AIMC Topic: Proteins

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Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning.

IEEE journal of biomedical and health informatics Jun 1, 2025
Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the ...
Humans Proteins Databases, Protein Genetic Predisposition to Disease Mutation Computational Biology Protein Interaction Mapping Deep Learning
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Protein function prediction using GO similarity-based heterogeneous network propagation.

Scientific reports May 31, 2025
Protein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. Despite significant advances, challenges persist due to data sparsity and functional ambiguity. ...
Computational Biology Proteins Humans Algorithms Gene Ontology Databases, Protein Saccharomyces cerevisiae
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DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.

Nature communications May 30, 2025
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug disco...
Pharmaceutical Preparations Drug Discovery Proteins Deep Learning Protein Binding Drug Development Humans Algorithms
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AI-Assisted Protein-Peptide Complex Prediction in a Practical Setting.

Journal of computational chemistry May 30, 2025
Accurate prediction of protein-peptide complex structures plays a critical role in structure-based drug design, including antibody design. Most peptide-docking benchmark studies were conducted using crystal structures of protein-peptide complexes; as...
Peptides Molecular Docking Simulation Proteins Protein Conformation Machine Learning
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Biomolecular Actuators for Soft Robots.

Chemical reviews May 28, 2025
Biomolecules present promising stimuli-responsive mechanisms to revolutionize soft actuators. Proteins, peptides, and nucleic acids foster specific intermolecular interactions, and their boundless sequence design spaces encode precise actuation capab...
Peptides Proteins Robotics Biocompatible Materials Nucleic Acids
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for Investigating Conformational Transitions and Environmental Interactions of Proteins.

Journal of chemical theory and computation May 27, 2025
Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dy...
Protein Conformation Thermodynamics Proteins Molecular Dynamics Simulation
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Machine-learning-guided identification of protein secondary structures using spectral and structural descriptors.

Biomaterials science May 27, 2025
Interrogation of the secondary structures of proteins is essential for designing and engineering more effective and safer protein-based biomaterials and other classes of theranostic materials. Protein secondary structures are commonly assessed using ...
Circular Dichroism Proteins Protein Structure, Secondary Algorithms Machine Learning
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Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening.

Journal of chemical information and modeling May 26, 2025
Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, the emergence of machine learning (ML)-based docking approaches has opened new possibilities for enhancing VS technologies. In this study...
Drug Evaluation, Preclinical Protein Conformation Molecular Docking Simulation Proteins Machine Learning
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Deep learning-guided design of dynamic proteins.

Science (New York, N.Y.) May 22, 2025
Deep learning has advanced the design of static protein structures, but the controlled conformational changes that are hallmarks of natural signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep learning-gui...
Molecular Dynamics Simulation Ligands Mutation Allosteric Regulation Protein Conformation Protein Engineering Deep Learning Proteins
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Enhanced Exploration of Protein Conformational Space through Integration of Ultra-Coarse-Grained Models to Multiscale Workflows.

The journal of physical chemistry. B May 22, 2025
Computational techniques such as all-atom (AA) molecular dynamics (MD) simulations and coarse-grained (CG) models have been essential to study various biological problems over a wide range of scales. While AA simulations provide detailed insights, th...
Molecular Dynamics Simulation Protein Conformation Machine Learning Proteins
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