Comparison of Protein Structure Prediction Methods

Aspect	Homology Modeling	Threading (Fold Recognition)	Ab Initio Modeling
Principle	Predicts structure based on similarity to known protein structures.	Identifies structural templates even when sequence similarity is low.	Predicts structure purely from sequence without templates.
Accuracy	High accuracy when a closely related template is available.	Moderate accuracy, depends on the alignment and template quality.	Low to moderate accuracy, often used for small proteins.
Strengths	– Reliable when good templates exist.	– Can work with distant homologs. – Useful when sequence similarity is low.	– Can model novel folds. – Works without templates.
Weaknesses	– Fails without a homologous template. – Depends on template quality.	– Performance drops with poor templates. – Sensitive to alignment errors.	– Computationally expensive. – Challenging for large proteins.
Computational Cost	Relatively low, as it uses template structures.	Moderate, involves aligning sequences to structural templates.	Very high, requires extensive simulations or deep learning.
Tools/Software	– SWISS-MODEL – MODELLER	– Phyre2 – HHpred	– Rosetta

Comparison of AlphaFold2, AlphaFold3 and ESMFold

Aspect	AlphaFold 2	AlphaFold 3	ESMFold
Principle	Deep learning model trained on protein structures and sequences to predict 3D structures.	Advanced version of AlphaFold 2 with improved accuracy and scalability.	Transformer-based model trained on protein sequences and embeddings to predict structures.
Training Data	Protein Data Bank (PDB) and multiple sequence alignments (MSAs).	Expanded datasets with enhanced integration of structural and sequence data.	Trained on large-scale protein databases like UniProt and embeddings from ESM models.
Key Features	-High-accuracy predictions. -Relies on MSAs and templates.	-Enhanced prediction accuracy and speed. -Likely improved handling of low-quality or sparse MSAs.	-Fast and lightweight. -Can work without MSAs. -Direct sequence-to-structure prediction.
Performance Accuracy	-Near-experimental accuracy for many proteins. -Struggles with disordered regions.	– Improved accuracy, especially for complex and multi-domain proteins.	– Good for many sequences but generally less accurate than AlphaFold models.
Input Requirements	Protein sequence, MSAs, and optional templates.	Protein sequence, MSAs, and optional templates (with better handling).	Protein sequence only; MSAs are optional.
Output	High-confidence 3D protein structure with per-residue confidence scores.	Higher-confidence 3D structures with potentially better speed and efficiency.	Predicted 3D structure with focus on speed rather than fine detail.
Computational Cost	High, requires powerful GPUs or TPUs for optimal performance.	High, but more optimized compared to AlphaFold 2.	Lower computational cost; designed for scalability and speed.
Strengths	-High accuracy for a wide range of proteins. -Handles complexes well.	-Even higher accuracy and speed. -Better scalability.	-Faster predictions. -Minimal input requirements.
Weaknesses	-Computationally intensive. -Struggles with disordered regions and some complexes.	-Likely still resource-intensive. -Details about limitations emerging.	– Less accurate for challenging cases like disordered regions or large complexes.
Best Use Cases	-Detailed structural predictions for research.- Function annotation. -Drug discovery.	– Similar to AlphaFold 2 but better for complex cases and scaling.	-High-throughput predictions where speed is essential. -Initial screening for structural hypotheses.
Release Year	2021	2024	2022