abstracts Lecture for the 3RD Indo-US Lecture Series on Discrete Mathematical Chemistry January 710, 2008, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India http://www.nrri.umn.edu/indouslecture/Trichy2008/
Applications of Quantitative Structure Activity Relationship (QSAR) and Molecular Docking in Drug Design
V. Subramanian, Chemical Laboratory, Central Leather Research Institute, Adyar, Chennai 600 020, India, [e-mail], [e-mail]
Quantitativestructure activity and structureproperty relationships (QSAR/QSPR) and molecular docking are of great importance in modern chemistry, biochemistry, and medicinal chemistry. The aim of QSAR/QSPR studies is to transform searches for optimal compounds with the desired properties based on chemical intuition and experience into searches with a mathematically founded and computerized form. Once a correlation between structure and activity/property is found, any number of chemical compounds, including those not yet synthesized, can be screened on the computer to select structures with the desired properties. Numerous studies have been made in the last decade on the application of QSAR to drug design. The binding affinity of the new compound with protein or nucleic acid can be obtained using molecular docking methods. Molecular docking is a method which predicts the preferred orientation of ligand with protein when bound to each other to form a stable complex. Knowledge of the preferred orientation of the ligand as obtained from the docking score can be used to predict the strength of association or binding affinity between protein and ligand. Various scoring functions have been proposed to derive the relationship between the binding affinity and relative orientation of the ligand in protein field. Hence for understanding the drug design strategies, it is necessary to know about the applications and limitations of QSAR and molecular docking. In this presentation, various aspects of QSAR and molecular docking will be described. Reference 1. Andrew R. Leach, Molecular modeling principles and applications, second edition. 2. C. A. Kontogiorgis and D. Hadjipavlou-Litina, Medicinal Research Reviews 2002, 22, 385418. 3. R. P. Verma and Corwin Hansch, Bioorg. & Med. Chem. 2005, 13, 45974621. 4. E. Besalua, Gironea S, L. Amat and R. CarboaDorca, Acc. Chem. Res. 2002, 35, 289-295. 5. C. Hansch, A. Kurup, R Garg, and H. Gao, Chem. Rev. 2001, 101, 619-672. 6. R. Parthasarathi, V. Subramanian, D. R. Roy and P. K. Chattaraj, Bioorg. & Med. Chem. 2004, 12, 5533-5543.
Automated E-Novo Workflow for Fast Lead Optimization Through Combination of CHARMm Applications
Amit Kulkarni, Accelrys Inc., USA, [e-mail]
We have compiled an automated E-Novo protocol through Pipeline Pilot (PP) using existing CHARMm components in Discovery Studio (DS)1.7, which will run enumeration of new ligand molecules from scaffold and fragments in addition to CHARMm-based core-constrained docking followed by physics-based binding energy as scoring function for ranking. Many studies with Structure-Based Design (SBD) focus on identifying new scaffolds, navigating around existing patents, or simply modeling analogs for medicinal chemistry. In most cases, experimental data shows that these analogs are in such a similar series that their scaffold is positioned very similarly in the receptor binding site. This means a full docking experiment is often unnecessary. In these instances, developing a fast and easy way to enumerate the fragment-scaffold combination becomes more critical. Furthermore, the workflow can be customized to incorporate further refinement and accurate physics-based scoring in an automated manner. In this talk we will highlight on the protocols that were built followed by two validation studies.
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Lecture abstracts for the 3RD Indo-US Lecture Series on Discrete Mathematical Chemistry January 710, 2008, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India http://www.nrri.umn.edu/indouslecture/Trichy2008/
Chaos Game Representation of Biological Sequences
S. Parthasarathy, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India, [e-mail]
Chaos Game Representation (CGR) of biological sequence analysis is an alignment free or sequence signature approach and it uses the patterns observed in graphical representation of sequences. CGR of DNA sequences consists of a square scatter plot with each corner of the square representing one of the four bases A, T, G or C. The applications of it range from sequence comparison, phylogeny, detection of horizontal transfers to extraction of representative motifs in regulation. Although much work has been done on the CGR analysis of DNA sequences, there is very little work on CGR analysis of protein sequences as there are twenty different amino acids to be considered and is a constraint on representation space dimensionality. This lecture will outline the basic concepts of CGR of DNA sequences. Further, I describe a novel way of applying CGR method to protein sequences of different families by considering the amino acids into three groups. Using this unique way of CGR method, I show that the protein sequences of different protein families produce the intrinsic fractal structure Sierpinski triangle type, possessing self-similar structure.
