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Gadiyaram V, Prabantu VM, Manjaly AA, Muthiah A, Vishveshwara S. GraSp-PSN: A web server for graph spectra based analysis of protein structure networks. Curr Res Struct Biol 2024; 7:100147. [PMID: 38766653 PMCID: PMC11098725 DOI: 10.1016/j.crstbi.2024.100147] [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: 11/30/2023] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/22/2024] Open
Abstract
The function of a protein is most of the time achieved due to minute conformational changes in its structure due to ligand binding or environmental changes or other interactions. Hence the analysis of structure of proteins should go beyond the analysis of mere atom contacts and should include the emergent global structure as a whole. This can be achieved by graph spectra based analysis of protein structure networks. GraSp-PSN is a web server that can assist in (1) acquiring weighted protein structure network (PSN) and network parameters ranging from atomic level to global connectivity from the three dimensional coordinates of a protein, (2) generating scores for comparison of a pair of protein structures with detailed information of local to global connectivity, and (3) assigning perturbation scores to the residues and their interactions, that can prioritise them in terms of residue clusters. The methods implemented in the server are generic in nature and can be used for comparing networks in any discipline by uploading adjacency matrices in the server. The webserver can be accessed using the following link: https://pople.mbu.iisc.ac.in/.
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Affiliation(s)
| | | | | | - Ananth Muthiah
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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Prabantu VM, Tandon H, Sandhya S, Sowdhamini R, Srinivasan N. The alteration of structural network upon transient association between proteins studied using graph theory. Proteins 2023. [PMID: 37902388 DOI: 10.1002/prot.26606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/31/2023] [Accepted: 09/14/2023] [Indexed: 10/31/2023]
Abstract
Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.
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Affiliation(s)
| | - Himani Tandon
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- Structural Studies Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Sankaran Sandhya
- Faculty of Life and Health Sciences, Department of Biotechnology, Ramaiah University of Applied Sciences, Bangalore, India
| | - Ramanathan Sowdhamini
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- National Centre for Biological Sciences (TIFR), Bangalore, India
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
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Prabantu VM, Gadiyaram V, Vishveshwara S, Srinivasan N. Understanding structural variability in proteins using protein structural networks. Curr Res Struct Biol 2022; 4:134-145. [PMID: 35586857 PMCID: PMC9108755 DOI: 10.1016/j.crstbi.2022.04.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/01/2022] [Accepted: 04/09/2022] [Indexed: 11/13/2022] Open
Abstract
Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures. Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable. The comparison of protein structural networks can better discriminate conformations with similar backbones. Specific interactions between solvent accessible and inaccessible residues are poorly preserved. Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function. These side-chain network-based studies provide a framework to correlate protein structure-function relationships.
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Vishwakarma P, Vattekatte AM, Shinada N, Diharce J, Martins C, Cadet F, Gardebien F, Etchebest C, Nadaradjane AA, de Brevern AG. V HH Structural Modelling Approaches: A Critical Review. Int J Mol Sci 2022; 23:3721. [PMID: 35409081 PMCID: PMC8998791 DOI: 10.3390/ijms23073721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 12/20/2022] Open
Abstract
VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.
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Affiliation(s)
- Poonam Vishwakarma
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Akhila Melarkode Vattekatte
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | | | - Julien Diharce
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
| | - Carla Martins
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Frédéric Cadet
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
- PEACCEL, Artificial Intelligence Department, Square Albin Cachot, F-75013 Paris, France
| | - Fabrice Gardebien
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Catherine Etchebest
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
| | - Aravindan Arun Nadaradjane
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Alexandre G. de Brevern
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
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Prabantu VM, Naveenkumar N, Srinivasan N. Influence of Disease-Causing Mutations on Protein Structural Networks. Front Mol Biosci 2021; 7:620554. [PMID: 33778000 PMCID: PMC7987782 DOI: 10.3389/fmolb.2020.620554] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/17/2020] [Indexed: 01/18/2023] Open
Abstract
The interactions between residues in a protein tertiary structure can be studied effectively using the approach of protein structure network (PSN). A PSN is a node-edge representation of the structure with nodes representing residues and interactions between residues represented by edges. In this study, we have employed weighted PSNs to understand the influence of disease-causing mutations on proteins of known 3D structures. We have used manually curated information on disease mutations from UniProtKB/Swiss-Prot and their corresponding protein structures of wildtype and disease variant from the protein data bank. The PSNs of the wildtype and disease-causing mutant are compared to analyse variation of global and local dissimilarity in the overall network and at specific sites. We study how a mutation at a given site can affect the structural network at a distant site which may be involved in the function of the protein. We have discussed specific examples of the disease cases where the protein structure undergoes limited structural divergence in their backbone but have large dissimilarity in their all atom networks and vice versa, wherein large conformational alterations are observed while retaining overall network. We analyse the effect of variation of network parameters that characterize alteration of function or stability.
