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Krishnan R S, Firzan Ca N, Mahendran KR. Functionally Active Synthetic α-Helical Pores. Acc Chem Res 2024; 57:1790-1802. [PMID: 38875523 DOI: 10.1021/acs.accounts.4c00101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
ConspectusTransmembrane pores are currently at the forefront of nanobiotechnology, nanopore chemistry, and synthetic chemical biology research. Over the past few decades, significant studies in protein engineering have paved the way for redesigning membrane protein pores tailored for specific applications in nanobiotechnology. Most previous efforts predominantly centered on natural β-barrel pores designed with atomic precision for nucleic acid sequencing and sensing of biomacromolecules, including protein fragments. The requirement for a more efficient single-molecule detection system has driven the development of synthetic nanopores. For example, engineering channels to conduct ions and biomolecules selectively could lead to sophisticated nanopore sensors. Also, there has been an increased interest in synthetic pores, which can be fabricated to provide more control in designing architecture and diameter for single-molecule sensing of complex biomacromolecules. There have been impressive advancements in developing synthetic DNA-based pores, although their application in nanopore technology is limited. This has prompted a significant shift toward building synthetic transmembrane α-helical pores, a relatively underexplored field offering novel opportunities. Recently, computational tools have been employed to design and construct α-helical barrels of defined structure and functionality.We focus on building synthetic α-helical pores using naturally occurring transmembrane motifs of membrane protein pores. Our laboratory has developed synthetic α-helical transmembrane pores based on the natural porin PorACj (Porin A derived from Corynebacterium jeikeium) that function as nanopore sensors for single-molecule sensing of cationic cyclodextrins and polypeptides. Our breakthrough lies in being the first to create a functional and large stable synthetic transmembrane pore composed of short synthetic α-helical peptides. The key highlight of our work is that these pores can be synthesized using easy chemical synthesis, which permits its easy modification to include a variety of functional groups to build charge-selective sophisticated pores. Additionally, we have demonstrated that stable functional pores can be constructed from D-amino acid peptides. The analysis of pores composed of D- and L-amino acids in the presence of protease showed that only the D pores are highly functional and stable. The structural models of these pores revealed distinct surface charge conformation and geometry. These new classes of synthetic α-helical pores are highly original systems of general interest due to their unique architecture, functionality, and potential applications in nanopore technology and chemical biology. We emphasize that these simplified transmembrane pores have the potential to be components of functional nanodevices and therapeutic tools. We also suggest that such designed peptides might be valuable as antimicrobial agents and can be targeted to cancer cells. This article will focus on the evolutions in assembling α-helical transmembrane pores and highlight their advantages, including structural and functional versatility.
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Affiliation(s)
- Smrithi Krishnan R
- Transdisciplinary Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India-695014
| | - Neilah Firzan Ca
- Transdisciplinary Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India-695014
- Manipal Academy of Higher Education, Manipal, Karnataka India-576104
| | - Kozhinjampara R Mahendran
- Transdisciplinary Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India-695014
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2
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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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3
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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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4
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Guerin N, Kaserer T, Donald BR. Protocol for predicting drug-resistant protein mutations to an ERK2 inhibitor using RESISTOR. STAR Protoc 2023; 4:102170. [PMID: 37115667 PMCID: PMC10173857 DOI: 10.1016/j.xpro.2023.102170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/11/2023] [Accepted: 02/21/2023] [Indexed: 04/29/2023] Open
Abstract
Prospective predictions of drug-resistant protein mutants could improve the design of therapeutics less prone to resistance. Here, we describe RESISTOR, an algorithm that uses structure- and sequence-based criteria to predict resistance mutations. We demonstrate the process of using RESISTOR to predict ERK2 mutants likely to arise in melanoma ablating the efficacy of the ERK1/2 inhibitor SCH779284. RESISTOR is included in the free and open-source computational protein design software OSPREY. For complete details on the use and execution of this protocol, please refer to Guerin et al..1.
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Affiliation(s)
- Nathan Guerin
- Department of Computer Science, Duke University, Durham, NC 27708, USA.
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, 6020 Innsbruck Austria
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University Medical Center, Durham, NC 22710, USA; Department of Chemistry, Duke University, Durham, NC 27708, USA; Department of Mathematics, Duke University, Durham, NC 27708, USA.
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5
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Abstract
Computational, in silico prediction of resistance-conferring escape mutations could accelerate the design of therapeutics less prone to resistance. This article describes how to use the Resistor algorithm to predict escape mutations. Resistor employs Pareto optimization on four resistance-conferring criteria-positive and negative design, mutational probability, and hotspot cardinality-to assign a Pareto rank to each prospective mutant. It also predicts the mechanism of resistance, that is, whether a mutant ablates binding to a drug, strengthens binding to the endogenous ligand, or a combination of these two factors, and provides structural models of the mutants. Resistor is part of the free and open-source computational protein design software OSPREY.
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Affiliation(s)
- Nathan Guerin
- Department of Computer Science, Duke University, Durham, North Carolina, USA
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina, USA
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, USA
- Department of Chemistry, Duke University, Durham, North Carolina, USA
- Department of Mathematics, Duke University, Durham, North Carolina, USA
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6
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Boyer Z, Kessler H, Brosman H, Ruud KJ, Falkowski AF, Viollet C, Bourne CR, O’Reilly MC. Synthesis and Characterization of Functionalized Amino Dihydropyrimidines Toward the Analysis of their Antibacterial Structure-Activity Relationships and Mechanism of Action. ACS OMEGA 2022; 7:37907-37916. [PMID: 36312355 PMCID: PMC9607683 DOI: 10.1021/acsomega.2c05071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Antibiotic resistance among bacteria puts immense strain on public health. The discovery of new antibiotics that work through unique mechanisms is one important pillar toward combating this threat of resistance. A functionalized amino dihydropyrimidine was reported to exhibit antibacterial activity via the inhibition of dihydrofolate reductase, an underexploited antibacterial target. Despite this promise, little is known about its structure-activity relationships (SAR) and mechanism of activity. Toward this goal, the aza-Biginelli reaction was optimized to allow for the preparation of focused libraries of functionalized amino dihydropyridines, which in some cases required the use of variable temperature NMR analysis for the conclusive assignment of compound identity and purity. Antibacterial activity was examined using microdilution assays, and compound interactions with dihydrofolate reductase were assessed using antimicrobial synergy studies alongside in vitro enzyme kinetics, differential scanning fluorimetry, and protein crystallography. Clear antibacterial SAR trends were unveiled (MIC values from >64 to 4 μg/mL), indicating that this compound class has promise for future development as an antibacterial agent. Despite this, the in vitro biochemical and biophysical studies performed alongside the synergy assays call the antibacterial mechanism into question, indicating that further studies will be required to fully evaluate the antibacterial potential of this compound class.