Characterizing Molecular Similarity and Tailoring Similarity Methods
Brian D. Gute, and Subhash C. Basak, Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN 55811,USA, [e-mail]
Similarity is understood by most people as a qualitative and intuitive concept, and attempts to quantify the similarity of any two objects tend to reveal the observers bias. After all, who is to say which aspects shared in common by two objects are more important in determining their degree of similarity. In computational chemistry, in which we include computer-aided drug discovery and environmental toxicology, scientists need to evaluate large numbers of new, unknown chemicals on a daily basis and it becomes necessary to have quantitative measures of molecular similarity so that the process can be automated for speed and consistency. These methods, founded on the structureproperty similarity principle, rely on unbiased methods to rapidly characterize and compare molecular structures. Such methods began to emerge over thirty years ago, and have come into their own in the past two decades. This lecture will outline some of the major concepts related to the evaluation of molecular similarity with some specific examples taken from the work of Basak et al.
Granular Support Vector Machines for Identification of Protein Functions
V.K. Jayaraman, National Chemical Laboratory, Pune, India, [e-mail]
This lecture will deal with hybrid combination of granular computing and support vector machines for maximizing classification performance. Granulation can aid in splitting the problem space into a set of subspaces such as classes, subsets, clusters and intervals. different information granules. Processing of these information granules obtained by the principle of divide and conquer can simplify bigger complex problems into computationally simpler problems. Support Vector Machines (SVM) algorithms are a class of classification problems rigorously based on statistically learning theory. We describe the procedure for hybridizing SVM with granular computing with a view to enhance classification accuracy. For the purpose of granulating we have employed association rules. Results on several bioinformatics case studies indicate that difficult classification problems can be successfully solved by hybrid combination of SVM and granular computing.
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Lecture abstracts for the 3RD Indo-US Lecture Series on Discrete Mathematical Chemistry January 710, 2008, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India http://www.nrri.umn.edu/indouslecture/Trichy2008/
Local Symmetry and Coset Representation
Dr. P. Venuvanalingam, School of Chemistry, Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu, India, [e-mail]
Molecular symmetry is usually denoted by point group that is group formed by a set of symmetry elements present in a molecule. This way of representing molecular symmetry is inadequate in two respects: 1. molecules are treated as rigid entities while looking for symmetry elements present in them. 2. Point represent groups only global symmetry of molecules and hence do not represent molecular symmetry uniquely. The present lecture highlights the latter aspect. Molecules are viewed as regular bodies constituted by different orbits called equivalence classes formed by sets of atoms. Now the orbits have subsets of symmetry elements present in them (local point groups?). Different orbits of a molecule can have different subsets and hence different local symmetries. This is an internal feature almost specific to every molecule. This aspect is exploited to uniquely represent molecular symmetries are better in terms of local symmetry. Local symmetries are better represented in terms of Coset Representation rather than by Frame work Group notation or Site Symmetry notation. A systematic way of representing molecular symmetry in terms of coset representations will be presented.
Mathematical Characterization of Chirality: The Hallmark of Life's Chemistry
Ramanathan Natarajan, Department of Chemical Engineering, Lakehead University, Thunder Bay, Ontario, Canada, P7B 5E1, [e-mail] and Subhash C. Basak, Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN 55811, USA [e-mail]
Enantiomers are the two stereoisomers that are non-superposable mirror images. This may arise due to the difference in the spatial orientation of the different groups (ligands) attached to an asymmetric carbon atom or the chiral center. The word chirality, meaning handedness, was introduced by Lord Kelvin in 1904. Several organic molecules such as the building blocks of natural polymers such as proteins and nucleic acids are chiral. Proteins consist of polypeptide chains made from combinations of 20 different amino acids (primary structure), almost all exclusively the L-enantiomers. This homochirality in the monomeric amino acid building blocks of proteins leads to homochirality in higher-order structures such as the right-handed -helix (secondary structure), and the fold (tertiary structure) that is unique to a specific protein in its native state. Nucleic acids consist of chains of deoxyribonucleosides (for DNA) or ribonucleosides (for RNA), connected by phosphodiester links, all based exclusively on the D-deoxyribose or D-ribose sugar ring, respectively. This homochirality in the monomeric sugar building blocks of nucleic acids leads to homochirality in their secondary structures such as the right-handed B-type DNA double helix. Thus, chirality of molecules provides the biosignature of life. Several drugs that are available in the market are chiral and this is due to the stereospecificity of receptors. Therefore, chirality is the hallmark of life's chemistry. In order to apply quantitative structure-activity relationship (QSAR) approach to the bioactivity and toxicity of chemicals and to model the receptor-ligand interactions mathematical characterization of molecular chirality is very important. An overview of numerical characterization of molecular chirality of organic molecules will be presented.
