1
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Szczepanik M, Wagner AS, Heunis S, Waite LK, Eickhoff SB, Hanke M. Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Neuroinformatics 2024:10.1007/s12021-024-09665-7. [PMID: 38713426 DOI: 10.1007/s12021-024-09665-7] [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] [Accepted: 04/22/2024] [Indexed: 05/08/2024]
Abstract
Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.
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Affiliation(s)
- Michał Szczepanik
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
| | - Adina S Wagner
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Stephan Heunis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Laura K Waite
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Hanke
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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2
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Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [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: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
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Affiliation(s)
- Doo Nam Kim
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Andrew D McNaughton
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
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3
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Kryś JD, Gront D. Coarse-grained potential for hydrogen bond interactions. J Mol Graph Model 2023; 124:108507. [PMID: 37295157 DOI: 10.1016/j.jmgm.2023.108507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Understanding protein structure and dynamics is crucial for investigating numerous biological processes. This however requires proper description of molecular interactions, most notably hydrogen bonds, which are the driving force behind the folding of protein sequences into working molecules. Due to the multi-body character of this interaction, proper mathematical formulation has been a matter of long debate in the literature. This description becomes even more complex in reduced protein models. In this contribution, we propose a novel hydrogen bond energy function definition that is based only on Cα positions and used for coarse-grained simulations. We show that this new method has the capability to recognize hydrogen bonds with over 80% accuracy and can successfully identify β-sheet in β-amyloid peptide simulations.
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Affiliation(s)
- Justyna D Kryś
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland.
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
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4
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Khmelinskaia A, Bethel NP, Fatehi F, Antanasijevic A, Borst AJ, Lai SH, Wang JYJ, Mallik BB, Miranda MC, Watkins AM, Ogohara C, Caldwell S, Wu M, Heck AJR, Veesler D, Ward AB, Baker D, Twarock R, King NP. Local structural flexibility drives oligomorphism in computationally designed protein assemblies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562842. [PMID: 37905007 PMCID: PMC10614843 DOI: 10.1101/2023.10.18.562842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. For example, clathrin coats adopt a wide variety of architectures to adapt to vesicular cargos of various sizes. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, most existing methods focus on designing static structures with high accuracy. Here we characterize the structures of three distinct computationally designed protein assemblies that each form multiple unanticipated architectures, and identify flexibility in specific regions of the subunits of each assembly as the source of structural diversity. Cryo-EM single-particle reconstructions and native mass spectrometry showed that only two distinct architectures were observed in two of the three cases, while we obtained six cryo-EM reconstructions that likely represent a subset of the architectures present in solution in the third case. Structural modeling and molecular dynamics simulations indicated that the surprising observation of a defined range of architectures, instead of non-specific aggregation, can be explained by constrained flexibility within the building blocks. Our results suggest that deliberate use of structural flexibility as a design principle will allow exploration of previously inaccessible structural and functional space in designed protein assemblies.
