AIMC Topic: Models, Molecular

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GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor.

Proteins Jun 8, 2016
G Protein-Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo- or hetero-d...
Mice Support Vector Machine Humans Databases, Protein Animals Receptors, G-Protein-Coupled Binding Sites ROC Curve Protein Structure, Secondary Protein Binding Protein Multimerization Area Under Curve Protein Interaction Domains and Motifs Models, Molecular Amino Acid Sequence
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Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

Journal of chemical theory and computation Jun 3, 2016
A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry on...
Machine Learning Models, Molecular Amino Acids Quantum Theory Static Electricity Thermodynamics
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Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

Regulatory toxicology and pharmacology : RTP Feb 13, 2016
Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guidelin...
Humans Risk Assessment Models, Molecular Molecular Structure Knowledge Bases Databases, Factual Computer Simulation Animals Quantitative Structure-Activity Relationship Data Mining Mutagenesis Pattern Recognition, Automated Mutagens Mutagenicity Tests Amines
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Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11.

Scientific reports Jan 14, 2016
Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolu...
Models, Molecular Amino Acids Protein Conformation ROC Curve Support Vector Machine Proteins Algorithms
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UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.

BMC systems biology Jan 11, 2016
BACKGROUND: The conjugation of ubiquitin to a substrate protein (protein ubiquitylation), which involves a sequential process--E1 activation, E2 conjugation and E3 ligation, is crucial to the regulation of protein function and activity in eukaryotes....
Datasets as Topic Lysine Ubiquitin Ubiquitination Amino Acid Motifs Sequence Analysis, Protein Mass Spectrometry Models, Molecular Protein Domains Amino Acid Sequence Software Proteomics Proteome Machine Learning
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Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

The Journal of membrane biology Nov 12, 2015
Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have ...
Databases, Protein Models, Molecular Workflow Protein Conformation Reproducibility of Results Binding Sites Protein Binding Machine Learning Protein Interaction Mapping Computational Biology Proteins
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Effects of mutations on the structure and function of silkworm type 1 acetylcholinesterase.

Pesticide biochemistry and physiology Nov 10, 2015
AChE is the target of organophosphate (OP) and carbamate (CB) pesticides, and mutations in the gene can significantly reduce insects' sensitivity to these pesticides. Bombyx mori is highly sensitive to pesticides. To investigate the effects of mutati...
Acetylcholinesterase Animals Models, Molecular Plasmids Mutation Bombyx
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Ligand-Based Virtual Screening in a Search for Novel Anti-HIV-1 Chemotypes.

Journal of chemical information and modeling Oct 9, 2015
In a search for new anti-HIV-1 chemotypes, we developed a multistep ligand-based virtual screening (VS) protocol combining machine learning (ML) methods with the privileged structures (PS) concept. In its learning step, the VS protocol was based on H...
Anti-HIV Agents Models, Molecular Molecular Structure Drug Evaluation, Preclinical Biological Assay HIV-1 Ligands Humans Machine Learning Cells, Cultured Inhibitory Concentration 50 HIV Integrase Inhibitors
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Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

Immunogenetics Sep 29, 2015
A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented ...
Algorithms Histocompatibility Antigens Class II Animals Protein Binding Mice Binding Sites Molecular Sequence Data Humans Sequence Homology, Amino Acid Neural Networks, Computer Models, Molecular Peptide Fragments Epitopes Amino Acid Sequence Computational Biology Cluster Analysis Databases, Protein
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Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology.

SAR and QSAR in environmental research Sep 21, 2015
Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales t...
Computer Communication Networks Computational Biology Models, Molecular Molecular Structure Information Dissemination Biological Ontologies Quantitative Structure-Activity Relationship Vocabulary, Controlled Computer Simulation
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