1
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Joron K, Zamel J, Kalisman N, Lerner E. Evidence for a compact σ 70 conformation in vitro and in vivo. iScience 2024; 27:110140. [PMID: 38957792 PMCID: PMC11217687 DOI: 10.1016/j.isci.2024.110140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/28/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
The initiation of transcription in Escherichia coli (E. coli) is facilitated by promoter specificity factors, also known as σ factors, which may bind a promoter only as part of a complex with RNA polymerase (RNAP). By performing in vitro cross-linking mass spectrometry (CL-MS) of apo-σ70, we reveal structural features suggesting a compact conformation compared to the known RNAP-bound extended conformation. Then, we validate the existence of the compact conformation using in vivo CL-MS by identifying cross-links similar to those found in vitro, which deviate from the extended conformation only during the stationary phase of bacterial growth. Conclusively, we provide information in support of a compact conformation of apo-σ70 that exists in live cells, which might represent a transcriptionally inactive form that can be activated upon binding to RNAP.
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Affiliation(s)
- Khalil Joron
- Department of Biological Chemistry, Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, Edmond J. Safra Campus, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Joanna Zamel
- Department of Biological Chemistry, Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, Edmond J. Safra Campus, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Nir Kalisman
- Department of Biological Chemistry, Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, Edmond J. Safra Campus, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center for Nanoscience and Nanotechnology, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Eitan Lerner
- Department of Biological Chemistry, Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, Edmond J. Safra Campus, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center for Nanoscience and Nanotechnology, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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2
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Cohen S, Schneidman-Duhovny D. A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data. Proteomics 2023; 23:e2200341. [PMID: 37070547 DOI: 10.1002/pmic.202200341] [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: 11/15/2022] [Revised: 04/02/2023] [Accepted: 04/03/2023] [Indexed: 04/19/2023]
Abstract
Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.
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Affiliation(s)
- Shon Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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3
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Cohen T, Halfon M, Carter L, Sharkey B, Jain T, Sivasubramanian A, Schneidman-Duhovny D. Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models. Methods Enzymol 2022; 678:237-262. [PMID: 36641210 DOI: 10.1016/bs.mie.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.
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Affiliation(s)
- Tomer Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Matan Halfon
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lester Carter
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Beth Sharkey
- High-Throughput Expression, Adimab LLC, Lebanon, NH, United States
| | - Tushar Jain
- Computational Biology, Adimab LLC, Palo Alto, CA, United States
| | | | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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4
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Lansky S, Salama R, Biarnés X, Shwartstein O, Schneidman-Duhovny D, Planas A, Shoham Y, Shoham G. Integrative structure determination reveals functional global flexibility for an ultra-multimodular arabinanase. Commun Biol 2022; 5:465. [PMID: 35577850 PMCID: PMC9110388 DOI: 10.1038/s42003-022-03054-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 07/15/2021] [Indexed: 11/08/2022] Open
Abstract
AbnA is an extracellular GH43 α-L-arabinanase from Geobacillus stearothermophilus, a key bacterial enzyme in the degradation and utilization of arabinan. We present herein its full-length crystal structure, revealing the only ultra-multimodular architecture and the largest structure to be reported so far within the GH43 family. Additionally, the structure of AbnA appears to contain two domains belonging to new uncharacterized carbohydrate-binding module (CBM) families. Three crystallographic conformational states are determined for AbnA, and this conformational flexibility is thoroughly investigated further using the "integrative structure determination" approach, integrating molecular dynamics, metadynamics, normal mode analysis, small angle X-ray scattering, dynamic light scattering, cross-linking, and kinetic experiments to reveal large functional conformational changes for AbnA, involving up to ~100 Å movement in the relative positions of its domains. The integrative structure determination approach demonstrated here may apply also to the conformational study of other ultra-multimodular proteins of diverse functions and structures.
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Affiliation(s)
- Shifra Lansky
- Institute of Chemistry, the Hebrew University of Jerusalem, Jerusalem, 91904, Israel.
| | - Rachel Salama
- Department of Biotechnology and Food Engineering, Technion, Haifa, 3200, Israel
| | - Xevi Biarnés
- Laboratory of Biochemistry, Institut Químic de Sarrià, Universitat Ramon Llull, Barcelona, 08017, Spain
| | - Omer Shwartstein
- Institute of Chemistry, the Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Dina Schneidman-Duhovny
- School of Computer Science and Engineering, the Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Antoni Planas
- Laboratory of Biochemistry, Institut Químic de Sarrià, Universitat Ramon Llull, Barcelona, 08017, Spain
| | - Yuval Shoham
- Department of Biotechnology and Food Engineering, Technion, Haifa, 3200, Israel.
| | - Gil Shoham
- Institute of Chemistry, the Hebrew University of Jerusalem, Jerusalem, 91904, Israel.
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5
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Puthenveetil R, Christenson ET, Vinogradova O. New Horizons in Structural Biology of Membrane Proteins: Experimental Evaluation of the Role of Conformational Dynamics and Intrinsic Flexibility. MEMBRANES 2022; 12:227. [PMID: 35207148 PMCID: PMC8877495 DOI: 10.3390/membranes12020227] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/08/2023]
Abstract
A plethora of membrane proteins are found along the cell surface and on the convoluted labyrinth of membranes surrounding organelles. Since the advent of various structural biology techniques, a sub-population of these proteins has become accessible to investigation at near-atomic resolutions. The predominant bona fide methods for structure solution, X-ray crystallography and cryo-EM, provide high resolution in three-dimensional space at the cost of neglecting protein motions through time. Though structures provide various rigid snapshots, only an amorphous mechanistic understanding can be inferred from interpolations between these different static states. In this review, we discuss various techniques that have been utilized in observing dynamic conformational intermediaries that remain elusive from rigid structures. More specifically we discuss the application of structural techniques such as NMR, cryo-EM and X-ray crystallography in studying protein dynamics along with complementation by conformational trapping by specific binders such as antibodies. We finally showcase the strength of various biophysical techniques including FRET, EPR and computational approaches using a multitude of succinct examples from GPCRs, transporters and ion channels.
