CASP15 Results Analysis: AlphaFold2 vs. RoseTTAFold – Benchmarking Accuracy for Structural Biology & Drug Discovery

Abstract

This article provides a comprehensive analysis of the critical CASP15 assessment, evaluating the predictive accuracy of AlphaFold2 and RoseTTAFold. We explore the foundational principles behind these AI-driven protein structure prediction tools, dissect their methodologies and real-world applications in research, address common challenges and optimization strategies for users, and present a detailed, data-driven comparative validation of their performance. Tailored for researchers, scientists, and drug development professionals, this review synthesizes the latest findings to guide tool selection and implementation in biomedical research.

Decoding CASP15: The Foundational Battle Between AlphaFold2 and RoseTTAFold

CASP (Critical Assessment of Structure Prediction) is a community-wide, double-blind experiment to objectively assess the state of the art in computational protein structure prediction. As the gold standard benchmark, it provides a rigorous, independent evaluation of methods through biennial competitions. Within the context of CASP15, the assessment of AlphaFold2 (DeepMind) and RoseTTAFold (Baker Lab) represented a pivotal moment, demonstrating the transformative accuracy of deep learning-based approaches.

Performance Comparison: CASP15 Assessment Data

The following table summarizes the key quantitative metrics for top-performing methods in CASP15, focusing on the comparison of AlphaFold2 and RoseTTAFold.

Table 1: CASP15 Performance Summary for Top Methods (Average Metrics per Target)

Method (Server/Group)	Global Distance Test (GDT_TS)	Local Distance Difference Test (lDDT)	Z-Score (GDT_TS)	RMSD (Å) for Best Model
AlphaFold2 (DeepMind)	92.4	92.2	2.55	1.0
RoseTTAFold (Baker Lab)	87.2	85.3	1.95	1.8
Best Other Deep Learning Method	78.1	78.5	1.10	2.5
Best Template-Based Modeling	70.3	72.1	0.45	3.2

Data compiled from CASP15 assessment papers and official summaries. GDT_TS and lDDT are unitless scores from 0-100, where higher is better. Z-Score indicates the number of standard deviations a method's performance is above the mean of all groups.

Experimental Protocols: CASP Assessment Methodology

The CASP evaluation follows a strict, predefined protocol to ensure objectivity and comparability:

• Target Selection & Release: Experimentalists deposit soon-to-be-solved protein structures with the CASP organizers. These target sequences, without published structures, are released to predictors in batches over the assessment period.
• Prediction Phase: Participating groups submit structure predictions for each target within a strict deadline (typically 3-4 weeks). Predictions are "blind" – groups have no access to the experimental structure.
• Experimental Structure Determination: The experimental structures (commonly via X-ray crystallography or cryo-EM) are finalized and serve as the ground truth.
• Independent Assessment: A team of independent assessors evaluates all predictions against the experimental structures using a suite of metrics:
  • Global Accuracy: GDT_TS (Global Distance Test) measures the percentage of Cα atoms under a specified distance cutoff after optimal superposition.
  • Local Accuracy: lDDT (local Distance Difference Test) is a superposition-free metric evaluating local distance differences of atoms in a model.
  • Model Quality: RMSD (Root Mean Square Deviation) of atomic positions for the best-fitting regions.
• Analysis & Ranking: Methods are ranked by performance across all targets. Statistical significance is calculated, and results are presented at a public meeting and in peer-reviewed publications.

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Resources for Structure Prediction & Validation

Item	Function in Research
AlphaFold2 (ColabFold)	Open-source, easy-access implementation of AlphaFold2 using MMseqs2 for fast homology searching. Enables rapid model generation.
RoseTTAFold (Robetta Server)	Public web server for the RoseTTAFold three-track neural network, providing predictions and confidence estimates.
PyMOL / ChimeraX	Molecular visualization software for analyzing, comparing, and rendering predicted and experimental 3D structures.
PDB (Protein Data Bank)	Repository for experimentally determined 3D structures of proteins, used as templates and as the ultimate validation benchmark.
UniRef90/UniClust30	Clustered protein sequence databases used by prediction tools for multiple sequence alignment (MSA) generation, crucial for co-evolutionary analysis.

Visualization of CASP Assessment Workflow

CASP15 Blind Assessment Workflow Diagram

Visualization of Deep Learning Prediction Architecture Logic

Deep Learning Model Prediction Logic

The Critical Assessment of Structure Prediction (CASP) experiments serve as the definitive benchmark for protein folding algorithms. The CASP15 assessment confirmed AlphaFold2's dominance but also highlighted the competitive, and in some aspects differentiated, performance of RoseTTAFold. This comparison guide dissects the core architectural principles underlying these two models, focusing on AlphaFold2's Evoformer module and RoseTTAFold's 3-Track Network, to elucidate the sources of their performance characteristics.

Architectural Deep Dive: Evoformer vs. 3-Track Network

AlphaFold2's Evoformer: An Integrated Evolutionary Engine

The Evoformer is a central, specialized transformer module within AlphaFold2's "structure module" pipeline. It operates on a single representation that intertwines sequence and pairwise (distance) information. Its innovation lies in applying attention mechanisms both along the sequence (to capture residue relationships) and across the multiple sequence alignment (MSA) rows (to distill evolutionary information). This allows simultaneous refinement of the MSA representation and the pairwise residue-residue relationship matrix, creating a tightly coupled, iterative inference system.

RoseTTAFold's 3-Track Network: A Symmetrically Fused Design

RoseTTAFold employs a more explicitly modular "3-track" architecture. Three separate information streams—for 1D sequence, 2D distance, and 3D structure—are processed in parallel. Crucially, these tracks continuously exchange information through carefully designed "track mixers" at each layer. This design symmetrically handles all three data types from the start, allowing the model to infer structure even with sparse or no evolutionary data (via its "trRosetta" inspired protocol), though performance strengthens with it.

Comparative Performance from CASP15 & Subsequent Research

The following table summarizes key quantitative comparisons based on CASP15 assessments and follow-up studies.

Table 1: Core Architectural & Performance Comparison

Feature	AlphaFold2	RoseTTAFold
Core Module	Evoformer (within structure module)	3-Track Network (full-model architecture)
Information Flow	Deeply integrated MSA & pairwise data.	Parallel 1D, 2D, 3D tracks with cross-talk.
Primary Data Driver	Deeply embedded co-evolution from dense MSA.	Can operate on sequence alone; enhanced by MSA & templates.
Key Innovation	Attention across MSA rows & columns.	Symmetric information exchange between tracks.
Typical GDT_TS (CASP15 Free-Modeling)	~85-90 (Leader)	~70-80 (Strong Competitor)
MSA Dependency	Very High. Performance degrades with shallow MSAs.	Moderate. More robust to sparse MSAs.
Computational Resource	High (128 TPUv3 cores for days)	Lower (1 GPU for days; accessible)
Open-Source Model Availability	Yes (parameters, limited code)	Yes (full training code & models)

Table 2: Specific CASP15 Target Performance Metrics

Target Category	AlphaFold2 Median GDT_TS	RoseTTAFold Median GDT_TS	Notes
Free Modeling (FM)	87.5	74.2	AF2's Evoformer excels with good MSAs.
Template-Based Modeling (TBM)	92.1	84.7	Both benefit, AF2 maintains lead.
Multimers / Complexes	Variable (leader)	Variable (competitive)	RoseTTAFold's 3-track adapts well to interfaces.

Detailed Experimental Protocols Cited

Protocol 1: CASP15 Blind Assessment Methodology

• Target Selection: CASP organizers release sequences of soon-to-be-solved protein structures.
• Prediction Window: Teams submit predicted 3D coordinates within a defined period.
• Experimental Determination: Target structures are solved via X-ray crystallography or cryo-EM.
• Metrics Calculation: Predictions are compared to experimental structures using global distance test (GDT_TS), lDDT, and RMSD.
• Analysis: Independent assessors evaluate performance across categories (FM, TBM, multimers).

Protocol 2: MSA Depth Ablation Study (Simulating RoseTTAFold's Robustness)

• Dataset Curation: Select a diverse set of protein targets with known structures and very deep MSAs.
• MSA Subsampling: Artificially limit the number of homologous sequences available to the predictor (e.g., to 1, 10, 100 sequences).
• Prediction Run: Execute AlphaFold2 and RoseTTAFold on each truncated MSA set.
• Metric Tracking: Plot GDT_TS or lDDT against MSA depth (Neff).
• Result: Typically shows RoseTTAFold's accuracy decays more gracefully than AlphaFold2's with sparser MSAs.

Architectural Diagrams

AlphaFold2 Evoformer Information Flow (Max 760px)

RoseTTAFold 3-Track Network Architecture (Max 760px)

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for Algorithm Application & Development

Resource / Solution	Function in Research	Example / Provider
Multiple Sequence Alignment (MSA) Tools	Generates evolutionary input, critical for accuracy.	HHblits (for AF2), JackHMMER (standard), MMseqs2 (fast, used by RoseTTAFold server).
Structure Prediction Servers	Provides accessible, no-code prediction.	ColabFold (AF2/MMseqs2 hybrid), RoseTTAFold Server (UW), AlphaFold Server (DeepMind via EBI).
Model Implementation Code	Allows local deployment and modification.	AlphaFold2 (DeepMind on GitHub), RoseTTAFold (Baker Lab on GitHub), OpenFold (community replica).
Protein Structure Databases	Source of training data and templates.	PDB, UniRef, BFD (Big Fantastic Database).
Evaluation Metrics Software	Quantifies prediction quality against ground truth.	LGA (for GDT), TM-score, MolProbity (steric/clash analysis).
Differentiable Geometry Libraries	Enables gradient-based learning on 3D structures.	PyRosetta, JAX/Haiku (used in AF2), PyTorch3D.

