Understanding how enzymes achieve their tremendous catalytic power is a major question in biochemistry. Greater understanding is also needed for enzyme engineering applications. In many cases, enzyme efficiency and specificity depend on residues not in direct contact with the substrate, termed remote residues. This work focuses on Escherichia coli ornithine transcarbamoylase (OTC), which plays a central role in amino acid metabolism. OTC has been reported to undergo an induced-fit conformational change upon binding its first substrate, carbamoyl phosphate (CP), and several residues important for activity have been identified. Using computational methods based on the computed chemical properties from theoretical titration curves, sequence-based scores derived from evolutionary history, and protein surface topology, residues important for catalytic activity were predicted. The roles of these residues in OTC activity were tested by constructing mutations at predicted positions, followed by steady-state kinetics assays and substrate binding studies with the variants. First-layer mutations R57A and D231A, second-layer mutation H272L, and thirdlayer mutation E299Q, result in 57-to 450-fold reductions in k cat /K M with respect to CP and 44-to 580-fold reductions with respect to ornithine. Second-layer mutations D140N and Y160S also reduce activity with respect to ornithine. Most variants had decreased stability relative to wild-type OTC, with variants H272L, H272N, and E299Q having the greatest decreases. Variants H272L, E299Q, and R57A also show compromised CP binding. In addition to direct effects on catalytic activity, effects on overall protein stability and substrate binding were observed that reveal the intricacies of how these residues contribute to catalysis.

The goal of this work is to understand the intrinsic properties that give natural enzymes their catalytic power and to learn how to build these properties in silico, for the design and expression of artificial enzymes that catalyze reactions that do not occur in nature. Partial Order Optimum Likelihood (POOL) is a machine learning method developed by us to predict residues important for function, using the 3D structure of the query protein. The input features to POOL are based on computed electrostatic and chemical properties from THEMATICS. These input features are effectively measures of the strength of coupling between protonation events. POOL is used to characterize the properties of natural enzymes that are necessary for efficient catalysis. Catalytic sites in proteins are characterized by networks of strongly coupled protonation states; these networks impart the necessary electrostatic and proton‐transfer properties to the active residues in the first layer around the reacting substrate molecule(s). Typically these networks include first‐, second‐, and sometimes third‐layer residues. POOL‐predicted, multi‐layer active sites with significant participation by distal residues have been verified experimentally by single‐point site‐directed mutagenesis and kinetics assays for Ps. putida nitrile hydratase, human phosphoglucose isomerase, E. coli replicative DNA polymerase Pol III, E. coli Y family DNA polymerase DinB, and E. coli ornithine transcarbamoylase. In designed enzymes, such as retroaldolases, the residue‐specific input features to POOL – measures of the strength of coupling between protonation equilibria – rise as the enzymes evolve to higher rates of catalytic turnover. An approach to build these properties into the initial designs is proposed.Support or Funding InformationNSF MCB‐1517290 (MJO & PJB) and a National Institute of Justice Fellowship (TAC)This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A diastereoselective synthesis of the β-anomer of glycinamide ribonucleotide (β-GAR) has been developed. The synthesis was accomplished in nine steps from D-ribose and occurred in 5% overall yield. The route provided material on the multi-milligram scale. The synthetic β-GAR formed was remarkably resistant to anomerization both in solution and as a solid.

Phosphorylation regulates protein conformation and function, and in turn, is regulated by phosphatases which rapidly dephosphorylate tyrosine, serine, and threonine residues through the hydrolysis of phosphomonoester bonds. The turnover number for phosphatases is generally high. For example, a single molecule of tissue non-specific alkaline phosphatase can catalyze nearly 1000 dephosphorylations per second. Therefore, residual phosphatase activity in a sample risks the omission of important phosphorylation events from the analysis. High pressure has been shown to enhance the proteolytic digestion of proteins preceding mass spectrometry, resulting in significantly reduced digestion times, fewer miscleavages, and improved sequence coverage. Lysyl endopeptidase (Lys-C) is commonly used for proteins resistant to trypsin digestion or may be used preemptively in combination with trypsin. The resiliency of Lys-C is clearly demonstrated where the rate of substrate conversion is accelerated by an order of magnitude at elevated pressure and temperature. In these experiments, high pressure optical spectroscopy (HiPOS) was used to measure in real time the activity of alkaline phosphatase (ALP). ALP is reversibly inactivated at high pressure and partial activity may be restored upon return to atmospheric pressure. HiPOS demonstrated the bufferdependent restoration of ALP activity following pressurization to 45 kpsi and return to atmospheric making the method a useful monitor of protein conformational dynamics. At the elevated temperature and chaotrope concentration typically used for proteolytic digestion, the inactivation of ALP by Lys-C was marginal at atmospheric pressure providing an opportunity for extensive artifactual dephosphorylation, whereas at high pressure, ALP is rapidly inactivated.

Advances have been made in understanding how enzymes achieve their exquisite catalytic power. However there are still gaps in our understanding, as computationally driven enzyme design efforts are still in their infancy and must be followed by years of directed evolution in order to achieve reasonable turnover rates. The goal of this work is to understand the intrinsic properties that give natural enzymes their catalytic capabilities and to learn how to build these properties in silico, for the design and expression of artificial enzymes that catalyze reactions that do not occur in nature. Partial Order Optimum Likelihood (POOL) is a machine learning method developed by us to predict residues important for function, using the 3D structure of the query protein. The input features to POOL are based on computed electrostatic and chemical properties from THEMATICS. These input features are effectively measures of the strength of coupling between protonation events. POOL is used to characterize the properties of natural enzymes that are necessary for efficient catalysis. Catalytic sites in proteins are characterized in part by networks of strongly coupled protonation states; these networks impart the necessary electrostatic and proton‐transfer properties to the active residues in the first layer around the reacting substrate molecule(s). Typically these networks include first‐, second‐, and sometimes third‐layer residues. POOL‐predicted, multi‐layer active sites with significant participation by distal residues have been verified experimentally by single‐point site‐directed mutagenesis and kinetics assays for Ps. putida nitrile hydratase, human phosphoglucose isomerase, E. coli replicative DNA polymerase Pol III, E. coli Y family DNA polymerase DinB, ornithine transcarbamoylase, and glycinamide ribonucleotide transformylase (GART). In designed enzymes, such as retroaldolases, the residue‐specific input features to POOL – measures of the strength of coupling between protonation equilibria – rise as the enzymes evolve to higher rates of catalytic turnover. These concepts can enhance current enzyme design protocols. Support or Funding Information Supported by NSF MCB‐1517290.