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Affiliation(s)
- Robbins Puthenveetil
- Section on Structural and Chemical Biology of Membrane Proteins, Neurosciences and Cellular and Structural Biology Division, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 35A Convent Dr., Bethesda, MD 20892, USA
| | | | - Olga Vinogradova
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Connecticut, Storrs, CT 06269, USA
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6
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Tyagi R, Singh A, Chaudhary KK, Yadav MK. Pharmacophore modeling and its applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00009-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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7
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Irshad N, Khan AU, Shah FA, Nadeem H, Ashraf Z, Tipu MK, Li S. Antihyperlipidemic effect of selected pyrimidine derivatives mediated through multiple pathways. Fundam Clin Pharmacol 2021; 35:1119-1132. [PMID: 33872413 DOI: 10.1111/fcp.12682] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 03/30/2021] [Accepted: 04/14/2021] [Indexed: 12/13/2022]
Abstract
Hyperlipidemia is worth-mentioning risk factor in quickly expanding atherosclerosis, myocardial infarction, and stroke. This study attempted to determine effectiveness of selected pyrimidine derivatives: 5-(3-Hydroxybenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-5), 5-(4-Hydroxybenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-8), 5-(3-Chlorobenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-9), and 5-(4-Chlorobenzylidene)-2, 4, 6(1H, 3H, 5H)-pyrimidinetrione (SR-10) against hyperlipidemia. In silico results revealed that SR-5, SR-8, SR-9, and SR-10 exhibited high affinity with 3-hydroxy-3-methylglutaryl coenzyme A (HMGCoA) possessing binding energy values of -8.2, -8.4, -8.6, and -9.5 Kcal/mol, respectively, and moderate (<-8 Kcal/mol) against other selected targets. In vivo findings showed that test drugs (25 and 50 mg/Kg) significantly decreased HFD rat total cholesterol, triglycerides, low-density lipoprotein, very-low-density lipoprotein, atherogenic index, coronary risk index, alkaline phosphatase, aspartate transaminase, alanine transaminase, and bilirubin and increased high-density lipoprotein (p < 0.05, p < 0.01, p < 0.001 vs HFD group). In animal liver tissues, SR-5, SR-8, SR-9, and SR-10 inhibited HMGCoA reductase enzyme, enhanced glutathione-s-transferase, reduced glutathione, catalase levels, improved cellular architecture in histopathological examination, and decreased expression of inflammatory markers: cyclo-oxygenase 2, tumor necrosis factor alpha, phosphorylated c-Jun N-terminal kinase, and phosphorylated-nuclear factor kappa B, evidenced in immunohistochemistry and enzyme-linked immunosorbent assay molecular investigations. This study indicates that SR-5, SR-8, SR-9, and SR-10 exhibit antihyperlipidemic action, mediated possibly through HMGCoA inhibition, hepatoprotection, antioxidant, and anti-inflammatory pathways.
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Affiliation(s)
- Nadeem Irshad
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan.,Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Arif-Ullah Khan
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan
| | - Fawad Ali Shah
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan
| | - Humaira Nadeem
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan
| | - Zaman Ashraf
- Department of Chemistry, Allama Iqbal Open University, Islamabad, Pakistan
| | - Muhammad Khalid Tipu
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Shupeng Li
- State Key Laboratory of Oncogenomics, School of Chemical Biology and Biotechnology, Shenzhen Graduate School, Peking University, Shenzhen, China
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8
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Xiang Y, Sang Z, Bitton L, Xu J, Liu Y, Schneidman-Duhovny D, Shi Y. Integrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies. Cell Syst 2021; 12:220-234.e9. [PMID: 33592195 PMCID: PMC7979497 DOI: 10.1016/j.cels.2021.01.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/13/2020] [Accepted: 01/20/2021] [Indexed: 12/15/2022]
Abstract
The antibody immune response is essential for the survival of mammals. However, we still lack a systematic understanding of the antibody repertoire. Here, we developed a proteomic strategy to survey, at an unprecedented scale, the landscape of antigen-engaged, circulating camelid heavy-chain antibodies, whose minimal binding fragments are called VHH antibodies or nanobodies. The sensitivity and robustness of this approach were validated with three antigens spanning orders of magnitude in immune responses; thousands of distinct, high-affinity nanobody families were reliably identified and quantified. Using high-throughput structural modeling, cross-linking mass spectrometry, mutagenesis, and deep learning, we mapped and analyzed the epitopes of >100,000 antigen-nanobody complexes. Our results revealed a surprising diversity of ultrahigh-affinity camelid nanobodies for specific antigen binding on various dominant epitope clusters. Nanobodies utilize both shape and charge complementarity to enable highly selective antigen binding. Interestingly, we found that nanobody-antigen binding can mimic conserved intracellular protein-protein interactions. A record of this paper's Transparent Peer Review process is included in the Supplemental information.
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Affiliation(s)
- Yufei Xiang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhe Sang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA; University of Pittsburgh, Carnegie Mellon University Program for Computational Biology, Pittsburgh, PA, USA
| | - Lirane Bitton
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel
| | - Jianquan Xu
- Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yang Liu
- Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dina Schneidman-Duhovny
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.
| | - Yi Shi
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA; University of Pittsburgh, Carnegie Mellon University Program for Computational Biology, Pittsburgh, PA, USA.
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9
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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10
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Xiang Y, Nambulli S, Xiao Z, Liu H, Sang Z, Duprex WP, Schneidman-Duhovny D, Zhang C, Shi Y. Versatile and multivalent nanobodies efficiently neutralize SARS-CoV-2. Science 2020; 370:1479-1484. [PMID: 33154108 PMCID: PMC7857400 DOI: 10.1126/science.abe4747] [Citation(s) in RCA: 254] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022]
Abstract
Cost-effective, efficacious therapeutics are urgently needed to combat the COVID-19 pandemic. In this study, we used camelid immunization and proteomics to identify a large repertoire of highly potent neutralizing nanobodies (Nbs) to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor binding domain (RBD). We discovered Nbs with picomolar to femtomolar affinities that inhibit viral infection at concentrations below the nanograms-per-milliliter level, and we determined a structure of one of the most potent Nbs in complex with the RBD. Structural proteomics and integrative modeling revealed multiple distinct and nonoverlapping epitopes and indicated an array of potential neutralization mechanisms. We bioengineered multivalent Nb constructs that achieved ultrahigh neutralization potency (half-maximal inhibitory concentration as low as 0.058 ng/ml) and may prevent mutational escape. These thermostable Nbs can be rapidly produced in bulk from microbes and resist lyophilization and aerosolization.
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MESH Headings
- Angiotensin-Converting Enzyme 2/chemistry
- Angiotensin-Converting Enzyme 2/genetics
- Angiotensin-Converting Enzyme 2/immunology
- Animals
- Antibodies, Neutralizing/chemistry
- Antibodies, Neutralizing/genetics
- Antibodies, Neutralizing/immunology
- Antibodies, Viral/chemistry
- Antibodies, Viral/genetics
- Antibodies, Viral/immunology
- Antibody Affinity
- COVID-19/therapy
- Camelids, New World
- Escherichia coli
- Humans
- Neutralization Tests
- Protein Binding
- Protein Domains
- Receptors, Virus/chemistry
- Receptors, Virus/genetics
- Receptors, Virus/immunology
- Recombinant Proteins/chemistry
- Recombinant Proteins/genetics
- Recombinant Proteins/immunology
- SARS-CoV-2/immunology
- Single-Domain Antibodies/chemistry
- Single-Domain Antibodies/genetics
- Single-Domain Antibodies/immunology
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Affiliation(s)
- Yufei Xiang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sham Nambulli
- Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhengyun Xiao
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Heng Liu
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhe Sang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh-Carnegie Mellon University Program in Computational Biology, Pittsburgh, PA, USA
| | - W Paul Duprex
- Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dina Schneidman-Duhovny
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Cheng Zhang
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Yi Shi
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA.
