Posts by Gaurav Bhardwaj

1) Message boards : Number crunching : Problems and Technical Issues with Rosetta@home (Message 87888) Posted 11 Dec 2017 by Gaurav Bhardwaj Post: Thanks for pointing out the issues with really long jobs to us. Some of these jobs are intended to predict the structures of cyclic peptides, and invoke a few different filters during their runs. For some peptides, passing all these filters is very low probability event, and therefore no structure makes it through even after hours of running. We are looking further into it, and will update you with more information very soon.
2) Message boards : Rosetta@home Science : Rosetta@home Research Updates (Message 80665) Posted 21 Sep 2016 by Gaurav Bhardwaj Post: Research Update: We recently published an article in Nature titled "Accurate de novo design of hyperstable constrained peptides". We would like to thank all Rosetta@Home participants for their help with this work. In the paper, we present computational methods for designing small stapled peptides with exceptional stabilities. These methods and designed peptides provide platform for rational design of new peptide-based therapeutics. Constrained (stapled) peptides combine the stability of conventional small-molecule drugs with the selectivity and potency of antibody therapeutics. Ability to precisely design these peptides in custom shapes and sizes opens up possibilities for "on-demand" design of peptide-based therapeutics. Other developments describe in the paper: 1) We can now accurately design 18-47 amino acid peptides that incorporate multiple cross-links. 2) We can now design peptides that incorporate unnatural amino acids. Specifically, we designed peptides with a mix of natural L-amino amino acids and D-amino acids (mirror images of L-amino acids). D-amino acids tend to provide better protease resistance and lower immunogenicity; both of which are desired properties in a therapeutic peptide. Unnatural amino acids also let us sample much more diverse shapes and functions. 3) We can now design peptides that are cyclized via a peptide bond between their N- and C-terminus. Cyclic peptides provide increased resistance against exopeptidases as they have no free ends, and thus are ideal candidates for engineering peptide therapeutics. We are now working to use these computational methods for designing peptides that target therapeutically relevant targets, such as, enzymes that impart antibiotic resistance in pathogenic bacteria. Structure prediction runs on Rosetta@Home for these designed peptide models played a key role in selection of good designs that were experimentally synthesized and characterized. So, thanks a lot to all of you for your help in making this work possible.