Computational Design of Allosteric Ribozymes via Genetic Algorithms.

In vitro selection of allosteric ribozymes has many challenges, such as complex and time-consuming experimental procedures, uncertain results, and the unwanted functionality of the enriched sequences. The precise computational design of allosteric ribozymes is achievable using RNA secondary structure folding principles. The computational design of allosteric ribozymes is based on experimentally validated EAs, random search algorithms, and a partition function for RNA folding. The in silico design achieves an accuracy exceeding 90%. Various algorithms with different logic gates have been automated via computer programs that can quickly create many allosteric sequences. This can eliminate the need for in vitro selection of allosteric ribozymes, thus vastly reducing the time and cost required.

Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

A molecularly imprinted polymer-based detection platform confirmed through molecular modeling for the highly sensitive and selective analysis of ipratropium bromide.

This study presented a new method to design a MIP-based electrochemical sensor that could improve the selective and sensitive detection of ipratropium bromide (IPR). The polymeric film was designed using 2-hydroxyethyl methacrylate (HEMA) as the basic monomer, 2-hydroxy-2-methylpropiophenone as the initiator, ethylene glycol dimethacrylate (EGDMA) as the crosslinking agent, and N-methacryloyl-L-aspartic acid (MAAsp) as the functional monomer. The presence of MAAsp results in the functional groups in imprinting binding sites, while the presence of poly(vinyl alcohol) (PVA) allows the generation of porous materials not only for sensitive sensing but also for avoiding electron transport limitations. Electrochemical characterizations of the changes at each stage of the MIP preparation process were confirmed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). In addition, morphological characterizations of the developed sensor were performed using scanning electron microscopy (SEM), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and contact angle measurements. Theoretical calculations were also performed to explain/confirm the experimental results better. It was found that the results of the calculations using the DFT approach agreed with the experimental data. The MAAsp-IPR@MIP/GCE sensor was developed using the photopolymerization method, and the sensor surface was obtained by exposure to UV lamp radiation at 365 nm. The improved MIP-based electrochemical sensor demonstrated the ability to measure IPR for standard solutions in the linear operating range of 1.0 × 10-12-1.0 × 10-11 M under optimized conditions. For standard solutions, the limit of detection (LOD) and limit of quantification (LOQ) were obtained as 2.78 × 10-13 and 9.27 × 10-13 M, respectively. The IPR recovery values for the inhalation form were calculated as 101.70 % and 100.34 %, and the mean relative standard deviations (RSD) were less than 0.76 % in both cases. In addition, the proposed modified sensor demonstrated remarkable sensitivity and selectivity for rapid assessment of IPR in inhalation forms. The sensor's unique selectivity is demonstrated by its successful performance even in the presence of IPR impurities.

Upcycling CO2 into succinic acid via electrochemical and engineered Escherichia coli.

Converting CO2 into value-added chemicals still remains a grand challenge. Succinic acid has long been considered as one of the top building block chemicals. This study reported efficiently upcycling CO2 into succinic acid by combining between electrochemical and engineered Escherichia coli. In this process, the Cu-organic framework catalyst was synthesized for electrocatalytic CO2-to-ethanol conversion with high Faradaic efficiency (FE, 84.7 %) and relative purity (RP, 95 wt%). Subsequently, an engineered E. coli with efficiently assimilating CO2-derived ethanol to produce succinic acid was constructed by combining computational design and metabolic engineering, and the succinic acid titer reached 53.8 mM with the yield of 0.41 mol/mol, which is 82 % of the theoretical yield. This study effort to link the two processes of efficient ethanol synthesis by electrocatalytic CO2 and succinic acid production from CO2-derived ethanol, paving a way for the production of succinic acid and other value-added chemicals by converting CO2 into ethanol.

Unlocking the potential of enzyme engineering via rational computational design strategies.

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Nanomaterial-assisted molecularly imprinted polymer strategies for highly sensitive and selective determination of cefdinir and its validation using computational approach.

