AIMC Topic: Proteins

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DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks.

Journal of chemical information and modeling Feb 25, 2019
Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural network-based method, fo...
Proteins Protein Stability Transition Temperature Point Mutation Neural Networks, Computer Computational Biology
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PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

Journal of computational chemistry Feb 15, 2019
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets...
Proteins Databases, Protein Models, Chemical Protein Binding Computational Biology Support Vector Machine Amino Acid Sequence
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Identification of coenzyme-binding proteins with machine learning algorithms.

Computational biology and chemistry Jan 28, 2019
The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on th...
Esters Acyl Coenzyme A Proteins Models, Molecular Molecular Structure Humans Machine Learning Software
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Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Journal of chemical information and modeling Jan 25, 2019
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds...
Binding Sites Small Molecule Libraries Databases, Pharmaceutical Models, Molecular Pharmaceutical Preparations High-Throughput Screening Assays Machine Learning ROC Curve Protein Binding Proteins
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Prediction of TF-Binding Site by Inclusion of Higher Order Position Dependencies.

IEEE/ACM transactions on computational biology and bioinformatics Jan 10, 2019
Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, we first propose a novel method to extract higher or...
DNA Binding Sites Humans Databases, Genetic Neural Networks, Computer Animals Models, Statistical Protein Binding Proteins Mice Computational Biology Transcription Factors
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DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one Jan 8, 2019
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...
Algorithms Deep Learning Databases, Protein Models, Molecular Amino Acid Sequence Computational Biology Support Vector Machine Proteins Protein Structure, Tertiary Sequence Analysis, Protein
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Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Amino Acids Molecular Dynamics Simulation Quantum Theory Dipeptides Static Electricity Thermodynamics Peptides Neural Networks, Computer Proteins Models, Chemical
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Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering.

Database : the journal of biological databases and curation Jan 1, 2019
Information about the interactions between chemical compounds and proteins is indispensable for understanding the regulation of biological processes and the development of therapeutic drugs. Manually extracting such information from biomedical litera...
Databases, Chemical Pharmaceutical Preparations Data Mining Semantics Humans Machine Learning Software Computational Biology Proteins Databases, Protein
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Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.

BMC bioinformatics Dec 31, 2018
BACKGROUND: Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph ...
DNA Deep Learning Models, Chemical Humans Protein Binding Binding Sites Pharmaceutical Preparations Neural Networks, Computer Proteins Software
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Gene ontology improves template selection in comparative protein docking.

Proteins Dec 27, 2018
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and a...
Computational Biology Proteins Databases, Protein Molecular Docking Simulation Protein Interaction Maps Protein Binding Binding Sites Models, Molecular Protein Conformation Protein Interaction Mapping Humans Gene Ontology Software Structural Homology, Protein
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