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Kranjc A, Narwani TJ, Abby SS, de Brevern AG. Structural Space of the Duffy Antigen/Receptor for Chemokines' Intrinsically Disordered Ectodomain 1 Explored by Temperature Replica-Exchange Molecular Dynamics Simulations. Int J Mol Sci 2023; 24:13280. [PMID: 37686086 PMCID: PMC10488288 DOI: 10.3390/ijms241713280] [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] [Received: 07/27/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Plasmodium vivax malaria affects 14 million people each year. Its invasion requires interactions between the parasitic Duffy-binding protein (PvDBP) and the N-terminal extracellular domain (ECD1) of the host's Duffy antigen/receptor for chemokines (DARC). ECD1 is highly flexible and intrinsically disordered, therefore it can adopt different conformations. We computationally modeled the challenging ECD1 local structure. With T-REMD simulations, we sampled its dynamic behavior and collected its most representative conformations. Our results suggest that most of the DARC ECD1 domain remains in a disordered state during the simulated time. Globular local conformations are found in the analyzed local free-energy minima. These globular conformations share an α-helix spanning residues Ser18 to Ser29 and in many cases they comprise an antiparallel β-sheet, whose β-strands are formed around residues Leu10 and Ala49. The formation of a parallel β-sheet is almost negligible. So far, progress in understanding the mechanisms forming the basis of the P. vivax malaria infection of reticulocytes has been hampered by experimental difficulties, along with a lack of DARC structural information. Our collection of the most probable ECD1 structural conformations will help to advance modeling of the DARC structure and to explore DARC-ECD1 interactions with a range of physiological and pathological ligands.
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Affiliation(s)
- Agata Kranjc
- Université Paris Cité and Université des Antilles and Université de la Réunion, BIGR, UMR_S1134, DSIMB Team, Inserm, F-75014 Paris, France;
- Institut National de la Transfusion Sanguine (INTS), F-75015 Paris, France
- Institute of Neuroscience and Medicine (INM-9)/Institute for Advanced Simulation (IAS-5), Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Tarun Jairaj Narwani
- Université Paris Cité and Université des Antilles and Université de la Réunion, BIGR, UMR_S1134, DSIMB Team, Inserm, F-75014 Paris, France;
- Institut National de la Transfusion Sanguine (INTS), F-75015 Paris, France
| | - Sophie S. Abby
- University Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, F-38000 Grenoble, France;
| | - Alexandre G. de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, BIGR, UMR_S1134, DSIMB Team, Inserm, F-75014 Paris, France;
- Institut National de la Transfusion Sanguine (INTS), F-75015 Paris, France
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2
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de Brevern AG. An agnostic analysis of the human AlphaFold2 proteome using local protein conformations. Biochimie 2023; 207:11-19. [PMID: 36417962 DOI: 10.1016/j.biochi.2022.11.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/14/2022] [Accepted: 11/17/2022] [Indexed: 11/21/2022]
Abstract
Knowledge of the 3D structure of proteins is a valuable asset for understanding their precise biological mechanisms. However, the cost of production of 3D structures and experimental difficulties limit their obtaining. The proposal of 3D structural models is consequently an appealing alternative. The release of the AlphaFold Deep Learning approach has revolutionized the field. The recent near-complete human proteome proposal makes it possible to analyse large amounts of data and evaluate the results of the approach in greater depth. The 3D human proteome was thus analysed in light of the classic secondary structures, and many less-used protein local conformations (PolyProline II helices, type of γ-turns, of β-turns and of β-bulges, curvature of the helices, and a structural alphabet). Without questioning the global quality of the approach, this analysis highlights certain local conformations, which maybe poorly predicted and they could therefore be better addressed.
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Affiliation(s)
- Alexandre G de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM UMR_S 1134, BIGR, DSIMB Bioinformatics team, F-75014, Paris, France.
