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Sivadas A, Rathore S, Sahana S, Jolly B, Bhoyar RC, Jain A, Sharma D, Imran M, Senthilvel V, Divakar MK, Mishra A, Sivasubbu S, Scaria V. The genomic landscape of CYP2D6 variation in the Indian population. Pharmacogenomics 2024; 25:147-160. [PMID: 38426301 DOI: 10.2217/pgs-2023-0233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Aim: The CYP2D6 gene is highly polymorphic, causing large interindividual variability in the metabolism of several clinically important drugs. Materials & methods: The authors investigated the diversity and distribution of CYP2D6 alleles in Indians using whole genome sequences (N = 1518). Functional consequences were assessed using pathogenicity scores and molecular dynamics simulations. Results: The analysis revealed population-specific CYP2D6 alleles (*86, *7, *111, *112, *113, *99) and remarkable differences in variant and phenotype frequencies with global populations. The authors observed that one in three Indians could benefit from a dose alteration for psychiatric drugs with accurate CYP2D6 phenotyping. Molecular dynamics simulations revealed large conformational fluctuations, confirming the predicted reduced function of *86 and *113 alleles. Conclusion: The findings emphasize the utility of comprehensive CYP2D6 profiling for aiding precision public health.
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Affiliation(s)
- Ambily Sivadas
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, Karnataka, 560034, India
| | - Surabhi Rathore
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - S Sahana
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Bani Jolly
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Rahul C Bhoyar
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
| | - Abhinav Jain
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Disha Sharma
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
| | - Mohamed Imran
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Vigneshwar Senthilvel
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Mohit Kumar Divakar
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Anushree Mishra
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
| | - Sridhar Sivasubbu
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
- Vishwanath Cancer Care Foundation, B 702, 7th Floor, Neelkanth Business Park Kirol Village, Vidya Vihar, West Mumbai, 400086, India
| | - Vinod Scaria
- CSIR Institute of Genomics & Integrative Biology, Mathura Road, New Delhi, 110025, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India
- Vishwanath Cancer Care Foundation, B 702, 7th Floor, Neelkanth Business Park Kirol Village, Vidya Vihar, West Mumbai, 400086, India
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Saih A, Bouqdayr M, Baba H, Hamdi S, Moussamih S, Bennani H, Saile R, Wakrim L, Kettani A. Computational Analysis of Missense Variants in the Human Transmembrane Protease Serine 2 ( TMPRSS2) and SARS-CoV-2. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9982729. [PMID: 34692848 PMCID: PMC8531787 DOI: 10.1155/2021/9982729] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/06/2021] [Accepted: 09/11/2021] [Indexed: 01/08/2023]
Abstract
The human transmembrane protease serine 2 (TMPRSS2) protein plays an important role in prostate cancer progression. It also facilitates viral entry into target cells by proteolytically cleaving and activating the S protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the current study, we used different available tools like SIFT, PolyPhen2.0, PROVEAN, SNAP2, PMut, MutPred2, I-Mutant Suite, MUpro, iStable, ConSurf, ModPred, SwissModel, PROCHECK, Verify3D, and TM-align to identify the most deleterious variants and to explore possible effects on the TMPRSS2 stability, structure, and function. The six missense variants tested were evaluated to have deleterious effects on the protein by SIFT, PolyPhen2.0, PROVEAN, SNAP2, and PMut. Additionally, V160M, G181R, R240C, P335L, G432A, and D435Y variants showed a decrease in stability by at least 2 servers; G181R, G432A, and D435Y are highly conserved and identified posttranslational modifications sites (PTMs) for proteolytic cleavage and ADP-ribosylation using ConSurf and ModPred servers. The 3D structure of TMPRSS2 native and mutants was generated using 7 meq as a template from the SwissModeller group, refined by ModRefiner, and validated using the Ramachandran plot. Hence, this paper can be advantageous to understand the association between these missense variants rs12329760, rs781089181, rs762108701, rs1185182900, rs570454392, and rs867186402 and susceptibility to SARS-CoV-2.
