Protein Structure Prediction

19 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Highly accurate protein structure prediction with AlphaFold

deepmind/alphafold Nature 2021

Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.

MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

mzweilin/EvadeML-Zoo 10 May 2016

Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics.

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

idrori/cu-tsp 9 Nov 2019

Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.

SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning

jonathanking/sidechainnet 16 Oct 2020

Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information.

ProteinNet: a standardized data set for machine learning of protein structure

EricAlcaide/MiniFold 1 Feb 2019

We have created the ProteinNet series of data sets to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships.

Iterative SE(3)-Transformers

FabianFuchsML/se3-transformer-public 26 Feb 2021

Motivated by this application, we implement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant attention-based model for graph data.

HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle

PaddlePaddle/PaddleHelix 12 Jul 2022

Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch.

HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative

PaddlePaddle/PaddleHelix 28 Jul 2022

Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs and templates for learning the co-evolution information.

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction

LucaAngioloni/ProteinSecondaryStructure-CNN 6 Mar 2014

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

matteofigliuzzi/bmDCA 12 Jan 2018

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.