Data di Pubblicazione:
2017
Citazione:
COMPUTATIONAL MODELING OF PROTEINS: FROM STATISTICAL MECHANICS TO IMMUNOLOGY / R. Capelli ; tutor: G. Tiana, G. Colombo; coordinatore: F. Ragusa. DIPARTIMENTO DI FISICA, 2017 Nov 24. 30. ciclo, Anno Accademico 2017. [10.13130/r-capelli_phd2017-11-24].
Abstract:
One of the biggest revolutions occurred during the second half of the 20th century in physics was the introduction of computers in research. In particular, the use of fast computing machines opened the possibility to study complex systems by simulating their dynamics, without the need to pursue analytical solutions, otherwise impossible to tackle. The consequences of this breakthrough were huge both in the study of equilibrium and non-equilibrium many-body problems, with the strong limitation given by the number of atoms involved in the calculation.
The first technique used in biology-related problems was the Monte Carlo Method, and some years later Molecular Dynamics (MD) was formalized. In MD, for each atom of the system one can solve its Newton equations of motion, obtaining a trajectory in the phase space for the entire system, and study its behavior in equilibrium and non-equilibrium conditions. The constant rise in computational power gave the possibility to scientists to study larger and larger systems, while the advances in experimental techniques enhanced the possibility for direct comparisons between wet and in silico data at similar levels of resolution. Despite the validity of Moore’s Law (i.e., the exponential growth of the computing power due to transistors miniaturization) until now, the timescale of the events that can be simulated has an upper limit of the millisecond with tailor-made computers, which is not enough to study all the biologically-relevant phenomena. Since the birth of computational chemistry, a huge number of different statistical mechanics-based methods has been implemented to permit, given the computing power limit, an effective reliable use of MD simulations in biochemistry.
One of the most relevant problems tackled by MD is the calculation of free energy differences, both in conformational changes and in sequence mutations of a protein. The main reason of this difficulty is represented by the frustrated nature of interactions in proteins and the size of these systems: this leads to a complex energy landscape which in principle needs very long sampling times to overcome all possible energy barriers. In the present thesis, we studied and improved a path-independent and system-independent free energy calculation technique, called Simplified Confinement Method. We describe this work in Chapter 1.
Although MD has been successful in most of its applications, there are still many open problems: as mentioned before, the available parametrizations of interaction potentials (called force fields) are not completely reliable. In particular, the choice of force field parameters is performed comparing experimental data on a fixed set of (usually small) molecules with computed data on the same molecules. This raises a significant problem: large molecules can have a more complex behavior, and using these potentials can lead to a systematic error; furthermore, the timescale in which the force field is tested needs to be limited. Another strong limitation of MD depends on the equilibrium experiments used for parametrization: the kinetic properties of a system are not considered. Given the impossibility to reparametrize a general force field with non-equilibrium experimental data, we implemented a technique that uses equilibrium-based force fields, adding a potential term based on time series resulting from kinetic experiments. This approach, based on the principle of Maximum Caliber, restrains the system with an experimental-based bias, returning a more realistic behavior of the simulation in condition where the usual force fields show their limitations. We describe this work in Chapter 2.
The application of computational methods in the study of proteins confirms its efficacy in other fields of life
Tipologia IRIS:
Tesi di dottorato
Keywords:
Free energy calculations; Structural vaccinology; non-equilibrium simulations; Protein folding; Protein design; Vaccine development
Elenco autori:
R. Capelli
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