Amnio acid substitution at position 298 of human glucose-6 phosphatase-α significantly impacts its stability in mammalian cells.

Glucose-6-phosphatase-α (G6Pase-α) catalyzes the hydrolysis of glucose-6-phosphate to glucose and functions as a key regulator in maintaining blood glucose homeostasis. Deficiency in G6Pase-α causes glycogen storage disease 1a (GSD1a), an inherited disorder characterized by life-threatening hypoglycemia and other long-term complications. We have developed a potential mRNA-based therapy for GSD1a and demonstrated that a human G6Pase-α (hG6Pase-α) variant harboring a single serine (S) to cysteine (C) substitution at the amino acid site 298 (S298C) had > twofold increase in protein expression, resulting in improved in vivo efficacy. Here, we sought to investigate the mechanisms contributing to the increased expression of the S298C variant. Mutagenesis of hG6Pase-α identified distinct protein variants at the 298 amino acid position with substantial reduction in protein expression in cultured cells. Kinetic analysis of expression and subcellular localization in mammalian cells, combined with cell-free in vitro translation assays, revealed that altered protein expression stemmed from differences in cellular protein stability rather than biosynthetic rates. Site-specific mutagenesis studies targeting other cysteines of the hG6Pase-α S298C variant suggest the observed improvements in stability are not due to additional disulfide bond formation. The glycosylation at Asparagine (N)-96 is critical in maintaining enzymatic activity and mutations at position 298 mainly affected glycosylated forms of hG6Pase-α. Finally, proteasome inhibition by lactacystin improved expression levels of unstable hG6Pase-α variants. Taken together, these data uncover a critical role for a single amino acid substitution impacting the stability of G6Pase-α and provide insights into the molecular genetics of GSD1a and protein engineering for therapeutic development.

An engineered T7 RNA polymerase that produces mRNA free of immunostimulatory byproducts.

In vitro transcription (IVT) is a DNA-templated process for synthesizing long RNA transcripts, including messenger RNA (mRNA). For many research and commercial applications, IVT of mRNA is typically performed using bacteriophage T7 RNA polymerase (T7 RNAP) owing to its ability to produce full-length RNA transcripts with high fidelity; however, T7 RNAP can also produce immunostimulatory byproducts such as double-stranded RNA that can affect protein expression. Such byproducts require complex purification processes, using methods such as reversed-phase high-performance liquid chromatography, to yield safe and effective mRNA-based medicines. To minimize the need for downstream purification processes, we rationally and computationally engineered a double mutant of T7 RNAP that produces substantially less immunostimulatory RNA during IVT compared with wild-type T7 RNAP. The resulting mutant allows for a simplified production process with similar mRNA potency, lower immunostimulatory content and quicker manufacturing time compared with wild-type T7 RNAP. Herein, we describe the computational design and development of this improved T7 RNAP variant.

Ionizable Amino Lipids Distribution and Effects on DSPC/Cholesterol Membranes: Implications for Lipid Nanoparticle Structure.

Lipid nanoparticles (LNPs) containing ionizable aminolipids are among the leading platforms for the successful delivery of nucleic-acid-based therapeutics, including messenger RNA (mRNA). The two recently FDA-approved COVID-19 vaccines developed by Moderna and Pfizer/BioNTech belong to this category. Ionizable aminolipids, cholesterol, and DSPC lipids are among the key components of such formulations, crucially modulating physicochemical properties of these formulations and, consequently, the potency of these therapeutics. Despite the importance of these components, the distribution of these molecules in LNPs containing mRNA is not clear. In this study, we used all-atom molecular dynamics (MD) simulations to investigate the distribution and effects of the Lipid-5 (apparent pKa of the lipid nanoparticle = 6.56), a rationally designed and previously reported ionizable aminolipid by Moderna, on lipid bilayers [Mol. Ther. 2018, 26, 1509-1519]. The simulations were conducted with half of the aminolipids charged and half neutral approximately to the expected ionization in the microenvironment of the LNP surface. In all five simulated systems in this work, the cholesterol content was kept constant, whereas the DSPC and Lipid-5 concentrations were changed systematically. We found that at higher concentrations of the ionizable aminolipids, the neutral aminolipids form a disordered aggregate in the membrane interior that preferentially includes cholesterol. The rules underlying the lipid redistribution could be used to rationally choose lipids to optimize the LNP function.

Therapeutic enzyme engineering using a generative neural network.

Enhancing the potency of mRNA therapeutics is an important objective for treating rare diseases, since it may enable lower and less-frequent dosing. Enzyme engineering can increase potency of mRNA therapeutics by improving the expression, half-life, and catalytic efficiency of the mRNA-encoded enzymes. However, sequence space is incomprehensibly vast, and methods to map sequence to function (computationally or experimentally) are inaccurate or time-/labor-intensive. Here, we present a novel, broadly applicable engineering method that combines deep latent variable modelling of sequence co-evolution with automated protein library design and construction to rapidly identify metabolic enzyme variants that are both more thermally stable and more catalytically active. We apply this approach to improve the potency of ornithine transcarbamylase (OTC), a urea cycle enzyme for which loss of catalytic activity causes a rare but serious metabolic disease.

mRNA therapy restores euglycemia and prevents liver tumors in murine model of glycogen storage disease.

