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Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As 3+ and 6.8 pM for Cr 6+ , 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy. 

Bacterial type IV pili (T4P) are polymeric protein nanofibers that have diverse biological roles. Their unique physicochemical properties mark them as a candidate biomaterial for various applications, yet difficulties in producing native T4P hinder their utilization. Recent effort to mimic the T4P of the metal‐reducingGeobacter sulfurreducensbacterium led to the design of synthetic peptide building blocks, which self‐assemble into T4P‐like nanofibers. Here, it is reported that the T4P‐like peptide nanofibers efficiently bind metal oxide particles and reduce Au ions analogously to their native counterparts, and thus give rise to versatile and multifunctional peptide–metal nanocomposites. Focusing on the interaction with Au ions, a combination of experimental and computational methods provides mechanistic insight into the formation of an exceptionally dense Au nanoparticle (AuNP) decoration of the nanofibers. Characterization of the thus‐formed peptide–AuNPs nanocomposite reveals enhanced thermal stability, electrical conductivity from the single‐fiber level up, and substrate‐selective adhesion. Exploring its potential applications, it is demonstrated that the peptide–AuNPs nanocomposite can act as a reusable catalytic coating or form self‐supporting immersible films of desired shapes. The films scaffold the assembly of cardiac cells into synchronized patches, and present static charge detection capabilities at the macroscale. The study presents a novel T4P‐inspired biometallic material.