- University of Pittsburgh-Carnegie Mellon University Program in Computational Biology, Pittsburgh, PA, USA
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11
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Braberg H, Echeverria I, Bohn S, Cimermancic P, Shiver A, Alexander R, Xu J, Shales M, Dronamraju R, Jiang S, Dwivedi G, Bogdanoff D, Chaung KK, Hüttenhain R, Wang S, Mavor D, Pellarin R, Schneidman D, Bader JS, Fraser JS, Morris J, Haber JE, Strahl BD, Gross CA, Dai J, Boeke JD, Sali A, Krogan NJ. Genetic interaction mapping informs integrative structure determination of protein complexes. Science 2020; 370:eaaz4910. [PMID: 33303586 PMCID: PMC7946025 DOI: 10.1126/science.aaz4910] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 07/23/2020] [Accepted: 10/23/2020] [Indexed: 12/17/2022]
Abstract
Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stefan Bohn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Peter Cimermancic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Anthony Shiver
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard Alexander
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jiewei Xu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Michael Shales
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Raghuvar Dronamraju
- Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Shuangying Jiang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Gajendradhar Dwivedi
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA
| | - Derek Bogdanoff
- Center for Advanced Technology, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kaitlin K Chaung
- Center for Advanced Technology, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Shuyi Wang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David Mavor
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Riccardo Pellarin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dina Schneidman
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joel S Bader
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - James S Fraser
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John Morris
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - James E Haber
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA
| | - Brian D Strahl
- Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Carol A Gross
- Department of Microbiology and Immunology and Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Junbiao Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jef D Boeke
- NYU Langone Health, New York, NY 10016, USA.
- High Throughput Biology Center and Department of Molecular Biology & Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY 11201, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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12
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Cárdenas R, Martínez-Seoane J, Amero C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules 2020; 25:E4783. [PMID: 33081072 PMCID: PMC7594097 DOI: 10.3390/molecules25204783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 01/01/2023] Open
Abstract
Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.
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Affiliation(s)
| | | | - Carlos Amero
- Laboratorio de Bioquímica y Resonancia Magnética Nuclear, Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (R.C.); (J.M.-S.)
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13
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Xiang Y, Nambulli S, Xiao Z, Liu H, Sang Z, Duprex WP, Schneidman-Duhovny D, Zhang C, Shi Y. Versatile, Multivalent Nanobody Cocktails Efficiently Neutralize SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 32869034 PMCID: PMC7457627 DOI: 10.1101/2020.08.24.264333] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The outbreak of COVID-19 has severely impacted global health and the economy. Cost-effective, highly efficacious therapeutics are urgently needed. Here, we used camelid immunization and proteomics to identify a large repertoire of highly potent neutralizing nanobodies (Nbs) to the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD). We discovered multiple elite Nbs with picomolar to femtomolar affinities that inhibit viral infection at sub-ng/ml concentration, more potent than some of the best human neutralizing antibodies. We determined a crystal structure of such an elite neutralizing Nb in complex with RBD. Structural proteomics and integrative modeling revealed multiple distinct and non-overlapping epitopes and indicated an array of potential neutralization mechanisms. Structural characterization facilitated the bioengineering of novel multivalent Nb constructs into multi-epitope cocktails that achieved ultrahigh neutralization potency (IC50s as low as 0.058 ng/ml) and may prevent mutational escape. These thermostable Nbs can be rapidly produced in bulk from microbes and resist lyophilization, and aerosolization. These promising agents are readily translated into efficient, cost-effective, and convenient therapeutics to help end this once-in-a-century health crisis.
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Affiliation(s)
| | - Sham Nambulli
- Center for Vaccine Research.,Department of Microbiology and Molecular Genetics School of Medicine
| | | | - Heng Liu
- Department of Pharmacology and Chemical Biology University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhe Sang
- Department of Cell Biology.,Pitt/CMU Program for Computational Biology
| | - W Paul Duprex
- Center for Vaccine Research.,Department of Microbiology and Molecular Genetics School of Medicine
| | - Dina Schneidman-Duhovny
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel
| | - Cheng Zhang
- Department of Pharmacology and Chemical Biology University of Pittsburgh, Pittsburgh, PA, USA
| | - Yi Shi
- Department of Cell Biology.,Pitt/CMU Program for Computational Biology
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14
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Kanagarajan S, Dhamodharan P, Mutharasappan N, Choubey SK, Jayaprakash P, Biswal J, Jeyaraman J. Structural insights on binding mechanism of CAD complexes (CPSase, ATCase and DHOase). J Biomol Struct Dyn 2020; 39:3144-3157. [PMID: 32338152 DOI: 10.1080/07391102.2020.1761877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Pyrimidine biosynthetic pathway enzymes constitute an important target for the development of antitumor drugs. To understand the role of binding mechanisms underlying the inborn errors of pyrimidine biosynthetic pathway, structure and function of enzymes have been analyzed. Pyrimidine biosynthetic pathway is initiated by CAD enzymes that harbor the first three enzymatic activities facilitated by Carbamoyl Phosphate Synthetase (CPSase), Aspartate Transcarbamoylase (ATCase) and Dihydroorotase (DHOase). While being an attractive therapeutic target, the lack of data driven us to study the CPSase (CarA and CarB) and its mode of binding to ATCase and DHOase which are the major limitation for its structural optimization. Understanding the binding mode of CPSase, ATCase and DHOase could help to identify the potential interface hotspot residues that favor the mechanism behind it. The mechanistic insight into the CAD complexes were achieved through Molecular modeling, Protein-Protein docking, Alanine scanning and Molecular dynamics (MD) Studies. The hotspot residues present in the CarB region of carboxy phosphate and carbamoyl phosphate synthetic domains are responsible for the assembly of CAD (CPSase-ATCase-DHOase) complexes. Overall analysis suggests that the identified hotspot residues were confirmed by alanine scanning and important for the regulation of pyrimidine biosynthesis. MD simulations analysis provided the prolonged stability of the interacting complexes. The present study reveals the novel hotspot residues such as Glu134, Glu147, Glu154, Asp266, Lys269, Glu274, Asp333, Trp459, Asp526, Asp528, Glu533, Glu544, Glu546, Glu800, Val855, Asp877, Tyr884 and Gln919 which could be targeted for structure-based inhibitor design to potentiate the CAD mediated regulation of aggressive tumors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Surekha Kanagarajan
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
| | - Prabhu Dhamodharan
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
| | - Nachiappan Mutharasappan
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
| | - Sanjay Kumar Choubey
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
| | - Prajisha Jayaprakash
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
| | - Jayashree Biswal
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
| | - Jeyakanthan Jeyaraman
- Structural Biology and Bio-Computing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India
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15
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Probing Surfaces in Dynamic Protein Interactions. J Mol Biol 2020; 432:2949-2972. [DOI: 10.1016/j.jmb.2020.02.032] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 02/22/2020] [Accepted: 02/24/2020] [Indexed: 01/09/2023]
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16
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Lansky S, Salama R, Shulami S, Lavid N, Sen S, Schapiro I, Shoham Y, Shoham G. Carbohydrate-Binding Capability and Functional Conformational Changes of AbnE, an Arabino-oligosaccharide Binding Protein. J Mol Biol 2020; 432:2099-2120. [PMID: 32067952 DOI: 10.1016/j.jmb.2020.01.041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 01/19/2020] [Accepted: 01/30/2020] [Indexed: 11/27/2022]
Abstract
ABC importers are membrane proteins responsible for the transport of nutrients into the cells of prokaryotes. Although the structures of ABC importers vary, all contain four conserved domains: two nucleotide-binding domains (NBDs), which bind and hydrolyze ATP, and two transmembrane domains (TMDs), which help translocate the substrate. ABC importers are also dependent on an additional protein component, a high-affinity substrate-binding protein (SBP) that specifically binds the target ligand for delivery to the appropriate ABC transporter. AbnE is a SBP belonging to the ABC importer for arabino-oligosaccharides in the Gram-positive thermophilic bacterium Geobacillus stearothermophilus. Using isothermal titration calorimetry (ITC), purified AbnE was shown to bind medium-sized arabino-oligosaccharides, in the range of arabino-triose (A3) to arabino-octaose (A8), all with Kd values in the nanomolar range. We describe herein the 3D structure of AbnE in its closed conformation in complex with a wide range of arabino-oligosaccharide substrates (A2-A8). These structures provide the basis for the detailed structural analysis of the AbnE-sugar complexes, and together with complementary quantum chemical calculations, site-specific mutagenesis, and isothermal titration calorimetry (ITC) experiments, provide detailed insights into the AbnE-substrate interactions involved. Small-angle X-ray scattering (SAXS) experiments and normal mode analysis (NMA) are used to study the conformational changes of AbnE, and these data, taken together, suggest clues regarding its binding mode to the full ABC importer.
