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Amino Acid Sequence

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MemDis: Predicting Disordered Regions in Transmembrane Proteins.

International journal of molecular sciences
Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a wel...

Learning the local landscape of protein structures with convolutional neural networks.

Journal of biological physics
One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received...

End-to-end learning for compound activity prediction based on binding pocket information.

BMC bioinformatics
BACKGROUND: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, do...

An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.

BMC bioinformatics
BACKGROUND: Several computational tools for predicting protein Ubiquitylation and SUMOylation sites have been proposed to study their regulatory roles in gene location, gene expression, and genome replication. However, existing methods generally rely...

Predicting subcellular location of protein with evolution information and sequence-based deep learning.

BMC bioinformatics
BACKGROUND: Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the p...

Support vector machine-based prediction of pore-forming toxins (PFT) using distributed representation of reduced alphabets.

Journal of bioinformatics and computational biology
Bacterial virulence can be attributed to a wide variety of factors including toxins that harm the host. Pore-forming toxins are one class of toxins that confer virulence to the bacteria and are one of the promising targets for therapeutic interventio...

CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation.

PLoS computational biology
MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates...

Fast activation maximization for molecular sequence design.

BMC bioinformatics
BACKGROUND: Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are fi...

CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.

BMC bioinformatics
BACKGROUND: Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduc...