AIMC Topic: Proteins

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Sequence-based prediction of protein-peptide binding sites using support vector machine.

Journal of computational chemistry
Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computational...

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.

Drug discovery today
Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning ...

Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11.

Scientific reports
Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolu...

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Scientific reports
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been...

Predicting Drug-Target Interactions With Multi-Information Fusion.

IEEE journal of biomedical and health informatics
Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most ...

simDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes.

Bioinformatics (Oxford, England)
MOTIVATION: Measures of protein functional similarity are essential tools for function prediction, evaluation of protein-protein interactions (PPIs) and other applications. Several existing methods perform comparisons between proteins based on the se...

A Turn-On Resonance Raman Scattering (BCS/Cu+) Sensor for Quantitative Determination of Proteins.

Applied spectroscopy
In this study, a new method for the quantitative evaluation of proteins is developed using competitive resonance Raman spectroscopy. A chelation reaction between bathocuproine disulfonate (BCS) and Cu(+) which is reduced by proteins in an alkaline en...

Determination of residual dextran sulfate in protein products by SEC-HPLC.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Dextran sulfate is a polyanionic derivative of dextran, produced by esterification of dextran with chlorosulphonic acid. Dextran sulfate with an average molecular weight of 8000Da can be added to the cell culture to inhibit binding of proteins to cel...

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

BMC bioinformatics
BACKGROUND: Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been develo...

A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs.

BMC bioinformatics
Protein O-GlcNAcylation, involving the β-attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues, is an O-linked glycosylation catalyzed by O-GlcNAc transferase (OGT). Molecular level investigation of t...