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Affiliation(s)
- Shifra Lansky
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Rachel Salama
- Department of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel
| | - Smadar Shulami
- Department of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel
| | - Noa Lavid
- Department of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel
| | - Saumik Sen
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel; Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Igor Schapiro
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel; Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Yuval Shoham
- Department of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel.
| | - Gil Shoham
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.
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17
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Liu S, Xiang X, Gao X, Liu H. Neighborhood Preference of Amino Acids in Protein Structures and its Applications in Protein Structure Assessment. Sci Rep 2020; 10:4371. [PMID: 32152349 PMCID: PMC7062742 DOI: 10.1038/s41598-020-61205-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 02/24/2020] [Indexed: 12/02/2022] Open
Abstract
Amino acids form protein 3D structures in unique manners such that the folded structure is stable and functional under physiological conditions. Non-specific and non-covalent interactions between amino acids exhibit neighborhood preferences. Based on structural information from the protein data bank, a statistical energy function was derived to quantify amino acid neighborhood preferences. The neighborhood of one amino acid is defined by its contacting residues, and the energy function is determined by the neighboring residue types and relative positions. The neighborhood preference of amino acids was exploited to facilitate structural quality assessment, which was implemented in the neighborhood preference program NEPRE. The source codes are available via https://github.com/LiuLab-CSRC/NePre.
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Affiliation(s)
- Siyuan Liu
- Complex Systems Division, Beijing Computational Science Research Center, Haidian, Beijing, 100193, China
- School of Software Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Xilun Xiang
- Complex Systems Division, Beijing Computational Science Research Center, Haidian, Beijing, 100193, China
- School of Software Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Xiang Gao
- Complex Systems Division, Beijing Computational Science Research Center, Haidian, Beijing, 100193, China
- School of Software Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Haiguang Liu
- Complex Systems Division, Beijing Computational Science Research Center, Haidian, Beijing, 100193, China.
- Physics Department, Beijing Normal University, Haidian, Beijing, 100875, China.
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18
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Abstract
A technical overview of the High Performance Collision Cross Section (HPCCS) software for accurate and efficient calculations of collision cross sections for molecular ions ranging from small organic molecules to large protein complexes is presented. The program uses helium or nitrogen as buffer gas with considerable gains in computer time compared to publicly available codes under the Trajectory Method approximation. HPCCS is freely available under the Academic Use License at https://github.com/cepid-cces/hpccs .
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19
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Gaber A, Gunčar G, Pavšič M. Proper evaluation of chemical cross-linking-based spatial restraints improves the precision of modeling homo-oligomeric protein complexes. BMC Bioinformatics 2019; 20:464. [PMID: 31500562 PMCID: PMC6734309 DOI: 10.1186/s12859-019-3032-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 08/16/2019] [Indexed: 11/22/2022] Open
Abstract
Background The function of oligomeric proteins is inherently linked to their quaternary structure. In the absence of high-resolution data, low-resolution information in the form of spatial restraints can significantly contribute to the precision and accuracy of structural models obtained using computational approaches. To obtain such restraints, chemical cross-linking coupled with mass spectrometry (XL-MS) is commonly used. However, the use of XL-MS in the modeling of protein complexes comprised of identical subunits (homo-oligomers) is often hindered by the inherent ambiguity of intra- and inter-subunit connection assignment. Results We present a comprehensive evaluation of (1) different methods for inter-residue distance calculations, and (2) different approaches for the scoring of spatial restraints. Our results show that using Solvent Accessible Surface distances (SASDs) instead of Euclidean distances (EUCs) greatly reduces the assignation ambiguity and delivers better modeling precision. Furthermore, ambiguous connections should be considered as inter-subunit only when the intra-subunit alternative exceeds the distance threshold. Modeling performance can also be improved if symmetry, characteristic for most homo-oligomers, is explicitly defined in the scoring function. Conclusions Our findings provide guidelines for proper evaluation of chemical cross-linking-based spatial restraints in modeling homo-oligomeric protein complexes, which could facilitate structural characterization of this important group of proteins. Electronic supplementary material The online version of this article (10.1186/s12859-019-3032-x) contains supplementary material, which is available to authorized users.
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20
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Vallat B, Webb B, Westbrook J, Sali A, Berman HM. Archiving and disseminating integrative structure models. JOURNAL OF BIOMOLECULAR NMR 2019; 73:385-398. [PMID: 31278630 PMCID: PMC6692293 DOI: 10.1007/s10858-019-00264-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 06/25/2019] [Indexed: 05/04/2023]
Abstract
Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.
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Affiliation(s)
- Brinda Vallat
- Institute for Quantitative Biomedicine, Piscataway, USA
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, 94143, USA
| | - John Westbrook
- Institute for Quantitative Biomedicine, Piscataway, USA
- RCSB Protein Data Bank, Piscataway, USA
| | - Andrej Sali
- RCSB Protein Data Bank, Piscataway, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, 94143, USA.
- Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA, 94143, USA.