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Affiliation(s)
- Asmae Saih
- Virology Unit, Immunovirology Laboratory, Institut Pasteur du Maroc, 20360 Casablanca, Morocco
- Laboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik Hassan II University of Casablanca, Morocco
| | - Meryem Bouqdayr
- Virology Unit, Immunovirology Laboratory, Institut Pasteur du Maroc, 20360 Casablanca, Morocco
- Laboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik Hassan II University of Casablanca, Morocco
| | - Hanâ Baba
- Virology Unit, Immunovirology Laboratory, Institut Pasteur du Maroc, 20360 Casablanca, Morocco
- Laboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik Hassan II University of Casablanca, Morocco
| | - Salsabil Hamdi
- Environmental Health Laboratory, Institut Pasteur du Maroc, 20360 Casablanca, Morocco
| | - Samya Moussamih
- Immunology and Biodiversity Laboratory, Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Morocco
| | - Houda Bennani
- Laboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik Hassan II University of Casablanca, Morocco
| | - Rachid Saile
- Laboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik Hassan II University of Casablanca, Morocco
| | - Lahcen Wakrim
- Virology Unit, Immunovirology Laboratory, Institut Pasteur du Maroc, 20360 Casablanca, Morocco
| | - Anass Kettani
- Laboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik Hassan II University of Casablanca, Morocco
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Pushpavanam K, Hellner B, Baneyx F. Interrogating biomineralization one amino acid at a time: amplification of mutational effects in protein-aided titania morphogenesis through reaction-diffusion control. Chem Commun (Camb) 2021; 57:4803-4806. [PMID: 33982711 DOI: 10.1039/d1cc01521d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
To emulate the control that biomineralizing organisms exert over reactant transport, we construct a countercurrent reaction-diffusion chamber in which an agarose hydrogel regulates the fluxes of inorganic precursor and precipitating solid-binding protein. We show that the morphology of the bioprecipitated titania can be changed from monolithic to interconnected particle networks and dispersed nanoparticles either by decreasing reaction time or by increasing agarose weight percentage at constant precursor and protein concentrations. More strikingly, protein variants with one or two substitutions in their metal oxide-binding domain yield unique peripheral morphologies (needles, threads, plates, and peapods) with distinct crystallography and photocatalytic activity. Our results suggest that diffusional control can magnify otherwise subtle mutational effects in biomineralizing proteins and provide a path for the green synthesis of morphologically and functionally diverse inorganic materials.
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Affiliation(s)
- Karthik Pushpavanam
- Department of Chemical Engineering, University of Washington, Box 351750, Seattle, WA, USA.
| | - Brittney Hellner
- Department of Chemical Engineering, University of Washington, Box 351750, Seattle, WA, USA.
| | - François Baneyx
- Department of Chemical Engineering, University of Washington, Box 351750, Seattle, WA, USA.
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Characterization of SdGA, a cold-adapted glucoamylase from Saccharophagus degradans. ACTA ACUST UNITED AC 2021; 30:e00625. [PMID: 34041001 PMCID: PMC8141877 DOI: 10.1016/j.btre.2021.e00625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 04/24/2021] [Accepted: 04/28/2021] [Indexed: 11/24/2022]
Abstract
We investigated the structural and functional properties of SdGA, a glucoamylase (GA) from Saccharophagus degradans, a marine bacterium which degrades different complex polysaccharides at high rate. SdGA is composed mainly by a N-terminal GH15_N domain linked to a C-terminal catalytic domain (CD) found in the GH15 family of glycosylhydrolases with an overall structure similar to other bacterial GAs. The protein was expressed in Escherichia coli cells, purified and its biochemical properties were investigated. Although SdGA has a maximum activity at 39 °C and pH 6.0, it also shows high activity in a wide range, from low to mild temperatures, like cold-adapted enzymes. Furthermore, SdGA has a higher content of flexible residues and a larger CD due to various amino acid insertions compared to other thermostable GAs. We propose that this novel SdGA, is a cold-adapted enzyme that might be suitable for use in different industrial processes that require enzymes which act at low or medium temperatures.