Glycogen Storage Disease 1a (GSD1a) is a rare, inherited metabolic disorder caused by deficiency of glucose 6-phosphatase (G6Pase-α). G6Pase-α is critical for maintaining interprandial euglycemia. GSD1a patients exhibit life-threatening hypoglycemia and long-term liver complications including hepatocellular adenomas (HCAs) and carcinomas (HCCs). There is no treatment for GSD1a and the current standard-of-care for managing hypoglycemia (Glycosade®/modified cornstarch) fails to prevent HCA/HCC risk. Therapeutic modalities such as enzyme replacement therapy and gene therapy are not ideal options for patients due to challenges in drug-delivery, efficacy, and safety. To develop a new treatment for GSD1a capable of addressing both the life-threatening hypoglycemia and HCA/HCC risk, we encapsulated engineered mRNAs encoding human G6Pase-α in lipid nanoparticles. We demonstrate the efficacy and safety of our approach in a preclinical murine model that phenotypically resembles the human condition, thus presenting a potential therapy that could have a significant therapeutic impact on the treatment of GSD1a.

Predicting protein folding cores by empirical potential functions.

Theoretical and in vitro experiments suggest that protein folding cores form early in the process of folding, and that proteins may have evolved to optimize both folding speed and native-state stability. In our previous work (Chen et al., Structure, 14 (2006) 1401), we developed a set of empirical potential functions and used them to analyze interaction energies among secondary-structure elements in two beta-sandwich proteins. Our work on this group of proteins demonstrated that the predicted folding core also harbors residues that form native-like interactions early in the folding reaction. In the current work, we have tested our empirical potential functions on structurally-different proteins for which the folding cores have been revealed by protein hydrogen-deuterium exchange experiments. Using a set of 29 unrelated proteins, which have been extensively studied in the literature, we demonstrate that the average prediction result from our method is significantly better than predictions based on other computational methods. Our study is an important step towards the ultimate goal of understanding the correlation between folding cores and native structures.

Normal-mode refinement of anisotropic thermal parameters for potassium channel KcsA at 3.2 A crystallographic resolution.

We report a normal-mode method for anisotropic refinement of membrane-protein structures, based on a hypothesis that the global near-native-state disordering of membrane proteins in crystals follows low-frequency normal modes. Thus, a small set of modes is sufficient to represent the anisotropic thermal motions in X-ray crystallographic refinement. By applying the method to potassium channel KcsA at 3.2 A, we obtained a structural model with an improved fit with the diffraction data. Moreover, the improved electron density maps allowed for large structural adjustments for 12 residues in each subunit, including the rebuilding of 3 missing side chains. Overall, the anisotropic KcsA structure at 3.2 A was systematically closer to a 2.0 A KcsA structure, especially in the selectivity filter. Furthermore, the anisotropic thermal ellipsoids from the refinement revealed functionally relevant structural flexibility. We expect this method to be a valuable tool for structural refinement of many membrane proteins with moderate-resolution diffraction data.

OPUS-Dom: applying the folding-based method VECFOLD to determine protein domain boundaries.

In this article, we present a de novo method for predicting protein domain boundaries, called OPUS-Dom. The core of the method is a novel coarse-grained folding method, VECFOLD, which constructs low-resolution structural models from a target sequence by folding a chain of vectors representing the predicted secondary-structure elements. OPUS-Dom generates a large ensemble of folded structure decoys by VECFOLD and labels the domain boundaries of each decoy by a domain parsing algorithm. Consensus domain boundaries are then derived from the statistical distribution of the putative boundaries and three empirical sequence-based domain profiles. OPUS-Dom generally outperformed several state-of-the-art domain prediction algorithms over various benchmark protein sets. Even though each VECFOLD-generated structure contains large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary-structure segments, rather than sequence-specific constraints.

OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing.

Here we report an orientation-dependent statistical all-atom potential derived from side-chain packing, named OPUS-PSP. It features a basis set of 19 rigid-body blocks extracted from the chemical structures of all 20 amino acid residues. The potential is generated from the orientation-specific packing statistics of pairs of those blocks in a non-redundant structural database. The purpose of such an approach is to capture the essential elements of orientation dependence in molecular packing interactions. Tests of OPUS-PSP on commonly used decoy sets demonstrate that it significantly outperforms most of the existing knowledge-based potentials in terms of both its ability to recognize native structures and consistency in achieving high Z-scores across decoy sets. As OPUS-PSP excludes interactions among main-chain atoms, its success highlights the crucial importance of side-chain packing in forming native protein structures. Moreover, OPUS-PSP does not explicitly include solvation terms, and thus the potential should perform well when the solvation effect is difficult to determine, such as in membrane proteins. Overall, OPUS-PSP is a generally applicable potential for protein structure modeling, especially for handling side-chain conformations, one of the most difficult steps in high-accuracy protein structure prediction and refinement.

OPUS-Rota: a fast and accurate method for side-chain modeling.

In this paper, we introduce a fast and accurate side-chain modeling method, named OPUS-Rota. In a benchmark comparison with the methods SCWRL, NCN, LGA, SPRUCE, Rosetta, and SCAP, OPUS-Rota is shown to be much faster than all the methods except SCWRL, which is comparably fast. In terms of overall chi (1) and chi (1+2) accuracies, however, OPUS-Rota is 5.4 and 8.8 percentage points better, respectively, than SCWRL. Compared with NCN, which has the best accuracy in the literature, OPUS-Rota is 1.6 percentage points better for overall chi (1+2) but 0.3 percentage points weaker for overall chi (1). Hence, our algorithm is much more accurate than SCWRL with similar execution speed, and it has accuracy comparable to or better than the most accurate methods in the literature, but with a runtime that is one or two orders of magnitude shorter. In addition, OPUS-Rota consistently outperforms SCWRL on the Wallner and Elofsson homology-modeling benchmark set when the sequence identity is greater than 40%. We hope that OPUS-Rota will contribute to high-accuracy structure refinement, and the computer program is freely available for academic users.