Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 10th International Congress on Structural Biology Helsinki, Finland.

Submit your Abstract
or e-mail to

events@conferenceseries.com
structuralbiology@memeetings.net
rohit.casper@memeetings.net

Scientific Program Day 1
Scientific Program Day 2

Day 2 :

  • Sequencing | Structural Molecular Biology | Structural Biology in Cancer Research| Signalling Biology
Location: Alto
Speaker

Chair

Annette G Beck-Sickinger

Leipzig University

Session Introduction

T V Koshlan

Peter the Great St. Petersburg Polytechnic University, Russia

Title: Identification of active sites interaction of different protein molecules in case of formation Nap1-Nap1, Mdm2-Mdm2 and P53-Mdm2

Time : 10:00 -11:00

Biography:

T V Koshlan is currently working as a Professor at Peter the Great St. Petersburg Polytechnic University, Institute of Applied Mathematics and Mechanics, Department of Higher Mathematics. He has completed his PhD in Physics and Mathematics with Mathematical modeling of the optical properties of multilayer biological systems and structures in their heterogeneous conjugation. He has habilitation at the State Polytechnic University of St. Petersburg, Russia. His research interests are diffraction theory, electrodynamics, physics of lasers, tissue optical methods of mathematical modeling in biological tissue optics and numerical method, biophysics.

Abstract:

In this report, two algorithms are developed, algorithm 1 and algorithm 2. Algorithm 1 was developed in order to search for the interaction of a polypeptide chain of a full-length protein with short active region. Algorithm 2 was developed to determine the most active sites of interaction between full-length proteins when dimers are formed in the direction from the N terminus to C terminus. Numerical calculations were made using proteins Mdm2, Nap1, P53. For modern proteomics, research and prediction of protein interactions are very important tasks, since they determine the function of proteins at levels from the cell to the whole organism. For proteins whose structure is known, the search for intermolecular interactions according to known data on the conformation of their tertiary structure reduces to the problem of searching for geometric complementarity of the sections of two interacting molecular surfaces and modeling their contacts, the so-called molecular docking. The task of molecular docking is the task of a conformational search algorithm, which reduces to a search for the conformational space of the formed biological complex due to the variation of the torsion angles of protein molecules. Modern conformational search algorithms in most cases find conformations that are generally close to the experimentally found structures in a relatively short time. However, there are factors that also have a significant impact on the success of the docking, which are often not taken into account in standard algorithms. One such factor is the conformational mobility of the target protein. The mobility range can be different beginning with a small adjustment of the side chains and ending with scale domain movements. These movements play an important role. At first glance, the most logical solution to this problem is to take into account the mobility of the protein in a docking program. Unfortunately, modern computational tools do not allow such modeling to be performed in an acceptable time frame since a protein molecule is very large, and allowing for mobility over all degrees of freedom can lead to a so-called combinatorial explosion (an astronomical increase in the number of possible variants). Only in some programs is there a limited mobility of protein binding sites (usually at the level of a small adaptation of conformations of the side chains of the active center residues). Another approach to this problem consists in docking the same protein in several different conformations and then selecting the best solutions from each docking run. The third approach is to find a universal structure of the target protein in which docking would produce fairly good results for different classes of ligands. In this case, the number of missed (but correct) solutions decreases, but the number of incorrect options also increases significantly. It should also be noted that most programs for the theoretical docking of proteins work according to the following principle: one protein is fixed in space, and the second is rotated around it in a variety of ways. At the same time, for each rotation configuration, estimates are made for the evaluation function. The evaluation function is based on surface complementarity (the mutual correspondence of complementary structures (macromolecules, radicals), determined by their chemical properties), electrostatic interactions, van der Waals repulsion and so on. The problem with this approach is that calculations throughout the configuration space require a lot of time, rarely leading to a single solution, which in turn does not allow us to speak of the uniqueness of the target protein and ligand interaction variant. So in the work while modeling by the methods of molecular dynamics, from 200 to 10 000 possible combinations of the formation of a protein complex with a ligand were found. Such a large number of modifications, along with the lack of a criterion for selecting the most probable variants of the bound structures of biological complexes (which would allow a radical reduction in their number) makes it very difficult to interpret the theoretical results obtained for practical use, namely, the finding of catalytic centers and a qualitative assessment of the dissociation constant of interacting substances. In contrast to the above computer simulation algorithms, mathematical algorithms have been developed in this chapter that allow determining the detection of proteins active regions and detecting the stability of different regions of protein complexes (linear docking) by analyzing the potential energy matrix of pairwise electrostatic interaction between different sites of the biological complex, such as the homodimer of the histone chaperone Nap1-Nap1, the heterodimer of the p53 Mdm2 proteins, and the homodimer Mdm2 Mdm2, which are responsible for the entry of a whole protein molecule into biochemical reactions.

