Biology subjects

Polizzi, N. F.

Publications and source records attributed to Polizzi, N. F..

6 recordsLinked to original sources

bioRxiv2026

Deep-learning-based design of an orthogonal self-labeling protein from K-Ras(G12C)

Self-labeling protein (SLP) tags enable versatile labeling of proteins in live cells, yet only two SLPs are commonly used, limiting multiplexing. Here, we repurposed the oncoprotein K-Ras(G12C) and its covalent inhibitors to create a third, orthogonal SLP system. We used a deep-learning-based approach to radically redesign the sequence of K-Ras(G12C) while preserving its covalent-inhibitor binding pocket. Our top design, LUCI-tag, is a 19 kDa, monomeric, thermostable SLP that rapidly and covalently reacts with commercially available K-Ras(G12C) inhibitors bearing diverse payloads. Unlike existing SLPs, LUCI-tag exhibited payload-agnostic labeling kinetics, outperforming HaloTag7 and SNAP-tag for a negatively charged payload. X-ray crystal structures of drug-bound and drug-free LUCI-tag showed the design was structurally accurate and contained a preorganized inhibitor-binding pocket that could explain its rapid labeling kinetics. Proteomics and cell-signaling experiments confirmed LUCI-tag is biologically inert. LUCI-tag enabled rapid, wash-free, live-cell imaging and simultaneous three-color multiplexed experiments with HaloTag7 and SNAP-tag. This work establishes LUCI-tag as an immediately useful orthogonal SLP and demonstrates that covalent drug-target pairs can be repurposed into a broadly applicable platform for protein labeling.

bioengineering↗

bioRxiv2025

Zero-shot design of drug-binding proteins via neural selection-expansion

Computational design of molecular recognition remains challenging despite advances in deep learning1-3. The design of proteins that bind to small molecules has been particularly difficult because it requires simultaneous optimization of protein sequence, protein structure, and ligand conformation1-7. Despite their promise, current deep-learning algorithms have struggled to navigate this landscape, precluding the zero- or few-shot design of binders. Here we show that the combination of two neural networks in an iterative design algorithm can create small-molecule binding proteins from scratch with high accuracy. To optimize a design in the joint distribution of sequence, structure, and ligand conformation, we use a pair of neural networks that were trained on reciprocal tasks. We train and use a graph neural network, LASErMPNN, to design protein sequence given protein-ligand co-structure, and we use RoseTTAFold-All Atom8 (RFAA) to predict protein-ligand co-structure given protein sequence. We iteratively apply these two networks to design proteins that bind the drug, exatecan, a topoisomerase I inhibitor that is prone to inactivation by hydrolysis9. Each of four experimentally tested designs bound the drug, with the lowest dissociation constant (Kd) near 100 nM. The hit rate and highest affinity design each surpassed the current state-of-the-art method by 5- and 70-fold, respectively. We further show that LASErMPNN can improve upon its own designs in a manner resembling chain-of-thought reasoning. Without experimental input, LASErMPNN suggested two mutations that increased affinity by over two orders of magnitude (Kd = 1.2 {+/-} 0.2 nM). Designs were selective, structurally accurate, and achieved their intended purpose to protect the drug from hydrolysis. Our work describes a recipe for using neural networks to automate the design of high affinity small-molecule binding proteins, which should have wide application in the creation of novel drug-delivery vehicles, antidotes, sensors, and enzymes.

bioengineering↗

bioRxiv2025

Emergence of binding and catalysis from a designed generalist binding protein

The evolution of binding and catalysis played a central role in the emergence of life. While natural proteins have finely tuned affinities for their primary ligands, they also bind weakly and promiscuously to other molecules, which serve as starting points for stepwise, incremental evolution of entirely new specificities. Thus, modern proteins emerged from the joint exploration of sequence and structural space. The ability of natural proteins to bind small molecule fragments in well-defined geometries has been widely evaluated using methods including crystallographic fragment screening. However, this approach had not been applied to de novo proteins. Here, we apply this method to explore the binding specificity of a de novo small molecule-binding protein ABLE. As in Nature, we found ABLE was capable of forming weak complexes, which were excellent starting points for designing entirely new functions, including a binder of a turn-on fluorophore and a highly efficient Kemp eliminase enzyme (kcat/KM = 2,200,000 M-1s-1) approaching the diffusion limit. This work illustrates how simultaneous consideration of both sequence and chemical structure diversity can guide the emergence of new function in designed proteins.

biophysics↗

bioRxiv2024

MreC-MreD structure reveals a multifaceted interface that controls MreC conformation

The peptidoglycan (PG) cell wall is critical for bacterial growth and survival and is a primary antibiotic target. MreD is an essential accessory factor of the Rod complex, which carries out PG synthesis during elongation, yet little is known about how MreD facilitates this process. Here, we present the cryo-electron microscopy structure of Thermus thermophilus MreD in complex with another essential Rod complex component, MreC. The structure reveals that a periplasmic-facing pocket of MreD interacts with multiple membrane-proximal regions of MreC. We use single-molecule FRET to show that MreD controls the conformation of MreC through these contacts, inducing a state primed for Rod complex activation. Using E. coli as a model, we demonstrate that disrupting these interactions abolishes Rod complex activity in vivo. Our findings reveal the role of MreD in bacterial cell shape determination and highlight its potential as an antibiotic target.

microbiology↗

bioRxiv2023

AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2

Predicting ligand-binding sites, particularly in the absence of previously resolved homologous structures, presents a significant challenge in structural biology. Here, we leverage the internal pairwise representation of AlphaFold2 (AF2) to train a model, AF2BIND, to accurately predict small-molecule-binding residues given only a target protein. AF2BIND uses 20 "bait" amino acids to optimally extract the binding signal in the absence of a small-molecule ligand. We find that the AF2 pair representation outperforms other neural-network representations for binding-site prediction. Moreover, unique combinations of the 20 bait amino acids are correlated with chemical properties of the ligand.

bioinformatics↗

bioRxiv2021

Allosteric mechanism of signal transduction in the two-component system histidine kinase PhoQ

Transmembrane signaling proteins couple extracytosolic sensors to cytosolic effectors. Here, we examine how binding of Mg2+ to the sensor domain of an E. coli two component histidine kinase (HK), PhoQ, modulates its cytoplasmic kinase domain. We use cysteine-crosslinking and reporter-gene assays to simultaneously and independently probe the signaling state of PhoQs sensor and autokinase domains in a set of over 30 mutants. Strikingly, conservative single-site mutants distant from the sensor or catalytic site strongly influence PhoQs ligand-sensitivity as well as the magnitude and direction of the signal, endowing diverse signaling characteristics without need for epistasis. Data from 35 mutants are explained by a semi-empirical 3-domain model in which the sensor, intervening HAMP, and catalytic domains can adopt kinase-promoting or inhibiting conformations, that are in allosteric communication. The catalytic and sensor domains intrinsically favor a constitutively kinase-on conformation, while the HAMP favors the off state; when coupled, they create a bistable system responsive to physiological [Mg2+]. Mutants alter signaling by locally modulating these intrinsic equilibrium constants and couplings. Our model suggests signals transmit via interdomain allostery rather than propagation of a single concerted conformational change, explaining the diversity of signaling structural transitions observed in individual HK domains.

biochemistry↗