# Messenger RNA Design via Expected Partition Function and Continuous Optimization

CoRR（2023）

Abstract

The tasks of designing RNAs are discrete optimization problems, and several
versions of these problems are NP-hard. As an alternative to commonly used
local search methods, we formulate these problems as continuous optimization
and develop a general framework for this optimization based on a generalization
of classical partition function which we call "expected partition function".
The basic idea is to start with a distribution over all possible candidate
sequences, and extend the objective function from a sequence to a distribution.
We then use gradient descent-based optimization methods to improve the extended
objective function, and the distribution will gradually shrink towards a
one-hot sequence (i.e., a single sequence). As a case study, we consider the
important problem of mRNA design with wide applications in vaccines and
therapeutics. While the recent work of LinearDesign can efficiently optimize
mRNAs for minimum free energy (MFE), optimizing for ensemble free energy is
much harder and likely intractable. Our approach can consistently improve over
the LinearDesign solution in terms of ensemble free energy, with bigger
improvements on longer sequences.

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