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5
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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6
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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7
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Wick ET, Treadway CJ, Li Z, Nicely NI, Ren Z, Baldwin AS, Xiong Y, Harrison JS, Brown NG. Insight into Viral Hijacking of CRL4 Ubiquitin Ligase through Structural Analysis of the pUL145-DDB1 Complex. J Virol 2022; 96:e0082622. [PMID: 35938868 PMCID: PMC9472758 DOI: 10.1128/jvi.00826-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/21/2022] [Indexed: 12/21/2022] Open
Abstract
Viruses evolve mechanisms to exploit cellular pathways that increase viral fitness, e.g., enhance viral replication or evade the host cell immune response. The ubiquitin-proteosome system, a fundamental pathway-regulating protein fate in eukaryotes, is hijacked by all seven classes of viruses. Members of the Cullin-RING family of ubiquitin (Ub) ligases are frequently co-opted by divergent viruses because they can target a broad array of substrates by forming multisubunit assemblies comprised of a variety of adapters and substrate receptors. For example, the linker subunit DDB1 in the cullin 4-RING (CRL4)-DDB1 Ub ligase (CRL4DDB1) interacts with an H-box motif found in several unrelated viral proteins, including the V protein of simian virus 5 (SV5-V), the HBx protein of hepatitis B virus (HBV), and the recently identified pUL145 protein of human cytomegalovirus (HCMV). In HCMV-infected cells, pUL145 repurposes CRL4DDB1 to target STAT2, a protein vital to the antiviral immune response. However, the details of how these divergent viral sequences hijack DDB1 is not well understood. Here, we use a combination of binding assays, X-ray crystallography, alanine scanning, cell-based assays, and computational analysis to reveal that viral H-box motifs appear to bind to DDB1 with a higher affinity than the H-box motifs from host proteins DCAF1 and DDB2. This analysis reveals that viruses maintain native hot-spot residues in the H-box motif of host DCAFs and also acquire favorable interactions at neighboring residues within the H-box. Overall, these studies reveal how viruses evolve strategies to produce high-affinity binding and quality interactions with DDB1 to repurpose its Ub ligase machinery. IMPORTANCE Many different viruses modulate the protein machinery required for ubiquitination to enhance viral fitness. Specifically, several viruses hijack the cullin-RING ligase CRL4DDB1 to degrade host resistance factors. Human cytomegalovirus (HCMV) encodes pUL145 that redirects CRL4DDB1 to evade the immune system through the targeted degradation of the antiviral immune response protein STAT2. However, it is unclear why several viruses bind specific surfaces on ubiquitin ligases to repurpose their activity. We demonstrate that viruses have optimized H-box motifs that bind DDB1 with higher affinity than the H-box of native binders. For viral H-boxes, native interactions are maintained, but additional interactions that are absent in host cell H-boxes are formed, indicating that rewiring CRL4DDB1 creates a selective advantage for the virus. The DDB1-pUL145 peptide structure reveals that water-mediated interactions are critical to the higher affinity. Together, our data present an interesting example of how viral evolution can exploit a weakness in the ubiquitination machinery.
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Affiliation(s)
- Elizaveta T. Wick
- Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Colton J. Treadway
- Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Zhijun Li
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Nathan I. Nicely
- Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Zhizhong Ren
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Albert S. Baldwin
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yue Xiong
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Joseph S. Harrison
- Department of Chemistry, University of the Pacific, Stockton, California, USA
| | - Nicholas G. Brown
- Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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8
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Aramyan S, McGregor K, Sandeep S, Haczku A. SP-A binding to the SARS-CoV-2 spike protein using hybrid quantum and classical in silico modeling and molecular pruning by Quantum Approximate Optimization Algorithm (QAOA) Based MaxCut with ZDOCK. Front Immunol 2022; 13:945317. [PMID: 36189278 PMCID: PMC9519185 DOI: 10.3389/fimmu.2022.945317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/10/2022] [Indexed: 11/18/2022] Open
Abstract
The pulmonary surfactant protein A (SP-A) is a constitutively expressed immune-protective collagenous lectin (collectin) in the lung. It binds to the cell membrane of immune cells and opsonizes infectious agents such as bacteria, fungi, and viruses through glycoprotein binding. SARS-CoV-2 enters airway epithelial cells by ligating the Angiotensin Converting Enzyme 2 (ACE2) receptor on the cell surface using its Spike glycoprotein (S protein). We hypothesized that SP-A binds to the SARS-CoV-2 S protein and this binding interferes with ACE2 ligation. To study this hypothesis, we used a hybrid quantum and classical in silico modeling technique that utilized protein graph pruning. This graph pruning technique determines the best binding sites between amino acid chains by utilizing the Quantum Approximate Optimization Algorithm (QAOA)-based MaxCut (QAOA-MaxCut) program on a Near Intermediate Scale Quantum (NISQ) device. In this, the angles between every neighboring three atoms were Fourier-transformed into microwave frequencies and sent to a quantum chip that identified the chemically irrelevant atoms to eliminate based on their chemical topology. We confirmed that the remaining residues contained all the potential binding sites in the molecules by the Universal Protein Resource (UniProt) database. QAOA-MaxCut was compared with GROMACS with T-REMD using AMBER, OPLS, and CHARMM force fields to determine the differences in preparing a protein structure docking, as well as with Goemans-Williamson, the best classical algorithm for MaxCut. The relative binding affinity of potential interactions between the pruned protein chain residues of SP-A and SARS-CoV-2 S proteins was assessed by the ZDOCK program. Our data indicate that SP-A could ligate the S protein with a similar affinity to the ACE2-Spike binding. Interestingly, however, the results suggest that the most tightly-bound SP-A binding site is localized to the S2 chain, in the fusion region of the SARS-CoV-2 S protein, that is responsible for cell entry Based on these findings we speculate that SP-A may not directly compete with ACE2 for the binding site on the S protein, but interferes with viral entry to the cell by hindering necessary conformational changes or the fusion process.
