Deterministic Search Methods for Computational Protein Design

Traoré, Seydou; Allouche, David; André, Isabelle; Schiex, Thomas; Barbe, Sophie

doi:10.1007/978-1-4939-6637-0_4

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…BBK � [32] is an efficient, multi-sequence design algorithm that calls the K � algorithm as a subroutine. Previous algorithms [12,27,29,30,[33][34][35] that design for binding affinity using ensembles are linear in the size of the sequence space N, where N is exponential in the number of simultaneously mutable residue positions. BBK � is the first provable ensemble-based algorithm to run in time sublinear in N, making it possible not only to perform K � designs over large sequence spaces, but also to enumerate a gap-free list of sequences in order of decreasing K � score.…”

Section: Introductionmentioning

confidence: 99%

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

et al. 2020

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The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy ε. Often, reaching an εapproximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two new algorithms into the OSPREY suite for protein design: FRIES, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. FRIES pre-processes the sequence space to limit a design to only the most stable, energetically favorable sequence possibilities. EWAK* then takes this pruned sequence space as input and, using a user-specified energy window, calculates K* scores using the lowest energy conformations. We expect FRIES/ EWAK* to be most useful in cases where there are many unstable sequences in the design sequence space and when users are satisfied with enumerating the low-energy ensemble of conformations. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable, effectively reducing runtime while still enriching for desirable sequences. This combined approach led to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*, while maintaining its efficiency and accuracy, which we show across 40 different protein systems and a total of 2,826 protein design problems. Additionally, as a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a protein implicated in difficult-to-treat cancers. FRIES/EWAK* accurately retrospectively predicted the effect of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used FRIES/ EWAK* for prospective design and discovered a novel point mutation that improves binding PLOS COMPUTATIONAL BIOLOGY

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Section: Introductionmentioning

confidence: 99%

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

et al. 2020

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“…Surprisingly, these algorithms can even be significantly faster than heuristic approaches, as they know when the global optimum is reached. On a large set of CPD benchmarks, the CFN prover toulbar2 has been shown to o↵er speed-ups of several orders of magnitude compared to other state-of-the-art provable methods, giving access to guaranteed GMECs for design problems that were previously out of reach of provable algorithms [63,1,57,62,64]. The energy function being only approximate, it is also possible to ask for weaker, easier to produce, proofs that just guarantee a bounded distance to the GMEC.…”

Section: Heuristic and Provable Algorithmsmentioning

confidence: 99%

Molecular flexibility in computational protein design: an algorithmic perspective

Bouchiba

Cortés

Schiex

et al. 2021

Protein Engineering, Design and Selection

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Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.

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“…The CFN formulation of computational protein design is straightforward [6,7,25,26] (see Figure 2): given a CPD instance with pairwise decomposable energy function E = , D, C) be a cost function network with variables X = (X 1 , . .…”

Section: Cpd As a Weighted Constraint Satisfaction Problemmentioning

confidence: 99%

Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Ruffini¹,

Vucinic²,

Givry³

et al. 2021

Preprint

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Proteins are the main active molecules of Life. While natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low energy solutions by extending Cost Function Network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

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Deterministic Search Methods for Computational Protein Design

Cited by 10 publications

References 39 publications

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

Molecular flexibility in computational protein design: an algorithmic perspective

Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

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