Graph Transformation for Enzymatic Mechanisms

TL;DR

This paper introduces a graph transformation framework to computationally model and generate plausible enzymatic mechanisms, enabling the design and elucidation of complex multi-step catalytic processes.

Contribution

It applies graph transformation rules to enzymatic chemistry, deriving mechanisms from databases and proposing novel plausible catalytic pathways.

Findings

Generated about 1000 rules for amino acid chemistry.

Proposed hundreds of hypothetical catalytic mechanisms.

Mechanisms combine known steps in chemically sound ways.

Abstract

Motivation: The design of enzymes is as challenging as it is consequential for making chemical synthesis in medical and industrial applications more efficient, cost-effective and environmentally friendly. While several aspects of this complex problem are computationally assisted, the drafting of catalytic mechanisms, i.e. the specification of the chemical steps-and hence intermediate states-that the enzyme is meant to implement, is largely left to human expertise. The ability to capture specific chemistries of multi-step catalysis in a fashion that enables its computational construction and design is therefore highly desirable and would equally impact the elucidation of existing enzymatic reactions whose mechanisms are unknown. Results: We use the mathematical framework of graph transformation to express the distinction between rules and reactions in chemistry. We derive about 1000 rules for amino acid side chain chemistry from the M-CSA database, a curated repository of enzymatic mechanisms. Using graph transformation we are able to propose hundreds of hypothetical catalytic mechanisms for a large number of unrelated reactions in the Rhea database. We analyze these mechanisms to find that they combine in chemically sound fashion individual steps from a variety of known multi-step mechanisms, showing that plausible novel mechanisms for catalysis can be constructed computationally.