PROJECT SUMMARY/ABSTRACT Characterization of the metabolic interactions between organisms is key to understanding the mechanisms of disease and symbioses between microbes and their animal hosts. Our long-term goal is to advance the applicability and accessibility of proteomic stable isotope probing (SIP) in ways that make it a valuable tool for microbiome researchers looking to measure in situ metabolic interactions of human microbiota. The objective of this proposal is to improve the performance and reproducibility of experimental measurements and accelerate the computational analysis of proteomic SIP experiments and to demonstrate the value of this method for studying the in vivo and in vitro metabolism of prebiotics by gut microbiota. Expected outcomes will represent a significant advance, because optimizing the use of prebiotics as therapeutics requires identification of the specific microorganisms capable of metabolizing prebiotics. By identifying proteins of specific taxa that are synthesized as a direct result of prebiotic assimilation, proteomic SIP will provide unambiguous links between prebiotic metabolism and the specific microorganisms responsible for this activity. We will accomplish this objective by pursuing three specific aims: 1) to increase the performance and reproducibility of mass spectrometry measurements by optimizing data-independent acquisition (DIA) methods for proteomic SIP; 2) to significantly accelerate the computing-intensive database search step by adapting the Sipros algorithm to use graphic processing units (GPUs) and cloud computing; and 3) to track in vivo and in vitro prebiotic assimilation patterns by microbial populations within simple consortia and complex natural communities. Our proposed work includes several innovations, such as the application of deep learning algorithms to improve the analysis mass spectrometry data, leveraging GPU-based parallel computing and cloud computing to accelerate the computational steps in the data analysis workflow, and using proteomic SIP for the first time to track prebiotic metabolism by gut microbes. The expected outcomes of the project include (a) a new DIA- based workflow for proteomic SIP that can identify significantly more labeled peptides at higher accuracy of enrichment estimation, (b) a new computational workflow that is faster to run, more scalable to large datasets, and more accessible to researchers, and (c) establish novel foundational knowledge on the specificity of prebiotic metabolism by microbes in the gut. These outcomes will establish proteomic SIP as a valuable -omics tool that will complement existing approaches to study the metabolism of gut microbiota, and specifically highlight its ability to investigate metabolism of prebiotics and probiotics as they relate to treating microbial dysbiosis and nutrition-related diseases.