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Bonadio A, Shifman JM. Computational design and experimental optimization of protein binders with prospects for biomedical applications. Protein Eng Des Sel 2021; 34:gzab020. [PMID: 34436606 PMCID: PMC8388154 DOI: 10.1093/protein/gzab020] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/11/2021] [Accepted: 07/11/2021] [Indexed: 11/12/2022] Open
Abstract
Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.
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Affiliation(s)
- Alessandro Bonadio
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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2
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Gainza P, Nisonoff HM, Donald BR. Algorithms for protein design. Curr Opin Struct Biol 2016; 39:16-26. [PMID: 27086078 PMCID: PMC5065368 DOI: 10.1016/j.sbi.2016.03.006] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 03/15/2016] [Accepted: 03/22/2016] [Indexed: 02/05/2023]
Abstract
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
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Affiliation(s)
- Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Hunter M Nisonoff
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, United States; Department of Biochemistry, Duke University Medical Center, Durham, NC, United States; Department of Chemistry, Duke University, Durham, NC, United States.
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Leaver-Fay A, Froning KJ, Atwell S, Aldaz H, Pustilnik A, Lu F, Huang F, Yuan R, Hassanali S, Chamberlain AK, Fitchett JR, Demarest SJ, Kuhlman B. Computationally Designed Bispecific Antibodies using Negative State Repertoires. Structure 2016; 24:641-651. [PMID: 26996964 DOI: 10.1016/j.str.2016.02.013] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 02/04/2016] [Accepted: 02/17/2016] [Indexed: 12/27/2022]
Abstract
A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA
| | - Karen J Froning
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Shane Atwell
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Hector Aldaz
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Anna Pustilnik
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Frances Lu
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Flora Huang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Richard Yuan
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Saleema Hassanali
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Aaron K Chamberlain
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Jonathan R Fitchett
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Stephen J Demarest
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA.
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, USA.
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Hallen MA, Donald BR. comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Protein Design Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence. J Comput Biol 2016; 23:311-21. [PMID: 26761641 DOI: 10.1089/cmb.2015.0188] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Practical protein design problems require designing sequences with a combination of affinity, stability, and specificity requirements. Multistate protein design algorithms model multiple structural or binding "states" of a protein to address these requirements. comets provides a new level of versatile, efficient, and provable multistate design. It provably returns the minimum with respect to sequence of any desired linear combination of the energies of multiple protein states, subject to constraints on other linear combinations. Thus, it can target nearly any combination of affinity (to one or multiple ligands), specificity, and stability (for multiple states if needed). Empirical calculations on 52 protein design problems showed comets is far more efficient than the previous state of the art for provable multistate design (exhaustive search over sequences). comets can handle a very wide range of protein flexibility and can enumerate a gap-free list of the best constraint-satisfying sequences in order of objective function value.
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Affiliation(s)
- Mark A Hallen
- 1 Department of Computer Science, Levine Science Research Center, Duke University , North Carolina
- 2 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Levine Science Research Center, Duke University , North Carolina
- 2 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina
- 3 Department of Chemistry, Duke University , Durham, North Carolina
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Leaver-Fay A, Jacak R, Stranges PB, Kuhlman B. A generic program for multistate protein design. PLoS One 2011; 6:e20937. [PMID: 21754981 PMCID: PMC3130737 DOI: 10.1371/journal.pone.0020937] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2011] [Accepted: 05/13/2011] [Indexed: 11/18/2022] Open
Abstract
Some protein design tasks cannot be modeled by the traditional single state design strategy of finding a sequence that is optimal for a single fixed backbone. Such cases require multistate design, where a single sequence is threaded onto multiple backbones (states) and evaluated for its strengths and weaknesses on each backbone. For example, to design a protein that can switch between two specific conformations, it is necessary to to find a sequence that is compatible with both backbone conformations. We present in this paper a generic implementation of multistate design that is suited for a wide range of protein design tasks and demonstrate in silico its capabilities at two design tasks: one of redesigning an obligate homodimer into an obligate heterodimer such that the new monomers would not homodimerize, and one of redesigning a promiscuous interface to bind to only a single partner and to no longer bind the rest of its partners. Both tasks contained negative design in that multistate design was asked to find sequences that would produce high energies for several of the states being modeled. Success at negative design was assessed by computationally redocking the undesired protein-pair interactions; we found that multistate design's accuracy improved as the diversity of conformations for the undesired protein-pair interactions increased. The paper concludes with a discussion of the pitfalls of negative design, which has proven considerably more challenging than positive design.
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Affiliation(s)
- Andrew Leaver-Fay
- Deptartment of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, United States of America.
