Accurate de novo design of membrane-traversing macrocycles.

We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6-12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6-12 residue size range cross membranes with an apparent permeability greater than 1 × 10-6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.

Directed Evolution of Protein Catalysts.

Directed evolution is a powerful technique for generating tailor-made enzymes for a wide range of biocatalytic applications. Following the principles of natural evolution, iterative cycles of mutagenesis and screening or selection are applied to modify protein properties, enhance catalytic activities, or develop completely new protein catalysts for non-natural chemical transformations. This review briefly surveys the experimental methods used to generate genetic diversity and screen or select for improved enzyme variants. Emphasis is placed on a key challenge, namely how to generate novel catalytic activities that expand the scope of natural reactions. Two particularly effective strategies, exploiting catalytic promiscuity and rational design, are illustrated by representative examples of successfully evolved enzymes. Opportunities for extending these approaches to more complex biocatalytic systems are also considered.

A double-switch pHLIP system enables selective enrichment of circulating tumor microenvironment-derived extracellular vesicles.

Circulating tumor microenvironment-derived extracellular vesicles (cTME-EVs) are gaining considerable traction in cancer research and liquid biopsy. However, the study of cTME-EVs is largely limited by the dearth of a general isolation technique to selectively enrich cTME-EVs from biological fluids for downstream analysis. In this work, we broke through this dilemma by presenting a double-switch pH-low insertion peptide (D-S pHLIP) system to exclusively harvest cTME-EVs from the blood serum of tumor mouse models. This D-S pHLIP system consists of a highly sensitive pH-driven conformational switch (pKa ≈ 6.8) that allows specific installation of D-S pHLIP on the EV membranes in TME (pH 6.5 to 6.8) and a unique hook-like switch to "lock" the peptide securely on the cTME-EVs during the systemic circulation. The D-S pHLIP-anchored cTME-EVs were magnetically enriched and then analyzed with high-resolution messenger RNA sequencing, by which more than 18 times the number of TME-related differentially expressed genes and 10 times the number of hub genes were identified, compared with those achieved by the gold-standard ultracentrifugation. This work could revolutionize basic TME research as well as clinical liquid biopsy for cancer.

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.

Broad Sarbecovirus Neutralizing Antibodies Obtained by Computational Design and Synthetic Library Screening.

Members of the Sarbecovirus subgenus of Coronaviridae have twice caused deadly threats to humans. There is increasing concern about the rapid mutation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has evolved into multiple generations of epidemic variants in 3 years. Broad neutralizing antibodies are of great importance for pandemic preparedness against SARS-CoV-2 variants and divergent zoonotic sarbecoviruses. Here, we analyzed the structural conservation of the receptor-binding domain (RBD) from representative sarbecoviruses and chose S2H97, a previously reported RBD antibody with ideal breadth and resistance to escape, as a template for computational design to enhance the neutralization activity and spectrum. A total of 35 designs were purified for evaluation. The neutralizing activity of a large proportion of these designs against multiple variants was increased from several to hundreds of times. Molecular dynamics simulation suggested that extra interface contacts and enhanced intermolecular interactions between the RBD and the designed antibodies are established. After light and heavy chain reconstitution, AI-1028, with five complementarity determining regions optimized, showed the best neutralizing activity across all tested sarbecoviruses, including SARS-CoV, multiple SARS-CoV-2 variants, and bat-derived viruses. AI-1028 recognized the same cryptic RBD epitope as the parental prototype antibody. In addition to computational design, chemically synthesized nanobody libraries are also a precious resource for rapid antibody development. By applying distinct RBDs as baits for reciprocal screening, we identified two novel nanobodies with broad activities. These findings provide potential pan-sarbecovirus neutralizing drugs and highlight new pathways to rapidly optimize therapeutic candidates when novel SARS-CoV-2 escape variants or new zoonotic coronaviruses emerge. IMPORTANCE The subgenus Sarbecovirus includes human SARS-CoV, SARS-CoV-2, and hundreds of genetically related bat viruses. The continuous evolution of SARS-CoV-2 has led to the striking evasion of neutralizing antibody (NAb) drugs and convalescent plasma. Antibodies with broad activity across sarbecoviruses would be helpful to combat current SARS-CoV-2 mutations and longer term animal virus spillovers. The study of pan-sarbecovirus NAbs described here is significant for the following reasons. First, we established a structure-based computational pipeline to design and optimize NAbs to obtain more potent and broader neutralizing activity across multiple sarbecoviruses. Second, we screened and identified nanobodies from a highly diversified synthetic library with a broad neutralizing spectrum using an elaborate screening strategy. These methodologies provide guidance for the rapid development of antibody therapeutics against emerging pathogens with highly variable characteristics.

