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Regular version of the site
Master 2019/2020

Computer Molecular Biology and Medicine

Area of studies: Applied Mathematics and Informatics
When: 1 year, 1, 2 module
Mode of studies: Full time
Instructors: Roman Efremov
Master’s programme: Mathematical Methods of Modelling and Computer Technologies
Language: English
ECTS credits: 4

Course Syllabus

Abstract

The course covers: basic physical principles of molecular simulations, mathematical algorithms and computationalprotocols employed to study supramolecular biological objects in the framework of classicalmechanics and empirical force fields; the methods of molecular mechanics, molecular dynamics, Monte Carlo,structuralbioinformatics and molecular docking along with their theoretical foundations and employedphysical models and mathematical algorithms; -Combined approaches to rational design of computational experiments with the help of in silicotechnologies; efficient application of the macroscopic approximation and classical mechanics in detailedanalysis of complex microscopic phenomena –individual molecules and their ensembles
Learning Objectives

Learning Objectives

  • The purpose of learning the discipline “Computer molecular biology and medicine” is the students’ introduction into modern methods of computer modeling of complex -multicomponent and mesoscopic -biomolecular systems. The modeling is carried out in the framework of classical Newtonian mechanics, using empirical energy functions -so-called force fields
Expected Learning Outcomes

Expected Learning Outcomes

  • will know: Basic computer technologies of the experimental data processing; Modern methods of computational analysis and prediction of properties and functioningmechanisms of the studied complex biomolecular systems and constructs; Basic physical models describing structural and dynamic properties of biomolecularsystems; Basic principles of computer-aided drug design and multiscale modeling;
  • be capable of: Analyzing scientific problems and physical processes, realizing in practice fundamentalknowledge obtained in the course of training; Adaptation new problematics, knowledge, scientific terminology and methodology, topossess the skills of independent learning; Application in the given subject area statistical methods of processing experimentaldata, numerical methods, methods of mathematical and computational modeling of complexsystems;
  • be capable of: Understanding meaning of the tasks appearing in the course of professional activity and employment the related physico-mathematical apparatus for description and solving the abovetasks; Using the knowledge of physical and mathematical subjects for further learningaccording to the training profile; Practical working with modern software in the field of computer modeling of complexsystems
  • get experience in: Formulation of computational tasks in studies of complex biomolecular systems; Preparing and running computer simulations of various biomolecular systems, includingsmall molecules, proteins, membranes and their complexes; Correct processing of modeling results and their comparison with available experimentaland literature data; Theoretical analysis of real problems related to atomic-scale studies of molecularsystems and their functioning mechanisms
Course Contents

Course Contents

  • Introduction: “Classical mechanics and in silico modeling in solving modern biomedical tasks (brief overview)”
    The term “in silico”. General characteristics of computer molecular modeling techniques based on classical mechanics formalism. Main directions of research. Types of the problems under study. Real and computational experiments. Bioinformatics. Electronic resources of biological information: databases of functional motifs and patterns, biomolecular sequences and spatial structures. Search in databases, alignment of homologous sequences. Delineation of motifs and patterns. Protein secondary structure prediction. Examples of applications to solving biomedical problems. Intermolecular interactions: molecular docking. Principle of the method. Limitations. Docking scoring functions and the problem of finding of correct docking solutions. Ligand-and target-specific scoring functions. Examples of applications in drug design. Homology modeling of protein structure. Principle of the method. Stages of model building. Choice of the structural template. Quality assessment of the resulting models (PROCHECK, D. Eisenberg’ 3D_1D profile), and so on. Application of the predicted models. Protein fold recognition techniques
  • Biomolecular simulations with empirical force fields
    Modern state-of-the-art. Origination of force fields (foundations of quantum chemistry). Approximate methods of solving Schrodinger equation for many-atom systems. Wave equations for electrons and nuclei. Methods of quantum chemistry. Task formulation and approximations used. Information obtained via quantum chemical calculations. Semi-empirical and ab initio approaches. Examples of applications. Definition of empirical force field. Classical or quantum mechanics: which one to choose? Analytical expressions for potential energy of molecular systems. Physical basis. Bonded and nonbonded energy terms. Definition of energy terms for various types of interactions.Development and application of force fields. Parameterization using experimental and computational data? Approximations used in force field calculations (periodic boundary conditions, cutoff functions, charge groups, constraints and restraints). Energy minimization algorithms. Examples of molecular mechanics applications in solving biomedical problems. Modern force fields, examples of biomolecular simulations. Perspectives of force fields
  • Molecular dynamics (MD)
    Basic principles. Problem formulation, integrators in MD, preparation of the starting configurations. Choice of the integration timestep, Verlet scheme, requirements to MD integrators. Computational protocols in MD. Concepts of temperature and pressure in MD, thermostat and barostat. Control of systems’ equilibration. Thermodynamic ensembles in MD. Algorithms of realization. Examples of MD applications in biomedicine, available software and MD-related resources.
  • Monte Carlo (MC) technique in biomolecular modeling
    Basic principles, Metropolis criterion. Typical computational schemes in MC simulations of biomolecular systems. Conformational analysis and modeling of equilibrated systems. Comparative analysis of MD and MC methods: advantages and limitations. Examples of MC applications in biomedicine
  • Methods of free energy calculations in molecular systems
    Relative free energy. Basic principles and formulation of the problem. Method of thermodynamic integration. Examples of applications
  • Solvation effects in biomolecular simulations.
    Role of solvation effects in formation and maintenance of biomolecular structure. Implicit solvation models. Simplest dielectric models, solution of Poisson-Boltzmann equation, atomic solvation parameters. Explicit solvent models. Periodic boundary conditions, boundary potential. Comparative analysis of implicit and explicit solvation models
  • Molecular modeling of biomembranes.
    Structure and physico-chemical properties of biomembranes. Theoretical models of biomembranes. Structure and dynamics of lipid bilayers. Macroscopic parameters, lateral heterogeneities and clusters. Examples of computer simulations of model biomembranes
  • Modern computational techniques for assessment of hydrophobic properties of molecular systems
    Hydrophobic effect. Quantitative characterization of spatial hydrophobic/hydrophilic properties of biomolecules. Method of molecular hydrophobicity potential and its application to protein modeling and drug design. Mapping and visualization of hydrophobic/hydrophilic properties of biomolecular surfaces –the Protein Surface Topography method. Examples of modern applications.
  • Numerical experiment in molecular biology and biophysics: modern possibilities and perspectives
    Recent "breakthrough" results of computational experiments in molecular biophysics. Supercomputing in simulations of mesoscopic biomolecular systems. Challenges and perspectives
Assessment Elements

Assessment Elements

  • non-blocking Control work
  • non-blocking Homework
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.2 * Homework + 0.2 * Control work + 0.6 * Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Finkelstein A.V., Ptitsyn O.B. Protein Physics: A Course of Lectures. –Academic Press, 2002.
  • Frenkel D., Smit B. Understanding Molecular Simulation: From Algorithms to Applications. –Elsevier, 2002.
  • Snurr, Randall Q, Adjiman, Claire, Kofke, David A.Foundations of Molecular Modeling and Simulation. –Springer, 2016.

Recommended Additional Bibliography

  • Rapaport, D. C. The art of molecular dynamics simulation. –Cambridge university press, 2004.