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3
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de Brevern AG. A Perspective on the (Rise and Fall of) Protein β-Turns. Int J Mol Sci 2022; 23:12314. [PMID: 36293166 PMCID: PMC9604201 DOI: 10.3390/ijms232012314] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/07/2022] [Accepted: 10/13/2022] [Indexed: 11/21/2022] Open
Abstract
The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software's difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era.
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Affiliation(s)
- Alexandre G de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM UMR_S 1134, BIGR, DSIMB Team, F-75014 Paris, France
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4
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Insights into Comparative Modeling of V HH Domains. Int J Mol Sci 2021; 22:ijms22189771. [PMID: 34575931 PMCID: PMC8466624 DOI: 10.3390/ijms22189771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/27/2021] [Accepted: 09/04/2021] [Indexed: 12/13/2022] Open
Abstract
In the particular case of the Camelidae family, immunoglobulin proteins have evolved into a unique and more simplified architecture with only heavy chains. The variable domains of these chains, named VHHs, have a number of Complementary Determining Regions (CDRs) reduced by half, and can function as single domains making them good candidates for molecular tools. 3D structure prediction of these domains is a beneficial and advantageous step to advance their developability as molecular tools. Nonetheless, the conformations of CDRs loops in these domains remain difficult to predict due to their higher conformational diversity. In addition to CDRs loop diversity, our earlier study has established that Framework Regions (FRs) are also not entirely conformationally conserved which establishes a need for more rigorous analyses of these regions that could assist in template selection. In the current study, VHHs models using different template selection strategies for comparative modeling using Modeller have been extensively assessed. This study analyses the conformational changes in both CDRs and FRs using an original strategy of conformational discretization based on a structural alphabet. Conformational sampling in selected cases is precisely reported. Some interesting outcomes of the structural analyses of models also draw attention towards the distinct difficulty in 3D structure prediction of VHH domains.
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5
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Brinkjost T, Ehrt C, Koch O, Mutzel P. SCOT: Rethinking the classification of secondary structure elements. Bioinformatics 2020; 36:2417-2428. [PMID: 31742326 DOI: 10.1093/bioinformatics/btz826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 10/02/2019] [Accepted: 11/16/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Secondary structure classification is one of the most important issues in structure-based analyses due to its impact on secondary structure prediction, structural alignment and protein visualization. There are still open challenges concerning helix and sheet assignments which are currently not addressed by a single multi-purpose software. RESULTS We introduce SCOT (Secondary structure Classification On Turns) as a novel secondary structure element assignment software which supports the assignment of turns, right-handed α-, 310- and π-helices, left-handed α- and 310-helices, 2.27- and polyproline II helices, β-sheets and kinks. We demonstrate that the introduction of helix Purity values enables a clear differentiation between helix classes. SCOT's unique strengths are highlighted by comparing it to six state-of-the-art methods (DSSP, STRIDE, ASSP, SEGNO, DISICL and SHAFT). The assignment approaches were compared concerning geometric consistency, protein structure quality and flexibility dependency and their impact on secondary structure element-based structural alignments. We show that only SCOT's combination of hydrogen bonds, geometric criteria and dihedral angles enables robust assignments independent of the structure quality and flexibility. We demonstrate that this combination and the elaborate kink detection lead to SCOT's clear superiority for protein alignments. As the resulting helices and strands are provided in a PDB conform output format, they can immediately be used for structure alignment algorithms. Taken together, the application of our new method and the straight-forward visualization using the accompanying PyMOL scripts enable the comprehensive analysis of regular backbone geometries in proteins. AVAILABILITY AND IMPLEMENTATION https://this-group.rocks. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tobias Brinkjost
- Department of Computer Science.,Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund 44227, Germany
| | - Christiane Ehrt
- Department of Computer Science.,Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund 44227, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund 44227, Germany
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6
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de Brevern AG. Impact of protein dynamics on secondary structure prediction. Biochimie 2020; 179:14-22. [PMID: 32946990 DOI: 10.1016/j.biochi.2020.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 09/04/2020] [Accepted: 09/10/2020] [Indexed: 02/08/2023]
Abstract
Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction. Interestingly, a direct link is observed between the quality of the prediction and the stability of the assignment to the secondary structure state. The more stable a local protein conformation is, the better the prediction will be. The secondary structure assignment not taken from the crystallized structures but from the conformations observed during the dynamics slightly increase the quality of the secondary structure prediction. These results show that evaluation of PSSPs can be done differently, but also that the notion of dynamics can be included in development of PSSPs and other approaches such as de novo approaches.