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Veevers R, Cawley G, Hayward S. Investigation of sequence features of hinge-bending regions in proteins with domain movements using kernel logistic regression. BMC Bioinformatics 2020; 21:137. [PMID: 32272894 PMCID: PMC7147021 DOI: 10.1186/s12859-020-3464-3] [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: 12/19/2019] [Accepted: 03/20/2020] [Indexed: 11/12/2022] Open
Abstract
Background Hinge-bending movements in proteins comprising two or more domains form a large class of functional movements. Hinge-bending regions demarcate protein domains and collectively control the domain movement. Consequently, the ability to recognise sequence features of hinge-bending regions and to be able to predict them from sequence alone would benefit various areas of protein research. For example, an understanding of how the sequence features of these regions relate to dynamic properties in multi-domain proteins would aid in the rational design of linkers in therapeutic fusion proteins. Results The DynDom database of protein domain movements comprises sequences annotated to indicate whether the amino acid residue is located within a hinge-bending region or within an intradomain region. Using statistical methods and Kernel Logistic Regression (KLR) models, this data was used to determine sequence features that favour or disfavour hinge-bending regions. This is a difficult classification problem as the number of negative cases (intradomain residues) is much larger than the number of positive cases (hinge residues). The statistical methods and the KLR models both show that cysteine has the lowest propensity for hinge-bending regions and proline has the highest, even though it is the most rigid amino acid. As hinge-bending regions have been previously shown to occur frequently at the terminal regions of the secondary structures, the propensity for proline at these regions is likely due to its tendency to break secondary structures. The KLR models also indicate that isoleucine may act as a domain-capping residue. We have found that a quadratic KLR model outperforms a linear KLR model and that improvement in performance occurs up to very long window lengths (eighty residues) indicating long-range correlations. Conclusion In contrast to the only other approach that focused solely on interdomain hinge-bending regions, the method provides a modest and statistically significant improvement over a random classifier. An explanation of the KLR results is that in the prediction of hinge-bending regions a long-range correlation is at play between a small number amino acids that either favour or disfavour hinge-bending regions. The resulting sequence-based prediction tool, HingeSeek, is available to run through a webserver at hingeseek.cmp.uea.ac.uk.
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Affiliation(s)
- Ruth Veevers
- Computational Biology Laboratory, School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
| | - Gavin Cawley
- Computational Biology Laboratory, School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
| | - Steven Hayward
- Computational Biology Laboratory, School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
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Gyulkhandanyan A, Rezaie AR, Roumenina L, Lagarde N, Fremeaux-Bacchi V, Miteva MA, Villoutreix BO. Analysis of protein missense alterations by combining sequence- and structure-based methods. Mol Genet Genomic Med 2020; 8:e1166. [PMID: 32096919 PMCID: PMC7196459 DOI: 10.1002/mgg3.1166] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play. METHODS Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB. RESULTS We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions. CONCLUSION Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.
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Affiliation(s)
- Aram Gyulkhandanyan
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Laboratory SABNP, University of Evry, INSERM U1204, Université Paris-Saclay, Evry, France
| | - Alireza R Rezaie
- Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Lubka Roumenina
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France
- Sorbonne Universités, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Nathalie Lagarde
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Laboratoire GBCM, EA7528, Conservatoire national des arts et métiers, Hesam Université, Paris, France
| | - Veronique Fremeaux-Bacchi
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France
- Sorbonne Universités, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
- Assistance Publique-Hôpitaux de Paris, Service d'Immunologie Biologique, Hôpital Européen Georges Pompidou, Paris, France
| | - Maria A Miteva
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Inserm U1268 MCTR, CNRS UMR 8038 CiTCoM, Faculté de Pharmacie de Paris, Univ. De Paris, Paris, France
| | - Bruno O Villoutreix
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- INSERM, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, Université de Lille, Lille, France
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Narwani TJ, Etchebest C, Craveur P, Léonard S, Rebehmed J, Srinivasan N, Bornot A, Gelly JC, de Brevern AG. In silico prediction of protein flexibility with local structure approach. Biochimie 2019; 165:150-155. [PMID: 31377194 DOI: 10.1016/j.biochi.2019.07.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 07/26/2019] [Indexed: 12/30/2022]
Abstract
Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein's inner flexibility and predicts them as rigid or flexible. PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. It also uses a specific representation of protein structures, named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix, the 120 LSPs are predicted with a good accuracy and directly used to predict (i) the protein flexibility in three categories (flexible, intermediate and rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a confidence index. Prediction accuracy among these three classes is equivalent to the best two class prediction methods, while the normalized B-factors and normalized RMSFs have a good correlation with experimental and in silico values. Thus, PredyFlexy is a unique approach, which is of major utility for the scientific community. It support parallelization features and can be run on a local cluster using multiple cores.
