Soman, J. | bioRxiv Science

bioRxiv2026

Novel Target Combinations in Lung Squamous Cell Carcinoma proposed by the Emet AI Research Environment and supported by discovery stage experimentation

Early drug discovery is frequently bottlenecked by target identification, a challenge that becomes particularly difficult in complex diseases driven by overlapping, redundant pathways rather than a single dominant driver. Lung squamous cell carcinoma (LUSC) is one such disease: mutationally complex, lacking a dominant actionable target, and marked by a long series of failed single-agent targeted trials. We hypothesized a research environment, capable of reasoning across interconnected datasets, would be well-suited to generate novel, mechanistically grounded therapeutic hypotheses for LUSC. Emet, an AI research environment utilizing a biomedical knowledge graph of more than 1.5 billion triples connected to over 100 specialised biological databases, was paired with an Agentic Research Director that pursues each hypothesis through a graph-of-thoughts search, invoking scoped link-prediction and retrieval tools and committing every round of findings to persistent memory. The top-ranked hypotheses were two-target combinations rather than a single target. The four highest-ranked combinations were advanced to dose-matrix testing in NCI-H520 and SK-MES-1 cells, and two produced combination effects exceeding either single agent; to our knowledge neither of these two pairings had previously been evaluated in LUSC. Dual inhibition of CDC7 (TAK-931) and PKMYT1 (RP-6306) produced statistically significant synergy in both lines that strengthened from day 5 to day 7 (HSA 19.24 to 24.19 in NCI-H520; 11.07 to 13.73 in SK-MES-1). Dual inhibition of USP13 (spautin-1) and PI3K (alpelisib) produced an additive, cell-line-dependent effect, and immunoblotting confirmed the predicted mechanism: time-dependent depletion of MCL-1, c-Myc and SOX-2 driven by the USP13 arm. Agentic reasoning over multi-domain biomedical evidence can therefore nominate testable, mechanism-bearing combination hypotheses that survive experimental scrutiny.

bioinformatics↗

bioRxiv2021

A de novo matrix for macroscopic living materials from bacteria

Summary ParagraphEngineered living materials (ELMs) are composites of living cells embedded in a biopolymer matrix that combine the desirable properties of natural biomaterials with non-natural, tailored properties. ELMs with a wide range of sophisticated biological functions have been created by engineering the embedded cells using synthetic biology. Engineering a de novo biomolecular matrix would offer control over material assembly, structure, and composition, thus enabling us to grow macroscopic ELMs with customizable mechanical properties. However, we have lacked the genetic tools and design rules to genetically encode a synthetic matrix that programs collective cell self-organization into macroscopic structures. Here we report growth of macroscopic ELMs from Caulobacter crescentus cells that display and secrete an engineered self-interacting protein. This protein formed an extracellular de novo matrix and assembled cells into hierarchically-ordered, centimeter-scale ELMs. We showed that the mechanical, catalytic, and morphological properties of these ELMs can be tuned through genetic modification of the matrix. Our work identifies novel genetic tools, design and assembly rules for growing macroscopic ELMs with both wide-ranging mechanical properties and customizable functions. We anticipate the modularity of this approach will permit the incorporation of different protein polymers in the de novo matrix, thus allowing to generate ELMs with a variety of desired structures and compositions of the bulk material. We envision specific matrix properties that can be combined synergistically with existing cellular functions to greatly expand the opportunities for ELMs in human health, energy, and the environment.

synthetic biology↗