AI Medical Compendium Topic:
Protein Conformation

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Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributi...

Protein classification using modified n-grams and skip-grams.

Bioinformatics (Oxford, England)
MOTIVATION: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used t...

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the ...

Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose predi...

Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces.

Methods in molecular biology (Clifton, N.J.)
The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly in...

DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years b...

A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy.

Bioinformatics (Oxford, England)
MOTIVATION: Residue-residue contacts are of great value for protein structure prediction, since contact information, especially from those long-range residue pairs, can significantly reduce the complexity of conformational sampling for protein struct...

SVMQA: support-vector-machine-based protein single-model quality assessment.

Bioinformatics (Oxford, England)
MOTIVATION: The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these prob...

NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.

Bioinformatics (Oxford, England)
MOTIVATION: Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact pr...

When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants.

Bioinformatics (Oxford, England)
MOTIVATION: Loss-of-function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evol...