In this study, the first nanomaterial-supported molecularly imprinted polymer (MIP)-based electrochemical approach was proposed to achieve the successful detection of cefdinir (CFD). Here, p-amino benzoic acid (p-ABA) was used as the monomer and the photopolymerization method was chosen to form MIP on a glassy carbon electrode (GCE). ZnO nanoparticles (ZnO NPs) were added to the MIP sensor to increase sensitivity and create high porosity. Through the use of cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), characterization investigations confirmed the alterations at each stage of the MIP production process. Electrochemical (cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS)) and scanning electron microscopy (SEM) methods were used for study the characterization studies of the MIP-based nanocomposite sensor. The measurement of MIP parameters, such as the addition of nanoparticles, the removal procedure, the rebinding period, the monomer ratio, etc., was done using the differential pulse voltammetry (DPV). The findings showed that when ZnO NPs were added, the signal was three times higher than when MIPs were used alone. Under the optimized conditions, CFD/4-ABA@ZnONPs/MIP/GCE showed a linear response in the concentration range between 7.5 pM and 100 pM with LOD and LOQ values of 2.06 pM and 6.86 pM, respectively. Anions, cations, and substances including uric acid, ascorbic acid, paracetamol, and dopamine were all used in the selectivity test. In addition, the imprinting factor (IF) study was carried out using compounds such as cefuroxime, cefazolin, cefixime, ceftazidime, and ceftriaxone, which have structural similarities with CFD, as well as impurities such as thiazolylacetyl glycine oxime (IMP-A), thiazolylacetyl glycine oxime acetal (IMP-B), and cefdinir lactone (IMP-E). The results showed that the proposed sensor was selective for CFD, as evidenced by the relative IF values of these impurities. The recovery studies of CFD were successfully applied to tablet dosage form samples, and the developed sensor demonstrated significant sensitivity and selectivity for rapid detection of CFD in tablet dosage form.

Inhibition of DEF-p65 Interactions as a Potential Avenue to Suppress Tumor Growth in Pancreatic Cancer.

The limited success of current targeted therapies for pancreatic cancer underscores an urgent demand for novel treatment modalities. The challenge in mitigating this malignancy can be attributed to the digestive organ expansion factor (DEF), a pivotal yet underexplored factor in pancreatic tumorigenesis. The study uses a blend of in vitro and in vivo approaches, complemented by the theoretical analyses, to propose DEF as a promising anti-tumor target. Analysis of clinical samples reveals that high expression of DEF is correlated with diminished survival in pancreatic cancer patients. Crucially, the depletion of DEF significantly impedes tumor growth. The study further discovers that DEF binds to p65, shielding it from degradation mediated by the ubiquitin-proteasome pathway in cancer cells. Based on these findings and computational approaches, the study formulates a DEF-mimicking peptide, peptide-031, designed to disrupt the DEF-p65 interaction. The effectiveness of peptide-031 in inhibiting tumor proliferation has been demonstrated both in vitro and in vivo. This study unveils the oncogenic role of DEF while highlighting its prognostic value and therapeutic potential in pancreatic cancer. In addition, peptide-031 is a promising therapeutic agent with potent anti-tumor effects.

A Piecewise Design Approach to Engineering a Miniature ACE2 Mimic to Bind SARS-CoV-2.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues its global spread, the exploration of novel therapeutic and diagnostic strategies is still needed. The virus enters host cells by binding the angiotensin-converting enzyme 2 (ACE2) receptor through the spike protein. Here, we develop an engineered, small, stable, and catalytically inactive version of ACE2, termed miniature ACE2 (mACE2), designed to bind the spike protein with high affinity. Employing a magnetic nanoparticle-based assay, we harnessed the strong binding affinity of mACE2 to develop a sensitive and specific platform for the detection or neutralization of SARS-CoV-2. Our findings highlight the potential of engineered mACE2 as a valuable tool in the fight against SARS-CoV-2. The success of developing such a small reagent based on a piecewise molecular design serves as a proof-of-concept approach for the rapid deployment of such agents to diagnose and fight other viral diseases.

Bioorthogonal Delivery of Carbon Disulfide in Living Cells.

Carbon disulfide (CS2) is an environmental contaminant, which is deadly hazardous to the workers under chronic or acute exposure. However, the toxicity mechanisms of CS2 are still unclear due to the scarcity of biocompatible donors, which can release CS2 in cells. Here we developed the first bioorthogonal CS2 delivery system based on the "click-and-release" reactions between mesoionic 1,3-thiazolium-5-thiolates (TATs) and strained cyclooctyne exo-BCN-OH. We successfully realized intracellular CS2 release and investigated the causes of CS2-induced hepatotoxicity, including oxidative stress, proteotoxic stress and copper-dependent cell death. It is found that CS2 can be copper vehicles bypassing copper transporters after reacting with nucleophiles in cytoplasm, and extra copper supplementation will exacerbate the loss of homeostasis of cells and ultimately cell death. These findings inspired us to explore the anticancer activity of CS2 in combination with copper by introducing a copper chelating group in our CS2 delivery system.

