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Abstract Protein sequence evolution in the presence of epistasis makes many previously acceptable amino acid residues at a site unfavorable over time. This phenomenon of entrenchment has also been observed with neutral substitutions using Potts Hamiltonian models. Here, we show that simulations using these models often evolve non-neutral proteins. We introduce a Neutral-with-Epistasis (N×E) model that incorporates purifying selection to conserve fitness, a requirement of neutral evolution. N×E protein evolution revealed a surprising lack of entrenchment, with site-specific amino-acid preferences remaining remarkably conserved, in biologically realistic time frames despite extensive residue coupling. Moreover, we found that the overdispersion of the molecular clock is caused by rate variation across sites introduced by epistasis in individual lineages, rather than by historical contingency. Therefore, substitutional entrenchment and rate contingency may indicate that adaptive and other non-neutral evolutionary processes were at play during protein evolution. 

Targeted delivery of nucleic acid therapeutics to the lungs could transform treatment options for pulmonary disease. We have previously developed oligomeric charge-altering releasable transporters (CARTs) for in vivo mRNA transfection and demonstrated their efficacy for use in mRNA-based cancer vaccination and local immunomodulatory therapies against murine tumors. While our previously reported glycine-based CART-mRNA complexes (G-CARTs/mRNA) show selective protein expression in the spleen (mouse, >99%), here, we report a new lysine-derived CART-mRNA complex (K-CART/mRNA) that, without additives or targeting ligands, shows selective protein expression in the lungs (mouse, >90%) following systemic IV administration. We further show that by delivering siRNA using the K-CART, we can significantly decrease expression of a lung-localized reporter protein. Blood chemistry and organ pathology studies demonstrate that K-CARTs are safe and well-tolerated. We report on the new step economical, organocatalytic synthesis (two steps) of functionalized polyesters and oligo-carbonate-co-α- aminoester K-CARTs from simple amino acid and lipid-based monomers. The ability to direct protein expression selectively in the spleen or lungs by simple, modular changes to the CART structure opens fundamentally new opportunities in research and gene therapy. 

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence “prevalences” expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins. 

Abstract Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general.