Mohd Athar

Central University of Gujarat, India

Title: First protein drug target’s appraisal of lead-likeness descriptors to unfold the intervening chemical space
Speaker
Biography:

Mohd Athar is a Senior Research Fellow in Computational Chemistry Group at Central University of Gujarat. He holds the DST-INSPIRE Fellowship awarded by the Ministry of Science and Technology (DST) India. He has completed his Master’s degree in Applied Chemistry at BBA Central University Lucknow and Bachelor’s degree in Biotechnology from Hemwati Nandan Bahuguna Garhwal University. His major research is in the area of Medicinal Chemistry ranging from Drug Discovery, Combinatorial Chemistry, In silico Virtual Screening, Lead Optimization/Designing, Target Identification to its Validation.
 

Abstract:

A plethora of literature has been published for unveiling the problems associated with lead and drug likeness. However, despite of these advances in combinatorial chemistry, high throughput methods and virtual screening, plethora of clinical studies disquiets due to lead and drug-likeness attrition. For mitigation, the knowledge of physicochemical properties is really useful for guiding the design and selection of compounds from libraries dictated by certain rule of thumbs. However, robust biotechnological and instrumental innovations have created exponential increase in novel compounds and databases which compelled rethinking of evaluation procedures. Known descriptive molecular property filters proposed by Lipinski, Verber and Hann are not efficient enough to encompass long array of compounds and do not take into account the specificity of biological target. In this pursuit, we have tried to appraise eight molecular properties for two major classes of biological targets viz. membrane proteins and ion channels binding ligands. It has been proposed that the target based knowledge of descriptors can guide the selection of molecules to pick compounds from high throughput screening. In this talk, efforts, challenges and success in filtering the compounds to answer the long pending questions on lead-likeness and drug-likeness will be addressed. 
Biography 

Marcin J Skwark

University of Cambridge, UK

Title: CHOPIN, MABELLINI and HANSEN-Enabling combatting emerging antibiotic resistance in mycobacteria through multi-dimensional structural proteome data resources
Speaker
Biography:

Marcin J Skwark has completed his PhD in Biochemistry from Stockholm University. He is currently working as a Research Associate in the Department of Chemistry at University of Cambridge, UK.
 

Abstract:

Here we propose a set of computational resources to inform experiments and facilitate drug discovery against Mycobacterium spp. Mycobacteria are etiological agents of some of the most notorious hazards to public health-tuberculosis (M. tuberculosis) and leprosy (M. leprae). They are also responsible for opportunistic infections in Cystic Fibrosis (CF) patients (M. abscessus). Finally, they are conjectured to be the underlying cause of ulcerative colitis and Crohn’s disease (M. avium subsp. paratuberculosis). Despite their importance for public health, there are very few successful treatment strategies against Mycobacteria, due to their intrinsic resistance to antimicrobials, as well as inherent experimental difficulties in testing drug candidates in vitro. Our resources (CHOPIN: M. tuberculosis, MABELLINI: M. abscessus, HANSEN: M. leprae) provide comprehensive set of high-confidence structural models for the entire bacterial proteome. Models are generated using Vivace pipeline, using Fugue for template identification and Modeller for model building-both developed in-house. On contrary to typical protein structure prediction approaches (e.g. I-TASSER or HHpred), our approach aims to produce models that inform experiments and not necessarily maximize the stereo chemical quality and superposition to crystal structure. As our models are not over-optimized, they can be readily used for analysis of drug ability, effects of mutations and assessing interactions. By combining the results of state-of-art methods developed locally, with comprehensive survey of local and publicly available experimental results, CHOPIN, MABELLINI and HANSEN form resources of unprecedented utility-delivering results of rigorous computational analysis in a user-friendly, approachable and understandable way. The resources are free to use, constantly updated and produced in close collaboration with mycobacterial research community. All the software developed and used is open source and all the data is open access. 

Daniela C Vaz

University of Coimbra, Portugal

Title: Use of molecular interaction fields to understand drug resistance in HIV 1 protease caused by single point mutations
Speaker
Biography:

Daniela C Vaz has completed her Ph.D. in Biological Chemistry from the University of Coimbra. Her research focuses mainly on protein structure, folding, and stability, in relation to function and disease. She is currently working as a Professor at the School of Health Sciences of Leiria and is also a Member of the Coimbra Chemistry Centre at the University of Coimbra, Portugal.
 

