ProFOLD Free modeling method for high-quality protein structure prediction
Introduction
Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end appoarch, ProFOLD, to estimate residue co-evolution directly from MSA. The key elements of ProFOLD include: (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures.
Publications
Credits
- Dongbo Bu dbu@ict.ac.cn Investigator
Advanced R&D lab, Institute of computing technology, Chinese Academy of Sciences, China
Community Ratings
Usability | Efficiency | Reliability | Rated By |
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Accession | BT007215 |
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Tool Type | Toolkit |
Category | 3D structure prediction |
Platforms | Linux/Unix |
Technologies | GPU, Python3 |
User Interface | Terminal Command Line |
Input Data | FASTA |
Download Count | 0 |
Country/Region | China |
Submitted By | Dongbo Bu |