Positive multistate protein design

Abstract

Motivation Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. Results We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. Availability and implementation https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. Supplementary information Supplementary data are available at Bioinformatics online.

Molecular flexibility in computational protein design: an algorithmic perspective

“…to optimize specificity or a bound vs. unbound state), a sequence that maximizes the di↵erence in optimal energies between desirable and undesirable states is often sought. These "negative design" problems define computationally far more challenging NP NP -complete problems [69]. This is consistent with the fact that solving a negative multi-state design problem requires to explore the sequence space, and for each sequence and state, to explore its conformation space.…”

“…When the aim is to stabilize any of the considered conformational states, the Boltzmann-weighted average of the energies (defined as the sum of optimal energies, weighted by their Boltzmann probabilities) in each state may be an attractive criteria [14]. Because this gives an exponential advantage to the backbone with lowest energy, the computation of this fitness has been approximated by the minimum optimal energy [12,33,69] defining what is called "multistate analysis" (MSA) [13]. MSA showed interesting results when combined with local backbone fluctuation search algorithms for each state.…”

“…When, instead, the aim is to design a sequence that fits several conformational states that must be adopted for the targeted function (e.g., states defining conformational switches), it is important that the energies of all states contribute to the definition of the fitness. In this case, it is usual to optimize the average of the optimal energies over all states, a problem we denote as ⌃-MSD [69,54]. Computationally speaking, SSD, MSA and ⌃-MSD define "positive design" problems that try to stabilize desirable states, and are just NP-complete [69].…”

“…In this case, it is usual to optimize the average of the optimal energies over all states, a problem we denote as ⌃-MSD [69,54]. Computationally speaking, SSD, MSA and ⌃-MSD define "positive design" problems that try to stabilize desirable states, and are just NP-complete [69].…”

“…Lately, by using a simple problem reduction, POMP d emerged as one of the most e cient provable positive MSD algorithm, outperforming iCFN for discrete rotamers positive design problems, whether with an MSA or ⌃-MSD fitness [69].…”