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Affiliation(s)
- Tarun J Narwani
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Catherine Etchebest
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Pierrick Craveur
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Sylvain Léonard
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Joseph Rebehmed
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Department of Computer Science and Mathematics, Lebanese American University, Byblos 1h401 2010, Lebanon
| | | | - Aurélie Bornot
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Jean-Christophe Gelly
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
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Wu X, Fraser K, Zha J, Dordick JS. Flexible Peptide Linkers Enhance the Antimicrobial Activity of Surface-Immobilized Bacteriolytic Enzymes. ACS APPLIED MATERIALS & INTERFACES 2018; 10:36746-36756. [PMID: 30281274 DOI: 10.1021/acsami.8b14411] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Chemical linkers are frequently used in enzyme immobilization to improve enzyme flexibility and activity, whereas peptide linkers, although ubiquitous in protein engineering, are much less explored in enzyme immobilization. Here, we report peptide-linker-assisted noncovalent immobilization of the bacteriolytic enzyme lysostaphin (Lst) to generate anti- Staphylococcus aureus surfaces. Lst was immobilized through affinity tags onto a silica surface (glass slides) and nickel nitrilotriacetic acid (NiNTA) agarose beads via silica-binding peptides (SiBPs) or a hexahistidine tag (His-tag) fused at the C-terminus of Lst, respectively. By inserting specific peptide linkers upstream of the SiBP or His-tag, the immobilized enzymes killed >99.5% of S. aureus ATCC 6538 cells (108 CFU/mL) within 3 h in buffer and could be reused multiple times without significant loss of activity. In contrast, immobilized Lst without a peptide linker was less active/stable. Molecular modeling of Lst-linker-affinity tag constructs illustrated that the presence of the peptide linkers enhanced the molecular flexibility of the proximal Lst binding domain, which interacts with the bacterial substrate, and such increased flexibility correlated with increased antimicrobial activity. We further show that Lst immobilized onto NiNTA beads retained the ability to kill ∼99% of a 108 CFU/mL microbial challenge even in the presence of 1% of a commercial anionic surfactant, C12-14 alcohol EO 3:1 sodium sulfate, when the Lst construct contained a decapeptide linker containing glycine, serine, and alanine residues. This linker-assisted immobilization strategy could be extended to an unrelated lytic enzyme, the endolysin PlyPH, to target Bacillus anthracis Sterne cells either in buffer or in the presence of anionic surfactants. Our approach, therefore, provides a facile route to the use of antimicrobial enzymes on surfaces.
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Khalid Z, Sezerman OU. Prediction of HIV Drug Resistance by Combining Sequence and Structural Properties. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:966-973. [PMID: 27992346 DOI: 10.1109/tcbb.2016.2638821] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Drug resistance is a major obstacle faced by therapist in treating HIV infected patients. The reason behind these phenomena is either protein mutation or the changes in gene expression level that induces resistance to drug treatments. These mutations affect the drug binding activity, hence resulting in failure of treatment. Therefore, it is necessary to conduct resistance testing in order to carry out HIV effective therapy. This study combines both sequence and structural features for predicting HIV resistance by applying SVM and Random Forests classifiers. The model was tested on the mutants of HIV-1 protease and reverse transcriptase. Taken together the features we have used in our method, total contact energies among multiple mutations have a strong impact in predicting resistance as they are crucial in understanding the interactions of HIV mutants. The combination of sequence-structure features offers high accuracy with support vector machines as compared to Random Forests classifier. Both single and acquisition of multiple mutations are important in predicting HIV resistance to certain drug treatments. We have discovered the practicality of these features; hence, these can be used in the future to predict resistance for other complex diseases.
