AIMC Topic: Amino Acid Sequence

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Sequence-based virtual screening using transformers.

Nature communications
Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identificatio...

BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information.

BMC bioinformatics
Bioactive peptides are beneficial or have physiological effects on the life activities of biological organisms. The functions of bioactive peptides are diverse, usually with one or more, so accurately detecting the multiple functions of multi-functio...

Cysteine pattern barcoding-based dataset filtration enhances the machine learning-assisted interpretation of Conus venom peptide therapeutics.

PloS one
Crude cone snail venom is a rich source of bioactive compounds with significant therapeutic potential. In this study, we conducted a comprehensive analysis of 5,985 cone snail peptides across 82 Conus species to identify unique cysteine (Cys) pattern...

In silico design of smaller size enzymatic protein by generative artificial intelligence (ProtGPT2).

Journal of bioscience and bioengineering
The construction of small proteins by removing amino acid subsequences that are not involved in function, activity, or structure is crucial for bioprocessing and drug development. Traditional design methods often focus on reconstructing functional mo...

Integrating Protein Language Models and Geometric Deep Learning for Peptide Toxicity Prediction.

Journal of chemical information and modeling
Peptide toxicity prediction is a critical task in biomedical research, influencing drug safety and therapeutic development. Traditional methods, relying on sequence similarity or handcrafted features, struggle to capture the complex relationship betw...

Deep learning-derived optimal annotation strategies to power the systematic mapping of peptide space.

Food chemistry
Rapid and reliable peptide identification techniques are essential for proteomics. High-resolution tandem mass spectrometry acquires a large amount of data through data-dependent acquisition (DDA) and data-independent acquisition (DIA), but tradition...

Sequence and Structure-based Prediction of Allosteric Sites.

Journal of molecular biology
Allosteric regulation in proteins is a critical aspect of cellular function, influencing various biological processes through conformational or dynamic changes induced by effector molecules. Allosteric drugs possess significant therapeutic value due ...

Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.

PloS one
The prevalence of Leukaemia, a malignant blood cancer that originates from hematopoietic progenitor cells, is increasing in Southeast Asia, with a worrisome fatality rate of 54%. Predicting outcomes in the early stages is vital for improving the chan...

The Highly Conserved Cys95 Residue of Fructose-1,6-Bisphosphatase 1 Mediates the pH-Driven Structure and Activity of the Enzyme and Photosynthesis.

Plant, cell & environment
In Arabidopsis, exposure to microbial volatile compounds promotes thiol reduction of the Cys95 residue of the photosynthetic enzyme fructose-1,6-bisphosphatase (cFBP1). Although highly conserved in plants, the Cys95 function still remains unknown. We...

Comparing models and experimental structures of the GPR101 receptor: Artificial intelligence yields highly accurate models.

Journal of molecular graphics & modelling
Experimental structures solved through cryo-electron microscopy have recently been published for GPR101, a G protein-coupled receptor (GPCR) implicated in the genetic condition X-linked acrogigantism (X-LAG). Here, we compared these experimental stru...