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Guerin N, Childs H, Zhou P, Donald BR. DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.12.579944. [PMID: 38405797 PMCID: PMC10888900 DOI: 10.1101/2024.02.12.579944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.
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2
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Guerin N, Childs H, Zhou P, Donald BR. DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors. Protein Eng Des Sel 2024; 37:gzae007. [PMID: 38757573 PMCID: PMC11099876 DOI: 10.1093/protein/gzae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 04/17/2024] [Indexed: 05/18/2024] Open
Abstract
With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.
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Affiliation(s)
- Nathan Guerin
- Department of Computer Science, Duke University, 308 Research Drive, Durham, NC 27708, United States
| | - Henry Childs
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, United States
| | - Pei Zhou
- Department of Biochemistry, Duke University School of Medicine, 307 Research Drive, Durham, NC 22710, United States
| | - Bruce R Donald
- Department of Computer Science, Duke University, 308 Research Drive, Durham, NC 27708, United States
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, United States
- Department of Biochemistry, Duke University School of Medicine, 307 Research Drive, Durham, NC 22710, United States
- Department of Mathematics, Duke University, 120 Science Drive, Durham, NC 27708, United States
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3
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Lemieux P, Bradley D, Dubé AK, Dionne U, Landry CR. Dissection of the role of a Src homology 3 domain in the evolution of binding preference of paralogous proteins. Genetics 2024; 226:iyad175. [PMID: 37793087 PMCID: PMC10763533 DOI: 10.1093/genetics/iyad175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/07/2023] [Accepted: 08/07/2023] [Indexed: 10/06/2023] Open
Abstract
Protein-protein interactions (PPIs) drive many cellular processes. Some interactions are directed by Src homology 3 (SH3) domains that bind proline-rich motifs on other proteins. The evolution of the binding specificity of SH3 domains is not completely understood, particularly following gene duplication. Paralogous genes accumulate mutations that can modify protein functions and, for SH3 domains, their binding preferences. Here, we examined how the binding of the SH3 domains of 2 paralogous yeast type I myosins, Myo3 and Myo5, evolved following duplication. We found that the paralogs have subtly different SH3-dependent interaction profiles. However, by swapping SH3 domains between the paralogs and characterizing the SH3 domains freed from their protein context, we find that very few of the differences in interactions, if any, depend on the SH3 domains themselves. We used ancestral sequence reconstruction to resurrect the preduplication SH3 domains and examined, moving back in time, how the binding preference changed. Although the most recent ancestor of the 2 domains had a very similar binding preference as the extant ones, older ancestral domains displayed a gradual loss of interaction with the modern interaction partners when inserted in the extant paralogs. Molecular docking and experimental characterization of the free ancestral domains showed that their affinity with the proline motifs is likely not the cause for this loss of binding. Taken together, our results suggest that a SH3 and its host protein could create intramolecular or allosteric interactions essential for the SH3-dependent PPIs, making domains not functionally equivalent even when they have the same binding specificity.
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Affiliation(s)
- Pascale Lemieux
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
| | - David Bradley
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biologie, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biologie, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
| | - Ugo Dionne
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Québec, Université Laval, Québec, QC, Canada G1R 2J6
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada M5G 1X5
| | - Christian R Landry
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biologie, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
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4
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Ahmed S, Chattopadhyay G, Manjunath K, Bhasin M, Singh N, Rasool M, Das S, Rana V, Khan N, Mitra D, Asok A, Singh R, Varadarajan R. Combining cysteine scanning with chemical labeling to map protein-protein interactions and infer bound structure in an intrinsically disordered region. Front Mol Biosci 2022; 9:997653. [PMID: 36275627 PMCID: PMC9585320 DOI: 10.3389/fmolb.2022.997653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
The Mycobacterium tuberculosis genome harbours nine toxin-antitoxin (TA) systems of the mazEF family. These consist of two proteins, a toxin and an antitoxin, encoded in an operon. While the toxin has a conserved fold, the antitoxins are structurally diverse and the toxin binding region is typically intrinsically disordered before binding. We describe high throughput methodology for accurate mapping of interfacial residues and apply it to three MazEF complexes. The method involves screening one partner protein against a panel of chemically masked single cysteine mutants of its interacting partner, displayed on the surface of yeast cells. Such libraries have much lower diversity than those generated by saturation mutagenesis, simplifying library generation and data analysis. Further, because of the steric bulk of the masking reagent, labeling of virtually all exposed epitope residues should result in loss of binding, and buried residues are inaccessible to the labeling reagent. The binding residues are deciphered by probing the loss of binding to the labeled cognate partner by flow cytometry. Using this methodology, we have identified the interfacial residues for MazEF3, MazEF6 and MazEF9 TA systems of M. tuberculosis. In the case of MazEF9, where a crystal structure was available, there was excellent agreement between our predictions and the crystal structure, superior to those with AlphaFold2. We also report detailed biophysical characterization of the MazEF3 and MazEF9 TA systems and measured the relative affinities between cognate and non-cognate toxin–antitoxin partners in order to probe possible cross-talk between these systems.