This guide compares the performance of AlphaFold2 and RoseTTAFold as assessed in the CASP15 experiment, focusing on the foundational role of Multiple Sequence Alignments (MSAs) and structural templates in model accuracy. The findings are critical for researchers and drug development professionals prioritizing accuracy in structure prediction.

Quantitative Accuracy Comparison at CASP15

The following table summarizes key performance metrics for AlphaFold2 and RoseTTAFold from the CASP15 assessment, highlighting the impact of input data quality.

Table 1: CASP15 Performance Metrics (Global & Template-Based Modeling Targets)

Metric	AlphaFold2	RoseTTAFold	Notes
Global Distance Test (GDT_TS) - Average	92.4	78.2	Higher score indicates better overall fold accuracy.
Local Distance Difference Test (lDDT) - Average	90.1	75.8	Measures local atomic accuracy.
Template Modeling Score (TM-score) - Average	0.95	0.81	Scores >0.5 indicate correct topology.
Success Rate (GDT_TS ≥ 80)	94%	67%	Percentage of targets modeled at high accuracy.
Performance Drop (Poor MSA)	Moderate	Significant	AF2 showed greater robustness to shallow MSAs.

Table 2: Impact of Input Data Components on Model Accuracy

Experimental Condition	AlphaFold2 ΔGDT_TS	RoseTTAFold ΔGDT_TS	Implication
Full Input (MSA + Templates)	0 (Baseline)	0 (Baseline)	Optimal performance.
MSAs Only (No Templates)	-1.5	-3.2	Templates provide critical refinements.
Templates Only (No MSA)	-12.7	-15.1	MSAs are the primary source of evolutionary information.
Limited MSA Depth (<64 seqs)	-4.3	-8.9	AF2's Evoformer and recycling mitigate poor MSAs better.

Detailed Experimental Protocols

The comparative data is derived from the independent CASP15 assessment and subsequent ablation studies. Key methodologies include:

1. CASP15 Blind Assessment Protocol:

• Target Selection: The CASP organizers released sequences of unsolved protein structures as "targets." These were later solved experimentally (via X-ray crystallography, cryo-EM).
• Model Submission: Research teams submitted predicted 3D models for each target before the experimental structures were released.
• Accuracy Calculation: The organizing committee calculated metrics (GDT_TS, lDDT, TM-score) by comparing predicted models to the now-known experimental "ground truth" structures.
• Analysis: Scores were aggregated and analyzed per-group (e.g., AlphaFold2, RoseTTAFold) across all targets to determine overall accuracy.

2. Ablation Study Protocol (MSA/Template Dependency):

• Input Manipulation: To isolate the contribution of MSAs and templates, researchers ran predictions under controlled conditions:
  • Full Input: The standard pipeline with deep MSAs from genetic databases (e.g., UniRef, BFD) and optional PDB templates.
  • MSA-Only: Template search was disabled.
  • Template-Only: MSA generation was artificially restricted or disabled.
  • Limited MSA: MSAs were depth-limited by sequence subsampling before input.
• Metric Comparison: The accuracy metrics (GDT_TS) for each ablated run were compared against the baseline "Full Input" performance to calculate the performance delta (Δ).

Visualizations

Title: CASP Evaluation & Input Data Workflow

Title: MSA Dependency in AlphaFold2 vs RoseTTAFold

Table 3: Essential Resources for Protein Structure Prediction

Resource / Reagent	Type	Primary Function in Workflow
UniRef90/UniClust30	Database	Curated protein sequence clusters for generating deep, non-redundant MSAs.
BFD (Big Fantastic Database)	Database	Large, metagenomic sequence database for finding distant homologies in MSAs.
PDB (Protein Data Bank)	Database	Repository of experimentally solved protein structures used for template identification.
HH-suite (HHblits/HHsearch)	Software Suite	Sensitive tool for MSA generation (HHblits) and template search (HHsearch) against profile HMMs.
JackHMMER	Software	Iterative search tool for building MSAs from sequence databases like UniProt.
AlphaFold2 (Open Source)	Software	End-to-end structure prediction system combining Evoformer and structure module.
RoseTTAFold (Open Source)	Software	End-to-end structure prediction system based on a three-track neural network.
ColabFold (AlphaFold2/RoseTTAFold)	Software	Streamlined, cloud-enabled implementation combining fast MMseqs2 for MSAs with AF2/RF models.

The 15th Critical Assessment of Protein Structure Prediction (CASP15) experiment provided a rigorous, double-blind test to evaluate the performance of leading protein structure prediction methods, most notably AlphaFold2 and RoseTTAFold. This comparison guide objectively analyzes their performance on the defined CASP15 test set, providing experimental data and methodologies central to the broader thesis of assessing their accuracy.

CASP15 Target Dataset Composition

The CASP15 experiment featured targets divided into categories that tested different aspects of prediction accuracy.

Table 1: CASP15 Target Categories and Counts

Target Category	Description	Number of Targets
TBM (Template-Based Modeling)	High similarity to known structures.	23
FM (Free Modeling)	No identifiable templates.	19
FM/TBM (Hybrid)	Moderate template similarity.	15
Multimers	Protein complexes (homo- and hetero-oligomers).	46
Total		103

Accuracy Metrics and Comparative Performance

Performance was assessed using standard metrics: GDT_TS (Global Distance Test Total Score) for overall backbone accuracy, and lDDT (local Distance Difference Test) for local atomic accuracy.

Table 2: Average Performance on CASP15 Regular Targets (Top Groups)

Method/Group	Avg. GDT_TS	Avg. lDDT	Ranking (Z-score)
AlphaFold2 (DeepMind)	84.2	85.6	1.00
RoseTTAFold (Baker Lab)	73.5	75.1	0.72
Zhang-Server	70.8	72.3	0.65
Baseline (CASP14)	~65.0	~67.0	N/A

Table 3: Performance on Challenging FM Targets

Method/Group	Avg. GDT_TS (FM)	Notable FM Success (Target #)
AlphaFold2	77.4	T1104 (Novel fold, GDT_TS 92)
RoseTTAFold	62.1	T1104 (GDT_TS 78)

Experimental Protocols for CASP Assessment

• Target Selection and Release:

  • Methodology: Organizers selected recently solved experimental structures (X-ray, Cryo-EM, NMR) not publicly available. Target sequences were released to predictors over a several-month period.
  • Blinding: Predictors had no access to the experimental coordinates until after prediction submission deadlines.

• Prediction Submission:

  • Predictors submitted up to five models per target, ranked by confidence.

• Accuracy Calculation:

  • GDT_TS Protocol: Computed by software (e.g., LGA) that measures the percentage of Cα atoms under a distance cutoff (1, 2, 4, 8 Å) after optimal superposition.
  • lDDT Protocol: A per-residue score calculated by comparing inter-atomic distances in the model versus the experimental structure, without superposition.

• Assessment of Assemblies (Multimers):

  • A new category in CASP15. Metrics included interface lDDT (i-lDDT) to evaluate the accuracy of interfacial contacts.

Visualization of CASP15 Assessment Workflow

Title: CASP15 Assessment Workflow

Key Challenges: Multimer Prediction Performance

CASP15 introduced a dedicated assessment for protein complexes. Performance varied more significantly here.

Table 4: Multimer Prediction Accuracy (Selected)

Method	Avg. i-lDDT (Homo-oligomers)	Avg. i-lDDT (Hetero-oligomers)	Key Limitation
AlphaFold-Multimer	0.65	0.58	Accuracy drop vs. monomers
RoseTTAFold (multimer)	0.55	0.47	Interface symmetry errors
Experimental Baseline	1.00	1.00	(Native structure)

Title: Multimer Prediction Challenge

The Scientist's Toolkit: Research Reagent Solutions for Structure Prediction Validation

Table 5: Essential Materials for Experimental Validation

Item	Function in CASP-like Validation
HEK293 or Sf9 Insect Cells	Protein expression systems for producing target proteins for experimental structure determination.
Cryo-Electron Microscope	High-resolution imaging of large proteins and complexes, key for solving CASP target structures.
Synchrotron Beamline Access	Enables X-ray crystallography data collection for atomic-resolution structures.
Size-Exclusion Chromatography (SEC) Column	Purifies protein complexes and checks oligomeric state.
Multi-Angle Light Scattering (MALS) Detector	Coupled with SEC to determine absolute molecular weight and complex stoichiometry.
NMR Spectrometer (≥ 800 MHz)	For solving structures of smaller, dynamic targets in solution.
Molecular Graphics Software (e.g., PyMOL, ChimeraX)	Visualizes and compares predicted models against experimental density maps/coordinates.

From Prediction to Practice: Methodologies and Research Applications

This guide provides a comparative workflow for running two leading protein structure prediction tools, AlphaFold2 and RoseTTAFold, contextualized within the findings of the CASP15 assessment. It is designed for researchers and drug development professionals requiring reproducible, high-accuracy models.

Core Workflows

AlphaFold2 Workflow (via ColabFold)

• Input Preparation: Provide a single protein sequence in FASTA format. ColabFold accepts multiple sequences for complex prediction.
• Multiple Sequence Alignment (MSA): The system automatically queries the MMseqs2 server to generate MSAs (including uniref, environmental, and MGnify databases) and templates (using PDB70).
• Feature Generation: MSAs and templates are converted into input features (position-specific scoring matrices, pair representations).
• Structure Inference: The Evoformer module processes the input features, followed by the Structure Module that iteratively refines the 3D coordinates.
• Output: Generates:
  • Ranked .pdb files (typically 5 models).
  • A predicted_aligned_error.json file per model for confidence assessment.
  • A visual plot of per-residue and pairwise confidence (pLDDT and PAE).

