TL;DR
This PhD research focuses on the development and analysis of Monte Carlo Tree Search (MCTS) and related stochastic search algorithms for de novo RNA design at the tertiary structure level to explore how such methods can efficiently navigate the high-dimensional sequence space while integrating feedback from modern structure prediction tools.
An RNA design approach, AlignIF, that leverages multiple structure alignment with cross-graph modeling and carefully hand-designed features to capture evolutionarily conserved structural patterns at the structural level, facilitating RNA sequence design outperforms existing state-of-the-art methods in test sets.
It is shown that discretized SHAPE data display context dependence within loop regions and within stem regions and Markov models are used to formally capture such dependencies and demonstrates that finer modeling of SHAPE data has the potential to push the limits of data-guided secondary structure prediction.
It is concluded that molecular dynamics has an important place in improving the physicality of existing protein structure prediction paradigms, leading to the development of the Subspace Relaxation Operator (SRO).
This review examines current computational strategies for exploring constrained protein fitness landscapes, including sequence-derived evolutionary descriptors, structural fitness assessment, energetic evaluation, and integrated multi-parameter scoring.
An approach to create novel, functional proteins through the integration of deep mutational scanning, structural analysis, and evolutionary mining within prompts for a generative protein language model (PLM) is described and the utility of this approach is demonstrated with the generation of novel compact RNA-guided nu...
Current gold-standard RNA structure prediction approaches and cutting-edge ML methods that can contribute to inferring RNA functions are introduced, and software protocols for generating RNA family sequences using a novel methodology called RfamGen are introduced, enabling readers to explore how cutting-edge ML can be...
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