Swapnil MAHAJAN
Post-doctorant CNRS
mars 2014 - mars 2016
| Équipe : |
Thèmes de recherche
Bioinformatique structurale
Parcours universitaire
Since Aug 2019 : Scientist, Bioinformatics, Medgenome, Bangalore
Apr 2018 – Jul 2019 : Scientist, Bioinformatics, La Jolla Institute for Allergy & Immunology
Jun 2016 – Mar 2018 : Post-Doctoral Fellow, Bioinformatics, La Jolla Institute for Allergy & Immunology
Mar 2014 – Mar 2016 : Post-Doctoral Fellow, Bioinformatics, CNRS, Université de Nantes
Feb 2014 : Visiting Course Instructor, Bioinformatics Center, University of Pune
2010 – 2013 : Phd Bioinformatics, Université de La Réunion, France
Aug 2009 – Sep 2010 : Junior Research Fellow, National Center for Biological Sciences (NCBS) and Indian Institute of Science (IISc), Bangalore
2007 – 2009 : MSc Bioinformatics, University of Pune
2004 – 2007 : BSc Biotechnology, University of Pune
Publications
2 publications
Vetrivel, Iyanar; Mahajan, Swapnil; Tyagi, Manoj; Hoffmann, Lionel; Sanejouand, Yves-Henri; Srinivasan, Narayanaswamy; Brevern, Alexandre G De; Cadet, Frédéric; Offmann, Bernard
Knowledge-based prediction of protein backbone conformation using a structural alphabet Article de journal
Dans: PLoS ONE, vol. 12, no. 11, 2017, ISSN: 19326203.
@article{Vetrivel2017,
title = {Knowledge-based prediction of protein backbone conformation using a structural alphabet},
author = {Iyanar Vetrivel and Swapnil Mahajan and Manoj Tyagi and Lionel Hoffmann and Yves-Henri Sanejouand and Narayanaswamy Srinivasan and Alexandre G {De Brevern} and Frédéric Cadet and Bernard Offmann},
doi = {10.1371/journal.pone.0186215},
issn = {19326203},
year = {2017},
date = {2017-11-01},
journal = {PLoS ONE},
volume = {12},
number = {11},
publisher = {Public Library of Science},
abstract = {Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/ or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/ or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.
Mahajan, Swapnil; Sanejouand, Yves-Henri
Jumping between protein conformers using normal modes Article de journal
Dans: Journal of Computational Chemistry, vol. 38, no. 18, p. 1622–1630, 2017, ISSN: 1096987X.
@article{Mahajan2017,
title = {Jumping between protein conformers using normal modes},
author = {Swapnil Mahajan and Yves-Henri Sanejouand},
doi = {10.1002/jcc.24803},
issn = {1096987X},
year = {2017},
date = {2017-01-01},
journal = {Journal of Computational Chemistry},
volume = {38},
number = {18},
pages = {1622--1630},
abstract = {The relationship between the normal modes of a protein and its functional conformational change has been studied for decades. However, using this relationship in a predictive context remains a challenge. In this work, we demonstrate that, starting from a given protein conformer, it is possible to generate in a single step model conformers that are less than 1 Å (Cα-RMSD) from the conformer which is the known endpoint of the conformational change, particularly when the conformational change is collective in nature. Such accurate model conformers can be generated by following either the so-called robust or the 50 lowest-frequency modes obtained with various Elastic Network Models (ENMs). Interestingly, the quality of many of these models compares well with actual crystal structures, as assessed by the ROSETTA scoring function and PROCHECK. The most accurate and best quality conformers obtained in the present study were generated by using the 50 lowest-frequency modes of an all-atom ENM. However, with less than ten robust modes, which are identified without any prior knowledge of the nature of the conformational change, nearly 90% of the motion described by the 50 lowest-frequency modes of a protein can be captured. Such results strongly suggest that exploring the robust modes of ENMs may prove efficient for sampling the functionally relevant conformational repertoire of many proteins. textcopyright 2017 Wiley Periodicals, Inc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The relationship between the normal modes of a protein and its functional conformational change has been studied for decades. However, using this relationship in a predictive context remains a challenge. In this work, we demonstrate that, starting from a given protein conformer, it is possible to generate in a single step model conformers that are less than 1 Å (Cα-RMSD) from the conformer which is the known endpoint of the conformational change, particularly when the conformational change is collective in nature. Such accurate model conformers can be generated by following either the so-called robust or the 50 lowest-frequency modes obtained with various Elastic Network Models (ENMs). Interestingly, the quality of many of these models compares well with actual crystal structures, as assessed by the ROSETTA scoring function and PROCHECK. The most accurate and best quality conformers obtained in the present study were generated by using the 50 lowest-frequency modes of an all-atom ENM. However, with less than ten robust modes, which are identified without any prior knowledge of the nature of the conformational change, nearly 90% of the motion described by the 50 lowest-frequency modes of a protein can be captured. Such results strongly suggest that exploring the robust modes of ENMs may prove efficient for sampling the functionally relevant conformational repertoire of many proteins. textcopyright 2017 Wiley Periodicals, Inc.
