AIMC Topic: Protein Conformation

Clear Filters Showing 431 to 440 of 609 articles

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...
Computational Biology Proteins Protein Conformation Protein Folding Humans Neural Networks, Computer Algorithms
View on PubMed DOI

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...
Protein Conformation Proteins Markov Chains Neural Networks, Computer Algorithms
View on PubMed DOI

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 Drug Evaluation, Preclinical Ligands Protein Conformation Computational Biology Proteins Models, Molecular
View on PubMed DOI

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 Homology, Amino Acid Sequence Analysis, Protein Computational Biology Principal Component Analysis Amino Acid Sequence Datasets as Topic Protein Conformation Protein Folding Machine Learning
View on PubMed DOI

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...
Humans Machine Learning Animals Protein Binding Models, Statistical Data Accuracy Proteins Models, Molecular Protein Conformation
View on PubMed DOI

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...
Software Computational Biology Support Vector Machine Internet Benchmarking Molecular Dynamics Simulation Models, Statistical Proteins Double-Blind Method Protein Conformation Monte Carlo Method
View on PubMed DOI

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 Proteins Models, Molecular Amino Acids Protein Conformation Static Electricity Machine Learning Peptides Models, Theoretical
View on PubMed DOI

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 Binding Proteins Databases, Protein Probability Protein Conformation Entropy Models, Molecular Knowledge Bases Ligands
View on PubMed DOI

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 ...
Protein Kinases Catalytic Domain Protein Conformation Machine Learning Models, Molecular
View on PubMed DOI

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...
Monte Carlo Method Computer Simulation Software Models, Molecular Protein Interaction Mapping Algorithms Molecular Docking Simulation Knowledge Bases Models, Chemical Protein Binding Linear Models Protein Conformation
View on PubMed DOI
Popular Topics
Recent Journals