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Affiliation(s)
- Zachary
W. Boyer
- Department
of Chemistry, Villanova University, Villanova, Pennsylvania 19085, United States
| | - Hannah Kessler
- Department
of Chemistry, Villanova University, Villanova, Pennsylvania 19085, United States
| | - Hannah Brosman
- Department
of Chemistry, Villanova University, Villanova, Pennsylvania 19085, United States
| | - Kirsten J. Ruud
- Department
of Chemistry and Biotechnology, University
of Wisconsin−River Falls, River Falls, Wisconsin 54022, United States
| | - Alan F. Falkowski
- Department
of Chemistry and Biotechnology, University
of Wisconsin−River Falls, River Falls, Wisconsin 54022, United States
| | - Constance Viollet
- Department
of Chemistry, Villanova University, Villanova, Pennsylvania 19085, United States
| | - Christina R. Bourne
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019, United States
| | - Matthew C. O’Reilly
- Department
of Chemistry, Villanova University, Villanova, Pennsylvania 19085, United States
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7
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Guerin N, Feichtner A, Stefan E, Kaserer T, Donald BR. Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures. Cell Syst 2022; 13:830-843.e3. [PMID: 36265469 PMCID: PMC9589925 DOI: 10.1016/j.cels.2022.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/29/2022] [Accepted: 09/13/2022] [Indexed: 01/26/2023]
Abstract
Resistance to pharmacological treatments is a major public health challenge. Here, we introduce Resistor-a structure- and sequence-based algorithm that prospectively predicts resistance mutations for drug design. Resistor computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity (ΔKa) of the (1) drug and (2) endogenous ligand upon a protein's mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. For validation, we applied Resistor to EGFR and BRAF kinase inhibitors treating lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the erlotinib and gefitinib "gatekeeper" T790M mutation and five known osimertinib resistance mutations. Furthermore, Resistor predictions are consistent with BRAF inhibitor sensitivity data from both retrospective and prospective experiments using KinCon biosensors. Resistor is available in the open-source protein design software OSPREY.
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Affiliation(s)
- Nathan Guerin
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Andreas Feichtner
- Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria
| | - Eduard Stefan
- Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria; Tyrolean Cancer Research Institute, Innsbruck, 6020 Tyrol, Austria
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria.
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University Medical Center, Durham, NC 27710, USA; Department of Chemistry, Duke University, Durham, NC 27708, USA; Department of Mathematics, Duke University, Durham, NC 27708, USA.
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8
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Wang S, Reeve SM, Holt GT, Ojewole AA, Frenkel MS, Gainza P, Keshipeddy S, Fowler VG, Wright DL, Donald BR. Chiral evasion and stereospecific antifolate resistance in Staphylococcus aureus. PLoS Comput Biol 2022; 18:e1009855. [PMID: 35143481 PMCID: PMC8865654 DOI: 10.1371/journal.pcbi.1009855] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 02/23/2022] [Accepted: 01/21/2022] [Indexed: 11/19/2022] Open
Abstract
Antimicrobial resistance presents a significant health care crisis. The mutation F98Y in Staphylococcus aureus dihydrofolate reductase (SaDHFR) confers resistance to the clinically important antifolate trimethoprim (TMP). Propargyl-linked antifolates (PLAs), next generation DHFR inhibitors, are much more resilient than TMP against this F98Y variant, yet this F98Y substitution still reduces efficacy of these agents. Surprisingly, differences in the enantiomeric configuration at the stereogenic center of PLAs influence the isomeric state of the NADPH cofactor. To understand the molecular basis of F98Y-mediated resistance and how PLAs' inhibition drives NADPH isomeric states, we used protein design algorithms in the osprey protein design software suite to analyze a comprehensive suite of structural, biophysical, biochemical, and computational data. Here, we present a model showing how F98Y SaDHFR exploits a different anomeric configuration of NADPH to evade certain PLAs' inhibition, while other PLAs remain unaffected by this resistance mechanism.
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Affiliation(s)
- Siyu Wang
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - Stephanie M. Reeve
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut, United States of America
| | - Graham T. Holt
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - Adegoke A. Ojewole
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - Marcel S. Frenkel
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Santosh Keshipeddy
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut, United States of America
| | - Vance G. Fowler
- Division of Infections Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Dennis L. Wright
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut, United States of America
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, United States of America
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
- Department of Chemistry, Duke University, Durham, North Carolina, United States of America
- * E-mail:
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9
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Benkoulouche M, Ben Imeddourene A, Barel LA, Lefebvre D, Fanuel M, Rogniaux H, Ropartz D, Barbe S, Guieysse D, Mulard LA, Remaud-Siméon M, Moulis C, André I. Computer-aided engineering of a branching sucrase for the glucodiversification of a tetrasaccharide precursor of S. flexneri antigenic oligosaccharides. Sci Rep 2021; 11:20294. [PMID: 34645865 PMCID: PMC8514537 DOI: 10.1038/s41598-021-99384-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/07/2021] [Indexed: 11/08/2022] Open
Abstract
Enzyme engineering approaches have allowed to extend the collection of enzymatic tools available for synthetic purposes. However, controlling the regioselectivity of the reaction remains challenging, in particular when dealing with carbohydrates bearing numerous reactive hydroxyl groups as substrates. Here, we used a computer-aided design framework to engineer the active site of a sucrose-active [Formula: see text]-transglucosylase for the 1,2-cis-glucosylation of a lightly protected chemically synthesized tetrasaccharide, a common precursor for the synthesis of serotype-specific S. flexneri O-antigen fragments. By targeting 27 amino acid positions of the acceptor binding subsites of a GH70 branching sucrase, we used a RosettaDesign-based approach to propose 49 mutants containing up to 15 mutations scattered over the active site. Upon experimental evaluation, these mutants were found to produce up to six distinct pentasaccharides, whereas only two were synthesized by the parental enzyme. Interestingly, we showed that by introducing specific mutations in the active site of a same enzyme scaffold, it is possible to control the regiospecificity of the 1,2-cis glucosylation of the tetrasaccharide acceptor and produce a unique diversity of pentasaccharide bricks. This work offers novel opportunities for the development of highly convergent chemo-enzymatic routes toward S. flexneri haptens.