2 publications
Mahajan, Swapnil; Brevern, Alexandre G De; Sanejouand, Yves-Henri; Srinivasan, Narayanaswamy; Offmann, Bernard
Use of a structural alphabet to find compatible folds for amino acid sequences Article de journal
Dans: Protein Science, vol. 24, no. 1, p. 145–153, 2015, ISSN: 1469896X.
@article{Mahajan2015a,
title = {Use of a structural alphabet to find compatible folds for amino acid sequences},
author = {Swapnil Mahajan and Alexandre G {De Brevern} and Yves-Henri Sanejouand and Narayanaswamy Srinivasan and Bernard Offmann},
doi = {10.1002/pro.2581},
issn = {1469896X},
year = {2015},
date = {2015-01-01},
journal = {Protein Science},
volume = {24},
number = {1},
pages = {145--153},
abstract = {The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence-search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino-acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as "Protein Blocks" (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence-search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z-score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales-up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web-server that is freely available at http://www.bo-protscience.fr/forsa.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence-search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino-acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as "Protein Blocks" (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence-search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z-score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales-up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web-server that is freely available at http://www.bo-protscience.fr/forsa.
Mahajan, Swapnil; Sanejouand, Yves-Henri
On the relationship between low-frequency normal modes and the large-scale conformational changes of proteins Article de journal
Dans: Archives of Biochemistry and Biophysics, vol. 567, p. 59–65, 2015, ISSN: 10960384.
@article{Mahajan2015b,
title = {On the relationship between low-frequency normal modes and the large-scale conformational changes of proteins},
author = {Swapnil Mahajan and Yves-Henri Sanejouand},
url = {http://dx.doi.org/10.1016/j.abb.2014.12.020},
doi = {10.1016/j.abb.2014.12.020},
issn = {10960384},
year = {2015},
date = {2015-01-01},
journal = {Archives of Biochemistry and Biophysics},
volume = {567},
pages = {59--65},
publisher = {Elsevier Inc.},
abstract = {Normal mode analysis is a computational technique that allows to study the dynamics of biological macromolecules. It was first applied to small protein cases, more than thirty years ago. The interest in this technique then raised when it was realized that it can provide insights about the large-scale conformational changes a protein can experience, for instance upon ligand binding. As it was also realized that studying highly simplified protein models can provide similar insights, meaning that this kind of analysis can be both quick and simple to handle, several applications were proposed, in the context of various structural biology techniques. This review focuses on these applications, as well as on how the functional relevance of the lowest-frequency modes of proteins was established.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Normal mode analysis is a computational technique that allows to study the dynamics of biological macromolecules. It was first applied to small protein cases, more than thirty years ago. The interest in this technique then raised when it was realized that it can provide insights about the large-scale conformational changes a protein can experience, for instance upon ligand binding. As it was also realized that studying highly simplified protein models can provide similar insights, meaning that this kind of analysis can be both quick and simple to handle, several applications were proposed, in the context of various structural biology techniques. This review focuses on these applications, as well as on how the functional relevance of the lowest-frequency modes of proteins was established.
2014
Mahajan, Swapnil; de Brevern, Alexandre G; Offmann, Bernard; Srinivasan, Narayanaswamy
Correlation between local structural dynamics of proteins inferred from NMR ensembles and evolutionary dynamics of homologues of known structure Article de journal
Dans: J Biomol Struct Dyn, vol. 32, no. 5, p. 751-8, 2014.
@article{Mahajan:2014aa,
title = {Correlation between local structural dynamics of proteins inferred from NMR ensembles and evolutionary dynamics of homologues of known structure},
author = {Swapnil Mahajan and Alexandre G {de Brevern} and Bernard Offmann and Narayanaswamy Srinivasan},
doi = {10.1080/07391102.2013.789989},
year = {2014},
date = {2014-01-01},
journal = {J Biomol Struct Dyn},
volume = {32},
number = {5},
pages = {751-8},
abstract = {Conformational changes in proteins are extremely important for their biochemical functions. Correlation between inherent conformational variations in a protein and conformational differences in its homologues of known structure is still unclear. In this study, we have used a structural alphabet called Protein Blocks (PBs). PBs are used to perform abstraction of protein 3-D structures into a 1-D strings of 16 alphabets (a-p) based on dihedral angles of overlapping pentapeptides. We have analyzed the variations in local conformations in terms of PBs represented in the ensembles of 801 protein structures determined using NMR spectroscopy. In the analysis of concatenated data over all the residues in all the NMR ensembles, we observe that the overall nature of inherent local structural variations in NMR ensembles is similar to the nature of local structural differences in homologous proteins with a high correlation coefficient of .94. High correlation at the alignment positions corresponding to helical and β-sheet regions is only expected. However, the correlation coefficient by considering only the loop regions is also quite high (.91). Surprisingly, segregated position-wise analysis shows that this high correlation does not hold true to loop regions at the structurally equivalent positions in NMR ensembles and their homologues of known structure. This suggests that the general nature of local structural changes is unique; however most of the local structural variations in loop regions of NMR ensembles do not correlate to their local structural differences at structurally equivalent positions in homologues.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Conformational changes in proteins are extremely important for their biochemical functions. Correlation between inherent conformational variations in a protein and conformational differences in its homologues of known structure is still unclear. In this study, we have used a structural alphabet called Protein Blocks (PBs). PBs are used to perform abstraction of protein 3-D structures into a 1-D strings of 16 alphabets (a-p) based on dihedral angles of overlapping pentapeptides. We have analyzed the variations in local conformations in terms of PBs represented in the ensembles of 801 protein structures determined using NMR spectroscopy. In the analysis of concatenated data over all the residues in all the NMR ensembles, we observe that the overall nature of inherent local structural variations in NMR ensembles is similar to the nature of local structural differences in homologous proteins with a high correlation coefficient of .94. High correlation at the alignment positions corresponding to helical and β-sheet regions is only expected. However, the correlation coefficient by considering only the loop regions is also quite high (.91). Surprisingly, segregated position-wise analysis shows that this high correlation does not hold true to loop regions at the structurally equivalent positions in NMR ensembles and their homologues of known structure. This suggests that the general nature of local structural changes is unique; however most of the local structural variations in loop regions of NMR ensembles do not correlate to their local structural differences at structurally equivalent positions in homologues.