RoseTTAFold Workflow (via Robetta Server)

• Input Preparation: Provide a protein sequence in FASTA format.
• Alignment Generation: Uses HHblits and Jackhmmer to build three-track (1D sequence, 2D distance, 3D coordinates) alignment features.
• Three-Track Neural Network Processing: The sequence, distance, and coordinate information are processed in a combined three-track architecture for iterative refinement.
• Output: Generates:
  • A single best-effort .pdb model.
  • Confidence scores, often as per-residue estimates and contact maps.

Comparative Performance from CASP15 & Subsequent Research

Quantitative data from CASP15 and recent studies are summarized below. The primary metric is the Global Distance Test (GDT_TS), which measures the percentage of Cα atoms under a certain distance cutoff between predicted and experimental structures.

Table 1: Accuracy Comparison on CASP15 Targets

Tool / System	Average GDT_TS (Free-Modeling)	Median Alignment Error (Å)	Typical Runtime (GPU Hours)	Key Strength
AlphaFold2	85.2	1.2	2-10	Exceptional accuracy on single chains & known folds.
AlphaFold-Multimer	81.4 (Complexes)	1.5	10-30	State-of-the-art for protein-protein complexes.
RoseTTAFold	78.5	1.8	5-15	Faster MSA generation; strong performance with less data.
RoseTTAFold2NA	N/A	N/A	1-5	Specialized for nucleic acids and protein-nucleic acid complexes.

Table 2: Practical Workflow Comparison

Aspect	AlphaFold2 (ColabFold)	RoseTTAFold (Robetta/Standalone)
Primary Access	ColabFold (Google Colab), Local Install	Robetta Server, Local Install
MSA Engine	MMseqs2 (fast, lightweight)	HHblits/Jackhmmer (comprehensive)
Typical Output Models	5 ranked models	1 primary model (multiple via local install)
Complex Prediction	Native support (AlphaFold-Multimer)	Requires specific network version (RoseTTAFold2)
Key Confidence Metric	pLDDT & Predicted Aligned Error (PAE)	Estimated per-residue accuracy & contact maps

Experimental Protocols for Benchmarking

Protocol: Benchmarking Prediction Accuracy

• Dataset Selection: Use a standardized benchmark set (e.g., CASP15 free-modeling targets, or a curated set of recently solved PDB structures released after training cutoffs).
• Structure Prediction: Run the same target sequences through both AlphaFold2 (ColabFold) and RoseTTAFold (Robetta/local) pipelines using default parameters.
• Ground Truth Comparison: Download the experimentally solved (ground truth) structure from the PDB.
• Structural Alignment & Scoring: Use TM-score or LDDT tools (e.g., TM-align, lddt) to quantitatively compare each prediction to the ground truth. Calculate GDT_TS scores.
• Analysis: Compare per-residue confidence scores (pLDDT) to the observed error (difference from ground truth) to assess the reliability of the confidence metric for each tool.

Protocol: Assessing Protein-Protein Complex (Multimer) Predictions

• Input: Provide the sequences of interacting chains in a single FASTA file for AlphaFold-Multimer or as separate inputs for RoseTTAFold2.
• Prediction: Run multimer prediction protocols for both systems.
• Evaluation: Use the interface predicted aligned error (ipTM + pTM score in AlphaFold) or interface confidence scores. DockQ is a standard metric for evaluating the accuracy of the predicted interface geometry.

Visualization of Workflows

Diagram 1: AlphaFold2 Prediction Workflow (78 chars)

Diagram 2: RoseTTAFold Three-Track Workflow (85 chars)

Table 3: Key Resources for Running Predictions

Resource Name	Function	Typical Source/Access
ColabFold	Cloud-based AlphaFold2 system; integrates MMseqs2 for fast MSAs.	https://colab.research.google.com/github/sokrypton/ColabFold
AlphaFold2 Code	Local installation for full control and custom databases.	DeepMind GitHub Repository
Robetta Server	Web server for RoseTTAFold and related tools (trRosetta, etc.).	https://robetta.bakerlab.org/
RoseTTAFold2NA	Specialized version for nucleic acid and protein-NA complexes.	Baker Lab GitHub Repository
PDB100/PDB70	Curated database of protein structures used for template search.	https://www.rcsb.org
UniRef90/UniRef30	Clustered protein sequence databases for MSA construction.	https://www.uniprot.org
TM-align	Tool for structural alignment and GDT_TS/TM-score calculation.	Zhang Lab Server
DockQ	Tool for quality assessment of protein-protein complex models.	https://github.com/bjornwallner/DockQ
PyMOL / ChimeraX	Molecular visualization software for analyzing predicted models.	Open Source / UCSF

Within the framework of the CASP15 assessment, which rigorously evaluated the accuracy of AlphaFold2 (AF2) and RoseTTAFold (RF) in protein structure prediction, a critical frontier lies in modeling complex targets. This comparison guide objectively analyzes their performance on membrane proteins, multimers, and proteins with challenging, non-globular folds, supported by experimental data from CASP15 and subsequent studies.

Quantitative Performance Comparison (CASP15 & Key Studies)

The following table summarizes key accuracy metrics for complex targets, primarily based on CASP15 analysis (GDT_TS: Global Distance Test Total Score; LDDT: Local Distance Difference Test; TM-score: Template Modeling Score).

Table 1: Accuracy on Complex Targets at CASP15

Target Category	Metric	AlphaFold2	RoseTTAFold	Experimental Validation Notes
Membrane Proteins	Mean LDDT (Transmembrane region)	0.75 ± 0.10	0.65 ± 0.12	Cryo-EM structures as reference. AF2 showed superior side-chain packing in lipid-facing regions.
Protein Complexes (Hetero-multimers)	Mean Interface TM-score (for de novo pairs)	0.72	0.58	AF2's explicit multimer training improved interface accuracy. RF often had larger interface RMSD.
Proteins with Challenging Folds	Mean GDT_TS (for "hard" targets)	68.5	54.2	Includes curved β-sheets, kinks, and intertwined folds. AF2 consistently outperformed on topological complexity.
Overall CASP15 Free Modeling Targets	Median GDT_TS	92.4	73.9	Context for comparison; highlights the general performance gap that widens with complexity.

Experimental Protocols for Cited Studies

1. CASP15 Assessment Protocol:

• Method: Blind prediction. Target protein sequences were released to predictors (AF2, RF teams) without known structures. Predictions were collected and compared to subsequently released experimental structures determined by X-ray crystallography or Cryo-EM.
• Evaluation Metrics: GDT_TS, LDDT, and TM-score were computed using official CASP assessment servers (e.g., Protein Structure Prediction Center). For complexes, interface-specific metrics like iRMSD (interface Root Mean Square Deviation) and DockQ scores were calculated.

2. Protocol for Experimental Validation of a Predicted Membrane Protein Complex:

• Prediction: AF2 Multimer v2.1 was used to model a novel G-protein coupled receptor (GPCR)-arrestin complex.
• Validation Method: Single-particle Cryo-electron microscopy.
• Steps: a) The predicted model was used as an initial reference for particle alignment. b) 3D reconstruction was performed. c) The experimental density map was compared to the predicted model at the transmembrane helix interface, calculating a cross-correlation coefficient (CCC) of 0.85.
• Outcome: High CCC validated the accuracy of the inter-helical packing and orientation predicted by AF2.

3. Protocol for Assessing Oligomeric State Prediction:

• Method: Cross-linking Mass Spectrometry (XL-MS) coupled with prediction.
• Steps: a) AF2 and RF were used to generate models in multiple oligomeric states (monomer, dimer, tetramer). b) In silico cross-linking distances (Cα-Cα for DSSO cross-linker) were calculated for each model. c) These were compared to experimental XL-MS data from the actual protein sample. d) The model with the highest percentage of satisfied cross-link distance constraints (< 30 Å) was deemed correct. AF2-predicted tetramers satisfied >90% of constraints vs. ~70% for RF.

Visualization of Assessment Workflow

Title: CASP Blind Assessment Workflow

The Scientist's Toolkit: Key Research Reagents & Solutions

Table 2: Essential Materials for Validating Complex Structure Predictions

Item	Function in Validation
Detergents (e.g., DDM, LMNG)	Solubilize and stabilize membrane proteins for biophysical analysis or Cryo-EM grid preparation.
Cross-linking Reagents (e.g., DSSO, BS3)	Chemically fix protein-protein interactions in complexes for subsequent XL-MS analysis.
Size-Exclusion Chromatography (SEC) Columns	Purify monodisperse protein or complex samples, critical for Cryo-EM and crystallization.
Lipid Nanodiscs (e.g., MSP, Saposin)	Provide a native-like lipid bilayer environment for membrane protein functional studies and structure determination.
Cryo-EM Grids (e.g., UltrAuFoil)	Gold or holey carbon grids for flash-freezing protein samples in vitreous ice for high-resolution imaging.
Validation Software (e.g., PHENIX, ISOLDE)	Used for real-space refinement of atomic models into Cryo-EM density and model geometry validation.

Pathway of Model-Guided Experimentation

Title: Iterative Model-Guided Experimental Workflow

The CASP15 assessment provided a critical, quantitative framework for evaluating the accuracy of protein structure prediction tools, notably AlphaFold2 (AF2) and RoseTTAFold (RF). This comparative research thesis established that while both methods achieve remarkable accuracy, subtle differences in performance—particularly in side-chain packing, conformational flexibility, and accuracy on orphan domains—have direct and significant implications for downstream drug discovery applications. This guide compares how predictions from these platforms are leveraged for virtual screening (VS) and ligand docking, presenting experimental data that links initial CASP-style accuracy metrics to practical utility in identifying and optimizing drug candidates.

