Protein Bioinformatics Lab

2026

S. Boopathi*, R. Garduno-Juarez and M. Michael Gromiha* (2026) Herpes Simplex Virus Glycoprotein D Associated with Aβ1-42 Tetramers Mediates Neurotoxicity by Perturbing Neuronal Membrane Integrity: A Molecular Dynamics Simulation. ACS Chem. Neurosci. (in press).
R.C. Kretsch, E. Posani, E.F. Baulin, J.M. Bujnicki, G. Bussi, T.E. Cheatham, S-J. Chen, A. Elofsson, M.A. Farsani, O.N. Fisher, M. Michael Gromiha , A. Gupta, M. Hamada, K. Harini, G. Hu, D. Huang, J. Iwakiri, A. Jain, Y. Kagaya, D. Kihara, S. Kmiecik, S.R. Krishnan, I. Kurisaki, O. Languin-Cattoën, J. Li, S. Li, K. Malekzadeh, T. Nakamura, W. Ni, C. Nithin, M. Z. Palo, J.H. Park, S. P. Pilla, S. Poblete, F. Pucci, P. Punuru, A. Saha, K. Sato , A. Srivastava, G. Terashi, E. Tugolukova, J. Verburgt, Q. Wuyun, G.H. Zerze, K. Zhang, S. Zhang, W. Zheng, Y. Zhou, W. Chiu, D.A. Case, R. Das (2026) Blind prediction of complex water and ion networks around RNA in CASP16. PROTEINS: Structure, Function, and Bioinformatics, 94(1), 381–402.
F. Ridha and M. Michael Gromiha* (2026) Binding affinity prediction of membrane protein-protein complexes using MPA-Pred. Meth. Mol. Biol. 2979, 267–276.
P.R. Reddy, F. Ridha and M Michael Gromiha* (2025) Integrating sequence, structure, and graph-based features for elucidating the stability of thermophilic proteins. J. Mol.Biol, 169593.

2025

S.R. Krishnan, A. Roy, L. Wong and M. Michael Gromiha* (2025) DRLiPS: A Novel Method for Prediction of Druggable RNA-Small Molecule Binding Pockets Using Machine Learning. Nucl. Acids Res. 53, gkaf239
F. Ridha, D. Frishman and M. Michael Gromiha* (2025) Structural and functional effects of disease mutations in the interface of human membrane protein complexes. Int. J. Biol. Macromol. 330, 148290.
S. Venkatachalam, N. Muaralidharan, S. Pandian, Y. Sayed and M.Michael Gromiha* (2025) Integrative Computational Approaches for Understanding Drug Resistance in HIV-1 Protease Subtype C. Viruses, 17, 850.
M. Michael Gromiha* , M. Pandey, A. Kulandaisamy, D. Sharma and F. Ridha (2025) Progress on the development of prediction tools for detecting disease causing mutations in proteins. Comp. Biol. Med. 185, 109510.
S.R. Krishnan, A. Roy and M. Michael Gromiha (2024) Comment on “Contrastive pre-training and 3D Convolution Neural Network for RNA and small molecule binding affinity prediction”. Bioinformatics 40(4), btae155.
S.V. Sankaran, S.R. Krishnan, R. Pandian, Y. Sayed and M. Michael Gromiha* (2025) Structural implications of HIV-1 protease subtype C bound to darunavir: A molecular dynamics study. PROTEINS. 93(9), 1426–1435.
F. Ridha and M. Michael Gromiha* (2025) Illustrative features and utilities of MPAD: Thermodynamic database for membrane protein-protein complexes. Meth. Mol. Biol. 2947, 341–354.
P. R. Reddy, A. Kulandaisamy and M. Michael Gromiha* (2025) TMB Stab-pred: Predicting the stability of transmembrane β-barrel proteins using their sequence and structural signatures. Biochim. Biophys. Acta: Proteins and Proteomics. 1873, 141070.
M. Michael Gromiha* , F. Ridha and S. Selvaraj (2025) Protein structural bioinformatics: an overview. Encyc Bioinf. Comp. Biol. 3, 519–533.
Harihar, K.M. Saravanan, M. Michael Gromiha, and S. Selvaraj (2025) Importance of inter-residue contacts for understanding protein folding andunfolding rates, remote homology and drug design. Mol. Biotech. 67, 862-884.
V.P. Warrier, S. Venkatachalam, R. Sakthivel, M. Michael Gromiha, D. Karunagaran (2025) Combinatorial Effects of 5-fluorouracil and Menadione on Wnt/β-catenin Pathway in Human Colorectal Cancer Cells. App. Biochem. Biotech. 197, 1280-1300.
K. Harini, M. Sekijima and M. Michael Gromiha* (2025) PRA-MutPred: Predicting the effect of point mutations in protein-RNA complexes using structural features. J. Chem. Inf. Model. 65, 1605-1614.
S. Rajendran, R.P. Surabhi, A.S. Kumar, P. Gopinath, V. Kanakaveti, G. Shanmugasundaram, M. Michael Gromiha , S.K. Rayala and G. Venkatraman (2025) P21-activated kinase 1 (PAK1) modulates therapeutic response to ionizing radiation in head and neck squamous cell carcinoma cells. Molecular Carcinogenesis 64(6), 970–984.
P. Rawat, R. Prabakaran, D. Sharma, V. Mandala, V. Greiff, S. Kumar, M Michael Gromiha* (2025) Investigating local sequence-structural attributes of light chain variable domains (VLs) in amyloidosis. PROTEINS 93(9), 1451–1464.
P.K. Praveen Kumar, H. Sundar, K. Balakrishnan, S. Subramaniam, R. Hemalath, M. Kevin and M. Michael Gromiha (2025) The Role of HSP90 and TRAP1 Targets on Treatment in Hepatocellular Carcinoma. Mol.Biotech. 67, 1367-1381
A. Phogat, S.R. Krishnan, M. Pandey and M. Michael Gromiha* (2025) ZFP-CanPred: Predicting the Effect of Mutations in Zinc-Finger Proteins in Cancers Using Protein Language Models. Methods, 235, 55–63.
V. Nithyasree, P. Magdalene, P.K. Praveen Kumar, J. Preethi and M. Michael Gromiha (2025) Role of HSP90 in Type2 Diabetes mellitus and its association with Liver diseases. Mol. Biotech. 67(8),2997–3009.
M. Michael Gromiha and K. Harini (2025) Protein-nucleic acid complexes: docking and binding affinity. Curr. Opin. Str. Biol. 90, 102955. INVITED REVIEW

