AIMC Topic: Proteins

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SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.

Journal of computational chemistry Jun 20, 2024
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific pro...
Machine Learning Proteins Software Ligands Molecular Docking Simulation
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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation Jun 18, 2024
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...
Static Electricity Neural Networks, Computer Thermodynamics Dipeptides Proteins Protein Conformation Molecular Dynamics Simulation
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Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.

Nature communications Jun 17, 2024
As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems a...
Humans Neural Networks, Computer Carbohydrates Binding Sites Proteins Deep Learning Protein Binding Models, Molecular
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DP-site: A dual deep learning-based method for protein-peptide interaction site prediction.

Methods (San Diego, Calif.) Jun 12, 2024
BACKGROUND: Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the year...
Proteins Databases, Protein Binding Sites Protein Interaction Mapping Protein Binding Computational Biology Peptides Deep Learning Neural Networks, Computer Software Humans
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The dynamic duo: cryo-EM teams up with machine learning to visualize biomolecules in motion.

BioTechniques Jun 12, 2024
Cryo-EM has been a key technique in our understanding of biomolecular structures. Now, machine learning techniques are being used to put these structures in motion, revealing dynamic interactions and processes happening on a molecular and cellular le...
Cryoelectron Microscopy Humans Proteins Machine Learning Motion
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CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.

Journal of chemical information and modeling Jun 7, 2024
We present a novel and interpretable approach for assessing small-molecule binding using context explanation networks. Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to...
Machine Learning Protein Binding Proteins Neural Networks, Computer Ligands Small Molecule Libraries
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Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Journal of chemical information and modeling Jun 6, 2024
Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibit...
Data Mining Ligands Algorithms Molecular Docking Simulation Artificial Intelligence Drug Design Proteins Machine Learning Molecular Dynamics Simulation
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ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins.

Computational biology and chemistry Jun 6, 2024
Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on informa...
Computational Biology Proteins Deep Learning Protein Interaction Maps Saccharomyces cerevisiae
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EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks.

International journal of molecular sciences Jun 6, 2024
Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell ...
Neural Networks, Computer Computational Biology Protein Conformation Protein Multimerization Proteins Models, Molecular
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PTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings.

IEEE journal of biomedical and health informatics Jun 6, 2024
Phosphorylation is pivotal in numerous fundamental cellular processes and plays a significant role in the onset and progression of various diseases. The accurate identification of these phosphorylation sites is crucial for unraveling the molecular me...
Humans Deep Learning A549 Cells Amino Acid Sequence Sequence Analysis, Protein Proteins Phosphorylation
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