- Lead Contacts, San Francisco, USA.
| | - Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Lead Contacts, Piscataway, USA.
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21
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Lei D, Liu J, Liu H, Cleveland TE, Marino JP, Lei M, Ren G. Single-Molecule 3D Images of "Hole-Hole" IgG1 Homodimers by Individual-Particle Electron Tomography. Sci Rep 2019; 9:8864. [PMID: 31221961 PMCID: PMC6586654 DOI: 10.1038/s41598-019-44978-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/30/2019] [Indexed: 12/20/2022] Open
Abstract
The engineering of immunoglobulin-G molecules (IgGs) is of wide interest for improving therapeutics, for example by modulating the activity or multiplexing the specificity of IgGs to recognize more than one antigen. Optimization of engineered IgG requires knowledge of three-dimensional (3D) structure of synthetic IgG. However, due to flexible nature of the molecules, their structural characterization is challenging. Here, we use our reported individual-particle electron tomography (IPET) method with optimized negative-staining (OpNS) for direct 3D reconstruction of individual IgG hole-hole homodimer molecules. The hole-hole homodimer is an undesired variant generated during the production of a bispecific antibody using the knob-into-hole heterodimer technology. A total of 64 IPET 3D density maps at ~15 Å resolutions were reconstructed from 64 individual molecules, revealing 64 unique conformations. In addition to the known Y-shaped conformation, we also observed an unusual X-shaped conformation. The 3D structure of the X-shaped conformation contributes to our understanding of the structural details of the interaction between two heavy chains in the Fc domain. The IPET approach, as an orthogonal technique to characterize the 3D structure of therapeutic antibodies, provides insight into the 3D structural variety and dynamics of heterogeneous IgG molecules.
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Affiliation(s)
- Dongsheng Lei
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jianfang Liu
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Hongbin Liu
- Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Thomas E Cleveland
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, MD, 20850, USA
| | - John P Marino
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, MD, 20850, USA
| | - Ming Lei
- Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA, 94080, USA.
| | - Gang Ren
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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22
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Braitbard M, Schneidman-Duhovny D, Kalisman N. Integrative Structure Modeling: Overview and Assessment. Annu Rev Biochem 2019; 88:113-135. [PMID: 30830798 DOI: 10.1146/annurev-biochem-013118-111429] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.
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Affiliation(s)
- Merav Braitbard
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Dina Schneidman-Duhovny
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel; .,School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Nir Kalisman
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
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23
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Debiec KT, Whitley MJ, Koharudin LMI, Chong LT, Gronenborn AM. Integrating NMR, SAXS, and Atomistic Simulations: Structure and Dynamics of a Two-Domain Protein. Biophys J 2019; 114:839-855. [PMID: 29490245 DOI: 10.1016/j.bpj.2018.01.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 12/19/2017] [Accepted: 01/02/2018] [Indexed: 12/21/2022] Open
Abstract
Multidomain proteins with two or more independently folded functional domains are prevalent in nature. Whereas most multidomain proteins are linked linearly in sequence, roughly one-tenth possess domain insertions where a guest domain is implanted into a loop of a host domain, such that the two domains are connected by a pair of interdomain linkers. Here, we characterized the influence of the interdomain linkers on the structure and dynamics of a domain-insertion protein in which the guest LysM domain is inserted into a central loop of the host CVNH domain. Expanding upon our previous crystallographic and NMR studies, we applied SAXS in combination with NMR paramagnetic relaxation enhancement to construct a structural model of the overall two-domain system. Although the two domains have no fixed relative orientation, certain orientations were found to be preferred over others. We also assessed the accuracies of molecular mechanics force fields in modeling the structure and dynamics of tethered multidomain proteins by integrating our experimental results with microsecond-scale atomistic molecular dynamics simulations. In particular, our evaluation of two different combinations of the latest force fields and water models revealed that both combinations accurately reproduce certain structural and dynamical properties, but are inaccurate for others. Overall, our study illustrates the value of integrating experimental NMR and SAXS studies with long timescale atomistic simulations for characterizing structural ensembles of flexibly linked multidomain systems.
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Affiliation(s)
- Karl T Debiec
- Molecular Biophysics and Structural Biology Graduate Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Matthew J Whitley
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Leonardus M I Koharudin
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Angela M Gronenborn
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
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24
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Wollacott AM, Robinson LN, Ramakrishnan B, Tissire H, Viswanathan K, Shriver Z, Babcock GJ. Structural prediction of antibody-APRIL complexes by computational docking constrained by antigen saturation mutagenesis library data. J Mol Recognit 2019; 32:e2778. [DOI: 10.1002/jmr.2778] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/21/2018] [Accepted: 12/06/2018] [Indexed: 12/29/2022]
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25
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Van Blarcom T, Rossi A, Foletti D, Sundar P, Pitts S, Melton Z, Telman D, Zhao L, Cheung WL, Berka J, Zhai W, Strop P, Pons J, Rajpal A, Chaparro-Riggers J. Epitope Mapping Using Yeast Display and Next Generation Sequencing. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2019; 1785:89-118. [PMID: 29714014 DOI: 10.1007/978-1-4939-7841-0_7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Monoclonal antibodies are the largest class of therapeutic proteins due in part to their ability to bind an antigen with a high degree of affinity and specificity. A precise determination of their epitope is important for gaining insights into their therapeutic mechanism of action and to help differentiate antibodies that bind the same antigen. Here, we describe a method to precisely and efficiently map the epitopes of multiple antibodies in parallel over the course of just several weeks. This approach is based on a combination of rational library design, yeast surface display, and next generation DNA sequencing and provides quantitative insights into the epitope residues most critical for the antibody-antigen interaction. As an example, we will use this method to map the epitopes of several antibodies that neutralize alpha toxin from Staphylococcus aureus.
