AIMC Topic: Models, Molecular

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modeling of the cryptic E2∼ubiquitin-binding site of E6-associated protein (E6AP)/UBE3A reveals the mechanism of polyubiquitin chain assembly.

The Journal of biological chemistry Sep 18, 2017
To understand the mechanism for assembly of Lys-linked polyubiquitin degradation signals, we previously demonstrated that the E6AP/UBE3A ligase harbors two functionally distinct E2∼ubiquitin-binding sites: a high-affinity Site 1 required for E6AP Cys...
Cattle Computational Biology Models, Molecular Iodine Radioisotopes Humans Animals Molecular Docking Simulation Cysteine Ubiquitin-Protein Ligases Amino Acid Substitution Recombinant Fusion Proteins Expert Systems Biocatalysis Protein Conformation Ubiquitin-Conjugating Enzymes Ubiquitination Protein Multimerization Isoenzymes Binding Sites Point Mutation Mutagenesis, Site-Directed Ubiquitin
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Analysis of deep learning methods for blind protein contact prediction in CASP12.

Proteins Sep 6, 2017
Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median...
Humans Neural Networks, Computer Software Models, Molecular Crystallography, X-Ray Protein Conformation Algorithms Protein Folding Deep Learning Computational Biology Proteins
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Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models.

European journal of medicinal chemistry Sep 1, 2017
There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening...
Factor XIIa Principal Component Analysis Models, Molecular Molecular Structure Support Vector Machine Dose-Response Relationship, Drug Blood Proteins Structure-Activity Relationship Humans Algorithms
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Elastic network model of learned maintained contacts to predict protein motion.

PloS one Aug 30, 2017
We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein's contact topology. Existing elastic network models assume that the contact topology r...
Protein Conformation Arachidonate 15-Lipoxygenase Proteins Models, Molecular Humans Machine Learning Motion Animals Elasticity
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Protein binding hot spots prediction from sequence only by a new ensemble learning method.

Amino acids Aug 1, 2017
UNLABELLED: Hot spots are interfacial core areas of binding proteins, which have been applied as targets in drug design. Experimental methods are costly in both time and expense to locate hot spot areas. Recently, in-silicon computational methods hav...
Databases, Protein Models, Molecular Sequence Analysis, Protein Machine Learning Algorithms
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EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

Biology direct Aug 1, 2017
BACKGROUND: Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high...
Models, Genetic Transcription Factors Models, Molecular Datasets as Topic Gene Expression Regulation Binding Sites Position-Specific Scoring Matrices Machine Learning Software ROC Curve Computational Biology
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In silico identification of milk antihypertensive di- and tripeptides involved in angiotensin I-converting enzyme inhibitory activity.

Nutrition research (New York, N.Y.) Jul 28, 2017
Identification of bioactive milk peptides could improve food technology through improved selection of food supplements with a focus on antihypertensive properties. We hypothesized that angiotensin I-converting enzyme (ACE) inhibitory activities of mi...
Proteolysis Binding Sites Catalytic Domain Milk Proteins Animals Oligopeptides Peptide Fragments Molecular Docking Simulation Angiotensin-Converting Enzyme Inhibitors Humans Dipeptides Cattle Hydrophobic and Hydrophilic Interactions Quantitative Structure-Activity Relationship Computational Biology Expert Systems Dietary Supplements Protein Conformation Hydrogen Bonding Peptidyl-Dipeptidase A Antihypertensive Agents Models, Molecular
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Machine learning-enabled discovery and design of membrane-active peptides.

Bioorganic & medicinal chemistry Jul 8, 2017
Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen s...
Peptides Computer-Aided Design Drug Design Antimicrobial Cationic Peptides Humans Models, Molecular Machine Learning Anti-Infective Agents Animals
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QSAR Study of Artemisinin Analogues as Antimalarial Drugs by Neural Network and Replacement Method.

Drug research May 30, 2017
Quantitative structure-activity relationship (QSAR) models were derived for 179 analogues of artemisinin, a potent antimalarial agent. Molecular descriptors derived solely from molecular structure were used to represent molecular structure. Utilizing...
Neural Networks, Computer Artemisinins Models, Molecular Quantitative Structure-Activity Relationship Antimalarials
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Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Computational biology and chemistry May 29, 2017
The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular...
Small Molecule Libraries Humans Machine Learning Models, Molecular Enzyme Inhibitors Drug Evaluation, Preclinical Quantitative Structure-Activity Relationship High-Throughput Screening Assays Kallikreins
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