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1. REvoLd: Ultra-Large Library Screening with an Evolutionary Algorithm in Rosetta

Author

Eisenhuth, Paul, Liessmann, Fabian, Moretti, Rocco, and Meiler, Jens

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Ultra-large make-on-demand compound libraries now contain billions of readily available compounds. This represents a golden opportunity for in-silico drug discovery. One challenge, however, is the time and computational cost of an exhaustive screen of such large libraries when receptor flexibility is taken into account. We propose an evolutionary algorithm to search combinatorial make-on-demand chemical space efficiently without enumerating all molecules. We exploit the feature of make-on-demand compound libraries, namely that they are constructed from lists of substrates and chemical reactions. Our novel algorithm RosettaEvolutionaryLigand (REvoLd) explores the vast search space of combinatorial libraries for protein-ligand docking with full ligand and receptor flexibility through RosettaLigand. A benchmark of REvoLd on five drug targets showed improvements in hit rates by factors between 869 and 1,622 compared to random selections. REvoLd is available as an application within the Rosetta software suite., Comment: 29 pages, 9 figures

Published
2024

3. Posttranslationally modified self-peptides promote hypertension in mouse models

Author

Bloodworth, Nathaniel, Chen, Wei, Hunter, Kuniko, Patrick, David, Palubinsky, Amy, Phillips, Elizabeth, Roeth, Daniel, Kalkum, Markus, Mallal, Simon, Davies, Sean, Ao, Mingfang, Moretti, Rocco, Meiler, Jens, and Harrison, David G.

Subjects
Thermo Fisher Scientific Inc., Lipid peroxidation, Scientific equipment and supplies industry, T cells, Lysine, HLA histocompatibility antigens, Lupus, Protein binding, B cells, Proteins, Histocompatibility antigens, Hypertension, Health care industry
Abstract

Posttranslational modifications can enhance immunogenicity of self-proteins. In several conditions, including hypertension, systemic lupus erythematosus, and heart failure, isolevuglandins (IsoLGs) are formed by lipid peroxidation and covalently bond with protein lysine residues. Here, we show that the murine class I major histocompatibility complex (MHC-I) variant [H-2D.sup.b] uniquely presents isoLG-modified peptides and developed a computational pipeline that identifies structural features for MHC-I accommodation of such peptides. We identified isoLG-adducted peptides from renal proteins, including sodium glucose transporter 2, cadherin 16, Kelch domain-containing protein 7A, and solute carrier family 23, that are recognized by [CD8.sup.+] T cells in tissues of hypertensive mice, induce T cell proliferation in vitro, and prime hypertension after adoptive transfer. Finally, we find patterns of isoLG-adducted antigen restriction in class I human leukocyte antigens that are similar to those in murine analogs. Thus, we have used a combined computational and experimental approach to define likely antigenic peptides in hypertension., Introduction Accumulating evidence from the last several decades implicates inflammation and immune activation in the genesis of hypertension and its sequelae (1). Both the innate and adaptive immune systems contribute [...]

Published
2024
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5. IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

Author

Vallat, Brinda, Webb, Benjamin M., Westbrook, John D., Goddard, Thomas D., Hanke, Christian A., Graziadei, Andrea, Peisach, Ezra, Zalevsky, Arthur, Sagendorf, Jared, Tangmunarunkit, Hongsuda, Voinea, Serban, Sekharan, Monica, Yu, Jian, Bonvin, Alexander A.M.J.J., DiMaio, Frank, Hummer, Gerhard, Meiler, Jens, Tajkhorshid, Emad, Ferrin, Thomas E., Lawson, Catherine L., Leitner, Alexander, Rappsilber, Juri, Seidel, Claus A.M., Jeffries, Cy M., Burley, Stephen K., Hoch, Jeffrey C., Kurisu, Genji, Morris, Kyle, Patwardhan, Ardan, Velankar, Sameer, Schwede, Torsten, Trewhella, Jill, Kesselman, Carl, Berman, Helen M., and Sali, Andrej

