1
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Krismer L, Schöppe H, Rauch S, Bante D, Sprenger B, Naschberger A, Costacurta F, Fürst A, Sauerwein A, Rupp B, Kaserer T, von Laer D, Heilmann E. Study of key residues in MERS-CoV and SARS-CoV-2 main proteases for resistance against clinically applied inhibitors nirmatrelvir and ensitrelvir. NPJ VIRUSES 2024; 2:23. [PMID: 38933182 PMCID: PMC11196219 DOI: 10.1038/s44298-024-00028-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 03/14/2024] [Indexed: 06/28/2024]
Abstract
The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an epidemic, zoonotically emerging pathogen initially reported in Saudi Arabia in 2012. MERS-CoV has the potential to mutate or recombine with other coronaviruses, thus acquiring the ability to efficiently spread among humans and become pandemic. Its high mortality rate of up to 35% and the absence of effective targeted therapies call for the development of antiviral drugs for this pathogen. Since the beginning of the SARS-CoV-2 pandemic, extensive research has focused on identifying protease inhibitors for the treatment of SARS-CoV-2. Our intention was therefore to assess whether these protease inhibitors are viable options for combating MERS-CoV. To that end, we used previously established protease assays to quantify inhibition of SARS-CoV-2, MERS-CoV and other main proteases. Nirmatrelvir inhibited several of these proteases, whereas ensitrelvir was less broadly active. To simulate nirmatrelvir's clinical use against MERS-CoV and subsequent resistance development, we applied a safe, surrogate virus-based system. Using the surrogate virus, we previously selected hallmark mutations of SARS-CoV-2-Mpro, such as T21I, M49L, S144A, E166A/K/V and L167F. In the current study, we selected a pool of MERS-CoV-Mpro mutants, characterized the resistance and modelled the steric effect of catalytic site mutants S142G, S142R, S147Y and A171S.
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Affiliation(s)
- Laura Krismer
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020 Austria
| | - Helge Schöppe
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, 6020 Austria
| | - Stefanie Rauch
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020 Austria
| | - David Bante
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020 Austria
| | - Bernhard Sprenger
- Institute of Biochemistry, University of Innsbruck, CMBI – Center for Molecular Biosciences Innsbruck, Innsbruck, 6020 Austria
| | - Andreas Naschberger
- Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology KAUST, Thuwal, Saudi Arabia
| | | | - Anna Fürst
- Institute of Molecular Immunology, Technical University of Munich, Munich, 81675 Germany
| | - Anna Sauerwein
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020 Austria
| | - Bernhard Rupp
- Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, 6020 Austria
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, 6020 Austria
| | - Dorothee von Laer
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020 Austria
| | - Emmanuel Heilmann
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020 Austria
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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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Costacurta F, Dodaro A, Bante D, Schöppe H, Sprenger B, Moghadasi SA, Fleischmann J, Pavan M, Bassani D, Menin S, Rauch S, Krismer L, Sauerwein A, Heberle A, Rabensteiner T, Ho J, Harris RS, Stefan E, Schneider R, Kaserer T, Moro S, von Laer D, Heilmann E. A comprehensive study of SARS-CoV-2 main protease (M pro) inhibitor-resistant mutants selected in a VSV-based system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.558628. [PMID: 37808638 PMCID: PMC10557589 DOI: 10.1101/2023.09.22.558628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Nirmatrelvir was the first protease inhibitor (PI) specifically developed against the SARS-CoV-2 main protease (3CLpro/Mpro) and licensed for clinical use. As SARS-CoV-2 continues to spread, variants resistant to nirmatrelvir and other currently available treatments are likely to arise. This study aimed to identify and characterize mutations that confer resistance to nirmatrelvir. To safely generate Mpro resistance mutations, we passaged a previously developed, chimeric vesicular stomatitis virus (VSV-Mpro) with increasing, yet suboptimal concentrations of nirmatrelvir. Using Wuhan-1 and Omicron Mpro variants, we selected a large set of mutants. Some mutations are frequently present in GISAID, suggesting their relevance in SARS-CoV-2. The resistance phenotype of a subset of mutations was characterized against clinically available PIs (nirmatrelvir and ensitrelvir) with cell-based and biochemical assays. Moreover, we showed the putative molecular mechanism of resistance based on in silico molecular modelling. These findings have implications on the development of future generation Mpro inhibitors, will help to understand SARS-CoV-2 protease-inhibitor-resistance mechanisms and show the relevance of specific mutations in the clinic, thereby informing treatment decisions.
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Affiliation(s)
- Francesco Costacurta
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Andrea Dodaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - David Bante
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Helge Schöppe
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Bernhard Sprenger
- Department of Biochemistry, University of Innsbruck, Innsbruck, 6020, Austria
| | - Seyed Arad Moghadasi
- Department of Biochemistry, Molecular Biology and Biophysics, Institute for Molecular Virology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jakob Fleischmann
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
- Tyrolean Cancer Research Institute (TKFI), Innrain 66, Innsbruck, 6020, Tyrol, Austria
| | - Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Silvia Menin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Stefanie Rauch
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Laura Krismer
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Anna Sauerwein
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Anne Heberle
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Toni Rabensteiner
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Joses Ho
- Bioinformatics Institute, Agency for Science Technology and Research, Singapore
| | - Reuben S. Harris
- Department of Biochemistry and Structural Biology, University of Texas Health San Antonio, San Antonio, TX 78229, United States
- Howard Hughes Medical Institute, University of Texas Health San Antonio, San Antonio, TX 78229, United States
| | - Eduard Stefan
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
- Tyrolean Cancer Research Institute (TKFI), Innrain 66, Innsbruck, 6020, Tyrol, Austria
| | - Rainer Schneider
- Department of Biochemistry, University of Innsbruck, Innsbruck, 6020, Austria
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Dorothee von Laer
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Emmanuel Heilmann
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
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5
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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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6
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Tahti EF, Blount JM, Jackson SN, Gao M, Gill NP, Smith SN, Pederson NJ, Rumph SN, Struyvenberg SA, Mackley IGP, Madden DR, Amacher JF. Additive energetic contributions of multiple peptide positions determine the relative promiscuity of viral and human sequences for PDZ domain targets. Protein Sci 2023; 32:e4611. [PMID: 36851847 PMCID: PMC10022582 DOI: 10.1002/pro.4611] [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: 12/31/2022] [Revised: 02/13/2023] [Accepted: 02/23/2023] [Indexed: 03/01/2023]
Abstract
Protein-protein interactions that involve recognition of short peptides are critical in cellular processes. Protein-peptide interaction surface areas are relatively small and shallow, and there are often overlapping specificities in families of peptide-binding domains. Therefore, dissecting selectivity determinants can be challenging. PDZ domains are a family of peptide-binding domains located in several intracellular signaling and trafficking pathways. These domains are also directly targeted by pathogens, and a hallmark of many oncogenic viral proteins is a PDZ-binding motif. However, amidst sequences that target PDZ domains, there is a wide spectrum in relative promiscuity. For example, the viral HPV16 E6 oncoprotein recognizes over double the number of PDZ domain-containing proteins as the cystic fibrosis transmembrane conductance regulator (CFTR) in the cell, despite similar PDZ targeting-sequences and identical motif residues. Here, we determine binding affinities for PDZ domains known to bind either HPV16 E6 alone or both CFTR and HPV16 E6, using peptides matching WT and hybrid sequences. We also use energy minimization to model PDZ-peptide complexes and use sequence analyses to investigate this difference. We find that while the majority of single mutations had marginal effects on overall affinity, the additive effect on the free energy of binding accurately describes the selectivity observed. Taken together, our results describe how complex and differing PDZ interactomes can be programmed in the cell.
