title: Accurate prediction for atomic-level protein design and its application in diversifying the near-optimal sequence space.
abstract: The task of engineering a protein to assume a target three-dimensional structure is known as protein design. Computational search algorithms are devised to predict a minimal energy amino acid sequence for a particular structure. In practice, however, an ensemble of low-energy sequences is often sought. Primarily, this is performed because an individual predicted low-energy sequence may not necessarily fold to the target structure because of both inaccuracies in modeling protein energetics and the nonoptimal nature of search algorithms employed. Additionally, some low-energy sequences may be overly stable and thus lack the dynamic flexibility required for biological functionality. Furthermore, the investigation of low-energy sequence ensembles will provide crucial insights into the pseudo-physical energy force fields that have been derived to describe structural energetics for protein design. Significantly, numerous studies have predicted low-energy sequences, which were subsequently synthesized and demonstrated to fold to desired structures. However, the characterization of the sequence space defined by such energy functions as compatible with a target structure has not been performed in full detail. This issue is critical for protein design scientists to successfully continue using these force fields at an ever-increasing pace and scale. In this paper, we present a conceptually novel algorithm that rapidly predicts the set of lowest energy sequences for a given structure. Based on the theory of probabilistic graphical models, it performs efficient inspection and partitioning of the near-optimal sequence space, without making any assumptions of positional independence. We benchmark its performance on a diverse set of relevant protein design examples and show that it consistently yields sequences of lower energy than those derived from state-of-the-art techniques. Thus, we find that previously presented search techniques do not fully depict the low-energy space as precisely. Examination of the predicted ensembles indicates that, for each structure, the amino acid identity at a majority of positions must be chosen extremely selectively so as to not incur significant energetic penalties. We investigate this high degree of similarity and demonstrate how more diverse near-optimal sequences can be predicted in order to systematically overcome this bottleneck for computational design. Finally, we exploit this in-depth analysis of a collection of the lowest energy sequences to suggest an explanation for previously observed experimental design results. The novel methodologies introduced here accurately portray the sequence space compatible with a protein structure and further supply a scheme to yield heterogeneous low-energy sequences, thus providing a powerful instrument for future work on protein design.
authors.affiliation: School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. fromer@cs.huji.ac.il
authors.forename: Menachem
authors.initials: M
authors.lastname: Fromer
authors.forename: Chen
authors.initials: C
authors.lastname: Yanover
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D017076:Computer-Aided Design; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D015202:Protein Engineering; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D015203:Reproducibility of Results; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D011506:Proteins