Published
2024
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10. Tissue factor binds to and inhibits interferon-α receptor 1 signaling

Author

Manoharan, Jayakumar, Rana, Rajiv, Kuenze, Georg, Gupta, Dheerendra, Elwakiel, Ahmed, Ambreen, Saira, Wang, Hongjie, Banerjee, Kuheli, Zimmermann, Silke, Singh, Kunal, Gupta, Anubhuti, Fatima, Sameen, Kretschmer, Stefanie, Schaefer, Liliana, Zeng-Brouwers, Jinyang, Schwab, Constantin, Al-Dabet, Moh’d Mohanad, Gadi, Ihsan, Altmann, Heidi, Koch, Thea, Poitz, David M., Baber, Ronny, Kohli, Shrey, Shahzad, Khurrum, Geffers, Robert, Lee-Kirsch, Min Ae, Kalinke, Ulrich, Meiler, Jens, Mackman, Nigel, and Isermann, Berend

Published
2024
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12. Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions

Author

Golkov, Vladimir, Becker, Alexander, Plop, Daniel T., Čuturilo, Daniel, Davoudi, Neda, Mendenhall, Jeffrey, Moretti, Rocco, Meiler, Jens, and Cremers, Daniel

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning, 68T07 (Primary) 62H30, 92E99, 68T10, 62F07 (Secondary), G.3, I.2.1, I.2.6, I.5.1, J.3
Abstract

Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. In this work we argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance, its ability to compromise over different decision thresholds, certain freedom to influence the relative weights in this compromise, fidelity to typical benchmarking measures, and equivalence to positive/unlabeled learning. We also propose new training schemes (coherent mini-batch arrangement, and usage of out-of-batch samples) for cost functions based on the ROC, as well as a cost function based on the logAUC metric that facilitates early enrichment (i.e. improves performance at high decision thresholds, as often desired when synthesizing predicted hit compounds). We demonstrate that these approaches outperform standard deep learning approaches on a series of PubChem high-throughput screening datasets that represent realistic and diverse drug discovery campaigns on major drug target families., Comment: 10 pages

Published
2020

14. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Author

Koehler Leman, Julia, Lyskov, Sergey, Lewis, Steven M, Adolf-Bryfogle, Jared, Alford, Rebecca F, Barlow, Kyle, Ben-Aharon, Ziv, Farrell, Daniel, Fell, Jason, Hansen, William A, Harmalkar, Ameya, Jeliazkov, Jeliazko, Kuenze, Georg, Krys, Justyna D, Ljubetič, Ajasja, Loshbaugh, Amanda L, Maguire, Jack, Moretti, Rocco, Mulligan, Vikram Khipple, Nance, Morgan L, Nguyen, Phuong T, Ó Conchúir, Shane, Roy Burman, Shourya S, Samanta, Rituparna, Smith, Shannon T, Teets, Frank, Tiemann, Johanna KS, Watkins, Andrew, Woods, Hope, Yachnin, Brahm J, Bahl, Christopher D, Bailey-Kellogg, Chris, Baker, David, Das, Rhiju, DiMaio, Frank, Khare, Sagar D, Kortemme, Tanja, Labonte, Jason W, Lindorff-Larsen, Kresten, Meiler, Jens, Schief, William, Schueler-Furman, Ora, Siegel, Justin B, Stein, Amelie, Yarov-Yarovoy, Vladimir, Kuhlman, Brian, Leaver-Fay, Andrew, Gront, Dominik, Gray, Jeffrey J, and Bonneau, Richard

Subjects
Information and Computing Sciences, Software Engineering, Bioengineering, Networking and Information Technology R&D (NITRD), Benchmarking, Binding Sites, Humans, Ligands, Macromolecular Substances, Molecular Docking Simulation, Protein Binding, Proteins, Reproducibility of Results, Software
Abstract

