AIMC Topic: Sequence Analysis, Protein

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SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins.

International journal of molecular sciences
Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational m...

DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation.

Methods (San Diego, Calif.)
As of April 2018, UniProtKB has collected more than 115 million protein sequences. Less than 0.15% of these proteins, however, have been associated with experimental GO annotations. As such, the use of automatic protein function prediction (AFP) to r...

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

Biomolecules
Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep lear...

Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.

Analytical biochemistry
Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutaryl...

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

Biomolecules
It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question u...

MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

Journal of molecular biology
Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO,...

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...

Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

Computational and mathematical methods in medicine
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or si...

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...

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...