Protein arginine methyltransferases (PRMTs) catalyse the methylation of arginine residues of target proteins. PRMTs utilise S-adenosyl methionine (SAM) as the methyl group donor, leading to S-adenosyl homocysteine (SAH) and monomethylarginine (mMA). A combination of homology modelling, molecular docking, Active Site Pressurisation, molecular dynamic simulations and MM-PBSA free energy calculations is used to investigate the binding poses of three PRMT1 inhibitors (ligands 1-3), which target both SAM and substrate arginine binding sites by containing a guanidine group joined by short linkers with the SAM derivative. It was assumed initially that the adenine moieties of the inhibitors would bind in sub-site 1 (PHE44, GLU137, VAL136 and GLU108), the guanidine side chain would occupy sub-site 2 (GLU 161, TYR160, TYR156 and TRP302), with the amino acid side chain occupying sub-site 3 (GLU152, ARG62, GLY86 and ASP84; pose 1). However, the SAH homocysteine moiety does not fully occupy sub-...

Molecular modeling method has been used for modeling a new molecule for Breast and colorectal cancer using Topotecan, a drug that’s already designed. This drug is drawn using HYPERCHEM and its R group is modified by replacing different functional groups like OH, CCl2OH, CF2OH, CH2CH2CH3, CH2CH3, CH3, Cl, F, H, and NH2, etc in its place. Molecules designed as such are optimized using different algorithms and their affinity is checked with the protein. The binding free energy of the protein is calculated by performing docking process. The docking process is done with the help of GOLD software. The molecule with minimum binding energy will have the maximum binding affinity. From the results obtained it’s clear that ligand “2(CCl2OH)”has the maximum binding affinity and this molecule is determined as the best lead molecule targets computationally. The calculated binding affinities between inhibitors 1,2,3,4,5,6, 7,8,9,10 are compared. The calculated binding affinities of the inhibitors indicate that inhibitor “2” (CCl2OH) would be expected to be better inhibitor than lead inhibitor 1,3,4,5,6,7,8,9 and 10. Inhibitor “2’’ predicted to be the most potent inhibitor of TOPOTECAN inhibitor as compared to all the other inhibitors considered in this study. For all the cases the minimization results provided qualitative agreement with experimental results. Therefore, this approach could be very useful for screening a larger set of compounds prior to synthesis accordingly; there is a need for methods that enable rapid assessment of large number of structurally unrelated molecules in a reasonably accurate manner. Energy components calculated by performing molecular mechanics calculations both in explicit solvent and complex states are sufficient to estimate the relative binding free energy differences between inhibitors qualitatively.

Computer methods can now be used on almost every stage of drug development, but the most common areas of computers application are virtual screening and lead generation/optimization stages. Accurate prediction of the protein-ligand binding affinities is a crucial step in the structure-based drug design approach. Current algorithms and tools for binding energy calculation that are used upon the development of new drug candidates with an emphasize on underlying principles, advantages and limitations, software and general considerations in the selection of specific methods are discussed in the paper. Four main classes of currently available physics-based computer methods (molecular docking, end point / approximate free energy, relative binding free energy, and absolute binding free energy) are reviewed in details. Molecular docking approaches are the method of choice to filter out compounds-nonbinders, but they are not accurate enough to predict binding affinity. The end point methods are more physically rigorous and closer to real free energy calculations, but they are more computationally-intensive and not predictive for some types of proteins. Relative binding free energy methods take into account conformational and entropic contributions, thus offering more accurate predictions. However, they have high computational requirements and can be used only to compare related ligands or receptors. The extremely computational-dependent method of absolute binding free energy calculation is the most powerful approach, giving predictions with good correlations to experimental binding affinities. 1. INTRODUCTION Rational structure-based computer-aided modeling of protein-ligand interactions is now a key component in modern drug discovery paradigm (Charifson,1997). It is widely accepted that computational methods have played an extremely important role in the design process for a growing number of marketed drugs, and in the development of new drug candidates (Mobley & Dill 2010). Moreover, by the aid of computer-aided drug design (CADD), the cost of drug development could be reduced by up to 50% (Tan, 2010). Computer methods can now be used on almost every stage of drug development, but the most common areas of computers application are virtual screening and lead generation/optimization stages (Xiang, 2012). Virtual screening methods, which are designed for searching large libraries of compounds in silico, are widely used within the drug R&D industry and play an indispensable role in modern CADD efforts. These methods usually give a much higher hit rate than the traditional high throughput screening (HTS) (Tang, 2006) and the hits from VS appear more drug-like than the ones from HTS (Shekhar, 2008). At the same time, there is concern that VS methods may have reached a limit in effectiveness (Schneider, 2010). Current virtual screening methods are not very effective in selecting molecules that are actually active against the selected target molecule, although they are undoubtedly useful in eliminating some inactive compounds (Chodera, 2012). Limitations of the VS methods come from a variety of approximations used to allow large numbers of compounds to be screened quickly, often neglecting statistical mechanical and chemical effects for computational efficiency (Chodera, 2012), thus leading to the inaccuracies in the estimation of protein-ligand binding energy. Lead optimization is another crucially important step (Keseru & Makara, 2006) among all of the stages of drug discovery process. From the computational side, the key step in lead optimization process is an accurate prediction of the protein-ligand binding affinities (Jorgensen, 2009), since it is currently accepted that the biological activity of a compound is closely related to the affinity of the compound to macromolecular receptor (Gohlke & Klebe, 2002). Unfortunately, available methods for binding affinity estimation do not possess enough balance between calculation efficiency and reliability, and in a typical situation the most accurate methods are the most time consuming, while the fastest algorithms usually are not very rigorous and accurate (Xiang, 2012). In this review we are going to discuss current approaches and tools for binding energy calculation that are used upon the development of new drug candidates with an emphasize on underlying principles, advantages and limitations, software and general considerations on the selection of specific methods for different users. Computational Approaches to Binding Energy Prediction: Currently available physics-based computer methods can be grouped in at least four different classes. Below are listed from the fastest to slowest, and from the least

Diabetes affects the routine of life, due to its potential complications such as nephropathy, neuropathy, retinopathy and stroke etc. Scientists increasingly use molecular docking for drug discovery because it provides more accurate data, helping to understand target sites, understanding and action of various enzymes, protein interactions, and selecting the best molecule from an array etc. it is a computational method for analyzing large collections of biological data which involves the interlinkage of two or more molecules to give the stable adduct. Depending upon binding properties of molecules and target site. The entire process takes place in a three-dimensional way.