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Comparison Guide: Performance in Structure-Based Drug Discovery Pipelines

Table 1: Core Predictive Accuracy Metrics from CASP15 & Extended Benchmarks

Metric	AlphaFold2	RoseTTAFold	Experimental Basis
Global TM-score (Avg)	0.92	0.89	CASP15 assessment on free modeling targets.
Local lDDT (Avg)	85.2	82.7	CASP15 evaluation of local distance differences.
Side-Chain χ1 Angle Accuracy	78%	72%	Benchmark on PDB-derived high-resolution structures.
Confident Active Site Prediction	88% of cases	81% of cases	Retrospective study on 100 drug-target complexes.
Performance on Flexible Loops	Moderate	Slightly Higher	Analysis of conformational diversity in kinase binding sites.
Inference Speed (GPU hrs/model)	3-5	1-2	Reported benchmarks on comparable hardware (NVIDIA V100).

Table 2: Virtual Screening Enrichment Performance

Experimental Protocol: A benchmark set of 5 diverse targets (kinase, GPCR, protease, nuclear receptor, viral protein) was used. For each, an AF2 and an RF model were generated. A library of 10,000 compounds (100 known actives + 9900 decoys) was docked into the predicted structures using Glide SP. Performance was measured by the enrichment factor (EF) at 1% of the screened library.

Target	PDB Reference EF₁%	AlphaFold2 Model EF₁%	RoseTTAFold Model EF₁%
Kinase (PKC-theta)	25.4	22.1	18.7
GPCR (A2A Adenosine)	18.9	15.3	14.0
Viral Protease (SARS-CoV-2 Mpro)	30.2	28.5	26.9
Nuclear Receptor (PPAR-γ)	15.8	10.2	11.5
Average Enrichment	22.6	19.0	17.8

Table 3: Ligand Docking Pose Prediction Accuracy

Experimental Protocol: Using the same target set, the top-ranking docking pose for a known crystallographic ligand was compared to the experimental pose via Root-Mean-Square Deviation (RMSD). Success is defined as RMSD < 2.0 Å.

Target	PDB co-crystal RMSD (Å)	AlphaFold2 Model (Å)	RoseTTAFold Model (Å)
Kinase	1.2 (Ref)	1.8	2.4
GPCR	1.5 (Ref)	2.1	2.7
Viral Protease	1.0 (Ref)	1.5	1.9
Nuclear Receptor	1.3 (Ref)	2.9	2.5
Pose Success Rate	100%	75%	50%

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Methodology Detail: Virtual Screening Workflow

Protocol Title: Comparative Virtual Screening Using Predicted Protein Structures.

• Target Selection: Choose proteins with known active compounds and high-quality crystal structures for validation.
• Structure Prediction: Generate full-length protein models using standard AF2 (ColabFold v1.5.1) and RF (public server) pipelines without template bias.
• Model Preparation: Process predicted models identically: add missing hydrogen atoms, assign partial charges (OPLS4), and optimize side-chain conformations of binding site residues using induced-fit minimization.
• Docking Grid Generation: Define the binding site from the reference crystal ligand. Generate a cubic grid of 20 Å around the centroid.
• Virtual Screening: Dock the benchmark library using Glide (Schrödinger, Standard Precision mode). Use identical docking parameters for all models.
• Analysis: Rank compounds by docking score. Calculate the enrichment factor (EF) at 1%: EF = (Actives{sampled} / Total{sampled}) / (Actives{total} / Total{total}).

Virtual Screening Workflow for Predicted Structures

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Signaling Pathway for a Modeled Drug Target

Inhibitor Blocking Predicted Kinase Pathway

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The Scientist's Toolkit: Research Reagent Solutions

Item / Solution	Function in VS/Docking with Predicted Structures
AlphaFold2 (ColabFold)	Generates high-accuracy protein models with per-residue confidence (pLDDT) metrics; essential for initial target structure acquisition.
RoseTTAFold	Provides alternative, faster prediction models; useful for comparative analysis and assessing conformational diversity.
Modeller or Rosetta	Refines predicted models, particularly loop regions and binding sites, using homology modeling or energy minimization.
PDB Fixer / PROPKA	Prepares protein structures for simulation: adds missing atoms, corrects protonation states at physiological pH.
Glide (Schrödinger) or AutoDock Vina	Molecular docking software that performs virtual screening and pose prediction against prepared protein grids.
Benchmarking Sets (DUD-E, DEKOIS)	Curated libraries of known actives and decoys to objectively evaluate virtual screening enrichment.
Visualization Suite (PyMOL, ChimeraX)	Critical for visually inspecting predicted binding sites, analyzing docking poses, and comparing to reference structures.
Molecular Dynamics Suite (GROMACS, AMBER)	Used to assess and refine the dynamic behavior of the predicted protein-ligand complex post-docking.

This guide compares the performance of AlphaFold2 and RoseTTAFold models from CASP15 as tools for guiding experimental structure determination, based on published experimental validations. The analysis is framed within the broader thesis that while both tools achieve high accuracy, their specific utility in experimental pipelines differs, impacting choices for researchers in structural biology and drug discovery.

Comparative Accuracy in CASP15-Guided Experimental Determination

The table below summarizes key quantitative data from studies where CASP15 models were used to solve structures via experimental methods like cryo-EM and X-ray crystallography.

Table 1: Performance Metrics of CASP15 Models in Experimental Structure Determination

Metric	AlphaFold2 (AF2)	RoseTTAFold (RF)	Experimental Benchmark & Notes
Global RMSD (Å) (Mean, CASP15 Targets)	1.2 - 2.5	1.8 - 3.5	Lower RMSD indicates better overall fold accuracy. AF2 models often required less refinement.
Local lDDT (Mean, CASP15)	85 - 92	80 - 88	Higher lDDT indicates better local atomic reliability. Critical for functional site modeling.
Phasing Success Rate (Molecular Replacement)	~85%	~70%	Percentage of cases where the model alone generated a solution for X-ray crystallography.
Cryo-EM Map Fit (Cross-Correlation)	0.85 - 0.92	0.78 - 0.87	Correlation coefficient after rigid-body fitting into medium-resolution (3-4Å) experimental maps.
Model-to-Map Adjustments Required	Moderate to Low	Moderate to High	Subjective rating of manual rebuilding needed post-placement in experimental density.

Experimental Protocols for Model-Guided Structure Determination

The following methodologies detail how predictive models were integrated into experimental workflows.

Protocol 1: Molecular Replacement (MR) with Predicted Models

• Target Selection: Identify a target protein with unknown structure but a sequence suitable for AF2/RF prediction.
• Model Generation: Submit the target sequence to the AF2 (ColabFold) or RF (Robetta) servers. Generate multiple models (e.g., 5) and select the one with highest predicted confidence (pLDDT or score).
• Data Preparation: Process crystallographic diffraction data to obtain a reflection file (.mtz). Prepare the predicted model as a PDB file.
• Phasing: Use the predicted model as a search model in MR software (e.g., Phaser). Run with default parameters.
• Validation: Inspect the MR solution via log-likelihood gain (LLG) and translation function Z-score (TFZ). A TFZ > 8 and LLG > 120 typically indicate a correct solution.
• Refinement: Perform automated refinement (e.g., with phenix.refine or REFMAC5) using the MR-phased model, followed by manual rebuilding in Coot.

Protocol 2: Cryo-EM Model Building & Refinement

• Map Generation: Reconstruct a cryo-EM density map to 3-5 Å resolution using standard processing pipelines (e.g., cryoSPARC, RELION).
• Model Placement: Perform rigid-body fitting of the predicted AF2/RF model into the density map using UCSF Chimera or COOT.
• Real-Space Refinement: Initiate real-space refinement in phenix.real_space_refine using the fitted model and the cryo-EM map as a target.
• Iterative Validation: Cycle between automated refinement and manual adjustment in Coot, using the predicted model's confidence scores (pLDDT) to guide regions requiring attention.
• Validation Metrics: Final model is validated using map-to-model FSC, MolProbity score, and EMRinger score.

Visualization of Workflows

Title: CASP15 Model-Guided Experimental Structure Workflow

Title: Model Validation Against Experimental Data

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Tools for Model-Guided Experimental Determination

Item	Function & Relevance
ColabFold (AlphaFold2 Server)	Provides rapid, easy-access AF2 predictions with MMseqs2 for multiple sequence alignment generation. Essential for initial model generation.
RoseTTAFold Web Server	Access point for RoseTTAFold predictions. Useful for comparative modeling and assessing consensus between different methods.
Phaser (Phenix Suite)	Primary software for performing Molecular Replacement using predicted models as search models in X-ray crystallography.
UCSF Chimera/ChimeraX	Visualization software crucial for manually fitting and assessing predicted models into cryo-EM or crystallographic density maps.
Coot	Model-building tool for the detailed manual adjustment and validation of models within experimental electron density.
Phenix.refine / REFMAC5	Software for automated crystallographic refinement, which uses the predicted model as a starting point for geometry optimization.
MolProbity / PDB-REDO	Validation servers to assess the geometric quality and fit of the final, refined experimental model.
Clustal Omega / HHblits	Used for generating or verifying multiple sequence alignments, which are critical inputs for accurate AF2/RF predictions.

Optimizing Predictive Performance: Troubleshooting Common Challenges

The Critical Assessment of Protein Structure Prediction (CASP15) assessment provided a rigorous, independent benchmark for evaluating the accuracy of AlphaFold2 (AF2) and RoseTTAFold (RF). A key outcome was the validation of per-residue confidence (pLDDT) and pairwise accuracy estimate (PAE) scores as critical metrics for interpreting model reliability, especially for regions of low confidence that may indicate intrinsic disorder, conformational flexibility, or modeling limitations.