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Affiliation(s)
- Alexandre G de Brevern
- Biologie Intégrée Du Globule Rouge UMR_S1134, Inserm, Université de Paris, Univ. de la Réunion, Univ. des Antilles, F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Institut National de la Transfusion Sanguine (INTS), F-75739, Paris, France; IBL, F-75015, Paris, France.
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7
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Akhila MV, Narwani TJ, Floch A, Maljković M, Bisoo S, Shinada NK, Kranjc A, Gelly JC, Srinivasan N, Mitić N, de Brevern AG. A structural entropy index to analyse local conformations in intrinsically disordered proteins. J Struct Biol 2020; 210:107464. [DOI: 10.1016/j.jsb.2020.107464] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 01/06/2020] [Accepted: 01/15/2020] [Indexed: 10/25/2022]
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8
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Narwani TJ, Craveur P, Shinada NK, Floch A, Santuz H, Vattekatte AM, Srinivasan N, Rebehmed J, Gelly JC, Etchebest C, de Brevern AG. Discrete analyses of protein dynamics. J Biomol Struct Dyn 2019; 38:2988-3002. [PMID: 31361191 DOI: 10.1080/07391102.2019.1650112] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how β-strand, β-turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β-turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. AbbreviationsNeqnumber of equivalentPBProtein BlocksPDBProtein DataBankRMSfroot mean square fluctuationsCommunicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Tarun Jairaj Narwani
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Pierrick Craveur
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nicolas K Shinada
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Discngine, SAS, Paris, France
| | - Aline Floch
- Laboratoire D'Excellence GR-Ex, Paris, France.,Etablissement Français du Sang Ile de France, Créteil, France.,IMRB - INSERM U955 Team 2 « Transfusion et Maladies du Globule Rouge », Paris Est- Créteil Univ, Créteil, France.,UPEC, Université Paris Est-Créteil, Créteil, France
| | - Hubert Santuz
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Akhila Melarkode Vattekatte
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France
| | | | - Joseph Rebehmed
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon
| | - Jean-Christophe Gelly
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France.,IBL, Paris, France
| | - Catherine Etchebest
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France
| | - Alexandre G de Brevern
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France.,IBL, Paris, France
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9
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Judy E, Kishore N. A look back at the molten globule state of proteins: thermodynamic aspects. Biophys Rev 2019; 11:365-375. [PMID: 31055760 PMCID: PMC6557940 DOI: 10.1007/s12551-019-00527-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 04/22/2019] [Indexed: 12/23/2022] Open
Abstract
Interest in protein folding intermediates lies in their significance to protein folding pathways. The molten globule (MG) state is one such intermediate lying on the kinetic (and sometimes thermodynamic) pathway between native and unfolded states. Development of our qualitative and quantitative understanding of the MG state can provide deeper insight into the folding pathways and hence potentially facilitate solution of the protein folding problem. An extensive look at literature suggests that most studies into protein MG states have been largely qualitative. Attempts to obtain quantitative insights into MG states have involved application of high-sensitivity calorimetry (differential scanning calorimetry and isothermal titration calorimetry). This review addresses the progress made in this direction by discussing the knowledge gained to date, along with the future promise of calorimetry, in providing quantitative information on the structural features of MG states. Particular attention is paid to the question of whether such states share common structural features or not. The difference in the nature of the transition from the MG state to the unfolded state, in terms of cooperativity, has also been addressed and discussed.
