Robust ω-Transaminases by Computational Stabilization of the Subunit Interface

Abstract

Transaminases are attractive catalysts for the production of enantiopure amines. However, the poor stability of these enzymes often limits their application in biocatalysis. Here, we used a framework for enzyme stability engineering by computational library design (FRESCO) to stabilize the homodimeric PLP fold type I ω-transaminase from Pseudomonas jessenii . A large number of surface-located point mutations and mutations predicted to stabilize the subunit interface were examined. Experimental screening revealed that 10 surface mutations out of 172 tested were indeed stabilizing (6% success), whereas testing 34 interface mutations gave 19 hits (56% success). Both the extent of stabilization and the spatial distribution of stabilizing mutations showed that the subunit interface was critical for stability. After mutations were combined, 2 very stable variants with 4 and 6 mutations were obtained, which in comparison to wild type ( T m app = 62 °C) displayed T m app values of 80 and 85 °C, respectively. These two variants were also 5-fold more active at their optimum temperatures and tolerated high concentrations of isopropylamine and cosolvents. This allowed conversion of 100 mM acetophenone to ( S )-1-phenylethylamine (>99% enantiomeric excess) with high yield (92%, in comparison to 24% with the wild-type transaminase). Crystal structures mostly confirmed the expected structural changes and revealed that the most stabilizing mutation, I154V, featured a rarely described stabilization mechanism: namely, removal of steric strain. The results show that computational interface redesign can be a rapid and powerful strategy for transaminase stabilization.

Asymmetric Synthesis of Optically Pure Aliphatic Amines with an Engineered Robust ω-Transaminase

“…The arginine side chain switches toward a more outward position upon 6-AHA binding. Arg417 and most surrounding residues were kept during the thermostability engineering that yielded PjTA-R6 [24]. During the redesign of other class III (S)-selective transaminases, the switching arginine is mostly preserved as amino donors or acceptors often feature a carboxylate functionality [27][28][29].…”

“…The design, isolation, and properties of the PjTA-R6 mutant have been previously described [24]. The PjTA-R6-R417L mutant was created by QuikChange site-directed mutagenesis.…”

“…Our group has deepened the understanding of this enzyme by solving crystal structures and measuring its activities using over 40 different amine substrates [23]. A robust six-fold mutant of PjTA (PjTA-R6) was recently constructed using a framework for rapid enzyme stabilization by computational library design (FRESCO) [24]. The natural substrate of PjTA is a linear aliphatic ω-amino acid, which suggests PjTA may have potential for the synthesis of aliphatic amines.…”

“…The natural substrate of PjTA is a linear aliphatic ω-amino acid, which suggests PjTA may have potential for the synthesis of aliphatic amines. Moreover, the robust mutant PjTA-R6 is expected to tolerate a high concentration of amino donor, the presence of cosolvent, and an enhanced reaction temperature [24]. Thus, in the work reported here, we explored PjTA-R6 for the synthesis of aliphatic amines for which the wild-type PjTA was not considered as attractive due to its lower stability under harsh conditions.…”

Computational design of noncanonical amino acid-based thioether staples at N/C-terminal domains of multi-modular pullulanase for thermostabilization in enzyme catalysis

“…Compared with irrational directed evolution, rational computational design is more rapid and targeted, with a comparably small library generally containing <10 2 variants [9] , [10] . Computational design of enzyme stabilization focuses on optimizing native interactions or reducing the configurational entropy of the unfolded state ensemble to stabilize proteins with the help of computational tools that utilize appropriate scoring functions, search algorithms, and amino acid rotamer libraries to predict potential stabilizing mutations [11] , [12] . Among computational design strategies including consensus sequence-based site-directed mutagenesis [13] , proline introduction [14] , disulfide bond introduction [15] , and surface-charge optimization [16] , disulfide bond introduction is regarded as an effective approach for enhancing enzyme thermostability [17] .…”