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.

An additive algorithm for origami design.

Inspired by the allure of additive fabrication, we pose the problem of origami design from a different perspective: How can we grow a folded surface in three dimensions from a seed so that it is guaranteed to be isometric to the plane? We solve this problem in two steps: by first identifying the geometric conditions for the compatible completion of two separate folds into a single developable fourfold vertex, and then showing how this foundation allows us to grow a geometrically compatible front at the boundary of a given folded seed. This yields a complete marching, or additive, algorithm for the inverse design of the complete space of developable quad origami patterns that can be folded from flat sheets. We illustrate the flexibility of our approach by growing ordered, disordered, straight, and curved-folded origami and fitting surfaces of given curvature with folded approximants. Overall, our simple shift in perspective from a global search to a local rule has the potential to transform origami-based metastructure design.

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-ß-lactamase 1.

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-ß-lactamase 1 (NDM-1), a bacterial enzyme that degrades ß-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l- and d-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.

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.

Computationally Supported Inversion of Ketoreductase Stereoselectivity.

Whereas directed evolution and rational design by structural inspection are established tools for enzyme redesign, computational methods are less mature but have the potential to predict small sets of mutants with desired properties without laboratory screening of large libraries. We have explored the use of computational enzyme redesign to change the enantioselectivity of a highly thermostable alcohol dehydrogenase from Thermus thermophilus in the asymmetric reduction of ketones. The enzyme reduces acetophenone to (S)-1-phenylethanol. To invert the enantioselectivity, we used an adapted CASCO workflow which included Rosetta for enzyme design and molecular dynamics simulations for ranking. To correct for unrealistic binding modes, we used Boltzmann weighing of binding energies computed by a linear interaction energy approach. This computationally cheap method predicted four variants with inverted enantioselectivity, each with 6-8 mutations around the substrate-binding site, causing only modest reduction (2- to 7-fold) of kcat /KM values. Laboratory testing showed that three variants indeed had inverted enantioselectivity, producing (R)-alcohols with up to 99 % enantiomeric excess. The broad substrate range allowed reduction of acetophenone derivatives with full conversion to highly enantioenriched alcohols. The results demonstrate the use of computational methods to control ketoreductase stereoselectivity in asymmetric transformations with minimal experimental screening.

RNA origami: design, simulation and application.

Design strategies for DNA and RNA nanostructures have developed along parallel lines for the past 30 years, from small structural motifs derived from biology to large 'origami' structures with thousands to tens of thousands of bases. With the recent publication of numerous RNA origami structures and improved design methods-even permitting co-transcriptional folding of kilobase-sized structures - the RNA nanotechnolgy field is at an inflection point. Here, we review the key achievements which inspired and enabled RNA origami design and draw comparisons with the development and applications of DNA origami structures. We further present the available computational tools for the design and the simulation, which will be key to the growth of the RNA origami community. Finally, we portray the transition from RNA origami structure to function. Several functional RNA origami structures exist already, their expression in cells has been demonstrated and first applications in cell biology have already been realized. Overall, we foresee that the fast-paced RNA origami field will provide new molecular hardware for biophysics, synthetic biology and biomedicine, complementing the DNA origami toolbox.

Engineered ACE2 receptor traps potently neutralize SARS-CoV-2.