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Affiliation(s)
- Elise F. Tahti
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Jadon M. Blount
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Sophie N. Jackson
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Melody Gao
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Nicholas P. Gill
- Department of BiochemistryGeisel School of Medicine at DartmouthHanoverNew HampshireUSA
| | - Sarah N. Smith
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Nick J. Pederson
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | | | | | - Iain G. P. Mackley
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Dean R. Madden
- Department of BiochemistryGeisel School of Medicine at DartmouthHanoverNew HampshireUSA
| | - Jeanine F. Amacher
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
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7
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Tahti EF, Blount JM, Jackson SN, Gao M, Gill NP, Smith SN, Pederson NJ, Rumph SN, Struyvenberg SA, Mackley IGP, Madden DR, Amacher JF. Additive energetic contributions of multiple peptide positions determine the relative promiscuity of viral and human sequences for PDZ domain targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2022.12.31.522388. [PMID: 36711692 PMCID: PMC9881875 DOI: 10.1101/2022.12.31.522388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Protein-protein interactions that include recognition of short sequences of amino acids, or peptides, are critical in cellular processes. Protein-peptide interaction surface areas are relatively small and shallow, and there are often overlapping specificities in families of peptide-binding domains. Therefore, dissecting selectivity determinants can be challenging. PDZ domains are an example of a peptide-binding domain located in several intracellular signaling and trafficking pathways, which form interactions critical for the regulation of receptor endocytic trafficking, tight junction formation, organization of supramolecular complexes in neurons, and other biological systems. These domains are also directly targeted by pathogens, and a hallmark of many oncogenic viral proteins is a PDZ-binding motif. However, amidst sequences that target PDZ domains, there is a wide spectrum in relative promiscuity. For example, the viral HPV16 E6 oncoprotein recognizes over double the number of PDZ domain-containing proteins as the cystic fibrosis transmembrane conductance regulator (CFTR) in the cell, despite similar PDZ targeting-sequences and identical motif residues. Here, we determine binding affinities for PDZ domains known to bind either HPV16 E6 alone or both CFTR and HPV16 E6, using peptides matching WT and hybrid sequences. We also use energy minimization to model PDZ-peptide complexes and use sequence analyses to investigate this difference. We find that while the majority of single mutations had a marginal effect on overall affinity, the additive effect on the free energy of binding accurately describes the selectivity observed. Taken together, our results describe how complex and differing PDZ interactomes can be programmed in the cell.
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Affiliation(s)
- Elise F. Tahti
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Jadon M. Blount
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Sophie N. Jackson
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Melody Gao
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Nicholas P. Gill
- Department of Biochemistry, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Sarah N. Smith
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Nick J. Pederson
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Simone N. Rumph
- Department of Biochemistry, Bowdoin College, Brunswick, ME, USA
| | | | - Iain G. P. Mackley
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
| | - Dean R. Madden
- Department of Biochemistry, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Jeanine F. Amacher
- Department of Chemistry, Western Washington University, Bellingham, WA, USA
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8
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Sheehan CT, Hampton TH, Madden DR. Tryptophan mutations in G3BP1 tune the stability of a cellular signaling hub by weakening transient interactions with Caprin1 and USP10. J Biol Chem 2022; 298:102552. [PMID: 36183834 PMCID: PMC9723946 DOI: 10.1016/j.jbc.2022.102552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 02/02/2023] Open
Abstract
Intrinsically disordered proteins (IDPs) often coordinate transient interactions with multiple proteins to mediate complex signals within large protein networks. Among these, the IDP hub protein G3BP1 can form complexes with cytoplasmic phosphoprotein Caprin1 and ubiquitin peptidase USP10; the resulting control of USP10 activity contributes to a pathogenic virulence system that targets endocytic recycling of the ion channel CFTR. However, while the identities of protein interactors are known for many IDP hub proteins, the relationship between pairwise affinities and the extent of protein recruitment and activity is not well understood. Here, we describe in vitro analysis of these G3BP1 affinities and show tryptophan substitutions of specific G3BP1 residues reduce its affinity for both USP10 and Caprin1. We show that these same mutations reduce the stability of complexes between the full-length proteins, suggesting that copurification can serve as a surrogate measure of interaction strength. The crystal structure of G3BP1 TripleW (F15W/F33W/F124W) mutant reveals a clear reorientation of the side chain of W33, creating a steric clash with USP10 and Caprin1. Furthermore, an amino-acid scan of USP10 and Caprin1 peptides reveals similarities and differences in the ability to substitute residues in the core motifs as well as specific substitutions with the potential to create higher affinity peptides. Taken together, these data show that small changes in component binding affinities can have significant effects on the composition of cellular interaction hubs. These specific protein mutations can be harnessed to manipulate complex protein networks, informing future investigations into roles of these networks in cellular processes.
