Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

Khare, Sagar D.; Kipnis, Yakov; Greisen, Per; Takeuchi, Ryo; Ashani, Yacov; Goldsmith, Moshe; Song, Yifan; Gallaher, Jasmine L.; Silman, Israel; Leader, Haim; Sussman, Joel L.; Stoddard, Barry; Tawfik, Dan S.; Baker, David

doi:10.1038/nchembio.777

Cited by 202 publications

(180 citation statements)

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“…CPD has been successfully applied to increase protein thermostability and solubility; to alter specificity towards some other molecules; and to design various binding sites and construct de novo enzymes (see for example [18]). …”

Section: Introductionmentioning

confidence: 99%

Computational Protein Design as a Cost Function Network Optimization Problem

Allouche¹,

Traoré

André

et al. 2012

Lecture Notes in Computer Science

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Abstract. Proteins are chains of simple molecules called amino acids. The three-dimensional shape of a protein and its amino acid composition define its biological function. Over millions of years, living organisms have evolved and produced a large catalog of proteins. By exploring the space of possible amino-acid sequences, protein engineering aims at similarly designing tailored proteins with specific desirable properties. In Computational Protein Design (CPD), the challenge of identifying a protein that performs a given task is defined as the combinatorial optimization problem of a complex energy function over amino acid sequences. In this paper, we introduce the CPD problem and some of the main approaches that have been used to solve it. We then show how this problem directly reduces to Cost Function Network (CFN) and 0/1LP optimization problems. We construct different real CPD instances to evaluate CFN and 0/1LP algorithms as implemented in the toulbar2 and cplex solvers. We observe that CFN algorithms bring important speedups compared to the CPD platform osprey but also to cplex.

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Section: Introductionmentioning

confidence: 99%

Computational Protein Design as a Cost Function Network Optimization Problem

Allouche¹,

Traoré

André

et al. 2012

Lecture Notes in Computer Science

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“…How can one demonstrate that the ability to engage in a variety of low-level biochemical functions without selection is an intrinsic property of proteins that holds not just for a limited number of designed proteins (5,8,17,18), but is likely true in general? Here, computational models of proteins can play a significant role.…”

mentioning

confidence: 99%

Interplay of physics and evolution in the likely origin of protein biochemical function

Skolnick

Gao

2013

Proc. Natl. Acad. Sci. U.S.A.

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The intrinsic ability of protein structures to exhibit the geometric and sequence properties required for ligand binding without evolutionary selection is shown by the coincidence of the properties of pockets in native, single domain proteins with those in computationally generated, compact homopolypeptide, artificial (ART) structures. The library of native pockets is covered by a remarkably small number of representative pockets (∼400), with virtually every native pocket having a statistically significant match in the ART library, suggesting that the library is complete. When sequences are selected for ART structures based on fold stability, pocket sequence conservation is coincident to native. The fact that structurally and sequentially similar pockets occur across fold classes combined with the small number of representative pockets in native proteins implies that promiscuous interactions are inherent to proteins. Based on comparison of PDB (real, single domain protein structures found in the Protein Data Bank) and ART structures and pockets, the widespread assumption that the co-occurrence of global structure, pocket similarity, and amino acid conservation demands an evolutionary relationship between proteins is shown to significantly underestimate the random background probability. Indeed, many features of biochemical function arise from the physical properties of proteins that evolution likely fine-tunes to achieve specificity. Finally, our study suggests that a repertoire of thermodynamically (marginally) stable proteins could engage in many of the biochemical reactions needed for living systems without selection for function, a conclusion with significant implications for the origin of life.protein evolution | protein pocket space | protein-ligand interactions

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“…The design principles and methodology we have described should allow the ready design of a wide range of robust and stable protein building blocks for the next generation of engineered functional proteins [33][34][35][36][37][38][39][40][41] . Almost all protein design and engineering efforts so far have repurposed naturally occurring proteins that evolved for some other, often unrelated, function [35][36][37][38][39][40][41] .…”

Section: Resultsmentioning

confidence: 99%

“…Almost all protein design and engineering efforts so far have repurposed naturally occurring proteins that evolved for some other, often unrelated, function [35][36][37][38][39][40][41] . It should now become possible to custom-design protein scaffolds ideal for the desired function, and to build larger assemblies 42,43 and materials from robust ideal building blocks.…”

Section: Resultsmentioning

confidence: 99%

Principles for Designing Ideal Protein Structures

Tatsumi-Koga¹,

Koga²

2013

Biophysics

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Unlike random heteropolymers, natural proteins fold into unique ordered structures. Understanding how these are encoded in amino-acid sequences is complicated by energetically unfavourable non-ideal features-for example kinked α-helices, bulged β-strands, strained loops and buried polar groups-that arise in proteins from evolutionary selection for biological function or from neutral drift. Here we describe an approach to designing ideal protein structures stabilized by completely consistent local and non-local interactions. The approach is based on a set of rules relating secondary structure patterns to protein tertiary motifs, which make possible the design of funnel-shaped protein folding energy landscapes leading into the target folded state. Guided by these rules, we designed sequences predicted to fold into ideal protein structures consisting of α-helices, β-strands and minimal loops. Designs for five different topologies were found to be monomeric and very stable and to adopt structures in solution nearly identical to the computational models. These results illuminate how the folding funnels of natural proteins arise and provide the foundation for engineering a new generation of functional proteins free from natural evolution.For proteins to fold, the interactions favouring the native state must collectively outweigh the non-native interactions, resulting in funnel-shaped energy landscapes [1][2][3] . However, it is not obvious how the ubiquitous non-covalent interactions that stabilize proteins-van der © 2012 Macmillan Publishers Limited. All rights reservedCorrespondence and requests for materials should be addressed to D.B. (dabaker@u.washington.edu) or G.T.M (guy@cabm.rutgers.edu). * These authors contributed equally to this work.Supplementary Information is available in the online version of the paper.Author Contributions N.K., R.T.-K., G.L., G.T.M. and D.B. designed the research. N.K. performed folding simulations and analysed natural proteins. N.K. wrote program code. N.K. and R.T.-K. performed computational design work: Di-I_5 and Di-IV_5 were designed by N.K., and Di-II_10, Di-III_14 and Di-V_7 were designed by R.T.-K. R.T.-K. expressed, purified and characterized the designed proteins by biochemical assay. R.X. and T.B.A. prepared isotope-enriched protein samples for NMR structure determination. G.L. collected NMR data and determined the solution NMR structures. N.K., R.T.-K., G.L., G.T.M. and D.B. wrote the manuscript. Author InformationThe NMR structures of the five designs have been deposited in the RCSB Protein Data Bank under the accession numbers 2KL8 (Di-I_5), 2LV8 (Di-II_10), 2LN3 (Di-III_14), 2LVB (Di-IV_5) and 2LTA (Di-V_7). NMR data have been deposited in the Biological Magnetic Resonance Data Bank under the accession numbers 16387 (Di-I_5), 18558 (Di-II_10), 18145 (Di-III_14), 18561 (Di-IV_5) and 18465 (Di-V_7).The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. NIH Public Access Author ManuscriptNature. Auth...

show abstract

Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

Cited by 202 publications

References 38 publications

Computational Protein Design as a Cost Function Network Optimization Problem

Computational Protein Design as a Cost Function Network Optimization Problem

Interplay of physics and evolution in the likely origin of protein biochemical function

Principles for Designing Ideal Protein Structures

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