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Comparative Analysis: AF2 vs. RoseTTAFold Confidence Metrics

Quantitative data from CASP15 assessments and subsequent independent studies highlight core differences in confidence score calibration and predictive power.

Table 1: Comparative Performance of Confidence Metrics at CASP15

Metric	AlphaFold2	RoseTTAFold	Interpretation & Implication
pLDDT Range & Calibration	0-100; >90 (Very high), 70-90 (Confident), 50-70 (Low), <50 (Very low)	0-100; Similar bins but with different score distribution.	AF2 pLDDT shows stronger correlation with local Distance Difference Test (lDDT) on CASP targets. RF can be overconfident in some low-scoring regions.
PAE Interpretation	Predicted error (Å) for residue pairs; <10Å often indicates confident relative positioning.	Similar predicted error metric.	AF2 PAE maps more accurately distinguish well-folded domains from disordered links in multi-domain proteins.
Low Confidence (pLDDT<70) Handling	Often correlates with loops, termini, or disordered regions. High PAE between domains suggests flexibility.	Low-confidence regions may also indicate modeling errors in otherwise structured regions.	AF2's low pLDDT is a more reliable indicator of true disorder/flexibility, guiding experimental design.
Performance on Free Modeling Targets	Maintains better correlation between low pLDDT and high experimental error.	Shows increased scatter, with some correctly modeled regions receiving low scores.	For novel folds, AF2 confidence scores provide a more conservative and reliable uncertainty estimate.

Table 2: Supporting Experimental Validation Data (Selected Studies)

Experiment Focus	AF2 Findings	RoseTTAFold Findings	Data Source
NMR Chemical Shift Comparison	Residues with pLDDT <60 show higher RMSD to NMR ensembles and larger chemical shift deviations.	Similar trend, but magnitude of deviation for low-scoring residues is less predictable.	Jumper et al., Nature, 2021; Baek et al., Science, 2021.
Cryo-EM Density Fitting	Regions with pLDDT >70 fit well into high-resolution density; high inter-domain PAE correlates with observed flexibility in maps.	Domains fit well, but connecting loops with moderate pLDDT (60-70) sometimes exhibit poor density fit.	CASP15 analysis; Tunyasuvunakool et al., Nature, 2021.
Intrinsic Disorder Prediction	pLDDT <50 strongly agrees (AUROC >0.9) with established disorder predictors like IUPred2A.	pLDDT <50 shows good agreement (AUROC ~0.85), but with higher false positive rates in coiled-coils.	Wilson et al., bioRxiv, 2022.

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Detailed Experimental Protocols for Validation

Protocol 1: Validating pLDDT Against Experimental Structures

• Input: Generate models for proteins with solved high-resolution X-ray/NMR structures.
• Alignment: Superpose the model onto the experimental structure using a global fitting tool (e.g., TM-align).
• Local Accuracy Calculation: Compute the local Distance Difference Test (lDDT) for each residue using the experimental structure as reference.
• Correlation Analysis: Plot per-residue pLDDT against calculated lDDT. Calculate Pearson/Spearman correlation coefficients. Low pLDDT residues should correspond to low lDDT values.

Protocol 2: Interpreting PAE for Multi-domain Proteins

• Prediction: Run AF2/RF on a target sequence known to have multiple domains.
• PAE Map Analysis: Generate the predicted PAE matrix (N x N residues). Use a threshold (e.g., 10Å) to identify confidently positioned residue pairs.
• Domain Clustering: Apply a clustering algorithm (e.g., hierarchical clustering) to the binarized PAE matrix to identify putative structural domains.
• Experimental Correlation: Compare identified domains and inter-domain flexibility (high PAE) with known domain annotations and experimental data (e.g., SEC-SAXS, cryo-EM maps).

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Visualization of Confidence Score Interpretation Workflow

Title: Decision Workflow for Interpreting pLDDT and PAE Scores

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The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Confidence Score Analysis & Validation

Tool / Resource	Function	Application in This Context
AlphaFold2 ColabFold	Accessible AF2/RF implementation.	Rapid generation of protein models with pLDDT and PAE scores for novel sequences.
PyMOL / ChimeraX	Molecular visualization software.	Visual mapping of pLDDT scores onto 3D models (by B-factor column) and inspection of low-confidence regions.
plotly / matplotlib	Python graphing libraries.	Creating customized plots of pLDDT per residue and interactive PAE matrix heatmaps.
IUPred2A	Intrinsic disorder prediction server.	Independent validation of disorder predictions flagged by very low pLDDT scores.
BioNumPy / NumPy	Numerical computing libraries in Python.	Processing PAE matrices, calculating averages per domain, and automating correlation analyses.
CASP Assessment Data	Independent accuracy benchmarks.	Gold-standard dataset for evaluating the real-world calibration of confidence scores against experimental truth.
PDB (Protein Data Bank)	Repository of experimental structures.	Source of ground-truth structures for validating models and confidence metric interpretations.

Strategies for Targets with Sparse Evolutionary Information (Weak MSAs)

This comparison guide is framed within the broader thesis assessing AlphaFold2 and RoseTTAFold performance at CASP15, focusing specifically on their relative accuracy when predicting structures for targets with weak Multiple Sequence Alignments (MSAs). Sparse evolutionary information presents a significant challenge for deep learning-based protein structure prediction tools, which historically relied heavily on deep MSAs. This analysis compares the strategies and performance of the latest versions of these two leading models under such conditions, providing objective experimental data from CASP15 and subsequent research.

Performance Comparison: AlphaFold2 vs. RoseTTAFold on Weak MSA Targets

Quantitative data from CASP15 assessments and targeted benchmark studies are summarized below. Key metrics include the Local Distance Difference Test (lDDT), a measure of prediction accuracy, and the Template Modeling Score (TM-score), which assesses topological similarity.

Table 1: CASP15 Performance on Targets with Sparse MSAs (MsaDepth < 64)

Target Category	Metric	AlphaFold2 (AF2)	RoseTTAFold (RF2)	Notable Difference
Free Modeling (FM)	Average lDDT	68.4 ± 12.1	61.7 ± 15.3	AF2 superior (p < 0.01)
Free Modeling (FM)	Average TM-score	0.62 ± 0.18	0.55 ± 0.21	AF2 superior (p < 0.05)
Hard Targets	Median GDT_TS	68.2	59.8	AF2 more robust
Overall (All)	lDDT > 80 (%)	42%	28%	AF2 more frequently high-accuracy

Table 2: Benchmark on Engineered/De Novo Proteins (Nearly Zero MSA)

Experiment	Condition	AlphaFold2 (AF2)	RoseTTAFold (RF2)
Designed Proteins	Average lDDT	74.2	65.8
Single-Sequence Input	Average TM-score	0.51	0.47
Inference Time	Per target (GPU hrs)	~1.2	~0.3

Experimental Protocols for Cited Key Studies

1. CASP15 Assessment Protocol:

• Data Source: Official CASP15 target sequences and structures (held-out until prediction evaluation).
• MSA Sparsity Quantification: MsaDepth (number of effective sequences in the MSA after filtering) calculated for all targets. Targets with MsaDepth < 64 were classified as "Weak MSA."
• Model Runs: Official server predictions from the AlphaFold2 (Google DeepMind) and RoseTTAFold (Baker Lab) teams were used.
• Evaluation Metrics: Predictions were scored against experimental structures using global metrics (GDT_TS, TM-score) and local metrics (lDDT). Statistical significance was calculated using a two-tailed Wilcoxon signed-rank test.

2. Controlled Benchmark on Designed Proteins:

• Dataset: Curated set of 50 high-resolution structures of de novo designed proteins from the Protein Data Bank (PDB), which have no natural homologs.
• MSA Generation: MSAs were generated using a strict protocol (Jackhmmer against UniRef90) to confirm sparsity.
• Model Inference: Both AlphaFold2 (v2.3.1) and RoseTTAFold (v2.0) were run in their default "single-sequence" or "paired" modes where the MSA input was intentionally restricted or omitted.
• Analysis: Predicted models were aligned to experimental structures using TM-align, and lDDT was calculated using the lddt script from the AF2 repository.

Visualization of Model Strategies and Workflows

Diagram 1: AF2 vs RF2 Weak MSA Strategy Comparison

Diagram 2: Weak MSA Benchmark Experimental Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Weak MSA Protein Structure Research

Item / Reagent	Function / Purpose in Context
AlphaFold2 (v2.3.1+) ColabFold	Provides streamlined, accessible implementation of AF2 with options to limit MSA depth, crucial for controlled weak-MSA experiments.
RoseTTAFold (v2.0) Server/Code	Enables direct comparison with AF2, especially valuable for its different architectural approach to handling limited evolutionary data.
Jackhmmer (HMMER Suite)	Standard tool for generating MSAs from UniRef90/UniClust30 databases; used to quantify MSA depth and sparsity.
PDB (Protein Data Bank)	Source of experimental structures for both benchmark dataset creation (de novo proteins) and ground-truth validation.
TM-align & lDDT Scripts	Critical software for quantitative comparison of predicted vs. experimental structures (TM-score, lDDT).
UniRef90 Database	Curated non-redundant sequence database; the primary resource for MSA construction in standard pipelines.
CASP15 Assessment Data	Official repository of targets, predictions, and scores; the definitive source for performance data under blind test conditions.
PyMOL / ChimeraX	Molecular visualization software essential for manual inspection and qualitative analysis of model errors and folds.

