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Amino Acid Sequence

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Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

European biophysics journal : EBJ
Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localizati...

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction.

Toxins
Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and...

ParaAntiProt provides paratope prediction using antibody and protein language models.

Scientific reports
Efficiently predicting the paratope holds immense potential for enhancing antibody design, treating cancers and other serious diseases, and advancing personalized medicine. Although traditional methods are highly accurate, they are often time-consumi...

DeepKlapred: A deep learning framework for identifying protein lysine lactylation sites via multi-view feature fusion.

International journal of biological macromolecules
Lysine lactylation (Kla) is a post-translational modification (PTM) that holds significant importance in the regulation of various biological processes. While traditional experimental methods are highly accurate for identifying Kla sites, they are bo...

Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.

PLoS computational biology
Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However,...

Improved enzyme functional annotation prediction using contrastive learning with structural inference.

Communications biology
Recent years have witnessed the remarkable progress of deep learning within the realm of scientific disciplines, yielding a wealth of promising outcomes. A prominent challenge within this domain has been the task of predicting enzyme function, a comp...

Discovery of anticancer peptides from natural and generated sequences using deep learning.

International journal of biological macromolecules
Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment due to their ability to selectively target and kill cancer cells. In recent years, numerous artificial intelligence (AI) algorithms have been developed. However...

PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms.

Nature communications
Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards p...

Examining the Influenza A Virus Sialic Acid Binding Preference Predictions of a Sequence-Based Convolutional Neural Network.

Influenza and other respiratory viruses
BACKGROUND: Though receptor binding specificity is well established as a contributor to host tropism and spillover potential of influenza A viruses, determining receptor binding preference of a specific virus still requires expensive and time-consumi...

SAMP: Identifying antimicrobial peptides by an ensemble learning model based on proportionalized split amino acid composition.

Briefings in functional genomics
It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune eff...