AIMC Topic: Protein Binding

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Gene ontology improves template selection in comparative protein docking.

Proteins Dec 27, 2018
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and a...
Structural Homology, Protein Proteins Databases, Protein Models, Molecular Binding Sites Protein Interaction Maps Protein Interaction Mapping Humans Protein Binding Protein Conformation Software Computational Biology Gene Ontology Molecular Docking Simulation
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Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE.

Journal of chemical theory and computation Dec 24, 2018
In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149...
Proteins Molecular Dynamics Simulation Ligands Thermodynamics Deep Learning Protein Binding
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An SVM-based method for assessment of transcription factor-DNA complex models.

BMC bioinformatics Dec 21, 2018
BACKGROUND: Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex struc...
Protein Binding Transcription Factors Algorithms Support Vector Machine DNA Models, Molecular
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SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

PLoS computational biology Dec 11, 2018
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational ...
Databases, Nucleic Acid RNA Processing, Post-Transcriptional Protein Binding Computational Biology RNA-Binding Proteins Amino Acid Sequence Base Sequence RNA, Long Noncoding Humans Databases, Protein Machine Learning Algorithms
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Discovering de novo peptide substrates for enzymes using machine learning.

Nature communications Dec 7, 2018
The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal ...
Bayes Theorem Bacterial Proteins Amino Acid Sequence Substrate Specificity Transferases (Other Substituted Phosphate Groups) Peptides Protein Binding Recombinant Proteins Machine Learning
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iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Proteins Dec 3, 2018
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using...
Computational Biology Evolution, Molecular Proteins Machine Learning Protein Binding Tumor Suppressor Protein p53 Thermodynamics Binding, Competitive Proto-Oncogene Proteins c-mdm2 Models, Molecular Mutation Protein Domains
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Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.

Computational biology and chemistry Dec 2, 2018
BACKGROUND: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target inte...
Protein Binding Computational Biology Cluster Analysis Machine Learning Algorithms Neoplasms Molecular Targeted Therapy Humans
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Learning protein binding affinity using privileged information.

BMC bioinformatics Nov 15, 2018
BACKGROUND: Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time an...
Support Vector Machine ROC Curve Proteins Machine Learning Ligands Amino Acid Sequence Protein Binding Algorithms Computational Biology Reproducibility of Results
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Application of molecular docking and PSO-SVR intelligent approaches in antimalarial activity prediction of enantiomeric cycloguanil analogues.

SAR and QSAR in environmental research Nov 1, 2018
A series of antifolate compounds, i.e. 1-(4-chlorophenyl)-6,6-dimethyl-1,3,5-triazine-2,4-diamine, or cycloguanil analogues, have shown effective inhibiting properties against Plasmodium falciparum dihydrofolate reductase (PfDHFR). In this work, the ...
Substrate Specificity Triazines Proguanil Quantitative Structure-Activity Relationship Machine Learning Reproducibility of Results Tetrahydrofolate Dehydrogenase Protein Binding Algorithms Stereoisomerism Molecular Docking Simulation Molecular Structure Plasmodium falciparum Folic Acid Antagonists Antimalarials Enzyme Inhibitors
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Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates.

Journal of computer-aided molecular design Oct 26, 2018
Feature selection is commonly used as a preprocessing step to machine learning for improving learning performance, lowering computational complexity and facilitating model interpretation. This paper proposes the application of boosting feature select...
Ligands Support Vector Machine Quantitative Structure-Activity Relationship Decision Trees ATP Binding Cassette Transporter, Subfamily B Molecular Structure Machine Learning Algorithms Protein Binding
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