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Sequence Analysis, Protein

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Deep Learning-Based Advances in Protein Structure Prediction.

International journal of molecular sciences
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine prot...

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Nature biomedical engineering
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 variants) expressed in mammalian cells, which typically results in fe...

The whole is greater than its parts: ensembling improves protein contact prediction.

Scientific reports
The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, c...

Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.

Scientific reports
Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distanc...

iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

IEEE/ACM transactions on computational biology and bioinformatics
Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association,...

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

PLoS computational biology
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by en...

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.

Communications biology
Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from...

PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.

International journal of molecular sciences
Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular functio...

PFP-WGAN: Protein function prediction by discovering Gene Ontology term correlations with generative adversarial networks.

PloS one
Understanding the functionality of proteins has emerged as a critical problem in recent years due to significant roles of these macro-molecules in biological mechanisms. However, in-laboratory techniques for protein function prediction are not as eff...

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.

Proteins
Deep learning has emerged as a revolutionary technology for protein residue-residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning-based contact predictions have been achie...