[HTML][HTML] Machine learning in protein structure prediction
M AlQuraishi - Current opinion in chemical biology, 2021 - Elsevier
Prediction of protein structure from sequence has been intensely studied for many decades,
owing to the problem's importance and its uniquely well-defined physical and computational …
owing to the problem's importance and its uniquely well-defined physical and computational …
OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with
exceptionally high accuracy. Its implementation, however, lacks the code and data required …
exceptionally high accuracy. Its implementation, however, lacks the code and data required …
Radiomics in radiooncology–challenging the medical physicist
Purpose Noticing the fast growing translation of artificial intelligence (AI) technologies to
medical image analysis this paper emphasizes the future role of the medical physicist in this …
medical image analysis this paper emphasizes the future role of the medical physicist in this …
A systematic survey on deep generative models for graph generation
Graphs are important data representations for describing objects and their relationships,
which appear in a wide diversity of real-world scenarios. As one of a critical problem in this …
which appear in a wide diversity of real-world scenarios. As one of a critical problem in this …
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
DT Jones, SM Kandathil - Bioinformatics, 2018 - academic.oup.com
Motivation In addition to substitution frequency data from protein sequence alignments,
many state-of-the-art methods for contact prediction rely on additional sources of …
many state-of-the-art methods for contact prediction rely on additional sources of …
OpenProteinSet: Training data for structural biology at scale
Multiple sequence alignments (MSAs) of proteins encode rich biological information and
have been workhorses in bioinformatic methods for tasks like protein design and protein …
have been workhorses in bioinformatic methods for tasks like protein design and protein …
ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks
Motivation Contact-map of a protein sequence dictates the global topology of structural fold.
Accurate prediction of the contact-map is thus essential to protein 3D structure prediction …
Accurate prediction of the contact-map is thus essential to protein 3D structure prediction …
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks
The topology of protein folds can be specified by the inter-residue contact-maps and
accurate contact-map prediction can help ab initio structure folding. We developed …
accurate contact-map prediction can help ab initio structure folding. We developed …
Protein contact prediction using metagenome sequence data and residual neural networks
Motivation Almost all protein residue contact prediction methods rely on the availability of
deep multiple sequence alignments (MSAs). However, many proteins from the poorly …
deep multiple sequence alignments (MSAs). However, many proteins from the poorly …
Recent developments in deep learning applied to protein structure prediction
SM Kandathil, JG Greener… - … : Structure, Function, and …, 2019 - Wiley Online Library
Although many structural bioinformatics tools have been using neural network models for a
long time, deep neural network (DNN) models have attracted considerable interest in recent …
long time, deep neural network (DNN) models have attracted considerable interest in recent …