Protein Structure Prediction
49 papers with code • 4 benchmarks • 1 datasets
Libraries
Use these libraries to find Protein Structure Prediction models and implementations
SidechainNet
Latest papers
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
alexduvalinho/geometric-gnns
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In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years.
Exploring Post-Training Quantization of Protein Language Models
In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications.
AtomSurf : Surface Representation for Learning on Protein Structures
An essential aspect of learning from protein structures is the choice of their representation as a geometric object (be it a grid, graph, or surface), which conditions the associated learning method.
APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics
hyunp2/alphafold
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The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation.
OpenProteinSet: Training data for structural biology at scale
aqlaboratory/openfold
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NeurIPS 2023
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades.
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction.
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision.
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
deepgraphlearning/diffpack
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NeurIPS 2023
In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space.
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function.