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Affiliation(s)
- Mounir Benkoulouche
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - Akli Ben Imeddourene
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - Louis-Antoine Barel
- Institut Pasteur, CNRS UMR3523 Unité de Chimie des Biomolécules, 28 Rue du Dr Roux, 75724, Paris Cedex 15, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Dorian Lefebvre
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - Mathieu Fanuel
- INRAE, UR BIA, 44316, Nantes, France
- INRAE, BIBS Facility, 44316, Nantes, France
| | - Hélène Rogniaux
- INRAE, UR BIA, 44316, Nantes, France
- INRAE, BIBS Facility, 44316, Nantes, France
| | - David Ropartz
- INRAE, UR BIA, 44316, Nantes, France
- INRAE, BIBS Facility, 44316, Nantes, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - David Guieysse
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - Laurence A Mulard
- Institut Pasteur, CNRS UMR3523 Unité de Chimie des Biomolécules, 28 Rue du Dr Roux, 75724, Paris Cedex 15, France
| | - Magali Remaud-Siméon
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - Claire Moulis
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France
| | - Isabelle André
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, 31077, Toulouse Cedex 04, France.
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10
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Shui S, Gainza P, Scheller L, Yang C, Kurumida Y, Rosset S, Georgeon S, Di Roberto RB, Castellanos-Rueda R, Reddy ST, Correia BE. A rational blueprint for the design of chemically-controlled protein switches. Nat Commun 2021; 12:5754. [PMID: 34599176 PMCID: PMC8486872 DOI: 10.1038/s41467-021-25735-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 08/24/2021] [Indexed: 12/20/2022] Open
Abstract
Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. However, the repertoire of small-molecule protein switches is insufficient for many applications, including those in the translational spaces, where properties such as safety, immunogenicity, drug half-life, and drug side-effects are critical. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions as OFF- and ON-switches. The designed binders and drug-receptors form chemically-disruptable heterodimers (CDH) which dissociate in the presence of small molecules. To design ON-switches, we converted the CDHs into a multi-domain architecture which we refer to as activation by inhibitor release switches (AIR) that incorporate a rationally designed drug-insensitive receptor protein. CDHs and AIRs showed excellent performance as drug responsive switches to control combinations of synthetic circuits in mammalian cells. This approach effectively expands the chemical space and logic responses in living cells and provides a blueprint to develop new ON- and OFF-switches. Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions into OFF- and ON-switches active in cells.
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Affiliation(s)
- Sailan Shui
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland
| | - Pablo Gainza
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland
| | - Leo Scheller
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland
| | - Che Yang
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland
| | - Yoichi Kurumida
- Department of Life Science, School and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Meguro-ku, Tokyo, 152-8550, Japan
| | - Stéphane Rosset
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland
| | - Sandrine Georgeon
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland
| | - Raphaël B Di Roberto
- Department of Biosystems Science and Engineering, ETH Zürich, 4058, Basel, Switzerland
| | | | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, 4058, Basel, Switzerland
| | - Bruno E Correia
- Laboratory of Protein Design and Immunoengineering (LPDI) - STI - EPFL, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland.
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11
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Padhi AK, Shukla R, Saudagar P, Tripathi T. High-throughput rational design of the remdesivir binding site in the RdRp of SARS-CoV-2: implications for potential resistance. iScience 2020; 24:101992. [PMID: 33490902 PMCID: PMC7807151 DOI: 10.1016/j.isci.2020.101992] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/28/2020] [Accepted: 12/21/2020] [Indexed: 02/09/2023] Open
Abstract
The use of remdesivir to treat COVID-19 will likely continue before clinical trials are completed. Due to the lengthening pandemic and evolving nature of the virus, predicting potential residues prone to mutation is crucial for the management of remdesivir resistance. Using a rational ligand-based interface design complemented with mutational mapping, we generated a total of 100,000 mutations and provided insight into the functional outcomes of mutations in the remdesivir-binding site in nsp12 subunit of RdRp. After designing 46 residues in the remdesivir-binding site of nsp12, the designs retained 97%–98% sequence identity, suggesting that very few mutations in nsp12 are required for SARS-CoV-2 to attain remdesivir resistance. Several mutants displayed decreased binding affinity to remdesivir, suggesting drug resistance. These hotspot residues had a higher probability of undergoing selective mutation and thus conferring remdesivir resistance. Identifying the potential residues prone to mutation improves our understanding of SARS-CoV-2 drug resistance and COVID-19 pathogenesis. SARS-CoV-2 may acquire mutations in nsp12 to develop remdesivir resistance Hotspot residues that exhibited the highest potential for mutation were identified Virus can undergo positive selection and attain resistance with very few mutations Data is crucial for the understanding and management of drug resistance
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Affiliation(s)
- Aditya K Padhi
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa 230-0045, Japan
| | - Rohit Shukla
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan 173234, India
| | - Prakash Saudagar
- Department of Biotechnology, National Institute of Technology, Warangal 506004, India
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
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12
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Safaei M, Mobini GR, Abiri A, Shojaeian A. Synthetic biology in various cellular and molecular fields: applications, limitations, and perspective. Mol Biol Rep 2020; 47:6207-6216. [PMID: 32507922 DOI: 10.1007/s11033-020-05565-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 05/28/2020] [Indexed: 01/10/2023]
Abstract
Synthetic biology breakthroughs have facilitated genetic circuit engineering to program cells through novel biological functions, dynamic gene expressions, as well as logic controls. SynBio can also participate in the rapid development of new treatments required for the human lifestyle. Moreover, these technologies are applied in the development of innovative therapeutic, diagnostic, as well as discovery-related methods within a wide range of cellular and molecular applications. In the present review study, SynBio applications in various cellular and molecular fields such as novel strategies for cancer therapy, biosensing, metabolic engineering, protein engineering, and tissue engineering were highlighted and summarized. The major safety and regulatory concerns about synthetic biology will be the environmental release, legal concerns, and risks of the engineered organisms. The final sections focused on limitations to SynBio.