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Meng F, Kurgan L. DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 2017; 32:i341-i350. [PMID: 27307636 PMCID: PMC4908364 DOI: 10.1093/bioinformatics/btw280] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Motivation: Disordered flexible linkers (DFLs) are disordered regions that serve as flexible linkers/spacers in multi-domain proteins or between structured constituents in domains. They are different from flexible linkers/residues because they are disordered and longer. Availability of experimentally annotated DFLs provides an opportunity to build high-throughput computational predictors of these regions from protein sequences. To date, there are no computational methods that directly predict DFLs and they can be found only indirectly by filtering predicted flexible residues with predictions of disorder. Results: We conceptualized, developed and empirically assessed a first-of-its-kind sequence-based predictor of DFLs, DFLpred. This method outputs propensity to form DFLs for each residue in the input sequence. DFLpred uses a small set of empirically selected features that quantify propensities to form certain secondary structures, disordered regions and structured regions, which are processed by a fast linear model. Our high-throughput predictor can be used on the whole-proteome scale; it needs <1 h to predict entire proteome on a single CPU. When assessed on an independent test dataset with low sequence-identity proteins, it secures area under the receiver operating characteristic curve equal 0.715 and outperforms existing alternatives that include methods for the prediction of flexible linkers, flexible residues, intrinsically disordered residues and various combinations of these methods. Prediction on the complete human proteome reveals that about 10% of proteins have a large content of over 30% DFL residues. We also estimate that about 6000 DFL regions are long with ≥30 consecutive residues. Availability and implementation:http://biomine.ece.ualberta.ca/DFLpred/. Contact:lkurgan@vcu.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada
| | - Lukasz Kurgan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada Department of Computer Science, Virginia Commonwealth University, Richmond, 23284, U.S.A
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Yi G, Ybe JA, Saha SS, Caviness G, Raymond E, Ganesan R, Mbow ML, Kao CC. Structural and Functional Attributes of the Interleukin-36 Receptor. J Biol Chem 2016; 291:16597-609. [PMID: 27307043 DOI: 10.1074/jbc.m116.723064] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Indexed: 12/22/2022] Open
Abstract
Signal transduction by the IL-36 receptor (IL-36R) is linked to several human diseases. However, the structure and function of the IL-36R is not well understood. A molecular model of the IL-36R complex was generated and a cell-based reporter assay was established to assess the signal transduction of recombinant subunits of the IL-36R. Mutational analyses and functional assays have identified residues of the receptor subunit IL-1Rrp2 needed for cytokine recognition, stable protein expression, disulfide bond formation and glycosylation that are critical for signal transduction. We also observed that, overexpression of ectodomain (ECD) of Il-1Rrp2 or IL-1RAcP exhibited dominant-negative effect on IL-36R signaling. The presence of IL-36 cytokine significantly increased the interaction of IL-1Rrp2 ECD with the co-receptor IL-1RAcP. Finally, we found that single nucleotide polymorphism A471T in the Toll-interleukin 1 receptor domain (TIR) of the IL-1Rrp2 that is present in ∼2% of the human population, down-regulated IL-36R signaling by a decrease of interaction with IL-1RAcP.
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Affiliation(s)
- Guanghui Yi
- From the Departments of Molecular and Cellular Biochemistry and
| | - Joel A Ybe
- Environmental Health, School of Public Health, Indiana University, Bloomington, Indiana 47405 and
| | | | - Gary Caviness
- Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut 06877
| | - Ernest Raymond
- Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut 06877
| | - Rajkumar Ganesan
- Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut 06877
| | - M Lamine Mbow
- Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut 06877
| | - C Cheng Kao
- From the Departments of Molecular and Cellular Biochemistry and
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12
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Yenenler A, Sezerman OU. Design and characterizations of two novel cellulases through single-gene shuffling of Cel12A (EG3) gene fromTrichoderma reseei. Protein Eng Des Sel 2016; 29:219-229. [DOI: 10.1093/protein/gzw011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 03/24/2016] [Indexed: 11/14/2022] Open
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13
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Palmitoylation controls DLK localization, interactions and activity to ensure effective axonal injury signaling. Proc Natl Acad Sci U S A 2015; 113:763-8. [PMID: 26719418 DOI: 10.1073/pnas.1514123113] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Dual leucine-zipper kinase (DLK) is critical for axon-to-soma retrograde signaling following nerve injury. However, it is unknown how DLK, a predicted soluble kinase, conveys long-distance signals and why homologous kinases cannot compensate for loss of DLK. Here, we report that DLK, but not homologous kinases, is palmitoylated at a conserved site adjacent to its kinase domain. Using short-hairpin RNA knockdown/rescue, we find that palmitoylation is critical for DLK-dependent retrograde signaling in sensory axons. This functional importance is because of three novel cellular and molecular roles of palmitoylation, which targets DLK to trafficking vesicles, is required to assemble DLK signaling complexes and, unexpectedly, is essential for DLK's kinase activity. By simultaneously controlling DLK localization, interactions, and activity, palmitoylation ensures that only vesicle-bound DLK is active in neurons. These findings explain how DLK specifically mediates nerve injury responses and reveal a novel cellular mechanism that ensures the specificity of neuronal kinase signaling.
