Language: English Lecturer: Gianni de Fabritiis. Teaching staff: Language: English Lecturer: Jordi Villà i Freixa

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1 MSI: Molecular Simulations Descriptive details concerning the subject: Name of the subject: Molecular Simulations Code : MSI Type of subject: Optional ECTS: 5 Total hours: Scheduling: 11:00-13:00 Course: 1 st course Time: 2nd trimester Coordination: Jordi Villà i Freixa Department: Ciències Experimentals i de la Salut Room: Mar Teaching staff: Language: English Lecturer: Jordi Villà i Freixa Department: Ciències Experimentals i de la Salut Language: English Lecturer: Gianni de Fabritiis Department: Ciències Experimentals i de la Salut Subject presentation: Molecular Simulations is a subject that represents the bridge between the isolated static representation of biomolecular structures (which is dealt with by the subject Structural Bioinformatics) and the use of this information for drug discovery and design (dealt with by the subject Computer Assisted Drug Discovery). The subject shows the student the way to use structural information, along with basic physical laws and more complex statistical thermodynamics tools, to obtain the relevant free energy values that will be comparable with several types of experimental information. The subject, however, does not restrict to concepts on simulations of biomolecular 3D structures, but tries to introduce the student to the general concepts of stochastic process, Monte Carlo approaches, sampling, and in general of simulation theory. This is an specialized subject, not critial but desirable to reach an appropriate level of understanding of the tools for drug discovery and design and to reach the appropriate knowledge on simulation techniques that may be expanded to the Computational Systems Biology subject. The subject is centered in the understanding of key physical and statistical physics concepts and tools, the application of common software found in the computational biochemistry and biophysics labs around the world as well as the ability to produce small demonstration codes with simple programming. As this is a completely incremental subject, the student is advised of the need of strong interaction with the lecturers and the need of keeping the class material up to date. Prerequisites in order to follow the itinerary:

2 This subject does not have any pre-requisite, although previous programming knowledge and notions of maths and physics are expected Competences to be attained in the subject General competences: Instrumentals: 1. Concept of stochastic process and sampling 2. Concept of free energy Interpersonal: 1. Group work 2. Ability to solve by yourself a given problem Systemic: 1. Analysis and synthesis abilities 2. Ability to search for information Specific: 1. To understand the concept of force field and potential energy surface (PES) 2. To understand and implement the concept of function optimization 3. To understand and implement the concept of numerical integration 4. To understand and implement the concept of thermostat 5. To master the basics of thermodynamics (1st, 2nd and 3rd laws) 6. To understand and apply the concept of partition function 7. To understand the relationship between partition functions and macroscopic thermodynamics state functions (S, H and G) 8. To know the different methods for adding solvation to molecular simulations (continuum, discrete and semidiscrete) 9. To understand the mathematical concept of random distribution and the computational concept of random generator function 10. To understand and apply a Metropolis algorithm 11. Ability to implement a Newtonian algorithm for molecular dynamics 12. To understand the different methods existing for docking calculations 13. To understand and implement simple algorithms for free energy calculations (FEP and umbrella samplig, potential of mean force...) Learning aims: To get the ability to understand, implement and apply molecular simulations to extract macroscopic observables from microscopic details of the dynamics of biomolecules. General assessment criteria: The evaluation will consists in four parts, with percentages of the total grade indicated in the following list: An incrementally built code in Python (or any other language mastered by the student) that will be evaluated regularly (40% of the total grade). The class will be divided in groups of 1-2 people to carry out the programs and the same grade will be given to the members of the group An exercise on small molecule docking (10%) A written term paper on the application of simulation codes to a given problem. The work will be presented in groups of 2 and each student will have a differet assessment depending on their

3 contribution to the article (that should be stated at the end). (30%) A short questions examination on different aspects of the course. The test will be individual. (20%) Competencies evaluation Instrumentals: Proficient reading/writing/listening scientific English related to the subject Expertise in UNIX syntax Expertise in at least one programming language (C, Perl or Python) Knowledge of office software to do quality scientific presentations and reports Elements of mathematics: concept of derivative and concept of integral Elements of Physics: Newton's law and Coulomb's law Elements of chemistry: pka, nucleophile/electrophile, aminoacids, nucelic acids, sugars Attainment indicator Correct understanding of proposed exercises and correct final presentation Ability to follow the class and exercises without any basic OS related handicap Ability to follow the proposed exercises High quality final presentation Ability to understand new proposed algorithms Ability to understand potential energy functions Ability to understand chemical concepts in the simulations performed Assessment procedure A presentation at the end of the course on a particular subject of interest in the field of molecular simulations. The class will be divided in groups of 3-4 people and the same grade will be given to all the members of the group. (25% of total grade) Implicit in the development of software Implicit in the development of software Implicit in final presentation Implicit in software development Implicit in the exercises A set of pencil and paper and practical exercises (5%) A short questions examination (25%) Scheduling End of term progressive End of term exercises taken during the course and final exam at the end of term. Interpersonal: Group work Ability to do team work both in programming and in preparing final presentation Implicit in developed software (45%) and final presentation

