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Sequence Analysis, Protein

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Learning the molecular grammar of protein condensates from sequence determinants and embeddings.

Proceedings of the National Academy of Sciences of the United States of America
Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of...

Deep learning for mining protein data.

Briefings in bioinformatics
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a power...

Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction.

Protein and peptide letters
BACKGROUND: The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment informat...

Improved Prediction of Protein-Protein Interaction Mapping on by Using Amino Acid Sequence Features in a Supervised Learning Framework.

Protein and peptide letters
BACKGROUND: Protein-Protein Interaction (PPI) has emerged as a key role in the control of many biological processes including protein function, disease incidence, and therapy design. However, the identification of PPI by wet lab experiment is a chall...

FastSK: fast sequence analysis with gapped string kernels.

Bioinformatics (Oxford, England)
MOTIVATION: Gapped k-mer kernels with support vector machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation...

MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.

Nucleic acids research
MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which res...

Machine learning predicts new anti-CRISPR proteins.

Nucleic acids research
The increasing use of CRISPR-Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that...

DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins....

Antioxidant Proteins' Identification Based on Support Vector Machine.

Combinatorial chemistry & high throughput screening
BACKGROUND: Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radical...

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield acc...