Protein Structure Prediction
49 papers with code • 4 benchmarks • 1 datasets
Libraries
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SidechainNet
Latest papers with no code
HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field.
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials.
The curious case of A31P, a topology-switching mutant of the Repressor of Primer protein : A molecular dynamics study of its folding and misfolding
The main problem with understanding the dramatic effect of this mutation on the folding of Rop is to understand its very existence : Most computational methods appear to agree that the mutation should have had no appreciable effect, with the majority of energy minimization methods and protein structure prediction protocols indicating that this mutation is fully consistent with the native Rop structure, requiring only a local and minor change at the mutation site.
Convergence of Continuous Normalizing Flows for Learning Probability Distributions
We establish non-asymptotic error bounds for the distribution estimator based on CNFs, in terms of the Wasserstein-2 distance.
AlphaFold2 for protein structure prediction: Best practices and critical analyses
AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms.
Deep Reinforcement Learning for Modelling Protein Complexes
In this work, by taking each chain as a node and assembly actions as edges, we show that an acyclic undirected connected graph can be used to predict the structure of multi-chain protein complexes (a. k. a., protein complex modelling, PCM).
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
SGNet: Folding Symmetrical Protein Complex with Deep Learning
Deep learning has made significant progress in protein structure prediction, advancing the development of computational biology.
Understanding Biology in the Age of Artificial Intelligence
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models.
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration
Proteins are essential for life, and their structure determines their function.