4 5. Ability to solve by yourself a given problem Correct answer of set of pen and pencil exercises and final examination Implicit in exercises and final examination Systemic: Analysis and synthesis abilities Ability to search for information Development of algorithms for proposed problems in molecular simulations Implicit in the software development Written term paper term paper End of term Specific: 1. To understand the concept of force field and potential energy surface (PES) 2. To understand and implement the concept of function optimization 3. To understand and implement the concept of numerical integration 4. To understand and implement the concept of thermostat 5. To master the basics of thermodynamics (1st, 2nd and 3rd laws) 6. To understand and apply the concept of partition function 7. To understand the relationship between partition functions and macroscopic thermodynamics state functions (S, H and G) 8. To know the different methods for adding solvation to molecular simulations (continuum, discrete and semidiscrete) 9. To understand the mathematical concept of random distribution and the computational concept of random generator function 10. To understand and apply a Metropolis algorithm 11. Ability to implement a Newtonian algorithm for molecular dynamics 12. Ability to implement a Brownian algorithm for molecular dynamics 13. To understand the different methods existing for docking calculations 14. To understand and implement simple algorithms for free energy calculations (FEP and umbrella samplig, potential of mean force...) 15. To understand the methods existing for multiscale molecular modelling 17. To understand the methods existing for experimental validation of molecular simulations Contents Contents 1: Block 1: Presentation of toy models for simple algorithms Concepts Procedures Attitudes

5 To abstract from a complex problem a simple entity To create a function determining an energy value for the saved configuration of Calpha s To create a simple algorithm of minimization and implement it in a code The coordinates of a protein will be simplified into the position of the Calpha Using Newton s law we will create a extremely simple initial force field C, Python or Perl will be used to implement a steepest descent algorithm on the saved coordinates To be able to edit a PDB file and extract a simple file with the coordinates of a given atom type To be able to translate structural information into the physical concept of energy To be able to use the basic concepts of algorithmics to create a short code of minimization Contents 2: Block 2: Molecular Dynamics and Monte Carlo simulations Conceptes Procedures Attitudes A simple numerical integrator Using random generators Langevin and Brownian dynamics for implicit inclusion of solvent friction Monte Carlo move The Verlet algorithm will be described and implemented on the beads-spring model To use the intrinsic functions in the selected programming language to generate random numbers Inclusion of the fluctuationdissipation term in the previous MD algorithm Using the same routines substitute the dynamical loop by a Metropolis algorithm to generate new conformations To be able to generate a loop containing the integration algorithm to generate time series of coordinates To be able to implement simple random generators and to understand their concept Being able to control the temperature term by simply adding its effect in the random term To be able to incorporate the Boltzmann term to proper select the probability of new conformations in the simulation Contents 3: Block 3: Basic concepts of statistical mechanics Conceptes Procedures Attitudes The three laws of thermodynamics Partition function Different models for solvation the inclusion of solvation in the simulated system Overview of typical probles in classical thermodynamics; thermic machines; entropy Building the vibrational partition function for a simple system like the beads/spring model A work will be done on the different aspects of solvation methods and their relationship with simulation and thermodynamic quantities To grasp the concepts that are essential to understand molecular simulations. To differentiate energy from free energy To understand how to relate microand macroscopic quantities Being able to get a synthesis of the different methodologies in a critical way

6 Contents 4: Block 4: Running simulations on real systems Concepts Procedures Attitudes Protein structure PDB file format Force fields NAMD and VMD Proteinsmall molecule docking Protein-protein docking Short overview of SBI To play with simple parsers for PDB file Recognizing standard force fields and implementing simple cases Practical training with NAMD and VMD Using AutoDock for practical runs A work will be done to review the recent achievements in the field, paying special attention to the CAPRI expermient To master the different folds one may encounter and to relate the to basic chemical and biochemical principles To be able to understand and implement a simple PDB parser To understand the concept of force field and how it has been implemented in standard programs To run simple simulations of proteins by using a combination of molecular visualization and simulation programs To understand the docking problem and to be able to critically assess the calculation of a given docking problem Being able to get a synthesis of the different methodologies in a critical way Contents 5: Block 5: Complex concepts in molecular simulations Conceptes Procedures Attitudes Free energy simulations and multiscale modelling To implement simple free energy methods (FEP/US, WHAM, PMF) to the outcome of the molecular simulators developed above. To learn ways to coarse grain a given simulation. To understand the sampling problem and ways to overcome its drawbacks To understand the time and space scaling problem in molecular simulations, being able to choose the appropriate technique for each question asked being able to get a synthesis of the different methodologies in a critical way Methodological focus of the subject: The subject focusses on practical implementation of different types of tools for simple molecular simulation protocols. Thus, the methods used are strongly based on the good understanding of basic principles of computer programming. Python will be the main language used in the class, but the students are allowed to do their assignments using any other programming language they master. The programs developed will be complemented by: exercises, individual and for particular chapters/blocks of the course an exam, individual and based on short questions/answers and

7 a term paper on the work done for a particular system The MS code The aim of the subject is not just to get a series of complex concepts but to be able to develop computer code for every proposed simple problem. The subject will be based on an incrementally built Python program (or in any other language the student feels comfortable) that the students will develop through the sessions. Regular checkpoints of the program will be set (see table above) and at those points evaluations of the performance of the student will be carried out. When a particular student has not reached the minimum level of knowledge the program generated by the teacher will be provided to the next checkpoint to be reached independently from the first one.