Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel (SAF) precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning (ML), permitted us to increase isoprenol titer 5-fold in six consecutive DBTL cycles. Moreover, ML enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven autom..., High-throughput proteomics data were generated to monitor the effects of CRISPRi-mediated gene knockdowns on protein expression levels across six DBTL cycles (DBTL0-DBTL6). The sample preparation protocol is detailed at Protocols.io dx.doi.org/10.17504/protocols.io.6qpvr6xjpvmk/v1. Protein was extracted from P. putida cell pellets using Qiagen P2 Lysis Buffer, precipitated with acetone, and digested with trypsin. Resulting tryptic peptides were analyzed using an Agilent 1290 UHPLC system coupled to a Thermo Scientific Orbitrap Exploris 480 mass spectrometer, employing data-independent acquisition (DIA) mode. The data processing protocol is detailed at Protocols.io dx.doi.org/10.17504/protocols.io.5qpvobk7xl4o/v2. DIA raw data were processed using DIA-NN software (library-free mode) against a database containing the P. putida KT2440 Uniprot proteome, heterologous proteins, and common contaminants. Protein quantification was performed using the Top3 method (Ahrne et al. 2013 DOI:10.1..., # Data from: Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Dataset DOI: [10.5061/dryad.gtht76hzh](10.5061/dryad.gtht76hzh)

## Description of the data and file structure

This dataset contains proteomic data key to the characterization and analyses of our CRISPRi study. It includes liquid chromatographic and mass spectrometric analysis of the proteomic samples of strains engineered for isoprenol production. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. In DBTL0, we used proteomics to determine whether a sgRNA downregulated a target gene based on the following three constraints: 1) below 90% of the library mean for the target; 2) in the bottom quartile of target expression; and 3) isoprenol titer greater than 66.6 mg/L (approximately 40% of control isoprenol titer). In DBTL1-6, the proteomics abundance measurements were used to identify CRI...,