Studying macromolecular interactions on the large scale : a bioinformatics approach
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Characterization of macromolecular interactions is not only critical for understanding how macromolecules perform their biological functions, such as promoting chemical reactions and acting as antibodies, but is also important for finding out molecular mechanisms behind the human diseases. Furthermore, the information of macromolecular binding is pivotal for elucidating metabolic, signal transduction, and other networks. Finally, our knowledge about macromolecular interactions may be critical in studying how complex genetic variations and alternative splicing affect the development and course of diseases such as cancer. Researchers are trying to understand the evolution and physics of macromolecular interactions by collecting and analyzing the interaction data, developing predictive models for characterization of macromolecular structure and function, and, applying the developed techniques to study specific biological systems or particular diseases. Some research methods like machine learning, statistical modeling and data mining based of the macromolecular interaction data derived from experimentally determined structures of macromolecule complexes are frequently used to discover the principle of interactions. Our work incorporates data mining, machine learning and statistical modeling methodology together into the location of macromolecular binding and establishes a comprehensive relational macromolecular database. Additionally, a sequence-based protein binding site prediction method was built using machine learning method and statistic model. This predictor intelligently integrates the information derived from the protein?s sequence and its homology model so that it can offer accurate predictions irrespective of the varying quality of comparative models. Our methods to analyze the mutations have been applied to studying the role of these interactions in diseases, like cancer.
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