Maxim V. Shapovalov

This paper expands the Rosetta software suite with tools for glycan modeling, density fitting, and rational sequon design, achieving high accuracy in predicting and building glycan structures.

The paper introduces a new test set, a convolutional neural network model, and evaluates factors affecting accuracy in protein secondary structure prediction.

The paper clusters beta turns from high-resolution protein structures into 18 types using DBSCAN and k-medoids, proposing a new nomenclature and providing tools for analysis.