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Published
2021

16. Macromolecular modeling and design in Rosetta: recent methods and frameworks

Author

Leman, Julia Koehler, Weitzner, Brian D, Lewis, Steven M, Adolf-Bryfogle, Jared, Alam, Nawsad, Alford, Rebecca F, Aprahamian, Melanie, Baker, David, Barlow, Kyle A, Barth, Patrick, Basanta, Benjamin, Bender, Brian J, Blacklock, Kristin, Bonet, Jaume, Boyken, Scott E, Bradley, Phil, Bystroff, Chris, Conway, Patrick, Cooper, Seth, Correia, Bruno E, Coventry, Brian, Das, Rhiju, De Jong, René M, DiMaio, Frank, Dsilva, Lorna, Dunbrack, Roland, Ford, Alexander S, Frenz, Brandon, Fu, Darwin Y, Geniesse, Caleb, Goldschmidt, Lukasz, Gowthaman, Ragul, Gray, Jeffrey J, Gront, Dominik, Guffy, Sharon, Horowitz, Scott, Huang, Po-Ssu, Huber, Thomas, Jacobs, Tim M, Jeliazkov, Jeliazko R, Johnson, David K, Kappel, Kalli, Karanicolas, John, Khakzad, Hamed, Khar, Karen R, Khare, Sagar D, Khatib, Firas, Khramushin, Alisa, King, Indigo C, Kleffner, Robert, Koepnick, Brian, Kortemme, Tanja, Kuenze, Georg, Kuhlman, Brian, Kuroda, Daisuke, Labonte, Jason W, Lai, Jason K, Lapidoth, Gideon, Leaver-Fay, Andrew, Lindert, Steffen, Linsky, Thomas, London, Nir, Lubin, Joseph H, Lyskov, Sergey, Maguire, Jack, Malmström, Lars, Marcos, Enrique, Marcu, Orly, Marze, Nicholas A, Meiler, Jens, Moretti, Rocco, Mulligan, Vikram Khipple, Nerli, Santrupti, Norn, Christoffer, Ó’Conchúir, Shane, Ollikainen, Noah, Ovchinnikov, Sergey, Pacella, Michael S, Pan, Xingjie, Park, Hahnbeom, Pavlovicz, Ryan E, Pethe, Manasi, Pierce, Brian G, Pilla, Kala Bharath, Raveh, Barak, Renfrew, P Douglas, Burman, Shourya S Roy, Rubenstein, Aliza, Sauer, Marion F, Scheck, Andreas, Schief, William, Schueler-Furman, Ora, Sedan, Yuval, Sevy, Alexander M, Sgourakis, Nikolaos G, Shi, Lei, Siegel, Justin B, Silva, Daniel-Adriano, Smith, Shannon, and Song, Yifan

Subjects
Biological Sciences, Networking and Information Technology R&D (NITRD), Bioengineering, Macromolecular Substances, Models, Molecular, Molecular Docking Simulation, Peptidomimetics, Protein Conformation, Proteins, Software, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Published
2020

17. Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures

Author

Berman, Helen M, Adams, Paul D, Bonvin, Alexandre A, Burley, Stephen K, Carragher, Bridget, Chiu, Wah, DiMaio, Frank, Ferrin, Thomas E, Gabanyi, Margaret J, Goddard, Thomas D, Griffin, Patrick R, Haas, Juergen, Hanke, Christian A, Hoch, Jeffrey C, Hummer, Gerhard, Kurisu, Genji, Lawson, Catherine L, Leitner, Alexander, Markley, John L, Meiler, Jens, Montelione, Gaetano T, Phillips, George N, Prisner, Thomas, Rappsilber, Juri, Schriemer, David C, Schwede, Torsten, Seidel, Claus AM, Strutzenberg, Timothy S, Svergun, Dmitri I, Tajkhorshid, Emad, Trewhella, Jill, Vallat, Brinda, Velankar, Sameer, Vuister, Geerten W, Webb, Benjamin, Westbrook, John D, White, Kate L, and Sali, Andrej

Subjects
Biological Sciences, Computational Biology, Crystallography, X-Ray, Databases, Protein, Magnetic Resonance Spectroscopy, Models, Molecular, Protein Conformation, Proteins, Biophysics
Abstract

Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.