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Affiliation(s)
- Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | | | | | - Munmun Bhasin
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Neelam Singh
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Mubashir Rasool
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Sayan Das
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Varsha Rana
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Neha Khan
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Debarghya Mitra
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Aparna Asok
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Ramandeep Singh
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- *Correspondence: Raghavan Varadarajan,
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5
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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6
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Barnes JE, Miller CR, Ytreberg FM. Searching for a mechanistic description of pairwise epistasis in protein systems. Proteins 2022; 90:1474-1485. [DOI: 10.1002/prot.26328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/05/2021] [Accepted: 02/22/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Jonathan E. Barnes
- Department of Physics University of Idaho Moscow Idaho USA
- Institute for Modeling Collaboration and Innovation, University of Idaho Moscow Idaho USA
| | - Craig R. Miller
- Institute for Modeling Collaboration and Innovation, University of Idaho Moscow Idaho USA
- Department of Biological Sciences University of Idaho Moscow Idaho USA
- Institute for Interdisciplinary Data Sciences, University of Idaho Moscow Idaho USA
| | - Frederick Marty Ytreberg
- Department of Physics University of Idaho Moscow Idaho USA
- Institute for Modeling Collaboration and Innovation, University of Idaho Moscow Idaho USA
- Institute for Interdisciplinary Data Sciences, University of Idaho Moscow Idaho USA
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7
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Mignon D, Druart K, Michael E, Opuu V, Polydorides S, Villa F, Gaillard T, Panel N, Archontis G, Simonson T. Physics-Based Computational Protein Design: An Update. J Phys Chem A 2020; 124:10637-10648. [DOI: 10.1021/acs.jpca.0c07605] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- David Mignon
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Karen Druart
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Eleni Michael
- Department of Physics, University of Cyprus, PO20537, CY1678 Nicosia, Cyprus
| | - Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Savvas Polydorides
- Department of Physics, University of Cyprus, PO20537, CY1678 Nicosia, Cyprus
| | - Francesco Villa
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Thomas Gaillard
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Georgios Archontis
- Department of Physics, University of Cyprus, PO20537, CY1678 Nicosia, Cyprus
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
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8
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Opuu V, Sun YJ, Hou T, Panel N, Fuentes EJ, Simonson T. A physics-based energy function allows the computational redesign of a PDZ domain. Sci Rep 2020; 10:11150. [PMID: 32636412 PMCID: PMC7341745 DOI: 10.1038/s41598-020-67972-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/08/2020] [Indexed: 11/30/2022] Open
Abstract
Computational protein design (CPD) can address the inverse folding problem, exploring a large space of sequences and selecting ones predicted to fold. CPD was used previously to redesign several proteins, employing a knowledge-based energy function for both the folded and unfolded states. We show that a PDZ domain can be entirely redesigned using a "physics-based" energy for the folded state and a knowledge-based energy for the unfolded state. Thousands of sequences were generated by Monte Carlo simulation. Three were chosen for experimental testing, based on their low energies and several empirical criteria. All three could be overexpressed and had native-like circular dichroism spectra and 1D-NMR spectra typical of folded structures. Two had upshifted thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting folding to a correct, PDZ structure. Evidently, the physical principles that govern folded proteins, with a dash of empirical post-filtering, can allow successful whole-protein redesign.
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Affiliation(s)
- Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Young Joo Sun
- Department of Biochemistry, Carver College of Medicine, University of Iowa, Iowa City, USA
| | - Titus Hou
- Department of Biochemistry, Carver College of Medicine, University of Iowa, Iowa City, USA
| | - Nicolas Panel
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Ernesto J Fuentes
- Department of Biochemistry, Carver College of Medicine, University of Iowa, Iowa City, USA.
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France.
