Drug designers working on protein-level chemistry have long been blocked by a hard computational wall: classical ...

Large protein machines in the body carry out many of the cell's most essential tasks, from energy production to the regulation of signal transmission. Although they can now be imaged in great detail ...

In a study published in Nature Methods, a research group developed a highly sensitive proteomics method called peptide-centric local stability assay (PELSA), which enables the simultaneous ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin introduces a breakthrough in protein simulation. The study, published in the ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has introduced CGSchNet, a machine-learned coarse-grained (CG) model that can ...

The ability to alter proteins to refine control over binding affinity and specificity can create tailored therapeutics with reduced side effects, highly sensitive diagnostic tools, efficient ...

A range of biophysical techniques is used in combination with computational analysis to understand whether glutamine-binding protein binds its ligand via the induced-fit or conformational selection ...

CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein engineering for cancer treatment. Operating significantly faster than traditional all ...

In a study published in Nature Methods, a research group led by Prof. YE Mingliang from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences (CAS), collaborating with Prof. LUO ...

Various approaches to such protein redesign have drawbacks. Traditional methods include time-consuming trial and error efforts, and many models in the emerging field ...