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Affiliation(s)
- Colin T Sheehan
- Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Thomas H Hampton
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Dean R Madden
- Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
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9
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Guerin N, Kaserer T, Donald BR. RESISTOR: A New OSPREY Module to Predict Resistance Mutations. J Comput Biol 2022; 29:1346-1352. [PMID: 36099194 PMCID: PMC9807075 DOI: 10.1089/cmb.2022.0254] [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] [Indexed: 01/12/2023] Open
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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10
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Brusa I, Sondo E, Falchi F, Pedemonte N, Roberti M, Cavalli A. Proteostasis Regulators in Cystic Fibrosis: Current Development and Future Perspectives. J Med Chem 2022; 65:5212-5243. [PMID: 35377645 PMCID: PMC9014417 DOI: 10.1021/acs.jmedchem.1c01897] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In cystic fibrosis (CF), the deletion of phenylalanine 508 (F508del) in the CF transmembrane conductance regulator (CFTR) leads to misfolding and premature degradation of the mutant protein. These defects can be targeted with pharmacological agents named potentiators and correctors. During the past years, several efforts have been devoted to develop and approve new effective molecules. However, their clinical use remains limited, as they fail to fully restore F508del-CFTR biological function. Indeed, the search for CFTR correctors with different and additive mechanisms has recently increased. Among them, drugs that modulate the CFTR proteostasis environment are particularly attractive to enhance therapy effectiveness further. This Perspective focuses on reviewing the recent progress in discovering CFTR proteostasis regulators, mainly describing the design, chemical structure, and structure-activity relationships. The opportunities, challenges, and future directions in this emerging and promising field of research are discussed, as well.
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Affiliation(s)
- Irene Brusa
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy.,Computational & Chemical Biology, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Elvira Sondo
- UOC Genetica Medica, IRCCS Istituto Giannina Gaslini, 16147 Genova, Italy
| | | | | | - Marinella Roberti
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy.,Computational & Chemical Biology, Istituto Italiano di Tecnologia, 16163 Genova, Italy
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11
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Delaunay M, Ha-Duong T. Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2405:205-230. [PMID: 35298816 DOI: 10.1007/978-1-0716-1855-4_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.
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Affiliation(s)
| | - Tâp Ha-Duong
- Université Paris-Saclay, CNRS, BioCIS, Châtenay-Malabry, France.
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12
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Düzgüneş N, Fernandez-Fuentes N, Konopka K. Inhibition of Viral Membrane Fusion by Peptides and Approaches to Peptide Design. Pathogens 2021; 10:1599. [PMID: 34959554 PMCID: PMC8709411 DOI: 10.3390/pathogens10121599] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 12/06/2021] [Accepted: 12/06/2021] [Indexed: 12/29/2022] Open
Abstract
Fusion of lipid-enveloped viruses with the cellular plasma membrane or the endosome membrane is mediated by viral envelope proteins that undergo large conformational changes following binding to receptors. The HIV-1 fusion protein gp41 undergoes a transition into a "six-helix bundle" after binding of the surface protein gp120 to the CD4 receptor and a co-receptor. Synthetic peptides that mimic part of this structure interfere with the formation of the helix structure and inhibit membrane fusion. This approach also works with the S spike protein of SARS-CoV-2. Here we review the peptide inhibitors of membrane fusion involved in infection by influenza virus, HIV-1, MERS and SARS coronaviruses, hepatitis viruses, paramyxoviruses, flaviviruses, herpesviruses and filoviruses. We also describe recent computational methods used for the identification of peptide sequences that can interact strongly with protein interfaces, with special emphasis on SARS-CoV-2, using the PePI-Covid19 database.
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Affiliation(s)
- Nejat Düzgüneş
- Department of Biomedical Sciences, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, CA 94103, USA;
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EE, UK;
| | - Krystyna Konopka
- Department of Biomedical Sciences, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, CA 94103, USA;
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13
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Pal A, Mulumudy R, Mitra P. Modularity-based parallel protein design algorithm with an implementation using shared memory programming. Proteins 2021; 90:658-669. [PMID: 34651333 DOI: 10.1002/prot.26263] [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: 06/17/2021] [Revised: 09/23/2021] [Accepted: 10/01/2021] [Indexed: 01/08/2023]
Abstract
Given a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three-dimensional structure. The protein design problem belongs to the non-deterministic polynomial-time-hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity-based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide-and-conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity-based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence-based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure-based comparison of the modeled structures of the design protein with the target structure exhibited an average root-mean-square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity-based protein design algorithm can be extended to protein interaction design as well.
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Affiliation(s)
- Abantika Pal
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Rohith Mulumudy
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
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14
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Zhang G, Li C, Quartararo AJ, Loas A, Pentelute BL. Automated affinity selection for rapid discovery of peptide binders. Chem Sci 2021; 12:10817-10824. [PMID: 34447564 PMCID: PMC8372318 DOI: 10.1039/d1sc02587b] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/13/2021] [Indexed: 12/14/2022] Open
Abstract
In-solution affinity selection (AS) of large synthetic peptide libraries affords identification of binders to protein targets through access to an expanded chemical space. Standard affinity selection methods, however, can be time-consuming, low-throughput, or provide hits that display low selectivity to the target. Here we report an automated bio-layer interferometry (BLI)-assisted affinity selection platform. When coupled with tandem mass spectrometry (MS), this method enables both rapid de novo discovery and affinity maturation of known peptide binders with high selectivity. The BLI-assisted AS-MS technology also features real-time monitoring of the peptide binding during the library selection process, a feature unattainable by current selection approaches. We show the utility of the BLI AS-MS platform toward rapid identification of novel nanomolar (dissociation constant, KD < 50 nM) non-canonical binders to the leukemia-associated oncogenic protein menin. To our knowledge, this is the first application of BLI to the affinity selection of synthetic peptide libraries. We believe our approach can significantly accelerate the use of synthetic peptidomimetic libraries in drug discovery. This work reports an automated affinity selection-mass spectrometry (AS-MS) approach amenable to both de novo peptide binder discovery and affinity maturation of known binders in a high-throughput and selective manner.![]()
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Affiliation(s)
- Genwei Zhang
- Department of Chemistry, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Chengxi Li
- Department of Chemistry, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Anthony J Quartararo
- Department of Chemistry, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Andrei Loas
- Department of Chemistry, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Bradley L Pentelute
- Department of Chemistry, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA .,The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology 500 Main Street Cambridge MA 02142 USA.,Center for Environmental Health Sciences, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA.,Broad Institute of MIT and Harvard 415 Main Street Cambridge MA 02142 USA
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15
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Vrancken JPM, Tame JRH, Voet ARD. Development and applications of artificial symmetrical proteins. Comput Struct Biotechnol J 2020; 18:3959-3968. [PMID: 33335692 PMCID: PMC7734218 DOI: 10.1016/j.csbj.2020.10.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/27/2020] [Accepted: 10/31/2020] [Indexed: 12/28/2022] Open
Abstract
Since the determination of the first molecular models of proteins there has been interest in creating proteins artificially, but such methods have only become widely successful in the last decade. Gradual improvements over a long period of time have now yielded numerous examples of non-natural proteins, many of which are built from repeated elements. In this review we discuss the design of such symmetrical proteins and their various applications in chemistry and medicine.
