Grant M. Landwehr

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We apply this platform to engineer amide synthetases by evaluating substrate preference for 1217 enzyme variants in 10,953 unique reactions. We use these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Over these nine compounds, ML-predicted enzyme variants demonstrate 1.6- to 42-fold improved activity relative to the parent. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel. While machine learning shows promise in expanding protein engineering efforts, its potential is limited by the challenge of gathering large datasets of sequence-function relationships. Here, authors introduce a platform that integrates cell-free DNA assembly and gene expression to accelerate enzyme engineering.

Recent developments in microfluidic devices, nanoparticle chemistry, fluorescent microscopy, and biochemical techniques such as genetic identification and antibody capture have provided easier and more sensitive platforms for detecting and diagnosing diseases as well as providing new fundamental insight into disease progression. These advancements have led to the development of new technology and assays capable of easy and early detection of pathogenicity as well as the enhancement of the drug discovery and development pipeline. While some studies have focused on treatment, many of these technologies have found initial success in laboratories as a precursor for clinical applications. This review highlights the current and future progress of microfluidic techniques geared toward the timely and inexpensive diagnosis of disease including technologies aimed at high-throughput single cell analysis for drug development. It also summarizes novel microfluidic approaches to characterize fundamental cellular behavior and heterogeneity.

Even though the majority of breast cancers respond well to primary therapy, a large percentage of patients relapse with metastatic disease, for which there is no treatment. In metastasis, a tumor sheds a small number of cancerous cells, termed circulating tumor cells (CTCs), into the local vasculature, from where they spread throughout the body to form new tumors. As CTCs move through the circulatory system, they experience physiological forces not present in the initial tumor environment, namely, fluid shear stress (FSS). Evidence suggests that CTCs respond to FSS by adopting a more aggressive phenotype; however, to date single-cell morphological changes have not been quantified to support this observation. Furthermore, the methodology of previous studies involves inducing FSS by flowing cells through the tubing, which lacks a precise and tunable control of FSS. Here, a microfluidic approach is used for isolating and characterizing the biophysical response of single breast cancer cells to conditions experienced in the circulatory system during metastasis. To evaluate the single-cell response of multiple breast cancer types, two model circulating tumor cell lines, MDA-MB-231 and MCF7, were challenged with FSS at precise magnitudes and durations. As expected, both MDA-MB-231 and MCF7 cells exhibited greater deformability due to increasing duration and magnitudes of FSS. However, wide variations in single-cell responses were observed. MCF7 cells were found to rapidly deform but reach a threshold value after 5 min of FSS, while MDA-MB-231 cells were observed to deform at a slower rate but with a larger threshold of deformation. This behavioral diversity suggests the presence of distinct cell subpopulations with different phenotypes.

Abstract Biotechnological processes hold tremendous potential for the efficient and sustainable conversion of one-carbon (C1) substrates into complex multi-carbon products. However, the development of robust and versatile biocatalytic systems for this purpose remains a significant challenge. In this study, we report a hybrid electrochemical-biochemical cell-free system for the conversion of C1 substrates into the universal biological building block acetyl-CoA. The synthetic reductive formate pathway (ReForm) consists of five core enzymes catalyzing non-natural reactions that were established through a cell-free enzyme engineering platform. We demonstrate that ReForm works in a plug-and-play manner to accept diverse C1 substrates including CO 2 equivalents. We anticipate that ReForm will facilitate efforts to build and improve synthetic C1 utilization pathways for a formate-based bioeconomy.