AIMC Topic: Protein Conformation

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Probing the Gate-Opening Transition in the Bacterial ClpP Peptidase Using Molecular Dynamics Simulations and Machine Learning.

Biochemistry
Preserving proteome integrity is crucial for maintaining cell viability across all kingdoms of life. The bacterial caseinolytic protease (ClpP) plays a critical role in maintaining protein homeostasis by degrading misfolded or damaged proteins within...

AQuaRef: machine learning accelerated quantum refinement of protein structures.

Nature communications
Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical enti...

TEMPL: A Template-Based Protein-Ligand Pose Prediction Baseline.

Journal of chemical information and modeling
Pose prediction of ligands to proteins remains a central challenge of structure-based drug design. Although data leakage and generalizability concerns remain, data-driven methods for pose prediction (i.e., based on deep learning and diffusion) now ro...

Mechanistic Basis for GPCR Phosphorylation-Dependent Allosteric Signaling Specificity of β-Arrestin 1 and 2.

Journal of chemical information and modeling
β-Arrestins (βarr1 and βarr2) are key transducers of G protein-coupled receptor (GPCR) signaling, orchestrating both shared and isoform-specific intracellular pathways. Phosphorylation of the receptor C-terminal tail by GPCR kinases encodes regulator...

ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis.

Nature communications
Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological ...

GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.

Proceedings of the National Academy of Sciences of the United States of America
While deep learning has advanced protein sequence and function design, engineering highly active and stable proteins still requires labor-intensive iterative computational design and experimentation. There is a critical need for methods capable of di...

SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions.

Genome biology
Intrinsically disordered proteins and regions (IDRs) lack stable 3D structures, posing challenges for interaction prediction. We present SpatPPI, a geometric deep learning model tailored for IDPPI prediction. SpatPPI leverages structural cues from fo...

Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.

Nature communications
Co-folding models represent a major innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom, AlphaFold3, and others have shown high-quality results on predicting the structures of protein...

AI-driven protein pocket detection through integrating deep Q-networks for structural analysis.

Journal of computer-aided molecular design
Protein pockets, or small cavities on the protein surface, are critical sites for enzymatic catalysis, molecular recognition, and drug binding. Accurately identifying these pockets is crucial for understanding protein function and designing therapeut...

Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.

Journal of chemical information and modeling
Network methods and molecular dynamics (MD) simulations have become essential tools for studying protein dynamics. However, applying network methods to MD simulations of flexible proteins is a major challenge, since the high conformational heterogene...