2024

M. Michael Gromiha, Editor (2024) Protein mutations: consequences on structure, function and diseases, World Scientific, Singapore.
F. Ridha and M. Michael Gromiha* (2024) MPA-MutPred: A novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes. Brief. Bioinf. 25, bbae598.
N. P. Sneha, S.A.P. Dharshini, Y.-h. Taguchi and M. Michael Gromiha* (2024) Tracing ALS Degeneration: Insights from Spinal Cord and Cortex Transcriptomes. Genes 15, 1431.
R. Nikam, S. Jemimah and M. Michael Gromiha* (2024) DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation. Bioinformatics 40(5), btae309
S. Venkatachalam, S.R. Krishnan, Y. Sayed and M. Michael Gromiha* (2024) Structural and functional studies on HIV protease: mechanism of action, subtypes, inhibitors and drug resistance. Meth. Mol. Biol. 185-200.
S.R. Krishnan, D. Sharma, Y. Nazeer, M.V. Bose, T. Rajkumar, G. Jayaraman, N. Madaboosi and M. Michael Gromiha* (2024) rAbDesFlow: A novel workflow for computational recombinant antibody design for healthcare engineering. Antibody Therapeutics. 7(3), 256-265.
N.R. Siva Shanmugam, A. Kulandaisamy, K. Veluraja and M. Michael Gromiha* (2024) CarbDisMut: Database on neutral and disease-causing mutations in human carbohydrate-binding proteins. Glycobiology. 34, cwae011.
K. Harini, M. Sekijima and M. Michael Gromiha* (2024) PRA-Pred: structure-based prediction of protein-RNA binding affinity. Int. J. Biol. Macromol. 259, 129490.
J. J. Blessy, N.R. Siva Shanmugam, K. Veluraja and M. Michael Gromiha (2024) Sialic acid analogue as inhibitor for human coronavirus OC43-A study by molecular dynamics simulations. J. Carb. Chem. 42, 197-215
S.V. Sankaran, S.R. Krishnan, Y. Sayed and M. Michael Gromiha (2024) Mechanism of drug resistance in HIV-1 protease subtype C in the presence of Atazanavir. Curr. Res. Str. Biol. 7, 100132
Fathima Ridha and M. Michael Gromiha (2024) MPA-Pred: A machine learning approach for predicting the binding affinity of membrane protein-protein complexes. Proteins. 92(4), 499–508.
M. Michael Gromiha*, P. Preethi and Medha Pandey (2024) From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications. BiomedInformatics 4, 542–548
D. Sharma, P. Rawat, V. Greiff, V. Janakiraman, and M. Michael Gromiha (2024) Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochim. Biophys. Acta: Mol. Bas. Dis. 1870:166959
M. Pandey, S.K. Shah, and M. Michael Gromiha (2024) Computational approaches for identifying disease-causing mutations in proteins. Adv. Prot. Chem. Str. Biol. 139, 141-171
K. Harini, M. Sekijima, and M. Michael Gromiha (2024) Bioinformatics approaches for understanding the binding affinity of protein-nucleic acid complexes. Meth. Mol. Biol. pp. 315-330
S.R. Krishnan, A. Roy and M. Michael Gromiha* (2024) Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. Brief. Bioinf. 25(2), bbae002

2023

M. Michael Gromiha* and K. Harini (2023) Comment on “Thermodynamic database supports deciphering protein–nucleic acid interactions”. Trends in Biotech. 41, 988-989.
M. Pandey and M. Michael Gromiha* (2023) MutBLESS: A tool to identify disease-prone sites in cancer using deep learning. Biochim. Biophys. Acta: Mol. Bas. Dis. 1869, 166721.
R. Nikam, K. Yugandhar, and M. Michael Gromiha* (2023) Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes. Biochim. Biophys. Acta: Proteins and Proteomics871: 140948.
N.P. Sneha, S.A.P. Dharshini, Y-h. Taguchi and M. Michael Gromiha (2023) Investigating neuron degeneration in Huntington’s disease using RNA-seq based transcriptome study. Genes. 14, 1801.
R. Reddy, A. Kulandaisamy and M. Michael Gromiha (2023) TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features. Methods. 18: 118-124.
J. Sun, A. Kulandaisamy, J. Ru, M. Michael Gromiha*, A.P. Cribbs* (2023) TMKit: a Python interface for computational analysis of transmembrane proteins. Brief. Bioinf. 24(5), bbad288.
M. Michael Gromiha*, P. Kundrotas, M.A. Marti, Č. Venclovas and M. Li (2023) Protein recognition and associated diseases. Front. Bioinf. 3:1215141.
S.R. Krishnan, A. Roy , and M. Michael Gromiha (2023) R-SIM: A database of binding affinities for RNA-small molecule interactions. J. Mol. Biol. 435(14), 167914.
R. Nikam, K. Yugandhar, and M. Michael Gromiha (2023) DeepBSRPred: Deep learning-based Binding Site Residue Prediction for proteins. Amino acids 55(10), 1305–1316
K. Harini, D. Kihara, and M. Michael Gromiha (2023) PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods 213, 10-17.
D. Sharma, T.L. Baas, A. Nogales*, L. Martinez-Sobrido* , and M. Michael Gromiha (2023) CoDe: A web-based tool for Codon Deoptimization. Bioinf. Adv. 3, vbac102.
S.V. Sankaran, N. Murlidharan, S. Krishnan, C. Ramakrishnan, M. Setshedi, R. Pandian, D. Barh, S. Tiwari, V. Azevedo, Y. Sayed and M. Michael Gromiha (2023) Understanding drug resistance of wild-type and L38HL insertion mutant of HIV-1 C protease to saquinavir Genes 14, 533.
M. Pandey, P. Anoosha, D. Yesudhas, and M. Michael Gromiha (2023) Identification of Cancer Hotspot Residues and Driver Mutations Using Machine Learning. In “Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics” (Ed. L. Kurgan), World Scientific, Singapore pp 289-306.
J. Sun, A. Kulandaisamy, K. Hu, M. Michael Gromiha* and Y. Zhang* (2023) Machine learning in computational modelling of membrane protein sequences and structures: from methodologies to applications. Comp. Str. Biotech. J. 21, 1205-1226.
S.R. Krishnan, R.R.G. Soares, N. Madaboosi , and M. Michael Gromiha (2023) AutoPLP: A padlock probe design pipeline for zoonotic pathogens. ACS Infect. Dis. 9(3), 459–469.
A. Kulandaisamy, S.A.P. Dharshini, and M. Michael Gromiha (2023) Alz-Disc: A tool to discriminate disease-causing and neutral mutations in Alzheimer's disease. Comb Chem High Throughput Screen. 26, 769-777.
D. Sharma, K.I. Notarte, R.A. Fernandez, G. Lippi, M. Michael Gromiha*, B.M. Henry* (2023) In silico evaluation of the impact of Omicron variant of concern sublineage BA.4 and BA.5 on the sensitivity of RT-qPCR assays for SARS-CoV-2 detection using whole genome sequencing. J. Med. Virol. 95, e28241.
F. Ridha, A. Kulandaisamy and M. Michael Gromiha (2023) MPAD: A database for binding affinity of membrane protein-protein complexes and their mutants. J. Mol. Biol. 435(14), 167870.

