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Databases, Protein

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ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons.

Journal of chemical information and modeling
Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there i...

EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features.

Journal of theoretical biology
Determining subcellular localization of proteins is considered as an important step towards understanding their functions. Previous studies have mainly focused solely on Gene Ontology (GO) as the main feature to tackle this problem. However, it was s...

Boosted neural networks scoring functions for accurate ligand docking and ranking.

Journal of bioinformatics and computational biology
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order p...

PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

Journal of theoretical biology
Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been s...

K: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Journal of chemical information and modeling
Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning appro...

iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition.

Journal of theoretical biology
Membrane proteins execute significant roles in cellular processes of living organisms, ranging from cell signaling to cell adhesion. As a major part of a cell, the identification of membrane proteins and their functional types become a challenging jo...

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

PLoS computational biology
This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules...

ProtDet-CCH: Protein Remote Homology Detection by Combining Long Short-Term Memory and Ranking Methods.

IEEE/ACM transactions on computational biology and bioinformatics
As one of the most challenging tasks in sequence analysis, protein remote homology detection has been extensively studied. Methods based on discriminative models and ranking approaches have achieved the state-of-the-art performance, and these two kin...

Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction.

Database : the journal of biological databases and curation
Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature. Having knowledge about these interactions is crucially important in many research areas such as pr...

Extracting chemical-protein relations using attention-based neural networks.

Database : the journal of biological databases and curation
Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) ...