Design of epitope-specific probes for sera analysis and antibody isolation

Georgiev, Ivelin S.; Acharya, Priyamvada; Schmidt, SD; Li, Y; Wycuff, Diane; Ofek, Gilad; Doria‐Rose, Nicole A.; Luongo, TS; Yang, Yi; Zhou, Tianhong; Donald, BR; Mascola, JR; Kwong, P.D.

doi:10.1186/1742-4690-9-s2-p50

Cited by 24 publications

(29 citation statements)

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“…Redesigns of the gp120 surface to achieve specific binding to particular antibodies has been instrumental in the development of probes to isolate these antibodies from sera. 28 Redesign of the antibody surface of a gp120-antibody complex has also been effective in optimizing antibody affinity, 25 which is useful for passive immunization and immunogen design. Interestingly, the redesign of the NIH45-46 complex yielded 12 top conformations within 0.06 kcal/mol of each other—two from the top sequence and ten from a double mutant.…”

Section: Resultsmentioning

confidence: 99%

Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design

Hallen

Gainza²,

Donald

2015

J. Chem. Theory Comput.

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In macromolecular design, conformational energies are sensitive to small changes in atom coordinates, so modeling the small, continuous motions of atoms around low-energy wells confers a substantial advantage in structural accuracy; however, modeling these motions comes at the cost of a very large number of energy function calls, which form the bottleneck in the design calculation. In this work, we remove this bottleneck by consolidating all conformational energy evaluations into the precomputation of a local polynomial expansion of the energy about the “ideal” conformation for each low-energy, “rotameric” state of each residue pair. This expansion is called Energy as Polynomials in Internal Coordinates (EPIC), where the internal coordinates can be sidechain dihedrals, backrub angles, and/or any other continuous degrees of freedom of a macromolecule, and any energy function can be used without adding any asymptotic complexity to the design. We demonstrate that EPIC efficiently represents the energy surface for both molecular-mechanics and quantum-mechanical energy functions, and apply it specifically to protein design to model both sidechain and backbone degrees of freedom.

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Section: Resultsmentioning

confidence: 99%

Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design

Hallen

Gainza²,

Donald

2015

J. Chem. Theory Comput.

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“…More recently, we used OSPREY to predict escape mutations that grant SaDHFR resistance to a different experimental inhibitor, compound 1; we showed that two novel, predicted mutants (V31L and V31G) were selected in resistance selection experiments along with an additional compensating mutation (F98Y) (1). Additionally, we used OSPREY to alter the specificity of Gramicidin S Synthetase A (12, 13), to design epitope-specific HIV antibody probes (14), to design peptides to inhibit the interaction between the protein CAL and cystic fibrosis transmembrane conductance regulator (CFTR) (15), and to screen inhibitors of a leukemia-associated protein-protein interaction (16). Furthermore, the Vaccine Research Center (VRC) used OSPREY to design HIV antibodies that are easier to induce (17).…”

Section: Introductionmentioning

confidence: 99%

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

Ojewole

Lowegard

Gainza

et al. 2016

Methods in Molecular Biology

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Summary Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (1), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme’s catalytic function but selectively ablate binding of an inhibitor.

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“…In this article we describe OSPREY ( O pen S ource P rotein Re design for Y ou), a free, open-source SCPR program. We have prospectively used OSPREY, with experimental validation, to redesign enzymes (Chen et al, 2009), design new drugs (Gorczynski et al, 2007), predict drug resistance (Frey et al, 2010), design peptide inhibitors of protein-protein interactions (Roberts et al, 2012), and design epitope-specific antibody probes (Georgiev, Acharya, et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

osprey

Gainza¹,

Roberts²,

Georgiev³

et al. 2013

Methods in Enzymology

111

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Summary We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally-optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically-relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application.

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Design of epitope-specific probes for sera analysis and antibody isolation

Cited by 24 publications

References 0 publications

Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design

Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

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