AIMC Topic: Protein Conformation

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Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling Dec 26, 2024
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad applicati...
Protein Binding Proteins Molecular Docking Simulation Deep Learning Ligands Protein Conformation Algorithms
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Topology-based protein classification: A deep learning approach.

Biochemical and biophysical research communications Dec 24, 2024
Utilizing Artificial Intelligence (AI) in computational biology techniques could offer significant advantages in alleviating the growing workloads faced by structural biologists, especially with the emergence of big data. In this study, we employed D...
Computational Biology Proteins Protein Conformation Databases, Protein Neural Networks, Computer Deep Learning
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Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

Molecules (Basel, Switzerland) Dec 24, 2024
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and im...
Allosteric Regulation Protein Tyrosine Phosphatase, Non-Receptor Type 11 Machine Learning Protein Binding Humans Molecular Dynamics Simulation Enzyme Inhibitors Protein Conformation
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An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

eLife Dec 18, 2024
The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defi...
Humans Machine Learning Protein Binding Entropy 1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine Protein Conformation Thermodynamics alpha-Synuclein Intrinsically Disordered Proteins
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S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Dec 12, 2024
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of pro...
Computational Biology Machine Learning Proteins Protein Conformation
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Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling Dec 10, 2024
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...
Machine Learning Protein Conformation Protein Binding Binding Sites Humans Molecular Docking Simulation Protein Kinase Inhibitors Protein Kinases
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Prediction of Inter-Residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction i...
Algorithms Computational Biology Proteins Deep Learning Protein Conformation Databases, Protein Models, Molecular
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SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Nature methods Nov 27, 2024
Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and su...
Deep Learning Ligands Algorithms Humans Proteins Protein Binding Protein Conformation Software Molecular Docking Simulation
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Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables.

Proceedings of the National Academy of Sciences of the United States of America Nov 26, 2024
Machine learning (ML) is transforming the investigation of complex biological processes. In enzymatic catalysis, one significant challenge is identifying the reactive conformations (RC) of the enzyme:substrate complex where the substrate assumes a pr...
Oligosaccharides Catalytic Domain Humans Machine Learning Pancreatic alpha-Amylases Protein Conformation Substrate Specificity
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ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks.

Journal of chemical information and modeling Nov 18, 2024
Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essenti...
Protein Interaction Mapping Models, Molecular Protein Conformation Neural Networks, Computer Proteins Protein Binding Databases, Protein
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