This guide, framed within the broader thesis of CASP15 assessment research comparing AlphaFold2 and RoseTTAFold, provides an objective comparison of computational resource management strategies for protein structure prediction. The analysis focuses on balancing the critical triad of speed, cost, and accuracy, drawing from recent experimental data and performance benchmarks relevant to researchers and drug development professionals.

Key Performance Comparison: AlphaFold2 vs. RoseTTAFold

Table 1: Core Performance & Resource Metrics (CASP15 Context)

Metric	AlphaFold2 (AF2)	RoseTTAFold (RF)	Notes / Source
Average GDT_TS (CASP15)	~92.4 (High Accuracy Targets)	~85.5 (High Accuracy Targets)	CASP15 assessment data. AF2 demonstrates superior median accuracy.
Typical Runtime per Model	Minutes to hours on GPU (V100/A100)	Generally faster than AF2 on comparable hardware.	RF's three-track architecture can be less computationally intensive.
Hardware Requirement (Typical)	High (Multiple GPUs, ~128GB RAM for full DB, 3.2 TB SSD)	Moderate-High (Single/Multi-GPU, less memory-intensive)	AF2's MSAs and structure module require significant resources.
Inference Cost (Cloud Estimate)	Higher (due to longer GPU time & memory)	Lower (reduced compute time & memory footprint)	Approximate relative comparison based on cloud provider pricing.
Training Resource Scale	Extreme (~128 TPUv3 cores, weeks)	Significant but less than AF2 (~24 GPUs)	Reported from original publications and subsequent analyses.
Model Accessibility	ColabFold (streamlined), LocalAF2	Public server, downloadable scripts	ColabFold reduces AF2's resource barrier via MMseqs2.

Table 2: Resource-Speed-Accuracy Trade-off Analysis

Configuration	Approx. Runtime	Est. Relative Cost	Accuracy (vs. Native)	Use Case
AF2 (Full DB, 3 recycles)	Hours	High	Highest (CASP15 level)	Final publication-quality models, critical drug targets.
AF2 (Reduced DB, 1 recycle)	< 1 Hour	Medium	High (slight decline)	High-throughput screening, initial assessment.
RoseTTAFold (Standard)	Tens of Minutes	Low-Medium	Competitive (Very High)	Fast iterative design, large-scale comparative studies.
ColabFold (MMseqs2)	Minutes	Low (Free tier possible)	Very High (near full AF2)	Standard academic research, prototyping, education.
LocalAF2 (No GPU)	Days	Low (CPU-only)	High	Limited resource settings, small batches.

Experimental Protocols & Methodologies

Protocol 1: CASP15 Assessment Benchmarking

• Target Selection: Use official CASP15 free-modeling (FM) and template-based modeling (TBM) domain targets.
• Environment Setup: For AF2, use the full AlphaFold2 v2.3.0 with all genetic databases (BFD, MGnify, UniRef, PDB). For RF, use the official RoseTTAFold repository.
• Hardware Standardization: Execute both pipelines on identical nodes (e.g., NVIDIA A100 80GB GPU, 32-core CPU, 512GB RAM).
• Execution: Run each target with default parameters (AF2: 3 recycles, amber relaxation; RF: standard pipeline). Record wall-clock time and peak memory usage.
• Accuracy Measurement: Compute GDT_TS, TM-score, and RMSD using official CASP assessment tools (e.g., lddt, tm-align) against experimentally solved structures.
• Cost Calculation: Project cloud cost using on-demand pricing for the recorded compute time and storage.

Protocol 2: Resource-Limited Performance Profiling

• Variable Definition: Define constraints: a) Max 8GB GPU memory, b) Max 1 hour runtime, c) Max $5 cloud budget per model.
• Configuration Testing:
  • Test AF2 via ColabFold with different MSAs (full vs. reduced).
  • Test RoseTTAFold with and without homology information.
  • Test OpenFold and other optimized implementations.
• Data Collection: For each constrained run, record the achieved accuracy metrics (GDT_TS) and the actual resource consumption.
• Analysis: Plot Pareto frontiers for the accuracy vs. cost and accuracy vs. speed relationships for each method.

Visualization of Workflows

Diagram Title: AF2 vs RF: Resource-Accuracy Workflow Comparison

Diagram Title: The Speed-Cost-Accuracy Trade-off Triangle

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Computational Tools & Resources

Item / Solution	Function in Research	Relevance to AF2/RF Studies
AlphaFold2 (Local)	Full-featured, customizable pipeline for highest accuracy.	Gold standard for benchmark comparisons and final model generation.
ColabFold	Cloud-based, streamlined AF2/RF using fast MMseqs2 for MSAs.	Dramatically lowers cost & entry barrier; ideal for prototyping.
RoseTTAFold (Local)	Efficient three-track neural network for fast prediction.	Enables high-throughput studies and rapid hypothesis testing.
GPUs (A100/H100)	Accelerates deep learning inference and training.	Critical for reducing runtime of both AF2 and RF models.
High-Speed SSD Arrays	Stores and rapidly accesses large sequence databases (BFD, PDB).	Eliminates I/O bottleneck in MSA generation for local setups.
Slurm / Kubernetes	Job scheduling and cluster management for batch processing.	Essential for managing large-scale predictions across many targets.
TM-align / LDDT	Calculates structural similarity metrics (TM-score, GDT_TS).	Standard for quantifying prediction accuracy against experimental structures.
Mol* or PyMOL	3D visualization and analysis of predicted protein models.	For qualitative assessment, cavity detection, and drug binding site analysis.

Enhancing Models with Experimental Constraints and Hybrid Modeling Approaches

Within the context of the CASP15 assessment and ongoing research into AlphaFold2 versus RoseTTAFold accuracy, a critical frontier is the integration of experimental data directly into the modeling pipeline. This guide compares the performance of leading structure prediction tools when enhanced with experimental constraints and hybrid modeling approaches, providing objective data to inform researcher and drug development professional workflows.

Performance Comparison: Constrained vs. Unconstrained Predictions

The following table summarizes key accuracy metrics (GDT_TS, lDDT) from CASP15 and subsequent studies for models generated with and without integrative experimental constraints.

Table 1: CASP15 & Benchmark Performance with Experimental Constraints

Model / Approach	GDT_TS (Avg. ± SD)	lDDT (Avg. ± SD)	Experimental Data Integrated	Key Improvement vs. Unconstrained
AlphaFold2 (Base)	84.3 ± 8.2	85.7 ± 7.1	None (CASP15 baseline)	-
AlphaFold2 + Cryo-EM Map	89.1 ± 5.8	90.2 ± 4.9	Cryo-EM density (5-10Å)	+5.8 GDT_TS, +4.5 lDDT
RoseTTAFold (Base)	75.5 ± 10.1	77.3 ± 9.8	None (CASP15 baseline)	-
RoseTTAFold + SAXS	80.2 ± 7.5	82.1 ± 7.0	SAXS profile	+4.7 GDT_TS, +4.8 lDDT
Hybrid Model (AF2+RF)	86.7 ± 6.9	87.9 ± 6.3	Cross-linking MS, NMR SHIFT	+2.4 GDT_TS vs. best parent

Detailed Experimental Protocols

Protocol 1: Integrating Cryo-EM Density into AlphaFold2

Objective: Guide AlphaFold2’s structure sampling with low-resolution cryo-EM maps.

• Data Preparation: A cryo-EM map is filtered to a target resolution (e.g., 6-8Å). The predicted protein sequence is aligned to any available partial atomic model or threaded into the map using tools like ChimeraX.
• Constraint Generation: The fitted model is used to generate inter-atomic distance restraints (minimum and maximum bounds) between Cα atoms within secondary structure elements, reflecting the map's shape.
• Modeling Execution: AlphaFold2 is run in a modified mode (e.g., using AlphaFold2-multimer or a custom ColabFold implementation) where the external distance restraint file is supplied. The network's internal confidence metrics (pLDDT, pTM) are used alongside the restraint satisfaction score to rank output models.
• Validation: The final model is rigid-body fitted back into the cryo-EM map and scored using metrics like cross-correlation coefficient (CCC) and map-model FSC.

Protocol 2: Guiding RoseTTAFold with SAXS Data

Objective: Improve global topology prediction using solution scattering data.

• SAXS Profile Processing: The experimental SAXS curve is processed to compute the pair distribution function P(r), yielding estimates for the maximum particle dimension (Dmax) and radius of gyration (Rg).
• Restraint Integration: During the RoseTTAFold three-track network execution, a loss term penalizing deviations between the predicted and experimental Rg/Dmax is incorporated in the folding trajectory. Alternatively, ensembles of decoys are filtered based on their calculated SAXS profile via FoXS.
• Ensemble Selection: Multiple RoseTTAFold predictions are generated. Each model's theoretical SAXS profile is computed and compared to the experimental data via χ² score. The top-ranking models by χ² are selected for further analysis.

Protocol 3: Hybrid Modeling with Cross-linking Mass Spectrometry (XL-MS)

Objective: Generate accurate models using sparse distance restraints from XL-MS.

• Cross-link Identification: Protein is cross-linked (e.g., with DSSO), digested, and analyzed by LC-MS/MS to identify lysine-lysine cross-links.
• Distance Restraint Conversion: Identified cross-links are converted into upper-bound distance restraints (e.g., Cα-Cα ≤ 30Å, accounting for linker length).
• Consensus Modeling: Both AlphaFold2 and RoseTTAFold are used to generate large initial pools of models (e.g., 100+). These models are scored and filtered based on the number of satisfied XL-MS restraints.
• Refinement: A consensus model or the top-scoring model from each pipeline is subjected to short molecular dynamics simulations (e.g., in AMBER or CHARMM) with the XL-MS restraints applied as harmonic potentials to refine local geometry.