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Abstract
BACKGROUND Neddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimer's and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features. RESULTS We have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder. Using 21 amino acid long lysine-centred windows, our model was able to predict neddylation sites successfully, with an average 5-fold stratified cross validation performance of 0.91, 0.91, 0.75, 0.44, 0.95 for accuracy, specificity, sensitivity, Matthew's correlation coefficient and area under curve, respectively. Independent test set results validated the robustness of reported new method. Additionally, we observed that neddylation sites are commonly flexible and there is a significant positively charged amino acid presence in neddylation sites. CONCLUSIONS In this study, a neddylation site prediction method was developed for the first time in literature. Common characteristics of neddylation sites and their discriminative properties were explored for further in silico studies on neddylation. Lastly, up-to-date neddylation dataset was provided for researchers working on post-translational modifications in the accompanying supplementary material of this article.
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15
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Craveur P, Joseph AP, Esque J, Narwani TJ, Noël F, Shinada N, Goguet M, Leonard S, Poulain P, Bertrand O, Faure G, Rebehmed J, Ghozlane A, Swapna LS, Bhaskara RM, Barnoud J, Téletchéa S, Jallu V, Cerny J, Schneider B, Etchebest C, Srinivasan N, Gelly JC, de Brevern AG. Protein flexibility in the light of structural alphabets. Front Mol Biosci 2015; 2:20. [PMID: 26075209 PMCID: PMC4445325 DOI: 10.3389/fmolb.2015.00020] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 04/30/2015] [Indexed: 01/01/2023] Open
Abstract
Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.
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Affiliation(s)
- Pierrick Craveur
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Agnel P Joseph
- Rutherford Appleton Laboratory, Science and Technology Facilities Council Didcot, UK
| | - Jeremy Esque
- Institut National de la Santé et de la Recherche Médicale U964,7 UMR Centre National de la Recherche Scientifique 7104, IGBMC, Université de Strasbourg Illkirch, France
| | - Tarun J Narwani
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Floriane Noël
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Nicolas Shinada
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Matthieu Goguet
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Sylvain Leonard
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Pierre Poulain
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Ets Poulain Pointe-Noire, Congo
| | - Olivier Bertrand
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Guilhem Faure
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health Bethesda, MD, USA
| | - Joseph Rebehmed
- Centre National de la Recherche Scientifique UMR7590, Sorbonne Universités, Université Pierre et Marie Curie - MNHN - IRD - IUC Paris, France
| | | | - Lakshmipuram S Swapna
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Hospital for Sick Children, and Departments of Biochemistry and Molecular Genetics, University of Toronto Toronto, ON, Canada
| | - Ramachandra M Bhaskara
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Department of Theoretical Biophysics, Max Planck Institute of Biophysics Frankfurt, Germany
| | - Jonathan Barnoud
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Laboratoire de Physique, École Normale Supérieure de Lyon, Université de Lyon, Centre National de la Recherche Scientifique UMR 5672 Lyon, France
| | - Stéphane Téletchéa
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Faculté des Sciences et Techniques, Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines, Centre National de la Recherche Scientifique UMR 6286, Université Nantes Nantes, France
| | - Vincent Jallu
- Platelet Unit, Institut National de la Transfusion Sanguine Paris, France
| | - Jiri Cerny
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Bohdan Schneider
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Catherine Etchebest
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | | | - Jean-Christophe Gelly
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Alexandre G de Brevern
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
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Yavuz AS, Sezerman OU. Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder. BMC Genomics 2014; 15 Suppl 9:S18. [PMID: 25521314 PMCID: PMC4290605 DOI: 10.1186/1471-2164-15-s9-s18] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. RESULTS In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. CONCLUSIONS By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.
