Abstract
Recent advances in distance-based protein folding have led to a paradigm shift in protein structure prediction. Through sufficiently precise estimation of the inter-residue distance matrix for a protein sequence, it is now feasible to predict the correct folds for new proteins much more accurately than ever before. Despite the exciting progress, a dedicated visualization system that can dynamically capture the distance-based folding process is still lacking. Most molecular visualizers typically provide only a static view of a folded protein conformation, but do not capture the folding process. Even among the selected few graphical interfaces that do adopt a dynamic perspective, none of them are distance-based. Here we present PolyFold, an interactive visual simulator for dynamically capturing the distance-based protein folding process through real-time rendering of a distance matrix and its compatible spatial conformation as it folds in an intuitive and easy-to-use interface. PolyFold integrates highly convergent stochastic optimization algorithms with on-demand customizations and interactive manipulations to maximally satisfy the geometric constraints imposed by a distance matrix. PolyFold is capable of simulating the complex process of protein folding even on modest personal computers, thus making it accessible to the general public for fostering citizen science. Open source code of PolyFold is freely available for download at https://github.com/Bhattacharya-Lab/PolyFold. It is implemented in cross-platform Java and binary executables are available for macOS, Linux, and Windows.
Funder
National Institute of General Medical Sciences
Division of Information and Intelligent Systems
Division of Biological Infrastructure
Publisher
Public Library of Science (PLoS)
Reference39 articles.
1. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments;LA Abriata;Proteins: Structure, Function, and Bioinformatics,2019
2. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13);AW Senior;Proteins: Structure, Function, and Bioinformatics,2019
3. Analysis of distance-based protein structure prediction by deep learning in CASP13;J Xu;Proteins: Structure, Function, and Bioinformatics,2019
4. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13;J Hou;Proteins: Structure, Function, and Bioinformatics,2019
5. Distance matrix-based approach to protein structure prediction;A Kloczkowski;J Struct Funct Genomics,2009
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献