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Affiliation(s)
- Sona Aramyan
- If and Only If (Iff) Technologies, Pleasanton, CA, United States
| | - Kirk McGregor
- If and Only If (Iff) Technologies, Pleasanton, CA, United States
| | - Samarth Sandeep
- If and Only If (Iff) Technologies, Pleasanton, CA, United States
- *Correspondence: Samarth Sandeep, ; Angela Haczku,
| | - Angela Haczku
- University of California (UC) Davis Lung Center Pulmonary, Critical Care and Sleep Division, Department of Medicine, School of Medicine, University of California, Davis, CA, United States
- *Correspondence: Samarth Sandeep, ; Angela Haczku,
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9
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Kretschmer S, Kortemme T. Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:659-674. [PMID: 36531560 PMCID: PMC9754107 DOI: 10.1109/jproc.2022.3157898] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.
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Affiliation(s)
- Simon Kretschmer
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158 USA, and affiliated with the California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158 USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158 USA, and affiliated with the California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158 USA
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10
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
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.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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11
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Wratten L, Wilm A, Göke J. Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat Methods 2021; 18:1161-1168. [PMID: 34556866 DOI: 10.1038/s41592-021-01254-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 07/29/2021] [Indexed: 02/08/2023]
Abstract
The rapid growth of high-throughput technologies has transformed biomedical research. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data. Workflow managers were developed in response to such challenges. They simplify pipeline development, optimize resource usage, handle software installation and versions, and run on different compute platforms, enabling workflow portability and sharing. In this Perspective, we highlight key features of workflow managers, compare commonly used approaches for bioinformatics workflows, and provide a guide for computational and noncomputational users. We outline community-curated pipeline initiatives that enable novice and experienced users to perform complex, best-practice analyses without having to manually assemble workflows. In sum, we illustrate how workflow managers contribute to making computational analysis in biomedical research shareable, scalable, and reproducible.
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Affiliation(s)
| | | | - Jonathan Göke
- Genome Institute of Singapore, Singapore, Singapore.
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12
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Plank G, Loewe A, Neic A, Augustin C, Huang YL, Gsell MAF, Karabelas E, Nothstein M, Prassl AJ, Sánchez J, Seemann G, Vigmond EJ. The openCARP simulation environment for cardiac electrophysiology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106223. [PMID: 34171774 DOI: 10.1016/j.cmpb.2021.106223] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. METHODS AND RESULTS openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. CONCLUSION As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.