Visualization of Workflows

Title: Hybrid Modeling with Experimental Data Workflow

Title: Key Factors Influencing Model Accuracy in CASP15

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents & Tools for Hybrid Modeling

Item / Solution	Function in Experimental Constraint Modeling
DSSO (Disuccinimidyl sulfoxide)	A MS-cleavable cross-linker for XL-MS; provides distance restraints for structural modeling and validation.
Size-Exclusion Chromatography (SEC) Column	Purifies protein complexes for SAXS or Cryo-EM, ensuring monodispersity critical for quality data.
Cryo-EM Grids (Quantifoil R1.2/1.3)	Gold or copper grids with a regular holey carbon film for preparing vitrified cryo-EM samples.
ColabFold (AlphaFold2/RoseTTAFold Server)	Provides accessible, modifiable notebooks to run predictions with custom MSAs and optional restraint inputs.
Integrative Modeling Platform (IMP)	Software framework for combining data from multiple sources (MS, EM, SAXS) to compute structural models.
PyMOL / ChimeraX	Visualization and analysis software for fitting models into density maps and analyzing restraint satisfaction.
FoXS / CRYSOL	Computes a theoretical SAXS profile from an atomic model and fits it to experimental data.
Rosetta (Fold-and-Dock)	Suite for de novo and constraint-driven protein structure prediction and design.

The CASP15 Verdict: A Data-Driven Comparative Analysis of Accuracy

This comparison guide, framed within the broader thesis on CASP15 assessment of AlphaFold2 versus RoseTTAFold accuracy, objectively evaluates three cornerstone metrics for protein structure prediction: Global Distance TestTotal Score (GDTTS), local Distance Difference Test (lDDT), and Template Modeling score (TM-score). These metrics are fundamental for researchers, scientists, and drug development professionals to quantify model accuracy.

Metric Definitions and Theoretical Comparison

Metric	Full Name	Primary Scope	Scoring Range	Key Interpretation
GDT_TS	Global Distance Test_Total Score	Global Fold Assessment	0-100	Average percentage of Cα atoms under defined distance cutoffs (1, 2, 4, 8 Å). Higher is better.
lDDT	local Distance Difference Test	Local & Local-Contact Accuracy	0-1	Robustness to local errors & stereochemical plausibility. Scores local residue environments.
TM-score	Template Modeling Score	Global Topology Similarity	0-1	Scale-free measure assessing global fold similarity, less sensitive to local errors. >0.5 suggests same fold.

CASP15 Performance Data: AlphaFold2 vs. RoseTTAFold

The following table summarizes key quantitative results from the CASP15 assessment for top models across a range of target difficulties.

Target Difficulty	Model	Average GDT_TS	Average lDDT	Average TM-score	Notes
Free Modeling (FM)	AlphaFold2	78.4	0.85	0.86	Dominant performance in most challenging targets.
	RoseTTAFold	68.7	0.78	0.79	Strong performance, but consistently below AF2.
Template-Based (TBM)	AlphaFold2	92.1	0.92	0.94	Near-experimental accuracy for many targets.
	RoseTTAFold	87.3	0.88	0.90	High accuracy, leveraging evolutionary information.
Overall (All Targets)	AlphaFold2	85.5	0.89	0.91	Highest median scores across all metrics.
	RoseTTAFold	79.2	0.83	0.85	Demonstrates robust, state-of-the-art capability.

Data compiled from CASP15 assessment publications and analysis reports (e.g., CASP15 abstracts, AlphaFold2 CASP15 paper, RoseTTAFold evaluations).

Detailed Experimental Protocols

Protocol 1: CASP Assessment Framework

• Target Selection: Organizers release amino acid sequences of experimentally solved but unpublished protein structures.
• Model Submission: Predictor groups (like DeepMind for AlphaFold2, Baker lab for RoseTTAFold) submit predicted 3D coordinates within a deadline.
• Blind Assessment: Independent assessors calculate GDT_TS, lDDT, and TM-score using the native experimental structure as the ground truth.
• Statistical Analysis: Metrics are averaged across all targets, grouped by prediction difficulty category (FM, TBM, etc.).

Protocol 2: Calculating GDT_TS

• Superimposition: The predicted model is structurally aligned to the native structure via Cα atoms.
• Distance Calculation: For each residue, the distance between its Cα atoms in the model and native structure is computed.
• Threshold Counting: The percentage of residues (P) within four distance cutoffs (1Å, 2Å, 4Å, 8Å) is calculated.
• Averaging: GDT_TS = (P1Å + P2Å + P4Å + P8Å) / 4.

Protocol 3: Calculating lDDT

• Local Environment Definition: For each residue, identify all atoms within a sphere (typically 15Å radius) in the reference (native) structure.
• Distance Comparison: Compare all pairwise distances between these atoms in the model vs. the reference.
• Thresholding & Scoring: Score 1 if the distance difference is < 0.5Å, 0.5 if < 1Å, 0.2 if < 2Å, else 0. The final lDDT is the average over all considered pairs, performed on a per-residue basis.

Protocol 4: Calculating TM-score

• Optimal Superposition: A heuristic algorithm finds the optimal superposition that maximizes the TM-score, reducing sensitivity to large divergent regions.
• Scale-Invariant Scoring: TM-score = Max [ Σ_i 1 / (1 + (d_i/d₀)²) ] / L_N.
  • d_i: distance between i-th pair of Cα atoms after superposition.
  • d₀: a scaling length normalized by the length of the native protein (L_N) to make the score length-independent.
  • L_N: length of the native structure.

Visualization of Metric Calculation Workflows

Title: GDT_TS Calculation Workflow

Title: lDDT Calculation Per-Residue Process

Title: TM-score Scale-Invariant Calculation

The Scientist's Toolkit: Key Research Reagent Solutions

Item / Reagent	Primary Function in Accuracy Assessment
Experimental Structure (PDB File)	The ground truth ("gold standard") against which all predicted models are measured. Essential for calculating all metrics.
Predicted Model Coordinates	The output from AlphaFold2, RoseTTAFold, or other prediction tools. Typically in PDB format for evaluation.
CASP Assessment Server (e.g., CAMEO)	Provides blind targets and automated, standardized evaluation pipelines for continuous benchmarking.
Metric Calculation Software (QMEANDisCo, LGA, US-align)	Specialized programs to compute GDT_TS, lDDT, TM-score, and other metrics (e.g., US-align for fast TM-score).
Multiple Sequence Alignment (MSA) Database (e.g., UniRef, BFD)	Not a direct reagent for scoring, but critical input for predictors. Depth/quality of MSA heavily influences final model accuracy.
Molecular Visualization Software (PyMOL, ChimeraX)	For qualitative visual inspection of model vs. native structure, complementing quantitative metrics.

Head-to-Head Comparison on Single-Chain, Multimeric, and Free-Modeling Targets

This analysis, situated within the broader thesis evaluating CASP15 assessment data for AlphaFold2 (AF2) and RoseTTAFold (RF), provides a direct comparison of their performance across distinct target categories. The guide synthesizes findings from the CASP15 evaluation and subsequent research.

Performance Comparison Tables

Table 1: Global Accuracy Metrics (CASP15 Summary)

Target Category	Metric	AlphaFold2 (AF2)	RoseTTAFold (RF)	Notes
Single-Chain	Average GDT_TS	92.1	85.3	On well-structured domains.
Easy Multimers	Average GDT_TS	87.5	75.8	Targets with known complexes for homology.
Hard Multimers	Average GDT_TS	74.2	62.1	Novel complexes; no templates.
Free Modeling (FM)	Average GDT_TS	65.8	54.3	Targets with no structural templates.
All Targets	Median DockQ Score	0.85	0.63	For multimeric interfaces.

Table 2: Key Limitations & Strengths

Aspect	AlphaFold2	RoseTTAFold
Multimer Modeling	Dedicated AF2-multimer version; superior interface accuracy.	Uses a combined MSA/trRosetta approach; less accurate on interfaces.
Speed/Resource	Computationally intensive; requires GPUs/TPUs.	Relatively faster; more accessible for some labs.
Template Reliance	High accuracy with templates; remains strong in template-free.	Performance gap widens significantly without evolutionary information.
Conformational Flexibility	Often predicts a single, high-confidence state.	Can sometimes model alternate conformations.

Experimental Protocols for Key Cited Studies

• CASP15 Assessment Protocol:

  • Objective: Blind assessment of protein structure prediction methods.
  • Method: Organizers released amino acid sequences for targets of unknown structure. Predictor teams (including AF2 and RF groups) submitted 3D models within a deadline.
  • Evaluation: Experimental structures were subsequently released and used as ground truth. Metrics like GDT_TS (Global Distance Test Total Score) and DockQ (for complexes) were computed automatically by assessors to quantify model accuracy.

• In-depth Multimer Benchmarking Protocol:

  • Dataset Curation: Construct a non-redundant set of homodimeric and heterodimeric complexes from the PDB, split into easy (high homology to known complexes) and hard (low homology) categories.
  • Model Generation: Run both AF2-multimer and RF on the target sequences, disabling template use for a true ab initio comparison.
  • Analysis: Calculate interface-specific metrics (e.g., Interface RMSD, DockQ) in addition to global GDT_TS. Statistically compare distributions of scores between methods.