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Affiliation(s)
- Mohsen Safaei
- Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Gholam-Reza Mobini
- Cellular and Molecular Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Ardavan Abiri
- Department of Medicinal Chemistry, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran
| | - Ali Shojaeian
- Department of Molecular Medicine, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran.
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13
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Lowegard AU, Frenkel MS, Holt GT, Jou JD, Ojewole AA, Donald BR. Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. PLoS Comput Biol 2020; 16:e1007447. [PMID: 32511232 PMCID: PMC7329130 DOI: 10.1371/journal.pcbi.1007447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 07/01/2020] [Accepted: 05/13/2020] [Indexed: 11/25/2022] Open
Abstract
The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy ε. Often, reaching an ε-approximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two new algorithms into the osprey suite for protein design: fries, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. fries pre-processes the sequence space to limit a design to only the most stable, energetically favorable sequence possibilities. EWAK* then takes this pruned sequence space as input and, using a user-specified energy window, calculates K* scores using the lowest energy conformations. We expect fries/EWAK* to be most useful in cases where there are many unstable sequences in the design sequence space and when users are satisfied with enumerating the low-energy ensemble of conformations. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable, effectively reducing runtime while still enriching for desirable sequences. This combined approach led to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*, while maintaining its efficiency and accuracy, which we show across 40 different protein systems and a total of 2,826 protein design problems. Additionally, as a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a protein implicated in difficult-to-treat cancers. fries/EWAK* accurately retrospectively predicted the effect of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used fries/EWAK* for prospective design and discovered a novel point mutation that improves binding of c-Raf-RBD to KRas in its active, GTP-bound state (KRasGTP). We combined this new mutation with two previously reported mutations (which were highly-ranked by osprey) to create a new variant of c-Raf-RBD, c-Raf-RBD(RKY). fries/EWAK* in osprey computationally predicted that this new variant binds even more tightly than the previous best-binding variant, c-Raf-RBD(RK). We measured the binding affinity of c-Raf-RBD(RKY) using a bio-layer interferometry (BLI) assay, and found that this new variant exhibits single-digit nanomolar affinity for KRasGTP, confirming the computational predictions made with fries/EWAK*. This new variant binds roughly five times more tightly than the previous best known binder and roughly 36 times more tightly than the design starting point (wild-type c-Raf-RBD). This study steps through the advancement and development of computational protein design by presenting theory, new algorithms, accurate retrospective designs, new prospective designs, and biochemical validation. Computational structure-based protein design is an innovative tool for redesigning proteins to introduce a particular or novel function. One such function is improving the binding of one protein to another, which can increase our understanding of important protein systems. Herein we introduce two novel, provable algorithms, fries and EWAK*, for more efficient computational structure-based protein design as well as their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. These new algorithms speed-up computational structure-based protein design while maintaining accurate calculations, allowing for larger, previously infeasible protein designs. Additionally, using fries and EWAK* within the osprey suite, we designed the tightest known binder of KRas, a heavily studied cancer target that interacts with a number of different proteins. This previously undiscovered variant of a KRas-binding domain, c-Raf-RBD, has potential to serve as a tool to further probe the protein-protein interface of KRas with its effectors and its discovery alone emphasizes the potential for more successful applications of computational structure-based protein design.
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Affiliation(s)
- Anna U. Lowegard
- Program in Computational Biology and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Marcel S. Frenkel
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Graham T. Holt
- Program in Computational Biology and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Jonathan D. Jou
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Adegoke A. Ojewole
- Program in Computational Biology and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
- * E-mail:
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14
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Surpeta B, Sequeiros-Borja CE, Brezovsky J. Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering. Int J Mol Sci 2020; 21:E2713. [PMID: 32295283 PMCID: PMC7215530 DOI: 10.3390/ijms21082713] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/10/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.
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Affiliation(s)
- Bartłomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
| | - Carlos Eduardo Sequeiros-Borja
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
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15
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Jou JD, Holt GT, Lowegard AU, Donald BR. Minimization-Aware Recursive K*: A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape. J Comput Biol 2019; 27:550-564. [PMID: 31855059 DOI: 10.1089/cmb.2019.0315] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE-A*-K*, computes provable ɛ-approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations, and using the A* enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A*-K* runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE-A*-K* is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K*. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K* (MARK*), which tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE-A*-K* versus MARK* by running the Branch and Bound over K* (BBK*) algorithm, which provably returns sequences in order of decreasing K* score, using either iMinDEE-A*-K* or MARK* to approximate partition functions. We show on 200 design problems that MARK* not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs up to 2 orders of magnitude faster. Finally, we show that MARK* not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK* is the first algorithm to do so. We use MARK* to analyze the change in energy landscape of the bound and unbound states of an HIV-1 capsid protein C-terminal domain in complex with a camelid VHH, and measure the change in conformational entropy induced by binding. Thus, MARK* both accelerates existing designs and offers new capabilities not possible with previous algorithms.
