Estimating conformational heterogeneity of tryptophan synthase with a template‐based Alphafold2 approach

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Abstract
The three‐dimensional structure of the enzymes provides very relevant information on the arrangement of the catalytic machinery and structural elements gating the active site pocket. The recent success of the neural network Alphafold2 in predicting the folded structure of proteins from the primary sequence with high levels of accuracy has revolutionized the protein design field. However, the application of Alphafold2 for understanding and engineering function directly from the obtained single static picture is not straightforward. Indeed, understanding enzymatic function requires the exploration of the ensemble of thermally accessible conformations that enzymes adopt in solution. In the present study, we evaluate the potential of Alphafold2 in assessing the effect of the mutations on the conformational landscape of the beta subunit of tryptophan synthase (TrpB). Specifically, we develop a template‐based Alphafold2 approach for estimating the conformational heterogeneity of several TrpB enzymes, which is needed for enhanced stand‐alone activity. Our results show the potential of Alphafold2, especially if combined with molecular dynamics simulations, for elucidating the changes induced by mutation in the conformational landscapes at a rather reduced computational cost, thus revealing its plausible application in computational enzyme design.
Funding Information
  • H2020 European Research Council (ERC‐2015‐StG‐679001)
  • Human Frontier Science Program (RGP0054/2020)
  • Ministerio de Ciencia e Innovación (PGC2018‐102192‐B‐I00)

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