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Affiliation(s)
- Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | | | - Christoph Augustin
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Yung-Lin Huang
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac-Bordeaux, France; Université Bordeaux, IMB, UMR 5251, F-33400 Talence, France
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13
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Akapo OO, Macnar JM, Kryś JD, Syed PR, Syed K, Gront D. In Silico Structural Modeling and Analysis of Interactions of Tremellomycetes Cytochrome P450 Monooxygenases CYP51s with Substrates and Azoles. Int J Mol Sci 2021; 22:7811. [PMID: 34360577 PMCID: PMC8346148 DOI: 10.3390/ijms22157811] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 11/16/2022] Open
Abstract
Cytochrome P450 monooxygenase CYP51 (sterol 14α-demethylase) is a well-known target of the azole drug fluconazole for treating cryptococcosis, a life-threatening fungal infection in immune-compromised patients in poor countries. Studies indicate that mutations in CYP51 confer fluconazole resistance on cryptococcal species. Despite the importance of CYP51 in these species, few studies on the structural analysis of CYP51 and its interactions with different azole drugs have been reported. We therefore performed in silico structural analysis of 11 CYP51s from cryptococcal species and other Tremellomycetes. Interactions of 11 CYP51s with nine ligands (three substrates and six azoles) performed by Rosetta docking using 10,000 combinations for each of the CYP51-ligand complex (11 CYP51s × 9 ligands = 99 complexes) and hierarchical agglomerative clustering were used for selecting the complexes. A web application for visualization of CYP51s' interactions with ligands was developed (http://bioshell.pl/azoledocking/). The study results indicated that Tremellomycetes CYP51s have a high preference for itraconazole, corroborating the in vitro effectiveness of itraconazole compared to fluconazole. Amino acids interacting with different ligands were found to be conserved across CYP51s, indicating that the procedure employed in this study is accurate and can be automated for studying P450-ligand interactions to cater for the growing number of P450s.
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Affiliation(s)
- Olufunmilayo Olukemi Akapo
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa;
| | - Joanna M. Macnar
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Stefana Banacha 2C, 02-097 Warsaw, Poland;
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - Justyna D. Kryś
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - Puleng Rosinah Syed
- Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa;
| | - Khajamohiddin Syed
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa;
| | - Dominik Gront
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
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14
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Kodali P, Schoeder CT, Schmitz S, Crowe JE, Meiler J. RosettaCM for antibodies with very long HCDR3s and low template availability. Proteins 2021; 89:1458-1472. [PMID: 34176159 PMCID: PMC8492515 DOI: 10.1002/prot.26166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/26/2021] [Indexed: 11/07/2022]
Abstract
Antibody-antigen co-crystal structures are a valuable resource for the fundamental understanding of antibody-mediated immunity. Determination of structures with antibodies in complex with their antigens, however, is a laborious task without guarantee of success. Therefore, homology modeling of antibodies and docking to their respective antigens has become a very important technique to drive antibody and vaccine design. The quality of the antibody modeling process is critical for the success of these endeavors. Here, we compare different computational protocols for predicting antibody structure from sequence in the biomolecular modeling software Rosetta-all of which use multiple existing antibody structures to guide modeling. Specifically, we compare protocols developed solely to predict antibody structure (RosettaAntibody, AbPredict) with a universal homology modeling protocol (RosettaCM). Following recent advances in homology modeling with multiple templates simultaneously, we propose that the use of multiple templates over the same antibody regions may improve modeling performance. To evaluate whether multi-template comparative modeling with RosettaCM can improve the modeling accuracy of antibodies over existing methods, this study compares the performance of the three modeling algorithms when modeling human antibodies taken from antibody-antigen co-crystal structures. In these benchmarking experiments, RosettaCM outperformed other methods when modeling antibodies with long HCDR3s and few available templates.
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Affiliation(s)
- Pranav Kodali
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Clara T Schoeder
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Samuel Schmitz
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - James E Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Departments of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA.,Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
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15
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Mulligan VK. Current directions in combining simulation-based macromolecular modeling approaches with deep learning. Expert Opin Drug Discov 2021; 16:1025-1044. [PMID: 33993816 DOI: 10.1080/17460441.2021.1918097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.