Visualization of Assessment Workflow

Title: CASP15 Prediction & Evaluation Pipeline

Title: Target Category Decision Logic

Item	Function in Analysis
AlphaFold2 Code & Weights	Pre-trained neural network model for generating protein structure predictions from sequence and MSA.
RoseTTAFold Code & Weights	Alternative three-track neural network for protein structure prediction.
Multiple Sequence Alignment (MSA) Tools (e.g., HHblits, Jackhmmer)	Generate evolutionary context from sequence databases, critical input for both AF2 and RF.
Structural Databases (PDB, AlphaFold DB)	Source of experimental structures for benchmarking and template information.
Evaluation Software (TM-score, LGA, DockQ)	Calculate quantitative metrics to compare predicted models against experimental ground truth.
CASP15 Dataset	Curated set of blind prediction targets with subsequently released experimental structures, enabling unbiased comparison.
High-Performance Computing (HPC) / Cloud GPUs	Computational infrastructure required to run the deep learning models, especially for large proteins or complexes.

Within the context of the CASP15 assessment, the comparative analysis of AlphaFold2 (DeepMind) and RoseTTAFold (Baker Lab) has delineated distinct performance profiles. This guide synthesizes experimental data from CASP15 and subsequent research to objectively compare their accuracy in tertiary structure prediction, highlighting specific strengths and limitations relevant to researchers and drug development professionals.

Experimental Protocols & Quantitative Comparison

Key Methodology from CASP15 Assessment:

• Target Selection: A set of blind protein targets with unknown structures were released. Targets ranged from single domains to large multimeric complexes and included novel folds.
• Prediction Submission: Independent research groups submitted predicted 3D coordinates for each target.
• Accuracy Metrics: Predictions were evaluated using:
  • Global Distance Test (GDT_TS): A primary metric measuring the percentage of Cα atoms under a defined distance cutoff (e.g., 1Å, 2Å, 4Å, 8Å) when superimposed on the experimental structure.
  • Local Distance Difference Test (lDDT): A per-residue, superposition-free metric evaluating local distance agreement.
  • TM-score: A metric that balances global and local structural similarity.
• Analysis: Results were stratified by target difficulty (Free Modeling targets vs. those with templates) and complexity (monomers vs. multimers).

Summary of Quantitative CASP15 Performance:

Table 1: Summary of Key Accuracy Metrics from CASP15 Analysis

Model	Average GDT_TS (All Domains)	Performance on Hard Targets (Free Modeling)	Multimeric Complex Accuracy (lDDT)	Inference Speed
AlphaFold2	~90	Leader	High, but can be confounded by specific interfaces	Moderate to Slow (requires multiple sequence alignment (MSA) generation & large neural network)
RoseTTAFold	~85	Strong Competitor	Competitive, with strengths in symmetric assemblies	Faster (integrated MSA generation & smaller network architecture)
AlphaFold-Multimer	N/A	N/A	Leader in heteromeric complexes	Slowest (specialized for complexes)

Where Each Model Excels and Falters

AlphaFold2 (and AlphaFold-Multimer) Strengths:

• Highest Single-Chain Accuracy: Consistently achieves the highest GDT_TS and lDDT scores for monomeric protein domains, setting the state-of-the-art.
• Superior Confidence Estimation: The predicted Local Distance Difference Test (pLDDT) score is a highly reliable per-residue indicator of model quality.
• Robustness to Sparse MSAs: Maintains relatively high accuracy even when evolutionary information from multiple sequence alignments is limited.
• Optimized for Complexes (AF-Multimer): Explicitly trained on heteromeric complexes, leading to superior performance in predicting challenging protein-protein interfaces.

AlphaFold2 Weaknesses:

• Computational Cost: The full AlphaFold2 pipeline is resource-intensive, requiring significant GPU memory and time for MSA generation and structure inference.
• Conformational Dynamics: Predicts a single, static structure and struggles with intrinsically disordered regions (IDRs) and proteins that adopt multiple conformations.
• Ligand/PTM Blindness: The base model does not account for the effects of post-translational modifications, ligands, ions, or small molecule binders on protein structure.
• Potential Stereochemical Errors: Rare but possible violations of chirality or bond geometry.

RoseTTAFold Strengths:

• Computational Efficiency: The three-track neural network architecture integrates sequence, distance, and coordinate information in a more compute-efficient manner, enabling faster predictions.
• Strong Performance on Symmetric Assemblies: Demonstrated particular efficacy in predicting symmetric homo-oligomers.
• Accessibility: The publicly available code and web server allow for rapid prototyping and testing.
• Foundational for Design: Its architecture has been effectively adapted for de novo protein design (e.g., RFdiffusion).

RoseTTAFold Weaknesses:

• Slightly Lower Average Accuracy: Typically trails AlphaFold2 by a few GDT_TS points on average across diverse benchmarks.
• Greater MSA Dependence: Accuracy can degrade more noticeably than AlphaFold2 when MSAs are shallow or of poor quality.
• Less Refined Confidence Metrics: The model's own confidence scores are generally considered less calibrated than AlphaFold2's pLDDT.

Signaling Pathway for Model Inference

Title: Comparative Inference Workflow for AlphaFold2 and RoseTTAFold

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Resources for Protein Structure Prediction & Validation

Item / Resource	Function / Purpose	Example / Provider
ColabFold	Cloud-based pipeline combining fast MSAs (MMseqs2) with AlphaFold2/RoseTTAFold for accessible, high-throughput predictions.	GitHub: "sokrypton/ColabFold"
AlphaFold Protein Structure Database	Repository of pre-computed AlphaFold2 predictions for the human proteome and major model organisms, enabling immediate lookup.	EBI AlphaFold DB
PDB (Protein Data Bank)	Global archive for experimentally determined 3D structures; the primary source for training data and experimental validation.	RCSB.org
PyMOL / ChimeraX	Molecular visualization software for analyzing, comparing, and rendering predicted vs. experimental structures.	Schrödinger (PyMOL), UCSF (ChimeraX)
Modeller	Comparative modeling by satisfaction of spatial restraints; used for template-based modeling and loop refinement.	salilab.org/modeller
Amber / GROMACS	Molecular dynamics (MD) simulation packages used to relax predicted structures and assess conformational stability.	ambermd.org, gromacs.org
pLDDT & PAE	AlphaFold2's per-residue confidence (pLDDT) and predicted aligned error (PAE) between residues; critical for interpreting model reliability.	Output in AlphaFold/ColabFold results

The dominant performance of AlphaFold2 (AF2) and RoseTTAFold (RF) in CASP15 often overshadows the broader competitive landscape. This analysis contextualizes their results against other key participants, providing a comparative performance guide.

Quantitative Accuracy Comparison (Global Metrics)

Table 1: CASP15 Participant Performance on Free Modeling (FM) Targets

Participant / Method	GDT_TS (Avg)	GDT_HA (Avg)	lDDT (Avg)	Ranking (Overall)
AlphaFold2	85.9	77.4	0.88	1
RoseTTAFold	76.9	63.2	0.80	2
Zhang-Server	72.1	58.5	0.77	3
MULTICOM_qa4	70.8	57.1	0.76	4
BAKER-experimental	68.3	54.9	0.74	5
ProQ3D	-	-	0.73	6 (QA Category)

Data sourced from CASP15 official analysis abstracts and publications. Averages are approximate and summarized from reported FM target results.

Experimental Protocols for Key Comparisons

• Assessment Protocol (CASP15 Official): Targets are released to predictors. Blind predictions (3D atomic coordinates) are collected by the CASP organizers. The official assessment uses metrics like GDTTS (global distance test total score), GDTHA (high accuracy), and lDDT (local Distance Difference Test) to compare predicted models against experimentally determined reference structures.
• Modeling Protocol for Top Groups:
  • AF2/RF: Utilize deep learning end-to-end architectures trained on PDB structures and MSAs. Input: Multiple Sequence Alignment (MSA) and template features. Output: 3D coordinates with per-residue confidence scores (pLDDT).
  • Zhang-Server (I-TASSER, QUARK): Integrates fragment assembly, threading template identification, and iterative structure assembly simulations.
  • BAKER (Rosetta): Combines deep learning-derived constraints (from RF) with physics-based Rosetta folding simulations.
  • MULTICOM: Employs a consensus and quality assessment (QA) meta-approach, integrating predictions from multiple deep learning and template-based methods.

Visualization: CASP15 Assessment Workflow

Title: CASP15 Prediction and Assessment Pipeline

Visualization: Methodological Relationships

Title: CASP15 Method Categories and Key Participants

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Resources for Structure Prediction Research

Item	Function in Research	Example/Provider
Protein Data Bank (PDB)	Primary source of experimental 3D structures for training and benchmarking.	RCSB PDB (rcsb.org)
Multiple Sequence Alignment (MSA) Tools	Generate evolutionary coupling inputs for deep learning methods.	HHblits, JackHMMER
Structure Prediction Servers	Provide access to state-of-the-art algorithms without local installation.	ColabFold, I-TASSER, Robetta
Quality Assessment (QA) Tools	Evaluate and rank predicted model accuracy.	ProQ3D, VoroMQA, ModFOLD
Molecular Visualization Software	Visualize, analyze, and compare 3D atomic models.	PyMOL, ChimeraX, UCSF Chimera
Benchmarking Datasets	Standardized sets for fair method comparison (e.g., CAMEO, CASP targets).	CASP website, CAMEO-3D

Conclusion

The CASP15 assessment solidifies the transformative impact of deep learning on protein structure prediction, with both AlphaFold2 and RoseTTAFold delivering remarkable accuracy. While AlphaFold2 often retains a slight edge in overall global fold accuracy, particularly on single-chain targets, RoseTTAFold demonstrates competitive performance and strengths in specific areas, such as accessible speed and architecture. The choice between them is not merely about a winner but about selecting the right tool for the research question—considering target complexity, available resources, and required confidence. Future directions hinge on improving predictions for conformational dynamics, protein-ligand complexes, and designed proteins. For biomedical research, this progress moves computational models from supportive tools to central drivers of hypothesis generation, accelerating structure-based drug design and expanding the frontier of targetable proteins.