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Affiliation(s)
- Jeroen P M Vrancken
- Laboratory of Biomolecular Modelling and Design, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
| | - Jeremy R H Tame
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro, Yokohama, Kanagawa 230-0045, Japan
| | - Arnout R D Voet
- Laboratory of Biomolecular Modelling and Design, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
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16
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Mafakher L, Rismani E, Rahimi H, Enayatkhani M, Azadmanesh K, Teimoori-Toolabi L. Computational design of antagonist peptides based on the structure of secreted frizzled-related protein-1 (SFRP1) aiming to inhibit Wnt signaling pathway. J Biomol Struct Dyn 2020; 40:2169-2188. [DOI: 10.1080/07391102.2020.1835718] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Ladan Mafakher
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Elham Rismani
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Hamzeh Rahimi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Maryam Enayatkhani
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | | | - Ladan Teimoori-Toolabi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
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17
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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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18
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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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19
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Whitby PW, Morton DJ, Mussa HJ, Mirea L, Stull TL. A bacterial vaccine polypeptide protective against nontypable Haemophilus influenzae. Vaccine 2020; 38:2960-2970. [PMID: 32111525 DOI: 10.1016/j.vaccine.2020.02.054] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 02/07/2020] [Accepted: 02/18/2020] [Indexed: 02/08/2023]
Abstract
Nontypeable strains of Haemophilus influenzae (NTHi) are one of the most common cause of otitis media and the most frequent infection associated with exacerbations of chronic obstructive pulmonary disease; there is currently no vaccine in the U.S. to prevent NTHi. Using bioinformatics and structural vaccinology, we previously identified several NTHi species-conserved and sequence-conserved peptides that mediate passive protection in the rat model of infection. Using these, and similar peptides, we designed Hi Poly 1, a Bacterial Vaccine Polypeptide, comprising 9 unique peptides from 6 different surface proteins. Recombinant Hi Poly 1 was purified by affinity chromatography. Forty chinchillas were immunized three times with 200 µg of Hi Poly 1 with alum adjuvant; similarly, 41 controls were immunized with adjuvant alone. The average Log2 IgG titer among immunized animals was 17.04, and IgG antibodies against each component peptide were detected. In the infant rat model, antisera from immunized chinchillas provided significant passive protection compared to PBS (p = 0.01) and pre-immune sera (p = 0.03). In the established chinchilla model of NTHi otitis media, the vaccinated group cleared infection faster than the control group as indicated by significantly decreased positive findings on video-otoscopy (p < 0.0001) and tympanometry (p = 0.0002) on day 7, and for middle ear fluid obtained by aspiration (p = 0.0001) on day 10 post-infection. Using 12 representative NTHi strains in a Live-Cell ELISA, greater antibody binding to each strain was detected with post Hi Poly 1 than the pre-immune chinchilla antisera. The data from this proof-of-principle study demonstrate the effectiveness of Hi Poly 1 against the NTHi in two relevant preclinical models of bacteremia and otitis media as well as surface antibody binding across the species. The Bacterial Vaccine Polypeptide approach to a vaccine against NTHi also serves as a paradigm for development of similar vaccines to protect against other bacteria.
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Affiliation(s)
- Paul W Whitby
- Department of Child Health, University of Arizona College of Medicine-Phoenix, United States; Phoenix Childrens Hospital, Phoenix, United States.
| | - Daniel J Morton
- Department of Child Health, University of Arizona College of Medicine-Phoenix, United States; Phoenix Childrens Hospital, Phoenix, United States
| | - Huda J Mussa
- Department of Child Health, University of Arizona College of Medicine-Phoenix, United States; Phoenix Childrens Hospital, Phoenix, United States
| | - Lucia Mirea
- Department of Child Health, University of Arizona College of Medicine-Phoenix, United States; Phoenix Childrens Hospital, Phoenix, United States
| | - Terrence L Stull
- Department of Child Health, University of Arizona College of Medicine-Phoenix, United States; Phoenix Childrens Hospital, Phoenix, United States
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20
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Amacher JF, Brooks L, Hampton TH, Madden DR. Specificity in PDZ-peptide interaction networks: Computational analysis and review. JOURNAL OF STRUCTURAL BIOLOGY-X 2020; 4:100022. [PMID: 32289118 PMCID: PMC7138185 DOI: 10.1016/j.yjsbx.2020.100022] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/26/2020] [Accepted: 02/29/2020] [Indexed: 01/03/2023]
Abstract
Globular PDZ domains typically serve as protein-protein interaction modules that regulate a wide variety of cellular functions via recognition of short linear motifs (SLiMs). Often, PDZ mediated-interactions are essential components of macromolecular complexes, and disruption affects the entire scaffold. Due to their roles as linchpins in trafficking and signaling pathways, PDZ domains are attractive targets: both for controlling viral pathogens, which bind PDZ domains and hijack cellular machinery, as well as for developing therapies to combat human disease. However, successful therapeutic interventions that avoid off-target effects are a challenge, because each PDZ domain interacts with a number of cellular targets, and specific binding preferences can be difficult to decipher. Over twenty-five years of research has produced a wealth of data on the stereochemical preferences of individual PDZ proteins and their binding partners. Currently the field lacks a central repository for this information. Here, we provide this important resource and provide a manually curated, comprehensive list of the 271 human PDZ domains. We use individual domain, as well as recent genomic and proteomic, data in order to gain a holistic view of PDZ domains and interaction networks, arguing this knowledge is critical to optimize targeting selectivity and to benefit human health.