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Preeprem T, Gibson G. SDS, a structural disruption score for assessment of missense variant deleteriousness. Front Genet 2014; 5:82. [PMID: 24795746 PMCID: PMC4001065 DOI: 10.3389/fgene.2014.00082] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2014] [Accepted: 03/26/2014] [Indexed: 11/17/2022] Open
Abstract
We have developed a novel structure-based evaluation for missense variants that explicitly models protein structure and amino acid properties to predict the likelihood that a variant disrupts protein function. A structural disruption score (SDS) is introduced as a measure to depict the likelihood that a case variant is functional. The score is constructed using characteristics that distinguish between causal and neutral variants within a group of proteins. The SDS score is correlated with standard sequence-based deleteriousness, but shows promise for improving discrimination between neutral and causal variants at less conserved sites. The prediction was performed on 3-dimentional structures of 57 gene products whose homozygous SNPs were identified as case-exclusive variants in an exome sequencing study of epilepsy disorders. We contrasted the candidate epilepsy variants with scores for likely benign variants found in the EVS database, and for positive control variants in the same genes that are suspected to promote a range of diseases. To derive a characteristic profile of damaging SNPs, we transformed continuous scores into categorical variables based on the score distribution of each measurement, collected from all possible SNPs in this protein set, where extreme measures were assumed to be deleterious. A second epilepsy dataset was used to replicate the findings. Causal variants tend to receive higher sequence-based deleterious scores, induce larger physico-chemical changes between amino acid pairs, locate in protein domains, buried sites or on conserved protein surface clusters, and cause protein destabilization, relative to negative controls. These measures were agglomerated for each variant. A list of nine high-priority putative functional variants for epilepsy was generated. Our newly developed SDS protocol facilitates SNP prioritization for experimental validation.
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Affiliation(s)
| | - Greg Gibson
- School of Biology, Georgia Institute of Technology Atlanta, GA, USA
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18
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Preeprem T, Gibson G. An association-adjusted consensus deleterious scheme to classify homozygous Mis-sense mutations for personal genome interpretation. BioData Min 2013; 6:24. [PMID: 24365473 PMCID: PMC3892026 DOI: 10.1186/1756-0381-6-24] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 12/17/2013] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Personal genome analysis is now being considered for evaluation of disease risk in healthy individuals, utilizing both rare and common variants. Multiple scores have been developed to predict the deleteriousness of amino acid substitutions, using information on the allele frequencies, level of evolutionary conservation, and averaged structural evidence. However, agreement among these scores is limited and they likely over-estimate the fraction of the genome that is deleterious. METHOD This study proposes an integrative approach to identify a subset of homozygous non-synonymous single nucleotide polymorphisms (nsSNPs). An 8-level classification scheme is constructed from the presence/absence of deleterious predictions combined with evidence of association with disease or complex traits. Detailed literature searches and structural validations are then performed for a subset of homozygous 826 mis-sense mutations in 575 proteins found in the genomes of 12 healthy adults. RESULTS Implementation of the Association-Adjusted Consensus Deleterious Scheme (AACDS) classifies 11% of all predicted highly deleterious homozygous variants as most likely to influence disease risk. The number of such variants per genome ranges from 0 to 8 with no significant difference between African and Caucasian Americans. Detailed analysis of mutations affecting the APOE, MTMR2, THSB1, CHIA, αMyHC, and AMY2A proteins shows how the protein structure is likely to be disrupted, even though the associated phenotypes have not been documented in the corresponding individuals. CONCLUSIONS The classification system for homozygous nsSNPs provides an opportunity to systematically rank nsSNPs based on suggestive evidence from annotations and sequence-based predictions. The ranking scheme, in-depth literature searches, and structural validations of highly prioritized mis-sense mutations compliment traditional sequence-based approaches and should have particular utility for the development of individualized health profiles. An online tool reporting the AACDS score for any variant is provided at the authors' website.