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Affiliation(s)
- Jonathan D Jou
- Department of Computer Science, Duke University, Durham, North Carolina
| | - Graham T Holt
- Department of Computer Science, Duke University, Durham, North Carolina.,Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina
| | - Anna U Lowegard
- Department of Computer Science, Duke University, Durham, North Carolina.,Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, North Carolina.,Department of Biochemistry, Duke University Medical Center, Durham, North Carolina.,Department of Chemistry, Duke University, Durham, North Carolina
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16
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Holt GT, Jou JD, Gill NP, Lowegard AU, Martin JW, Madden DR, Donald BR. Computational Analysis of Energy Landscapes Reveals Dynamic Features That Contribute to Binding of Inhibitors to CFTR-Associated Ligand. J Phys Chem B 2019; 123:10441-10455. [PMID: 31697075 DOI: 10.1021/acs.jpcb.9b07278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The CFTR-associated ligand PDZ domain (CALP) binds to the cystic fibrosis transmembrane conductance regulator (CFTR) and mediates lysosomal degradation of mature CFTR. Inhibition of this interaction has been explored as a therapeutic avenue for cystic fibrosis. Previously, we reported the ensemble-based computational design of a novel peptide inhibitor of CALP, which resulted in the most binding-efficient inhibitor to date. This inhibitor, kCAL01, was designed using osprey and evinced significant biological activity in in vitro cell-based assays. Here, we report a crystal structure of kCAL01 bound to CALP and compare structural features against iCAL36, a previously developed inhibitor of CALP. We compute side-chain energy landscapes for each structure to not only enable approximation of binding thermodynamics but also reveal ensemble features that contribute to the comparatively efficient binding of kCAL01. Finally, we compare the previously reported design ensemble for kCAL01 vs the new crystal structure and show that, despite small differences between the design model and crystal structure, significant biophysical features that enhance inhibitor binding are captured in the design ensemble. This suggests not only that ensemble-based design captured thermodynamically significant features observed in vitro, but also that a design eschewing ensembles would miss the kCAL01 sequence entirely.
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Affiliation(s)
- Graham T Holt
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Program in Computational Biology and Bioinformatics , Duke University , Durham , North Carolina 27708 , United States
| | - Jonathan D Jou
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States
| | - Nicholas P Gill
- Department of Biochemistry & Cell Biology , Geisel School of Medicine at Dartmouth , Hanover , New Hampshire 03755 , United States
| | - Anna U Lowegard
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Program in Computational Biology and Bioinformatics , Duke University , Durham , North Carolina 27708 , United States
| | - Jeffrey W Martin
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States
| | - Dean R Madden
- Department of Biochemistry & Cell Biology , Geisel School of Medicine at Dartmouth , Hanover , New Hampshire 03755 , United States
| | - Bruce R Donald
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Department of Biochemistry , Duke University , Durham , North Carolina 27710 , United States.,Department of Chemistry , Duke University , Durham , North Carolina 27710 , United States
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17
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Kuhlman B, Bradley P. Advances in protein structure prediction and design. Nat Rev Mol Cell Biol 2019; 20:681-697. [PMID: 31417196 PMCID: PMC7032036 DOI: 10.1038/s41580-019-0163-x] [Citation(s) in RCA: 387] [Impact Index Per Article: 77.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2019] [Indexed: 12/18/2022]
Abstract
The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.
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Affiliation(s)
- Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Philip Bradley
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
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18
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HALLEN MARKA, DONALD BRUCER. Protein Design by Provable Algorithms. COMMUNICATIONS OF THE ACM 2019; 62:76-84. [PMID: 31607753 PMCID: PMC6788629 DOI: 10.1145/3338124] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein design algorithms can leverage provable guarantees of accuracy to provide new insights and unique optimized molecules.
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Affiliation(s)
- MARK A. HALLEN
- Research assistant professor at the Toyota Technological Institute at Chicago, IL, USA
| | - BRUCE R. DONALD
- James B. Duke Professor of Computer Science at Duke University, as well as a
professor of chemistry and biochemistry in the Duke University Medical
Center, Durham, NC, USA
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19
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Li Y, Netherland MD, Zhang C, Hong H, Gong P. In silico identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of Kochia scoparia. PLoS One 2019; 14:e0216116. [PMID: 31063467 PMCID: PMC6504096 DOI: 10.1371/journal.pone.0216116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 04/14/2019] [Indexed: 12/17/2022] Open
Abstract
Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis.
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Affiliation(s)
- Yan Li
- Bennett Aerospace, Inc., Cary, North Carolina, United States of America
| | - Michael D. Netherland
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, United States of America
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, Mississippi, United States of America
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, United States of America
- * E-mail:
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20
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Hallen MA, Martin JW, Ojewole A, Jou JD, Lowegard AU, Frenkel MS, Gainza P, Nisonoff HM, Mukund A, Wang S, Holt GT, Zhou D, Dowd E, Donald BR. OSPREY 3.0: Open-source protein redesign for you, with powerful new features. J Comput Chem 2018; 39:2494-2507. [PMID: 30368845 PMCID: PMC6391056 DOI: 10.1002/jcc.25522] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 06/14/2018] [Indexed: 12/14/2022]
Abstract
We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Mark A. Hallen
- Department of Computer Science, Duke University, Durham, NC
27708
- Toyota Technological Institute at Chicago, Chicago, IL
60637
| | | | - Adegoke Ojewole
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - Jonathan D. Jou
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Anna U. Lowegard
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - Marcel S. Frenkel
- Department of Biochemistry, Duke University Medical Center,
Durham, NC 27710
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC
27708
| | | | - Aditya Mukund
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Siyu Wang
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - Graham T. Holt
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - David Zhou
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Elizabeth Dowd
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, NC
27708
- Department of Chemistry, Duke University, Durham, NC
27708
- Department of Biochemistry, Duke University Medical Center,
Durham, NC 27710
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21
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Kaserer T, Blagg J. Combining Mutational Signatures, Clonal Fitness, and Drug Affinity to Define Drug-Specific Resistance Mutations in Cancer. Cell Chem Biol 2018; 25:1359-1371.e2. [PMID: 30146241 PMCID: PMC6242700 DOI: 10.1016/j.chembiol.2018.07.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 06/12/2018] [Accepted: 07/26/2018] [Indexed: 12/26/2022]
Abstract
The emergence of mutations that confer resistance to molecularly targeted therapeutics is dependent upon the effect of each mutation on drug affinity for the target protein, the clonal fitness of cells harboring the mutation, and the probability that each variant can be generated by DNA codon base mutation. We present a computational workflow that combines these three factors to identify mutations likely to arise upon drug treatment in a particular tumor type. The Osprey-based workflow is validated using a comprehensive dataset of ERK2 mutations and is applied to small-molecule drugs and/or therapeutic antibodies targeting KIT, EGFR, Abl, and ALK. We identify major clinically observed drug-resistant mutations for drug-target pairs and highlight the potential to prospectively identify probable drug resistance mutations.