Abstract:

Molecular Interaction Fields (MIF) is an archetypal computational chemistry technique that can be applied to capture a singular fingerprint of an ensemble of atoms on a protein and encode its physicochemical environment. Thus, MIFs have particular relevance in the context of binding hot spots and binding site analysis. Taking HIV 1 Protease (HIVPR) as case study, the present work focuses on a MIF-based in silico approach to achieve a qualitative interpretation and quantitative determination of mutation effects on HIVPR’s binding site, to help to understand translated changes in the enzyme’s structure and physicochemical environment. Assuming that binding sites with similar chemical environments have similar affinity for inhibitors, our method calculates and compares MIF similarities, visually assessing structural differences and quantifying their overlap through a Tanimoto coefficient. To assess the method’s ability to capture mutation induced chemical perturbations within HIVPR’s binding site, we collected 48 X-ray structures from the Protein Data Bank (PDB), from HIV strains either resistant or susceptible to protease inhibitors and quantified their binding site MIF similarities against a high quality, susceptible, reference structure. We observed and defined a threshold that discriminated most susceptible and resistant structures, confirming the MIF's suitability for our approach. Subsequently, we built homology models containing different reported single point resistance-conferring mutations using a single high-quality PDB structure as template. Root-Mean-Square Deviation (RMSD) values between template and model structures were calculated on residue by residue basis, confirming that the mutation was the only structural change. Then, the MIF similarities were determined, showing that this technique effectively captured subtle changes on HIVPR’s binding sites induced by the studied mutations. Along with the perspective of following an equivalent ligand based approach, we believe our results can be a promising starting point for developing an algorithm with drug resistance predictive power.
 

Daniela C Vaz

University of Coimbra

Title: RGA & GAI: On the characterization of two DELLA plant growth repressor proteins
Speaker
Biography:

Daniela C Vaz has completed her PhD in Biological Chemistry from the University of Coimbra. Her research focuses on protein structure, folding and stability in relation to function and disease. She is currently working as a Professor at the School of Health Sciences of Leiria and is also a Member of the Coimbra Chemistry Centre at the University of Coimbra, Portugal.
 

Abstract:

DELLA proteins are a family of nuclear proteins responsible for plant growth modulation. They act as growth repressor proteins in response to gibberellin signaling pathways. Five DELLA protein homologs were found in Arabidopsis thaliana, namely, RGA (Repressor of Gibberellic Acid), GAI (Gibberellic Acid Insensitive) and three RGA-like proteins (RGL-1, RGL-2 and RGL-3). The RGA- DELLA and GAI-DELLA protein homologs have been classified as Intrinsically Unstructured Proteins (IUPs) that undergo a disorder-to-order transition upon receptor binding. This structural change has found to be physiologically relevant for biological signaling and molecular recognition. Thus, in order to better characterize the structural features and molecular changes that govern these conformational variations of the polypeptide chain, we have produced recombinant RGADELLA and GAI-DELLA proteins in three length-versions, i.e. full-length, N-terminal and C-terminal versions. Full-length and terminal versions present different sequence motifs, attributed to different biological functions. All proteins were analyzed spectro-photometrically, via Light Scattering (LS), Circular Dichroism (CD) and intrinsic and extrinsic fluorescence (ANS binding), in order to compare spectral profiles, secondary structure propensities, levels of solvent exposure and structural compactness. Full-length and terminal variants exhibit different behaviors, spectral profiles and levels of compactness that can be related to different protein domains and ultimately to different functional implications.
 

Premkumar Dinadayala

Sanofi Pasteur

Title: Structural and functional characterization of various forms of glycated heat labile toxin protein
Biography:

Premkumar Dinadayala has completed his PhD from University of Toulouse and Postdoctoral studies from Colorado State University, Colorado. He is working as a Scientist since 12 years at Sanofi Pasteur. He has published more than 10 papers in journals.
 

Abstract:

Enterotoxigenic Escherichia coli (ETEC) are responsible for a high diarrheal disease burden, especially in children living in endemic countries and travelers visiting those countries. After oro-fecal transmission, ETEC reaches the small intestine where adhesion occurs through colonization factors. Then heat Labile Toxin (LT), one of the two enterotoxins produced by ETEC, is secreted and causes aqueous diarrhea. LT consists of five B sub-units, which are able to bind the monosialoganglioside GM1 and a single, catalytically-active A subunit stimulating the intracellular synthesis of cyclic Adenosine Monophosphate (cAMP), leading ultimately to fluid and electrolyte secretions into the intestinal lumen. Herein we characterized various purified forms of LT: (1) recombinant B subunit of LT (rLTB) (2) native LT purified from ETEC strain (nLT) and (3) recombinant LT purified from E. coli expressing the protein (rLT). SDS-PAGE analysis showed a difference of migration between the different LT forms confirmed by liquid chromatography coupled to MS (mass shift of 162 Da). This modification was found to be due to the glycation of LT subunits by galactose, a reducing sugar that is used in the LT purification process and remains present during the LT Lyophilization process. This observation has to be taken into account for the purification and storage of LT. Experiments are ongoing to determine if LT glycation could have an impact on the functional activity of LT using an in vitro assay based on cAMP release by epithelial cell line.