AIMC Topic: Computational Biology

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TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

Bioinformatics (Oxford, England)
MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding ...

DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimen...

Improving generalizability of drug-target binding prediction by pre-trained multi-view molecular representations.

Bioinformatics (Oxford, England)
MOTIVATION: Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug-target binding during drug development. However, the inherent divers...

BetaAlign: a deep learning approach for multiple sequence alignment.

Bioinformatics (Oxford, England)
MOTIVATION: Multiple sequence alignments (MSAs) are extensively used in biology, from phylogenetic reconstruction to structure and function prediction. Here, we suggest an out-of-the-box approach for the inference of MSAs, which relies on algorithms ...

DPAM-AI: a domain parser for AlphaFold models powered by artificial intelligence.

Bioinformatics (Oxford, England)
MOTIVATION: Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering...

TPepPro: a deep learning model for predicting peptide-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-pr...

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...

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.

Briefings in functional genomics
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing...

Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework.

Briefings in functional genomics
RNA interference (RNAi) technology is widely used in the biological prevention and control of terrestrial insects. One of the main factors with the application of RNAi in insects is the difference in RNAi efficiency, which may vary not only in differ...

GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

IEEE journal of biomedical and health informatics
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...