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9
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Netzer R, Listov D, Lipsh R, Dym O, Albeck S, Knop O, Kleanthous C, Fleishman SJ. Ultrahigh specificity in a network of computationally designed protein-interaction pairs. Nat Commun 2018; 9:5286. [PMID: 30538236 PMCID: PMC6290019 DOI: 10.1038/s41467-018-07722-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/21/2018] [Indexed: 01/21/2023] Open
Abstract
Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity. The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.
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Affiliation(s)
- Ravit Netzer
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Rosalie Lipsh
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orly Dym
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Shira Albeck
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orli Knop
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Colin Kleanthous
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel.
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10
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Storz JF. Compensatory mutations and epistasis for protein function. Curr Opin Struct Biol 2018; 50:18-25. [PMID: 29100081 PMCID: PMC5936477 DOI: 10.1016/j.sbi.2017.10.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 10/05/2017] [Accepted: 10/12/2017] [Indexed: 01/09/2023]
Abstract
Adaptive protein evolution may be facilitated by neutral amino acid mutations that confer no benefit when they first arise but which potentiate subsequent function-altering mutations via direct or indirect structural mechanisms. Theoretical and empirical results indicate that such compensatory interactions (intramolecular epistasis) can exert a strong influence on trajectories of protein evolution. For this reason, assessing the form and prevalence of intramolecular epistasis and characterizing biophysical mechanisms of compensatory interaction are important research goals at the nexus of structural biology and molecular evolution. Here I review recent insights derived from protein-engineering studies, and I describe an approach for identifying and characterizing mechanisms of epistasis that integrates experimental data on structure-function relationships with analyses of comparative sequence data.
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Affiliation(s)
- Jay F Storz
- University of Nebraska, School of Biological Sciences, Lincoln, NE 68588-0114, United States.
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11
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Swint-Kruse L. Using Evolution to Guide Protein Engineering: The Devil IS in the Details. Biophys J 2017; 111:10-8. [PMID: 27410729 DOI: 10.1016/j.bpj.2016.05.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 04/18/2016] [Accepted: 05/20/2016] [Indexed: 10/21/2022] Open
Abstract
For decades, protein engineers have endeavored to reengineer existing proteins for novel applications. Overall, protein folds and gross functions can be readily transferred from one protein to another by transplanting large blocks of sequence (i.e., domain recombination). However, predictably fine-tuning function (e.g., by adjusting ligand affinity, specificity, catalysis, and/or allosteric regulation) remains a challenge. One approach has been to use the sequences of protein families to identify amino acid positions that change during the evolution of functional variation. The rationale is that these nonconserved positions could be mutated to predictably fine-tune function. Evolutionary approaches to protein design have had some success, but the engineered proteins seldom replicate the functional performances of natural proteins. This Biophysical Perspective reviews several complexities that have been revealed by evolutionary and experimental studies of protein function. These include 1) challenges in defining computational and biological thresholds that define important amino acids; 2) the co-occurrence of many different patterns of amino acid changes in evolutionary data; 3) difficulties in mapping the patterns of amino acid changes to discrete functional parameters; 4) the nonconventional mutational outcomes that occur for a particular group of functionally important, nonconserved positions; 5) epistasis (nonadditivity) among multiple mutations; and 6) the fact that a large fraction of a protein's amino acids contribute to its overall function. To overcome these challenges, new goals are identified for future studies.
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Affiliation(s)
- Liskin Swint-Kruse
- Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, Kansas.
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12
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Mignon D, Panel N, Chen X, Fuentes EJ, Simonson T. Computational Design of the Tiam1 PDZ Domain and Its Ligand Binding. J Chem Theory Comput 2017; 13:2271-2289. [PMID: 28394603 DOI: 10.1021/acs.jctc.6b01255] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
PDZ domains direct protein-protein interactions and serve as models for protein design. Here, we optimized a protein design energy function for the Tiam1 and Cask PDZ domains that combines a molecular mechanics energy, Generalized Born solvent, and an empirical unfolded state model. Designed sequences were recognized as PDZ domains by the Superfamily fold recognition tool and had similarity scores comparable to natural PDZ sequences. The optimized model was used to redesign the two PDZ domains, by gradually varying the chemical potential of hydrophobic amino acids; the tendency of each position to lose or gain a hydrophobic character represents a novel hydrophobicity index. We also redesigned four positions in the Tiam1 PDZ domain involved in peptide binding specificity. The calculated affinity differences between designed variants reproduced experimental data and suggest substitutions with altered specificities.
