BM12016/10.26125/e1gd-3089

Enhanced sampling techniques for protein folding simulations

Author(s): Emanuel Karl Peter

Publication: Bunsenmagazin, Issue 1 2016, Aspekte, Seiten: 8 - 16

Publisher: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V., Frankfurt

Language: English

DOI: 10.26125/e1gd-3089

 

Introduction

Proteins fold into a unique 3-dimensional structure after they have been expressed by the Ribosome in the living cell. The structure and stability of a protein are primarily determined by its amino acid sequence, which also controls its function in intra- and extra-cellular processes. With the development of experimental techniques, i.e. X-ray crystallography and solution nuclear magnetic resonance (NMR) techniques, structural information of proteins on atomic resolution became broadly accessible. Based on this structural information, an abundance of biochemical questions could be resolved, e.g. the elucidation of G protein coupled receptor (GPCR) structures which was awarded with the Nobel prize in Chemistry in 2012. GPCRs are cell-receptors for ligands, including light-sensitive compounds, odor-molecules, pheromones, hormones and neurotransmitters. Thus, an understanding of GPCR structure and function opened the gateway to a vast number of treatment options of various diseases [1]. At present, over 100.000 protein structures have been determined and are available from the RCSB protein data bank [2]. Despite this huge amount of available information based on experiments, proteins are dynamic in order to be able to execute their function, i.e. their conformation space (the number of 3-dimensional structures accessible to the protein at a defined temperature) is larger than the experimentally defined native state. Additionally, the effect of single mutations (exchanges of amino acids in the protein sequence) can dramatically affect the structure of proteins and change their function. For example, pathogenic mutations such as the Arctic Amyloid Precursor Protein (APP) mutation E693G, have been detected to cause an increased risk for a severe form of Alzheimer's disease in a Swedish family [3]. In order to understand protein function and the effect of mutations, pathways of protein folding, i.e. the process of structure-formation after protein expression, have to be investigated. For these studies of protein dynamics (conformation space) and folding pathways on an atomistic level, computational methods have emerged as a complementary tool to experiments.

As it has been stated in Levinthal's paradox, the protein folding pathway cannot be a combinatorial process, i.e. the search of the polypeptide chain for its native conformer has to be directed and is tens of orders of magnitude faster than any combinatorial process. In this way, a reaction consisting of combinatorial search problems would contradict the experimental observation of comparably short time-scales of protein folding reactions [4, 5]. Instead, the folding process is dependent on conformation-states accessible from the instantaneous configuration including the polypeptide chain, the surrounding solvent and electrolytes. In other words, starting from one instantaneous configuration (at time t) only a limited number of new configurations are available at a time t+dt, while structurally independent permutations for finding a new conformer are impossible. In specific cases, e.g. ultra-fast folding proteins, the number of newly accessible structures in a period from t to t+dt throughout the folding pathway is restricted to a extremely low number. Subsequently, if we observe any protein in a hypothetically infinite time-frame, the folded state of a polypeptide chain (corresponding to the experimental structure) is then the ensemble of conformations (a collection of structures with the same structural properties) occurring with the highest probability. From a thermodynamic perspective, this ensemble of folded configurations (with the largest occurrence) in the native state corresponds to the global free energy minimum for this specific protein. It is worth mentioning, that the system containing the peptide is at equilibrium, while the protein in this very folding process might dissipate energy into its surrounding. In other words, the system might be locally out of equilibrium at a defined time-frame, but not globally. This behaviour is also reflected by changes in the specific temperature dependent heat capacities obtained in simulations.

A vast number of theories for protein folding have been developed in the last decades, while very few developments are actually based on first principles, i.e. on simplest and irreducible physical conjectures. At present, there are 2 models representing different boundary cases of the rate-limiting event in protein folding reactions : First, the diffusion collision model and second, the nucleation condensation model. In the diffusion collision model, minor structural motifs, i.e. spatially separated sub-structures, which already contain partial native secondary or tertiary structures (depending on the size of the protein), are formed slowly after the expression by the Ribosome. Subsequently, the different motifs diffuse and collide fast, driven by thermal fluctuations, until the complete native tertiary structure has aligned. The nucleation condensation model assumes that the protein forms a nucleus in a slow process at the beginning. Through the formation of this nucleus, the global tertiary structure is already pre-defined. This nucleation event is then followed by a fast condensation-process, consisting of the subsequent formation of structural elements (motifs) around this nucleus leading to the native state. It should be noted, that actual folding pathways might be described by more complex intermediate models, e.g. an initial diffusion-collision process might lead to a nucleation event, followed by condensation and the formation of the native structure. Alternatively, large proteins (> 400 amino-acids) might react through several local nucleation-condensation mechanisms, but globally within one single diffusion-collision process. [...]

 

Cite this: Emanuel Karl Peter (2016): Enhanced sampling techniques for protein folding simulations. Bunsenmagazin 2016, 1: 8-16. Frankfurt am Main: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V. DOI: 10.26125/e1gd-3089

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