Automated design of specificity in molecular recognition

Havranek, James J.; Harbury, Pehr B.

doi:10.1038/nsb877

Cited by 298 publications

(317 citation statements)

References 60 publications

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“…An appealing approach to this problem was pioneered by Havranek and Harbury [55], but applying this broadly confronts obstacles. We recently developed a framework for specificity design that is flexible and computationally efficient.…”

Section: Coiled-coil Partners For Native Proteinsmentioning

confidence: 99%

Structural specificity in coiled-coil interactions

Grigoryan

Keating

2008

Current Opinion in Structural Biology

267

283

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Coiled coils have a rich history in the field of protein design and engineering. Novel structures, such as the first 7-helix coiled coil, continue to provide surprises and insights. Large-scale data sets quantifying the influence of systematic mutations on coiled-coil stability are a valuable new asset to the area. Scoring methods based on sequence and/or structure can predict interaction preferences in coiled-coil-mediated bZIP transcription factor dimerization. Experimental and computational methods for dealing with the near-degeneracy of many coiled-coil structures appear promising for future design applications.

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Section: Coiled-coil Partners For Native Proteinsmentioning

confidence: 99%

Structural specificity in coiled-coil interactions

Grigoryan

Keating

2008

Current Opinion in Structural Biology

267

283

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“…Previous work has addressed many problems related to the design of improved protein-protein binding affinity, such as the design of stable protein folds 2-4 , binding pockets for peptides and small molecules [5][6][7] , altered protein-protein specificity [8][9][10][11][12] , and altered enzymatic activity [13][14][15] . The design of improved antigen-binding affinity has met with limited success, however [16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Computational design of antibody-affinity improvement beyond in vivo maturation

2007

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Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages, and increasing drug efficacy. However, antibody affinity maturation in vivo often fails to produce antibody drugs of the targeted potency 1 , making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a 10-fold affinity improvement to 52 pM was engineered into the anti-EGFR drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods were further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate novel computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.Computational design depends critically on two capabilities: accurate energetic evaluation and thorough conformational search. Previous work has addressed many problems related to the design of improved protein-protein binding affinity, such as the design of stable protein folds 2-4 , binding pockets for peptides and small molecules [5][6][7] , altered protein-protein specificity [8][9][10][11][12] , and altered enzymatic activity [13][14][15] . The design of improved antigen-binding affinity has met with limited success, however [16][17][18][19] . Challenges for protein-protein affinity design include conformational change upon binding, interfacial trapped water molecules, polar and charged side chains, and the trade-off of protein-solvent with protein-protein interactions from the unbound to bound state. Fine free energy discrimination for redesign from nanomolar to picomolar affinities is a particular challenge.Correspondence and requests for materials should be addressed to B.T. (tidor@mit.edu) or K.D.W. (wittrup@mit.edu).. ‡ Present address: Codon Devices, Inc., One Kendall Square, Building 300, Cambridge, MA 02139, USA Author Contributions B.T. oversaw all computational aspects of the work, and K.D.W. oversaw all experimental aspects of the work. S.M.L. developed and adopted the design methods and software and carried out all computational and experimental studies. The authors as a group interpreted the results of the calculations and selected the mutants to create experimentally. S.M.L. drafted the manuscript, and all authors contributed to its editing. Competing Interests StatementThe authors declare that they have no competing financial interests. NIH Public Access NIH-PA Author ManuscriptA robust design strategy should both produce a considerable fraction of designs that are successful when tested experimentally, and yield su...

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“…This may be accomplished by combining states into a single objective, such as the Boltzmann probability of forming the target over competing states (Seeman and Kallenbach 1983;Havranek and Harbury 2003;Stapleton et al 2015). In the case where multiple target states are desired, summing the stabilities of the sequence mapped onto each target structure (Ambroggio and Kuhlman 2006) or by separating sequence optimization for a set of states while gradually enforcing coupling constraints to drive convergence to a single solution (Sevy et al 2015).…”

Section: Multi-objective Protein Designmentioning

confidence: 99%

Searching for the Pareto frontier in multi-objective protein design

2017

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The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multiobjective protein design, the development of Pareto optimization methods, and present a specific case study using multiobjective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

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Automated design of specificity in molecular recognition

Cited by 298 publications

References 60 publications

Structural specificity in coiled-coil interactions

Structural specificity in coiled-coil interactions

Computational design of antibody-affinity improvement beyond in vivo maturation

Searching for the Pareto frontier in multi-objective protein design

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