Engineering highly thermostable Cas12b via de novo structural analyses for one-pot detection of nucleic acids.

CRISPR-Cas-based diagnostics have the potential to elevate nucleic acid detection. CRISPR-Cas systems can be combined with a pre-amplification step in a one-pot reaction to simplify the workflow and reduce carryover contamination. Here, we report an engineered Cas12b with improved thermostability that falls within the optimal temperature range (60°C-65°C) of reverse transcription-loop-mediated isothermal amplification (RT-LAMP). Using de novo structural analyses, we introduce mutations to wild-type BrCas12b to tighten its hydrophobic cores, thereby enhancing thermostability. The one-pot detection assay utilizing the engineered BrCas12b, called SPLENDID (single-pot LAMP-mediated engineered BrCas12b for nucleic acid detection of infectious diseases), exhibits robust trans-cleavage activity up to 67°C in a one-pot setting. We validate SPLENDID clinically in 80 serum samples for hepatitis C virus (HCV) and 66 saliva samples for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high specificity and accuracy. We obtain results in as little as 20 min, and with the extraction process, the entire assay can be performed within an hour.

Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus.

The human infectious disease COVID-19 caused by the SARS-CoV-2 virus has become a major threat to global public health. Developing a vaccine is the preferred prophylactic response to epidemics and pandemics. However, for individuals who have contracted the disease, the rapid design of antibodies that can target the SARS-CoV-2 virus fulfils a critical need. Further, discovering antibodies that bind multiple variants of SARS-CoV-2 can aid in the development of rapid antigen tests (RATs) which are critical for the identification and isolation of individuals currently carrying COVID-19. Here we provide a proof-of-concept study for the computational design of high-affinity antibodies that bind to multiple variants of the SARS-CoV-2 spike protein using RosettaAntibodyDesign (RAbD). Well characterized antibodies that bind with high affinity to the SARS-CoV-1 (but not SARS-CoV-2) spike protein were used as templates and re-designed to bind the SARS-CoV-2 spike protein with high affinity, resulting in a specificity switch. A panel of designed antibodies were experimentally validated. One design bound to a broad range of variants of concern including the Omicron, Delta, Wuhan, and South African spike protein variants.

Characterization and protein engineering of glycosyltransferases for the biosynthesis of diverse hepatoprotective cycloartane-type saponins in Astragalus membranaceus.

Although plant secondary metabolites are important source of new drugs, obtaining these compounds is challenging due to their high structural diversity and low abundance. The roots of Astragalus membranaceus are a popular herbal medicine worldwide. It contains a series of cycloartane-type saponins (astragalosides) as hepatoprotective and antivirus components. However, astragalosides exhibit complex sugar substitution patterns which hindered their purification and bioactivity investigation. In this work, glycosyltransferases (GT) from A. membranaceus were studied to synthesize structurally diverse astragalosides. Three new GTs, AmGT1/5 and AmGT9, were characterized as 3-O-glycosyltransferase and 25-O-glycosyltransferase of cycloastragenol respectively. AmGT1G146V/I variants were obtained as specific 3-O-xylosyltransferases by sequence alignment, molecular modelling and site-directed mutagenesis. A combinatorial synthesis system was established using AmGT1/5/9, AmGT1G146V/S and the reported AmGT8 and AmGT8A394F . The system allowed the synthesis of 13 astragalosides in Astragalus root with conversion rates from 22.6% to 98.7%, covering most of the sugar-substitution patterns for astragalosides. In addition, AmGT1 exhibited remarkable sugar donor promiscuity to use 10 different donors, and was used to synthesize three novel astragalosides and ginsenosides. Glycosylation remarkably improved the hepatoprotective and SARS-CoV-2 inhibition activities for triterpenoids. This is one of the first attempts to produce a series of herbal constituents via combinatorial synthesis. The results provided new biocatalytic tools for saponin biosynthesis.

Engineered soluble ACE2 receptor: Responding to change with change.

SARS coronavirus 2 (SARS-CoV-2) invades the human body by binding to major receptors such as ACE2 via its S-spike protein, so the interaction of receptor-binding sites has been a hot topic in the development of coronavirus drugs. At present, the clinical progress in monoclonal antibody therapy that occurred early in the pandemic is gradually showing signs of slowing. While recombinant soluble ACE2, as an alternative therapy, has been modified by many engineering methods, both the safety and functional aspects are approaching maturity, and this therapy shows great potential for broadly neutralizing coronaviruses, but its progress in clinical development remains stalled. Therefore, there are still several key problems to be considered and solved for recombinant soluble ACE2 to be approved as a clinical treatment as soon as possible.

