Due to vast improvements in sequencing methods over the past few decades, the availability of genomic data is rapidly increasing, thus bringing about the need for functional characterization tools. Considering the breadth of data involved, functional assays would be impractical and only a computational method could afford fast and cost-effective functional annotations. Therefore, homology-based computational methods are routinely used to assign putative molecular functions that can later be confirmed with targeted experiments. These methods are particularly well suited to predict the function of enzymes because most metabolic pathways are conserved across organisms. However, the current methods have limitations, especially when considering enzymes that have very low sequence and structure homology to well-annotated enzymes.
We hypothesized that two enzymes with the same molecular function shared significant sequence homology in the region surrounding the active site, even if they appear diverged at the global sequence level. First, we have investigated the limits of sequence and structure conservation for enzymes with the same function during divergent evolution. The goal of this was to determine the sequence identity threshold beyond which functional annotations should not be transferred between two sequences; that is the level of homology beyond which the pair of proteins would not be expected to have the same function. Our analysis, which compares several models of sequence evolution, shows that the sequences of orthologous proteins catalyzing the same reaction rarely diverge beyond 30 % identity, even after approximately 3.5 billion years of evolution. As for structure conservation, enzymes catalyzing the same reactions rarely diverge beyond 3 Ã… root-mean-square distance (RMSD). We have also explored sequence conservation constraints as a function of the distance to the active site. Although residues closer to the protein active site (within a radius of 10 Ã… around the catalytic residues) are mutating significantly slower, the requirement to preserve the molecular function also constrains residues at other parts of the protein.
From these results, we have developed a structure-based function prediction method where we employ active site conservation in addition to global sequence homology for functional characterization. We then integrated this method with a probabilistic whole-genome function prediction framework previously developed in the Vitkup group, GLOBUS. The original version of GLOBUS uses sampling of probability space to assign functions to all putative metabolic genes in an input genome by considering sequence homology to known enzymes, gene-gene context and EC co-occurrence. Applying this novel method to the whole-genome metabolic reconstruction of Mycobacterium tuberculosis, we made several novel predictions for genes with apparent links to pathogenesis. Notably, our predictions allowed us to reconstruct the cholesterol degradation pathway in M. tuberculosis, which has been implicated in bacterial persistence in the literature but remains to be fully characterized. This pathway is absent from previously published metabolic models of M. tuberculosis. Our new model can now be used to simulate different environments and conditions in order to gain a better understanding of the metabolic adaptability of M. tuberculosis during pathogenesis.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8GT5KRF |
| Date | January 2014 |
| Creators | Konate, Mariam |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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