Journal of chemical information and modeling
40309917
While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effec...
Journal of chemical information and modeling
40273444
The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully sha...
Journal of chemical information and modeling
40272990
The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. Certain segments in the sequence strongly influence specific functions, but identifying these segments, or so-called motifs, is c...
In post-translational modification, covalent bonds on lysine and attached chemical groups significantly change proteins' physical and chemical properties. They shape protein structures, enhance function and stability, and are vital for physiological ...
Protein aggregation is a pathological hallmark of more than 50 human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experi...
The 2024 Nobel Prize in chemistry has been awarded to Demis Hassabis and John M. Jumper (Google DeepMind) for the development of artificial intelligence-guided protein structure prediction and to David Baker (University of Washington, Seattle, USA) f...
Journal of chemical theory and computation
40270304
Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to...
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...
BACKGROUND: Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative effor...
Proceedings of the National Academy of Sciences of the United States of America
40314982
Understanding the effects of missense mutations or single amino acid variants (SAVs) on protein function is crucial for elucidating the molecular basis of diseases/disorders and designing rational therapies. We introduce here , a machine learning too...