AIMC Topic: Proteins

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Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method.

Journal of computer-aided molecular design Mar 17, 2020
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-meta...
Proteins Metals Databases, Protein Protein Binding Quantum Theory Machine Learning Ligands Computational Biology Drug Design
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Energy-based graph convolutional networks for scoring protein docking models.

Proteins Mar 16, 2020
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for su...
Protein Multimerization Protein Interaction Domains and Motifs Structural Homology, Protein Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Thermodynamics Computer Graphics Protein Interaction Mapping Amino Acid Sequence Ligands Binding Sites Peptides Molecular Docking Simulation Deep Learning Protein Binding Proteins Benchmarking Humans Software Research Design
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Machine learning prediction of oncology drug targets based on protein and network properties.

BMC bioinformatics Mar 14, 2020
BACKGROUND: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, r...
Computer Simulation Humans Drug Discovery Machine Learning Antineoplastic Agents Area Under Curve Protein Interaction Maps Genomics Proteins
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Machine learning models for drug-target interactions: current knowledge and future directions.

Drug discovery today Mar 12, 2020
Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compound...
Computational Biology Humans Drug Development Ligands Machine Learning Drug Delivery Systems Proteins Drug Discovery Deep Learning
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Druggability Assessment in TRAPP Using Machine Learning Approaches.

Journal of chemical information and modeling Mar 11, 2020
Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Because of the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. ...
Proteins Protein Conformation Machine Learning Binding Sites Protein Binding
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DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet.

Journal of chemical information and modeling Mar 9, 2020
Computational protein design remains a challenging task despite its remarkable success in the past few decades. With the rapid progress of deep-learning techniques and the accumulation of three-dimensional protein structures, the use of deep neural n...
Amino Acid Sequence Neural Networks, Computer Computational Biology Proteins Amino Acids
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Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps.

Scientific reports Mar 9, 2020
Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on a...
Software Algorithms Deep Learning Proteins Models, Molecular Amino Acid Sequence Humans Neural Networks, Computer Protein Conformation Cryoelectron Microscopy Sequence Homology
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ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence.

Journal of molecular biology Mar 4, 2020
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system...
Humans Neural Networks, Computer Protein Binding Computational Biology Proteins Machine Learning Software Animals Sequence Analysis, Protein Nucleic Acid Conformation Prokaryotic Cells DNA Binding Sites RNA Eukaryota Protein Conformation
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Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach.

Journal of chemical information and modeling Mar 3, 2020
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard...
Ligands Deep Learning Protein Binding Proteins Molecular Docking Simulation
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Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

Journal of chemical information and modeling Mar 3, 2020
In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scoring functions. However, several published studies have questioned that the superior performance of m...
Algorithms Proteins Bayes Theorem Neural Networks, Computer Machine Learning Ligands
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