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Affiliation(s)
- Eva Judy
- Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076 India
| | - Nand Kishore
- Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076 India
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10
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Are protein hubs faster folders? Exploration based on Escherichia coli proteome. Amino Acids 2016; 48:2747-2753. [DOI: 10.1007/s00726-016-2309-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 08/05/2016] [Indexed: 10/21/2022]
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11
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Extension of the classical classification of β-turns. Sci Rep 2016; 6:33191. [PMID: 27627963 PMCID: PMC5024104 DOI: 10.1038/srep33191] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 08/22/2016] [Indexed: 11/29/2022] Open
Abstract
The functional properties of a protein primarily depend on its three-dimensional (3D) structure. These properties have classically been assigned, visualized and analysed on the basis of protein secondary structures. The β-turn is the third most important secondary structure after helices and β-strands. β-turns have been classified according to the values of the dihedral angles φ and ψ of the central residue. Conventionally, eight different types of β-turns have been defined, whereas those that cannot be defined are classified as type IV β-turns. This classification remains the most widely used. Nonetheless, the miscellaneous type IV β-turns represent 1/3rd of β-turn residues. An unsupervised specific clustering approach was designed to search for recurrent new turns in the type IV category. The classical rules of β-turn type assignment were central to the approach. The four most frequently occurring clusters defined the new β-turn types. Unexpectedly, these types, designated IV1, IV2, IV3 and IV4, represent half of the type IV β-turns and occur more frequently than many of the previously established types. These types show convincing particularities, in terms of both structures and sequences that allow for the classical β-turn classification to be extended for the first time in 25 years.
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12
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Noël F, Malpertuy A, de Brevern AG. Global analysis of VHHs framework regions with a structural alphabet. Biochimie 2016; 131:11-19. [PMID: 27613403 DOI: 10.1016/j.biochi.2016.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/05/2016] [Accepted: 09/05/2016] [Indexed: 02/08/2023]
Abstract
The VHHs are antigen-binding region/domain of camelid heavy chain antibodies (HCAb). They have many interesting biotechnological and biomedical properties due to their small size, high solubility and stability, and high affinity and specificity for their antigens. HCAb and classical IgGs are evolutionary related and share a common fold. VHHs are composed of regions considered as constant, called the frameworks (FRs) connected by Complementarity Determining Regions (CDRs), a highly variable region that provide interaction with the epitope. Actually, no systematic structural analyses had been performed on VHH structures despite a significant number of structures. This work is the first study to analyse the structural diversity of FRs of VHHs. Using a structural alphabet that allows approximating the local conformation, we show that each of the four FRs do not have a unique structure but exhibit many structural variant patterns. Moreover, no direct simple link between the local conformational change and amino acid composition can be detected. These results indicate that long-range interactions affect the local conformation of FRs and impact the building of structural models.
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Affiliation(s)
- Floriane Noël
- INSERM, U 1134, DSIMB, F-75739 Paris, France; Univ Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, 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, F-75739 Paris, France; Univ Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France; Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France; Laboratoire d'Excellence GR-Ex, F-75739 Paris, France.
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13
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Chebrek R, Leonard S, de Brevern AG, Gelly JC. PolyprOnline: polyproline helix II and secondary structure assignment database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau102. [PMID: 25380779 PMCID: PMC4224144 DOI: 10.1093/database/bau102] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The polyproline helix type II (PPII) is a regular protein secondary structure with remarkable features. Many studies have highlighted different crucial biological roles supported by this local conformation, e.g. in the interactions between biological macromolecules. Although PPII is less frequently present than regular secondary structures such as canonical alpha helices and beta strands, it corresponds to 3–10% of residues. Up to now, PPII is not assigned by most popular assignment tools, and therefore, remains insufficiently studied. PolyprOnline database is, therefore, dedicated to PPII structure assignment and analysis to facilitate the study of PPII structure and functional roles. This database is freely accessible from www.dsimb.inserm.fr/dsimb_tools/polyproline.