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Affiliation(s)
- Teresa Kaserer
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
| | - Julian Blagg
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
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22
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23
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Gainza-Cirauqui P, Correia BE. Computational protein design-the next generation tool to expand synthetic biology applications. Curr Opin Biotechnol 2018; 52:145-152. [PMID: 29729544 DOI: 10.1016/j.copbio.2018.04.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 04/03/2018] [Accepted: 04/04/2018] [Indexed: 11/25/2022]
Abstract
One powerful approach to engineer synthetic biology pathways is the assembly of proteins sourced from one or more natural organisms. However, synthetic pathways often require custom functions or biophysical properties not displayed by natural proteins, limitations that could be overcome through modern protein engineering techniques. Structure-based computational protein design is a powerful tool to engineer new functional capabilities in proteins, and it is beginning to have a profound impact in synthetic biology. Here, we review efforts to increase the capabilities of synthetic biology using computational protein design. We focus primarily on computationally designed proteins not only validated in vitro, but also shown to modulate different activities in living cells. Efforts made to validate computational designs in cells can illustrate both the challenges and opportunities in the intersection of protein design and synthetic biology. We also highlight protein design approaches, which although not validated as conveyors of new cellular function in situ, may have rapid and innovative applications in synthetic biology. We foresee that in the near-future, computational protein design will vastly expand the functional capabilities of synthetic cells.
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Affiliation(s)
- Pablo Gainza-Cirauqui
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Bruno Emanuel Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland.
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24
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Ojewole AA, Jou JD, Fowler VG, Donald BR. BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces. J Comput Biol 2018; 25:726-739. [PMID: 29641249 DOI: 10.1089/cmb.2017.0267] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Computational protein design (CPD) algorithms that compute binding affinity, Ka, search for sequences with an energetically favorable free energy of binding. Recent work shows that three principles improve the biological accuracy of CPD: ensemble-based design, continuous flexibility of backbone and side-chain conformations, and provable guarantees of accuracy with respect to the input. However, previous methods that use all three design principles are single-sequence (SS) algorithms, which are very costly: linear in the number of sequences and thus exponential in the number of simultaneously mutable residues. To address this computational challenge, we introduce BBK*, a new CPD algorithm whose key innovation is the multisequence (MS) bound: BBK* efficiently computes a single provable upper bound to approximate Ka for a combinatorial number of sequences, and avoids SS computation for all provably suboptimal sequences. Thus, to our knowledge, BBK* is the first provable, ensemble-based CPD algorithm to run in time sublinear in the number of sequences. Computational experiments on 204 protein design problems show that BBK* finds the tightest binding sequences while approximating Ka for up to 105-fold fewer sequences than the previous state-of-the-art algorithms, which require exhaustive enumeration of sequences. Furthermore, for 51 protein-ligand design problems, BBK* provably approximates Ka up to 1982-fold faster than the previous state-of-the-art iMinDEE/[Formula: see text]/[Formula: see text] algorithm. Therefore, BBK* not only accelerates protein designs that are possible with previous provable algorithms, but also efficiently performs designs that are too large for previous methods.
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Affiliation(s)
- Adegoke A Ojewole
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,2 Computational Biology and Bioinformatics Program, Duke University , Durham, North Carolina
| | - Jonathan D Jou
- 1 Department of Computer Science, Duke University , Durham, North Carolina
| | - Vance G Fowler
- 3 Division of Infectious Diseases, Duke University Medical Center , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,4 Department of Biochemistry, Duke University Medical Center , Durham North Carolina
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25
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Rational design of proteins that exchange on functional timescales. Nat Chem Biol 2017; 13:1280-1285. [DOI: 10.1038/nchembio.2503] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/18/2017] [Indexed: 12/12/2022]
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26
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Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity. Proc Natl Acad Sci U S A 2017; 114:E5085-E5093. [PMID: 28607051 DOI: 10.1073/pnas.1621233114] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Therapeutic proteins of wide-ranging function hold great promise for treating disease, but immune surveillance of these macromolecules can drive an antidrug immune response that compromises efficacy and even undermines safety. To eliminate widespread T-cell epitopes in any biotherapeutic and thereby mitigate this key source of detrimental immune recognition, we developed a Pareto optimal deimmunization library design algorithm that optimizes protein libraries to account for the simultaneous effects of combinations of mutations on both molecular function and epitope content. Active variants identified by high-throughput screening are thus inherently likely to be deimmunized. Functional screening of an optimized 10-site library (1,536 variants) of P99 β-lactamase (P99βL), a component of ADEPT cancer therapies, revealed that the population possessed high overall fitness, and comprehensive analysis of peptide-MHC II immunoreactivity showed the population possessed lower average immunogenic potential than the wild-type enzyme. Although similar functional screening of an optimized 30-site library (2.15 × 109 variants) revealed reduced population-wide fitness, numerous individual variants were found to have activity and stability better than the wild type despite bearing 13 or more deimmunizing mutations per enzyme. The immunogenic potential of one highly active and stable 14-mutation variant was assessed further using ex vivo cellular immunoassays, and the variant was found to silence T-cell activation in seven of the eight blood donors who responded strongly to wild-type P99βL. In summary, our multiobjective library-design process readily identified large and mutually compatible sets of epitope-deleting mutations and produced highly active but aggressively deimmunized constructs in only one round of library screening.
