Poster number 64 submitted by Sara Ali
Mutate2test: New Algorithm and Software for Mutational Design
Sara E. Ali (Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester), Yan Sun (Department of Microbiology & Immunology, University of Rochester), Ela Kierzek (Institute of Bioorganic Chemistry Polish Academy of Sciences), Ryszard Kierzek (Institute of Bioorganic Chemistry Polish Academy of Sciences), David H. Mathews (Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester)
It is common that a hypothesized RNA secondary structure needs to be tested. A traditional method is the site-directed mutation of a sequence to abolish function, followed by restoration of the structure with compensating changes. This approach, however, is complicated by the need to carefully design the mutants, which can be especially difficult when the sequence falls within an open reading frame. It is essential to maintain the amino acid identity so that the structure is the only changing variable in the test. Manual mutation design can be complicated, nonoptimal and time-consuming.
The goal of this study is to write new software to automate the design of sequences to test putative RNA structures. We created a new program, mutate2test, that, given an RNA secondary structure, designs mutants to abolish the structure and designs the set of compensating mutations to restore the structure. The program iterates over all possible disrupting and restoring mutations within an open reading frame that maintain the amino acid identity of the input sequence. The objective function used to accept or reject mutations is based on Ensemble Defect (ED). For a candidate sequence and a given target secondary structure, the ensemble defect is the average number of incorrectly paired nucleotides at equilibrium evaluated over the ensemble of secondary structures. Thus, it is used to quantify the level of disruption or restoration for a sequence given a model structure. It is key that the sequence restoring the original structure by compensating mutations have low ensemble defect to the hypothesized structure, the loss of function mutants have high average ensemble defect and the total number of mutations be minimized to facilitate the experiments. The mutations are then ranked to find the most optimal disrupting and restoring mutations. We are benchmarking mutate2test by designing mutants for a conserved RNA secondary structure model we developed in respiratory syncytial virus (RSV).
Keywords: RNA secondary structure, mutation design, open reading frame (ORF)