1
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Scaini JLR, Camargo AD, Seus VR, von Groll A, Werhli AV, da Silva PEA, Machado KDS. Molecular modelling and competitive inhibition of a Mycobacterium tuberculosis multidrug-resistance efflux pump. J Mol Graph Model 2018; 87:98-108. [PMID: 30529931 DOI: 10.1016/j.jmgm.2018.11.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 11/29/2018] [Accepted: 11/29/2018] [Indexed: 02/08/2023]
Abstract
Tuberculosis is a major cause of mortality and morbidity in developing countries, and the emergency of multidrug and extensive drug resistance cases is an utmost issue on the control of the disease. Despite the efforts on the development of new antibiotics, eventually there will be strains resistant to them as well. Efflux plays an important role in the evolution of resistance in Mycobacterium tuberculosis. Tap is an important efflux pump associated with tuberculosis resistant to isoniazid, rifampicine and ofloxacin and with multidrug resistance. The development of efflux inhibitors for Tap could raise the effectiveness of second line drugs and reduce the duration of the current treatment. Therefore the objective of this study is to build a reliable molecular model of Tap efflux pump and test the possible competitive inhibition between efflux inhibitors and antibiotics in the optimized structure. We built twenty five Tap models with molecular modelling to elect the best according to the results of the validation analysis. The elected model went through to a 100 ns molecular dynamics simulation in a lipid bilayer, and the resulting optimized structure was used in docking studies to test if the used efflux inhibitors may act via competitive inhibition on antibiotics. The validation results pointed the model built by Phyre2 as the closest to a possible native Tap structure, and therefore it was the elected model. RSMD analysis revealed the model is stable, where the predicted binding site stabilized between 15 and 20 ns, maintaining the RMSD at around 0.35 Å throughout the molecular dynamics simulation in a lipid bilayer. Therefore this model is reliable and can also be used for further studies. The docking studies showed a possibility of competitive inhibition by NUNL02 on ofloxacin and bedaquiline, and by verapamil on ofloxacin and rifampicin. This presents the possibility that NUNL02 and verapamil are possible inhibitors of Tap efflux and highlights the importance of including efflux inhibitors as adjuvants to the tuberculosis therapy, as it indicates a possible extrusion of ofloxacin, rifampicin and bedaquilin by Tap.
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Affiliation(s)
- Joāo Luís Rheingantz Scaini
- Laboratory of Computational Biology, Computational Sciences Center of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil; Research Center in Medical Microbiology of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil.
| | - Alex Dias Camargo
- Laboratory of Computational Biology, Computational Sciences Center of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil
| | - Vinicius Rosa Seus
- Laboratory of Computational Biology, Computational Sciences Center of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil
| | - Andrea von Groll
- Research Center in Medical Microbiology of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil
| | - Adriano Velasque Werhli
- Laboratory of Computational Biology, Computational Sciences Center of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil
| | - Pedro Eduardo Almeida da Silva
- Research Center in Medical Microbiology of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil
| | - Karina Dos Santos Machado
- Laboratory of Computational Biology, Computational Sciences Center of the Universidade Federal do Rio Grande, Avenida Itlia, Km8, Rio Grande, RS, Brazil
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2
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Soni N, Madhusudhan M. Computational modeling of protein assemblies. Curr Opin Struct Biol 2017; 44:179-89. [DOI: 10.1016/j.sbi.2017.04.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
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3
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Abstract
Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.
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Affiliation(s)
- Roman A Laskowski
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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4
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Abstract
Temperature sensitive (Ts) mutants of proteins provide experimentalists with a powerful and reversible way of conditionally expressing genes. The technique has been widely used in determining the role of gene and gene products in several cellular processes. Traditionally, Ts mutants are generated by random mutagenesis and then selected though laborious large-scale screening. Our web server, TSpred (http://mspc.bii.a-star.edu.sg/TSpred/), now enables users to rationally design Ts mutants for their proteins of interest. TSpred uses hydrophobicity and hydrophobic moment, deduced from primary sequence and residue depth, inferred from 3D structures to predict/identify buried hydrophobic residues. Mutating these residues leads to the creation of Ts mutants. Our method has been experimentally validated in 36 positions in six different proteins. It is an attractive proposition for Ts mutant engineering as it proposes a small number of mutations and with high precision. The accompanying web server is simple and intuitive to use and can handle proteins and protein complexes of different sizes.
