Rojan Shrestha

TL;DR: The accuracy of sequence‐based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting and it suggests that there is much room left for further improvement.

TL;DR: EdaFold is presented, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm that learns from previously generated decoys and steers the search toward native-like regions and showed a higher success rate in molecular replacement when compared to Rosetta.

TL;DR: This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases, but suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.

TL;DR: This work proposes a method using propagation of geometric constraints to accelerate exact clustering, without compromising the distance measure, and shows that this approach can even outperform the speed of an approximate method.

TL;DR: This work has proposed a fast method that works even on large decoy sets, and has further enhanced the performance of Durandal by incorporating a Quaternion‐based characteristic polynomial method that has increased the speed of Durandals between 13% and 27% compared with the previous version.