1
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Harris BJ, Nguyen PT, Zhou G, Wulff H, DiMaio F, Yarov-Yarovoy V. Toward high-resolution modeling of small molecule-ion channel interactions. Front Pharmacol 2024; 15:1411428. [PMID: 38919257 PMCID: PMC11196768 DOI: 10.3389/fphar.2024.1411428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 05/13/2024] [Indexed: 06/27/2024] Open
Abstract
Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.
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Affiliation(s)
- Brandon J. Harris
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
- Biophysics Graduate Group, University of California, Davis, Davis, CA, United States
| | - Phuong T. Nguyen
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
| | - Guangfeng Zhou
- Department of Biochemistry, University of Washington, Seattle, WA, United States
- Institute for Protein Design, University of Washington, Seattle, WA, United States
| | - Heike Wulff
- Department of Pharmacology, School of Medicine, University of California, Davis, Davis, CA, United States
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, United States
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
- Biophysics Graduate Group, University of California, Davis, Davis, CA, United States
- Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, CA, United States
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2
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Okwei E, Smith ST, Bender BJ, Allison B, Ganguly S, Geanes A, Zhang X, Ledwitch K, Meiler J. Rosetta's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery. Biochemistry 2023; 62:700-709. [PMID: 36626571 DOI: 10.1021/acs.biochem.2c00649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 μM. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.
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Affiliation(s)
- Elleansar Okwei
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee37240, United States
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee37240, United States.,Program in Chemical and Physical Biology, Vanderbilt University, Nashville, Tennessee37240, United States
| | - Brian J Bender
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee37240, United States.,Department of Pharmacology, Vanderbilt University, Nashville, Tennessee37240, United States
| | - Brittany Allison
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee37240, United States
| | - Soumya Ganguly
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States
| | - Alexander Geanes
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States
| | - Xuan Zhang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States
| | - Kaitlyn Ledwitch
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee37240, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee37240, United States.,Program in Chemical and Physical Biology, Vanderbilt University, Nashville, Tennessee37240, United States.,Department of Pharmacology, Vanderbilt University, Nashville, Tennessee37240, United States.,Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103Leipzig, Germany
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3
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Bloodworth N, Barbaro NR, Moretti R, Harrison DG, Meiler J. Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids. PLoS One 2022; 17:e0275759. [PMID: 36512534 PMCID: PMC9746977 DOI: 10.1371/journal.pone.0275759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 09/22/2022] [Indexed: 12/15/2022] Open
Abstract
Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation. These modifications fundamentally alter peptide immunogenicity. Similarly, existing structure-based methods are biased towards canonical peptide backbone structures, which may or may not be preserved when NCAAs are present. Rosetta FlexPepDock ab-initio is a structure-based computational protocol able to evaluate peptide-receptor interaction where no prior information of the peptide backbone is known. We benchmarked FlexPepDock ab-initio for docking canonical peptides to MHC-I, and illustrate for the first time the method's ability to accurately model MHC-I bound epitopes containing NCAAs. FlexPepDock ab-initio protocol was able to recapitulate near-native structures (≤1.5Å) in the top lowest-energy models for 20 out of 25 cases in our initial benchmark. Using known experimental binding affinities of twenty peptides derived from an influenza-derived peptide, we showed that FlexPepDock protocol is able to predict relative binding affinity as Rosetta energies correlate well with experimental values (r = 0.59, p = 0.006). ROC analysis revealed 80% true positive and a 40% false positive rate, with a prediction power of 93%. Finally, we demonstrate the protocol's ability to accurately recapitulate HLA-A*02:01 bound phosphopeptide backbone structures and relative binding affinity changes, the theoretical structure of the lymphocytic choriomeningitis derived glycosylated peptide GP392 bound to MHC-I H-2Db, and isolevuglandin-adducted peptides. The ability to use non-canonical amino acids in the Rosetta FlexPepDock protocol may provide useful insight into critical amino acid positions where the post-translational modification modulates immunologic responses.