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Affiliation(s)
- Kuan Pern Tan
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671 School of Computer Engineering, Nanyang Technological University, Singapore 639798
| | - Shruti Khare
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
| | | | - Mallur Srivatsan Madhusudhan
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671 Indian Institute of Science Education and Research, Pune 411008, India Department of Biological Sciences, National University of Singapore, Singapore 117543
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Pieper U, Webb BM, Dong GQ, Schneidman-Duhovny D, Fan H, Kim SJ, Khuri N, Spill YG, Weinkam P, Hammel M, Tainer JA, Nilges M, Sali A. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 2013; 42:D336-46. [PMID: 24271400 PMCID: PMC3965011 DOI: 10.1093/nar/gkt1144] [Citation(s) in RCA: 216] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein–protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein–ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.
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Affiliation(s)
- Ursula Pieper
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, USA, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, USA, Graduate Group in Biophysics, University of California at San Francisco, CA 94158, USA, Structural Bioinformatics Unit, Structural Biology and Chemistry department, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France, Université Paris Diderot-Paris 7, école doctorale iViv, Paris Rive Gauche, 5 rue Thomas Mann, 75013 Paris, France, Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, Department of Molecular Biology, Skaggs Institute of Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA, Life Sciences Division, Department of Molecular Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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6
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Topham CM, Rouquier M, Tarrat N, André I. Adaptive Smith-Waterman residue match seeding for protein structural alignment. Proteins 2013; 81:1823-39. [DOI: 10.1002/prot.24327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2013] [Revised: 04/22/2013] [Accepted: 05/15/2013] [Indexed: 12/30/2022]
Affiliation(s)
- Christopher M. Topham
- Université de Toulouse, INSA, UPS, INP, LISBP; 135 Avenue de Rangueil F-31077 Toulouse France
- CNRS, UMR5504; F-31400 Toulouse France
- INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés; F-31400 Toulouse France
| | - Mickaël Rouquier
- Université de Toulouse, INSA, UPS, INP, LISBP; 135 Avenue de Rangueil F-31077 Toulouse France
- CNRS, UMR5504; F-31400 Toulouse France
- INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés; F-31400 Toulouse France
| | - Nathalie Tarrat
- Université de Toulouse, INSA, UPS, INP, LISBP; 135 Avenue de Rangueil F-31077 Toulouse France
- CNRS, UMR5504; F-31400 Toulouse France
- INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés; F-31400 Toulouse France
| | - Isabelle André
- Université de Toulouse, INSA, UPS, INP, LISBP; 135 Avenue de Rangueil F-31077 Toulouse France
- CNRS, UMR5504; F-31400 Toulouse France
- INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés; F-31400 Toulouse France
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Braberg H, Webb BM, Tjioe E, Pieper U, Sali A, Madhusudhan MS. SALIGN: a web server for alignment of multiple protein sequences and structures. Bioinformatics 2012; 28:2072-3. [PMID: 22618536 DOI: 10.1093/bioinformatics/bts302] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Accurate alignment of protein sequences and/or structures is crucial for many biological analyses, including functional annotation of proteins, classifying protein sequences into families, and comparative protein structure modeling. Described here is a web interface to SALIGN, the versatile protein multiple sequence/structure alignment module of MODELLER. The web server automatically determines the best alignment procedure based on the inputs, while allowing the user to override default parameter values. Multiple alignments are guided by a dendrogram computed from a matrix of all pairwise alignment scores. When aligning sequences to structures, SALIGN uses structural environment information to place gaps optimally. If two multiple sequence alignments of related proteins are input to the server, a profile-profile alignment is performed. All features of the server have been previously optimized for accuracy, especially in the contexts of comparative modeling and identification of interacting protein partners. AVAILABILITY The SALIGN web server is freely accessible to the academic community at http://salilab.org/salign. SALIGN is a module of the MODELLER software, also freely available to academic users (http://salilab.org/modeller). CONTACT sali@salilab.org; madhusudhan@bii.a-star.edu.sg.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
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8
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Abstract
