AIMC Topic: Models, Molecular

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GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

PloS one Aug 27, 2015
Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is...
RNA Algorithms Game Theory Models, Molecular RNA Folding Knowledge Bases
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Accurate contact predictions using covariation techniques and machine learning.

Proteins Aug 14, 2015
Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effectiv...
Computational Biology Models, Statistical Proteins Databases, Protein Software Computer Simulation Bacteria Protein Structure, Secondary Protein Folding Sequence Alignment Protein Interaction Domains and Motifs Models, Molecular Humans Internet Neural Networks, Computer Amino Acid Sequence Machine Learning Viruses
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LoopIng: a template-based tool for predicting the structure of protein loops.

Bioinformatics (Oxford, England) Aug 6, 2015
MOTIVATION: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are n...
Software Proteins Databases, Protein Models, Molecular Protein Conformation Machine Learning
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Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

Molecular informatics Aug 5, 2015
The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important resea...
Nucleotides Machine Learning Peptides Models, Molecular
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Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

Toxicological sciences : an official journal of the Society of Toxicology Jul 21, 2015
Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligan...
Catalytic Domain Molecular Conformation Quantitative Structure-Activity Relationship Humans Binding Sites Ligands Artificial Intelligence Peptide Mapping Animals Acetylcholinesterase Nootropic Agents Molecular Docking Simulation Cholinesterase Inhibitors Imaging, Three-Dimensional Expert Systems Models, Molecular Computational Biology GPI-Linked Proteins Databases, Protein Insecticides Nerve Tissue Proteins
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Binding Activity Prediction of Cyclin-Dependent Inhibitors.

Journal of chemical information and modeling Jul 10, 2015
The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust est...
Algorithms Protein Binding Support Vector Machine Decision Trees Machine Learning Protein Kinase Inhibitors Models, Molecular Chromosomes Bayes Theorem Cyclin-Dependent Kinases Protein Conformation
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SPECTRUS: A Dimensionality Reduction Approach for Identifying Dynamical Domains in Protein Complexes from Limited Structural Datasets.

Structure (London, England : 1993) Jul 9, 2015
Identifying dynamical, quasi-rigid domains in proteins provides a powerful means for characterizing functionally oriented structural changes via a parsimonious set of degrees of freedom. In fact, the relative displacements of few dynamical domains us...
Thermodynamics Protein Structure, Tertiary Capsid Multifactor Dimensionality Reduction Escherichia coli Models, Molecular Internet Ion Channels Adenylate Kinase Tobacco mosaic satellite virus Software
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Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

Scientific reports Jun 22, 2015
Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independentl...
Protein Structure, Secondary Databases, Protein Machine Learning Models, Molecular Algorithms Amino Acids Proteins Solvents
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Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism.

BMC bioinformatics Jun 6, 2015
BACKGROUND: People with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is kn...
Nerve Tissue Proteins Models, Molecular High-Throughput Nucleotide Sequencing Synapses Autistic Disorder Gene Ontology Genome Animals Mice Genomics Rats Humans Phenotype Calcium-Binding Proteins Cell Physiological Phenomena Social Behavior Cell Adhesion Molecules, Neuronal Membrane Potentials Neural Cell Adhesion Molecules Synaptic Potentials
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Using support vector machines to improve elemental ion identification in macromolecular crystal structures.

Acta crystallographica. Section D, Biological crystallography Apr 25, 2015
In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific know...
Calcium Macromolecular Substances Manganese Crystallography, X-Ray Models, Molecular Protein Conformation Iron Algorithms Nickel Zinc Metals Support Vector Machine
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