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Lecture abstracts for the 3RD Indo-US Lecture Series on Discrete Mathematical Chemistry January 710, 2008, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India http://www.nrri.umn.edu/indouslecture/Trichy2008/
Mathematical Chemodescriptors and Biodescriptors: Development and Applications
Subhash C. Basak, Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN 55811, USA [e-mail]
A contemporary trend in chemoinformatics and bioinformatics is the characterization of structures relevant to chemistry and biology using discrete mathematical methods like graph theory and information theory. Molecules can be represented by various types of weighted graphs. Such graphs and matrices derived from them can be used as the source of various numerical invariants, also called topological indices (TIs). Such chemodescriptors have been used for the discrimination of closely related molecular structures, quantification of similarity/ dissimilarity of chemicals as well as prediction of property/ bioactivity/ toxicity of congeneric and diverse sets of molecules. In the postgenomic era, the three "omics" sciences, viz., genomics, proteomics, and metabolomics, are generating an enormous amount of data which need to be properly interpreted to be useful. Techniques of graph theory and information theory have been used to develop numerical biodescriptors which quantitatively characterize proteomics maps and their perturbations following treatment of cells/ tissues with toxicants. This presentation will review recent progress in the development and applications of mathematical chemodescriptors and biodescriptors mentioned above.
Some Bioinformatics Algorithms
R. Balakrishnan, DST Chair Professor in Discrete Mathematics, Srinivasa Ramanujan Centre, SASTRA University, Kumbakonam 612001, Tamil Nadu, India, [e-mail]
In this lecture, we present some algorithms to determine the regulatory motifs in DNA sequences.
Reactivity of Candida rugosa Lipase: A Molecular Dynamics Study
James Jayasundar, B.S. Lakshmi, Aswin Sai Narain, P. Kangueanne , S. Anishetty and P. Gautam, Centre for Biotechnology, Anna University Chennai, Chennai 600025, India, [e-mail]
An interesting observation was found during our continued studies on the hydrolysis of ibuprofen esters by Candida rugosa lipase (CRL). An important role is played by pH in the stereo specific hydrolysis of these esters. The flap region of CRL plays a significant role in the access of the substrate to the active site of the enzyme. At pH 5.6, 48%of the methyl ester and 5% of the butyl ester of ibuprofen was hydrolyzed in a identical reaction time using CRL. Thus CRL prefers the methyl ester of ibuprofen as a substrate at an acidic pH and the butyl ester of ibuprofen at a neutral pH. Therefore, in order to understand the role of pH in the substrate selection by CRL for the esters of ibuprofen we used the crystallographic coordinates of the open form of the CRL (1CRL) for molecular dynamics (MD) simulations under acidic and neutral conditions for 2 ns using GROMACS. The final structures obtained after simulation in acidic and neutral conditions were compared with the energy-minimized structure, and the root-mean-square deviations (r.m.s.ds) were calculated. The r.m.s.d. of the CRL flap at neutral pH was found to be greater than that of the CRL flap at acidic pH. The extent to which the flap opens at neutral pH allowed the bulkier substrate, the butyl ester of ibuprofen, to diffuse into the active site and provides the best enzyme substrate fit for this specific substrate. At acidic pH there is a decreased opening of the flap thereby accommodating a more compact substrate, namely the methyl ester of ibuprofen. Thus, simulation experiments using MD provide reasonable insight for the pHdependent substrate selectivity of CRL in aqueous environments. The effect of solvent hydrophobicity on activation of Candida rugosa lipase (CRL) was investigated by performing molecular dynamics simulations for four nano seconds (ns). The
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Lecture abstracts for the 3RD Indo-US Lecture Series on Discrete Mathematical Chemistry January 710, 2008, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India http://www.nrri.umn.edu/indouslecture/Trichy2008/
closed/inactive conformerm of CRL(PDB code 1TRH) was solvated in three alkan-aqueous environments. The alkanes aggregated in a predominantly aqueous environment and by 1ns a stable spherical alkane-aqueous interface had formed. This lead to the interfacial activation of CRL. On analyzing the simulated conformers with the closed conformer of CRL, the flap was found to have opened from a closed state by 7.7A, 13.1 A at hexane-aqueous, octane-aqueous, and decane-aqueous interfaces. Further, essential dynamics analysis revealed that major anharmonic fluctuations were confirmed to residues 64-81, the flap of CRL. Reference 1. Jayasundar J. James, Baddireddi S. Lakshmi, Venkateshamurthy Raviprasad, Mathuranthagam simulations into pH-dependent enantioselective hydrolysis of ibuprofen esters by Candida rugosa lipase, Protein Engineering 16, 1017,2003. 2. Jayasundar Jayant James, Baddireddi Subadra Lakshmi, Aswin Sai Narain Seshasayee and Penathur Gautam. Activation of Candida rugosa lipase at alkane-aqueous interfaces: A molecular dynamics study. FEBS Lett. 581, 4377,2007.
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