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Binding Sites

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BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction....

Automatic recognition of ligands in electron density by machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without so...

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fie...

Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding ...

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.

Bioinformatics (Oxford, England)
MOTIVATION: Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription...

pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.

Bioinformatics (Oxford, England)
SUMMARY: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and diffi...

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Bioinformatics (Oxford, England)
MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successf...

DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

Nucleic acids research
The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address ...

Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally r...

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...