Fast and accurate side‐chain topology and energy refinement (FASTER) as a new method for protein structure optimization

Desmet, Jan; Spriet, J.A.; Lasters, Ignace

doi:10.1002/prot.10131

Cited by 105 publications

(87 citation statements)

References 41 publications

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“…Side-chain-TS interaction energies were biased to favor those contacts that satisfy the geometries in SI Appendix, Table S1 as in the active site search. Sequence optimization was carried out with FASTER (28,29), and a Monte Carlo-based algorithm (30, 31) was used to sample sequences around the minimum energy sequence.…”

Section: Methodsmentioning

confidence: 99%

Iterative approach to computational enzyme design

Privett

Kiss

Lee

et al. 2012

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

295

357

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A general approach for the computational design of enzymes to catalyze arbitrary reactions is a goal at the forefront of the field of protein design. Recently, computationally designed enzymes have been produced for three chemical reactions through the synthesis and screening of a large number of variants. Here, we present an iterative approach that has led to the development of the most catalytically efficient computationally designed enzyme for the Kemp elimination to date. Previously established computational techniques were used to generate an initial design, HG-1, which was catalytically inactive. Analysis of HG-1 with molecular dynamics simulations (MD) and X-ray crystallography indicated that the inactivity might be due to bound waters and high flexibility of residues within the active site. This analysis guided changes to our design procedure, moved the design deeper into the interior of the protein, and resulted in an active Kemp eliminase, HG-2. The cocrystal structure of this enzyme with a transition state analog (TSA) revealed that the TSA was bound in the active site, interacted with the intended catalytic base in a catalytically relevant manner, but was flipped relative to the design model. MD analysis of HG-2 led to an additional point mutation, HG-3, that produced a further threefold improvement in activity. This iterative approach to computational enzyme design, including detailed MD and structural analysis of both active and inactive designs, promises a more complete understanding of the underlying principles of enzymatic catalysis and furthers progress toward reliably producing active enzymes.computational protein design | de novo enzyme design | proton transfer T he high efficiency, chemoselectivity, regio-and stereospecificity, and biodegradability of enzymes make them extremely attractive catalysts. However, the finite repertoire of naturally occurring enzymes limits their applicability to broad problems in biotechnology. A general method for the computational design of enzymes that can efficiently catalyze arbitrary chemical reactions would allow the benefits of enzymatic catalysis to be applied to chemical transformations of interest that are currently inaccessible via natural enzymes. Bolon and Mayo provided important early evidence that such an approach is feasible (1), which motivated significant progress toward this goal in recent years. Using quantum mechanics-based active site design and the Rosetta software suite, Baker, Houk, and coworkers designed enzymes for three chemically unrelated nonnatural reactions in a variety of catalytically inert scaffolds (2-4).In early incarnations of computational protein design, a strategy for methods development was put forth in terms of the so-called "protein design cycle" in which experimental evaluation of an initial design is used to inform adjustments to the design process for subsequent rounds of design (5, 6). Ideally, these steps would be continued iteratively until the protein sequences predicted by the algorithm exhibit the desired char...

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Section: Methodsmentioning

confidence: 99%

Iterative approach to computational enzyme design

Privett

Kiss

Lee

et al. 2012

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

295

357

View full text Add to dashboard Cite

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“…The amino acid substitutions required to obtain the correct target sequence are initially introduced with standard side-chain conformations. In the final step, all models are energy-optimized, first by global side-chain conformation optimization, using the FASTER algorithm (39), then by 200 steps of conjugate gradient energy minimization.…”

Section: Methodsmentioning

confidence: 99%

Affinity maturation of antibodies assisted by in silico modeling

Barderas

Desmet²,

Timmerman³

et al. 2008

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

Self Cite

125

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Rational engineering methods can be applied with reasonable success to optimize physicochemical characteristics of proteins, in particular, antibodies. Here, we describe a combined CDR3 walking randomization and rational design-based approach to enhance the affinity of the human anti-gastrin TA4 scFv. The application of this methodology to TA4 scFv, displaying only a weak overall affinity for gastrin17 (K D ‫؍‬ 6 M), resulted in a set of nine affinity-matured scFv variants with near-nanomolar affinity (KD ‫؍‬ 13.2 nM for scFv TA4.112). First, CDR-H3 and CDR-L3 randomization resulted in three scFvs with an overall affinity improvement of 15-to 35-fold over the parental. Then, the modeling of two scFv constructs selected from the previous step (TA4.11 and TA4.13) was followed by a combination of manual and molecular dynamics-based docking of gastrin17 into the respective binding sites, analysis of apparent packing defects, and selection of residues for mutagenesis through phage display. Nine scFv mutants were obtained from the second maturation step. A final 454-fold improvement in affinity compared with TA4 was obtained. These scFvs showed an enhanced potency to inhibit gastrin-induced proliferation in Colo 320 WT and BxPc3 tumoral cells. In conclusion, we propose a structure-based rational method to accelerate the development of affinity-matured antibody constructs with enhanced potential for therapeutic use.antibody engineering ͉ gastrin ͉ in vitro affinity maturation ͉ pancreatic cancer

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“…Hence, a variety of meta-heuristics have been applied to it, including Monte Carlo simulated annealing [21], genetic algorithms [33], and other algorithms [10]. The main weakness of these approaches is that they may remain stuck in local minima and miss the GMEC without notice.…”

Section: Existing Approaches For the Cpdmentioning

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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Fast and accurate side‐chain topology and energy refinement (FASTER) as a new method for protein structure optimization

Cited by 105 publications

References 41 publications

Iterative approach to computational enzyme design

Iterative approach to computational enzyme design

Affinity maturation of antibodies assisted by in silico modeling

Computational Protein Design as a Cost Function Network Optimization Problem

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