Synopsis of the thesis on

Transcription

1 Synopsis of the thesis on COMPUTATIONAL INVESTIGATION OF PROTEIN-LIGAND INTERACTIONS IN ANTI-DIABETIC AGENTS A dissertation submitted in the partial fulfillment for the award of the degree of DOCTOR OF PHILOSOPHY IN COMPUTER SCIENCE AND ENGINEERING By Naresh Babu Muppalaneni Y9CSR043 Under the Esteemed Guidance of Prof. Col. ALLAM APPA RAO Vice-Chancellor JNTU Kakinada DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING ACHARYA NAGARJUNA UNIVERSITY NAGARJUNA NAGAR INDIA 2011

2 ABSTRACT Effort to apply computational power to the combined chemical and biological space in order to streamline computer aided drug discovery and development is the spirit of scientific method. India is a world capital of diabetes. Immediate attention is required for the development of a novel drug for diabetes. Though Type 2 Diabetes has many drugs, it lacks 100% effective cure. The drug design is mainly based on target identification, protein-ligand interactions and the active site residues. Our study concentrates mainly on the highly active and conserved amino acid residues of 5 important proteins responsible for causing Diabetes. An investigation has been carried out to study the mode of binding as well as the affinities of drug-like compounds from ZINC database(a free database for virtual screening), some plant compounds and chalcones as anti-diabetic agents by performing protein ligand interactions using various docking softwares. 3-dimensional structural coordinates of 1AH3 (Aldose Reductase) from protein data bank was selected for analysis based on the Root Mean Square Deviation (RMSD) and reports in literature. All default parameters are used in docking runs. A total of 1001 and 837 (search result based on range and average physico-chemical properties of co-crystallized aldose reductase ligands) compounds from ZINC database; 85 cuminum cyminum and 267 compounds from 7 different plants, 722 chalcone compounds from in-house chalcone database were docked with 1AH3 protein and the dock scores were recorded. There were many compounds which had a dock score greater than the average dock score ( kcal/mol) of the inhibitors taken from literature. ZINC ( kcal/mol) and ZINC ( kcal/mol) resulted as best compounds from 1001 and 837 hits of ZINC database. Further consensus scoring was implemented to evaluate the efficiency of docking program as a combination of these scoring functions have been shown to outperform one single scoring function. Apigetrin ranked high and 1

3 was reported to be the best compound that can bind with high affinity to Aldose reductase enzyme. Similarly, ZINC (out of 1001 hits) and ZINC (out of 837 hits) from ZINC database; Allium38 from in-house plant database; 44[IC]COX,LOinhibitor-Me-UCH3 from chalcone database gave the best results with a binding energy better than the original cocrystallized ligand of 1AH3. These results are used for clinical trials for drug design. INTRODUCTION India is a world capital of diabetes, hence it is required immediate attention of drug design and development of novel drug. Though Type 2 Diabetes has many drugs, it lacks 100% effective cure. Drug design is mainly based on protein-ligand interactions and the active site residues. To help innovative drug designers a consensus database with 5 important candidate proteins causing diabetes has been developed. Relational database concepts of computer science and Information retrieval concepts of digital libraries are important for understanding biological databases. Biological database design, development, and long-term management is a core area of the discipline of Bioinformatics. Data contents include gene sequences, textual descriptions, attributes and ontology classifications, citations, and tabular data. These are often described as semi-structured data, and can be represented as tables, key delimited records, and XML structures. Cross-references among databases are common, using database accession numbers. A mutation in a protein may leads to malfunction which result in causing disease. A Protein may cause more diseases, a disease can caused by many proteins. There are many proteins which causes diabetes, all the proteins are not having ligands to correct the sequence. Designing drug 2

