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Adaptive local learning in sampling based motion planning for protein folding.

BMC systems biology Aug 1, 2016
BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods ...
Machine Learning Protein Conformation Thermodynamics Movement Protein Folding Computational Biology Proteins Models, Molecular
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In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms.

Journal of chemical information and modeling Jul 20, 2016
The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many appli...
Neural Networks, Computer Machine Learning Computer Simulation Linear Models Molecular Conformation Solutions Support Vector Machine Models, Molecular Ionic Liquids
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ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Molecular pharmaceutics Jul 18, 2016
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is qu...
Machine Learning Potassium Channel Blockers Models, Molecular Bayes Theorem Quantitative Structure-Activity Relationship Drug Discovery Support Vector Machine Cluster Analysis
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gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence.

Journal of theoretical biology Jul 1, 2016
DNA-binding proteins are the functional proteins in cells, which play an important role in various essential biological activities. An effective and fast computational method gDNA-Prot is proposed to predict DNA-binding proteins in this paper, which ...
Amino Acid Sequence Genome Numerical Analysis, Computer-Assisted Models, Molecular Protein Domains DNA-Binding Proteins ROC Curve Computational Biology Principal Component Analysis DNA Support Vector Machine Databases, Protein
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Protease Inhibitors in View of Peptide Substrate Databases.

Journal of chemical information and modeling Jun 9, 2016
Protease substrate profiling has nowadays almost become a routine task for experimentalists, and the knowledge on protease peptide substrates is easily accessible via the MEROPS database. We present a shape-based virtual screening workflow using vROC...
Peptides User-Computer Interface Peptide Hydrolases Machine Learning Databases, Pharmaceutical Protein Conformation Models, Molecular Protease Inhibitors Substrate Specificity Drug Evaluation, Preclinical Small Molecule Libraries Ligands
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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...
Protein Structure, Secondary Protein Multimerization Protein Interaction Domains and Motifs Support Vector Machine Databases, Protein Receptors, G-Protein-Coupled Amino Acid Sequence Binding Sites Area Under Curve Models, Molecular ROC Curve Protein Binding Mice Humans Animals
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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...
Thermodynamics Machine Learning Quantum Theory Static Electricity Models, Molecular Amino Acids
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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...
Quantitative Structure-Activity Relationship Mutagenesis Databases, Factual Mutagenicity Tests Computer Simulation Amines Data Mining Animals Risk Assessment Pattern Recognition, Automated Mutagens Humans Models, Molecular Molecular Structure Knowledge Bases
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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...
Support Vector Machine Proteins Models, Molecular Algorithms ROC Curve Amino Acids Protein Conformation
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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....
Sequence Analysis, Protein Protein Domains Proteome Machine Learning Amino Acid Sequence Mass Spectrometry Software Datasets as Topic Models, Molecular Proteomics Lysine Ubiquitin Ubiquitination Amino Acid Motifs
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