Richa Naveed Ahamed

1. T. Richa, R Bayoumi, Lansberg P, Banerjee Y. Blending Gagne's Instructional Model with Peyton's Approach to Design an Introductory Bioinformatics Lesson-plan for Medical Students. JMIR Med Educ. 2018. Accepted 10.2196/11122.

2. T. Richa, M Gentaro, M Taiji and Y. Kuroda. All-atom molecular dynamics simulations of alanine-scanned amyloidogenic octapeptides derived from IAPP using large multi-peptide systems. 2018 Submitted.


3. T. Richa, S. Ide, R. Suzuki, T. Ebina and Y. Kuroda. Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. Journal of Computer-Aided Molecular Design 2017; 31: 237-244.


4. K. P. Rao*, T. Richa*, K. Kumar, B. Raghuram and A. K. Sinha. In silico analysis reveals 75 members of mitogen-activated protein kinase kinase kinase gene family in rice. DNA Research 2010;17(3):139-153.
*Equal contribution


5. T. Richa and T. Sivaraman. OneG-Vali: A Webserver for Detecting, Estimating and Validating Cryptic Intermediates of Proteins under Native Conditions. RSC Advances 2014; 4(69): 36325-36335.


6. T. Richa and T. Sivaraman. OneG: A computational tool for predicting cryptic intermediates in the unfolding kinetics of proteins under native conditions. PLoS ONE 2012;7(3) e32465.


7. D. Sivakumar, T. Richa, S. S. Rajesh, B. Gorai and T. Sivaraman. In silico methods for designing antagonists to anti-apoptotic members of bcl-2 family proteins. Mini-Reviews in Medicinal Chemistry 2012;12(11):1144-1153.

8. D. P. Wankhede, M. Kumari, T. Richa, J. Aravind and S. Rajkumar. Genome wide identification and characterization of Calcium Dependent Protein Kinase gene family in Cajanus cajan. Journal of Environmental Biology 2017; 38: 169-177.


9. T. Richa and T. Sivaraman Computational analyses of cryptic intermediates in the native unfolding pathways of barnase and thioredoxin Biologia 2015; 70: 420-427.


10. P. Arasu, T.Richa and T. Sivaraman. Review on Computational Methods for Predicting Residue-Specific Stabilities of Proteins. Journal of Pharmaceutical Sciences and Research 2015; 7(3): 159-162.


11. P. Das, T. Richa and T. Sivaraman. A computational strategy for predicting residue-specific stabilities of cardiotoxin III, an all β-sheet protein. Research Journal of Pharmaceutical, Biological and Chemical Sciences. 2014; 5(3): 1824-1831.


12. T. Richa and T. Sivaraman. Cooperative unfolding units and metastable states of cytochrome c551 from Pseudomonas aeruginosa under native conditions. Journal of Pharmaceutical Sciences and Research. 2014; 6(3):144-147.


13. T. Richa and T. Sivaraman. Structural stability and folding pathways of proteins under native conditions as monitored by hydrogen/deuterium (H/D) exchange methods. International Journal of Research in Pharmaceutical Sciences. 2013; 4(4): 550-562.


14. T. Richa and T. Sivaraman. CIntX: A software tool for calculating the intrinsic exchange rates of labile protons in proteins. Journal of Pharmaceutical Sciences and Research. 2012;4(6):1852-1858.


15. T. Richa and T. Sivaraman. META: A computational tool for predicting metastable states in the folding pathways of proteins. Journal of Pharmaceutical Sciences and Research. 2011;3(9):1486-1490.