Cellular function arises from the dynamics of biomolecules. While fast dynamics can be treated on the quantum or molecular mechanics level using molecular dynamics, many biological processes are too slow to be amenable to simulation by molecular dynamics, which is currently limited to the microsecond time-scale (10^-6  – 10^-5 s). This is often referred to as the time-scale problem of molecular dynamics.

Atomistic Markov Chain Monte Carlo (MCMC) is an interesting and complementary approach to studying long time scale phenomena like protein folding and peptide aggregation.

The main objectives of this tutorial are to provide researchers with a solid background knowledge of the principal characteristics and capabilities of atomistic MCMC simulations and to introduce them to practical MCMC simulation using the software package ProFASi developed by the lecturers of this tutorial [1].

ProFASi is an open source software for MCMC simulation of biomolecules and provides a versatile toolkit for using modern Monte Carlo methods such as the replica exchange or the multi-canonical methods. ProFASi is fast enough to fold some small helical proteins within a minute which makes it a good tool for this tutorial. It has been successfully applied to study long time scale processes like protein folding, peptide aggregation and the dynamics of intrinsically
unstructured proteins [2-5]. A recent highlight has been simulating the folding of the 92 amino acid protein Top7 [6], a process operating at a time scale of 1 second.

The three practical afternoon session will introduce the ProFASi package at sufficient depth for productive use. Using HPC clusters of the Jülich  Supercomputing Centre the participants will perform increasingly sophisticated simulations and learn how to apply MCMC simulation to various areas of biomolecular research.

The tutorial will highlight commonalities and differences of atomistic MCMC simulations to other simulation techniques and discuss both their advantages and limitations to allow the attendants to judge where MCMC simulations may be helpful in their research and where not.