GMMA Can Stabilize Proteins Across Different Functional Constraints.

Stabilizing proteins without otherwise hampering their function is a central task in protein engineering and design. PYR1 is a plant hormone receptor that has been engineered to bind diverse small molecule ligands. We sought a set of generalized mutations that would provide stability without affecting functionality for PYR1 variants with diverse ligand-binding capabilities. To do this we used a global multi-mutant analysis (GMMA) approach, which can identify substitutions that have stabilizing effects and do not lower function. GMMA has the added benefit of finding substitutions that are stabilizing in different sequence contexts and we hypothesized that applying GMMA to PYR1 with different functionalities would identify this set of generalized mutations. Indeed, conducting FACS and deep sequencing of libraries for PYR1 variants with two different functionalities and applying a GMMA analysis identified 5 substitutions that, when inserted into four PYR1 variants that each bind a unique ligand, provided an increase of 2-6 °C in thermal inactivation temperature and no decrease in functionality.

Coiled-Coil Protein Hydrogels Engineered with Minimized Fiber Diameters for Sustained Release of Doxorubicin in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) lacks expressed protein targets, making therapy development challenging. Hydrogels offer a promising new route in this regard by improving the chemotherapeutic efficacy through increased solubility and sustained release. Moreover, subcutaneous hydrogel administration reduces patient burden by requiring less therapy and shorter treatment times. We recently established the design principles for the supramolecular assembly of single-domain coiled-coils into hydrogels. Using a modified computational design algorithm, we designed Q8, a hydrogel with rapid assembly for faster therapeutic hydrogel preparation. Q8 encapsulates and releases doxorubicin (Dox), enabling localized sustained release via subcutaneous injection. Remarkably, a single subcutaneous injection of Dox-laden Q8 (Q8•Dox) significantly suppresses tumors within just 1 week. This work showcases the bottom-up engineering of a fully protein-based drug delivery vehicle for improved TBNC treatment via noninvasive localized therapy.

Computational-Design Enabled Wearable and Tunable Metamaterials via Freeform Auxetics for Magnetic Resonance Imaging.

Metamaterials hold significant promise for enhancing the imaging capabilities of magnetic resonance imaging (MRI) machines as an additive technology, due to their unique ability to enhance local magnetic fields. However, despite their potential, the metamaterials reported in the context of MRI applications have often been impractical. This impracticality arises from their predominantly flat configurations and their susceptibility to shifts in resonance frequencies, preventing them from realizing their optimal performance. Here, a computational method for designing wearable and tunable metamaterials via freeform auxetics is introduced. The proposed computational-design tools yield an approach to solving the complex circle packing problems in an interactive and efficient manner, thus facilitating the development of deployable metamaterials configured in freeform shapes. With such tools, the developed metamaterials may readily conform to a patient's knee, ankle, head, or any part of the body in need of imaging, and while ensuring an optimal resonance frequency, thereby paving the way for the widespread adoption of metamaterials in clinical MRI applications.

Computational Design of Phosphotriesterase Improves V-Agent Degradation Efficiency.

Organophosphates (OPs) are a class of neurotoxic acetylcholinesterase inhibitors including widely used pesticides as well as nerve agents such as VX and VR. Current treatment of these toxins relies on reactivating acetylcholinesterase, which remains ineffective. Enzymatic scavengers are of interest for their ability to degrade OPs systemically before they reach their target. Here we describe a library of computationally designed variants of phosphotriesterase (PTE), an enzyme that is known to break down OPs. The mutations G208D, F104A, K77A, A80V, H254G, and I274N broadly improve catalytic efficiency of VX and VR hydrolysis without impacting the structure of the enzyme. The mutation I106 A improves catalysis of VR and L271E abolishes activity, likely due to disruptions of PTE's structure. This study elucidates the importance of these residues and contributes to the design of enzymatic OP scavengers with improved efficiency.

Fin-Bayes: A Multi-Objective Bayesian Optimization Framework for Soft Robotic Fingers.

