Literature DB >> 31855059

Minimization-Aware Recursive K*: A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape.

Jonathan D Jou1, Graham T Holt1,2, Anna U Lowegard1,2, Bruce R Donald1,3,4.   

Abstract

Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE-A*-K*, computes provable ɛ-approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations, and using the A* enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A*-K* runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE-A*-K* is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K*. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K* (MARK*), which tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE-A*-K* versus MARK* by running the Branch and Bound over K* (BBK*) algorithm, which provably returns sequences in order of decreasing K* score, using either iMinDEE-A*-K* or MARK* to approximate partition functions. We show on 200 design problems that MARK* not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs up to 2 orders of magnitude faster. Finally, we show that MARK* not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK* is the first algorithm to do so. We use MARK* to analyze the change in energy landscape of the bound and unbound states of an HIV-1 capsid protein C-terminal domain in complex with a camelid VHH, and measure the change in conformational entropy induced by binding. Thus, MARK* both accelerates existing designs and offers new capabilities not possible with previous algorithms.

Entities:  

Keywords:  K*; OSPREY; computational protein design; energy landscapes; partition function; provable algorithms; thermodynamics

Mesh:

Substances:

Year:  2019        PMID: 31855059      PMCID: PMC7185359          DOI: 10.1089/cmb.2019.0315

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  44 in total

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Journal:  Proteins       Date:  2009-01

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Authors:  Sarel J Fleishman; Sagar D Khare; Nobuyasu Koga; David Baker
Journal:  Protein Sci       Date:  2011-04       Impact factor: 6.725

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Authors:  B I Dahiyat; S L Mayo
Journal:  Science       Date:  1997-10-03       Impact factor: 47.728

6.  Improved energy bound accuracy enhances the efficiency of continuous protein design.

Authors:  Kyle E Roberts; Bruce R Donald
Journal:  Proteins       Date:  2015-05-08

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Authors:  Shiou-Ru Tzeng; Charalampos G Kalodimos
Journal:  Nature       Date:  2012-08-09       Impact factor: 49.962

8.  BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces.

Authors:  Adegoke A Ojewole; Jonathan D Jou; Vance G Fowler; Bruce R Donald
Journal:  J Comput Biol       Date:  2018-03-13       Impact factor: 1.479

9.  Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design.

Authors:  Mark A Hallen; Pablo Gainza; Bruce R Donald
Journal:  J Chem Theory Comput       Date:  2015-05-12       Impact factor: 6.006

10.  Fast gap-free enumeration of conformations and sequences for protein design.

Authors:  Kyle E Roberts; Pablo Gainza; Mark A Hallen; Bruce R Donald
Journal:  Proteins       Date:  2015-08-24
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  3 in total

1.  Computational Analysis of Energy Landscapes Reveals Dynamic Features That Contribute to Binding of Inhibitors to CFTR-Associated Ligand.

Authors:  Graham T Holt; Jonathan D Jou; Nicholas P Gill; Anna U Lowegard; Jeffrey W Martin; Dean R Madden; Bruce R Donald
Journal:  J Phys Chem B       Date:  2019-11-27       Impact factor: 2.991

2.  Minimization-Aware Recursive K*: A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape.

Authors:  Jonathan D Jou; Graham T Holt; Anna U Lowegard; Bruce R Donald
Journal:  J Comput Biol       Date:  2019-12-06       Impact factor: 1.479

3.  Perturbing the energy landscape for improved packing during computational protein design.

Authors:  Jack B Maguire; Hugh K Haddox; Devin Strickland; Samer F Halabiya; Brian Coventry; Jermel R Griffin; Surya V S R K Pulavarti; Matthew Cummins; David F Thieker; Eric Klavins; Thomas Szyperski; Frank DiMaio; David Baker; Brian Kuhlman
Journal:  Proteins       Date:  2020-12-11
  3 in total

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