Comparing and classifying the three-dimensional (3D) structures of proteins is of crucial importance to molecular biology, from helping to determine the function of a protein to determining its evolutionary relationships. Traditionally, 3D structures are classified into groups of families that closely resemble the grouping according to their primary sequence. However, significant structural similarities exist at multiple levels between proteins that belong to these different structural families. In this study, we propose a new algorithm, CLICK, to capture such similarities. The method optimally superimposes a pair of protein structures independent of topology. Amino acid residues are represented by the Cartesian coordinates of a representative point (usually the Cα atom), side chain solvent accessibility, and secondary structure. Structural comparison is effected by matching cliques of points. CLICK was extensively benchmarked for alignment accuracy on four different sets: (i) 9537 pair-wise alignments between two structures with the same topology; (ii) 64 alignments from set (i) that were considered to constitute difficult alignment cases; (iii) 199 pair-wise alignments between proteins with similar structure but different topology; and (iv) 1275 pair-wise alignments of RNA structures. The accuracy of CLICK alignments was measured by the average structure overlap score and compared with other alignment methods, including HOMSTRAD, MUSTANG, Geometric Hashing, SALIGN, DALI, GANGSTA+, FATCAT, ARTS and SARA. On average, CLICK produces pair-wise alignments that are either comparable or statistically significantly more accurate than all of these other methods. We have used CLICK to uncover relationships between (previously) unrelated proteins. These new biological insights include: (i) detecting hinge regions in proteins where domain or sub-domains show flexibility; (ii) discovering similar small molecule binding sites from proteins of different folds and (iii) discovering topological variants of known structural/sequence motifs. Our method can generally be applied to compare any pair of molecular structures represented in Cartesian coordinates as exemplified by the RNA structure superimposition benchmark.
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Affiliation(s)
- Minh N Nguyen
- Bioinformatics Institute, 30 Biopolis Street, #07-01 Matrix, Singapore 138671
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Saqib U, Siddiqi MI. Insights into structure and function of SHIP2-SH2: homology modeling, docking, and molecular dynamics study. J Chem Biol 2011; 4:149-58. [PMID: 22328908 DOI: 10.1007/s12154-011-0057-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2010] [Accepted: 01/27/2011] [Indexed: 01/18/2023] Open
Abstract
SRC homology 2 (SH2)-containing inositol 5'-phosphatase protein (SHIP2) is a potential target for type 2 diabetes. Its ability to dephosphorylate the lipid messenger phosphatidylinositol 3,4,5-trisphosphate [PtdIns(3,4,5)P3], important for insulin signaling, makes it an important target against type 2 diabetes. The insulin-induced SHIP2 interaction with Shc is very important for the membrane localization and functioning of SHIP2. There is a bidentate relationship between the two proteins where two domains each from SHIP2 and Shc are involved in mutual binding. However in the present study, the SHIP2-SH2 domain binding with the phosphorylated tyrosine 317 on the collagen-homology (CH) domain of Shc, has been studied due to the indispensability of this interaction in SHIP2 localization. In the absence of the crystal structure of SHIP2-SH2, its structural model was developed followed by tracking its molecular interactions with Shc through molecular docking and dynamics studies. This study revealed much about the structural interactions between the SHIP2-SH2 and Shc-CH. Finally, docking study of a nonpeptide inhibitor into the SHIP2-SH2 domain further confirmed the structural interactions involved in ligand binding and also proposed the inhibitor as a major starting point against SHIP2-SH2 inhibition. The insights gained from the current study should prove useful in the design of more potent inhibitors against type 2 diabetes.
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10
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Abstract
Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these there are a vast number of databases for the most specialized user, dealing with specific families, diseases, structural features and so on.
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11
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Pieper U, Webb BM, Barkan DT, Schneidman-Duhovny D, Schlessinger A, Braberg H, Yang Z, Meng EC, Pettersen EF, Huang CC, Datta RS, Sampathkumar P, Madhusudhan MS, Sjölander K, Ferrin TE, Burley SK, Sali A. ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 2010; 39:D465-74. [PMID: 21097780 PMCID: PMC3013688 DOI: 10.1093/nar/gkq1091] [Citation(s) in RCA: 256] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence–structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains 10 355 444 reliable models for domains in 2 421 920 unique protein sequences. ModBase allows users to update comparative models on demand, and request modeling of additional sequences through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are available through the ModBase interface as well as the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the SALIGN server for multiple sequence and structure alignment (http://salilab.org/salign), the ModEval server for predicting the accuracy of protein structure models (http://salilab.org/modeval), the PCSS server for predicting which peptides bind to a given protein (http://salilab.org/pcss) and the FoXS server for calculating and fitting Small Angle X-ray Scattering profiles (http://salilab.org/foxs).