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Affiliation(s)
| | - Nagarajan Naveenkumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,National Centre for Biological Sciences, TIFR, Bangalore, India.,Bharathidasan University, Tiruchirappalli, India
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Gadiyaram V, Dighe A, Ghosh S, Vishveshwara S. Network Re-Wiring During Allostery and Protein-Protein Interactions: A Graph Spectral Approach. Methods Mol Biol 2021; 2253:89-112. [PMID: 33315220 DOI: 10.1007/978-1-0716-1154-8_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The process of allostery is often guided by subtle changes in the non-covalent interactions between residues of a protein. These changes may be brought about by minor perturbations by natural processes like binding of a ligand or protein-protein interaction. The challenge lies in capturing minute changes at the residue interaction level and following their propagation at local as well as global distances. While macromolecular effects of the phenomenon of allostery are inferred from experiments, a computational microscope can elucidate atomistic-level details leading to such macromolecular effects. Network formalism has served as an attractive means to follow this path and has been pursued further for the past couple of decades. In this chapter some concepts and methods are summarized, and recent advances are discussed. Specifically, the changes in strength of interactions (edge weight) and their repercussion on the overall protein organization (residue clustering) are highlighted. In this review, we adopt a graph spectral method to probe these subtle changes in a quantitative manner. Further, the power of this method is demonstrated for capturing re-ordering of side-chain interactions in response to ligand binding, which culminates into formation of a protein-protein complex in β2-adrenergic receptors.
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Affiliation(s)
- Vasundhara Gadiyaram
- IISc Mathematics Initiative (IMI), Indian Institute of Science, Bangalore, India
| | - Anasuya Dighe
- IISc Mathematics Initiative (IMI), Indian Institute of Science, Bangalore, India
| | - Sambit Ghosh
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA
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Papo D. Gauging Functional Brain Activity: From Distinguishability to Accessibility. Front Physiol 2019; 10:509. [PMID: 31139089 PMCID: PMC6517676 DOI: 10.3389/fphys.2019.00509] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Accepted: 04/11/2019] [Indexed: 11/13/2022] Open
Abstract
Standard neuroimaging techniques provide non-invasive access not only to human brain anatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how neuroimaging's ability to extract genuine functional brain activity is bounded by functional representations' structure. To do so, we first provide a simple definition of functional brain activity from a system-level brain imaging perspective. We then review how the properties of the space on which brain activity is represented induce relations on observed imaging data which allow determining the extent to which two observations are functionally distinguishable and quantifying how far apart they are. It is also proposed that genuine functional distances would require defining accessibility, i.e., how a given observed condition can be accessed from another given one, under the dynamics of some neurophysiological process. We show how these properties result from the structure defined on dynamical data and dynamics-to-function projections, and consider some implications that the way and extent to which these are defined have for the interpretation of experimental data from standard system-level brain recording techniques.
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Affiliation(s)
- David Papo
- SCALab, UMR CNRS 9193, Université de Lille, Villeneuve d’Ascq, France
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Gadiyaram V, Vishveshwara S, Vishveshwara S. From Quantum Chemistry to Networks in Biology: A Graph Spectral Approach to Protein Structure Analyses. J Chem Inf Model 2019; 59:1715-1727. [DOI: 10.1021/acs.jcim.9b00002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Vasundhara Gadiyaram
- IISc Mathematics Initiative (IMI), Indian Institute of Science, C V Raman Road, Bengaluru, Karnataka 560012, India
| | - Smitha Vishveshwara
- Department of Physics, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801-3080, United States
| | - Saraswathi Vishveshwara
- Molecular Biophysics Unit, Indian Institute of Science, C V Raman Road, Bengaluru, Karnataka 560012, India
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Pražnikar J, Tomić M, Turk D. Validation and quality assessment of macromolecular structures using complex network analysis. Sci Rep 2019; 9:1678. [PMID: 30737447 PMCID: PMC6368557 DOI: 10.1038/s41598-019-38658-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 01/07/2019] [Indexed: 02/06/2023] Open
Abstract
Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.
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Affiliation(s)
- Jure Pražnikar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia.
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia.
| | - Miloš Tomić
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia
| | - Dušan Turk
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia
- Center of excellence for Integrated Approaches in Chemistry and Biology of Proteins, Jamova 39, Ljubljana, Slovenia
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