Protein Structure Prediction
49 papers with code • 4 benchmarks • 1 datasets
Libraries
Use these libraries to find Protein Structure Prediction models and implementations
SidechainNet
Most implemented papers
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
magiccircuit/msa-augmentor
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The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
LucaAngioloni/ProteinSecondaryStructure-CNN
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Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations.
How pairwise coevolutionary models capture the collective residue variability in proteins
We show how correlations are built up in a highly collective way by a large number of coupling paths, which are based on the protein's three-dimensional structure.
Universal Transforming Geometric Network
JinLi711/3DProteinPrediction
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The recurrent geometric network (RGN), the first end-to-end differentiable neural architecture for protein structure prediction, is a competitive alternative to existing models.
PolyFold: an interactive visual simulator for distance-based protein folding
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.
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.
Energy-based models for atomic-resolution protein conformations
facebookresearch/protein-ebm
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ICLR 2020
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale.
AlphaCrystal: Contact map based crystal structure prediction using deep learning
To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations.
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference.
Generative De Novo Protein Design with Global Context
chengtan9907/gca-generative-protein-design
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Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.
