Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mos...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
38083556
Recent advances in Natural Language Processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream t...
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the effi...
BACKGROUND: The relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of prote...
International journal of molecular sciences
37958639
Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks ...
Journal of biomolecular structure & dynamics
37850427
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can...
MOTIVATION: In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based pr...
The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel clas...
Journal of computational biology : a journal of computational molecular cell biology
38100126
Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years....
Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional ...