A PROSS-designed extensively mutated estrogen receptor α variant displays enhanced thermal stability while retaining native allosteric regulation and structure

Biomimetic androgen receptor-based AIE biosensor for detecting bisphenol analogues: An integrating in silico topological analysis, molecular docking, and experimental validation study

Bisphenol analogues are the typical class of endocrine disrupting chemicals (EDCs) that interfere with binding of endogenous hormones to androgen receptor (AR). With the expansion of industrial activities and the intensification of environmental pollution, an increasing array of bisphenol analogues is being released into the environment and food chain. This highlights the urgency to develop sensitive methods for the detection of bisphenol analogues. Here, we propose a biomimetic AR-based biosensor platform for detecting bisphenol analogues (BPF, TBBPA, and TBBPS) by binding with Aggregation-Induced Emission (AIE) probes. Following a comparison of the PROSS and ABACUS methods, biomimetic AR was designed using the ABACUS approach and subsequently expressed in vitro via the E. coli expression system. Through molecular docking and the observation of fluorescence changes upon binding with biomimetic AR, BS-46006 was selected as the AIE probe for the biosensor. The biomimetic AR-based biosensor showed sensitive detections of BPF, TBBPA, and TBBPS within a range of 0-50 mM. To further elucidate the multi-residue recognition mechanism, molecular orbitals, Electron Localization Function (ELF), and Localized Orbital Locator (LOL) were systematically calculated in this study. Lowest unoccupied molecular orbital and highest occupied molecular orbital indicated the energy gap of BPF, TBBPA, and TBBPS, which correspond to 0.12812, 0.19689, and 0.18711 eV, respectively. ELF and LOL offered clearer perspective through heat maps to visually represent the electron delocalization in BPF, TBBPA, and TBBPS. The matrix effect analysis suggested that the responses of bisphenol analogues in soil matrices could be effectively mitigated through sample pretreatment. The analysis of spiked soil samples showed the acceptable recoveries ranged from 91 % to 105 %. Additionally, the biomimetic AR-based AIE biosensor, which combines multi-residue detection with Tolerable Daily Intakes, shows great promise for the risk assessment of bisphenol analogues. This research may present a viable approach for the analysis of environmental pollutants.

One-shot design elevates functional expression levels of a voltage-gated potassium channel

Membrane proteins play critical physiological roles as receptors, channels, pumps, and transporters. Despite their importance, however, low expression levels often hamper the experimental characterization of membrane proteins. We present an automated and web-accessible design algorithm called mPROSS (https://mPROSS.weizmann.ac.il), which uses phylogenetic analysis and an atomistic potential, including an empirical lipophilicity scale, to improve native-state energy. As a stringent test, we apply mPROSS to the Kv1.2-Kv2.1 paddle chimera voltage-gated potassium channel. Four designs, encoding 9-26 mutations relative to the parental channel, were functional and maintained potassium-selective permeation and voltage dependence in Xenopus oocytes with up to 14-fold increase in whole-cell current densities. Additionally, single-channel recordings reveal no significant change in the channel-opening probability nor in unitary conductance, indicating that functional expression levels increase without impacting the activity profile of individual channels. Our results suggest that the expression levels of other dynamic channels and receptors may be enhanced through one-shot design calculations.

Rational evolution for altering the ligand preference of estrogen receptor alpha

Estrogen receptor α is commonly used in synthetic biology to control the activity of genome editing tools. The activating ligands, estrogens, however, interfere with various cellular processes, thereby limiting the applicability of this receptor. Altering its ligand preference to chemicals of choice solves this hurdle but requires adaptation of unspecified ligand-interacting residues. Here, we provide a solution by combining rational protein design with multi-site-directed mutagenesis and directed evolution of stably integrated variants in Saccharomyces cerevisiae. This method yielded an estrogen receptor variant, named TERRA, that lost its estrogen responsiveness and became activated by tamoxifen, an anti-estrogenic drug used for breast cancer treatment. This tamoxifen preference of TERRA was maintained in mammalian cells and mice, even when fused to Cre recombinase, expanding the mammalian synthetic biology toolbox. Not only is our platform transferable to engineer ligand preference of any steroid receptor, it can also profile drug-resistance landscapes for steroid receptor-targeted therapies.

Improving the thermal stability and branching efficiency of Pyrococcus horikoshii OT3 glycogen branching enzyme

In practical applications, the gelatinisation temperature of starch is high. Most current glycogen branching enzymes (GBEs, EC 2.4.1.18) exhibit optimum activity at moderate or low temperatures and quickly lose their activity at higher temperatures, limiting the application of GBEs in starch modification. Therefore, we used the PROSS strategy combined with PDBePISA analysis of the dimer interface to further improve the heat resistance of hyperthermophilic bacteria Pyrococcus horikoshii OT3 GBE. The results showed that the melting temperature of mutant T508K increased by 3.1 °C compared to wild-type (WT), and the optimum reaction temperature increased by 10 °C for all mutants except V140I. WT almost completely lost its activity after incubation at 95 °C for 60 h, while all of the combined mutants maintained >40 % of their residual activity. Further, the content of the α-1,6 glycosidic bond of corn starch modified by H415W and V140I/H415W was approximately 2.68-fold and 1.92-fold higher than that of unmodified corn starch and corn starch modified by WT, respectively. Additionally, the glucan chains of DP < 13 were significantly increased in mutant modified corn starch. This method has potential for improving the thermal stability of GBE, which can be applied in starch branching in the food industry.

Computational redesign of taxane-10β-hydroxylase for de novo biosynthesis of a key paclitaxel intermediate

Paclitaxel (Taxol®) is the most popular anticancer diterpenoid predominantly present in Taxus. The core skeleton of paclitaxel is highly modified, but researches on the cytochrome P450s involved in post-modification process remain exceedingly limited. Herein, the taxane-10β-hydroxylase (T10βH) from Taxus cuspidata, which is the third post-modification enzyme that catalyzes the conversion of taxadiene-5α-yl-acetate (T5OAc) to taxadiene-5α-yl-acetoxy-10β-ol (T10OH), was investigated in Escherichia coli by combining computation-assisted protein engineering and metabolic engineering. The variant of T10βH, M3 (I75F/L226K/S345V), exhibited a remarkable 9.5-fold increase in protein expression, accompanied by respective 1.3-fold and 2.1-fold improvements in turnover frequency (TOF) and total turnover number (TTN). Upon integration into the engineered strain, the variant M3 resulted in a substantial enhancement in T10OH production from 0.97 to 2.23 mg/L. Ultimately, the titer of T10OH reached 3.89 mg/L by fed-batch culture in a 5-L bioreactor, representing the highest level reported so far for the microbial de novo synthesis of this key paclitaxel intermediate. This study can serve as a valuable reference for further investigation of other P450s associated with the artificial biosynthesis of paclitaxel and other terpenoids. KEY POINTS: • The T10βH from T. cuspidata was expressed and engineered in E. coli unprecedentedly. • The expression and activity of T10βH were improved through protein engineering. • De novo biosynthesis of T10OH was achieved in E. coli with a titer of 3.89 mg/L.

De novo prediction of explicit water molecule positions by a novel algorithm within the protein design software MUMBO

By mediating interatomic interactions, water molecules play a major role in protein-protein, protein-DNA and protein-ligand interfaces, significantly affecting affinity and specificity. This notwithstanding, explicit water molecules are usually not considered in protein design software because of high computational costs. To challenge this situation, we analyzed the binding characteristics of 60,000 waters from high resolution crystal structures and used the observed parameters to implement the prediction of water molecules in the protein design and side chain-packing software MUMBO. To reduce the complexity of the problem, we incorporated water molecules through the solvation of rotamer pairs instead of relying on solvated rotamer libraries. Our validation demonstrates the potential of our algorithm by achieving recovery rates of 67% for bridging water molecules and up to 86% for fully coordinated waters. The efficacy of our algorithm is highlighted further by the prediction of 3 different proteinligand complexes. Here, 91% of water-mediated interactions between protein and ligand are correctly predicted. These results suggest that the new algorithm could prove highly beneficial for structure-based protein design, particularly for the optimization of ligand-binding pockets or protein-protein interfaces.

Improvement of the stability and catalytic efficiency of heparan sulfate N-sulfotransferase for preparing N-sulfated heparosan

The chemo-enzymatic and enzymatic synthesis of heparan sulfate and heparin are considered as an attractive alternative to the extraction of heparin from animal tissues. Sulfation of the hydroxyl group at position 2 of the deacetylated glucosamine is a prerequisite for subsequent enzymatic modifications. In this study, multiple strategies, including truncation mutagenesis based on B-factor values, site-directed mutagenesis guided by multiple sequence alignment, and structural analysis were performed to improve the stability and activity of human N-sulfotransferase. Eventually, a combined variant Mut02 (MBP-hNST-NΔ599-602/S637P/S741P/E839P/L842P/K779N/R782V) was successfully constructed, whose half-life at 37°C and catalytic activity were increased by 105-fold and 1.35-fold, respectively. After efficient overexpression using the Escherichia coli expression system, the variant Mut02 was applied to N-sulfation of the chemically deacetylated heparosan. The N-sulfation content reached around 82.87% which was nearly 1.88-fold higher than that of the wild-type. The variant Mut02 with high stability and catalytic efficiency has great potential for heparin biomanufacturing.

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

Labeling of a mutant estrogen receptor with an Affimer in a breast cancer cell line

Mutations of the intracellular estrogen receptor alpha (ERα) is implicated in 70% of breast cancers. Therefore, it is of considerable interest to image various mutants (L536S, Y537S, D538G) in living cancer cell lines, particularly as a function of various anticancer drugs. We therefore developed a small (13 kDa) Affimer, which, after fluorescent labeling, is able to efficiently label ERα by traveling through temporary pores in the cell membrane, created by the toxin streptolysin O. The Affimer, selected by a phage display, predominantly labels the Y537S mutant and can tell the difference between L536S and D538G mutants. The vast majority of Affimer-ERαY537S is in the nucleus and is capable of an efficient, unrestricted navigation to its target DNA sequence, as visualized by single-molecule fluorescence. The Affimer can also differentiate the effect of selective estrogen receptor modulators. More generally, this is an example of a small binding reagent-an Affimer protein-that can be inserted into living cells with minimal perturbation and high efficiency, to image an endogenous protein.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

pH and the Breast Cancer Recurrent Mutation D538G Affect the Process of Activation of Estrogen Receptor α

Estrogen receptor α (ERα) is a regulatory protein that can access a set of distinct structural configurations. ERα undergoes extensive remodeling as it interacts with different agonists and antagonists, as well as transcription activation and repression factors. Moreover, breast cancer tumors resistant to hormone therapy have been associated with the imbalance between the active and inactive ERα states. Cancer-activating mutations in ERα play a crucial role in this imbalance and can promote the progression of cancer. However, the rate of this progression can also be increased by dysregulated pH in the tumor microenvironment. Many molecular aspects of the process of activation of ERα that can be affected by these pH changes and mutations are still unclear. Thus, we applied computational and experimental techniques to explore the activation process dynamics of ER for environments with different pHs and in the presence of one of the most recurrent cancer-activating mutations, D538G. Our results indicated that the effect of the pH increase associated with the D538G mutation promoted a robust stabilization of the active state of ER. We were also able to determine the main protein regions that have the most potential to influence the activation process under different pH conditions, which may provide targets of future therapeutics for the treatment of hormone-resistant breast cancer tumors. Finally, the approach used here can be applied for proteins associated with the proliferation of other cancer types, which can also have their function affected by small pH changes.

The crystal structure of the varicella-zoster Orf24-Orf27 nuclear egress complex spotlights multiple determinants of herpesvirus subfamily specificity

Varicella-zoster virus (VZV) is a human pathogen from the α-subfamily of herpesviruses. The VZV Orf24-Orf27 complex represents the essential viral core nuclear egress complex (NEC) that orchestrates the egress of the preassembled virus capsids from the nucleus. While previous studies have primarily emphasized that the architecture of core NEC complexes is highly conserved among herpesviruses, the present report focuses on subfamily-specific structural and functional features that help explain the differences in the autologous versus nonautologous interaction patterns observed for NEC formation across herpesviruses. Here, we describe the crystal structure of the Orf24-Orf27 complex at 2.1 Å resolution. Coimmunoprecipitation and confocal imaging data show that Orf24-Orf27 complex formation displays some promiscuity in a herpesvirus subfamily-restricted manner. At the same time, analysis of thermodynamic parameters of NEC formation of three prototypical α-, β-, and γ herpesviruses, i.e., VZV, human cytomegalovirus (HCMV), and Epstein–Barr virus (EBV), revealed highly similar binding affinities for the autologous interaction with specific differences in enthalpy and entropy. Computational alanine scanning, structural comparisons, and mutational data highlight intermolecular interactions shared among α-herpesviruses that are clearly distinct from those seen in β- and γ-herpesviruses, including a salt bridge formed between Orf24-Arg167 and Orf27-Asp126. This interaction is located outside of the hook-into-groove interface and contributes significantly to the free energy of complex formation. Combined, these data explain distinct properties of specificity and permissivity so far observed in herpesviral NEC interactions. These findings will prove valuable in attempting to target multiple herpesvirus core NECs with selective or broad-acting drug candidates.

An Engineered Thermostable Laccase with Great Ability to Decolorize and Detoxify Malachite Green

Laccases can catalyze the remediation of hazardous synthetic dyes in an eco-friendly manner, and thermostable laccases are advantageous to treat high-temperature dyeing wastewater. A novel laccase from Geothermobacter hydrogeniphilus (Ghlac) was cloned and expressed in Escherichia coli. Ghlac containing 263 residues was characterized as a functional laccase of the DUF152 family. By structural and biochemical analyses, the conserved residues H78, C119, and H136 were identified to bind with one copper atom to fulfill the laccase activity. In order to make it more suitable for industrial use, Ghlac variant Mut2 with enhanced thermostability was designed. The half-lives of Mut2 at 50 °C and 60 °C were 80.6 h and 9.8 h, respectively. Mut2 was stable at pH values ranging from 4.0 to 8.0 and showed a high tolerance for organic solvents such as ethanol, acetone, and dimethyl sulfoxide. In addition, Mut2 decolorized approximately 100% of 100 mg/L of malachite green dye in 3 h at 70 °C. Furthermore, Mut2 eliminated the toxicity of malachite green to bacteria and Zea mays. In summary, the thermostable laccase Ghlac Mut2 could effectively decolorize and detoxify malachite green at high temperatures, showing great potential to remediate the dyeing wastewater.