Estimation of binding free energy based on the MM/3D-RISM method | 33428
Journal of Proteomics & Bioinformatics

Journal of Proteomics & Bioinformatics
Open Access

ISSN: 0974-276X

+44 1223 790975

Estimation of binding free energy based on the MM/3D-RISM method for the Pim1-ligand system

6th International Conference on Structural Biology

August 22-23, 2016 New Orleans, USA

Takeshi Haesegawa, Masatake Sugita, Takeshi Kikuchi and Fumio Hirata

Ritsumeikan University, Japan
Toyota Physical and Chemical Institute, Japan

Posters & Accepted Abstracts: J Proteomics Bioinform

Abstract :

To seek the drug candidate molecules, it is necessary to assess the binding affinity of a protein with its inhibitors. A large number of computational methods for estimation the binding free energy have been proposed. MD simulation is a powerful method for computationally estimating the physical quantity including the binding free energy. Although the estimation of the physical quantity of the system which has a huge amount of the degree of the freedom requires enormous computational cost, we sometimes cannot explore the enough structural space to evaluate an accurate value. On the other hand, 3D-RISM theory, a statistical theory for the molecular liquid, can estimate the solvation free energy in a reasonable computational cost by analytically calculating configuration integral of the solvent. In this regards, the combination of the 3D-RISM theory and MD simulation can escape the insufficient sampling. However, the quantitative capability of the MM/3D-RISM method has been ambiguous so far since this is a somehow novel method and has been applied to few systems. We apply this method to estimate binding free energy between Pim1 kinase and its inhibitors. Pim1 kinase is a famous target protein for treating hematopoietic malignancies, such as leukemia, lymphoma and prostate cancer. As a result, we get an approximately R=0.7 of the correlation coefficient between experimental and calculated values. Furthermore, we suggest a way of possible a lead optimization procedure by means of the 3D-RISM theory and MM/3D-RISM method.

Biography :

Takeshi Hasegawa is currently a graduate student of Ritsumeikan University at Department of Bioinformatics of College of Life Sciences.