2022

M. Pandey, P. Anoosha, D. Yesudhas and M. Michael Gromiha (2022) Identification of potential driver mutations in glioblastoma using machine learning. Brief. Bioinf. 23, bbac451.
S. A. P. Dharshini, N.P. Sneha, D. Yesudhas, A. Kulandaisamy, U. Rangaswamy, A. Shanmugam, Y-h. Taguchi and M. Michael Gromiha (2022) Exploring plausible therapeutic targets for Alzheimer's disease using multi-omics approach, machine learning and docking. Curr. Topics Med. Chem.22, 1868-1879.
N.P. Sneha, S.A.P. Dharshini, Y-h. Taguchi and M. Michael Gromiha (2022) Integrative Meta-Analysis of Huntington’s Disease Transcriptome Landscape. Genes 13, 428.
P. Rawat, D. Sharma, R. Prabakaran, F. Ridha, M. Mugdha, V. Janakiraman, and M. Michael Gromiha (2022) Ab-CoV: a curated database for binding affinity and neutralization profiles of coronavirus related antibodies. Bioinformatics. 38, 4051-4052.
V. Kanakaveti, R. Sakthivel, R. Kanumari, B. Vaishnavi, R. Saravanan, I. Ezhil, R. Pitani, G. Venkatraman, S.K. Rayala, and M. Michael Gromiha (2022) Novel BH4-BCL-2 domain antagonists induce BCL-2 mediated apoptosis in triple negative breast cancer. Cancers 14, 5241.
A. Kulandaisamy, F. Ridha, D. Frishman and M. Michael Gromiha (2022) Computational approaches for investigating disease-causing mutations in membrane proteins: database development, analysis and prediction Curr. Topics Med. Chem. 22, 1766-1775.
P. Rawat, D. Sharma, M. Pandey, R. Prabakaran, and M. Michael Gromiha (2022) Understanding the mutational frequency in SARS-CoV-2 proteome using structural features Comp. Biol. Med. 147, 105708.
G. Lippi, J. Favresse, M. Michael Gromiha, J. SoRelle, EPH. Yap, M. Plebani, BM. Henry (2022) Ad interim recommendations for diagnosing SARS-CoV-2 infection by the IFCC SARS-CoV-2 Variants Working Group. Clin. Chem. Lab. Med. 60:975-981.
N.R. Siva Shanmugam, K. Veluraja, and M. Michael Gromiha (2022) PCA-MutPred: Prediction of binding free energy change upon missense mutation in protein-carbohydrate complexes. J. Mol. Biol. 434, 167526.
D. Yesudhas, S.A.P. Dharshini, Y-h. Taguchi, and M. Michael Gromiha (2022) Tumor heterogeneity and molecular characteristics of glioblastoma revealed by single-cell RNA-seq data analysis. Genes. 13, 428.
R.A. Fernandez, M.T. Quimque, K.I. Notarted, J.A. Manzanoa, D.Y. Pilapil, V.N. de Leon, J.J.S. Josea, O.Villalobosf, N.H. Muralidharan, M. Michael Gromiha, S. Brogi and A.P.G. Macabeoa (2022) Myxobacterial depsipeptide chondramides interrupt SARS-CoV-2 entry by targeting its broad, cell tropic spike protein. J Biomol Struct Dyn. 40(22), 12209–12220.
M. Michael Gromiha, C. Orengo, R. Sowdhamini and J. Thornton (2022) Srinivasan (1962-2021) in Bioinformatics and beyond. Bioinformatics 38(8), 2377–2379.
D. Sharma, P. Rawat, V. Janakiraman, and M. Michael Gromiha (2022) Elucidating important structural features for the binding affinity of spike - SARS-CoV-2 neutralizing antibody complexes. PROTEINS: Structure, Function and Bioinformatics. 90: 824-834.
J.J. Blessy, N.R.S. Shanmugam, K. Veluraja and M. Michael Gromiha (2022) Investigations on the binding specificity of β-galactoside analogues with human galectin-1 using molecular dynamics simulations. J Biomol Struct Dyn. 40(20), 10094–10105.
A. Shanmugam, A. Venkattappan, and M. Michael Gromiha (2022) Structure Based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design. Curr. Topics Med. Chem. 22, 2396-2409.
A. Srivastava, D. Yesudhas, S. Ahmad and M. Michael Gromiha (2022) Understanding disorder-to-order transitions in protein–RNA complexes using molecular dynamics simulations. J Biomol Struct Dyn. 40(17), 7915–7925.
R. Prabakaran, P. Rawat, N. Yasuo, M. Sekijima, S. Kumar and M. Michael Gromiha (2022) Effect of Charged Mutation on Aggregation of a Pentapeptide: Insights from Molecular Dynamics Simulations. PROTEINS: Structure, Function and Bioinformatics 90(2):405-417.
K. Harini, A. Srivastava, A. Kulandaisamy and M. Michael Gromiha (2022) ProNAB: Database for binding affinities of protein-nucleic acid complexes and their mutants. Nucl. Acids Res.50(D1):D1528-D1534.