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Affiliation(s)
- David Mignon
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Xingyu Chen
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Ernesto J Fuentes
- Department of Biochemistry, Roy J. & Lucille A. Carver College of Medicine and Holden Comprehensive Cancer Center, University of Iowa , Iowa City, Iowa 52242-1109, United States
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
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13
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Abstract
Protein-protein interactions play critical roles in essentially every cellular process. These interactions are often mediated by protein interaction domains that enable proteins to recognize their interaction partners, often by binding to short peptide motifs. For example, PDZ domains, which are among the most common protein interaction domains in the human proteome, recognize specific linear peptide sequences that are often at the C-terminus of other proteins. Determining the set of peptide sequences that a protein interaction domain binds, or it's "peptide specificity," is crucial for understanding its cellular function, and predicting how mutations impact peptide specificity is important for elucidating the mechanisms underlying human diseases. Moreover, engineering novel cellular functions for synthetic biology applications, such as the biosynthesis of biofuels or drugs, requires the design of protein interaction specificity to avoid crosstalk with native metabolic and signaling pathways. The ability to accurately predict and design protein-peptide interaction specificity is therefore critical for understanding and engineering biological function. One approach that has recently been employed toward accomplishing this goal is computational protein design. This chapter provides an overview of recent methodological advances in computational protein design and highlights examples of how these advances can enable increased accuracy in predicting and designing peptide specificity.
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Affiliation(s)
- Noah Ollikainen
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd., Pasadena, CA, 91125, USA
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14
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Liu X, Speckhard DC, Shepherd TR, Sun YJ, Hengel SR, Yu L, Fowler CA, Gakhar L, Fuentes EJ. Distinct Roles for Conformational Dynamics in Protein-Ligand Interactions. Structure 2016; 24:2053-2066. [PMID: 27998539 DOI: 10.1016/j.str.2016.08.019] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 07/27/2016] [Accepted: 09/30/2016] [Indexed: 11/29/2022]
Abstract
Conformational dynamics has an established role in enzyme catalysis, but its contribution to ligand binding and specificity is largely unexplored. Here we used the Tiam1 PDZ domain and an engineered variant (QM PDZ) with broadened specificity to investigate the role of structure and conformational dynamics in molecular recognition. Crystal structures of the QM PDZ domain both free and bound to ligands showed structural features central to binding (enthalpy), while nuclear-magnetic-resonance-based methyl relaxation experiments and isothermal titration calorimetry revealed that conformational entropy contributes to affinity. In addition to motions relevant to thermodynamics, slower microsecond to millisecond switching was prevalent in the QM PDZ ligand-binding site consistent with a role in ligand specificity. Our data indicate that conformational dynamics plays distinct and fundamental roles in tuning the affinity (conformational entropy) and specificity (excited-state conformations) of molecular interactions. More broadly, our results have important implications for the evolution, regulation, and design of protein-ligand interactions.
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Affiliation(s)
- Xu Liu
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | | | - Tyson R Shepherd
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | - Young Joo Sun
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | - Sarah R Hengel
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | - Liping Yu
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Carver College of Medicine Medical Nuclear Magnetic Resonance Facility, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | - C Andrew Fowler
- Carver College of Medicine Medical Nuclear Magnetic Resonance Facility, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | - Lokesh Gakhar
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Protein Crystallography Facility, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA
| | - Ernesto J Fuentes
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242-1109, USA; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242-1109, USA; Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109, USA.
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15
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Starr TN, Thornton JW. Epistasis in protein evolution. Protein Sci 2016; 25:1204-18. [PMID: 26833806 PMCID: PMC4918427 DOI: 10.1002/pro.2897] [Citation(s) in RCA: 297] [Impact Index Per Article: 37.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 01/25/2016] [Accepted: 01/27/2016] [Indexed: 01/18/2023]
Abstract
The structure, function, and evolution of proteins depend on physical and genetic interactions among amino acids. Recent studies have used new strategies to explore the prevalence, biochemical mechanisms, and evolutionary implications of these interactions-called epistasis-within proteins. Here we describe an emerging picture of pervasive epistasis in which the physical and biological effects of mutations change over the course of evolution in a lineage-specific fashion. Epistasis can restrict the trajectories available to an evolving protein or open new paths to sequences and functions that would otherwise have been inaccessible. We describe two broad classes of epistatic interactions, which arise from different physical mechanisms and have different effects on evolutionary processes. Specific epistasis-in which one mutation influences the phenotypic effect of few other mutations-is caused by direct and indirect physical interactions between mutations, which nonadditively change the protein's physical properties, such as conformation, stability, or affinity for ligands. In contrast, nonspecific epistasis describes mutations that modify the effect of many others; these typically behave additively with respect to the physical properties of a protein but exhibit epistasis because of a nonlinear relationship between the physical properties and their biological effects, such as function or fitness. Both types of interaction are rampant, but specific epistasis has stronger effects on the rate and outcomes of evolution, because it imposes stricter constraints and modulates evolutionary potential more dramatically; it therefore makes evolution more contingent on low-probability historical events and leaves stronger marks on the sequences, structures, and functions of protein families.