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Affiliation(s)
- Jeanine F Amacher
- Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.,Department of Chemistry, Western Washington University, Bellingham, WA 98225, USA
| | - Lionel Brooks
- Department of Biology, Western Washington University, Bellingham, WA 98225, USA
| | - Thomas H Hampton
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Dean R Madden
- Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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21
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Sala V, Murabito A, Ghigo A. Inhaled Biologicals for the Treatment of Cystic Fibrosis. ACTA ACUST UNITED AC 2020; 13:19-26. [PMID: 30318010 PMCID: PMC6751348 DOI: 10.2174/1872213x12666181012101444] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 10/09/2018] [Accepted: 10/09/2018] [Indexed: 12/20/2022]
Abstract
Background: Cystic Fibrosis (CF), one of the most frequent genetic diseases, is characterized by the production of viscous mucus in several organs. In the lungs, mucus clogs the airways and traps bacteria, leading to recurrent/resistant infections and lung damage. For cystic fibrosis patients, respiratory failure is still lethal in early adulthood since available treatments display incomplete efficacy. Objective: The objective of this review is to extend the current knowledge in the field of available treat-ments for cystic fibrosis. A special focus has been given to inhaled peptide-based drugs. Methods: The current review is based on recent and/or relevant literature and patents already available in various scientific databases, which include PubMed, PubMed Central, Patentscope and Science Direct. The information obtained through these diverse databases is compiled, critically interpreted and presented in the current study. An in-depth but not systematic approach to the specific research question has been adopted. Results: Recently, peptides have been proposed as possible pharmacologic agents for the treatment of respiratory diseases. Of note, peptides are suitable to be administered by inhalation to maximize efficacy and reduce systemic side effects. Moreover, innovative delivery carriers have been developed for drug administration through inhalation, allowing not only protection against proteolysis, but also a prolonged and controlled release. Conclusion: Here, we summarize newly patented peptides that have been developed in the last few years and advanced technologies for inhaled drug delivery to treat cystic fibrosis.
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Affiliation(s)
- Valentina Sala
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy.,S.C. Medicina d'Urgenza, A.O.U. Città della Salute e della Scienza, Molinette Hospital, Torino, Italy
| | - Alessandra Murabito
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy
| | - Alessandra Ghigo
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy
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22
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Zhou J, Panaitiu AE, Grigoryan G. A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures. Proc Natl Acad Sci U S A 2020; 117:1059-1068. [PMID: 31892539 PMCID: PMC6969538 DOI: 10.1073/pnas.1908723117] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.
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Affiliation(s)
- Jianfu Zhou
- Department of Computer Science, Dartmouth College, Hanover, NH 03755
| | | | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755;
- Department of Biological Sciences, Dartmouth College, Hanover, NH 03755
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23
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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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24
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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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25
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Valgardson J, Cosbey R, Houser P, Rupp M, Van Bronkhorst R, Lee M, Jagodzinski F, Amacher JF. MotifAnalyzer-PDZ: A computational program to investigate the evolution of PDZ-binding target specificity. Protein Sci 2019; 28:2127-2143. [PMID: 31599029 DOI: 10.1002/pro.3741] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 09/27/2019] [Accepted: 09/30/2019] [Indexed: 12/15/2022]
Abstract
Recognition of short linear motifs (SLiMs) or peptides by proteins is an important component of many cellular processes. However, due to limited and degenerate binding motifs, prediction of cellular targets is challenging. In addition, many of these interactions are transient and of relatively low affinity. Here, we focus on one of the largest families of SLiM-binding domains in the human proteome, the PDZ domain. These domains bind the extreme C-terminus of target proteins, and are involved in many signaling and trafficking pathways. To predict endogenous targets of PDZ domains, we developed MotifAnalyzer-PDZ, a program that filters and compares all motif-satisfying sequences in any publicly available proteome. This approach enables us to determine possible PDZ binding targets in humans and other organisms. Using this program, we predicted and biochemically tested novel human PDZ targets by looking for strong sequence conservation in evolution. We also identified three C-terminal sequences in choanoflagellates that bind a choanoflagellate PDZ domain, the Monsiga brevicollis SHANK1 PDZ domain (mbSHANK1), with endogenously-relevant affinities, despite a lack of conservation with the targets of a homologous human PDZ domain, SHANK1. All three are predicted to be signaling proteins, with strong sequence homology to cytosolic and receptor tyrosine kinases. Finally, we analyzed and compared the positional amino acid enrichments in PDZ motif-satisfying sequences from over a dozen organisms. Overall, MotifAnalyzer-PDZ is a versatile program to investigate potential PDZ interactions. This proof-of-concept work is poised to enable similar types of analyses for other SLiM-binding domains (e.g., MotifAnalyzer-Kinase). MotifAnalyzer-PDZ is available at http://motifAnalyzerPDZ.cs.wwu.edu.
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Affiliation(s)
- Jordan Valgardson
- Department of Computer Science, Western Washington University, Bellingham, Washington.,Department of Chemistry, Western Washington University, Bellingham, Washington
| | - Robin Cosbey
- Department of Computer Science, Western Washington University, Bellingham, Washington
| | - Paul Houser
- Department of Computer Science, Western Washington University, Bellingham, Washington
| | - Milo Rupp
- Department of Computer Science, Western Washington University, Bellingham, Washington
| | - Raiden Van Bronkhorst
- Department of Computer Science, Western Washington University, Bellingham, Washington
| | - Michael Lee
- Department of Computer Science, Western Washington University, Bellingham, Washington
| | - Filip Jagodzinski
- Department of Computer Science, Western Washington University, Bellingham, Washington
| | - Jeanine F Amacher
- Department of Chemistry, Western Washington University, Bellingham, Washington
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26
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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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27
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Christensen NR, Čalyševa J, Fernandes EFA, Lüchow S, Clemmensen LS, Haugaard‐Kedström LM, Strømgaard K. PDZ Domains as Drug Targets. ADVANCED THERAPEUTICS 2019; 2:1800143. [PMID: 32313833 PMCID: PMC7161847 DOI: 10.1002/adtp.201800143] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/25/2019] [Indexed: 12/14/2022]
Abstract
Protein-protein interactions within protein networks shape the human interactome, which often is promoted by specialized protein interaction modules, such as the postsynaptic density-95 (PSD-95), discs-large, zona occludens 1 (ZO-1) (PDZ) domains. PDZ domains play a role in several cellular functions, from cell-cell communication and polarization, to regulation of protein transport and protein metabolism. PDZ domain proteins are also crucial in the formation and stability of protein complexes, establishing an important bridge between extracellular stimuli detected by transmembrane receptors and intracellular responses. PDZ domains have been suggested as promising drug targets in several diseases, ranging from neurological and oncological disorders to viral infections. In this review, the authors describe structural and genetic aspects of PDZ-containing proteins and discuss the current status of the development of small-molecule and peptide modulators of PDZ domains. An overview of potential new therapeutic interventions in PDZ-mediated protein networks is also provided.