4 using conventional process is time consuming and expensive, but using Bioinformatic tools can be minimized and also cheaper. BACKGROUND Protein coding genes related to Diabetes are figured out from the gene cards website. Many of them are screened as they don t have PDB id, and some doesn t have ligands. All those are eliminated and only the closely linked proteins with ligand are selected and the best suited 5 proteins are filtered finally (dipeptidyl-peptidase 4 (DPP-4), peroxisome proliferator-activated receptor gamma (PPAR-γ), protein tyrosine phosphatase, non-receptor type 1 (PTPN1), Glycogen synthase kinase -3 beta (GSK-3β) and Aldose Reductase). Aldose Reductase exihibts more consenus from the remaining. The average docking score of the ligands, inhibitors is kcal/mol. PROBLEM STATEMENT The problem addressed in my study is to identify protein ligand which inhibits high affinity than the existing ligands associated with diabetes causing proteins. Finding the best protein ligand for diabetes from various sources like plant database, chalcon database, ZINC database such that best ligand which is having high affinity than kcal/mol. Advances in computational techniques have enabled virtual screening to have a positive impact on the discovery process. In ligand-based virtual screening, the strategy is to use information provided by a compound or set of compounds that are known to bind to the desired target and to use this to identify other compounds in the corporate database or external databases with similar properties. 3

5 Design and development of novel drug with fewer side effects and less costly using various bioinformatic tools. We developed a technique to minimize the human intervention in the calculation of the ligand properties. NATURE OF THE STUDY The current research extracted the data from the databases available online. The data thus extracted is assembled in a desired format. To identify high consensus protein causing diabetes by using Root Mean Square Deviation, Rank sum technique. In addition to protein ligand docking can be performed to identify the ligand that binds with high affinity. It also compares the results of different software to identify best ligand for protein causing diabetes. RESEARCH QUESTIONS What is the computational approach to highlight the crucial amino acid residues responsible for functional attributes? How to predict the structures of ligands for protein How to understand the interactions at binding sites of ligands. How computer technology can be used to reduce the time spent in the synthesis of compounds and the use of experimental methods designed compounds would lead to effective compounds with drug use computer aided design? THESIS OVERVIEW The principal topic of this work is the application of Root mean square deviation, rank sum technique to identify ligands with high affinity. 4

6 Figure 1: Potential areas for in silico intervention in drug discovery process Chapter 1 provides motivation, role of bioinformatics in drug discovery, background information on present research of me on diabetes, diabetes causing protein Aldose reductase. Chapter 2 provides literature reviews of the present study and describes about anti diabetic agents. In addition to that various bioinformatic tools and techniques are reviewed. The active ingredients present in medicinal plants have been reported regeneration of pancreatic beta cells, insulin and the release of combating the problem of insulin resistance. Not only in Ayurveda, but also in several other systems of traditional medicine, described the plants useful in diabetes also have strong antioxidant / free radical properties [1, 2]. Chapter 3 provides materials and methodologies to predict protein ligand interactions by using root mean square deviation (RMSD), Tsar-rank sum technique to figure out high affinity ligands. Chapter 4 provides results and discussions based on the RMSD value and literature references [3, 4, 5] 1AH3 is selected for further docking studies. The co-crystallized ligands of 15 5

7 proteins(pdb ids) and the inhibitors from literature[6,7,8] were docked with 1AH3 protein using Molegro, Virtual Docker. Chapter 5 provides conclusions and further work. Finally it is concluded that Apigetrin ranked high and was reported to be the best compound that can bind with high affinity to Aldose reductase enzyme. Similarly, ZINC (out of 1001 hits) and ZINC (out of 837 hits) from ZINC database; Allium38 from in-house plant database; 44[IC]COX, LOinhibitor- Me-UCH3 from chalcone database gave the best results with a binding energy better than the original co-crystallized ligand of 1AH3. To improve the process and mechanism to optimize the non conventional drug discovery and that leads to efficient drug design through computer aided drug discovery. MATERIALS AND METHODS Drug discovery and development is an intense, lengthy and an interdisciplinary endeavor. Drug discovery is mostly portrayed as a linear, consecutive process that starts with target and lead discovery, followed by lead optimization and pre-clinical in vitro and in vivo studies to determine if such compounds satisfy a number of pre-set criteria for initiating clinical development. For the pharmaceutical industry, the number of years for a drug from discovery to market is approximately years and costs up to $1.2 - $1.4 billion dollars. In silico methods can help in the identification of therapeutic targets through bioinformatics tools. They can also be used to analyze the target structures for possible binding/ active sites, generate candidate molecules, check for their drug likeness, dock these molecules with the target, rank 6