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Affiliation(s)
- Nathaniel Bloodworth
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Natália Ruggeri Barbaro
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Rocco Moretti
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - David G. Harrison
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University, Leipzig, Germany
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4
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Smith ST, Shub L, Meiler J. PlaceWaters: Real-time, explicit interface water sampling during Rosetta ligand docking. PLoS One 2022; 17:e0269072. [PMID: 35639743 PMCID: PMC9154094 DOI: 10.1371/journal.pone.0269072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/13/2022] [Indexed: 01/29/2023] Open
Abstract
Water molecules at the protein-small molecule interface often form hydrogen bonds with both the small molecule ligand and the protein, affecting the structural integrity and energetics of a binding event. The inclusion of these 'bridging waters' has been shown to improve the accuracy of predicted docked structures; however, due to increased computational costs, this step is typically omitted in ligand docking simulations. In this study, we introduce a resource-efficient, Rosetta-based protocol named "PlaceWaters" to predict the location of explicit interface bridging waters during a ligand docking simulation. In contrast to other explicit water methods, this protocol is independent of knowledge of number and location of crystallographic waters in homologous structures. We test this method on a diverse protein-small molecule benchmark set in comparison to other Rosetta-based protocols. Our results suggest that this coarse-grained, structure-based approach quickly and accurately predicts the location of bridging waters, improving our ability to computationally screen drug candidates.
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Affiliation(s)
- Shannon T. Smith
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Laura Shub
- Biomedical Informatics Program, University of California San Francisco, San Francisco, California, United States of America
- Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, California, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Departments of Chemistry, Pharmacology, and Biomedical Informatics, Center for Structural Biology and Institute of Chemical Biology, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, SAC, Leipzig, Germany
- * E-mail:
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5
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Brown BP, Vu O, Geanes AR, Kothiwale S, Butkiewicz M, Lowe EW, Mueller R, Pape R, Mendenhall J, Meiler J. Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery. Front Pharmacol 2022; 13:833099. [PMID: 35264967 PMCID: PMC8899505 DOI: 10.3389/fphar.2022.833099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/24/2022] [Indexed: 01/31/2023] Open
Abstract
The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.
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Affiliation(s)
- Benjamin P. Brown
- Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- *Correspondence: Jens Meiler, ; Jeffrey Mendenhall, ; Benjamin P. Brown,
| | - Oanh Vu
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Alexander R. Geanes
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Sandeepkumar Kothiwale
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Mariusz Butkiewicz
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Edward W. Lowe
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Ralf Mueller
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Richard Pape
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - Jeffrey Mendenhall
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- *Correspondence: Jens Meiler, ; Jeffrey Mendenhall, ; Benjamin P. Brown,
| | - Jens Meiler
- Department of Chemistry, Departments of Pharmacology and Biomedical Informatics, Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
- *Correspondence: Jens Meiler, ; Jeffrey Mendenhall, ; Benjamin P. Brown,
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6
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Moretti R, Lyskov S, Das R, Meiler J, Gray JJ. Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE). Protein Sci 2017; 27:259-268. [PMID: 28960691 DOI: 10.1002/pro.3313] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 09/21/2017] [Accepted: 09/25/2017] [Indexed: 12/12/2022]
Abstract
The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pKa determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.
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Affiliation(s)
- Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, California.,Department of Physics, Stanford University, Stanford, California
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland.,Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland
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7
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Bender BJ, Cisneros A, Duran AM, Finn JA, Fu D, Lokits AD, Mueller BK, Sangha AK, Sauer MF, Sevy AM, Sliwoski G, Sheehan JH, DiMaio F, Meiler J, Moretti R. Protocols for Molecular Modeling with Rosetta3 and RosettaScripts. Biochemistry 2016; 55:4748-63. [PMID: 27490953 PMCID: PMC5007558 DOI: 10.1021/acs.biochem.6b00444] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
![]()
Previously, we published an article
providing an overview of the
Rosetta suite of biomacromolecular modeling software and a series
of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive
response to this publication we received motivates us to here share
the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking,
small molecule docking, and protein design. This updated and expanded
set of tutorials is needed, as since 2010 Rosetta has been fully redesigned
into an object-oriented protein modeling program Rosetta3. Notable
improvements include a substantially improved energy function, an
XML-like language termed “RosettaScripts” for flexibly
specifying modeling task, new analysis tools, the addition of the
TopologyBroker to control conformational sampling, and support for
multiple templates in comparative modeling. Rosetta’s ability
to model systems with symmetric proteins, membrane proteins, noncanonical
amino acids, and RNA has also been greatly expanded and improved.