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27
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Jeukens J, Freschi L, Kukavica-Ibrulj I, Emond-Rheault JG, Tucker NP, Levesque RC. Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa. Ann N Y Acad Sci 2017; 1435:5-17. [PMID: 28574575 PMCID: PMC7379567 DOI: 10.1111/nyas.13358] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/10/2017] [Accepted: 03/22/2017] [Indexed: 12/17/2022]
Abstract
Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.
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Affiliation(s)
- Julie Jeukens
- Institut de biologie intégrative et des systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
| | - Luca Freschi
- Institut de biologie intégrative et des systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
| | - Irena Kukavica-Ibrulj
- Institut de biologie intégrative et des systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
| | | | - Nicholas P Tucker
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, Scotland, UK
| | - Roger C Levesque
- Institut de biologie intégrative et des systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
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28
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Jain S, Jou JD, Georgiev IS, Donald BR. A critical analysis of computational protein design with sparse residue interaction graphs. PLoS Comput Biol 2017; 13:e1005346. [PMID: 28358804 PMCID: PMC5391103 DOI: 10.1371/journal.pcbi.1005346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 04/13/2017] [Accepted: 01/03/2017] [Indexed: 11/19/2022] Open
Abstract
Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies. Computational structure-based protein design algorithms have successfully redesigned proteins to fold and bind target substrates in vitro, and even in vivo. Because the complexity of a computational design increases dramatically with the number of mutable residues, many design algorithms employ cutoffs (distance or energy) to neglect some pairwise residue interactions, thereby reducing the effective search space and computational cost. However, the energies neglected by such cutoffs can add up, which may have nontrivial effects on the designed sequence and its function. To study the effects of using cutoffs on protein design, we computed the optimal sequence both with and without cutoffs, and showed that neglecting long-range interactions can significantly change the computed conformation and sequence. Designs on proteins with experimentally measured thermostability showed the benefits of computing the optimal sequences (and their conformations), both with and without cutoffs, efficiently and accurately. Therefore, we also showed that a provable, ensemble-based algorithm can efficiently compute the optimal conformation and sequence, both with and without applying cutoffs, by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine cutoffs with provable, ensemble-based algorithms to reap the computational efficiency of cutoffs while avoiding their potential inaccuracies.
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Affiliation(s)
- Swati Jain
- Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Jonathan D. Jou
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Ivelin S. Georgiev
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Chemistry, Duke University, Durham, North Carolina, United States of America
- * E-mail:
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29
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Ojewole A, Lowegard A, Gainza P, Reeve SM, Georgiev I, Anderson AC, Donald BR. OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design. Methods Mol Biol 2017; 1529:291-306. [PMID: 27914058 PMCID: PMC5192561 DOI: 10.1007/978-1-4939-6637-0_15] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
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 (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), 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 continuous 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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Affiliation(s)
- Adegoke Ojewole
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Anna Lowegard
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | - Stephanie M Reeve
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Ivelin Georgiev
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD, 20892, USA
| | - Amy C Anderson
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, 27708, USA.
- Department of Biochemistry, Duke University, Durham, NC, 27708, USA.
- Department of Chemistry, Duke University, Durham, NC, 27708, USA.
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30
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Abstract
Computational structure-based protein design (CSPD) is an important problem in computational biology, which aims to design or improve a prescribed protein function based on a protein structure template. It provides a practical tool for real-world protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational protein design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab (Gainza et al., Methods Enzymol 523:87, 2013) to implement a GPU-based massively parallel A* algorithm for improving protein design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large protein design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE (Gainza et al., PLoS Comput Biol 8:e1002335, 2012) and DEEPer (Hallen et al., Proteins 81:18-39, 2013) to also consider continuous backbone and side-chain flexibility.
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Affiliation(s)
- Yichao Zhou
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, P. R. China
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, USA
- Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, P. R. China.
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31
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Reeve SM, Scocchera EW, G-Dayanadan N, Keshipeddy S, Krucinska J, Hajian B, Ferreira J, Nailor M, Aeschlimann J, Wright DL, Anderson AC. MRSA Isolates from United States Hospitals Carry dfrG and dfrK Resistance Genes and Succumb to Propargyl-Linked Antifolates. Cell Chem Biol 2016; 23:1458-1467. [PMID: 27939900 DOI: 10.1016/j.chembiol.2016.11.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Revised: 09/15/2016] [Accepted: 11/14/2016] [Indexed: 10/20/2022]
Abstract
Antibiotic resistance is a rapidly evolving health concern that requires a sustained effort to understand mechanisms of resistance and to develop new agents that overcome those mechanisms. The dihydrofolate reductase (DHFR) inhibitor, trimethoprim (TMP), remains one of the most important orally administered antibiotics. However, resistance through chromosomal mutations and mobile, plasmid-encoded insensitive DHFRs threatens the continued use of this agent. We are pursuing the development of new propargyl-linked antifolate (PLA) DHFR inhibitors designed to evade these mechanisms. While analyzing contemporary TMP-resistant clinical isolates of methicillin-resistant and sensitive Staphylococcus aureus, we discovered two mobile resistance elements, dfrG and dfrK. This is the first identification of these resistance mechanisms in the United States. These resistant organisms were isolated from a variety of infection sites, show clonal diversity, and each contain distinct resistance genotypes for common antibiotics. Several PLAs showed significant activity against these resistant strains by direct inhibition of the TMP resistance elements.