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Affiliation(s)
- Tyler N Starr
- Graduate Program in Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, 60637
| | - Joseph W Thornton
- Departments of Ecology and Evolution and Human Genetics, University of Chicago, Chicago, Illinois, 60637
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16
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Sandhya S, Mudgal R, Kumar G, Sowdhamini R, Srinivasan N. Protein sequence design and its applications. Curr Opin Struct Biol 2016; 37:71-80. [PMID: 26773478 DOI: 10.1016/j.sbi.2015.12.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 12/07/2015] [Accepted: 12/15/2015] [Indexed: 01/14/2023]
Abstract
Design of proteins has far-reaching potentials in diverse areas that span repurposing of the protein scaffold for reactions and substrates that they were not naturally meant for, to catching a glimpse of the ephemeral proteins that nature might have sampled during evolution. These non-natural proteins, either in synthesized or virtual form have opened the scope for the design of entities that not only rival their natural counterparts but also offer a chance to visualize the protein space continuum that might help to relate proteins and understand their associations. Here, we review the recent advances in protein engineering and design, in multiple areas, with a view to drawing attention to their future potential.
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Affiliation(s)
- Sankaran Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India
| | - Richa Mudgal
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India; IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560 012, India
| | - Gayatri Kumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences-TIFR, UAS-GKVK Campus, Bangalore 560065, India
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17
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Sahoo A, Khare S, Devanarayanan S, Jain PC, Varadarajan R. Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis. eLife 2015; 4. [PMID: 26716404 PMCID: PMC4758949 DOI: 10.7554/elife.09532] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 12/29/2015] [Indexed: 12/16/2022] Open
Abstract
Identification of residue-residue contacts from primary sequence can be used to guide protein structure prediction. Using Escherichia coli CcdB as the test case, we describe an experimental method termed saturation-suppressor mutagenesis to acquire residue contact information. In this methodology, for each of five inactive CcdB mutants, exhaustive screens for suppressors were performed. Proximal suppressors were accurately discriminated from distal suppressors based on their phenotypes when present as single mutants. Experimentally identified putative proximal pairs formed spatial constraints to recover >98% of native-like models of CcdB from a decoy dataset. Suppressor methodology was also applied to the integral membrane protein, diacylglycerol kinase A where the structures determined by X-ray crystallography and NMR were significantly different. Suppressor as well as sequence co-variation data clearly point to the X-ray structure being the functional one adopted in vivo. The methodology is applicable to any macromolecular system for which a convenient phenotypic assay exists. DOI:http://dx.doi.org/10.7554/eLife.09532.001 Common techniques to determine the three-dimensional structures of proteins can help researchers to understand these molecules’ activities, but are often time-consuming and do not work for all proteins. Proteins are made of chains of amino acids. When a protein chain folds, some of these amino acids interact with other amino acids and these contacts dictate the overall shape of the protein. This means that identifying the pairs of contacting amino acids could make it possible to predict the protein’s structure. Interactions between pairs of contacting amino acids tend to remain conserved throughout evolution, and if a mutation alters one of the amino acids in a pair then a 'compensatory' change often occurs to alter the second amino acid as well. Compensatory mutations can suggest that two amino acids are close to each other in the three-dimensional shape of a protein, but the computational methods used to identify such amino acid pairs can sometimes be inaccurate. In 2012, researchers generated mutants of a bacterial protein called CcdB with changes to single amino acids that caused the protein to fail to fold correctly. Now, Sahoo et al. – who include two of the researchers involved in the 2012 work – have developed an experimental method to identify contacting amino acids and use the CcdB protein as a test case. The approach involved searching for additional mutations that could restore the activity of five of the original mutant proteins when the proteins were produced in yeast cells. The rationale was that any secondary mutations that restored the activity must have corrected the folding defect caused by the original mutation. Sahoo et al. then predicted how close the amino acids affected by the secondary mutations were to the amino acids altered by the original mutations. This information was used to select reliable three-dimensional models of CcdB from a large set of possible structures that had been generated previously using computer models. Next, the technique was applied to a protein called diacylglycerol kinase A. The structure of this protein had previously been inferred using techniques such as X-ray crystallography and nuclear magnetic resonance, but there was a mismatch between the two methods. Sahoo et al. found that the amino acid contacts derived from their experimental method matched those found in the crystal structure, suggesting that the functional protein structure in living cells is similar to the crystal structure. In the future, the experimental approach developed in this work could be combined with existing methods to reliably guide protein structure prediction. DOI:http://dx.doi.org/10.7554/eLife.09532.002
