In recent years, there has been significant progress in the ability to predict the three-
dimensional structure of proteins from their amino acid sequence. Progress has been due to …

The state-of-the-art for Monte Carlo (MC) simulations of biomacromolecules is reviewed.
Available methodologies for sampling conformational equilibria and associations of …

Motion planning is a fundamental problem in robotics that has motivated research since
more than three decades ago. A large variety of algorithms have been proposed to compute …

Background Deep learning is one of the most powerful machine learning methods that has
achieved the state-of-the-art performance in many domains. Since deep learning was …

The effects of amino acid insertions and deletions (InDels) remain a rather under-explored
area of structural biology. These variations oftentimes are the cause of numerous disease …

Background: Macromolecule structure prediction remains a fundamental challenge of
bioinformatics. Over the past several decades, the Rosetta framework has provided …

Background Protein pairs that have the same secondary structure packing arrangement but
have different topologies have attracted much attention in terms of both evolution and …

Proteins are active, flexible machines that perform a range of different functions. Innovative
experimental approaches may now provide limited partial information about conformational …

Characterizing protein flexibility is an important goal for understanding the physical–
chemical principles governing biological function. This paper presents a Fragment …

Physics-based simulation represents a powerful method for investigating the time-varying
behavior of dynamic protein systems at high spatial and temporal resolution. Such …