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Natural selection and algorithmic design of mRNA.
1Department of Computer Science, University Heights, New Jersey Institute of Technology, Newark, NJ 07102, USA. bcohen@cs.njit.edu
Summary
Messenger RNA (mRNA) stability is influenced by natural selection, with organisms favoring certain sequences. This study develops algorithms to design both highly stable and unstable mRNA sequences, offering new possibilities for RNA sequence design.
Area of Science:
- Computational Biology
- Bioinformatics
- Molecular Biology
Background:
- Messenger RNA (mRNA) sequences encode proteins using a degenerate triplet code, allowing for multiple RNA sequences to code for the same protein.
- This degeneracy provides nature with flexibility in selecting mRNA sequences that are informationally equivalent but structurally and energetically diverse.
Purpose of the Study:
- To investigate the role of natural selection in shaping mRNA sequence stability.
- To develop algorithmic methods for designing mRNA sequences with optimized stability (most stable and least stable) for a target protein.
Main Methods:
- Conducted a large-scale computational experiment analyzing over 27,000 mRNA sequences from 34 microbial species to compare their stability against random synonymous sequences.
- Developed a polynomial-time dynamic programming algorithm for the Most Stable Sequence Problem (MSSP).
- Established the NP-completeness of the Least Stable Sequence Problem (LSSP) and proposed heuristic algorithms for its solution.
Main Results:
- Evidence suggests mRNA sequence stability is under natural selection, with highly stable sequences disproportionately abundant across all analyzed genomic structures.
- In a significant number of cases (19 out of 36), highly unstable sequences were also found to be disproportionately abundant, indicating selection for both extremes.
- Experimental results contextualize designed high/low stability sequences against wildtype and random encodings, demonstrating the practical application of the algorithms.
Conclusions:
- Natural selection actively influences mRNA sequence stability, favoring both highly stable and, in some cases, highly unstable sequences.
- The developed algorithms provide efficient solutions for designing mRNA sequences with extreme stability properties.
- These findings and algorithms have potential applications in areas like RNA computing and other sequence design challenges.