8 them according to their binding affinites, further optimize the molecules to improve binding characteristics[9]. Molegro Virtual Docker was used for docking compounds to generate an ensemble of docked conformations and each scoring function is applied to generate classes based on the obtained dock scores followed by ranking the best conformations. During ranking, signs of some scoring functions are changed to make certain that a lower score always indicates a higher affinity. The application software has also been developed to divide the Structure Data File (SDF), the calculation of the properties of ligands as molecular weight, the number of hydrogen bond acceptors and donors on the basis that you can search in the ZINC database. RESULTS AND DISCUSSIONS Consensus scoring is a widely used approach to improve the scoring reliability and hit rate in virtual screening and four standalone programs (GOLD, Molegro, AutoDock and e-hits) and two online servers (PatchDock and MEDock) are utilized to rank top hits. From the complete analysis, Apigetrin ranked high and reported to be the best compound that can bind with high affinity to Aldose reductase enzyme. Similarly, ZINC (out of 1001 hits) and ZINC (out of 837 hits) from ZINC database; Allium38 from in-house plant database; 44[IC]COX,LOinhibitor-Me-UCH3 from chalcone database resulted in best hits with a better binding energy than the original co-crystallized ligand of 1AH3. The result may vary depending on the structures available in the database and database remain online in time and time updates. 7

9 CONCLUSION AND FURTHER STUDIES The primary objective of this consensus active site residue database is to encourage the design of novel drugs for Type 2 Diabetes. It also provides an easy access of the active amino acid residues that take part in hydrogen bonding with the ligands to the scientific community and to other users of the database. Most effective ligand binding to the protein can be known by this database which makes the drug design effective and easy. Valuable details of highly active amino acid residues for the 5 diabetes proteins are deposited. Hence this database can be described as a helpful one, for those drugs which are to be designed with high ligand affinity. For the current database construction, the protein coding genes related to Diabetes are figured out from the gene cards website. Many of them are screened as they don t have PDB id, and some doesn t have ligands. All those are eliminated and only the closely linked proteins with ligands are selected and the best suited 5 proteins are filtered finally (dipeptidyl-peptidase 4 (DPP-4), peroxisome proliferator-activated receptor gamma (PPAR-γ), protein tyrosine phosphatase, non-receptor type 1 (PTPN1), Glycogen synthase kinase -3 beta (GSK-3β) and Aldose Reductase). All the PDB structures of these 5 proteins are accessed in PDB and the conserved amino acids are found out through LIGPLOT interactions. This database shall be updated time to time continuously. Following the importance of consensus amino acid residues within the functional region of a protein, protein ligand interactions using various docking softwares resulted in binding affinities with 1AH3 extracted from protein data bank. All default parameters are used in docking runs. The average Mol Dock Score of all the aldose reductase inhibitors was kcal/mol. 8

10 A total of 1001 and 837 (search result based on range and average physico-chemical properties of co-crystallized aldose reductase ligands) compounds from ZINC database docked with 1AH3 resulted in ZINC ( kcal/mol) and ZINC ( kcal/mol) as best compounds from 1001 and 837 hits of ZINC database. Also, 85 cuminum cyminum and 267 compounds from 7 different plants, 722 chalcone compounds from in-house chalcone database were docked and the binding energies were reported. However, post-docking analysis revealed few best hits that represented a binding pose much better than the original co-crystallized ligand and hence to retrieve the best pose from the dataset, a python based program was implemented to minimize the task. In other words, the sdf (structure data file) from ZINC database was split into individual files using a python based program and the properties essential for ligand binding such as molecular weight, h-bond donors, h-bond acceptors etc. were also implemented in the program. This structural information is necessary to perform docking and isolate few best hits from the database file. Future Direction In the conventional process customization process is tedious work because the process of what follows is more economical in terms of resources. Once you fall in computer-aided design of personalized medicine is more effective and provides immediate service to the needy. As our methodology used in our study greatly helps researchers of Pharma Industry. This computational thinking facilitates customization of each treatment. We can also make the detection of anti-cancer compounds and study the best way I can link to the 1AH3. The best compounds which we resulted may apply for clinical trials for designing drug. 9