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Affiliation(s)
- Nikolaj R. Christensen
- Center for BiopharmaceuticalsDepartment of Drug Design and PharmacologyUniversity of CopenhagenUniversitetsparken 22100CopenhagenDenmark
| | - Jelena Čalyševa
- European Molecular Biology Laboratory (EMBL)Structural and Computational Biology UnitMeyerhofstraße 169117HeidelbergGermany
- EMBL International PhD ProgrammeFaculty of BiosciencesEMBL–Heidelberg UniversityGermany
| | - Eduardo F. A. Fernandes
- Center for BiopharmaceuticalsDepartment of Drug Design and PharmacologyUniversity of CopenhagenUniversitetsparken 22100CopenhagenDenmark
| | - Susanne Lüchow
- Department of Chemistry – BMCUppsala UniversityBox 576SE75123UppsalaSweden
| | - Louise S. Clemmensen
- Center for BiopharmaceuticalsDepartment of Drug Design and PharmacologyUniversity of CopenhagenUniversitetsparken 22100CopenhagenDenmark
| | - Linda M. Haugaard‐Kedström
- Center for BiopharmaceuticalsDepartment of Drug Design and PharmacologyUniversity of CopenhagenUniversitetsparken 22100CopenhagenDenmark
| | - Kristian Strømgaard
- Center for BiopharmaceuticalsDepartment of Drug Design and PharmacologyUniversity of CopenhagenUniversitetsparken 22100CopenhagenDenmark
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28
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Frappier V, Jenson JM, Zhou J, Grigoryan G, Keating AE. Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1. Structure 2019; 27:606-617.e5. [PMID: 30773399 PMCID: PMC6447450 DOI: 10.1016/j.str.2019.01.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 12/20/2018] [Accepted: 01/18/2019] [Indexed: 12/25/2022]
Abstract
Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.
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Affiliation(s)
- Vincent Frappier
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Justin M Jenson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jianfu Zhou
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, USA; Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA.
| | - Amy E Keating
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Koch Center for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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29
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Abstract
PDZ domains contain 80-100 amino acids and bind short C-terminal sequences of target proteins. Their specificity is essential for cellular signaling pathways. We studied the binding of the Tiam1 PDZ domain to peptides derived from the C-termini of its Syndecan-1 and Caspr4 targets. We used free energy perturbation (FEP) to characterize the binding energetics of one wild-type and 17 mutant complexes by simulating 21 alchemical transformations between pairs of complexes. Thirteen complexes had known experimental affinities. FEP is a powerful tool to understand protein/ligand binding. It depends, however, on the accuracy of molecular dynamics force fields and conformational sampling. Both aspects require continued testing, especially for ionic mutations. For six mutations that did not modify the net charge, we obtained excellent agreement with experiment using the additive, AMBER ff99SB force field, with a root mean square deviation (RMSD) of 0.37 kcal/mol. For six ionic mutations that modified the net charge, agreement was also good, with one large error (3 kcal/mol) and an RMSD of 0.9 kcal/mol for the other five. The large error arose from the overstabilization of a protein/peptide salt bridge by the additive force field. Four of the ionic mutations were also simulated with the polarizable Drude force field, which represents the first test of this force field for protein/ligand binding free energy changes. The large error was eliminated and the RMS error for the four mutations was reduced from 1.8 to 1.2 kcal/mol. The overall accuracy of FEP indicates it can be used to understand PDZ/peptide binding. Importantly, our results show that for ionic mutations in buried regions, electronic polarization plays a significant role.
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30
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Liu X, Fuentes EJ. Emerging Themes in PDZ Domain Signaling: Structure, Function, and Inhibition. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2019; 343:129-218. [PMID: 30712672 PMCID: PMC7185565 DOI: 10.1016/bs.ircmb.2018.05.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Post-synaptic density-95, disks-large and zonula occludens-1 (PDZ) domains are small globular protein-protein interaction domains widely conserved from yeast to humans. They are composed of ∼90 amino acids and form a classical two α-helical/six β-strand structure. The prototypical ligand is the C-terminus of partner proteins; however, they also bind internal peptide sequences. Recent findings indicate that PDZ domains also bind phosphatidylinositides and cholesterol. Through their ligand interactions, PDZ domain proteins are critical for cellular trafficking and the surface retention of various ion channels. In addition, PDZ proteins are essential for neuronal signaling, memory, and learning. PDZ proteins also contribute to cytoskeletal dynamics by mediating interactions critical for maintaining cell-cell junctions, cell polarity, and cell migration. Given their important biological roles, it is not surprising that their dysfunction can lead to multiple disease states. As such, PDZ domain-containing proteins have emerged as potential targets for the development of small molecular inhibitors as therapeutic agents. Recent data suggest that the critical binding function of PDZ domains in cell signaling is more than just glue, and their binding function can be regulated by phosphorylation or allosterically by other binding partners. These studies also provide a wealth of structural and biophysical data that are beginning to reveal the physical features that endow this small modular domain with a central role in cell signaling.
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Affiliation(s)
- Xu Liu
- Department of Biochemistry, University of Iowa, Iowa City, IA, United States
| | - Ernesto J. Fuentes
- Department of Biochemistry, University of Iowa, Iowa City, IA, United States
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States
- Corresponding author: E-mail:
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31
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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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32
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Hallen MA. PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design. Proteins 2018; 87:62-73. [PMID: 30378699 DOI: 10.1002/prot.25623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 10/10/2018] [Accepted: 10/16/2018] [Indexed: 12/29/2022]
Abstract
Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs-the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).
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Affiliation(s)
- Mark A Hallen
- Toyota Technological Institute at Chicago, Chicago, Illinois
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33
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Charpentier A, Mignon D, Barbe S, Cortes J, Schiex T, Simonson T, Allouche D. Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems. J Chem Inf Model 2018; 59:127-136. [DOI: 10.1021/acs.jcim.8b00510] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - David Mignon
- Laboratoire de Biochimie (CNRS UMR 7654), École Polytechnique, 91128 Palaiseau, France
| | - Sophie Barbe
- Laboratoire d’Ingénierie des Systèmes Biologiques et Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, 31077 Toulouse, France
| | - Juan Cortes
- LAAS-CNRS, Université de Toulouse, CNRS, 31400 Toulouse, France
| | - Thomas Schiex
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR 7654), École Polytechnique, 91128 Palaiseau, France
| | - David Allouche
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
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34
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Lechner H, Ferruz N, Höcker B. Strategies for designing non-natural enzymes and binders. Curr Opin Chem Biol 2018; 47:67-76. [PMID: 30248579 DOI: 10.1016/j.cbpa.2018.07.022] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 07/16/2018] [Accepted: 07/17/2018] [Indexed: 12/20/2022]
Abstract
The design of tailor-made enzymes is a major goal in biochemical research that can result in wide-range applications and will lead to a better understanding of how proteins fold and function. In this review we highlight recent advances in enzyme and small molecule binder design. A focus is placed on novel strategies for the design of scaffolds, developments in computational methods, and recent applications of these techniques on receptors, sensors, and enzymes. Further, the integration of computational and experimental methodologies is discussed. The outlined examples of designed enzymes and binders for various purposes highlight the importance of this topic and underline the need for tailor-made proteins.