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Affiliation(s)
| | - Andrea Rossi
- Rinat, Pfizer Inc., South San Francisco, CA, USA
| | - Davide Foletti
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,23andMe Inc., South San Francisco, CA, USA
| | | | - Steven Pitts
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,23andMe Inc., South San Francisco, CA, USA
| | - Zea Melton
- Rinat, Pfizer Inc., South San Francisco, CA, USA
| | | | - Lora Zhao
- Rinat, Pfizer Inc., South San Francisco, CA, USA
| | - Wai Ling Cheung
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,Princeton University, Princeton, NJ, USA
| | - Jan Berka
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,Roche Sequencing Solutions, Pleasanton, CA, USA
| | - Wenwu Zhai
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,NGM Biopharmaceuticals Inc., South San Francisco, CA, USA
| | - Pavel Strop
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,Bristol-Myers Squibb Inc., Redwood City, CA, USA
| | - Jaume Pons
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,Alexo Therapeutics Inc., South San Francisco, CA, USA
| | - Arvind Rajpal
- Rinat, Pfizer Inc., South San Francisco, CA, USA.,Bristol-Myers Squibb Inc., Redwood City, CA, USA
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26
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Jiménez-García B, Roel-Touris J, Romero-Durana M, Vidal M, Jiménez-González D, Fernández-Recio J. LightDock: a new multi-scale approach to protein-protein docking. Bioinformatics 2018; 34:49-55. [PMID: 28968719 DOI: 10.1093/bioinformatics/btx555] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/01/2017] [Indexed: 12/18/2022] Open
Abstract
Motivation Computational prediction of protein-protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. Results We describe here a new multi-scale protein-protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases. Availability and implementation The source code of the software and installation instructions are available for download at https://life.bsc.es/pid/lightdock/. Contact juanf@bsc.es. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Brian Jiménez-García
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Jorge Roel-Touris
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Miguel Romero-Durana
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Miquel Vidal
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Daniel Jiménez-González
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Department of Computer Architecture, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Structural Biology Unit, IBMB-CSIC, 08028 Barcelona, Spain
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27
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Chu F, Thornton DT, Nguyen HT. Chemical cross-linking in the structural analysis of protein assemblies. Methods 2018; 144:53-63. [PMID: 29857191 DOI: 10.1016/j.ymeth.2018.05.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/22/2018] [Accepted: 05/25/2018] [Indexed: 12/31/2022] Open
Abstract
For decades, chemical cross-linking of proteins has been an established method to study protein interaction partners. The chemical cross-linking approach has recently been revived by mass spectrometric analysis of the cross-linking reaction products. Chemical cross-linking and mass spectrometric analysis (CXMS) enables the identification of residues that are close in three-dimensional (3D) space but not necessarily close in primary sequence. Therefore, this approach provides medium resolution information to guide de novo structure prediction, protein interface mapping and protein complex model building. The robustness and compatibility of the CXMS approach with multiple biochemical methods have made it especially appealing for challenging systems with multiple biochemical compositions and conformation states. This review provides an overview of the CXMS approach, describing general procedures in sample processing, data acquisition and analysis. Selection of proper chemical cross-linking reagents, strategies for cross-linked peptide identification, and successful application of CXMS in structural characterization of proteins and protein complexes are discussed.
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Affiliation(s)
- Feixia Chu
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States; Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH 03824, United States.
| | - Daniel T Thornton
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States
| | - Hieu T Nguyen
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States
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28
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Vreven T, Schweppe DK, Chavez JD, Weisbrod CR, Shibata S, Zheng C, Bruce JE, Weng Z. Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking. J Mol Biol 2018; 430:1814-1828. [PMID: 29665372 DOI: 10.1016/j.jmb.2018.04.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 03/19/2018] [Accepted: 04/10/2018] [Indexed: 12/23/2022]
Abstract
Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Devin K Schweppe
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Juan D Chavez
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Chad R Weisbrod
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Sayaka Shibata
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Chunxiang Zheng
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - James E Bruce
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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29
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Abstract
Small-angle X-ray scattering (SAXS) is an increasingly common and useful technique for structural characterization of molecules in solution. A SAXS experiment determines the scattering intensity of a molecule as a function of spatial frequency, termed SAXS profile. SAXS profiles can be utilized in a variety of molecular modeling applications, such as comparing solution and crystal structures, structural characterization of flexible proteins, assembly of multi-protein complexes, and modeling of missing regions in the high-resolution structure. Here, we describe protocols for modeling atomic structures based on SAXS profiles. The first protocol is for comparing solution and crystal structures including modeling of missing regions and determination of the oligomeric state. The second protocol performs multi-state modeling by finding a set of conformations and their weights that fit the SAXS profile starting from a single-input structure. The third protocol is for protein-protein docking based on the SAXS profile of the complex. We describe the underlying software, followed by demonstrating their application on interleukin 33 (IL33) with its primary receptor ST2 and DNA ligase IV-XRCC4 complex.
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Affiliation(s)
- Dina Schneidman-Duhovny
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Michal Hammel
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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30
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Harnessing the Combined Power of SAXS and NMR. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:171-180. [DOI: 10.1007/978-981-13-2200-6_11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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31
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Moal IH, Barradas-Bautista D, Jiménez-García B, Torchala M, van der Velde A, Vreven T, Weng Z, Bates PA, Fernández-Recio J. IRaPPA: information retrieval based integration of biophysical models for protein assembly selection. Bioinformatics 2017; 33:1806-1813. [PMID: 28200016 PMCID: PMC5783285 DOI: 10.1093/bioinformatics/btx068] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 01/26/2017] [Accepted: 02/12/2017] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. RESULTS Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. AVAILABILITY AND IMPLEMENTATION IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. CONTACT moal@ebi.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iain H Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Didier Barradas-Bautista
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Brian Jiménez-García
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | | | - Arjan van der Velde
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Juan Fernández-Recio
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
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32
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Joseph AP, Lagerstedt I, Patwardhan A, Topf M, Winn M. Improved metrics for comparing structures of macromolecular assemblies determined by 3D electron-microscopy. J Struct Biol 2017; 199:12-26. [PMID: 28552721 PMCID: PMC5479444 DOI: 10.1016/j.jsb.2017.05.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Revised: 05/19/2017] [Accepted: 05/23/2017] [Indexed: 11/28/2022]
Abstract
Recent developments in 3-dimensional electron microcopy (3D-EM) techniques and a concomitant drive to look at complex molecular structures, have led to a rapid increase in the amount of volume data available for biomolecules. This creates a demand for better methods to analyse the data, including improved scores for comparison, classification and integration of data at different resolutions. To this end, we developed and evaluated a set of scoring functions that compare 3D-EM volumes. To test our scores we used a benchmark set of volume alignments derived from the Electron Microscopy Data Bank. We find that the performance of different scores vary with the map-type, resolution and the extent of overlap between volumes. Importantly, adding the overlap information to the local scoring functions can significantly improve their precision and accuracy in a range of resolutions. A combined score involving the local mutual information and overlap (LMI_OV) performs best overall, irrespective of the map category, resolution or the extent of overlap, and we recommend this score for general use. The local mutual information score itself is found to be more discriminatory than cross-correlation coefficient for intermediate-to-low resolution maps or when the map size and density distribution differ significantly. For comparing map surfaces, we implemented two filters to detect the surface points, including one based on the 'extent of surface exposure'. We show that scores that compare surfaces are useful at low resolutions and for maps with evident surface features. All the scores discussed are implemented in TEMPy (http://tempy.ismb.lon.ac.uk/).
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Affiliation(s)
- Agnel Praveen Joseph
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom; Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Ingvar Lagerstedt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom; Computational Chemistry and Cheminformatics, Lilly UK, Windlesham GU20 6PH, United Kingdom
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom.
| | - Martyn Winn
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom.
