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.

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, OPUSRota is shown to be much faster than all the methods except SCWRL, which is comparably fast. In terms of overall x 1 and x 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, OPUSRota is 1.6 percentage points better for overall x 1+2 but 0.3 percentage points weaker for overall x 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.Keywords: rotamers; side-chain modeling; structure prediction; high-accuracy refinement In the post-genomic era, computational structure prediction has becoming increasingly important and powerful. Accurately determining a protein structure from its amino acid sequence, however, is still very challenging. One of the most pressing bottlenecks is the fast and accurate modeling of side-chain conformations, which is particularly important in high-accuracy refinement of predicted structure models.The most successful side-chain modeling methods employ rotamer libraries to reduce the space of conformations that must be sampled (Ponder and Richards 1987;Dunbrack Jr. and Karplus 1993;DeMaeyer et al. 1997;Dunbrack Jr. and Cohen 1997;Lovell et al. 2000). In the rotamer approach, side-chain conformations are restricted to a small set of positions drawn from a rotamer library of likely conformations, which are, in turn, derived from a database of high-resolution X-ray structures. Many rotamer-based side-chain modeling methods have been developed, such as those with enhanced sampling schemes

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.

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.