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Erijman A, Aizner Y, Shifman JM. Multispecific Recognition: Mechanism, Evolution, and Design. Biochemistry 2011; 50:602-11. [DOI: 10.1021/bi101563v] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ariel Erijman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Yonatan Aizner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Julia M. Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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Wallnoefer HG, Lingott T, Gutiérrez JM, Merfort I, Liedl KR. Backbone flexibility controls the activity and specificity of a protein-protein interface: specificity in snake venom metalloproteases. J Am Chem Soc 2010; 132:10330-7. [PMID: 20617834 DOI: 10.1021/ja909908y] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Protein-protein interfaces have crucial functions in many biological processes. The large interaction areas of such interfaces show complex interaction motifs. Even more challenging is the understanding of (multi)specificity in protein-protein binding. Many proteins can bind several partners to mediate their function. A perfect paradigm to study such multispecific protein-protein interfaces are snake venom metalloproteases (SVMPs). Inherently, they bind to a variety of basement membrane proteins of capillaries, hydrolyze them, and induce profuse bleeding. However, despite having a high sequence homology, some SVMPs show a strong hemorrhagic activity, while others are (almost) inactive. We present computer simulations indicating that the activity to induce hemorrhage, and thus the capability to bind the potential reaction partners, is related to the backbone flexibility in a certain surface region. A subtle interplay between flexibility and rigidity of two loops seems to be the prerequisite for the proteins to carry out their damaging function. Presumably, a significant alteration in the backbone dynamics makes the difference between SVMPs that induce hemorrhage and the inactive ones.
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Affiliation(s)
- Hannes G Wallnoefer
- Institute of General, Inorganic and Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck, Innrain 52a, A-6020 Innsbruck, Austria
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Fromer M, Yanover C, Harel A, Shachar O, Weiss Y, Linial M. SPRINT: side-chain prediction inference toolbox for multistate protein design. Bioinformatics 2010; 26:2466-7. [DOI: 10.1093/bioinformatics/btq445] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Fromer M, Linial M. Exposing the co-adaptive potential of protein-protein interfaces through computational sequence design. ACTA ACUST UNITED AC 2010; 26:2266-72. [PMID: 20679332 DOI: 10.1093/bioinformatics/btq412] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
MOTIVATION In nature, protein-protein interactions are constantly evolving under various selective pressures. Nonetheless, it is expected that crucial interactions are maintained through compensatory mutations between interacting proteins. Thus, many studies have used evolutionary sequence data to extract such occurrences of correlated mutation. However, this research is confounded by other evolutionary pressures that contribute to sequence covariance, such as common ancestry. RESULTS Here, we focus exclusively on the compensatory mutations deriving from physical protein interactions, by performing large-scale computational mutagenesis experiments for >260 protein-protein interfaces. We investigate the potential for co-adaptability present in protein pairs that are always found together in nature (obligate) and those that are occasionally in complex (transient). By modeling each complex both in bound and unbound forms, we find that naturally transient complexes possess greater relative capacity for correlated mutation than obligate complexes, even when differences in interface size are taken into account.
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Affiliation(s)
- Menachem Fromer
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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Friedland GD, Kortemme T. Designing ensembles in conformational and sequence space to characterize and engineer proteins. Curr Opin Struct Biol 2010; 20:377-84. [DOI: 10.1016/j.sbi.2010.02.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2010] [Accepted: 02/19/2010] [Indexed: 11/16/2022]
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Hub promiscuity in protein-protein interaction networks. Int J Mol Sci 2010; 11:1930-43. [PMID: 20480050 PMCID: PMC2871146 DOI: 10.3390/ijms11041930] [Citation(s) in RCA: 116] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2010] [Revised: 03/17/2010] [Accepted: 04/18/2010] [Indexed: 11/17/2022] Open
Abstract
Hubs are proteins with a large number of interactions in a protein-protein interaction network. They are the principal agents in the interaction network and affect its function and stability. Their specific recognition of many different protein partners is of great interest from the structural viewpoint. Over the last few years, the structural properties of hubs have been extensively studied. We review the currently known features that are particular to hubs, possibly affecting their binding ability. Specifically, we look at the levels of intrinsic disorder, surface charge and domain distribution in hubs, as compared to non-hubs, along with differences in their functional domains.
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Fromer M, Shifman JM. Tradeoff between stability and multispecificity in the design of promiscuous proteins. PLoS Comput Biol 2009; 5:e1000627. [PMID: 20041208 PMCID: PMC2790338 DOI: 10.1371/journal.pcbi.1000627] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2009] [Accepted: 11/24/2009] [Indexed: 12/23/2022] Open
Abstract
Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions. In nature, some proteins are more social than others, interacting with a large number of partners. These “promiscuous” proteins play key roles in cellular signaling pathways whose disruption may lead to diseases such as cancer. The amino acid sequences of such proteins must have evolved to be optimal for combined interactions with all natural partners. However, the evolutionary process leading to this promiscuity is not fully understood. We address this subject by predicting amino acid sequences that would be most compatible for interaction with each partner on its own and those most compatible for binding multiple proteins. We find that these two types of sequences are substantially different, the latter more closely resembling the natural sequences of promiscuous proteins. We also find that promiscuous proteins contain certain regions that are necessary for interfacing with all of their partners, while other regions convey specific interactions with each particular target protein. We analyze the tradeoffs required for such proteins to bind multiple partners and find that only some degree of compromise is typically needed in order to permit interactions that are seemingly antagonistic. We conclude that the simulations reported here mimic well the natural evolution of proteins that associate with multiple partners.
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Affiliation(s)
- Menachem Fromer
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julia M. Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- * E-mail:
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