| December 29th, 2020 04:09 PM | |
| vikash | Sathyabama Institute of Science and Technology M.Sc - Bioinformatics and Datascience SBIA5203 Bio-molecular Modelling Syllabus Sathyabama Institute of Science and Technology M.Sc - Bioinformatics and Datascience SBIA5203 Bio-molecular Modelling Syllabus SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF BIO AND CHEMICAL ENGINEERING SBIA5203 BIO-MOLECULAR MODELLING L T P Credits Total Marks 3 0 0 3 100 UNIT I: BASIC CONCEPTS IN MOLECULAR MODELLING Internal parameters - Z-matrix – Introduction to quantum chemistry –basic postulates – Schrodinger wave equation – Derivation – Hydrogen atom – Born-Oppenheimer approximation - Laws of thermodynamics - entropy – enthalpy - free energy calculations-chemical potential - Calculating thermodynamic properties using force field; Transferability of force field parameters, treatment of delocaliised pi system; Force field for metals and inorganic systems – Application of energy minimization. UNIT II MOLECULAR GEOMETRY Conformational parameters - Potential energy Surface - Molecular mechanics: empirical forces fields - bond stretching - angle bending - torsional terms – non-bonded and electrostatic interaction. UNIT III TYPES OF FORCE FIELDS AND ENERGY MINIMIZATION simplex – sequential univariate method - steepest descent - conjugate gradient method - Newton–Raphson method – Molecular Dynamics with continuous potentials and at constant temperature and pressure; Timedependent properties; Solvent effects in Molecular Dynamics - Conformational analysis - Molecular visualization - Molecular graphics – Rendering - Rasmol. UNIT IV-MOLECULAR DYNAMICS (MD) SIMULATION OF BIOPOLYMERS Ttime steps - Setting up MD - energy conservation in MD Simulation -continuous potentials and constraint dynamics - MD at constant temperature and pressure - incorporating solvent effects - examples-random number generator - Monte Carlo simulation of biological macromolecules - MD softwares. UNIT V-PREDICTION OF SECONDARY STRUCTURE Membrane prediction – Comparative modeling - Sequence Alignment Homolog’s - analogs - Homology modeling - steps in homology modeling – side chain modeling – loop modeling - fold recognition – ab initio prediction – Predicting protein structures by threading protein folding – active site/binding site prediction – tools – databases - CASP. Course Outcomes: CO1: Students will learn the basics of molecular modeling aspects. CO2: They will be able to relate the conformational parameters to the conformation of the molecule. CO3: They will be able to predict the effect of the force field applied on a molecule. CO4: Students will be able to examine and analyze the significance of simulation of macromolecules. CO5: Students will be able to appreciate the predicted models. CO6: Assessment of novel algorithm in structure prediction References: 1. Leach A.R., Molecular Modelling - Principles and Applications, 2nd Edition, Prentice Hall, 2001. 2. Vinter J.G. and Mark Gardener, Molecular modeling and drug design, Mac Millan, 1994. 3. Prasad, R.K., Quantum Chemistry, Halsted Press, 1992. 4. Ramachandran, K.I, Deepa, G, Namboori, K, Computational Chemistry and Molecular Modeling: Principles & Application, Springer 2008. 5. 3. Young, D.C., Computational Chemistry: A Practical Guide for Applying Techniques to Real- WorldProblems, Wiley-Indescience 2001 END SEMESTER EXAM QUESTION PAPER PATTERN Max. Marks: 100 Exam Duration: 3 Hrs. PART A: 6 Questions for 5 Marks each –without choice 30 Marks PART B: 2 Questions from each unit with internal choice, each carrying 14 marks 70 Marks |
