Prediction of amino acid sequence from structure

Raha, Kaushik; Wollacott, Andrew M.; Italia, Michael J.; Desjarlais, John R.

doi:10.1110/ps.9.6.1106

Cited by 79 publications

(93 citation statements)

References 47 publications

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“…Design calculations were carried out by using Protein Design Automation technology (39) and Sequence Prediction Algorithm technology (40). Detailed description is provided in Supporting Text.…”

Section: Methodsmentioning

confidence: 99%

Engineered antibody Fc variants with enhanced effector function

Lazar

Dang²,

Karki³

et al. 2006

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

705

583

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Antibody-dependent cell-mediated cytotoxicity, a key effector function for the clinical efficacy of monoclonal antibodies, is mediated primarily through a set of closely related Fc␥ receptors with both activating and inhibitory activities. By using computational design algorithms and high-throughput screening, we have engineered a series of Fc variants with optimized Fc␥ receptor affinity and specificity. The designed variants display >2 orders of magnitude enhancement of in vitro effector function, enable efficacy against cells expressing low levels of target antigen, and result in increased cytotoxicity in an in vivo preclinical model. Our engineered Fc regions offer a means for improving the next generation of therapeutic antibodies and have the potential to broaden the diversity of antigens that can be targeted for antibody-based tumor therapy.antibody-dependent cell-mediated cytotoxicity ͉ Fc␥R ͉ protein engineering ͉ cancer

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Section: Methodsmentioning

confidence: 99%

Engineered antibody Fc variants with enhanced effector function

Lazar

Dang²,

Karki³

et al. 2006

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

705

583

View full text Add to dashboard Cite

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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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“…It combines the all atom force field of CHARMM (24, 25) with a simple empirical surface area-dependent hydration term (26). It is noteworthy that unlike most other studies (27)(28)(29)(30)) the parameters of this fitness function have not been adjusted to yield native-like sequences, and no constraints are imposed on the amino acid composition of the designed sequences.…”

mentioning

confidence: 99%