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Databases, Protein

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Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.

Biomolecules
Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab ex...

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.

BMC genomics
BACKGROUND: Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical protein...

Discovering nuclear targeting signal sequence through protein language learning and multivariate analysis.

Analytical biochemistry
Nuclear localization signals (NLSs) are peptides that target proteins to the nucleus by binding to carrier proteins in the cytoplasm that transport their cargo across the nuclear membrane. Accurate identification of NLSs can help elucidate the functi...

Modeling aspects of the language of life through transfer-learning protein sequences.

BMC bioinformatics
BACKGROUND: Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applica...

Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation.

Journal of chemical information and modeling
Protein associations are at the core of biological activity, and the drug-based disruption of dysfunctional associations poses a major challenge to targeted therapy. The problem becomes daunting when the structure and regulated modulation of the comp...

Multimodal deep representation learning for protein interaction identification and protein family classification.

BMC bioinformatics
BACKGROUND: Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve ...

Learning a functional grammar of protein domains using natural language word embedding techniques.

Proteins
In this paper, using Word2vec, a widely-used natural language processing method, we demonstrate that protein domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to the multi-domain proteins in whi...

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Journal of computer-aided molecular design
Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for...

Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions.

Journal of computer-aided molecular design
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accu...

MiPepid: MicroPeptide identification tool using machine learning.

BMC bioinformatics
BACKGROUND: Micropeptides are small proteins with length < = 100 amino acids. Short open reading frames that could produces micropeptides were traditionally ignored due to technical difficulties, as few small peptides had been experimentally confirme...