bioRxivBioengineeringPreprint
In-depth sequence-function characterization reveals multiple paths to enhance phenylalanine ammonia-lyase (PAL) activity.
V. D. Trivedi, T. C. Chappell, N. B. Krishna, A. Shetty, G. G. Sigamani, K. Mohan, A. S. Ramesh, P. Kumar R., N. U. Nair
Abstract
Phenylalanine ammonia-lyases (PALs) deaminate L-phenylalanine to trans-cinnamic acid and ammonium and have idespread application in chemo-enzymatic synthesis, agriculture, and medicine. In particular, the PAL from Anabaena variabilis (Trichormus variabilis) has garnered significant attention as the active ingredient in Pegvaliase(R), the only FDA-approved drug treating classical phenylketonuria (PKU). Although an extensive body of literature exists on structure, substrate-specificity, and catalytic mechanism, protein-wide sequence determinants of function remain unknown, which limits the ability to rationally engineer these enzymes. Previously, we developed a high-throughput screen (HTS) for PAL, and here, we leverage it to create a detailed sequence-function landscape of PAL by performing deep mutational scanning (DMS). Our method revealed 79 hotspots that affected a positive change in enzyme fitness, many of which have not been reported previously. Using fitness values and structure-function analysis, we picked a subset of residues for comprehensive single- and multi-site saturation mutagenesis to improve the catalytic activity of PAL and identified combinations of mutations that led to improvement in reaction kinetics in cell-free and cellular contexts. To understand the mechanistic role of the most beneficial mutations, we performed QM/MM and MD and observed that different mutants confer improved catalytic activity via different mechanisms, including stabilizing first transition and intermediate states and improving substrate diffusion into the active site, and decreased product inhibition. Thus, this work provides a comprehensive sequence-function relationship for PAL, identifies positions that improve PAL activity when mutated and assesses their mechanisms of action.
- assay: protein sequence
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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