Toward real-world automated antibody design with combinatorial Bayesian optimization.

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Molecular engineering of a cryptic epitope in Spike RBD improves manufacturability and neutralizing breadth against SARS-CoV-2 variants.

There is a continued need for sarbecovirus vaccines that can be manufactured and distributed in low- and middle-income countries (LMICs). Subunit protein vaccines are manufactured at large scales at low costs, have less stringent temperature requirements for distribution in LMICs, and several candidates have shown protection against SARS-CoV-2. We previously reported an engineered variant of the SARS-CoV-2 Spike protein receptor binding domain antigen (RBD-L452K-F490W; RBD-J) with enhanced manufacturability and immunogenicity compared to the ancestral RBD. Here, we report a second-generation engineered RBD antigen (RBD-J6) with two additional mutations to a hydrophobic cryptic epitope in the RBD core, S383D and L518D, that further improved expression titers and biophysical stability. RBD-J6 retained binding affinity to human convalescent sera and to all tested neutralizing antibodies except antibodies that target the class IV epitope on the RBD core. K18-hACE2 transgenic mice immunized with three doses of a Beta variant of RBD-J6 displayed on a virus-like particle (VLP) generated neutralizing antibodies (nAb) to nine SARS-CoV-2 variants of concern at similar levels as two doses of Comirnaty. The vaccinated mice were also protected from challenge with Alpha or Beta SARS-CoV-2. This engineered antigen could be useful for modular RBD-based subunit vaccines to enhance manufacturability and global access, or for further development of variant-specific or broadly acting booster vaccines.

Exploring Highly Conserved Regions of SARS-CoV-2 Spike S2 Subunit as Targets for Fusion Inhibition Using Chimeric Proteins.

Since the beginning of the COVID-19 pandemic, considerable efforts have been made to develop protective vaccines against SARS-CoV-2 infection. However, immunity tends to decline within a few months, and new virus variants are emerging with increased transmissibility and capacity to evade natural or vaccine-acquired immunity. Therefore, new robust strategies are needed to combat SARS-CoV-2 infection. The viral spike composed of S1 and S2 subunits mediates viral attachment and membrane fusion to infect the host cell. In this process, interaction between the highly conserved heptad repeat 1 and 2 regions (HR1 and HR2) of S2 is crucial and for this reason; these regions are promising targets to fight SARS-CoV-2. Here, we describe the design and characterization of chimeric proteins that structurally imitate the S2 HR1 region in a trimeric coiled-coil conformation. We biophysically characterized the proteins and determined their capacity to bind the HR2 region, as well as their inhibitory activity of SARS-CoV-2 infection in vitro. HR1 mimetic proteins showed conformational heterogeneity and a propensity to form oligomers. Moreover, their structure is composed of subdomains with varied stability. Interestingly, the full HR1 proteins showed high affinity for HR2-derived peptides and SARS-CoV-2 inhibitory activity, whereas smaller proteins mimicking HR1 subdomains had a decreased affinity for their complementary HR2 region and did not inhibit the virus. The results provide insight into effective strategies to create mimetic proteins with broad inhibitory activity and therapeutic potential against SARS-CoV-2.

ADAPTABLE ENGINEERING OF CELLULOSE-BASED VERTICAL FLOW ASSAYS FOR RAPID DIAGNOSTICS - THE CASE OF COVID-19

Paper microfluidics has had a rich history in medical diagnostics owing to their portability, low-cost and capacity for mass manufacture. While nitrocellulose has widespread use in commercial paper-based assays, shortages can become a bottleneck for deployment. Here, we seek to overcome this limitation by enabling swift and efficient production of cellulose-based paper assays with minimal substrate processing via protein engineering. We demonstrate good clinical and lab-based performance for both serological and antigen rapid tests and their compatibility with roll-to-roll mass manufacturing, which validates our proposed workflow for commercialization. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Steric accessibility of the N-terminus improves the titer and quality of recombinant proteins secreted from Komagataella phaffii.

