New methods to model complex biological systems and analyze complex data are needed to take advantage of rapid increases in computing power and approaches that enable the generation of vast experimental datasets. This Focus issue features a collection of articles that showcase how such developments can drive discoveries in chemical biology.

The gut microbiome converts dietary fiber into metabolites that shape human health, but identifying the right combinations of microorganisms and nutrients has been a challenge. A machine learning framework can now find optimal microbial communities that maximize targeted health benefits in the gut.

Sequence biases at individual positions can be used to design more stable protein variants, while correlation between pairs of residues has also been shown to be important for specifying protein structure and function. Here, a Potts model is used to separate the contributions of single-site biases and pair correlations, revealing that protein structure is stabilized by single-site biases rather than pairwise coupling, while pairwise coupling is important for protein function.

Bowl-shaped mechanosensitive PIEZO channels sense membrane tension by flattening curved transmembrane domains, but how these mechanosensory motions couple with and open the channel pore is unclear. Now, multi-scale molecular dynamics simulations provide insight into this mechano-electrical coupling in PIEZO2, uncovering clockwork-like gating motions of the central pore.

Small molecules can covalently modify proteins to generate hapten-modified peptides, triggering T cell-mediated immune responses. Now a chemoproteomic platform enables the discovery of covalent neoantigens and harnessing them for immunotherapeutic applications.

Dietary fibers are a primary energy source for gut bacteria and shape the composition and function of the microbiome. Now, an active learning approach integrating explainable machine learning, Bayesian optimization and high-throughput community construction designs microbial communities with desired functions in response to key nutrients.

Here d’Oelsnitz et al. generate enantioselective transcription factors that convert chiral reaction products into fluorescent signals, enabling the rapid evolution of asymmetric enzymes with inverted selectivity.

Cells have evolved defense pathways to maintain homeostasis against external perturbations, but not against new-to-nature nanomaterials, which can irreversibly accumulate and induce cell stress. Now, programmable peptide nanoassemblies have been designed that can morphologically transform in influx–efflux cycles in response to restoring redox homeostasis in bacterial cells.

Host antimicrobial peptides (AMPs) are a frontline defense against infection. Now, it has been shown that maturation of AMP repertoires early in life can predict stable host coexistence with a highly invasive pathogen.

Resolution of protein phosphorylation at specific sites is challenging. Now, synthetic receptors have been developed that enable high-resolution enrichment of ultralow-abundance phosphopeptide positional isomers from complex human proteomes.

New methods to model complex biological systems and analyze complex data are needed to take advantage of rapid increases in computing power and approaches that enable the generation of vast experimental datasets. This Focus issue features a collection of articles that showcase how such developments can drive discoveries in chemical biology.