AIMC Topic: Proteins

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Interpretable and explainable predictive machine learning models for data-driven protein engineering.

Biotechnology advances Dec 5, 2024
Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein ...
Proteins Biotechnology Protein Engineering Machine Learning
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Structure-Aware Graph Attention Diffusion Network for Protein-Ligand Binding Affinity Prediction.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Accurate prediction of protein-ligand binding affinities can significantly advance the development of drug discovery. Several graph neural network (GNN)-based methods learn representations of protein-ligand complexes via modeling intermolecule intera...
Algorithms Humans Databases, Protein Protein Binding Drug Discovery Ligands Neural Networks, Computer Proteins
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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 Algorithms Proteins Ligands Humans Protein Binding Molecular Docking Simulation Protein Conformation Software
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Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.

Journal of chemical information and modeling Nov 22, 2024
The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between liga...
Ligands Proteins Databases, Protein Deep Learning Protein Binding Molecular Docking Simulation
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MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning.

Journal of molecular biology Nov 20, 2024
Multi-ligand binding residues (MLBRs) are amino acids in protein sequences that interact with multiple different ligands that include proteins, peptides, nucleic acids, and a variety of small molecules. MLBRs are implicated in a number of cellular fu...
Software Deep Learning Protein Binding Neural Networks, Computer Humans Machine Learning Databases, Protein Computational Biology Ligands Proteins Binding Sites
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PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network.

International journal of biological macromolecules Nov 19, 2024
The involvement of protein intrinsic disorder in essential biological processes, it is well known in structural biology. However, experimental methods for detecting intrinsic structural disorder and directly measuring highly dynamic behavior of prote...
Neural Networks, Computer Algorithms Computational Biology Software Intrinsically Disordered Proteins Proteins Databases, Protein
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CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein-Ligand Binding Interactions.

Journal of chemical information and modeling Nov 19, 2024
Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In t...
Proteins Ligands Small Molecule Libraries Deep Learning Protein Binding Drug Discovery
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DeepKlapred: A deep learning framework for identifying protein lysine lactylation sites via multi-view feature fusion.

International journal of biological macromolecules Nov 19, 2024
Lysine lactylation (Kla) is a post-translational modification (PTM) that holds significant importance in the regulation of various biological processes. While traditional experimental methods are highly accurate for identifying Kla sites, they are bo...
Deep Learning Lysine Computational Biology Proteins Amino Acid Sequence Algorithms Protein Processing, Post-Translational
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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...
Models, Molecular Protein Conformation Proteins Databases, Protein Protein Interaction Mapping Neural Networks, Computer Protein Binding
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DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Artificial intelligence in medicine Nov 17, 2024
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions predicti...
Algorithms Drug Discovery Humans Neural Networks, Computer Computational Biology Drug Interactions Proteins Pharmaceutical Preparations
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