Published
2019

20. An Empirical Bayes Approach to Regularization Using Previously Published Models

Author

Smith, Derek K, Smith, Loren E, Kroncke, Brett, Billings, Frederic T, Meiler, Jens, and Blume, Jeffrey

Subjects
Statistics - Applications
Abstract

This manuscript proposes a novel empirical Bayes technique for regularizing regression coefficients in predictive models. When predictions from a previously published model are available, this empirical Bayes method provides a natural mathematical framework for shrinking coefficients toward the estimates implied by the body of existing research rather than the shrinkage toward zero provided by traditional L1 and L2 penalization schemes. The method is applied to two different prediction problems. The first involves the construction of a model for predicting whether a single nucleotide polymorphism (SNP) of the KCNQ1 gene will result in dysfunction of the corresponding voltage gated ion channel. The second involves the prediction of preoperative serum creatinine change in patients undergoing cardiac surgery.

Published
2017

21. On-target Resistance to the Mutant-Selective EGFR Inhibitor Osimertinib Can Develop in an Allele-Specific Manner Dependent on the Original EGFR-Activating Mutation

Author

Brown, Benjamin P, Zhang, Yun-Kai, Westover, David, Yan, Yingjun, Qiao, Huan, Huang, Vincent, Du, Zhenfang, Smith, Jarrod A, Ross, Jeffrey S, Miller, Vincent A, Ali, Siraj, Bazhenova, Lyudmila, Schrock, Alexa B, Meiler, Jens, and Lovly, Christine M

Subjects
Genetics, Cancer, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Good Health and Well Being, Acrylamides, Alleles, Aniline Compounds, Carcinoma, Non-Small-Cell Lung, Cell Line, Tumor, Dose-Response Relationship, Drug, Drug Resistance, Neoplasm, ErbB Receptors, Exons, Gene Expression Profiling, Humans, Lung Neoplasms, Models, Molecular, Mutation, Protein Binding, Protein Kinase Inhibitors, Structure-Activity Relationship, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

PurposeThe third-generation EGFR inhibitor, osimertinib, is the first mutant-selective inhibitor that has received regulatory approval for the treatment of patients with EGFR-mutant lung cancer. Despite the development of highly selective third-generation inhibitors, acquired resistance remains a significant clinical challenge. Recently, we and others have identified a novel osimertinib resistance mutation, G724S, which was not predicted in in vitro screens. Here, we investigate how G724S confers resistance to osimertinib.Experimental Design: We combine structure-based predictive modeling of G724S in combination with the 2 most common EGFR-activating mutations, exon 19 deletion (Ex19Del) and L858R, with in vitro drug-response models and patient genomic profiling.ResultsOur simulations suggest that the G724S mutation selectively reduces osimertinib-binding affinity in the context of Ex19Del. Consistent with our simulations, cell lines transduced with Ex19Del/G724S demonstrate resistance to osimertinib, whereas cells transduced with L858R/G724S are sensitive to osimertinib. Subsequent clinical genomic profiling data further suggest G724S occurs with Ex19Del but not L858R. Furthermore, we demonstrate that Ex19Del/G724S retains sensitivity to afatinib, but not to erlotinib, suggesting a possible therapy for patients at the time of disease relapse.ConclusionsAltogether, these data suggest that G724S is an allele-specific resistance mutation emerging in the context of Ex19Del but not L858R. Our results fundamentally reframe the problem of targeted therapy resistance from one focused on the "drug-resistance mutation" pair to one focused on the "activating mutation-drug-resistance mutation" trio. This has broad implications across clinical oncology.