2021

R. Prabakaran, P. Rawat, S. Kumar and M. Michael Gromiha (2021) Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Brief. Bioinf. 22, bbab240.
A. Kulandaisamy, R. Nikam, K. Harini, D. Sharma and M. Michael Gromiha (2021) Illustrative tutorials for ProThermDB: Thermodynamic database for proteins and mutants. Curr. Protocols 1 (11), e306.
R. Prabakaran, S. Jemimah, P. Rawat, D. Sharma and M. Michael Gromiha (2021) A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19. Sci Rep. 11(1):24073.
A. Sharma, F. Tobar-Tosse, T. C. Dakal, H. Liu, A. Biswas, A. Menon, P. Anoosha, P. Katsonis, O. Lichtarge, M. Michael Gromiha , M. Ludwig, F.G. Holz, K.U. Löffler, M.C. Herwig-Carl (2021) PPAR-responsive elements (PPREs) enriched with Alu repeats revealed a descriptive contribution to PPARγ- DNMT1 interactions in the genome. Cancers. 13, 3993.
P. Rawat, R. Prabakaran, S. Kumar and M. Michael Gromiha (2021) Exploring the sequence features determining amyloidosis in human antibody light chains. Scientific Reports. 11, 13785.
P. Rawat, R. Prabakaran, S. Kumar and M. Michael Gromiha (2021) AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins. Biophys. Biochim. Acta - Proteins and Proteomics 1869, 140682.
D. Yesudhas, A. Srivastava, M. Sekijima and M. Michael Gromiha (2021) Tackling Covid-19 using disordered-to-order transition of residues in the spike protein upon ACE2 binding. PROTEINS: Structure, Function and Bioinformatics 89, 1158-1166.
P. Rawat, D. Sharma, A. Srivastava, V. Janakiraman and M. Michael Gromiha (2021) Exploring antibody repurposing for COVID-19: Beyond presumed roles of therapeutic antibodies. Scientific Reports 11, 10220.
R. Prabakaran, P. Rawat, A. M. Thangakani, S. Kumar and M. Michael Gromiha (2021) Protein Aggregation: In silico Algorithms and Applications. Biophys Rev. 13, 71-89.
A. Srivastava, D. Yesudhas, S. Ahmad, M. Michael Gromiha (2021) Deciphering the role of residues involved in disorder-to-order transition regions in archaeal tRNA methyltransferase 5. Genes 12, 399.
S. Akila Parvathy Dharshini, S. Jemimah, Y-h. Taguchi and M. Michael Gromiha (2021) Exploring common therapeutic targets for neurodegenerative disorders using transcriptome study. Front Genet. 12, 639160.
D. Yesudhas, A. Srivastava and M. Michael Gromiha (2021) COVID-19 outbreak: history, mechanism, transmission, structural studies and therapeutics. Infection. 49, 199–213.
M. Pandey and M. Michael Gromiha (2021) Predicting potential residues associated with lung cancer using deep neural networks. Mutation Res. 822, 111737.
J. Zaucha, M. Heinzinger, A. Kulandaisamy, E. Kataka, Ó.L. Salvádor, P. Popov, B. Rost, M. Michael Gromiha, B.S. Zhorov and D. Frishman (2021) Mutations in transmembrane proteins: diseases, evolutionary insights and prediction. Brief. Bioinf. 22, bbaa132.
P. Rawat, S. Jemimah, P.K. Ponnuswamy and M. Michael Gromiha (2021) Why are ACE2 binding coronavirus strains SARS-CoV/SARS-CoV-2 wild and NL63 mild? Proteins: Struct Funct Bioinf. 89:389-398.
Y-h. Taguchi, S.A.P. Dharshini and M. Michael Gromiha (2021) Identification of Transcription Factors, Biological Pathways, and Diseases as Mediated by N6-methyladenosine using Tensor Decomposition-Based Unsupervised Feature Extraction. Appl Sci. 11, 213.
R. Prabakaran, P. Rawat, S. Kumar and M. Michael Gromiha (2021) ANuPP: A versatile tool to predict aggregation nucleating regions in peptides and proteins. J Mol Biol. 433, 166707.
R. Nikam, A. Kulandaisamy, K. Harini, D. Sharma and M. Michael Gromiha (2021) ProThermDB: Thermodynamic database for proteins and mutants revisited after 15 years. Nucleic Acids Res. 49, D420-424.
A. Kulandaisamy, R. Sakthivel and M. Michael Gromiha (2021) MPTherm: Database for Membrane Protein Thermodynamics for understanding folding and stability. Brief Bioinf. 22, 2119-2125.
C. Ramakrishnan, R. Nagarajan, M. Sekijima and M. Michael Gromiha (2021). Molecular dynamics simulations of cognate and non-cognate AspRS-tRNA Asp complexes. J Biomol Str Dyn. 39, 493-501.
A. Kulandaisamy, Jan Zaucha, Dmitrij Frishman and M. Michael Gromiha (2021) MPTherm-pred: Analysis and prediction of thermal stability changes upon mutations in transmembrane proteins. J Mol Biol. 433, 166646.
N. Muralidharan, R. Sakthivel, D. Velmurugan and M. Michael Gromiha (2021) Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19. J Biomol Struct Dyn. 39, 2673-2678.
N.R.S. Shanmugam, J. Blessy, K. Veluraja and M. Michael Gromiha (2021) Prediction of protein-carbohydrate complex binding affinity using structural features. Brief Bioinf. bbaa319.

