AIMC Topic: Proteins

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Contextual AI models for single-cell protein biology.

Nature methods Jul 22, 2024
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challen...
Protein Interaction Maps Deep Learning Proteins Computational Biology Single-Cell Analysis Humans Algorithms
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Binding and sensing diverse small molecules using shape-complementary pseudocycles.

Science (New York, N.Y.) Jul 18, 2024
We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying sha...
Small Molecule Libraries Molecular Docking Simulation Thyroxine Ligands Deep Learning Protein Binding Methotrexate Protein Multimerization Binding Sites Nanopores Proteins
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MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics.

Journal of computer-aided molecular design Jul 17, 2024
Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and an...
Drug Design Protein Binding Workflow Molecular Dynamics Simulation Proteins Software Humans Molecular Docking Simulation Machine Learning Binding Sites Ligands Protein Conformation
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Deep learning methods for protein function prediction.

Proteomics Jul 12, 2024
Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in p...
Computational Biology Databases, Protein Humans Proteins Deep Learning
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Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Journal of chemical information and modeling Jul 8, 2024
Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due t...
Pharmaceutical Preparations Ligands Proteins Protein Domains Drug Discovery Deep Learning
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RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints.

Methods (San Diego, Calif.) Jul 6, 2024
Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementat...
Markov Chains Humans Neural Networks, Computer Proteins Molecular Dynamics Simulation Deep Learning
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The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.

Topics in current chemistry (Cham) Jul 4, 2024
In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the i...
Deep Learning Proteins Algorithms Databases, Protein Protein Conformation
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Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

Journal of chemical information and modeling Jul 3, 2024
In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuraci...
Humans Machine Learning Molecular Docking Simulation Drug Evaluation, Preclinical Ligands Drug Discovery User-Computer Interface Antineoplastic Agents Proteins
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Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.

Journal of chemical information and modeling Jul 2, 2024
Message passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the proximity graph network (...
Protein Binding Neural Networks, Computer Molecular Docking Simulation Proteins Ligands
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Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

Nature communications Jul 2, 2024
The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and...
Molecular Dynamics Simulation Protein Conformation Cryoelectron Microscopy Proteins Databases, Protein Deep Learning Neural Networks, Computer Computational Biology
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