@article{Tripathi2019,

title = {Investigation of phospholipase Cγ1 interaction with SLP76 using molecular modeling methods for identifying novel inhibitors},

author = {Neha Tripathi and Iyanar Vetrivel and Stéphane Téletchéa and Mickaël Jean and Patrick Legembre and Adèle D Laurent},

doi = {10.3390/ijms20194721},

issn = {14220067},

year  = {2019},

date = {2019-10-01},

journal = {International Journal of Molecular Sciences},

volume = {20},

number = {19},

publisher = {MDPI AG},

abstract = {The enzyme phospholipase C gamma 1 (PLCγ1) has been identified as a potential drug target of interest for various pathological conditions such as immune disorders, systemic lupus erythematosus, and cancers. Targeting its SH3 domain has been recognized as an efficient pharmacological approach for drug discovery against PLCγ1. Therefore, for the first time, a combination of various biophysical methods has been employed to shed light on the atomistic interactions between PLCγ1 and its known binding partners. Indeed, molecular modeling of PLCγ1 with SLP76 peptide and with previously reported inhibitors (ritonavir, anethole, daunorubicin, diflunisal, and rosiglitazone) facilitated the identification of the common critical residues (Gln805, Arg806, Asp808, Glu809, Asp825, Gly827, and Trp828) as well as the quantification of their interaction through binding energies calculations. These features are in agreement with previous experimental data. Such an in depth biophysical analysis of each complex provides an opportunity to identify new inhibitors through pharmacophore mapping, molecular docking and MD simulations. From such a systematic procedure, a total of seven compounds emerged as promising inhibitors, all characterized by a strong binding with PLCγ1 and a comparable or higher binding affinity to ritonavir (ΔGbind < -25 kcal/mol), one of the most potent inhibitor reported till now.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vetrivel2019,

title = {Structural variations within proteins can be as large as variations observed across their homologues},

author = {Iyanar Vetrivel and Alexandre G de Brevern and Frédéric Cadet and Narayanaswamy Srinivasan and Bernard Offmann},

doi = {10.1016/j.biochi.2019.09.013},

issn = {61831638},

year  = {2019},

date = {2019-01-01},

journal = {Biochimie},

volume = {167},

pages = {162--170},

abstract = {Understanding the structural plasticity of proteins is key to understanding the intricacies of their functions and mechanistic basis. In the current study, we analyzed the available multiple crystal structures of the same protein for the structural differences. For this purpose we used an abstraction of protein structures referred as Protein Blocks (PBs) that was previously established. We also characterized the nature of the structural variations for a few proteins using molecular dynamics simulations. In both the cases, the structural variations were summarized in the form of substitution matrices of PBs. We show that certain conformational states are preferably replaced by other specific conformational states. Interestingly, these structural variations are highly similar to those previously observed across structures of homologous proteins (r2 = 0.923) or across the ensemble of conformations from NMR data (r2 = 0.919). Thus our study quantitatively shows that overall trends of structural changes in a given protein are nearly identical to the trends of structural differences that occur in the topologically equivalent positions in homologous proteins. Specific case studies are used to illustrate the nature of these structural variations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cadet2018,

title = {A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes},

author = {Frédéric Cadet and Nicolas Fontaine and Guangyue Li and Joaquin Sanchis and Matthieu {Ng Fuk Chong} and Rudy Pandjaitan and Iyanar Vetrivel and Bernard Offmann and Manfred T Reetz},

doi = {10.1038/s41598-018-35033-y},

issn = {20452322},

year  = {2018},

date = {2018-01-01},

journal = {Scientific Reports},

volume = {8},

number = {1},

pages = {1--15},

abstract = {Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov'SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}