Published
2019

22. IgG4‐related disease: Association with a rare gene variant expressed in cytotoxic T cells

Author

Newman, John H, Shaver, Aaron, Sheehan, Jonathan H, Mallal, Simon, Stone, John H, Pillai, Shiv, Bastarache, Lisa, Riebau, Derek, Allard‐Chamard, Hugues, Stone, William M, Perugino, Cory, Pilkinton, Mark, Smith, Scott A, McDonnell, Wyatt J, Capra, John A, Meiler, Jens, Cogan, Joy, Xing, Kelly, Mahajan, Vinay S, Mattoo, Hamid, Hamid, Rizwan, Phillips, John A, Adams, David R, Aday, Aaron, Alejandro, Mercedes E, Allard, Patrick, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Balasubramanyam, Ashok, Barseghyan, Hayk, Batzli, Gabriel F, Beggs, Alan H, Behnam, Babak, Bellen, Hugo J, Bernstein, Jonathan A, Bican, Anna, Bick, David P, Birch, Camille L, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Briere, Lauren C, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Chen, Shan, Clark, Gary D, Coakley, Terra R, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D'Souza, Precilla, Davids, Mariska, Davidson, Jean M, Dayal, Jyoti G, Dell'Angelica, Esteban C, Dhar, Shweta U, Dipple, Katrina M, Donnell‐Fink, Laurel A, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Dries, Annika M, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Enns, Gregory M, Eskin, Ascia, Esteves, Cecilia, Estwick, Tyra, Fernandez, Liliana, Ferreira, Carlos, Fisher, Paul G, Fogel, Brent L, Friedman, Noah D, Gahl, William A, Glanton, Emily, Godfrey, Rena A, Goldman, Alica M, Goldstein, David B, Gould, Sarah E, Gourdine, Jean‐Philippe F, Groden, Catherine A, Gropman, Andrea L, Haendel, Melissa, Hanchard, Neil A, Handley, Lori H, Herzog, Matthew R, High, Francis, Holm, Ingrid A, Hom, Jason, Howerton, Ellen M, Huang, Yong, Jamal, Fariha, Jiang, Yong‐hui, and Johnston, Jean M

Subjects
Biological Sciences, Genetics, Rare Diseases, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Adolescent, CD4-Positive T-Lymphocytes, Genetic Variation, Humans, Immunoglobulin G, Immunoglobulin G4-Related Disease, Male, Middle Aged, T-Lymphocytes, Cytotoxic, cytotoxic lymphocytes, heritable, IgG4-RD, Undiagnosed Disease Network, Medicinal and Biomolecular Chemistry, Clinical Sciences, Medicinal and biomolecular chemistry
Abstract

BackgroundFamily screening of a 48-year-old male with recently diagnosed IgG4-related disease (IgG4-RD) revealed unanticipated elevations in plasma IgG4 in his two healthy teenaged sons.MethodsWe performed gene sequencing, immune cell studies, HLA typing, and analyses of circulating cytotoxic CD4+ T lymphocytes and plasmablasts to seek clues to pathogenesis. DNA from a separate cohort of 99 patients with known IgG4-RD was also sequenced for the presence of genetic variants in a specific gene, FGFBP2.ResultsThe three share a previously unreported heterozygous single base deletion in fibroblast growth factor binding protein type 2 (FGFBP2), which causes a frameshift in the coding sequence. The FGFBP2 protein is secreted by cytotoxic T-lymphocytes and binds fibroblast growth factor. The variant sequence in the FGFBP2 protein is predicted to form a disordered random coil rather than a helical-turn-helix structure, unable to adopt a stable conformation. The proband and the two sons had 5-10-fold higher numbers of circulating cytotoxic CD4 + T cells and plasmablasts compared to matched controls. The three members also share a homozygous missense common variant in FGFBP2 found in heterozygous form in ~40% of the population. This common variant was found in 73% of an independent, well characterized IgG4-RD cohort, showing enrichment in idiopathic IgG4-RD.ConclusionsThe presence of a shared deleterious variant and homozygous common variant in FGFBP2 in the proband and sons strongly implicates this cytotoxic T cell product in the pathophysiology of IgG4-RD. The high prevalence of a common FGFBP2 variant in sporadic IgG4-RD supports the likelihood of participation in disease.