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Affiliation(s)
- Anusmita Sahoo
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Shruti Khare
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | | | - Pankaj C Jain
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Jawaharlal Nehru Center for Advanced Scientific Research, Bangalore, India
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18
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Ollikainen N, de Jong RM, Kortemme T. Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity. PLoS Comput Biol 2015; 11:e1004335. [PMID: 26397464 PMCID: PMC4580623 DOI: 10.1371/journal.pcbi.1004335] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 05/10/2015] [Indexed: 11/25/2022] Open
Abstract
Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution. Designing new protein–ligand interactions has tremendous potential for engineering sensitive biosensors for diagnostics or new enzymes useful in biotechnology, but these applications are extremely challenging, both because of inaccuracies of the energy functions used in modeling and design, and because protein active and binding sites are highly sensitive to subtle changes in structure. Here we describe a new method that addresses the second problem and couples changes in the structure of the protein backbone and of the amino acid side chains, the amino acid sequence, and the conformation of the ligand and its orientation in the binding site. We show that our method improvements significantly increase the accuracy of designing protein–ligand interactions compared to current state-of-the-art design methods. We assess these improvements in two important tests: the first predicts mutations that change ligand-binding preferences in enzymes, and the second predicts protein sequences that bind a given ligand. In these tests, subtle conformational changes made in our model are essential to recapitulate both the results from engineering experiments and the sequence diversity occurring in natural protein families. These results therefore shed light on the mechanisms of how new protein functions might have emerged and can be engineered in the laboratory.
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Affiliation(s)
- Noah Ollikainen
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, United States of America
| | - René M. de Jong
- DSM Biotechnology Center, Alexander Fleminglaan 1, Delft, The Netherlands
| | - Tanja Kortemme
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, United States of America
- California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Science, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
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19
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Goncearenco A, Shaytan AK, Shoemaker BA, Panchenko AR. Structural Perspectives on the Evolutionary Expansion of Unique Protein-Protein Binding Sites. Biophys J 2015. [PMID: 26213149 DOI: 10.1016/j.bpj.2015.06.056] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
Structures of protein complexes provide atomistic insights into protein interactions. Human proteins represent a quarter of all structures in the Protein Data Bank; however, available protein complexes cover less than 10% of the human proteome. Although it is theoretically possible to infer interactions in human proteins based on structures of homologous protein complexes, it is still unclear to what extent protein interactions and binding sites are conserved, and whether protein complexes from remotely related species can be used to infer interactions and binding sites. We considered biological units of protein complexes and clustered protein-protein binding sites into similarity groups based on their structure and sequence, which allowed us to identify unique binding sites. We showed that the growth rate of the number of unique binding sites in the Protein Data Bank was much slower than the growth rate of the number of structural complexes. Next, we investigated the evolutionary roots of unique binding sites and identified the major phyletic branches with the largest expansion in the number of novel binding sites. We found that many binding sites could be traced to the universal common ancestor of all cellular organisms, whereas relatively few binding sites emerged at the major evolutionary branching points. We analyzed the physicochemical properties of unique binding sites and found that the most ancient sites were the largest in size, involved many salt bridges, and were the most compact and least planar. In contrast, binding sites that appeared more recently in the evolution of eukaryotes were characterized by a larger fraction of polar and aromatic residues, and were less compact and more planar, possibly due to their more transient nature and roles in signaling processes.
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Affiliation(s)
- Alexander Goncearenco
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland
| | - Alexey K Shaytan
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland
| | - Benjamin A Shoemaker
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland
| | - Anna R Panchenko
- Computational Biology Branch of the National Center for Biotechnology Information, Bethesda, Maryland.
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