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Affiliation(s)
- Horst Lechner
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Noelia Ferruz
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Birte Höcker
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany.
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35
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Hallen MA, Donald BR. CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions. Bioinformatics 2018; 33:i5-i12. [PMID: 28882005 PMCID: PMC5870559 DOI: 10.1093/bioinformatics/btx277] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation When proteins mutate or bind to ligands, their backbones often move significantly, especially in loop regions. Computational protein design algorithms must model these motions in order to accurately optimize protein stability and binding affinity. However, methods for backbone conformational search in design have been much more limited than for sidechain conformational search. This is especially true for combinatorial protein design algorithms, which aim to search a large sequence space efficiently and thus cannot rely on temporal simulation of each candidate sequence. Results We alleviate this difficulty with a new parameterization of backbone conformational space, which represents all degrees of freedom of a specified segment of protein chain that maintain valid bonding geometry (by maintaining the original bond lengths and angles and ω dihedrals). In order to search this space, we present an efficient algorithm, CATS, for computing atomic coordinates as a function of our new continuous backbone internal coordinates. CATS generalizes the iMinDEE and EPIC protein design algorithms, which model continuous flexibility in sidechain dihedrals, to model continuous, appropriately localized flexibility in the backbone dihedrals ϕ and ψ as well. We show using 81 test cases based on 29 different protein structures that CATS finds sequences and conformations that are significantly lower in energy than methods with less or no backbone flexibility do. In particular, we show that CATS can model the viability of an antibody mutation known experimentally to increase affinity, but that appears sterically infeasible when modeled with less or no backbone flexibility. Availability and implementation Our code is available as free software at https://github.com/donaldlab/OSPREY_refactor. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC, USA.,Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, USA.,Department of Chemistry, Duke University, Durham, NC, USA.,Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
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36
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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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37
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Zheng F, Grigoryan G. Simplifying the Design of Protein-Peptide Interaction Specificity with Sequence-Based Representations of Atomistic Models. Methods Mol Biol 2018; 1561:189-200. [PMID: 28236239 DOI: 10.1007/978-1-4939-6798-8_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Computationally designed peptides targeting protein-protein interaction interfaces are of great interest as reagents for biological research and potential therapeutics. In recent years, it has been shown that detailed structure-based calculations can, in favorable cases, describe relevant determinants of protein-peptide recognition. Yet, despite large increases in available computing power, such accurate modeling of the binding reaction is still largely outside the realm of protein design. The chief limitation is in the large sequence spaces generally involved in protein design problems, such that it is typically infeasible to apply expensive modeling techniques to score each sequence. Toward addressing this issue, we have previously shown that by explicitly evaluating the scores of a relatively small number of sequences, it is possible to synthesize a direct mapping between sequences and scores, such that the entire sequence space can be analyzed extremely rapidly. The associated method, called Cluster Expansion, has been used in a number of studies to design binding affinity and specificity. In this chapter, we provide instructions and guidance for applying this technique in the context of designing protein-peptide interactions to enable the use of more detailed and expensive scoring approaches than is typically possible.
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Affiliation(s)
- Fan Zheng
- Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, 6211 Sudikoff Lab, Room 113, Hanover, NH, 03755, USA. .,Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA.
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38
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Foight GW, Chen TS, Richman D, Keating AE. Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design. Methods Mol Biol 2018; 1561:213-232. [PMID: 28236241 DOI: 10.1007/978-1-4939-6798-8_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.
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Affiliation(s)
- Glenna Wink Foight
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA
- Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - T Scott Chen
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA
- Google Inc., Mountain View, CA, 94043, USA
| | - Daniel Richman
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA
| | - Amy E Keating
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA.
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA.
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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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CFTR-NHERF2-LPA₂ Complex in the Airway and Gut Epithelia. Int J Mol Sci 2017; 18:ijms18091896. [PMID: 28869532 PMCID: PMC5618545 DOI: 10.3390/ijms18091896] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 08/25/2017] [Accepted: 08/25/2017] [Indexed: 01/02/2023] Open
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is a cAMP- and cGMP-regulated chloride (Cl−) and bicarbonate (HCO3−) channel localized primarily at the apical plasma membrane of epithelial cells lining the airway, gut and exocrine glands, where it is responsible for transepithelial salt and water transport. Several human diseases are associated with altered CFTR channel function. Cystic fibrosis (CF) is caused by the absence or dysfunction of CFTR channel activity, resulting from mutations in the gene. Secretory diarrhea is caused by the hyperactivation of CFTR channel activity in the gastrointestinal tract. CFTR is a validated target for drug development to treat CF, and extensive research has been conducted to develop CFTR inhibitors for therapeutic interventions of secretory diarrhea. The intracellular processing, trafficking, apical membrane localization, and channel function of CFTR are regulated by dynamic protein–protein interactions in a complex network. In this paper, we review the current knowledge of a macromolecular complex of CFTR, Na+/H+ exchanger regulatory factor 2 (NHERF2), and lysophosphatidic acids (LPA) receptor 2 (LPA2) at the apical plasma membrane of airway and gut epithelial cells, and discuss its relevance in human physiology and diseases. We also explore the possibilities of targeting this complex to fine tune CFTR channel activity, with a hope to open up new avenues to develop novel therapies for CF and secretory diarrhea.
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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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Mignon D, Panel N, Chen X, Fuentes EJ, Simonson T. Computational Design of the Tiam1 PDZ Domain and Its Ligand Binding. J Chem Theory Comput 2017; 13:2271-2289. [PMID: 28394603 DOI: 10.1021/acs.jctc.6b01255] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
PDZ domains direct protein-protein interactions and serve as models for protein design. Here, we optimized a protein design energy function for the Tiam1 and Cask PDZ domains that combines a molecular mechanics energy, Generalized Born solvent, and an empirical unfolded state model. Designed sequences were recognized as PDZ domains by the Superfamily fold recognition tool and had similarity scores comparable to natural PDZ sequences. The optimized model was used to redesign the two PDZ domains, by gradually varying the chemical potential of hydrophobic amino acids; the tendency of each position to lose or gain a hydrophobic character represents a novel hydrophobicity index. We also redesigned four positions in the Tiam1 PDZ domain involved in peptide binding specificity. The calculated affinity differences between designed variants reproduced experimental data and suggest substitutions with altered specificities.