Published
2019

28. 3D Deep Learning for Biological Function Prediction from Physical Fields

Author

Golkov, Vladimir, Skwark, Marcin J., Mirchev, Atanas, Dikov, Georgi, Geanes, Alexander R., Mendenhall, Jeffrey, Meiler, Jens, and Cremers, Daniel

Subjects
Quantitative Biology - Biomolecules, Computer Science - Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning, I.2.6, J.3
Abstract

Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. Function is dictated by structure, since it is by spatial interactions that molecules interact with each other, both in terms of steric complementarity, as well as intermolecular forces. Thus, the electron density field and electrostatic potential field of a molecule contain the "raw fingerprint" of how this molecule can fit to binding partners. In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields. Protein function based on EC numbers is predicted from the approximated electron density field. In another experiment, the activity of small molecules is predicted with quality comparable to state-of-the-art descriptor-based methods. We propose several alternative computational models for the GPU with different memory and runtime requirements for different sizes of molecules and of databases. We also propose application-specific multi-channel data representations. With future improvements of training datasets and neural network settings in combination with complementary information sources (sequence, genomic context, expression level), deep learning can be expected to show its generalization power and revolutionize the field of molecular function prediction.

Published
2017

29. Community‐Science‐ Projekte in der Wissenschaft: Wie Bürger neue Arzneistoffe mitentwickeln.

Author

Ließmann, Fabian, Landsmann, Anja, Rossa, Jan, and Meiler, Jens

Subjects
PROTEIN engineering, PROTEIN folding, CREATIVE ability in science, LIGANDS (Biochemistry), SMALL molecules
Abstract

Copyright of Chemie in unserer Zeit is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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33. Structure and Function of the Transmembrane Domain of NsaS, an Antibiotic Sensing Histidine Kinase in Staphylococcus aureus

Author

Bhate, Manasi P, Lemmin, Thomas, Kuenze, Georg, Mensa, Bruk, Ganguly, Soumya, Peters, Jason M, Schmidt, Nathan, Pelton, Jeffrey G, Gross, Carol A, Meiler, Jens, and DeGrado, William F

Subjects
Infectious Diseases, Emerging Infectious Diseases, Anti-Bacterial Agents, Bacitracin, Bacterial Proteins, Gene Knockout Techniques, Histidine Kinase, Hydrophobic and Hydrophilic Interactions, Magnetic Resonance Spectroscopy, Membrane Proteins, Microbial Sensitivity Tests, Molecular Dynamics Simulation, Nisin, Protein Conformation, alpha-Helical, Protein Domains, Staphylococcus aureus, Chemical Sciences, General Chemistry
Abstract

NsaS is one of four intramembrane histidine kinases (HKs) in Staphylococcus aureus that mediate the pathogen's response to membrane active antimicrobials and human innate immunity. We describe the first integrative structural study of NsaS using a combination of solution state NMR spectroscopy, chemical-cross-linking, molecular modeling and dynamics. Three key structural features emerge: First, NsaS has a short N-terminal amphiphilic helix that anchors its transmembrane (TM) bundle into the inner leaflet of the membrane such that it might sense neighboring proteins or membrane deformations. Second, the transmembrane domain of NsaS is a 4-helix bundle with significant dynamics and structural deformations at the membrane interface. Third, the intracellular linker connecting the TM domain to the cytoplasmic catalytic domains of NsaS is a marginally stable helical dimer, with one state likely to be a coiled-coil. Data from chemical shifts, heteronuclear NOE, H/D exchange measurements and molecular modeling suggest that this linker might adopt different conformations during antibiotic induced signaling.

