AIMC Topic: Protein Conformation

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Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

Journal of theoretical biology Mar 27, 2017
Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid ...
Machine Learning Computational Biology Principal Component Analysis Amino Acid Sequence Sequence Homology, Amino Acid Sequence Analysis, Protein Datasets as Topic Protein Conformation Protein Folding
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Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

BMC bioinformatics Mar 23, 2017
BACKGROUND: One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex i...
Protein Binding Models, Statistical Proteins Humans Machine Learning Animals Models, Molecular Data Accuracy Protein Conformation
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Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Proteins Mar 21, 2017
Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during t...
Computational Biology Support Vector Machine Models, Statistical Software Benchmarking Molecular Dynamics Simulation Proteins Protein Conformation Monte Carlo Method Double-Blind Method Internet
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FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.

Journal of computational chemistry Mar 10, 2017
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a krigi...
Thermodynamics Physical Phenomena Peptides Models, Theoretical Proteins Protein Conformation Machine Learning Static Electricity Models, Molecular Amino Acids
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A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016.

Journal of chemical information and modeling Feb 27, 2017
We present the third generation of our scoring function for the prediction of protein-ligand binding free energy. This function is now a hybrid between a knowledge-based potential and an empirical function. We constructed a diversified set of ∼1000 c...
Protein Conformation Entropy Knowledge Bases Ligands Probability Proteins Databases, Protein Models, Molecular Protein Binding
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Classifying kinase conformations using a machine learning approach.

BMC bioinformatics Feb 2, 2017
BACKGROUND: Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and ...
Machine Learning Catalytic Domain Protein Conformation Models, Molecular Protein Kinases
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Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

PloS one Jan 24, 2017
Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scorin...
Protein Conformation Monte Carlo Method Protein Interaction Mapping Software Algorithms Computer Simulation Knowledge Bases Models, Molecular Molecular Docking Simulation Linear Models Models, Chemical Protein Binding
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Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

Tuberculosis (Edinburgh, Scotland) Jan 20, 2017
There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target...
Topoisomerase I Inhibitors Protein Conformation Mycobacterium smegmatis Structure-Activity Relationship Computer-Aided Design Humans Bayes Theorem DNA Topoisomerases, Type I Tuberculosis Machine Learning Antitubercular Agents Drug Design Mycobacterium tuberculosis Molecular Docking Simulation Dose-Response Relationship, Drug Molecular Targeted Therapy
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A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

Proteins Jan 20, 2017
Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and th...
Machine Learning Benchmarking Software Protein Conformation Structural Homology, Protein Computational Biology Binding Sites Thermodynamics Research Design Cluster Analysis Molecular Docking Simulation Protein Interaction Mapping Proteins Protein Binding
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Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

PLoS computational biology Jan 5, 2017
MOTIVATION: Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predict...
Computational Biology Proteins Databases, Protein Protein Conformation Sequence Analysis, Protein Neural Networks, Computer Machine Learning Algorithms
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