2013
Mahajan, Swapnil; Agarwal, Garima; Iftekhar, Mohammed; Offmann, Bernard; de Brevern, Alexandre G; Srinivasan, Narayanaswamy
DoSA: Database of Structural Alignments Article de journal
Dans: Database (Oxford), vol. 2013, p. bat048, 2013.
@article{Mahajan:2013aa,
title = {DoSA: Database of Structural Alignments},
author = {Swapnil Mahajan and Garima Agarwal and Mohammed Iftekhar and Bernard Offmann and Alexandre G {de Brevern} and Narayanaswamy Srinivasan},
doi = {10.1093/database/bat048},
year = {2013},
date = {2013-01-01},
journal = {Database (Oxford)},
volume = {2013},
pages = {bat048},
abstract = {Protein structure alignment is a crucial step in protein structure-function analysis. Despite the advances in protein structure alignment algorithms, some of the local conformationally similar regions are mislabeled as structurally variable regions (SVRs). These regions are not well superimposed because of differences in their spatial orientations. The Database of Structural Alignments (DoSA) addresses this gap in identification of local structural similarities obscured in global protein structural alignments by realigning SVRs using an algorithm based on protein blocks. A set of protein blocks is a structural alphabet that abstracts protein structures into 16 unique local structural motifs. DoSA provides unique information about 159,780 conformationally similar and 56,140 conformationally dissimilar SVRs in 74 705 pairwise structural alignments of homologous proteins. The information provided on conformationally similar and dissimilar SVRs can be helpful to model loop regions. It is also conceivable that conformationally similar SVRs with conserved residues could potentially contribute toward functional integrity of homologues, and hence identifying such SVRs could be helpful in understanding the structural basis of protein function. Database URL: http://bo-protscience.fr/dosa/},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Protein structure alignment is a crucial step in protein structure-function analysis. Despite the advances in protein structure alignment algorithms, some of the local conformationally similar regions are mislabeled as structurally variable regions (SVRs). These regions are not well superimposed because of differences in their spatial orientations. The Database of Structural Alignments (DoSA) addresses this gap in identification of local structural similarities obscured in global protein structural alignments by realigning SVRs using an algorithm based on protein blocks. A set of protein blocks is a structural alphabet that abstracts protein structures into 16 unique local structural motifs. DoSA provides unique information about 159,780 conformationally similar and 56,140 conformationally dissimilar SVRs in 74 705 pairwise structural alignments of homologous proteins. The information provided on conformationally similar and dissimilar SVRs can be helpful to model loop regions. It is also conceivable that conformationally similar SVRs with conserved residues could potentially contribute toward functional integrity of homologues, and hence identifying such SVRs could be helpful in understanding the structural basis of protein function. Database URL: http://bo-protscience.fr/dosa/
2010
Joseph, Agnel Praveen; Agarwal, Garima; Mahajan, Swapnil; Gelly, Jean-Christophe; Swapna, Lakshmipuram S; Offmann, Bernard; Cadet, Frédéric; Bornot, Aurélie; Tyagi, Manoj; Valadié, Hélène; Schneider, Bohdan; Etchebest, Catherine; Srinivasan, Narayanaswamy; de Brevern, Alexandre G
A short survey on protein blocks Article de journal
Dans: Biophys Rev, vol. 2, no. 3, p. 137-147, 2010.
@article{Joseph:2010aa,
title = {A short survey on protein blocks},
author = {Agnel Praveen Joseph and Garima Agarwal and Swapnil Mahajan and Jean-Christophe Gelly and Lakshmipuram S Swapna and Bernard Offmann and Frédéric Cadet and Aurélie Bornot and Manoj Tyagi and Hélène Valadié and Bohdan Schneider and Catherine Etchebest and Narayanaswamy Srinivasan and Alexandre G {de Brevern}},
doi = {10.1007/s12551-010-0036-1},
year = {2010},
date = {2010-08-01},
journal = {Biophys Rev},
volume = {2},
number = {3},
pages = {137-147},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.