AIMC Topic: Proteins

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Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

Interdisciplinary sciences, computational life sciences Nov 14, 2024
The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural ...
Neural Networks, Computer Algorithms Ligands Protein Conformation Graph Neural Networks Protein Binding Proteins
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deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction.

Biomolecules Nov 14, 2024
Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-ato...
Models, Molecular Deep Learning Computational Biology Proteins Neural Networks, Computer Software
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An experimental analysis of graph representation learning for Gene Ontology based protein function prediction.

PeerJ Nov 14, 2024
Understanding protein function is crucial for deciphering biological systems and facilitating various biomedical applications. Computational methods for predicting Gene Ontology functions of proteins emerged in the 2000s to bridge the gap between the...
Proteins Algorithms Gene Ontology Humans Protein Interaction Maps Computational Biology Machine Learning
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Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

Journal of bioinformatics and computational biology Nov 11, 2024
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end,...
Deep Learning Pharmaceutical Preparations Protein Binding Algorithms Neural Networks, Computer Ligands Humans Computational Biology Drug Discovery Binding Sites Machine Learning Proteins Databases, Protein
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LGS-PPIS: A Local-Global Structural Information Aggregation Framework for Predicting Protein-Protein Interaction Sites.

Proteins Nov 9, 2024
Exploring protein-protein interaction sites (PPIS) is of significance to elucidating the intrinsic mechanisms of diverse biological processes. On this basis, recent studies have applied deep learning-based technologies to overcome the high cost of we...
Humans Deep Learning Protein Binding Algorithms Protein Interaction Domains and Motifs Databases, Protein Binding Sites Computational Biology Proteins Protein Interaction Mapping
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Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.

IET systems biology Nov 8, 2024
Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, cont...
Proteins Databases, Protein Position-Specific Scoring Matrices Deep Learning Computational Biology Neural Networks, Computer
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AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation Nov 8, 2024
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-...
Machine Learning Proteins Thermodynamics Molecular Dynamics Simulation Neural Networks, Computer
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A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Nature computational science Nov 8, 2024
Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, ins...
Bayes Theorem Ligands Deep Learning Proteins Humans Algorithms Drug Design Neural Networks, Computer
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Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.

Nature communications Nov 7, 2024
Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While...
Algorithms Protein Binding Computational Biology Neural Networks, Computer Machine Learning Proteins Protein Interaction Mapping Models, Molecular
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Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

IEEE transactions on pattern analysis and machine intelligence Nov 6, 2024
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels o...
Protein Conformation Proteins Ligands Machine Learning COVID-19 SARS-CoV-2 Protein Binding Algorithms Humans Neural Networks, Computer
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