Kal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA
A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry
With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways. Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists. As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery. While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories. Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site. As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty. In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases. We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase. We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.
V. Nagaraja Ph.D.
Professor, Indian Institute of Science, Bengaluru, India
Perturbation of DNA topology in mycobacteria
To maintain the topological homeostasis of the genome in the cell, DNA topoisomerases catalyse DNA cleavage, strand passage and rejoining of the ends. Thus, although they are essential house- keeping enzymes, they are the most vulnerable targets; arrest of the reaction after the first trans-esterification step leads to breaks in DNA and cell death. Some of the successful antibacterial or anticancer drugs target the step ie arrest the reaction or stabilize the topo -DNA covalent complex. I will describe our efforts in this direction – to target DNA gyrase and also topoisomerase1 from mycobacteria. The latter, although essential, has no inhibitors described so far. The new inhibitors being characterized are also used to probe topoisomerase control of gene expression.
In the biological warfare between the organisms, a diverse set of molecules encoded by invading genomes target the above mentioned most vulnerable step of topoisomerase reaction, leading to the accumulation of double strand breaks. Bacteria, on their part appear to have developed defense strategies to protect the cells from genomic double strand breaks. I will describe a mechanism involving three distinct gyrase interacting proteins which inhibit the enzyme in vitro. However, in vivo all these topology modulators protect DNA gyrase from poisoning effect by sequestering the enzyme away from DNA.
Next, we have targeted a topology modulator protein, a nucleoid associated protein(NAP) from Mycobacterium tuberculosis to develop small molecule inhibitors by structure based design. Over expression of HU leads to alteration in the nucleoid architecture. The crystal structure of the N-terminal half of HU reveals a cleft that accommodates duplex DNA. Based on the structural feature, we have designed inhibitors which bind to the protein and affect its interaction with DNA, de-compact the nucleoid and inhibit cell growth. Chemical probing with the inhibitors reveal the importance of HU regulon in M.tuberculosis.
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.