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Affiliation(s)
- David Mignon
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Xingyu Chen
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Ernesto J Fuentes
- Department of Biochemistry, Roy J. & Lucille A. Carver College of Medicine and Holden Comprehensive Cancer Center, University of Iowa , Iowa City, Iowa 52242-1109, United States
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
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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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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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45
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Callebaut I, Hoffmann B, Lehn P, Mornon JP. Molecular modelling and molecular dynamics of CFTR. Cell Mol Life Sci 2017; 74:3-22. [PMID: 27717958 PMCID: PMC11107702 DOI: 10.1007/s00018-016-2385-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 09/28/2016] [Indexed: 12/11/2022]
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) protein is a member of the ATP-binding cassette (ABC) transporter superfamily that functions as an ATP-gated channel. Considerable progress has been made over the last years in the understanding of the molecular basis of the CFTR functions, as well as dysfunctions causing the common genetic disease cystic fibrosis (CF). This review provides a global overview of the theoretical studies that have been performed so far, especially molecular modelling and molecular dynamics (MD) simulations. A special emphasis is placed on the CFTR-specific evolution of an ABC transporter framework towards a channel function, as well as on the understanding of the effects of disease-causing mutations and their specific modulation. This in silico work should help structure-based drug discovery and design, with a view to develop CFTR-specific pharmacotherapeutic approaches for the treatment of CF in the context of precision medicine.
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Affiliation(s)
- Isabelle Callebaut
- UMR CNRS 7590, Museum National d'Histoire Naturelle, IRD UMR 206, IUC, Case 115, IMPMC, Sorbonne Universités, UPMC Univ Paris 06, 4 Place Jussieu, 75005, Paris Cedex 05, France.
| | - Brice Hoffmann
- UMR CNRS 7590, Museum National d'Histoire Naturelle, IRD UMR 206, IUC, Case 115, IMPMC, Sorbonne Universités, UPMC Univ Paris 06, 4 Place Jussieu, 75005, Paris Cedex 05, France
| | - Pierre Lehn
- INSERM U1078, SFR ScInBioS, Université de Bretagne Occidentale, Brest, France
| | - Jean-Paul Mornon
- UMR CNRS 7590, Museum National d'Histoire Naturelle, IRD UMR 206, IUC, Case 115, IMPMC, Sorbonne Universités, UPMC Univ Paris 06, 4 Place Jussieu, 75005, Paris Cedex 05, France
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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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Abstract
Protein-protein interactions play critical roles in essentially every cellular process. These interactions are often mediated by protein interaction domains that enable proteins to recognize their interaction partners, often by binding to short peptide motifs. For example, PDZ domains, which are among the most common protein interaction domains in the human proteome, recognize specific linear peptide sequences that are often at the C-terminus of other proteins. Determining the set of peptide sequences that a protein interaction domain binds, or it's "peptide specificity," is crucial for understanding its cellular function, and predicting how mutations impact peptide specificity is important for elucidating the mechanisms underlying human diseases. Moreover, engineering novel cellular functions for synthetic biology applications, such as the biosynthesis of biofuels or drugs, requires the design of protein interaction specificity to avoid crosstalk with native metabolic and signaling pathways. The ability to accurately predict and design protein-peptide interaction specificity is therefore critical for understanding and engineering biological function. One approach that has recently been employed toward accomplishing this goal is computational protein design. This chapter provides an overview of recent methodological advances in computational protein design and highlights examples of how these advances can enable increased accuracy in predicting and designing peptide specificity.
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Affiliation(s)
- Noah Ollikainen
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd., Pasadena, CA, 91125, USA
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48
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Hallen MA, Jou JD, Donald BR. LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid Rotamer-Like Efficiency. J Comput Biol 2016; 24:536-546. [PMID: 27681371 DOI: 10.1089/cmb.2016.0136] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Most protein design algorithms search over discrete conformations and an energy function that is residue-pairwise, that is, a sum of terms that depend on the sequence and conformation of at most two residues. Although modeling of continuous flexibility and of non-residue-pairwise energies significantly increases the accuracy of protein design, previous methods to model these phenomena add a significant asymptotic cost to design calculations. We now remove this cost by modeling continuous flexibility and non-residue-pairwise energies in a form suitable for direct input to highly efficient, discrete combinatorial optimization algorithms such as DEE/A* or branch-width minimization. Our novel algorithm performs a local unpruned tuple expansion (LUTE), which can efficiently represent both continuous flexibility and general, possibly nonpairwise energy functions to an arbitrary level of accuracy using a discrete energy matrix. We show using 47 design calculation test cases that LUTE provides a dramatic speedup in both single-state and multistate continuously flexible designs.
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Affiliation(s)
- Mark A Hallen
- 1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina
| | - Jonathan D Jou
- 1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina.,2 Department of Chemistry, Duke University , Durham, North Carolina.,3 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina
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49
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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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50
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Hallen MA, Gainza P, Donald BR. Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design. J Chem Theory Comput 2016; 11:2292-306. [PMID: 26089744 DOI: 10.1021/ct501031m] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In macromolecular design, conformational energies are sensitive to small changes in atom coordinates; thus, modeling the small, continuous motions of atoms around low-energy wells confers a substantial advantage in structural accuracy. However, modeling these motions comes at the cost of a very large number of energy function calls, which form the bottleneck in the design calculations. In this work, we remove this bottleneck by consolidating all conformational energy evaluations into the pre-computation of a local polynomial expansion of the energy about the "ideal" conformation for each low-energy, "rotameric" state of each residue pair. This expansion is called "energy as polynomials in internal coordinates" (EPIC), where the internal coordinates can be side-chain dihedrals, backrub angles, and/or any other continuous degrees of freedom of a macromolecule, and any energy function can be used without adding any asymptotic complexity to the design. We demonstrate that EPIC efficiently represents the energy surface for both molecular-mechanics and quantum-mechanical energy functions, and apply it specifically to protein design for modeling both side chain and backbone degrees of freedom.
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