Computational design is a critical tool to realize the full potential of Soft Robotics, maximizing their inherent benefits of high performance, flexibility, robustness, and safe interaction. Practically, computational design entails a rapid iterative search process over a parameterized design space, with assessment using (frequently) computational modeling and (more rarely) physical experimentation. Bayesian approaches work well for these expensive-to-analyze systems and can lead to efficient exploration of design space than comparative algorithms. However, such computational design typically entails weaknesses related to a lack of fidelity in assessment, a lack of sufficient iterations, and/or optimizing to a singular objective function. Our work directly addresses these shortcomings. First, we harness a sophisticated nonlinear Finite Element Modeling suite that explicitly considers geometry, material, and contact nonlinearity to perform rapid accurate characterization. We validate this through extensive physical testing using an automated test rig and printed robotic fingers, providing far more experimental data than that reported in the literature. Second, we explore a significantly larger design space than comparative approaches, with more free variables and more opportunity to discover novel, high performance designs. Finally, we use a multiobjective Bayesian optimizer that allows for the identification of promising trade-offs between two critical objectives, compliance and contact force. We test our framework on optimizing Fin Ray grippers, which are ubiquitous throughout research and industry due to their passive compliance and durability. Results demonstrate the benefits of our approach, allowing for the optimization and identification of promising gripper designs within an extensive design space, which are then 3D printed and usable in reality.

Multi-strategy orthogonal enhancement and analysis of aldo-keto reductase thermal stability.

Given their outstanding efficiency and selectivity, enzymes are integral in various domains such as drug synthesis, the food industry, and environmental management. However, the inherent instability of natural enzymes limits their widespread industrial application. In this study, we underscore the efficacy of enhancing protein thermal stability through comprehensive protein design strategies, encompassing elements such as the free energy of protein folding, internal forces within proteins, and the overall structural design. We also demonstrate the efficiency and precision of combinatorial screening in the thermal stability design of aldo-keto reductase (AKR7-2-1). In our research, three single-point mutations and five combinatorial mutations were strategically introduced into AKR7-2-1, using multiple computational techniques. Notably, the E12I/S235I mutant showed a significant increase of 25.4 °C in its melting temperature (Tm). Furthermore, the optimal mutant, E12V/S235I, maintained 80 % of its activity while realizing a 16.8 °C elevation in Tm. Remarkably, its half-life at 50 °C was increased to twenty times that of the wild type. Structural analysis indicates that this enhanced thermal stability primarily arises from reduced oscillation in the loop region and increased internal hydrogen bonding. The promising results achieved with AKR7-2-1 demonstrate that our strategy could serve as a valuable reference for enhancing the thermal stability of other industrial enzymes.

Rational design of MIPs for the detection of Myxovirus resistance protein A (MxA), a biomarker for viral infection.

Accurate diagnosis is crucial for effective patient care and the containment of antimicrobial resistance outbreaks. The intricate challenge of distinguishing bacterial from viral infections, coupled with limited diagnostic tools and overlapping symptoms has driven the utilization of molecular imprinting techniques. This study focuses on developing cost-effective, chemically stable antibody analogs for the interferon-induced protein myxovirus resistance protein A (MxA). MxA is an intracellular, cytoplasmic GTPase having activity against a wide range of viruses and serves as a distinctive biomarker for viral infections. We utilized computational design to guide the polymer assembly, centering on epitope imprinting to target MxA-specific regions crucial for interaction. Molecular docking calculations, alongside a pioneering multi-monomer simultaneous docking (MMSD) protocol, efficiently elucidate cooperativity during pre-polymerization. Monomer binding affinity scores, such as for APTMS, exhibited notable increase, ranging from -3.11 to -13.03 kcal/mol across various MMSD combinations compared to a maximum of -2.78 kcal/mol in single monomer docking, highlighting the capacity of MMSD in elucidating crucial monomer-monomer interactions. This computational approach provides a theoretical alternative to labor-intensive experimental optimization, streamlining the development process for synthetic receptors. Simulations reveal unique interactions enhancing MIP-peptide complementarity, yielding optimized receptors selectively binding to MxA epitopes. The obtained MIPs demonstrated a maximum adsorption capacity of approximately 12 mg/g and captured 1.6 times more epitope and 2.6 times more epitope containing MxA protein than corresponding NIPs. A proof-of-concept study demonstrates MxA protein binding to synthetic receptors, highlighting the potential of MIPs, analogous to antibodies, in overcoming current diagnostic challenges for precise detection of viral infection.

Computational design and engineering of self-assembling multivalent microproteins with therapeutic potential against SARS-CoV-2.

