Biological applications of discrete molecular dynamics
Sequence, structure and dynamics are an indivisible tandem to understand protein function. Luckily, evolution imposed a hierarchical rational between that facilitates the analysis: dynamics are encoded in the structure, which in turn, is encoded in the sequence. Decipher the mechanisms governing pro...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/397796 |
| Acceso en línea: | http://hdl.handle.net/10803/397796 |
| Access Level: | acceso abierto |
| Palabra clave: | Biologia molecular Biología molecular Molecular biology Dinàmica molecular Dinámica molecular Molecular dynamics Proteïnes Proteínas Proteins Ciències Experimentals i Matemàtiques 577 |
| Sumario: | Sequence, structure and dynamics are an indivisible tandem to understand protein function. Luckily, evolution imposed a hierarchical rational between that facilitates the analysis: dynamics are encoded in the structure, which in turn, is encoded in the sequence. Decipher the mechanisms governing protein function requires contributions from diverse fields, particularly to follow molecular motions. There are technological limitations to monitor local, elemental, protein movements, since they are too fast to be followed by current experimental set-ups. Theoretical models provide necessary assistance in this regard mainly through molecular simulations. But atomistically simulations of large functional motions make computations, currently, unaffordable. The problem is that large-scale motions are rooted in the very fast elemental ones; so, in order to observe a biological-functional conformational change we have to keep track of all the elemental motions occurring. The gap in the time scale of both extremes of motions is devastating: fast motions are over 1015 times faster than functional ones. In this Thesis, I present our contribution to extend the simulation time range, in an effort towards more predictive computational models. We explored alternative methods to retrieve molecular motions from the underlying physical forces governing proteins. The method used is named Discrete Molecular Dynamics and represents by itself a significant improvement in computational efficiency. In order to go further, we lower the resolution of protein models to a coarse-grained representation both in terms of number of particles and interaction functions. We benefited from several existing algorithms to simplify calculations keeping the models as much accurate as possible. Putting all this methodological innovations together, we developed models to follow conformational transitions of proteins, from local re-arrangements to motions changing drastically the protein structure. Also, we applied novel computational approaches to account for protein flexibility upon recognizing and binding other interacting proteins. In a second stage, we investigated the echo of protein flexibility and dynamics printed out in the sequence of the protein. We observed over the history of the sequence that instead of one single native structure, proteins were tuned to have several conformations. We exploited this flexibility signature in the sequence to predict protein motions and eventually alternative protein conformations. Finally, we use our efficient tools to move protein dynamics analysis to the proteome scale. We searched for all proteins having two known conformations, a symptom of a conformational transition, and then, we used those conformations to follow the motion from one state to the other. We analyzed and structured all that dynamical information of proteins and connected our results to the most detailed simulation methods available to dissect the fine details of proteins dynamical behavior when required. |
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