Functional Studies of Interactions in Proteins

Interactions between proteins form the basis of almost all biological mechanisms. The majority of proteins perform their functions as a part of an assembled complex, rather than as an isolated species. Understanding the functional pathways of these protein complexes helps in uncovering the molecular mechanisms involved in the interactions. In this thesis, this has been explored in two fundamental ways. First, a biohybrid complex was assembled using the photosystem I (PSI) protein complex to translate the biochemical pathways into a non-cellular environment. This involved incorporating PSI on a porous antimony-doped tin oxide electrode using cytochrome c. Photocurrent was generated upon illumination of the PSI/electrode system alone at microamp/cm2 levels, with reduced oxygen apparently as the primary carrier. When the PSI/electrode system was coupled with ferredoxin, ferredoxin-NADP+ reductase (FNR), and NADP+, the resulting light-powered NADPH production was coupled to a dehydrogenase system for enzymatic carbon reduction. The results demonstrated that light-dependent reduction readily takes place. However, the pathways do not always match the biological pathways of PSI in nature. To create a complex self-assembled system such as the one involving PSI that is structurally well defined, there is a need to develop ways to guide the molecular interactions. In the second part of the thesis, this problem was approached by studying a well-defined system involving monoclonal antibodies (mAbs) binding their cognate epitope sequences to understand the molecular recognition properties associated with protein-protein interactions. This approach used a neural network model to derive a comprehensive and quantitative relationship between an amino acid sequence and its function by using sparse measurements of mAb binding to peptides on a high density peptide microarray. The resulting model can be used to predict the function of any peptide in the possible combinatorial sequence space. The results demonstrated that by training the model on just ~105 peptides out of the total combinatorial space of ~1010, the target sequences of the mAbs (cognate epitopes) can be predicted with high statistical accuracy. Furthermore, the biological relevance of the algorithm’s predictive ability has also been demonstrated.