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Virtual Screening Studies and Computational Investigations of Protein-Drug Interactions in Acetylcholinesterase
Turkey (TurkMSIC) - Bahcesehir University, Istanbul
Computational Biology & Molecular Simulations Lab, Department of Biophysics, School of Medicine Sahrayi Cedid Mh. Batman Sk No: 66 - 68, 34734, Yenisahra/Kadikoy ISTANBUL/TURKEY
Prof. Dr. Serdar DURDAGI
Prof. Dr. Serdar DURDAGI
Type of Research Project
- Basic science
What is the background of the project?
Please, provide the background of the project (Provide information about the project; min. 10 lines, max. 25 lines) A common feature in the Alzheimer’s disease (AD) brain is the presence of acetylcholinesterase (AChE) (also known acetylhydrolase, is the primary cholinesterase in the body. It is an enzyme that catalyzes the breakdown of acetylcholine and of some other choline esters that function as neurotransmitters. AChE is found at mainly neuromuscular junctions and in chemical synapses of the cholinergic type, where its activity serves to terminate synaptic transmission. It belongs to carboxylesterase family of enzymes. It is the primary target of inhibition by organophosphorus compounds such as nerve agents and pesticides.) which is commonly associated with β-amyloid plaques and neurofibrillary tangles (NFT). Although our understanding of the relationship between AChE and the pathological features of AD is incomplete, increasing evidence suggests that both β-amyloid protein (Aβ) and abnormally hyperphosphorylated tau (P-tau) can influence AChE expression. Therefore, it is important to understand the molecular mechanisms behind the AChE, and novel therapeutic compounds should be developed to inhibit its undesired activity.
What is the aim of the project?
To understand the basics and main concepts about computer-aided-drug-design studies, applied methods and techniques.
What techniques and methods are used?
What techniques and methods are used? Mention the steps/stages in this project. (Provide examples and avoid abbreviations; which tools will be used in general in this project?) Meta Core - Meta Drug: MetaCore delivers 100% manually curated knowledge on biological systems and provides researchers with a platform to mine knowledge, analyze their data and visualize important results. This knowledge base contains information on molecular interactions including protein-protein, drug-protein, metabolic reactions, and more to provide researchers with the knowledge needed to accelerate their research. Meta Drug: Incorporates curated information on biological effects of small molecule compounds. MetaDrug predictions rely on manually curated information about compound targets, metabolic fate, ADME (ADME is an abbreviation in pharmacokinetics and pharmacology for "absorption, distribution, metabolism, and excretion", and describes the disposition of a pharmaceutical compound within an organism. The four criteria all influence the drug levels and kinetics of drug exposure to the tissues and hence influence the performance and pharmacological activity of the compound as a drug.) properties, and therapeutic and side effects. Nearly 6,000 human proteins are covered by compound information. Every target in MetaDrug comes with protein interactions to explore biological pathways affected by the user’s compounds and network neighborhood of drug targets. QSAR (Quantitative Structure Activity Relationships): Quantitative structure–activity relationship models are regression or classification models used in the chemical and biological sciences and engineering. In QSAR modeling, the predictors consist of physico-chemical properties or theoretical molecular descriptors of chemicals; the QSAR response-variable could be a biological activity of the chemicals. QSAR models first summarize a supposed relationship between chemical structures and biological activity in a data-set of chemicals. Second, QSAR models predict the activities of new chemicals Molecular Docking: In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.) The associations between biologically relevant molecules such as proteins, peptides, nucleic acids, carbohydrates, and lipids play a central role in signal transduction. Furthermore, the relative orientation of the two interacting partners may affect the type of signal produced (e.g., agonism vs antagonism). Therefore, docking is useful for predicting both the strength and type of signal produced. Virtual Screening: Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. VS has largely been a numbers game focusing on how the enormous chemical space of over 1060 conceivable compounds can be filtered to a manageable number that can be synthesized, purchased, and tested. Although searching the entire chemical universe may be a theoretically interesting problem, more practical VS scenarios focus on designing and optimizing targeted combinatorial libraries and enriching libraries of available compounds from in-house compound repositories or vendor offerings. Molecular Dynamics: Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanics force fields. The method is applied mostly in chemical physics and the biophysics. Because molecular systems typically consist of a vast number of particles, it is impossible to determine the properties of such complex systems analytically; MD simulation circumvents this problem by using numerical methods Homology Modeling: Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the "template"). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. The homology modeling procedure can be broken down into four sequential steps: template selection, target-template alignment, model construction, and model assessment. The first two steps are often essentially performed together, as the most common methods of identifying templates rely on the production of sequence alignments; however, these alignments may not be of sufficient quality because database search techniques prioritize speed over alignment quality.
What is the role of the student?
- If the project includes “lab work”
- the student will take active part in the practical aspect of the project
What are the tasks expected to be accomplished by the student?
The theoretical basis of molecular simulations; including the 3D structural information about proteins, ligands and nucleic acids. In addition; the thermodynamics of binding, calculation of binding energy, force field topics should be theoretically learned. This knowledge would be the base to the student for the practical applications such as molecular docking, QSAR (Quantitative Structure Activity Relationships), virtual screening and molecular dynamics.
Will there be any theoretical teaching provided (preliminary readings, lectures, courses, seminars etc)
The following topics will be provided in the form of seminars by Prof. Dr. Serdar Durdagi. Lessons will be provided according to the availability of the teacher, at least once per week: -Computerized drug design steps -Molecular design programs -Virtual Screening -Molecular Dynamics There will be several textbooks chapter, reviews and papers reading about similar to the topic which will be given by Prof. Dr. Serdar Durdagi
What is expected from the student at the end of the research exchange? What will be the general outcome of the student?
- The student will prepare a scientific report
What skills are required of the student? Is there any special knowledge or a certain level of studies needed?
No specific skills are required.
Are there any legal limitations in the student’s involvement
Type of students accepted
This project accepts: - Medical students - Graduated students (less than 6 months)
- For the use of students considering participating in the project; further information can be found from the following articles (Please add at least one specific article that is not a book; with correct reference) Serdar Durdagi; Busecan Aksoydan; Ismail Erol; Isik Kantarcioglu et. al. Integration of Multi-Scale Molecular Modeling Approaches with Experiments for the in silico Guided Design and Discovery of Novel hERG-Neutral Antihypertensive Oxazalone and Imidazolone Derivatives and Analysis of Their Potential Restrictive Effects on Cell Proliferation. European Journal of Medicinal Chemistry; 2018; DOI:10.1016/j.ejmech.2017.12.021
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