2020

M. Michael Gromiha, Editor (2020) Protein Interactions: computational methods, analysis and applications, World Scientific, Singapore.
D. Barh, S. Tiwari, M.E. Weener, V. Azevedo, A. Góes-Neto, M. Michael Gromiha and P. Ghosh (2020) Multi-omics-based identification of SARS-CoV-2 infection biology and candidate drugs against COVID-19. Comp Biol Med. 126, 104051.
U. Rangaswamy, S.A.P. Dharshini, D. Yesudhas and M. Michael Gromiha (2020) VEPAD- Predicting the effect of variants associated with Alzheimer’s disease using machine learning. Comp Biol Med. 103933.
A. Shanmugam, N. Muralidharan, D. Velmurugan and M. Michael Gromiha (2020) Therapeutic targets and computational approaches on drug development for COVID-19. Curr Top Med Chem. 20, 2210-20.
S. Sudhakara, C. Ramakrishnan, M. Michael Gromiha and A. Chadha (2020) New insights into the stereospecific reduction by an (S) specific carbonyl reductase from Candida parapsilosis ATCC 7330: Experimental and QM/MM studies. Catal Sci Technol. 10, 5925-34.
S. Jemimah and M. Michael Gromiha (2020) Insights into changes in binding affinity caused by disease mutations in protein-protein complexes. Comp Biol Med. 103829.
S. Jemimah, K. Yugandhar and M. Michael Gromiha (2020) Binding affinity of protein-protein complexes: experimental techniques, databases and computational methods. In “Protein interactions: computational methods, analysis and applications”, (Ed. M.M. Gromiha), World Scientific, Singapore, pp 87-108.
A. Srivastava, D. Yesudhas, A. Kulandaisamy, N. Muralidharan, C. Ramakrishnan, R. Nagarajan and M. Michael Gromiha (2020) Computational approaches for understanding the recognition mechanism of protein–nucleic acid complexes. In “Protein interactions: computational methods, analysis and applications”, (Ed. M.M. Gromiha), World Scientific, Singapore, pp 169-216.
D. Yesudhas, A. Srivastava, N. Muralidharan, A. Kulandaisamy, R. Nagarajan and M. Michael Gromiha (2020) Prediction of nucleic acid binding proteins and their binding sites. In “Protein interactions: computational methods, analysis and applications”, (Ed. M.M. Gromiha), World Scientific, Singapore, pp 217-242.
K. Veluraja, N. R. Siva Shanmugam, J. Jino Blessy, R. A. Jeyaram, B. Lalithamaheswari and M. Michael Gromiha (2020) Protein-carbohydrate complexes: binding site analysis, prediction, binding affinity and molecular dynamics simulations. In “Protein interactions: computational methods, analysis and applications”, (Ed. M.M. Gromiha), World Scientific, Singapore, pp 299-332.
V. Kanakaveti, P. Anoosha, R. Sakthivel, S.K. Rayala and and M. Michael Gromiha (2020) Quantitative structure-activity relationship in ligand-based drug design: concepts and applications. In “Protein interactions: computational methods, analysis and applications”, (Ed. M.M. Gromiha), World Scientific, Singapore, pp 333-349.
N.R.S. Shanmugam, J. Blessy, K. Veluraja and M. Michael Gromiha (2020). ProCaff: protein-carbohydrate complex binding affinity database. Bioinformatics 36(11), 3615-3617.
V.R. Muddapu, S.A.P. Dharshini, V. Srinivasa Chakravarthy and M. Michael Gromiha (2020). Neurodegenerative diseases - Is metabolic deficiency the root cause? Frontiers Neurosci. 14, 213.
A. Shanmugam, C. Ramakrishnan, D. Velmurugan and M. Michael Gromiha (2020). Identification of potential inhibitors for targets involved in dengue fever. Curr Topics Med Chem. 20, 1738-1756.
A. Srivastava, D. Yesudhas, C. Ramakrishnan, S. Ahmad and M. Michael Gromiha (2020). Role of disordered regions in transferring tyrosine to its cognate tRNA. Int J Biol Macromol. 150:705-713
P. Rawat, R. Prabakaran, R.Sakthivel, A. Mary Thangakani, Sandeep Kumar and M. Michael Gromiha (2020). CPAD 2.0: A repository of curated experimental data on aggregating proteins and peptides. Amyloid 27, 128-133.
V. Kanakaveti, A. Shanmugam, C. Ramakrishnan, P. Anoosha, R. Sakthivel, S.K. Rayala and M. Michael Gromiha (2020). Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery. Adv Prot Chem Str Biol. 121, 25-47.
S. Akila Parvathy Dharshini, Y-h. Taguchi and M. Michael Gromiha (2020). Identifying suitable tools for variant detection and differential gene expression using RNA-seq data. Genomics. 112(3):2166-2172.
A. Kulandaisamy, J. Zaucha, R. Sakthivel, D. Frishman and M. Michael Gromiha (2020) Pred-MutHTP: Prediction of disease-causing and neutral mutations in human transmembrane proteins. Human Mutation. 41, 581-590.
S. Jemimah, M. Sekijima and M. Michael Gromiha (2020) ProAffiMuSeq: Sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification. Bioinformatics. 36:1725-1730.
P. Rawat, R. Prabakaran, S. Kumar and M. Michael Gromiha (2020). AggreRATE-Pred: A mathematical model for the prediction of change in aggregation rate upon point mutation. Bioinformatics. 36:1439-1444.
R. A. Jeyaram, C. Anu Radha, M. Michael Gromiha and K. Veluraja (2020). Design of fluorinated sialic acid analog inhibitor to H5 Hemagglutinin of H5N1 Influenza virus through Molecular Dynamics simulation study. J Biomol Str Dyn. 38, 3504-3513.