An essential mechanism for severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection begins with the viral spike protein binding to the human receptor protein angiotensin-converting enzyme II (ACE2). Here, we describe a stepwise engineering approach to generate a set of affinity optimized, enzymatically inactivated ACE2 variants that potently block SARS-CoV-2 infection of cells. These optimized receptor traps tightly bind the receptor binding domain (RBD) of the viral spike protein and prevent entry into host cells. We first computationally designed the ACE2-RBD interface using a two-stage flexible protein backbone design process that improved affinity for the RBD by up to 12-fold. These designed receptor variants were affinity matured an additional 14-fold by random mutagenesis and selection using yeast surface display. The highest-affinity variant contained seven amino acid changes and bound to the RBD 170-fold more tightly than wild-type ACE2. With the addition of the natural ACE2 collectrin domain and fusion to a human immunoglobulin crystallizable fragment (Fc) domain for increased stabilization and avidity, the most optimal ACE2 receptor traps neutralized SARS-CoV-2-pseudotyped lentivirus and authentic SARS-CoV-2 virus with half-maximal inhibitory concentrations (IC50s) in the 10- to 100-ng/mL range. Engineered ACE2 receptor traps offer a promising route to fighting infections by SARS-CoV-2 and other ACE2-using coronaviruses, with the key advantage that viral resistance would also likely impair viral entry. Moreover, such traps can be predesigned for viruses with known entry receptors for faster therapeutic response without the need for neutralizing antibodies isolated from convalescent patients.

Carbon-Nanotube-Encapsulated-Sulfur Cathodes for Lithium-Sulfur Batteries: Integrated Computational Design and Experimental Validation.

To mitigate lithium-polysulfides (Li-PSs) shuttle in lithium-sulfur batteries (LiSBs), a unique carbon-nanotube-encapsulated-sulfur (S@CNT) cathode material with optimum open-ring sizes (ORSs) on the CNT walls were designed using an integrated computational approach followed by experimental validation. By calculating the transport barrier of Li+ ion through ORSs on the CNT walls and comparing the molecular size of solvents and Li-PSs with ORSs, optimum open-rings with 16-30 surrounding carbon atoms were predicted to selectively allow transportation of Li+ ion and evaporated sulfur while blocking both Li-PS and solvent molecules. A CNT oxidation process was proposed and simulated to generate these ORSs, and the results indicated that the optimum ORSs can be achieved by narrowly controlling the oxidation parameters. Subsequently, S@CNT cathodes were experimentally synthesized, confirming that optimum ORSs were generated in CNT oxidized at 475 K and exhibited more stable cycling behavior.

Scalable Combinatorial Assembly of Synthetic DNA for Tracking Applications.

Synthetic DNA barcodes are double-stranded DNA molecules designed to carry recoverable information, information that can be used to represent and track objects and organisms. DNA barcodes offer robust, sensitive detection using standard amplification and sequencing techniques. While numerous research groups have promoted DNA as an information storage medium, less attention has been devoted to the design of economical, scalable DNA barcode libraries. Here, we present an alternative modular approach to sequence design. Barcode sequences were constructed from smaller, interchangeable blocks, allowing for the combinatorial assembly of numerous distinct tags. We demonstrated the design and construction of first-generation (N = 256) and second-generation (N = 512) modular barcode libraries, from fewer than 50 total single-stranded oligonucleotides for each library. To avoid contamination during experimental validation, a liquid-handling robot was employed for oligonucleotide mixing. Generating barcode sequences in-house reduces dependency upon external entities for unique tag generation, increasing flexibility in barcode generation and deployment. Next generation sequencing (NGS) detection of 256 different samples in parallel highlights the multiplexing afforded by the modular barcode design coupled with high-throughput sequencing. Deletion variant analysis of the first-generation library informed sequence design for enhancing barcode assembly specificity in the second-generation library.

Rational In Silico Design of Molecularly Imprinted Polymers: Current Challenges and Future Potential.

The rational design of molecularly imprinted polymers has evolved along with state-of-the-art experimental imprinting strategies taking advantage of sophisticated computational tools. In silico methods enable the screening and simulation of innovative polymerization components and conditions superseding conventional formulations. The combined use of quantum mechanics, molecular mechanics, and molecular dynamics strategies allows for macromolecular modelling to study the systematic translation from the pre- to the post-polymerization stage. However, predictive design and high-performance computing to advance MIP development are neither fully explored nor practiced comprehensively on a routine basis to date. In this review, we focus on different steps along the molecular imprinting process and discuss appropriate computational methods that may assist in optimizing the associated experimental strategies. We discuss the potential, challenges, and limitations of computational approaches including ML/AI and present perspectives that may guide next-generation rational MIP design for accelerating the discovery of innovative molecularly templated materials.