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16
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Schoeder CT, Schmitz S, Adolf-Bryfogle J, Sevy AM, Finn JA, Sauer MF, Bozhanova NG, Mueller BK, Sangha AK, Bonet J, Sheehan JH, Kuenze G, Marlow B, Smith ST, Woods H, Bender BJ, Martina CE, Del Alamo D, Kodali P, Gulsevin A, Schief WR, Correia BE, Crowe JE, Meiler J, Moretti R. Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design. Biochemistry 2021; 60:825-846. [PMID: 33705117 PMCID: PMC7992133 DOI: 10.1021/acs.biochem.0c00912] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
![]()
Structure-based antibody
and antigen design has advanced greatly
in recent years, due not only to the increasing availability of experimentally
determined structures but also to improved computational methods for
both prediction and design. Constant improvements in performance within
the Rosetta software suite for biomolecular modeling have given rise
to a greater breadth of structure prediction, including docking and
design application cases for antibody and antigen modeling. Here,
we present an overview of current protocols for antibody and antigen
modeling using Rosetta and exemplify those by detailed tutorials originally
developed for a Rosetta workshop at Vanderbilt University. These tutorials
cover antibody structure prediction, docking, and design and antigen
design strategies, including the addition of glycans in Rosetta. We
expect that these materials will allow novice users to apply Rosetta
in their own projects for modeling antibodies and antigens.
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Affiliation(s)
- Clara T Schoeder
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Samuel Schmitz
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California 92037, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California 92037, 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.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States
| | - Jessica A Finn
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States.,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, 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.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States
| | - Nina G Bozhanova
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Benjamin K Mueller
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Amandeep K Sangha
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jonathan H Sheehan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Institute for Drug Discovery, University Leipzig Medical School, 04103 Leipzig, Germany
| | - Brennica Marlow
- 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
| | - Shannon T Smith
- 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
| | - Hope Woods
- 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
| | - Brian J Bender
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Cristina E Martina
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Diego Del Alamo
- 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
| | - Pranav Kodali
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Alican Gulsevin
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - William R Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California 92037, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - James E Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States.,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Institute for Drug Discovery, University Leipzig Medical School, 04103 Leipzig, Germany
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
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17
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Brown BP, Mendenhall J, Geanes AR, Meiler J. General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps. J Chem Inf Model 2021; 61:603-620. [PMID: 33496578 PMCID: PMC7903419 DOI: 10.1021/acs.jcim.0c01001] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Indexed: 12/20/2022]
Abstract
The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure-activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein-ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on http://meilerlab.org/.
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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, Tennessee 37232, United States
| | - Jeffrey Mendenhall
- Department
of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Alexander R. Geanes
- Department
of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Departments
of Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37212, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig SAC 04103, Germany
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18
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Minkiewicz P, Darewicz M, Iwaniak A, Turło M. Proposal of the Annotation of Phosphorylated Amino Acids and Peptides Using Biological and Chemical Codes. Molecules 2021; 26:molecules26030712. [PMID: 33573096 PMCID: PMC7866520 DOI: 10.3390/molecules26030712] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/21/2021] [Accepted: 01/26/2021] [Indexed: 01/04/2023] Open
Abstract
Phosphorylation represents one of the most important modifications of amino acids, peptides, and proteins. By modifying the latter, it is useful in improving the functional properties of foods. Although all these substances are broadly annotated in internet databases, there is no unified code for their annotation. The present publication aims to describe a simple code for the annotation of phosphopeptide sequences. The proposed code describes the location of phosphate residues in amino acid side chains (including new rules of atom numbering in amino acids) and the diversity of phosphate residues (e.g., di- and triphosphate residues and phosphate amidation). This article also includes translating the proposed biological code into SMILES, being the most commonly used chemical code. Finally, it discusses possible errors associated with applying the proposed code and in the resulting SMILES representations of phosphopeptides. The proposed code can be extended to describe other modifications in the future.
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19
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Hameduh T, Haddad Y, Adam V, Heger Z. Homology modeling in the time of collective and artificial intelligence. Comput Struct Biotechnol J 2020; 18:3494-3506. [PMID: 33304450 PMCID: PMC7695898 DOI: 10.1016/j.csbj.2020.11.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/04/2020] [Accepted: 11/04/2020] [Indexed: 12/12/2022] Open
Abstract
Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.
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Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 395] [Impact Index Per Article: 98.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
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.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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