Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.

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Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiquity and great interest in related processes, how and to what extent spatial partitioning affects the structures and dynamics of microbial communities is poorly understood. Using modeling and quantitative experiments with simple and complex microbial communities, we demonstrate that spatial partitioning modulates the community dynamics by altering the local interaction types and global interaction strength. Partitioning promotes the persistence of populations with negative interactions but suppresses those with positive interactions. For a community consisting of populations with both positive and negative interactions, an intermediate level of partitioning maximizes the overall diversity of the community. Our results reveal a general mechanism underlying the maintenance of microbial diversity and have implications for natural and engineered communities.