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Affiliation(s)
| | - Greg Gibson
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
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19
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Sai Ramesh A, Sethumadhavan R, Thiagarajan P. Structure–Function Studies on Non-synonymous SNPs of Chemokine Receptor Gene Implicated in Cardiovascular Disease: A Computational Approach. Protein J 2013; 32:657-65. [DOI: 10.1007/s10930-013-9529-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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20
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Yu H, Huang H. Engineering proteins for thermostability through rigidifying flexible sites. Biotechnol Adv 2013; 32:308-15. [PMID: 24211474 DOI: 10.1016/j.biotechadv.2013.10.012] [Citation(s) in RCA: 163] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Revised: 09/04/2013] [Accepted: 10/29/2013] [Indexed: 01/06/2023]
Abstract
Engineering proteins for thermostability is an exciting and challenging field since it is critical for broadening the industrial use of recombinant proteins. Thermostability of proteins arises from the simultaneous effect of several forces such as hydrophobic interactions, disulfide bonds, salt bridges and hydrogen bonds. All of these interactions lead to decreased flexibility of polypeptide chain. Structural studies of mesophilic and thermophilic proteins showed that the latter need more rigid structures to compensate for increased thermal fluctuations. Hence flexibility can be an indicator to pinpoint weak spots for enhancing thermostability of enzymes. A strategy has been proven effective in enhancing proteins' thermostability with two steps: predict flexible sites of proteins firstly and then rigidify these sites. We refer to this approach as rigidify flexible sites (RFS) and give an overview of such a method through summarizing the methods to predict flexibility of a protein, the methods to rigidify residues with high flexibility and successful cases regarding enhancing thermostability of proteins using RFS.
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Affiliation(s)
- Haoran Yu
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin 300072, China
| | - He Huang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin 300072, China.
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21
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Verma R, Schwaneberg U, Roccatano D. Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering. Comput Struct Biotechnol J 2012; 2:e201209008. [PMID: 24688649 PMCID: PMC3962222 DOI: 10.5936/csbj.201209008] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2012] [Revised: 10/07/2012] [Accepted: 10/12/2012] [Indexed: 12/01/2022] Open
Abstract
The combination of computational and directed evolution methods has proven a winning strategy for protein engineering. We refer to this approach as computer-aided protein directed evolution (CAPDE) and the review summarizes the recent developments in this rapidly growing field. We will restrict ourselves to overview the availability, usability and limitations of web servers, databases and other computational tools proposed in the last five years. The goal of this review is to provide concise information about currently available computational resources to assist the design of directed evolution based protein engineering experiment.
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Affiliation(s)
- Rajni Verma
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany ; Department of Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany
| | - Ulrich Schwaneberg
- Department of Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany
| | - Danilo Roccatano
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
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de Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly JC. PredyFlexy: flexibility and local structure prediction from sequence. Nucleic Acids Res 2012; 40:W317-22. [PMID: 22689641 PMCID: PMC3394303 DOI: 10.1093/nar/gks482] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Protein structures are necessary for understanding protein function at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations. First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures. We developed a prediction method that proposes structural candidates in terms of LSPs and predict protein flexibility along a given sequence. Second, we examine flexibility according to two different descriptors: X-ray B-factors considered as good indicators of flexibility and the root mean square fluctuations, based on molecular dynamics simulations. We then define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. This method does not resort to sophisticate learning of flexibility but predicts flexibility from average flexibility of predicted local structures. The method is implemented in PredyFlexy web server. Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. PredyFlexy can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/predyflexy/.
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Hirose S, Yokota K, Kuroda Y, Wako H, Endo S, Kanai S, Noguchi T. Prediction of protein motions from amino acid sequence and its application to protein-protein interaction. BMC STRUCTURAL BIOLOGY 2010; 10:20. [PMID: 20626880 PMCID: PMC3245509 DOI: 10.1186/1472-6807-10-20] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2009] [Accepted: 07/13/2010] [Indexed: 11/10/2022]
Abstract
BACKGROUND Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information. RESULTS In this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors. CONCLUSIONS Finally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the predicted high internal motion regions and the observed conformational change regions.
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Affiliation(s)
- Shuichi Hirose
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST),2-42, Aomi, Koto-ku, Tokyo, 135-0064, Japan.
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Liu YC, Yang MH, Lin WL, Huang CK, Oyang YJ. A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins. BMC Genomics 2009; 10 Suppl 3:S22. [PMID: 19958486 PMCID: PMC2788375 DOI: 10.1186/1471-2164-10-s3-s22] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research. Results In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode. Conclusion Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.
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Affiliation(s)
- Yu-Cheng Liu
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
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