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33
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Vreven T, Pierce BG, Borrman TM, Weng Z. Performance of ZDOCK and IRAD in CAPRI rounds 28-34. Proteins 2016; 85:408-416. [PMID: 27718275 DOI: 10.1002/prot.25186] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/20/2016] [Accepted: 09/29/2016] [Indexed: 11/11/2022]
Abstract
We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking. Proteins 2017; 85:408-416. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Tyler M Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
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34
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Schulze-Gahmen U, Echeverria I, Stjepanovic G, Bai Y, Lu H, Schneidman-Duhovny D, Doudna JA, Zhou Q, Sali A, Hurley JH. Insights into HIV-1 proviral transcription from integrative structure and dynamics of the Tat:AFF4:P-TEFb:TAR complex. eLife 2016; 5. [PMID: 27731797 PMCID: PMC5072841 DOI: 10.7554/elife.15910] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 10/07/2016] [Indexed: 01/04/2023] Open
Abstract
HIV-1 Tat hijacks the human superelongation complex (SEC) to promote proviral transcription. Here we report the 5.9 Å structure of HIV-1 TAR in complex with HIV-1 Tat and human AFF4, CDK9, and CycT1. The TAR central loop contacts the CycT1 Tat-TAR recognition motif (TRM) and the second Tat Zn2+-binding loop. Hydrogen-deuterium exchange (HDX) shows that AFF4 helix 2 is stabilized in the TAR complex despite not touching the RNA, explaining how it enhances TAR binding to the SEC 50-fold. RNA SHAPE and SAXS data were used to help model the extended (Tat Arginine-Rich Motif) ARM, which enters the TAR major groove between the bulge and the central loop. The structure and functional assays collectively support an integrative structure and a bipartite binding model, wherein the TAR central loop engages the CycT1 TRM and compact core of Tat, while the TAR major groove interacts with the extended Tat ARM. DOI:http://dx.doi.org/10.7554/eLife.15910.001
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Affiliation(s)
- Ursula Schulze-Gahmen
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Ignacia Echeverria
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.,California Institute of Quantitative Biosciences, University of California San, Francisco, San Francisco, United States
| | - Goran Stjepanovic
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States
| | - Yun Bai
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Huasong Lu
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.,California Institute of Quantitative Biosciences, University of California San, Francisco, San Francisco, United States
| | - Jennifer A Doudna
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States.,Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States.,Department of Chemistry, University of California, Berkeley, Berkeley, United States
| | - Qiang Zhou
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.,California Institute of Quantitative Biosciences, University of California San, Francisco, San Francisco, United States
| | - James H Hurley
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, United States.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States
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35
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Small-angle scattering and 3D structure interpretation. Curr Opin Struct Biol 2016; 40:1-7. [DOI: 10.1016/j.sbi.2016.05.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 05/12/2016] [Accepted: 05/12/2016] [Indexed: 12/29/2022]
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36
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Schindler C, de Vries S, Sasse A, Zacharias M. SAXS Data Alone can Generate High-Quality Models of Protein-Protein Complexes. Structure 2016; 24:1387-1397. [DOI: 10.1016/j.str.2016.06.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 06/08/2016] [Accepted: 06/08/2016] [Indexed: 11/29/2022]
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37
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Abstract
A detailed understanding of chemical and biological function and the mechanisms underlying the molecular activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy, and neutron diffraction are well established and they have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last twenty years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches, and high-speed computing and visualization, now provide specialists and nonspecialists alike with a steady flow of molecular images of unprecedented detail. The present unit combines a general overview of diffraction methods with a detailed description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis, and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. © 2016 by John Wiley & Sons, Inc.
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Affiliation(s)
- Martin Egli
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee
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38
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Carter L, Kim SJ, Schneidman-Duhovny D, Stöhr J, Poncet-Montange G, Weiss TM, Tsuruta H, Prusiner SB, Sali A. Prion Protein-Antibody Complexes Characterized by Chromatography-Coupled Small-Angle X-Ray Scattering. Biophys J 2016; 109:793-805. [PMID: 26287631 DOI: 10.1016/j.bpj.2015.06.065] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Revised: 06/22/2015] [Accepted: 06/30/2015] [Indexed: 10/23/2022] Open
Abstract
Aberrant self-assembly, induced by structural misfolding of the prion proteins, leads to a number of neurodegenerative disorders. In particular, misfolding of the mostly α-helical cellular prion protein (PrP(C)) into a β-sheet-rich disease-causing isoform (PrP(Sc)) is the key molecular event in the formation of PrP(Sc) aggregates. The molecular mechanisms underlying the PrP(C)-to-PrP(Sc) conversion and subsequent aggregation remain to be elucidated. However, in persistently prion-infected cell-culture models, it was shown that treatment with monoclonal antibodies against defined regions of the prion protein (PrP) led to the clearing of PrP(Sc) in cultured cells. To gain more insight into this process, we characterized PrP-antibody complexes in solution using a fast protein liquid chromatography coupled with small-angle x-ray scattering (FPLC-SAXS) procedure. High-quality SAXS data were collected for full-length recombinant mouse PrP [denoted recPrP(23-230)] and N-terminally truncated recPrP(89-230), as well as their complexes with each of two Fab fragments (HuM-P and HuM-R1), which recognize N- and C-terminal epitopes of PrP, respectively. In-line measurements by fast protein liquid chromatography coupled with SAXS minimized data artifacts caused by a non-monodispersed sample, allowing structural analysis of PrP alone and in complex with Fab antibodies. The resulting structural models suggest two mechanisms for how these Fabs may prevent the conversion of PrP(C) into PrP(Sc).
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Affiliation(s)
- Lester Carter
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California
| | - Seung Joong Kim
- Department of Bioengineering and Therapeutic Sciences and Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences and Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California
| | - Jan Stöhr
- Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, California; Department of Neurology, University of California San Francisco, San Francisco, California
| | - Guillaume Poncet-Montange
- Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, California
| | - Thomas M Weiss
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California
| | - Hiro Tsuruta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California
| | - Stanley B Prusiner
- Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, California; Department of Neurology, University of California San Francisco, San Francisco, California.
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences and Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California.
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39
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Schneidman-Duhovny D, Hammel M, Tainer JA, Sali A. FoXS, FoXSDock and MultiFoXS: Single-state and multi-state structural modeling of proteins and their complexes based on SAXS profiles. Nucleic Acids Res 2016; 44:W424-9. [PMID: 27151198 PMCID: PMC4987932 DOI: 10.1093/nar/gkw389] [Citation(s) in RCA: 349] [Impact Index Per Article: 43.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 04/27/2016] [Indexed: 11/14/2022] Open
Abstract
Small Angle X-ray Scattering (SAXS) is an increasingly common and useful technique for structural characterization of molecules in solution. A SAXS experiment determines the scattering intensity of a molecule as a function of spatial frequency, termed SAXS profile. Here, we describe three web servers for modeling atomic structures based on SAXS profiles. FoXS (Fast X-Ray Scattering) rapidly computes a SAXS profile of a given atomistic model and fits it to an experimental profile. FoXSDock docks two rigid protein structures based on a SAXS profile of their complex. MultiFoXS computes a population-weighted ensemble starting from a single input structure by fitting to a SAXS profile of the protein in solution. We describe the interfaces and capabilities of the servers (salilab.org/foxs), followed by demonstrating their application on Interleukin-33 (IL-33) and its primary receptor ST2.