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Affiliation(s)
- Romain Chebrek
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
| | - Sylvain Leonard
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
| | - Alexandre G de Brevern
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
| | - Jean-Christophe Gelly
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
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14
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Hafsa NE, Wishart DS. CSI 2.0: a significantly improved version of the Chemical Shift Index. JOURNAL OF BIOMOLECULAR NMR 2014; 60:131-146. [PMID: 25273503 DOI: 10.1007/s10858-014-9863-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Accepted: 09/17/2014] [Indexed: 06/03/2023]
Abstract
Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for secondary structure identification is called the Chemical Shift Index (CSI). The CSI method uses a simple digital chemical shift filter to locate secondary structures along the protein chain using backbone (13)C and (1)H chemical shifts. While the CSI method is simple to use and easy to implement, it is only about 75-80% accurate. Here we describe a significantly improved version of the CSI (2.0) that uses machine-learning techniques to combine all six backbone chemical shifts ((13)Cα, (13)Cβ, (13)C, (15)N, (1)HN, (1)Hα) with sequence-derived features to perform far more accurate secondary structure identification. Our tests indicate that CSI 2.0 achieved an average identification accuracy (Q3) of 90.56% for a training set of 181 proteins in a repeated tenfold cross-validation and 89.35% for a test set of 59 proteins. This represents a significant improvement over other state-of-the-art chemical shift-based methods. In particular, the level of performance of CSI 2.0 is equal to that of standard methods, such as DSSP and STRIDE, used to identify secondary structures via 3D coordinate data. This suggests that CSI 2.0 could be used both in providing accurate NMR constraint data in the early stages of protein structure determination as well as in defining secondary structure locations in the final protein model(s). A CSI 2.0 web server (http://csi.wishartlab.com) is available for submitting the input queries for secondary structure identification.
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Affiliation(s)
- Noor E Hafsa
- Department of Computing Science, University of Alberta, Edmonton, Canada
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Zacharias J, Knapp EW. Protein secondary structure classification revisited: processing DSSP information with PSSC. J Chem Inf Model 2014; 54:2166-79. [PMID: 24866861 DOI: 10.1021/ci5000856] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A first step toward three-dimensional protein structure description is the characterization of secondary structure. The most widely used program for secondary structure assignment remains DSSP, introduced in 1983, with currently more than 400 citations per year. DSSP output is in a one-letter representation, where much of the information on DSSP's internal description is lost. Recently it became evident that DSSP overlooks most π-helical structures, which are more prevalent and important than anticipated before. We introduce an alternative concept, representing the internal structure characterization of DSSP as an eight-character string that is human-interpretable and easy to parse by software. We demonstrate how our protein secondary structure characterization (PSSC) code allows for inspection of complicated structural features. It recognizes ten times more π-helical residues than does the standard DSSP. The plausibility of introduced changes in interpreting DSSP information is demonstrated by better clustering of secondary structures in (φ, ψ) dihedral angle space. With a sliding sequence window (SSW), helical assignments with PSSC remain invariant compared with an assignment based on the complete structure. In contrast, assignment with DSSP can be changed by residues in the neighborhood that are in fact not interacting with the residue under consideration. We demonstrate how one can easily define new secondary structure classification schemes with PSSC and perform the classifications. Our approach works without changing the DSSP source code and allows for more detailed protein characterization.
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Affiliation(s)
- Jan Zacharias
- Fachbereich Biologie, Chemie, Pharmazie/Institute of Chemistry and Biochemistry, Freie Universität Berlin , Fabeckstrasse 36A, 14195 Berlin, Germany
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Joseph AP, de Brevern AG. From local structure to a global framework: recognition of protein folds. J R Soc Interface 2014; 11:20131147. [PMID: 24740960 DOI: 10.1098/rsif.2013.1147] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.
