Data di Pubblicazione:
2010
Citazione:
PROTEIN SURFACE SIMILARITIES EVALUATION FOR FUNCTIONAL ANNOTATION STUDIES / P.a. Cozzi ; direttore della scuola: Maria Luisa Villa ; tutore: Paola Comi; correlatore: Luciano Milanesi. Universita' degli Studi di Milano, 2010 Dec 09. 23. ciclo, Anno Accademico 2010. [10.13130/cozzi-paolo-alessandro_phd2010-12-09].
Abstract:
One of the main targets of bioinformatics is to assign functions to proteins whose function is unknown relying on homologies identifications with proteins with known functions. Several approaches are currently available: the best choice depends on the evolutionary distance that separates the protein of interest from its homologous. Recently attention has been focused on molecular surfaces since they do not depend on the three-dimensional structure and allow similarities to be identified which other methods can’t identify. Furthermore, molecular surfaces are the interface of interaction between molecules, and their geometrical and physical descriptions will lead to the comprehension of the molecular recognition process, since the geometrical component has a fundamental role in the early stage of complex formation. This particular aspect would have a major impact in the field of drug design and in the understanding of the side effects due to interactions between proteins.
During this thesis a protocol for similarities identification on molecular surfaces has been developed and optimized. In this process, molecular surfaces are calculated according to Lee Richard’s model, and then are represented through triangular meshes. Successively surfaces are transformed into a set of object oriented images using a computer vision approach. This type of representation has the advantage of being independent from the position of the objects represented, and thus similar surfaces can be described by similar images. The search for similarities is then performed by indentifying correspondences between pairs of similar images, by filtering matches relying on geometrical criteria and then by clustering correspondences in high similarity groups. These groups are then used to align surfaces in order to evaluate results both by visual inspection and through appropriate indexes. This process can be applied in the field of functional annotation, through the identification of similarities between surfaces of homologous proteins, and in study of interaction between proteins, through the identification of complementary areas between interacting proteins.
The whole process of similarities detection depends on the configuration of 15 parameters that balance the time needed to perform calculation with the quality of results found. The problem of parameters estimation has been addressed using an implementation of genetic algorithm, which allowed representing different configuration parameters as a population in which individuals that are able to align surfaces satisfactory are rewarded with an high fitness score. The effectiveness of the algorithm was then improved by the introduction of neighbor heuristic which reduced the computational time required for correspondence clustering on surfaces.
Particular interest was placed in results displaying and in the construction of indices that can quantify the quality of results. Regarding the visualization problem, a display system was implemented based on the Visualization ToolKit libraries in order to represent surfaces aligned as objects in three-dimensional space, enabling the user to interact with the scene represented by changing the point of view or enlarging details of the scene represented.
Regarding the definition of useful indexes for results evaluation, two indexes had a fundamental role. The first one, called overlap index, measures the percentage of vertices of two surfaces that are closer than 1 A° after the alignment. This index in particular is useful for evaluating the surface similarity since similar aligned surfaces will have a large number of vertices closer than this distance. The second index, called RMSD, is important because it evaluates the Root Mean Square Deviation of alpha carbons of
Tipologia IRIS:
Tesi di dottorato
Keywords:
molecular surface ; molecular visualization ; surface similarities
Elenco autori:
P.A. Cozzi
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