Protein Structure Prediction
57 papers with code • 4 benchmarks • 1 datasets
Libraries
Use these libraries to find Protein Structure Prediction models and implementations
SidechainNet
Most implemented papers
Highly accurate protein structure prediction with AlphaFold
deepmind/alphafold
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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
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
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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
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.
Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making.
ProteinNet: a standardized data set for machine learning of protein structure
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
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Motivated by this application, we implement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant attention-based model for graph data.
ProteinBERT: a universal deep-learning model of protein sequence and function
nadavbra/protein_bert
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Bioinformatics, Volume 38, Issue 8 2022
We introduce ProteinBERT, a deep language model specifically designed for proteins.
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development.