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Affiliation(s)
- Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, CA 94143, USA
| | - Michal Hammel
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - John A Tainer
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA Department of Molecular and Cellular Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, CA 94143, USA
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de Vries SJ, Chauvot de Beauchêne I, Schindler CEM, Zacharias M. Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling. Biophys J 2016; 110:785-97. [PMID: 26846888 DOI: 10.1016/j.bpj.2015.12.038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 11/18/2015] [Accepted: 12/14/2015] [Indexed: 12/29/2022] Open
Abstract
Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.
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Affiliation(s)
- Sjoerd J de Vries
- Physik-Department T38, Technische Universität München, Garching, Germany.
| | | | - Christina E M Schindler
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
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41
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DksA regulates RNA polymerase in Escherichia coli through a network of interactions in the secondary channel that includes Sequence Insertion 1. Proc Natl Acad Sci U S A 2015; 112:E6862-71. [PMID: 26604313 DOI: 10.1073/pnas.1521365112] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Sensing and responding to nutritional status is a major challenge for microbial life. In Escherichia coli, the global response to amino acid starvation is orchestrated by guanosine-3',5'-bisdiphosphate and the transcription factor DksA. DksA alters transcription by binding to RNA polymerase and allosterically modulating its activity. Using genetic analysis, photo-cross-linking, and structural modeling, we show that DksA binds and acts upon RNA polymerase through prominent features of both the nucleotide-access secondary channel and the active-site region. This work is, to our knowledge, the first demonstration of a molecular function for Sequence Insertion 1 in the β subunit of RNA polymerase and significantly advances our understanding of how DksA binds to RNA polymerase and alters transcription.
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42
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Marcoux J, Cianférani S. Towards integrative structural mass spectrometry: Benefits from hybrid approaches. Methods 2015; 89:4-12. [DOI: 10.1016/j.ymeth.2015.05.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 05/06/2015] [Accepted: 05/25/2015] [Indexed: 01/10/2023] Open
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43
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Integrative Modeling of Biomolecular Complexes: HADDOCKing with Cryo-Electron Microscopy Data. Structure 2015; 23:949-960. [DOI: 10.1016/j.str.2015.03.014] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Revised: 03/12/2015] [Accepted: 03/13/2015] [Indexed: 12/13/2022]
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44
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Janin J, Wodak SJ, Lensink MF, Velankar S. Assessing Structural Predictions of Protein-Protein Recognition: The CAPRI Experiment. REVIEWS IN COMPUTATIONAL CHEMISTRY 2015. [DOI: 10.1002/9781118889886.ch4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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45
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Shih ESC, Hwang MJ. NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues. BIOLOGY 2015; 4:282-97. [PMID: 25811640 PMCID: PMC4498300 DOI: 10.3390/biology4020282] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Accepted: 03/16/2015] [Indexed: 11/16/2022]
Abstract
Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.
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Affiliation(s)
- Edward S C Shih
- Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei 115, Taiwan.
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei 115, Taiwan.
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46
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Precise and Efficient Antibody Epitope Determination through Library Design, Yeast Display and Next-Generation Sequencing. J Mol Biol 2015; 427:1513-1534. [DOI: 10.1016/j.jmb.2014.09.020] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 09/17/2014] [Accepted: 09/26/2014] [Indexed: 01/18/2023]
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47
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Chaudhuri BN. Emerging applications of small angle solution scattering in structural biology. Protein Sci 2015; 24:267-76. [PMID: 25516491 PMCID: PMC4353354 DOI: 10.1002/pro.2624] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2014] [Accepted: 12/05/2014] [Indexed: 12/12/2022]
Abstract
Small angle solution X-ray and neutron scattering recently resurfaced as powerful tools to address an array of biological problems including folding, intrinsic disorder, conformational transitions, macromolecular crowding, and self or hetero-assembling of biomacromolecules. In addition, small angle solution scattering complements crystallography, nuclear magnetic resonance spectroscopy, and other structural methods to aid in the structure determinations of multidomain or multicomponent proteins or nucleoprotein assemblies. Neutron scattering with hydrogen/deuterium contrast variation, or X-ray scattering with sucrose contrast variation to a certain extent, is a convenient tool for characterizing the organizations of two-component systems such as a nucleoprotein or a lipid-protein assembly. Time-resolved small and wide-angle solution scattering to study biological processes in real time, and the use of localized heavy-atom labeling and anomalous solution scattering for applications as FRET-like molecular rulers, are amongst promising newer developments. Despite the challenges in data analysis and interpretation, these X-ray/neutron solution scattering based approaches hold great promise for understanding a wide variety of complex processes prevalent in the biological milieu.
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Affiliation(s)
- Barnali N Chaudhuri
- Faculty of Life Sciences and Biotechnology, South Asian UniversityAkbar Bhawan, Chanakyapuri, New Delhi, India
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48
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Moal IH, Dapkūnas J, Fernández-Recio J. Inferring the microscopic surface energy of protein-protein interfaces from mutation data. Proteins 2015; 83:640-50. [PMID: 25586563 DOI: 10.1002/prot.24761] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/04/2014] [Accepted: 12/21/2014] [Indexed: 11/11/2022]
Abstract
Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain
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49
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Sarti E, Granata D, Seno F, Trovato A, Laio A. Native fold and docking pose discrimination by the same residue-based scoring function. Proteins 2015; 83:621-30. [DOI: 10.1002/prot.24764] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 12/15/2014] [Accepted: 12/31/2014] [Indexed: 11/10/2022]
Affiliation(s)
| | - Daniele Granata
- SISSA, Physics Faculty; Trieste I-34136 Italy
- Institute of Computational and Molecular Science; Temple University; Philadelphia, PA 19122 USA
| | - Flavio Seno
- CNISM, Padova Unit, and Department of Physics and Astronomy; University of Padova; 35131 Padova Italy
| | - Antonio Trovato
- INFN, Padova Section, and Department of Physics and Astronomy; University of Padova; 35131 Padova Italy
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50
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Hopf TA, Schärfe CPI, Rodrigues JPGLM, Green AG, Kohlbacher O, Sander C, Bonvin AMJJ, Marks DS. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 2014; 3. [PMID: 25255213 PMCID: PMC4360534 DOI: 10.7554/elife.03430] [Citation(s) in RCA: 332] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 09/23/2014] [Indexed: 12/24/2022] Open
Abstract
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.
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Affiliation(s)
- Thomas A Hopf
- Department of Systems Biology, Harvard University, Boston, United States
| | | | - João P G L M Rodrigues
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Anna G Green
- Department of Systems Biology, Harvard University, Boston, United States
| | - Oliver Kohlbacher
- Applied Bioinformatics, Quantitative Biology Center, University of Tübingen, Tübingen, Germany
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Debora S Marks
- Department of Systems Biology, Harvard University, Boston, United States
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