2019

R Nikam and M. Michael Gromiha (2019). Seq2Feature: a comprehensive web-based feature extraction tool. Bioinformatics 35:4797-4799.
S. Akila Parvathy Dharshini, Y-h. Taguchi and M. Michael Gromiha (2019) Investigating the energy crisis in Alzheimer disease using transcriptome study. Sci Rep 9, 18509.
S. Chiba, M. Ohue, A. Gryniukova, P. Borysko, S. Zozulya, N. Yasuo, R. Yoshino, K. Ikeda, W-H. Shin, D. Kihara, M. Iwadate, H. Umeyama, T. Ichikawa, R. Teramoto, K-Y. Hsin, V. Gupta, H. Kitano, M. Sakamoto, A. Higuchi, N. Miura, K. Yura, M. Mochizuki, C. Ramakrishnan, A. Mary Thangakani, D. Velmurugan, M. Michael Gromiha, I. Nakane, N. Uchida, H. Hakariya, M. Tan, H. Nakamura, S.D. Suzuki, T. Ito, M. Kawatani, K. Kudoh, S. Takashina, K. Yamamoto, Y. Moriwaki, K. Oda, D. Kobayashi, T. Okuno, S. Minami, G. Chikenji, P. Prathipati, C. Nagao, A. Mohsen, M. Ito, K. Mizuguchi, T. Honma, T. Ishida, T. Hirokawa, Y. Akiyama and M. Sekijima (2019) A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Scientific Reports, 9(1), 19585.
A. Sharma, A. Biswas, L. Hongde, S. Sen, P. Anoosha, P. Katsonis, O. Lichtarge, T.C. Dakal, U. Maulik, M. Michael Gromiha, S. Bandyopadhyay, M. Ludwig, F.G. Holz, K.U Loeffler, M.C. Herwig-Carl (2019) Mutational landscape of the BAP1 locus reveals an intrinsic control to regulate the miRNA network and the binding of protein complexes in uveal melanoma. Cancers 11(10), E1600.
P. Parasuraman, J.F.A. Selvin, M. Michael Gromiha , K. Fukui and K. Veluraja (2019). Investigation on the binding specificity of Agrocybe cylindracea galectin towards α(2,6)-linked sialyllactose by molecular modelling and molecular dynamics simulations. J Carb Chem. 38, 566-585
Popov P, Bizin I, A. Kulandaisamy, M. Michael Gromiha and Frishman D(2019). BorodaTM: prediction of disease-associated mutations in membrane proteins with known 3D structure. PLoS ONE, 14(7):e0219452.
P Uma Rani , K VishnuPriya, M.K.N. Sai Varsha, M Nikunj, C Shivam, N Manoj, R Thiagarajan, R Manikandan, M. Michael Gromiha and D Madhulika (2019). Amarogentin, a secoiridoid glycoside, activates AMP-activated protein kinase (AMPK) to exert beneficial vasculo-metabolic effects. BBA - General Subjects 1863:1270-1282.
V. Kanakaveti, R. Sakthivel, S.K. Rayala and M. Michael Gromiha (2019). Forging new scaffolds from old: Combining Scaffold hopping and Hierarchical virtual screening for identifying novel Bcl-2 inhibitors. Curr Top Med Chem. 19(13):1162-1172.
T. Amemiya, M. Michael Gromiha K. Horimoto and K. Fukui (2019). Drug repositioning for dengue hemorrhagic fever by integrating multiple omics analyses. Sci. Rep. 9, 523.
R.A. Jeyaram , T.R.K Priyadarzini , C. Anu Radha , N.R. Siva Shanmugam , C. Ramakrishnan , M. Michael Gromiha and K. Veluraja (2019). Molecular dynamics simulation studies on influenza A virus H5N1 complexed with sialic acid and fluorinated sialic acid. J Biomol Struct Dyn. 37(18):4813-4824.
A. Kulandaisamy, S. Binny Priya, R. Sakthivel, Dmitrij Frishman and M. Michael Gromiha(2019). Statistical analysis of disease-causing and neutral mutations in human membrane proteins. PROTEINS 87, 452-466.
K. Ganesan, A. Kulandaisamy,S. Binny Priya, and M. Michael Gromiha(2019). HuVarBase: A human variant database with comprehensive information at gene and protein levels. PLoS ONE 14(1), e0210475.
M. Michael Gromiha, R. Nagarajan and S. Selvaraj (2019). Protein structural bioinformatics: an overview. Encyc Bioinf Comp Biol. 2, 445-459. INVITED REVIEW.
V. Kanakaveti, P. Anoosha, R. Sakthivel, S. K. Rayala and M. Michael Gromiha (2019). Influence of amino acid mutations and small molecules for targeted inhibition of proteins involved in cancer. Curr Top Med Chem. 19, 457-466.
S. Akila Parvathy Dharshini, Y-h. Taguchi and M. Michael Gromiha(2019). Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis. Genomics. 111, 936-949.
S. Binny Priya and M. Michael Gromiha (2019). Structural insights into the aggregation mechanism of huntingtin exon 1 protein fragment with different polyQ-lengths. J Cell Biochem. 120, 10519-10529.
S. Anusuya, M. Michael Gromiha (2019). Structural basis of flavonoids as dengue polymerase inhibitors: Insights from QSAR and docking studies. J Biomol Struct Dyn. 37:104-115.