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Affiliation(s)
- Brian J Bender
- Department of Pharmacology, Vanderbilt University , Nashville, Tennessee 37232-6600, United States.,Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States
| | - Alberto Cisneros
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States
| | - Amanda M Duran
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Jessica A Finn
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Pathology, Microbiology and Immunology, Vanderbilt University , Nashville, Tennessee 37232-2561, United States
| | - Darwin Fu
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Alyssa D Lokits
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Neuroscience Program, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Benjamin K Mueller
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Amandeep K Sangha
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Marion F Sauer
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States
| | - Alexander M Sevy
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States
| | - Gregory Sliwoski
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Jonathan H Sheehan
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States
| | - Frank DiMaio
- Department of Biochemistry, University of Washington , Seattle, Washington 98195, United States
| | - Jens Meiler
- Department of Pharmacology, Vanderbilt University , Nashville, Tennessee 37232-6600, United States.,Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States.,Department of Pathology, Microbiology and Immunology, Vanderbilt University , Nashville, Tennessee 37232-2561, United States.,Neuroscience Program, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
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8
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Abstract
RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial 'low-resolution' docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the 'high-resolution' full atom refinement step.
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Affiliation(s)
- Samuel DeLuca
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States of America
| | - Karen Khar
- Center for Computational Biology, University of Kansas, Lawrence, KS, United States of America
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States of America
- * E-mail:
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9
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Willis JR, Sapparapu G, Murrell S, Julien JP, Singh V, King HG, Xia Y, Pickens JA, LaBranche CC, Slaughter JC, Montefiori DC, Wilson IA, Meiler J, Crowe JE. Redesigned HIV antibodies exhibit enhanced neutralizing potency and breadth. J Clin Invest 2015; 125:2523-31. [PMID: 25985274 DOI: 10.1172/jci80693] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 03/19/2015] [Indexed: 01/08/2023] Open
Abstract
Several HIV envelope-targeting (Env-targeting) antibodies with broad and potent neutralizing activity have been identified and shown to have unusual features. Of these, the PG9 antibody has a long heavy chain complementarity determining region 3 (HCDR3) and possesses unique structural elements that interact with protein and glycan features of the HIV Env glycoprotein. Here, we used the Rosetta software suite to design variants of the PG9 antibody HCDR3 loop with the goal of identifying variants with increased potency and breadth of neutralization for diverse HIV strains. One variant, designated PG9_N100(F)Y, possessed increased potency and was able to neutralize a diverse set of PG9-resistant HIV strains, including those lacking the Env N160 glycan, which is critical for PG9 binding. An atomic resolution structure of the PG9_N100(F)Y fragment antigen binding (Fab) confirmed that the mutated residue retains the paratope surface when compared with WT PG9. Differential scanning calorimetry experiments revealed that the mutation caused a modest increase in thermodynamic stability of the Fab, a feature predicted by the computational model. Our findings suggest that thermodynamic stabilization of the long HCDR3 in its active conformation is responsible for the increased potency of PG9_N100(F)Y, and strategies aimed at stabilizing this region in other HIV antibodies could become an important approach to in silico optimization of antibodies.
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10
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Jensen JH, Willemoës M, Winther JR, De Vico L. In silico prediction of mutant HIV-1 proteases cleaving a target sequence. PLoS One 2014; 9:e95833. [PMID: 24796579 PMCID: PMC4010418 DOI: 10.1371/journal.pone.0095833] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Accepted: 03/31/2014] [Indexed: 11/17/2022] Open
Abstract
HIV-1 protease represents an appealing system for directed enzyme re-design, since it has various different endogenous targets, a relatively simple structure and it is well studied. Recently Chaudhury and Gray (Structure (2009) 17: 1636–1648) published a computational algorithm to discern the specificity determining residues of HIV-1 protease. In this paper we present two computational tools aimed at re-designing HIV-1 protease, derived from the algorithm of Chaudhuri and Gray. First, we present an energy-only based methodology to discriminate cleavable and non cleavable peptides for HIV-1 proteases, both wild type and mutant. Secondly, we show an algorithm we developed to predict mutant HIV-1 proteases capable of cleaving a new target substrate peptide, different from the natural targets of HIV-1 protease. The obtained in silico mutant enzymes were analyzed in terms of cleavability and specificity towards the target peptide using the energy-only methodology. We found two mutant proteases as best candidates for specificity and cleavability towards the target sequence.