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Affiliation(s)
- Ursula Pieper
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, CA 94158, USA
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Pierri CL, Parisi G, Porcelli V. Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening. Biochim Biophys Acta 2010; 1804:1695-712. [PMID: 20433957 DOI: 10.1016/j.bbapap.2010.04.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2010] [Revised: 03/04/2010] [Accepted: 04/14/2010] [Indexed: 12/12/2022]
Abstract
The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.
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Affiliation(s)
- Ciro Leonardo Pierri
- Department of Pharmaco-Biology, Laboratory of Biochemistry and Molecular Biology, University of Bari, Va E. Orabona, 4 - 70125 Bari, Italy.
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Abstract
Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds--particularly useful for newly solved structures, and especially those of unknown function. Beyond these there are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.
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Affiliation(s)
- Roman A Laskowski
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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14
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Abstract
Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.
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Affiliation(s)
- Hao Fan
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, San Francisco, California 94158, USA
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15
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Madhusudhan MS, Webb BM, Marti-Renom MA, Eswar N, Sali A. Alignment of multiple protein structures based on sequence and structure features. Protein Eng Des Sel 2009; 22:569-74. [PMID: 19587024 DOI: 10.1093/protein/gzp040] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Comparing the structures of proteins is crucial to gaining insight into protein evolution and function. Here, we align the sequences of multiple protein structures by a dynamic programming optimization of a scoring function that is a sum of an affine gap penalty and terms dependent on various sequence and structure features (SALIGN). The features include amino acid residue type, residue position, residue accessible surface area, residue secondary structure state and the conformation of a short segment centered on the residue. The multiple alignment is built by following the 'guide' tree constructed from the matrix of all pairwise protein alignment scores. Importantly, the method does not depend on the exact values of various parameters, such as feature weights and gap penalties, because the optimal alignment across a range of parameter values is found. Using multiple structure alignments in the HOMSTRAD database, SALIGN was benchmarked against MUSTANG for multiple alignments as well as against TM-align and CE for pairwise alignments. On the average, SALIGN produces a 15% improvement in structural overlap over HOMSTRAD and 14% over MUSTANG, and yields more equivalent structural positions than TM-align and CE in 90% and 95% of cases, respectively. The utility of accurate multiple structure alignment is illustrated by its application to comparative protein structure modeling.
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Affiliation(s)
- M S Madhusudhan
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158, USA
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16
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Abstract
Ligand virtual screening is a widely used tool to assist in new pharmaceutical
discovery. In practice, virtual screening approaches have a number of
limitations, and the development of new methodologies is required. Previously,
we showed that remotely related proteins identified by threading often share a
common binding site occupied by chemically similar ligands. Here, we demonstrate
that across an evolutionarily related, but distant family of proteins, the
ligands that bind to the common binding site contain a set of strongly conserved
anchor functional groups as well as a variable region that accounts for their
binding specificity. Furthermore, the sequence and structure conservation of
residues contacting the anchor functional groups is significantly higher than
those contacting ligand variable regions. Exploiting these insights, we
developed FINDSITELHM that employs structural information extracted
from weakly related proteins to perform rapid ligand docking by homology
modeling. In large scale benchmarking, using the predicted anchor-binding mode
and the crystal structure of the receptor, FINDSITELHM outperforms
classical docking approaches with an average ligand RMSD from native of
∼2.5 Å. For weakly homologous receptor protein models, using
FINDSITELHM, the fraction of recovered binding residues and
specific contacts is 0.66 (0.55) and 0.49 (0.38) for highly confident (all)
targets, respectively. Finally, in virtual screening for HIV-1 protease
inhibitors, using similarity to the ligand anchor region yields significantly
improved enrichment factors. Thus, the rather accurate, computationally
inexpensive FINDSITELHM algorithm should be a useful approach to
assist in the discovery of novel biopharmaceuticals. As an integral part of drug development, high-throughput virtual screening is a
widely used tool that could in principle significantly reduce the cost and time
to discovery of new pharmaceuticals. In practice, virtual screening algorithms
suffer from a number of limitations. The high sensitivity of all-atom ligand
docking approaches to the quality of the target receptor structure restricts the
selection of drug targets to those for which high-quality X-ray structures are
available. Furthermore, the predicted binding affinity is typically strongly
correlated with the molecular weight of the ligand, independent of whether or
not it really binds. To address these significant problems, we developed
FINDSITELHM, a novel threading-based approach that employs
structural information extracted from weakly related proteins to perform rapid
ligand docking and ranking that is very much in the spirit of homology modeling
of protein structures. Particularly for low-quality modeled receptor structures,
FINDSITELHM outperforms classical all-atom ligand docking
approaches in terms of the accuracy of ligand binding pose prediction and
requires considerably less CPU time. As an attractive alternative to classical
molecular docking, FINDSITELHM offers the possibility of rapid
structure-based virtual screening at the proteome level to improve and speed up
the discovery of new biopharmaceuticals.
