Extracellular expression of heat shock protein 90 (eHsp90) by tumor cells is correlated with malignancy. Development of small molecule probes that can detect eHsp90 in vivo may therefore have utility in the early detection of malignancy. We synthesized a cell impermeable far-red fluorophore-tagged Hsp90 inhibitor to target eHsp90 in vivo. High resolution confocal and lattice light sheet microscopy show that probe-bound eHsp90 accumulates in punctate structures on the plasma membrane of breast tumor cells and is actively internalized. The extent of internalization correlates with tumor cell aggressiveness, and this process can be induced in benign cells by overexpressing p110HER2. Whole body cryoslicing, imaging, and histology of flank and spontaneous tumor-bearing mice strongly suggests that eHsp90 expression and internalization is a phenomenon unique to tumor cells in vivo and may provide an "Achilles heel" for the early diagnosis of metastatic disease and targeted drug delivery.

Efficient genome editing methods are essential for biotechnology and fundamental research. Homologous recombination (HR) is the most versatile method of genome editing, but techniques that rely on host RecA-mediated pathways are inefficient and laborious. Phage-encoded ssDNA annealing proteins (SSAPs) improve HR 1000-fold above endogenous levels; however, they are not broadly functional. Using Escherichia coli , Lactococcus lactis , Mycobacterium smegmatis , Lactobacillus rhamnosus , and Caulobacter crescentus we investigated the limited portability of SSAPs. We find that these proteins specifically recognize the C-terminal tail of the host’s single-stranded DNA-binding protein (SSB), and are portable between species if compatibility with this host domain is maintained. Furthermore, we find that co-expressing SSAPs with a paired SSB can significantly improve activity, in some species enabling SSAP functionality even without host-compatibility. Finally, we find that high-efficiency HR far surpasses the mutational capacity of commonly used random mutagenesis methods, generating exceptional phenotypes inaccessible through sequential nucleotide conversions.

The multisubunit protein assemblies that play critical roles in biology are the result of evolutionary selection for function of the entire assembly, and hence the subunits in structures such as icosahedral viral capsids often fit together with remarkable shape complementarity1,2. In contrast, the large multisubunit assemblies that have been created by de novo protein design, notably the icosahedral nanocages used in a new generation of potent vaccines3–7, have been built by first designing symmetric oligomers with cyclic symmetry and then assembling these into nanocages while keeping the internal structure fixed8–14, which results in more porous structures with less extensive shape matching between the components. Such hierarchical “bottom-up” design approaches have the advantage that one interface can be designed and validated in the context of the cyclic oligomer building block15,16, but the disadvantage that the structural and functional features of the assemblies are limited by the properties of the predesigned building blocks. To overcome this limitation, we set out to develop a “top-down” reinforcement learning based approach to protein nanomaterial design in which both the structures of the subunits and the interactions between them are built up coordinately in the context of the entire assembly. We developed a Monte Carlo tree search (MCTS) method17,18 which assembles protein monomer structures in the context of an overall architecture guided by a loss function which enables specification of any desired overall structural properties such as shape and porosity. We demonstrate the power of the approach by designing hyperstable icosahedral assemblies more compact than any previously observed protein icosahedral structure (designed or naturally occurring), that have very low porosity and are robust to fusion and display of proteins as complex as influenza hemagglutinin. CryoEM structures of two designs are very close to the computational design models. Our top-down reinforcement learning approach should enable the design of a wide variety of complex protein nanomaterials by direct optimization of overall system properties.

As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a “top-down” reinforcement learning–based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo–electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.