Multivalent drugs targeting homo-oligomeric viral surface proteins, such as the SARS-CoV-2 trimeric spike (S) protein, have the potential to elicit more potent and broad-spectrum therapeutic responses than monovalent drugs by synergistically engaging multiple binding sites on viral targets. However, rational design and engineering of nanoscale multivalent protein drugs are still lacking. Here, we developed a computational approach to engineer self-assembling trivalent microproteins that simultaneously bind to the three receptor binding domains (RBDs) of the S protein. This approach involves four steps: structure-guided linker design, molecular simulation evaluation of self-assembly, experimental validation of self-assembly state, and functional testing. Using this approach, we first designed trivalent constructs of the microprotein miniACE2 (MP) with different trimerization scaffolds and linkers, and found that one of the constructs (MP-5ff) showed high trimerization efficiency, good conformational homogeneity, and strong antiviral neutralizing activity. With its trimerization unit (5ff), we then engineered a trivalent nanobody (Tr67) that exhibited potent and broad neutralizing activity against the dominant Omicron variants, including XBB.1 and XBB.1.5. Cryo-EM complex structure confirmed that Tr67 stably binds to all three RBDs of the Omicron S protein in a synergistic form, locking them in the "3-RBD-up" conformation that could block human receptor (ACE2) binding and potentially facilitate immune clearance. Therefore, our approach provides an effective strategy for engineering potent protein drugs against SARS-CoV-2 and other deadly coronaviruses.

Computer-Aided Reconstruction and Application of Bacillus halodurans S7 Xylanase with Heat and Alkali Resistance.

ß-1,4-Endoxylanase is the most critical hydrolase for xylan degradation during lignocellulosic biomass utilization. However, its poor stability and activity in hot and alkaline environments hinder its widespread application. In this study, BhS7Xyl from Bacillus halodurans S7 was improved using a computer-aided design through isothermal compressibility (ßT) perturbation engineering and by combining three thermostability prediction algorithms (ICPE-TPA). The best variant with remarkable improvement in specific activity, heat resistance (70 °C), and alkaline resistance (both pH 9.0 and 70 °C), R69F/E137M/E145L, exhibited a 4.9-fold increase by wild-type in specific activity (1368.6 U/mg), a 39.4-fold increase in temperature half-life (458.1 min), and a 57.6-fold increase in pH half-life (383.1 min). Furthermore, R69F/E137M/E145L was applied to the hydrolysis of agricultural waste (corncob and hardwood pulp) to efficiently obtain a higher yield of high-value xylooligosaccharides. Overall, the ICPE-TPA strategy has the potential to improve the functional performance of enzymes under extreme conditions for the high-value utilization of lignocellulosic biomass.

A pH-Responsive Virus-Like Particle as a Protein Cage for a Targeted Delivery.

A stimuli-responsive protein self-assembly offers promising utility as a protein nanocage for biotechnological and medical applications. Herein, the development of a virus-like particle (VLP) that undergoes a transition between assembly and disassembly under a neutral and acidic pH, respectively, for a targeted delivery is reported. The structure of the bacteriophage P22 coat protein is used for the computational design of coat subunits that self-assemble into a pH-responsive VLP. Subunit designs are generated through iterative computational cycles of histidine substitutions and evaluation of the interaction energies among the subunits under an acidic and neutral pH. The top subunit designs are tested and one that is assembled into a VLP showing the highest pH-dependent structural transition is selected. The cryo-EM structure of the VLP is determined, and the structural basis of a pH-triggered disassembly is delineated. The utility of the designed VLP is exemplified through the targeted delivery of a cytotoxic protein cargo into tumor cells in a pH-dependent manner. These results provide strategies for the development of self-assembling protein architectures with new functionality for diverse applications.

Machine Learning-Based Shear Optimal Adhesive Microstructures with Experimental Validation.

Bioinspired fibrillar structures are promising for a wide range of disruptive adhesive applications. Especially micro/nanofibrillar structures on gecko toes can have strong and controllable adhesion and shear on a wide range of surfaces with residual-free, repeatable, self-cleaning, and other unique features. Synthetic dry fibrillar adhesives inspired by such biological fibrils are optimized in different aspects to increase their performance. Previous fibril designs for shear optimization are limited by predefined standard shapes in a narrow range primarily based on human intuition, which restricts their maximum performance. This study combines the machine learning-based optimization and finite-element-method-based shear mechanics simulations to find shear-optimized fibril designs automatically. In addition, fabrication limitations are integrated into the simulations to have more experimentally relevant results. The computationally discovered shear-optimized structures are fabricated, experimentally validated, and compared with the simulations. The results show that the computed shear-optimized fibrils perform better than the predefined standard fibril designs. This design optimization method can be used in future real-world shear-based gripping or nonslip surface applications, such as robotic pick-and-place grippers, climbing robots, gloves, electronic devices, and medical and wearable devices.