Investigating the Formation of Structural Elements in Proteins Using Local Sequence-Dependent Information and a Heuristic Search Algorithm

• Published in: Molecules (Basel, Switzerland) Switzerland), 2019-03, Vol.24 (6), p.1150
• Main Authors: Estaña, Alejandro, Ghallab, Malik, Bernadó, Pau, Cortés, Juan
• Format: Article
• Language: English
• Subjects: Algorithms; Amino acids; Artificial Intelligence; Biochemistry; Biochemistry, Molecular Biology; Bioinformatics; Computer applications; Computer Science; conformational transitions; Folding; Heuristic; heuristic search algorithms; Investigations; Libraries; Life Sciences; Metabolism; Mimicry; Modeling and Simulation; Mutation; Polypeptides; Problem solving; Protein folding; Proteins; Robotics; Search algorithms; Simulation; structural database; structural elements; Structural members
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules24061150

Structural elements inserted in proteins are essential to define folding/unfolding mechanisms and partner recognition events governing signaling processes in living organisms. Here, we present an original approach to model the folding mechanism of these structural elements. Our approach is based on the exploitation of local, sequence-dependent structural information encoded in a database of three-residue fragments extracted from a large set of high-resolution experimentally determined protein structures. The computation of conformational transitions leading to the formation of the structural elements is formulated as a discrete path search problem using this database. To solve this problem, we propose a heuristically-guided depth-first search algorithm. The domain-dependent heuristic function aims at minimizing the length of the path in terms of angular distances, while maximizing the local density of the intermediate states, which is related to their probability of existence. We have applied the strategy to two small synthetic polypeptides mimicking two common structural motifs in proteins. The folding mechanisms extracted are very similar to those obtained when using traditional, computationally expensive approaches. These results show that the proposed approach, thanks to its simplicity and computational efficiency, is a promising research direction.