2018

P. Rawat, S. Kumar and M. Michael Gromiha(2018). An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins. Int J Biol Macromol. 118, 1157-1167.
D-S. Huang, M. Michael Gromiha, K. Han A. Hussain (Editors) Lecture Notes in Artificial Intelligence(2018). Springer, Heidelberg, Vol. 10956.
A. Kumaraswamy, A. Mamidi, P. Desai, A. Sivagnanam, L.R. Perumalsamy, C. Ramakrishnan, M. Michael Gromiha K. Rajalingam and S. Mahalingam (2018). The non-enzymatic RAS effector RASSF7 inhibits oncogenic c-Myc function. J Biol Chem. 293(40),15691-15705.
D-S. Huang , V. Bevilacqua and M. Michael Gromiha(2018). Guest Editorial for Special Section on the 12th International Conference on Intelligent Computing (ICIC) IEEE/ACM Trans Comput Biol Bioinform. 15(5),1433-1435.
A. Srivastava, S. Ahmad and M. Michael Gromiha(2018). Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins. Int J Mol Sci. 19(6), 1595.
N.R. Siva Shanmugam, J. Fermin Angelo Selvin, K. Veluraja and M. Michael Gromiha. Identification and analysis of key residues involved in folding and binding of protein-carbohydrate complexes. Protein Pept Lett. 25(4),379-389.
S. Jemimah, M. Michael Gromiha (2018). Exploring additivity effects of double mutations on the binding affinity of protein-protein complexes. Proteins. 86(5), 536-547.
A. Kulandaisamy, S. Binny Priya, R. Sakthivel, S. Tarnovskaya, I. Bizin, P. Hönigschmid, D. Frishman and M. M. Gromiha (2018). MutHTP: Mutations in Human Transmembrane Proteins. Bioinformatics. 34(13), 2325-2326.
C. Ramakrishnan, A. M. Thangakani, D. Velmurugan, D. A. Krishnan, M. Sekijima, Y. Akiyama and M. Michael Gromiha (2018). Identification of type I and type II inhibitors of c-Yes kinase using in silico and experimental techniques. J Biomol Struct Dyn.36(6), 1566-1576.
A. Kulandaisamy, A. Srivastava,R. Nagarajan and M. Michael Gromiha (2018). Dissecting and analyzing key residues in protein-DNA complexes. J Mol Recog. 31(4).
A. Kulandaisamy, A. Srivastava, P. Kumar, R. Nagarajan, S. Binny Priya and M. Michael Gromiha (2018).Identification and analysis of key residues in protein-RNA complexes. IEEE/ACM Trans Comput Biol Bioinform. 15(5), 1436-1444.

2017

M. Michael Gromiha, K. Yugandhar, and S. Jemimah (2017) Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol., 44, 31-38.
P. Anoosha, R. Sakthivel, M. Michael Gromiha (2017). Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer. Mutat Res. , 806, 19-26.
S. Chiba, T. Ishida, K. Ikeda, M. Mochizuki, R. Teramoto, Y-h Taguchi, M. Iwadate, H. Umeyama, C. Ramakrishnan, A. M. Thangakani, D. Velmurugan, M. Michael Gromiha, T. Okuno, K. Kato, S. Minami, G. Chikenji, S.D. Suzuki, K. Yanagisawa, W.H. Shin, D. Kihara, K. Yamamoto, Y. Moriwaki, N. Yasuo, R. Yoshino, S. Zozulya, P. Borysko, R. Stavniichuk, T. Honma, T. Hirokawa, Y. Akiyama, M. Sekijima (2017). An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes. Sci Rep.,7(1):12038.
S. Jemimah, K. Yugandhar, M. Michael Gromiha (2017). PROXiMATE: a database of mutant protein–protein complex thermodynamics and kinetics. Bioinformatics, 33(17), 2787-2788.
S. D. Huang, A. Hussain,,K. Han,M. Michael Gromiha (Editors) Lecture Notes in Artificial Intelligence(2017), Springer, Heidelberg, Vol. 1036.
R. Prabakaran, D. Goel, S. Kumar and M. Michael Gromiha (2017) Aggregation prone regions in human proteome: Insights from large-scale data analyses. Proteins, 85(6):1099-1118.
M. Michael Gromiha and K. Yugandhar (2017) Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes. Prog Biophys Mol Biol. , 128, 33-38.
V. Kanakaveti, R. Sakthivel, Suresh K. Rayala and M. Michael Gromiha (2017) Importance of functional groups in predicting the activity of small molecule inhibitors of Bcl-2 and Bcl-xL. Chem Biol Drug Des., 90, 308-316.
A. Kulandaisamy, V. Lathi, K. ViswaPoorani, K. Yugandhar, and M. Michael Gromiha (2017) Important amino acid residues involved in folding and binding of protein-protein complexes. Int J Biol Macromol., 94 (Pt A), 438-444.
K. Yugandhar and M. Michael Gromiha (2017) Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes. Methods Mol Biol., 1484, 237-253.
S. Anusuya and M. Michael Gromiha (2017) Quercetin derivatives as non-nucleoside inhibitors for dengue polymerase: molecular docking, molecular dynamics simulation and binding free energy calculation, J Biomol Struct Dyn., 35, 2895-2909.
S. Anusuya, M. Kesherwani, K. Vishnupriya, A. Vimala, G. Shanmugam, D. Velmurugan and M. Michael Gromiha (2017) Drug-Target Interactions: Prediction Methods and Applications. Curr Protein Pept Sci., 18, 1-25.
M. Kesherwani, M. Michael Gromiha, K. Fukui and D. Velmurugan (2017) Identification of novel natural inhibitor for NorM -A Multidrug and Toxic compound extrusion Transporter-An in silico molecular modeling and simulation studies. J Biomol Struct Dyn., 35, 58-77.