Published
2018

39. Unveiling the crucial neuronal role of the proteasomal ATPase subunit genePSMC5in neurodevelopmental proteasomopathies

Author

Küry, Sébastien, primary, Stanton, Janelle E., additional, van Woerden, Geeske, additional, Hsieh, Tzung-Chien, additional, Rosenfelt, Cory, additional, Pier Scott-Boyer, Marie, additional, Most, Victoria, additional, Wang, Tianyun, additional, Papendorf, Jonas Johannes, additional, de Konink, Charlotte, additional, Deb, Wallid, additional, Vignard, Virginie, additional, Studencka-Turski, Maja, additional, Besnard, Thomas, additional, Hajdukowicz, Anna Marta, additional, Thiel, Franziska, additional, Moller, Sophie, additional, Florenceau, Laetitia, additional, Cuinat, Silvestre, additional, Marsac, Sylvain, additional, Wentzensen, Ingrid, additional, Tuttle, Annabelle, additional, Forster, Cara, additional, Striesow, Johanna, additional, Golnik, Richard, additional, Ortiz, Damara, additional, Jenkins, Laura, additional, Rosenfeld, Jill A., additional, Ziegler, Alban, additional, Houdayer, Clara, additional, Bonneau, Dominique, additional, Torti, Erin, additional, Begtrup, Amber, additional, Monaghan, Kristin G., additional, Mullegama, Sureni V., additional, Volker-Touw, C.M.L. (Nienke), additional, van Gassen, Koen L.I., additional, Oegema, Renske, additional, de Pagter, Mirjam, additional, Steindl, Katharina, additional, Rauch, Anita, additional, Ivanovski, Ivan, additional, McDonald, Kimberly, additional, Boothe, Emily, additional, Dauber, Andrew, additional, Baker, Janice, additional, Fabie, Noelle Andrea V., additional, Bernier, Raphael A., additional, Turner, Tychele N., additional, Srivastava, Siddharth, additional, Dies, Kira A., additional, Swanson, Lindsay, additional, Costin, Carrie, additional, Jobling, Rebekah K., additional, Pappas, John, additional, Rabin, Rachel, additional, Niyazov, Dmitriy, additional, Tsai, Anne Chun-Hui, additional, Kovak, Karen, additional, Beck, David B., additional, Malicdan, May Christine V, additional, Adams, David R, additional, Wolfe, Lynne, additional, Ganetzky, Rebecca D., additional, Muraresku, Colleen, additional, Babikyan, Davit, additional, Sedlacek, Zdenek, additional, Hancarova, Miroslava, additional, Timberlake, Andrew T., additional, Al Saif, Hind, additional, Schmidt, Berkley, additional, King, Kayla, additional, Hajianpour, MJ, additional, Costain, Gregory, additional, Prendergast, D'Arcy, additional, Li, Chumei, additional, Genevieve, David, additional, Vitobello, Antonio, additional, Sorlin, Arthur, additional, Philippe, Christophe, additional, Harel, Tamar, additional, Toker, Ori, additional, Sabir, Ataf, additional, Lim, Derek, additional, Hamilton, Mark, additional, Bryson, Lisa, additional, Cleary, Elaine, additional, Weber, Sacha, additional, Hoffman, Trevor L., additional, Cueto-Gonzalez, Anna Maria, additional, Tizzano, Eduardo Fidel, additional, Gomez-Andres, David, additional, Codina-Sola, Marta, additional, Ververi, Athina, additional, Pavlidou, Efterpi, additional, Lambropoulos, Alexandros, additional, Garganis, Kyriakos, additional, Rio, Marlene, additional, Levy, Jonathan, additional, Jurgensmeyer, Sarah, additional, McRae, Anne M., additional, Lessard, Mathieu K., additional, DAgostino, Maria Daniela, additional, De Bie, Isabelle, additional, Wegler, Meret, additional, Abou Jamra, Rami, additional, Kamphausen, Susanne B., additional, Bothe, Viktoria, additional, Busch, Larissa M., additional, Volker, Uwe, additional, Hammer, Elke, additional, Wende, Kristian, additional, Cogne, Benjamin, additional, Isidor, Bertrand, additional, Meiler, Jens, additional, Bosc-Rosati, Amelie, additional, Marcoux, Julien, additional, Bousquet, Marie-Pierre, additional, Poschmann, Jeremie, additional, Laumonnier, Frederic, additional, Hildebrand, Peter W., additional, Eichler, Evan E., additional, McWalter, Kirsty, additional, Krawitz, Peter M., additional, Droit, Arnaud, additional, Elgersma, Ype, additional, Grabrucker, Andreas M., additional, Bolduc, Francois, additional, Bézieau, Stéphane, additional, Ebstein, Frédéric, additional, and Krüger, Elke, additional

