AIMC Topic: Protein Conformation

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SARST2 high-throughput and resource-efficient protein structure alignment against massive databases.

Nature communications
The flood of protein structural Big Data is coming. With the belief that biotech researchers deserve powerful analysis engines to overcome the challenge of rapidly increasing computational demands, we are devoted to developing efficient protein struc...

Molecular Dynamics and Neural Network Analysis Reveal Sequential Gating and Allosteric Communication in FMRFamide-Activated Sodium Channels.

Journal of chemical information and modeling
FMRFamide-activated sodium channels (FaNaCs) represent a unique class of neuropeptide-gated ion channels within the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. While cryo-electron microscopy has revealed static binding architectures, ...

A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction.

Scientific reports
The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformatio...

Revisiting Protein-Protein Docking: A Systematic Evaluation Framework.

Journal of chemical information and modeling
Protein-protein interactions play pivotal roles in a wide range of biological processes. Determining the atomic-level structures of protein-protein complexes is indispensable for elucidating macromolecular interaction mechanisms and advancing structu...

CGBack: Diffusion Model for Backmapping Large-Scale and Complex Coarse-Grained Molecular Systems.

Journal of chemical information and modeling
Molecular dynamics simulations based on coarse-grained (CG) models are used to accelerate conformational dynamics of biomolecules and other chemical systems with reduced computational costs. CG models achieve this by discarding atomic information nec...

Sampling and Ranking of Protein Conformations Using Machine Learning Techniques Do Not Improve the Quality of Rigid Protein-Protein Docking.

Journal of chemical information and modeling
Rigid docking remains the most popular method of predicting protein-protein interactions in cases when experimental 3D structures of the complexes are not available. The docking often relies on known unbound (Apo) protein structures, which may differ...

Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca-Binding Proteins.

Journal of chemical theory and computation
Protein function is driven by transitions between metastable conformations, many of which are not conserved across homologues, offering opportunities for selective drug design. Accurately modeling both backbone and side chain metastability, and gener...

Molecular Mechanism of Na/H Antiporting in NhaA.

Journal of chemical theory and computation
Sodium-proton antiporter NhaA of is a paradigm to investigate the mechanistic basis of the fundamental Na/H exchange in cells. However, all existing crystal structures of NhaA are inward-facing (IF), and the putative outward-facing (OF) structures a...

Prediction of protein structural changes mediated by NS-SNPs in antibiotic resistance determinants in Streptococcus pneumoniae.

Archives of microbiology
Streptococcus pneumoniae (S. pneumoniae) is a gram-positive bacterium, which is a human pathogen that colonises the human nasopharyngeal region. The evolution of its resistance to many antibiotics has become a major clinical and public health problem...

Protein functional site annotation using local structure embeddings.

Proceedings of the National Academy of Sciences of the United States of America
The rapid expansion of protein sequence and structure databases has resulted in a significant number of proteins with ambiguous or unknown function. While advances in machine learning techniques hold great potential to fill this annotation gap, curre...