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Sequence Analysis, Protein

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CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Improving the accuracy of predicting protein crystallization is very important for protein crystallization projects, which is a critical step for the determination of protein structure by X-ray crystallography. At present, many machine learning metho...

Template-based prediction of protein structure with deep learning.

BMC genomics
BACKGROUND: Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary struct...

Noninvasive diagnostic of periprosthetic joint infection by urinary peptide markers: A preliminary study.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Previous immunohistochemical analyses revealed altered protein expression in the periprosthetic membranes of patients with periprosthetic joint infection (PJI). Proteins are degraded to peptides that may pass the blood-kidney barrier depending on the...

ReFold-MAP: Protein remote homology detection and fold recognition based on features extracted from profiles.

Analytical biochemistry
Protein remote homology detection and protein fold recognition are two important tasks in protein structure and function prediction. There are three kinds of methods in this field, including the discriminative methods, the alignment methods, and the ...

A novel fusion based on the evolutionary features for protein fold recognition using support vector machines.

Scientific reports
Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting fe...

Prediction of antioxidant proteins using hybrid feature representation method and random forest.

Genomics
Natural antioxidant proteins are mainly found in plants and animals, which interact to eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat some diseases. Therefore, accurate identification of antioxidant protein...

cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks.

Proteins
Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurat...

Machine-learning approach expands the repertoire of anti-CRISPR protein families.

Nature communications
The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechani...

Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes.

International journal of molecular sciences
Predicting protein-protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to h...

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.

Scientific reports
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and f...