Computational Biochemistry and Biophysics

General information

Course instructor: Assist. Prof. Vedran Miletić
Name of the course: Computational Biochemistry and Biophysics
Study programme: Postgraduate University Doctoral Study Programme Informatics
Status of the course: compulsory/elective
Year of study: 1./2.
ECTS credits and manner of instruction:

• ECTS credits: 6
• Number of class hours (L+E+S): 15+0+15

Course description

Course objectives

The human genome project began in the 1990s to identify and sequence all human genes. As early as the early 2000s, a large amount of data on genes encoding proteins was publicly available for research. These data and the information derived from them, the availability of ever faster supercomputers, and the advancement of methods used in computational biochemistry and biophysics in the next two decades enabled the rapid development of a branch of molecular biology called structural biology, which links the structure and function of biological macromolecules proteins, nucleic acids, and membranes.

The objective of the course is to acquire knowledge about data structures and algorithms that form the basis of modern software in the field of computational biochemistry and biophysics and the possibilities of application and procedures for further development of existing software by scientific research needs. There is a specific focus on data structures and algorithms that enable the execution of this software on exascalar supercomputers. The objective of the course is also to get acquainted with current scientific research issues in this area and approaches that answer these questions.

Course enrolment requirements

There are no enrolment requirements.

Expected learning outcomes

After fulfilling all the obligations anticipated by the course, students are expected to be able to:

O1. Propose an improvement of an existing algorithm or method in the molecular dynamics simulation.
O2. Predict the performance of molecular dynamics simulators on supercomputers and in cloud computing.
O3. Design an extension of the molecular dynamics simulator with a new feature.
O4. Develop a new feature of the molecular dynamics simulator.

Course content

The course includes the following topics:

• Historical development of computational biochemistry and biophysics. Implementation of atom models within molecular and quantum mechanics.
• Molecular dynamics simulation. Algorithms, data structures, and file formats for storing parameters, molecular structures, and simulation results. Implementation of force fields and interaction functions. Parallelization methods and software adaptation for performing molecular dynamics simulation on heterogeneous computer systems.
• Implementation of methods based on molecular dynamics: calculation of free energy, non-equilibrium withdrawal, adaptive bias, imposed rotation, simulation of uniform and shear flow, and interactive molecular dynamics.
• Performing molecular dynamics simulation in cloud computing and on supercomputers. Analysis and visualization of simulation results. Customizations of software for performing simulation on exascale supercomputers.
• Applications of machine learning in computer biochemistry and biophysics.

Manner of instruction

• lectures
• seminars and workshops
• exercises
• distance learning
• fieldwork
• individual assignments
• multimedia and network
• laboratories
• mentorship
• other _______

Comments

Relevant scientific papers by the course instructor:

1. Svedružić, Ž. M, Vrbnjak, K., Martinović, M. & Miletić, V. Structural Analysis of the Simultaneous Activation and Inhibition of γ-Secretase Activity in the Development of Drugs for Alzheimer's Disease. Pharmaceutics 13(4), 514 (2021). doi:10.3390/pharmaceutics13040514 (WoS-SCIE, Q1 (2020), JIF: 6.321 (2020); times cited: 2)
2. Miletić, V., Ašenbrener Katić, M. & Svedružić, Ž. High-throughput Virtual Screening Web Service Development for SARS-CoV-2 Drug Design. in 2020 43rd International Convention on Information, Communication, and Electronic Technology (MIPRO), 371–376 (2020). doi:10.23919/MIPRO48935.2020.9245082
3. Herrera-Rodríguez, A., Miletić, V., Aponte-Santamaría, C. & Gräter, F. Molecular dynamics simulations of molecules in uniform flow. Biophys. J. 116(6), 621–632 (2019). doi:10.1016/j.bpj.2018.12.025 (WoS-SCIE, Q1, JIF: 3.854; times cited: 5)
4. Franz, F., Aponte-Santamaría, C., Daday, C., Miletić, V. & Gräter, F. Stability of Biological Membranes upon Mechanical Indentation. J. Phys. Chem. B 122(28), 7073–7079 (2018). doi:10.1021/acs.jpcb.8b01861 (WoS-SCIE, Q2, JIF: 2.923; times cited: 2)
5. Miletić, V., Odorčić, I., Nikolić, P. & Svedružić, Ž. M. In silico design of the first DNA-independent mechanism-based inhibitor of mammalian DNA methyltransferase Dnmt1. PLOS ONE 12(4), e0174410 (2017). doi:10.1371/journal.pone.0174410 (WoS-SCIE, Q1, JIF: 2.766; times cited: 14)

Student responsibilities

Students should actively participate in all course activities.

Monitoring¹ of student work

• Class attendance: 1
• Class participation:
• Seminar paper: 1
• Experimental work:
• Written exam:
• Oral exam:
• Essay:
• Research: 2
• Project:
• Continuous assessment:
• Report:
• Practical work: 2
• Portfolio:

Assessment of learning outcomes in class and at the final exam (procedure and examples)

Learning outcomes are assessed through scientific research in which the student applies theoretical and practical knowledge of the subject. The student should describe the results of the research in a seminar paper, which can also be written in the form of a scientific paper. In this way, the seminar paper can serve as the basis for the publication of a scientific paper that will be published at a conference or in a journal in consultation with the course instructor and the student's mentor.

Mandatory literature (at the time of submission of study programme proposal)

1. Advances in Molecular Simulation. (MDPI, 2021). doi:10.3390/books978-3-0365-2711-6. Available online: www.mdpi.com/books/pdfview/book/4780
2. Molecular Dynamics Simulation. (MDPI, 2014). doi:10.3390/books978-3-906980-66-9. Available online: www.mdpi.com/books/pdfview/book/75
3. Garmon, A. Accelerated Molecular Dynamics for the Exascale. (Clemson Libraries, 2020). Available online: tigerprints.clemson.edu/all_dissertations/2716
4. GROMACS Reference Manual, User Guide, and Developer Guide. Available online: manual.gromacs.org
5. Content prepared for learning through a learning system.

Optional/additional literature (at the time of submission of the study programme proposal)

1. Computational Biochemistry and Biophysics. (CRC Press, 2001). doi:10.1201/9780203903827.
2. Frenkel, D. & Smit, B. Understanding Molecular Simulation: From Algorithms to Applications. (Academic Press, 2001).
3. Cramer, C. J. Essentials of Computational Chemistry: Theories and Models. (Wiley, 2004).
4. Jensen, F. Introduction to Computational Chemistry. (John Wiley & Sons, 2017).
5. Griebel, M., Knapek, S. & Zumbusch, G. Numerical Simulation in Molecular Dynamics: Numerics, Algorithms, Parallelization, Applications. (Springer, 2010).
6. Todd, B. D. & Daivis, P. J. Nonequilibrium Molecular Dynamics: Theory, Algorithms and Applications. (Cambridge University Press, 2017).
7. OpenMM User Guide and Developer Guide. Available online: openmm.org/documentation
8. LAMMPS User Guide and Programmer Guide. Available online: docs.lammps.org/Manual.html

Number of assigned reading copies in relation to the number of students currently attending the course

Quality monitoring methods that ensure the acquisition of exit knowledge, skills and competences

Regular evaluation is planned in order to ensure and continuously improve the quality of teaching and study programs (as part of the activities of the Quality Assurance Committee of the Faculty of Informatics and Digital Technologies).