Sibe: a computation tool to apply protein sequence statistics to folding and design

Abstract

Statistical analysis plays a significant role in both protein sequences and structures, expanding in recent years from the studies of co-evolution guided single-site mutations to protein folding in silico. Here we describe a computational tool, termed Sibe, with a particular focus on protein sequence analysis, folding and design. Since Sibe has various easy-interface modules, expressive architecture and extensible codes, it is powerful in statistically analyzing sequence data and building energetic potentials in boosting both protein folding and design. In this study, Sibe is used to capture positionally conserved couplings between pairwise amino acids and help rational protein design, in which the pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several blocks. A human {beta}2-adrenergic receptor ({beta}2AR) was used to demonstrated that those blocks could contribute rational design at functional residues. In addition, Sibe provides protein folding modules based on both the positionally conserved couplings and well-established statistical potentials. Sibe provides various easy to use command-line interfaces in C++ and/or Python. Sibe was developed for compatibility with the big data era, and it primarily focuses on protein sequence analysis, in silico folding and design, but it is also applicable to extend for other modeling and predictions of experimental measurements.