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Affiliation(s)
- Agnel Praveen Joseph
- Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, , Didcot OX11 0QX, UK
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Craveur P, Joseph AP, Rebehmed J, de Brevern AG. β-Bulges: extensive structural analyses of β-sheets irregularities. Protein Sci 2013; 22:1366-78. [PMID: 23904395 DOI: 10.1002/pro.2324] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Revised: 07/19/2013] [Accepted: 07/22/2013] [Indexed: 12/30/2022]
Abstract
β-Sheets are quite frequent in protein structures and are stabilized by regular main-chain hydrogen bond patterns. Irregularities in β-sheets, named β-bulges, are distorted regions between two consecutive hydrogen bonds. They disrupt the classical alternation of side chain direction and can alter the directionality of β-strands. They are implicated in protein-protein interactions and are introduced to avoid β-strand aggregation. Five different types of β-bulges are defined. Previous studies on β-bulges were performed on a limited number of protein structures or one specific family. These studies evoked a potential conservation during evolution. In this work, we analyze the β-bulge distribution and conservation in terms of local backbone conformations and amino acid composition. Our dataset consists of 66 times more β-bulges than the last systematic study (Chan et al. Protein Science 1993, 2:1574-1590). Novel amino acid preferences are underlined and local structure conformations are highlighted by the use of a structural alphabet. We observed that β-bulges are preferably localized at the N- and C-termini of β-strands, but contrary to the earlier studies, no significant conservation of β-bulges was observed among structural homologues. Displacement of β-bulges along the sequence was also investigated by Molecular Dynamics simulations.
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Affiliation(s)
- Pierrick Craveur
- INSERM, U665, DSIMB, F-75739, Paris, France; University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, F-75739, Paris, France; Institut National de la Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire d'Excellence GR-Ex, F-75739, Paris, France
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Miller EB, Murrett CS, Zhu K, Zhao S, Goldfeld DA, Bylund JH, Friesner RA. Prediction of Long Loops with Embedded Secondary Structure using the Protein Local Optimization Program. J Chem Theory Comput 2013; 9:1846-4864. [PMID: 23814507 DOI: 10.1021/ct301083q] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. Further, as loop prediction advances to success with larger loops, the exclusion of loops containing secondary structure becomes awkward. Here, we extend the applicability of the Protein Local Optimization Program (PLOP) to loops up to 17 residues in length that contain either helical or hairpin segments. In general, PLOP hierarchically samples conformational space and ranks candidate loops with a high-quality molecular mechanics force field. For loops identified to possess α-helical segments, we employ an alternative dihedral library composed of (ϕ,ψ) angles commonly found in helices. The alternative library is searched over a user-specified range of residues that define the helical bounds. The source of these helical bounds can be from popular secondary structure prediction software or from analysis of past loop predictions where a propensity to form a helix is observed. Due to the maturity of our energy model, the lowest energy loop across all experiments can be selected with an accuracy of sub-Ångström RMSD in 80% of cases, 1.0 to 1.5 Å RMSD in 14% of cases, and poorer than 1.5 Å RMSD in 6% of cases. The effectiveness of our current methods in predicting hairpin-containing loops is explored with hairpins up to 13 residues in length and again reaching an accuracy of sub-Ångström RMSD in 83% of cases, 1.0 to 1.5 Å RMSD in 10% of cases, and poorer than 1.5 Å RMSD in 7% of cases. Finally, we explore the effect of an imprecise surrounding environment, in which side chains, but not the backbone, are initially in perturbed geometries. In these cases, loops perturbed to 3Å RMSD from the native environment were restored to their native conformation with sub-Ångström RMSD.