Published
2024
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44. Protein structure prediction guided by cross-linking restraints - A systematic evaluation of the impact of the cross-linking spacer length

Author

Hofmann, Tommy, Fischer, Axel W., Meiler, Jens, and Kalkhof, Stefan

Subjects
Physics - Biological Physics, Physics - Computational Physics, Quantitative Biology - Biomolecules
Abstract

Recent development of high-resolution mass spectrometry (MS) instruments enables chemical cross-linking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein structure prediction algorithm, it must also determine an optimal cross-linker type and length for protein structure determination. While a longer cross-linker will yield more restraints, the value of each restraint for protein structure prediction decreases as the restraint is consistent with a larger conformational space. In this study, the number of cross-links and their discriminative power was systematically analyzed in silico on a set of 2,055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive cross-linkers between 1 {\AA} and 60 {\AA}. Depending on the protein size a heuristic was developed that determines the optimal cross-linker length. Next, simulated restraints of variable length were used to de novo predict the tertiary structure of fifteen proteins using the BCL::Fold algorithm. The results demonstrate that a distinct cross-linker length exists for which information content for de novo protein structure prediction is maximized. The sampling accuracy improves on average by 1.0 {\AA} and up to 2.2 {\AA} in the most prominent example. XL-MS restraints enable consistently an improved selection of native-like models with an average enrichment of 2.1., Comment: 21 pages, 8 figures, 3 tables

Published
2015
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45. BCL::MP-Fold: membrane protein structure prediction guided by EPR restraints

Author

Fischer, Axel Walter, Alexander, Nathan Scott, Woetzel, Nils, Karakas, Mert, Weiner, Brian, and Meiler, Jens

Subjects
Physics - Biological Physics, Physics - Chemical Physics, Physics - Computational Physics, Quantitative Biology - Biomolecules
Abstract

For many membrane proteins the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 {\AA} for twenty-seven, better than 6 {\AA} for twenty-two and better than 4 {\AA} for fifteen out of twenty-nine proteins, demonstrating the algorithms ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data., Comment: 23 pages, 6 figures, 6 tables

Published
2015
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46. Time-resolved live-cell spectroscopy reveals EphA2 multimeric assembly

Author

Shi, Xiaojun, primary, Lingerak, Ryan, additional, Herting, Cameron J., additional, Ge, Yifan, additional, Kim, Soyeon, additional, Toth, Paul, additional, Wang, Wei, additional, Brown, Benjamin P., additional, Meiler, Jens, additional, Sossey-Alaoui, Khalid, additional, Buck, Matthias, additional, Himanen, Juha, additional, Hambardzumyan, Dolores, additional, Nikolov, Dimitar B., additional, Smith, Adam W., additional, and Wang, Bingcheng, additional

Published
2023
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49. Structural, functional, and behavioral insights of dopamine dysfunction revealed by a deletion in SLC6A3

Author

Campbell, Nicholas G., Shekar, Aparna, Aguilar, Jenny I., Peng, Dungeng, Navratna, Vikas, Yang, Dongxue, Morley, Alexander N., Duran, Amanda M., Galli, Greta, O’Grady, Brian, Ramachandran, Ramnarayan, Sutcliffe, James S., Sitte, Harald H., Erreger, Kevin, Meiler, Jens, Stockner, Thomas, Bellan, Leon M., Matthies, Heinrich J. G., Gouaux, Eric, Mchaourab, Hassane S., and Galli, Aurelio

Published
2019

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