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Affiliation(s)
- Michal Brylinski
- Center for the Study of Systems Biology, School of Biology, Georgia
Institute of Technology, Atlanta, Georgia, United States of America
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biology, Georgia
Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
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17
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Abstract
The COPS (Classification Of Protein Structures) web server provides access to the complete repertoire of known protein structures and protein structural domains. The COPS classification encodes pairwise structural similarities as quantified metric relationships. The resulting metrical structure is mapped to a hierarchical tree, which is largely equivalent to the structure of a file browser. Exploiting this relationship we implemented the Fold Space Navigator, a tool that makes navigation in fold space as convenient as browsing through a file system. Moreover, pairwise structural similarities among the domains can be visualized and inspected instantaneously. COPS is updated weekly and stays concurrent with the PDB repository. The server also exposes the COPS classification pipeline. Newly determined structures uploaded to the server are chopped into domains, the locations of the new domains in the classification tree are determined, and their neighborhood can be immediately explored through the Fold Space Navigator. The COPS web server is accessible at http://cops.services.came.sbg.ac.at/.
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Affiliation(s)
- Stefan J Suhrer
- Center of Applied Molecular Engineering, Division of Bioinformatics, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria
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Matavel A, Fleury C, Oliveira LC, Molina F, de Lima ME, Cruz JS, Cordeiro MN, Richardson M, Ramos CHI, Beirão PSL. Structure and activity analysis of two spider toxins that alter sodium channel inactivation kinetics. Biochemistry 2009; 48:3078-88. [PMID: 19231838 DOI: 10.1021/bi802158p] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this work, Phoneutria nigriventer toxins PnTx2-5 and PnTx2-6 were shown to markedly delay the fast inactivation kinetics of neuronal-type sodium channels. Furthermore, our data show that they have significant differences in their interaction with the channel. PnTx2-6 has an affinity 6 times higher than that of PnTx2-5, and its effects are not reversible within 10-15 min of washing. PnTx2-6 partially (59%) competes with the scorpion alpha-toxin AaHII, but not with the scorpion beta-toxin CssIV, thus suggesting a mode of action similar to that of site 3 toxins. However, PnTx2-6 is not removed by strong depolarizing pulses, as in the known site 3 toxins. We have also established the correct PnTx2-5 amino acid sequence and confirmed the sequence of PnTx2-6, in both cases establishing that the cysteines are in their oxidized form. A structural model of each toxin is proposed. They show structures with poor alpha-helix content. The model is supported by experimental and theoretical tests. A likely binding region on PnTx2-5 and PnTx2-6 is proposed on the basis of their different affinities and sequence differences.
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Affiliation(s)
- Alessandra Matavel
- Department of Biochemistry and Immunology, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
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Ortí L, Carbajo RJ, Pieper U, Eswar N, Maurer SM, Rai AK, Taylor G, Todd MH, Pineda-Lucena A, Sali A, Marti-Renom MA. A kernel for open source drug discovery in tropical diseases. PLoS Negl Trop Dis 2009; 3:e418. [PMID: 19381286 PMCID: PMC2667270 DOI: 10.1371/journal.pntd.0000418] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2008] [Accepted: 03/23/2009] [Indexed: 01/28/2023] Open
Abstract
Background Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private–public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues—open source drug discovery—has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such “kernels”. Methodology/Principal Findings Here, we use a computational pipeline for: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. Conclusions/Significance The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases. Open source drug discovery, a promising alternative avenue to conventional patent-based drug development, has so far remained elusive with few exceptions. A major stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. This paper introduces the results from a newly assembled computational pipeline for identifying protein targets for drug discovery in ten organisms that cause tropical diseases. We have also experimentally tested two promising targets for their binding to commercially available drugs, validating one and invalidating the other. The resulting kernel provides a base of drug targets and lead candidates around which an open source community can nucleate. We invite readers to donate their judgment and in silico and in vitro experiments to develop these targets to the point where drug optimization can begin.
