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Protein Conformation

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Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase.

Scientific reports Jun 16, 2017
The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are impo...
Humans Machine Learning Protein Interaction Mapping Protein Interaction Maps Protein Disulfide-Isomerases Oxidation-Reduction Protein Interaction Domains and Motifs Molecular Dynamics Simulation Protein Conformation
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Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture.

IEEE/ACM transactions on computational biology and bioinformatics May 19, 2017
MOTIVATION: Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interactio...
Protein Conformation Binding Sites Molecular Docking Simulation Computational Biology Deep Learning Proteins Protein Binding Surface Properties
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A novel model-based on FCM-LM algorithm for prediction of protein folding rate.

Journal of bioinformatics and computational biology Apr 25, 2017
The prediction of protein folding rates is of paramount importance in describing the protein folding mechanism, which has broad applications in fields such as enzyme engineering and protein engineering. Therefore, predicting protein folding rates usi...
Protein Folding Proteins Protein Conformation Humans Neural Networks, Computer Algorithms Computational Biology
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Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images.

Computational biology and chemistry Apr 13, 2017
Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpr...
Neural Networks, Computer Algorithms Proteins Markov Chains Protein Conformation
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Protein-Ligand Scoring with Convolutional Neural Networks.

Journal of chemical information and modeling Apr 11, 2017
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affini...
Neural Networks, Computer User-Computer Interface Computational Biology Drug Evaluation, Preclinical Proteins Models, Molecular Ligands 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 ...
Sequence Analysis, Protein Machine Learning Protein Conformation Computational Biology Protein Folding Amino Acid Sequence Principal Component Analysis Sequence Homology, Amino Acid Datasets as Topic
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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 Machine Learning Humans Models, Molecular Animals 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...
Monte Carlo Method Benchmarking Molecular Dynamics Simulation Protein Conformation Double-Blind Method Internet Software Computational Biology Support Vector Machine Models, Statistical Proteins
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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...
Physical Phenomena Machine Learning Peptides Protein Conformation Models, Theoretical Models, Molecular Static Electricity Proteins Amino Acids Thermodynamics
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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...
Ligands Databases, Protein Protein Binding Probability Protein Conformation Proteins Models, Molecular Entropy Knowledge Bases
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