11 REFERENCES 1. J Welihinda et al., The insulin-releasing activity of the tropical plant momordica charantia Acta Biol. Med. Ger., 41: 1229 (1982) [PMID: ] 2. L McCune et al., Antioxidant activity in medicinal plants associated with the symptoms of diabetes mellitus used by the indigenous peoples of the North American boreal forest J. Ethnopharmacol., 82 : 197 (2002), [PMID: ] 3. Urzhumtsev A, Teˆte-Favier F, Mitschler A, Barbanton J, Barth P, Urzhumtseva L, Biellmann J-F, Podjarny AD, and Moras D (1997) A specificity pocket inferred from the crystal structures of the complexes of aldose reductase with the pharmaceutically important inhibitors tolrestat and sorbinil. Structure 1997, 5: Won Suck Sun et. al Rational Design of an Indolebutanoic Acid Derivative as a Novel Aldose Reductase Inhibitor Based on Docking and 3D QSAR Studies of Phenethylamine Derivatives J. Med. Chem. 2003, 46, Kubata Bruno Kilunga Structural and Mutational Analysis of Trypanosoma brucei Prostaglandin H2 Reductase Provides Insight into the Catalytic Mechanism of Aldoketoreductases THE JOURNAL OF BIOLOGICAL CHEMISTRY Vol. 280, No. 28, Issue of July 15, pp , Shuichi Miyamoto Molecular Modeling and Structure-based Drug Discovery Studies of Aldose Reductase Inhibitors Chem-Bio Informatics Journal, vol2, no 3, pp (2002) 7. B.L. Mylari, E. R. Larson, T. A. Beyer, W. J. Zembrowski, C. E. Aldinger, M. F. Dee, T. W. Siegel and D. H. Singleton, J. Med. Chem., 34, (1991). 8. M. Kawamura and N. Hamanaka, J. Synth. Org. Chem., Japan, 37, (1997)

12 PUBLICATIONS 1. Naresh Babu Muppalaneni and Allam Appa Rao Computational Analysis on Cuminum Cyminum Compounds Against Aldose Reductase as Anti-diabetic Agents to appear in 2011 International Conference on Bioinformatics and Computational Biology (July 18-21, 2011, Las Vegas, USA) 2. M Naresh Babu, P Satheesh, R Satish Babu, A Chandra Sekhar, Allam Appa Rao "Computational Analysis, Bioinformatic Tools helps in Predicting the Function of an Unknown Protein" Int J Engg Techsci Vol 2(1) 2010, , January M.Naresh Babu, Patchikolla Satheesh, Datta Teja R S Grandhi and Prof. Allam Appa Rao, Comparative Study of Ligand Docking, International Journal of Computer Applications 6(1):36 39, September Patchikolla Sateesh, Prof. Allam Appa Rao, Suresh Kumar Sangeeta, M.Naresh Babu, R.S. Datta Teja Grandhi HOMOLOGY MODELLINGAND SEQUENCE ANALYSIS OF anxc3.1, International Journal of Engineering Science & Technology, ISSN: , Vol. 2(5), 2010, , May M.Naresh Babu, Palika Harikanth, P Pavan Krishna Chaitanya, Microarray and Gene Expression : Applications and Challenges, International Conference on Bioinformatics & Diabetes Mellitus, March 2006,