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Affiliation(s)
- Leticia Ortí
- Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain
- Structural Biology Laboratory, Medicinal Chemistry Department, Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Rodrigo J. Carbajo
- Structural Biology Laboratory, Medicinal Chemistry Department, Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Ursula Pieper
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, United States of America
| | - Narayanan Eswar
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, United States of America
| | - Stephen M. Maurer
- Gould School of Law, University of Southern California, Los Angeles, California, United States of America
| | - Arti K. Rai
- School of Law, Duke University, Durham, North Carolina, United States of America
| | - Ginger Taylor
- The Synaptic Leap, San Ramon, California, United States of America
| | - Matthew H. Todd
- School of Chemistry, University of Sydney, Sydney, New South Wales, Australia
| | - Antonio Pineda-Lucena
- Structural Biology Laboratory, Medicinal Chemistry Department, Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (AS); (MAM-R)
| | - Marc A. Marti-Renom
- Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain
- * E-mail: (AS); (MAM-R)
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Pieper U, Eswar N, Webb BM, Eramian D, Kelly L, Barkan DT, Carter H, Mankoo P, Karchin R, Marti-Renom MA, Davis FP, Sali A. MODBASE, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 2009; 37:D347-54. [PMID: 18948282 PMCID: PMC2686492 DOI: 10.1093/nar/gkn791] [Citation(s) in RCA: 132] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2008] [Accepted: 10/08/2008] [Indexed: 11/14/2022] Open
Abstract
MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).
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Affiliation(s)
- Ursula Pieper
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Narayanan Eswar
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Ben M. Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - David Eramian
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Libusha Kelly
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - David T. Barkan
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Hannah Carter
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Parminder Mankoo
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Rachel Karchin
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Marc A. Marti-Renom
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Fred P. Davis
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, Graduate Group in Biophysics, Graduate Group in Bioinformatics, University of California at San Francisco, CA, Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA, Structural Genomics Unit, Bioinformatics & Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Avda. Autopista del Saler 16, Valencia 46012, Spain and Howard Hughes Medical Institute, Janelia Farm, 19700 Helix Drive, Ashburn, VA 20147, USA
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Chiang RA, Sali A, Babbitt PC. Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies. PLoS Comput Biol 2008; 4:e1000142. [PMID: 18670595 PMCID: PMC2453236 DOI: 10.1371/journal.pcbi.1000142] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2007] [Accepted: 06/24/2008] [Indexed: 11/19/2022] Open
Abstract
The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies.
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Affiliation(s)
- Ranyee A. Chiang
- Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, United States of America
| | - Andrej Sali
- Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, United States of America
| | - Patricia C. Babbitt
- Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, United States of America
- * E-mail:
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Topf M, Lasker K, Webb B, Wolfson H, Chiu W, Sali A. Protein structure fitting and refinement guided by cryo-EM density. Structure 2008; 16:295-307. [PMID: 18275820 PMCID: PMC2409374 DOI: 10.1016/j.str.2007.11.016] [Citation(s) in RCA: 263] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2007] [Revised: 11/20/2007] [Accepted: 11/26/2007] [Indexed: 11/23/2022]
Abstract
For many macromolecular assemblies, both a cryo-electron microscopy map and atomic structures of its component proteins are available. Here we describe a method for fitting and refining a component structure within its map at intermediate resolution (<15 A). The atomic positions are optimized with respect to a scoring function that includes the crosscorrelation coefficient between the structure and the map as well as stereochemical and nonbonded interaction terms. A heuristic optimization that relies on a Monte Carlo search, a conjugate-gradients minimization, and simulated annealing molecular dynamics is applied to a series of subdivisions of the structure into progressively smaller rigid bodies. The method was tested on 15 proteins of known structure with 13 simulated maps and 3 experimentally determined maps. At approximately 10 A resolution, Calpha rmsd between the initial and final structures was reduced on average by approximately 53%. The method is automated and can refine both experimental and predicted atomic structures.
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Affiliation(s)
- Maya Topf
- School of Crystallography, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom.
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