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Affiliation(s)
- Jan H Jensen
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
| | - Martin Willemoës
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jakob R Winther
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Luca De Vico
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
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11
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Combs SA, Deluca SL, Deluca SH, Lemmon GH, Nannemann DP, Nguyen ED, Willis JR, Sheehan JH, Meiler J. Small-molecule ligand docking into comparative models with Rosetta. Nat Protoc 2013; 8:1277-98. [PMID: 23744289 DOI: 10.1038/nprot.2013.074] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Structure-based drug design is frequently used to accelerate the development of small-molecule therapeutics. Although substantial progress has been made in X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, the availability of high-resolution structures is limited owing to the frequent inability to crystallize or obtain sufficient NMR restraints for large or flexible proteins. Computational methods can be used to both predict unknown protein structures and model ligand interactions when experimental data are unavailable. This paper describes a comprehensive and detailed protocol using the Rosetta modeling suite to dock small-molecule ligands into comparative models. In the protocol presented here, we review the comparative modeling process, including sequence alignment, threading and loop building. Next, we cover docking a small-molecule ligand into the protein comparative model. In addition, we discuss criteria that can improve ligand docking into comparative models. Finally, and importantly, we present a strategy for assessing model quality. The entire protocol is presented on a single example selected solely for didactic purposes. The results are therefore not representative and do not replace benchmarks published elsewhere. We also provide an additional tutorial so that the user can gain hands-on experience in using Rosetta. The protocol should take 5-7 h, with additional time allocated for computer generation of models.
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Affiliation(s)
- Steven A Combs
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
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12
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Willis JR, Briney BS, DeLuca SL, Crowe JE, Meiler J. Human germline antibody gene segments encode polyspecific antibodies. PLoS Comput Biol 2013; 9:e1003045. [PMID: 23637590 PMCID: PMC3636087 DOI: 10.1371/journal.pcbi.1003045] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Accepted: 03/15/2013] [Indexed: 11/25/2022] Open
Abstract
Structural flexibility in germline gene-encoded antibodies allows promiscuous binding to diverse antigens. The binding affinity and specificity for a particular epitope typically increase as antibody genes acquire somatic mutations in antigen-stimulated B cells. In this work, we investigated whether germline gene-encoded antibodies are optimal for polyspecificity by determining the basis for recognition of diverse antigens by antibodies encoded by three VH gene segments. Panels of somatically mutated antibodies encoded by a common VH gene, but each binding to a different antigen, were computationally redesigned to predict antibodies that could engage multiple antigens at once. The Rosetta multi-state design process predicted antibody sequences for the entire heavy chain variable region, including framework, CDR1, and CDR2 mutations. The predicted sequences matched the germline gene sequences to a remarkable degree, revealing by computational design the residues that are predicted to enable polyspecificity, i.e., binding of many unrelated antigens with a common sequence. The process thereby reverses antibody maturation in silico. In contrast, when designing antibodies to bind a single antigen, a sequence similar to that of the mature antibody sequence was returned, mimicking natural antibody maturation in silico. We demonstrated that the Rosetta computational design algorithm captures important aspects of antibody/antigen recognition. While the hypervariable region CDR3 often mediates much of the specificity of mature antibodies, we identified key positions in the VH gene encoding CDR1, CDR2, and the immunoglobulin framework that are critical contributors for polyspecificity in germline antibodies. Computational design of antibodies capable of binding multiple antigens may allow the rational design of antibodies that retain polyspecificity for diverse epitope binding.
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Affiliation(s)
- Jordan R. Willis
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Chemical Biology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Bryan S. Briney
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Samuel L. DeLuca
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Chemical Biology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - James E. Crowe
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Chemical Biology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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