The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization

Abstract

Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism.

Systematic mapping of protein‐metabolite interactions in central metabolism of Escherichia coli

“…About 60% of the detected binders were of high chemical similarity (> 0.5) to the native reactants of their protein targets, suggesting competitive binding at the active site. This conclusion concurs with the observation that most known metabolite regulators are chemically similar to either the substrates or products of their enzyme target (Alam et al , ). The remaining 40 interactions with low chemical similarity scores (≤ 0.5) are hence suspected to allosterically bind at some distance from the active site.…”

Charting the cross-functional map between transcription factors and cancer metabolism

“…Most interactions included the combined action of a metabolite and a kinase, acting as allosteric effectors or post-translational modifiers of TFs. An extensive network of these two types of physical interactions has emerged in model organisms such as E. coli or Yeast 24,65,66 , and based on our findings we expect a similar picture to hold true in humans 59 . However, the identity and conditiondependent relevance of such interactions is much harder to determine experimentally in human cells than in unicellular model organisms.…”

“…Here, we established an in silico framework for generating hypotheses on regulatory interactions between TFs, metabolites and kinases ( Figure 4a). To that end, we used modelbased fitting analysis to integrate TF activity and metabolome profiles with proteome data 22 Overall, our approach opens the door for a systematic investigation of a previously largely unexplored 59 interaction space between transcriptional regulators and signaling effectors in human cells. The herein-presented experimental workflow for large-scale comparison of metabolomes in different cancer types, and in silico modeling of regulatory actions of metabolites and/or kinases can become an invaluable methodology in finding unique effectors that can trigger changes in the cell's transcriptional program.…”

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

“…How does the cell balance between these two responsibilities, especially in bacteria that have no intracellular compartments that could offer spatial separation (Alam et al, 2017)? More specifically, are the cellular concentrations of these metabolites and the affinities of their interactions with the different enzymes in E. coli tuned such that they can inhibit some reactions while efficiently serving as substrates for others?…”

“…An alternative strategy for studying small molecule regulation is to leverage the vast record of biochemical studies to informatically reconstruct a small molecule regulatory network (SMRN) (Alam et al, 2017). Such an approach would produce a network of interactions between enzymes and metabolites/small molecules (terms we will use interchangeably) that mirrors the native interactions of metabolites as substrates for enzymes, and could be naturally integrated with genome-scale metabolic models (GSMMs, e.g.…”