B-SIDER: Computational Algorithm for the Design of Complementary β-sheet Sequences

Abstract

The {beta}-sheet is an element of protein secondary structure, and intra-/inter-molecular {beta}-sheet interactions play pivotal roles in biological regulatory processes including scaffolding, transporting, and oligomerization. In nature, a {beta}-sheet formation is tightly regulated because dysregulated {beta}-stacking often leads to severe diseases such as Alzheimers, Parkinsons, systemic amyloidosis, or diabetes. Thus, the identification of intrinsic {beta}-sheet forming propensities can provide valuable insight into protein designs for the development of novel therapeutics. However, structure-based design methods may not be generally applicable to such amyloidogenic peptides mainly owing to high structural plasticity and complexity. Therefore, an alternative design strategy based on complementary sequence information is of significant importance. Herein, we developed a database search method called B-SIDER for the design of complementary {beta}-strands. This method makes use of the structural database information and generates query-specific score matrices. The discriminatory power of the B-SIDER score function was tested on representative amyloidogenic peptide substructures against a sequence-based score matrix (PASTA2.0) and two popular ab initio protein design score functions (Rosetta and FoldX). B-SIDER is able to distinguish wild-type amyloidogenic {beta}-strands as favored interactions in a more consistent manner than other methods. B-SIDER was prospectively applied to the design of complementary {beta}-strands for a splitGFP scaffold. Three variants were identified to have stronger interactions than the original sequence selected through a directed evolution, emitting higher fluorescence intensities. Our results indicate that B-SIDER can be applicable to the design of other {beta}-strands, assisting in the development of therapeutics against disease-related amyloidogenic peptides.