Comprehensive sequence-to-function mapping of cofactor-dependent RNA catalysis in the glmS ribozyme.

Massively parallel, quantitative measurements of biomolecular activity across sequence space can greatly expand our understanding of RNA sequence-function relationships. We report the development of an RNA-array assay to perform such measurements and its application to a model RNA: the core glmS ribozyme riboswitch, which performs a ligand-dependent self-cleavage reaction. We measure the cleavage rates for all possible single and double mutants of this ribozyme across a series of ligand concentrations, determining kcat and KM values for active variants. These systematic measurements suggest that evolutionary conservation in the consensus sequence is driven by maintenance of the cleavage rate. Analysis of double-mutant rates and associated mutational interactions produces a structural and functional mapping of the ribozyme sequence, revealing the catalytic consequences of specific tertiary interactions, and allowing us to infer structural rearrangements that permit certain sequence variants to maintain activity

Improving Protein Optimization with Smoothed Fitness Landscapes

The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine. Modeling the combinatorially large space of sequences is infeasible; prior methods often constrain optimization to a small mutational radius, but this drastically limits the design space. Instead of heuristics, we propose smoothing the fitness landscape to facilitate protein optimization. First, we formulate protein fitness as a graph signal then use Tikunov regularization to smooth the fitness landscape. We find optimizing in this smoothed landscape leads to improved performance across multiple methods in the GFP and AAV benchmarks. Second, we achieve state-of-the-art results utilizing discrete energy-based models and MCMC in the smoothed landscape. Our method, called Gibbs sampling with Graph-based Smoothing (GGS), demonstrates a unique ability to achieve 2.5 fold fitness improvement (with in-silico evaluation) over its training set. GGS demonstrates potential to optimize proteins in the limited data regime. Code: https://github.com/kirjner/GGSComment: ICLR 2024. Code: https://github.com/kirjner/GG

Analysis of proteins in the light of mutations

Proteins have evolved through mutations, amino acid substitutions, since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. Three of the most recent findings in these areas deserve to be highlighted. First, an innovative method has made it feasible to massively determine the impact of mutations on protein stability. Second, a theoretical analysis showed how mutations impact the evolution of protein folding rates. Lastly, it has been shown that native-state structural changes brought on by mutations can be explained in detail by the amide hydrogen exchange protection factors. This study offers a new perspective on how those findings can be used to analyze proteins in the light of mutations. The preliminary results indicate that: (i) mutations can be viewed as sensitive probes to identify "typos" in the amino-acid sequence and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (ii) the presence of "typos" in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (iii) the mutation site is far more important than the substituted amino acid in terms of the protein's marginal stability changes, and (iv) the protein evolution unpredictability at the molecular level by mutations exists even in the absence of epistasis effects. Finally, the study results support the Darwinian concept of evolution as "descent with modification" by demonstrating that some regions of any protein sequence are susceptible to mutations while others are not.Comment: Manuscript of 10 pages and 5 figure

Unsupervised inference of protein fitness landscape from deep mutational scan

The recent technological advances underlying the screening of large combinatorial libraries in high- throughput mutational scans, deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold