Project Abstract/Summary More than 75% of the data generated from mass spectrometry (MS) - based omics experiments are wasted due to inefficiency of existing algorithmic methods that deduce peptides. The peptides that do get identified by existing computational methods usually come from abundant proteins – and hence recent calls by scientists to study overlooked proteins are gaining traction. These non-abundant and overlooked proteins might have the same (or more) importance in human systems biology health, and disease. Yet, all downstream analysis and conclusions – related to human health – are based on suboptimal and incomplete peptide deductions indicating formal investigation is warranted and urgently needed. In the recent decade, advances in machine-learning (ML) models have provided a critical step and have made it possible to develop more accurate and deeper pipelines for MS data analysis. Our preliminary work and experiments suggest that the limited training search-space exhibited by labelled spectral libraries makes robustness, and generalizability of existing ML models highly susceptible and may not effectively work for real- world data. The overall objective of my research lab using this MIRA mechanism is to design and develop robust, reliable, and generalizable machine-learning models for peptide deduction from MS data from omics experiments. Our proposed work fills four key knowledge gaps in development of ML models pursued via this MIRA grant that, if filled, will lead to superior computational techniques capable of inferring both abundant and non-abundant peptides. Our general strategy will involve design and development of generative models, self-learning models, biologically inspired models, and methods to infer uncertainty quantification. In addition, we will strive to focus on two key gaps in adaptation of ML models that will be filled via developing ML-ready workflows and developing easy-to-use software infrastructure that can be used by scientists. All this effort via MIRA grant mechanism will fill a critical gap in our understanding and ability to deduce peptide (that are novel) and will contribute a fundamental tool for studying complex communities in proteomics, and meta-proteomics data. At the end of this grant funding cycle, it is our expectation that we will have designed and developed highly accurate ML peptide deduction engine capable of end-to-end analysis of the MS based omics data– that is robust, generalizable, and more accurate than their algorithmic counterpart. Our proposed work will facilitate reproducibility by developing ML models that perform well – irrespective of underlying MS data quality or completeness – will be a highly impactful outcome. This proposed work will also serve as the foundation for analysis of more complex data sets related to meta-proteomics, and proteogenomics as one of our long-term goals that we hope to achieve using this MIRA grant mechanism.

Project Narrative The proposed research is relevant to public health because understanding Mass Spectrometry (MS) based omics can allow systematic analysis of thousands of proteins with the promise of discovering new proteins, and biomarkers for various disease conditions and better understanding of human systems biology. Artificial intelligence and machine- learning tools are needed to explain the high-dimensional MS data and such accurate tools will be instrumental in elucidating the microbiome and cell function which affects virtually all aspects of human health. Therefore, this MIRA proposal is relevant to NIH’s broader mission which support fundamental and innovative research strategies which can become the basis of protecting and improving human health.