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Affiliation(s)
- Edward B Miller
- Department of Chemistry, Columbia University, New York, New York
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Species specific amino acid sequence–protein local structure relationships: An analysis in the light of a structural alphabet. J Theor Biol 2011; 276:209-17. [DOI: 10.1016/j.jtbi.2011.01.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2010] [Revised: 01/28/2011] [Accepted: 01/31/2011] [Indexed: 11/24/2022]
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Mansiaux Y, Joseph AP, Gelly JC, de Brevern AG. Assignment of PolyProline II conformation and analysis of sequence--structure relationship. PLoS One 2011; 6:e18401. [PMID: 21483785 PMCID: PMC3069088 DOI: 10.1371/journal.pone.0018401] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2010] [Accepted: 03/07/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Secondary structures are elements of great importance in structural biology, biochemistry and bioinformatics. They are broadly composed of two repetitive structures namely α-helices and β-sheets, apart from turns, and the rest is associated to coil. These repetitive secondary structures have specific and conserved biophysical and geometric properties. PolyProline II (PPII) helix is yet another interesting repetitive structure which is less frequent and not usually associated with stabilizing interactions. Recent studies have shown that PPII frequency is higher than expected, and they could have an important role in protein-protein interactions. METHODOLOGY/PRINCIPAL FINDINGS A major factor that limits the study of PPII is that its assignment cannot be carried out with the most commonly used secondary structure assignment methods (SSAMs). The purpose of this work is to propose a PPII assignment methodology that can be defined in the frame of DSSP secondary structure assignment. Considering the ambiguity in PPII assignments by different methods, a consensus assignment strategy was utilized. To define the most consensual rule of PPII assignment, three SSAMs that can assign PPII, were compared and analyzed. The assignment rule was defined to have a maximum coverage of all assignments made by these SSAMs. Not many constraints were added to the assignment and only PPII helices of at least 2 residues length are defined. CONCLUSIONS/SIGNIFICANCE The simple rules designed in this study for characterizing PPII conformation, lead to the assignment of 5% of all amino as PPII. Sequence-structure relationships associated with PPII, defined by the different SSAMs, underline few striking differences. A specific study of amino acid preferences in their N and C-cap regions was carried out as their solvent accessibility and contact patterns. Thus the assignment of PPII can be coupled with DSSP and thus opens a simple way for further analysis in this field.
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Affiliation(s)
- Yohann Mansiaux
- INSERM, UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Paris, France
- Université Paris Diderot - Paris 7, Paris, France
- Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Agnel Praveen Joseph
- INSERM, UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Paris, France
- Université Paris Diderot - Paris 7, Paris, France
- Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Jean-Christophe Gelly
- INSERM, UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Paris, France
- Université Paris Diderot - Paris 7, Paris, France
- Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Alexandre G. de Brevern
- INSERM, UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Paris, France
- Université Paris Diderot - Paris 7, Paris, France
- Institut National de la Transfusion Sanguine (INTS), Paris, France
- * E-mail:
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Joseph AP, Agarwal G, Mahajan S, Gelly JC, Swapna LS, Offmann B, Cadet F, Bornot A, Tyagi M, Valadié H, Schneider B, Etchebest C, Srinivasan N, De Brevern AG. A short survey on protein blocks. Biophys Rev 2010; 2:137-147. [PMID: 21731588 DOI: 10.1007/s12551-010-0036-1] [Citation(s) in RCA: 97] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Protein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications.
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Affiliation(s)
- Agnel Praveen Joseph
- DSIMB, Dynamique des Structures et Interactions des Macromolécules Biologiques Université Paris-Diderot - Paris VII INTS INSERM : U665 INTS, 6 rue Alexandre Cabanel, 75739 Paris Cedex 15 FRANCE,FR
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Influence of assignment on the prediction of transmembrane helices in protein structures. Amino Acids 2010; 39:1241-54. [DOI: 10.1007/s00726-010-0559-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2009] [Accepted: 03/08/2010] [Indexed: 02/01/2023]
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