2016

P. Chaudhary, A. N. Naganathan, M. Michael Gromiha (2016) Prediction of change in protein unfolding rates upon point mutations in two state proteins. Biochim Biophys Acta. 1864(9), 1104-1109.
D-S. Huang, V. Bevilacqua, and M. Michael Gromiha (2016) Guest Editorial for Special Section on the 10th International Conference on Intelligent Computing (ICIC). IEEE Trans. Comp. Biol. Bioinf., 13, 1-3.
S. Muthusamy, M. Prakash, C. Ramakrishnan, M. Michael Gromiha and V. Kesavan (2016) Organocatalytic Enantioselective Assembly of Spirooxindole-naphthopyrans through Tandem Friedel-Crafts Type/Hemiketalization. ChemCatChem, 8, 1708-1712.
R. Nagarajan and M. Michael Gromiha (2016) Computational Analysis of SimilarProtein-DNA Complexes from Different Organisms to Understand Organism Specific Recognition. Lect. Notes Comp. Sci., 9772, 888-894.
E. L. Folador, P. V. S. D. de Carvalho, W. M. Silva, R. S. Ferreira, A. Silva, M. Michael Gromiha, P. Ghosh, D. Barh, V. Azevedo, and R. Rottger (2016) In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks. BMC Systems Biology, 1, 103.
K. Yugandhar and M. Michael Gromiha (2016) Analysis of protein-protein interaction networks based on binding affinity. Current Protein & Peptide Science, 17(1): 72-81.
S. Anusuya, D. Velmurugan and M. Michael Gromiha (2016) Identification of Dengue Viral RNA Dependent RNA Polymerase Inhibitor using Computational Fragment Based Approaches and Molecular Dynamics Study. Journal of Biomolecular Structure and Dynamics, 34(7), 1512-32.
R. Nagarajan, A. Archana, A. Mary Thangakani, S. Jemimah, D. Velmurugan and M. Michael Gromiha (2016) PDBparam: Online Resource for Computing Structural Parameters of Proteins. Bioinformatics and Biology Insights, 10, 73-80.
C. Magyar, M. Michael Gromiha, Z. Savoly, and I. Simon (2016) The role of stabilization centers in protein thermal stability. Biochemical and biophysical research communications, 471(1), 57-62.
M. Michael Gromiha, P. Anoosha, and Liang-Tsung Huang (2016) Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants. Methods Mol Biol., 1415, 71-89.
A. Mary Thangakani, R. Nagarajan, S. Kumar, R. Sakthivel, D. Velmurugan and M. Michael Gromiha* (2016) CPAD, Curated Protein Aggregation Database: A Repository of Manually Curated Experimental Data on Protein and Peptide Aggregation. PLoS One, 11(4), e0152949.
S. Kumar, A. Mary Thangakani, R. Nagarajan, Satish K. Singh, D. Velmurugan and M. Michael Gromiha* (2016) Autoimmune Responses to Soluble Aggregates of Amyloidogenic Proteins Involved in Neurodegenerative Diseases: Overlapping Aggregation Prone and Autoimmunogenic regions. Scientific Reports, 6, 22258.
D. R. Tompa, M. Michael Gromiha and K. Saraboji* (2016) Contribution of main chain and side chain atoms and their locations to the stability of thermophilic proteins. J. Mol. Graph. Model, 64, 85-93.
P. Anoosha, R. Sakthivel, M. Michael Gromiha*(2016) Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1862(2), 155-165
N. Saranya, KM Saravanan, M. Michael Gromiha, S. Selvaraj* (2016) Analysis of secondary structural and physico-chemical changes in protein-protein complexes. J Biomol Struct Dyn., 34(3):508-16.

2015

P. Anoosha, Liang-Tsung Huang, R. Sakthivel, D. Karunagaran, M. Michael Gromiha*(2015) Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer.Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 780, 24-34.
R. Nagarajan, Sonia P Chothani, C. Ramakrishnan, M. Sekijima, M. Michael Gromiha* (2015) Structure based approach for understanding organism specific recognition of protein-RNA complexes, Biology Direct, 10:8.
Priyashree Chaudhary, Athi N. Naganathan, M. Michael Gromiha* (2015) Folding RaCe: A Robust Method for Predicting Changes in Protein Folding Rates upon Point Mutations. Bioinformatics, 31(13), 2091-2097.
S. Kumar, R.H. Robins, P.M. Buck, T.P. Hickling, A. M. Thangakani, L. Li, S. K. Singh, M Michael Gromiha (2015) Biopharmaceutical Informatics: Applications of Computation in Biologic Drug Development. In "Developability of Biotherapeutics" (Eds) S. Kumar and S.K. Singh, CRC Press, New York, pp 3-34.
Veeramani Murugan, Ponnusamy Parasuraman, Jeyasigamani FA Selvin, Thanu RK Priyadarzini, M Michael Gromiha, Kazuhiko Fukui, Kasinadar Veluraja* (2015) Geometry Optimization of Carbohydrate Binding Sites of Influenza: A Quantum Mechanical Approach.Journal of Carbohydrate Chemistry, 34(7), 409-429.
Ponnusamy Parasuraman, Veeramani Murugan, Jeyasigamani FA Selvin, M Michael Gromiha, Kazuhiko Fukui, Kasinadar Veluraja* (2015) Theoretical investigation on the glycan-binding specificity of Agrocybe cylindracea galectin using molecular modeling and molecular dynamics simulation studies.Journal of Molecular Recognition, 28(9), 528–538.
Huntaro Chiba, Kazuyoshi Ikeda, Takashi Ishida, M. Michael Gromiha, Y-h Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Kun-Yi Hsin, Hiroaki Kitano, Kazuki Yamamoto, Nobuyoshi Sugaya, Koya Kato, Tatsuya Okuno, George Chikenji, Masahiro Mochizuki, Nobuaki Yasuo, Ryunosuke Yoshino, Keisuke Yanagisawa, Tomohiro Ban, Reiji Teramoto, C. Ramakrishnan, A. Mary Thangakani, D Velmurugan, Philip Prathipati, Junichi Ito, Yuko Tsuchiya, Kenji Mizuguchi, Teruki Honma, Takatsugu Hirokawa , Yutaka Akiyama* (2015) Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Scientific Reports, 5, 17209.
P. Anoosha, R. Sakthivel, M. Michael Gromiha*(2015) Prediction of protein disorder upon amino acid substitutions, Analytical Biochemistry, 491, 18-22
S. Anandakumar, S. Vijayakumar, N. Arumugam, M Michael Gromiha (2015) Mammalian Mitochondrial ncRNA Database. Bioinformation 11, 512-513.
A. Vimala*, C. Ramakrishnan, M. Michael Gromiha* (2015) Identifying a potential receptor for the antibacterial peptide of sponge Axinella donnani endosymbiont. Gene, 566(2), 166-174.
M. Xavier Suresh, M. Michael Gromiha, Makiko Suwa (2015) Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information. Advances in Bioinformatics, 1-7.
M. Michael Gromiha*, P. Anoosha, D. Velmurugan, K. Fukui (2015) Mutational studies to understand the structure-function relationship in multidrug efflux transporters: application for distinguishing mutants with high specificity. International Journal of Biological Macromolecules,75, 218-224.