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Affiliation(s)
- Stephanie M Reeve
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Eric W Scocchera
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Narendran G-Dayanadan
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Santosh Keshipeddy
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Jolanta Krucinska
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Behnoush Hajian
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Jacob Ferreira
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
| | - Michael Nailor
- Department of Pharmacy Practice, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA; Department of Pharmacy Services, Hartford Hospital, 80 Seymour Street, Hartford, CT 06102, USA
| | - Jeffrey Aeschlimann
- Department of Pharmacy Practice, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA; Division of Infectious Diseases and Department of Pharmacy Services, UConn Health/John Dempsey Hospital, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Dennis L Wright
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA.
| | - Amy C Anderson
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, CT 06269, USA
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32
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Schiffer C. Remembering Professor Amy Christine Anderson. Cell Chem Biol 2016. [DOI: 10.1016/j.chembiol.2016.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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33
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Pan Y, Dong Y, Zhou J, Hallen M, Donald BR, Zeng J, Xu W. cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design. J Comput Biol 2016; 23:737-49. [PMID: 27154509 PMCID: PMC5586165 DOI: 10.1089/cmb.2015.0234] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches.
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Affiliation(s)
- Yuchao Pan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Yuxi Dong
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Jingtian Zhou
- Department of Pharmacology and Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Mark Hallen
- Department of Computer Science, Duke University, Durham, North Carolina
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Wei Xu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
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34
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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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35
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Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A. Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 2016; 12:e1004619. [PMID: 27124275 PMCID: PMC4849799 DOI: 10.1371/journal.pcbi.1004619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.
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Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Ryan Moffatt
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
- Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
- School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
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36
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Pachov DV, Fonseca R, Arnol D, Bernauer J, van den Bedem H. Coupled Motions in β2AR:Gαs Conformational Ensembles. J Chem Theory Comput 2016; 12:946-56. [DOI: 10.1021/acs.jctc.5b00995] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Dimitar V. Pachov
- Department
of Chemistry, Stanford University, Stanford, California 94305, United States
- Division
of Biosciences, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California 94025, United States
| | - Rasmus Fonseca
- Division
of Biosciences, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California 94025, United States
- AMIB
INRIA - Bioinformatics group, LIX, École Polytechnique, 91128 Palaiseau, France
| | - Damien Arnol
- INRIA Saclay-Île de France, 1 rue Honoré d'Estienne
d'Orves, Bâtiment Alan Turing, Campus de l'École
Polytechnique, 91120 Palaiseau, France
- Laboratoire
d'Informatique de l'École Polytechnique (LIX), CNRS
UMR 7161, École Polytechnique, 91128 Palaiseau, France
| | - Julie Bernauer
- INRIA Saclay-Île de France, 1 rue Honoré d'Estienne
d'Orves, Bâtiment Alan Turing, Campus de l'École
Polytechnique, 91120 Palaiseau, France
- Laboratoire
d'Informatique de l'École Polytechnique (LIX), CNRS
UMR 7161, École Polytechnique, 91128 Palaiseau, France
| | - Henry van den Bedem
- Division
of Biosciences, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California 94025, United States
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37
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Jou JD, Jain S, Georgiev IS, Donald BR. BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design. J Comput Biol 2016; 23:413-24. [PMID: 26744898 DOI: 10.1089/cmb.2015.0194] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.
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Affiliation(s)
- Jonathan D Jou
- 1 Department of Computer Science, Duke University , Durham, North Carolina
| | - Swati Jain
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,2 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina.,3 Department of Computational Biology and Bioinformatics Program, Duke University , Durham, North Carolina
| | - Ivelin S Georgiev
- 1 Department of Computer Science, Duke University , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,2 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina.,4 Department of Chemistry, Duke University , Durham, North Carolina
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38
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Roberts KE, Gainza P, Hallen MA, Donald BR. Fast gap-free enumeration of conformations and sequences for protein design. Proteins 2015; 83:1859-1877. [PMID: 26235965 DOI: 10.1002/prot.24870] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/14/2015] [Accepted: 07/21/2015] [Indexed: 12/12/2022]
Abstract
Despite significant successes in structure-based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest-energy structures and sequences are found. DEE/A*-based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap-free list of low-energy protein conformations, which is necessary for ensemble-based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*-based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically relevant designs.
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Affiliation(s)
- Kyle E Roberts
- Department of Computer Science, Duke University, Durham, NC
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC
| | - Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC.,Department of Biochemistry, Duke University Medical Center, Durham, NC.,Department of Chemistry, Duke University, Durham, NC
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Roberts KE, Donald BR. Improved energy bound accuracy enhances the efficiency of continuous protein design. Proteins 2015; 83:1151-64. [PMID: 25846627 DOI: 10.1002/prot.24808] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Accepted: 03/24/2015] [Indexed: 11/07/2022]
Abstract
Flexibility and dynamics are important for protein function and a protein's ability to accommodate amino acid substitutions. However, when computational protein design algorithms search over protein structures, the allowed flexibility is often reduced to a relatively small set of discrete side-chain and backbone conformations. While simplifications in scoring functions and protein flexibility are currently necessary to computationally search the vast protein sequence and conformational space, a rigid representation of a protein causes the search to become brittle and miss low-energy structures. Continuous rotamers more closely represent the allowed movement of a side chain within its torsional well and have been successfully incorporated into the protein design framework to design biomedically relevant protein systems. The use of continuous rotamers in protein design enables algorithms to search a larger conformational space than previously possible, but adds additional complexity to the design search. To design large, complex systems with continuous rotamers, new algorithms are needed to increase the efficiency of the search. We present two methods, PartCR and HOT, that greatly increase the speed and efficiency of protein design with continuous rotamers. These methods specifically target the large errors in energetic terms that are used to bound pairwise energies during the design search. By tightening the energy bounds, additional pruning of the conformation space can be achieved, and the number of conformations that must be enumerated to find the global minimum energy conformation is greatly reduced.
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Affiliation(s)
- Kyle E Roberts
- Department of Computer Science, Duke University, Durham, North Carolina
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, North Carolina.,Department of Biochemistry, Duke University Medical Center, Durham, North Carolina.,Department of Chemistry, Duke University, Durham, North Carolina
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