Rational Design of a Facially Coordinating P,N,N Ligand for Manganese-Catalysed Enantioselective Hydrogenation of Cyclic Ketones.

DFT calculations on the full catalytic cycle for manganese catalysed enantioselective hydrogenation of a selection of ketones have been carried out at the PBE0-D3PCM //RI-BP86PCM level. Mn complexes of an enantiomerically pure chiral P,N,N ligand have been found to be most reactive when adopting a facial coordination mode. The use of a new ligand with an ortho-substituted dimethylamino-pyridine motif has been calculated to completely transform the levels of enantioselectivity possible for the hydrogenation of cyclic ketones relative to the first-generation Mn catalysts. In silico evaluation of substrates has been used to identify those likely to be reduced with high enantiomer ratios (er), and others that would exhibit less selectivity; good agreements were then found in experiments. Various cyclic ketones and some acetophenone derivatives were hydrogenated with er's up to 99 : 1.

Modular peptide binders - development of a predictive technology as alternative for reagent antibodies.

Current biomedical research and diagnostics critically depend on detection agents for specific recognition and quantification of protein molecules. Monoclonal antibodies have been used for this purpose over decades and facilitated numerous biological and biomedical investigations. Recently, however, it has become apparent that many commercial reagent antibodies lack specificity or do not recognize their target at all. Thus, synthetic alternatives are needed whose complex designs are facilitated by multidisciplinary approaches incorporating experimental protein engineering with computational modeling. Here, we review the status of such an engineering endeavor based on the modular armadillo repeat protein scaffold and discuss challenges in its implementation.

Prevention of hospital pathogen biofilm formation by antimicrobial peptide KWI18.

This study investigated the antimicrobial and antibiofilm activity of KWI18, a new synthetic peptide. KWI18 was tested against planktonic cells and Pseudomonas aeruginosa and Candida parapsilosis biofilms. Time-kill and synergism assays were performed. Sorbitol, ergosterol, lipid peroxidation, and protein oxidation assays were used to gain insight into the mechanism of action of the peptide. Toxicity was evaluated against erythrocytes and Galleria mellonella. KWI18 showed antimicrobial activity, with minimum inhibitory concentration (MIC) values ranging from 0.5 to 10 µM. KWI18 at 10 × MIC reduced P. aeruginosa and C. parapsilosis biofilm formation and cell viability. Time-kill assays revealed that KWI18 inhibited the growth of P. aeruginosa in 4 h and that of C. parapsilosis in 6 h. The mechanism of action was related to ergosterol as well as induction of oxidative damage in cells and biofilms. Furthermore, KWI18 demonstrated low toxicity to erythrocytes and G. mellonella. KWI18 proved to be an effective antibiofilm agent, opening opportunities for the development of new antimicrobials.

Theoretical Perspectives in Organocatalysis.

It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state-of-the-art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual-driven, to the most recent one, machine-driven.

Study on the Preparation of Estrone Molecularly Imprinted Polymers and Their Application in a Quartz Crystal Microbalance Sensor via a Computer-Assisted Design.

Computer simulations are widely used for the selection of conditions for the synthesis of molecularly imprinted polymers and can rapidly reduce the experimental cycle time and save labor and materials. In this paper, estrone molecularly imprinted polymers (E1-MIPs) are designed at the M062X/6-311+G(d,p) level with itaconic acid (IA) as the functional monomer. The imprinted molar ratio between E1 and IA was optimized, cross-linkers and solvents were screened, and the nature of interactions between E1 and IA was explored. The simulated results showed that pentaerythritol triacrylate was the best cross-linker. Meanwhile, when the imprinted molar ratio between E1 and IA was 1:4, the E1-IA complex had the largest amount of hydrogen bonds, the lowest binding energy, and the strongest stability. Using the simulation results as guidance, the E1-MIPs were prepared to modify the electrons of a quartz crystal microbalance (QCM) sensor. The experimental studies showed that the E1-MIPs-QCM sensor had the highest adsorption capacity to E1 in comparison with their analogues, and the lowest detection value of the sensor was 16.00 µg/L. The computer simulations and experimental studies could provide guidance for synthesize novel E1-MIPs materials. It also could provide important references and directions for the application of E1-MIPs.