BACKGROUND: Komagataella phaffii is a commonly used alternative host for manufacturing therapeutic proteins, in part because of its ability to secrete recombinant proteins into the extracellular space. Incorrect processing of secreted proteins by cells can, however, cause non-functional product-related variants, which are expensive to remove in purification and lower overall process yields. The secretion signal peptide, attached to the N-terminus of the recombinant protein, is a major determinant of the quality of the protein sequence and yield. In K. phaffii, the signal peptide from the Saccharomyces cerevisiae alpha mating factor often yields the highest secreted titer of recombinant proteins, but the quality of secreted protein can vary highly. RESULTS: We determined that an aggregated product-related variant of the SARS-CoV-2 receptor binding domain is caused by N-terminal extension from incomplete cleavage of the signal peptide. We eliminated this variant and improved secreted protein titer up to 76% by extension of the N-terminus with a short, functional peptide moiety or with the EAEA residues from the native signal peptide. We then applied this strategy to three other recombinant subunit vaccine antigens and observed consistent elimination of the same aggregated product-related variant. Finally, we demonstrated that this benefit in quality and secreted titer can be achieved with addition of a single amino acid to the N-terminus of the recombinant protein. CONCLUSIONS: Our observations suggest that steric hindrance of proteases in the Golgi that cleave the signal peptide can cause unwanted N-terminal extension and related product variants. We demonstrated that this phenomenon occurs for multiple recombinant proteins, and can be addressed by minimal modification of the N-terminus to improve steric accessibility. This strategy may enable consistent secretion of a broad range of recombinant proteins with the highly productive alpha mating factor secretion signal peptide.

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

Engineered multivalent self-assembled binder protein against SARS-CoV-2 RBD.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic since December 2019, and with it, a push for innovations in rapid testing and neutralizing antibody treatments in an effort to solve the spread and fatality of the disease. One such solution to both of these prevailing issues is targeting the interaction of SARS-CoV-2 spike receptor binding domain (RBD) with the human angiotensin-converting enzyme 2 (ACE2) receptor protein. Structural studies have shown that the N-terminal alpha-helix comprised of the first 23 residues of ACE2 plays an important role in this interaction. Where it is typical to design a binding domain to fit a target, we have engineered a protein that relies on multivalency rather than the sensitivity of a monomeric ligand to provide avidity to its target by fusing the N-terminal helix of ACE2 to the coiled-coil domain of the cartilage oligomeric matrix protein. The resulting ACE-MAP is able to bind to the SARS-CoV-2 RBD with improved binding affinity, is expressible in E. coli, and is thermally stable and relatively small (62 kDa). These properties suggest ACE-MAP and the MAP scaffold to be a promising route towards developing future diagnostics and therapeutics to SARS-CoV-2.

Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies.

Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments.

Leveraging Bacteriophages in Vaccine Development

Bacteriophages, or Phages for short, are viruses that specifically infect bacterial cells. Their name is derived from Greek for "bacteria eater" and it's an apt description: To reproduce, they must coopt the cellular machinery of a bacterium, sometimes destroying it in the process [1]. Phages thrive anywhere bacteria exist-on land, in water, and inside plants and animals. In fact, they are considered the most successful organisms on earth due to their abundance and genetic diversity. Scientists estimate there are a trillion phages for every grain of sand on earth. That's 10(31) phages [2].

ACE2 decoy receptor generated by high-throughput saturation mutagenesis efficiently neutralizes SARS-CoV-2 and its prevalent variants.

The recent global pandemic was a spillover from the SARS-CoV-2 virus. Viral entry involves the receptor binding domain (RBD) of the viral spike protein interacting with the protease domain (PD) of the cellular receptor, ACE2. We hereby present a comprehensive mutational landscape of the effects of ACE2-PD point mutations on RBD-ACE2 binding using a saturation mutagenesis approach based on microarray-based oligo synthesis and a single-cell screening assay. We observed that changes in glycosylation sites and directly interacting sites of ACE2-PD significantly influenced ACE2-RBD binding. We further engineered an ACE2 decoy receptor with critical point mutations, D30I, L79W, T92N, N322V, and K475F, named C4-1. C4-1 shows a 200-fold increase in neutralization for the SARS-CoV-2 D614G pseudotyped virus compared to wild-type soluble ACE2 and a sevenfold increase in binding affinity to wild-type spike compared to the C-terminal Ig-Fc fused wild-type soluble ACE2. Moreover, C4-1 efficiently neutralized prevalent variants, especially the omicron variant (EC50=16 ng/mL), and rescued monoclonal antibodies, vaccine, and convalescent sera neutralization from viral immune-escaping. We hope to next investigate translating the therapeutic potential of C4-1 for the treatment of SARS-CoV-2.

Engineering protein activity into off-the-shelf DNA devices.

DNA-based devices are straightforward to design by virtue of their predictable folding, but they lack complex biological activity such as catalysis. Conversely, protein-based devices offer a myriad of functions but are much more difficult to design due to their complex folding. This study combines DNA and protein engineering to generate an enzyme that is activated by a DNA sequence of choice. A single protein switch, engineered from nanoluciferase using the alternate-frame-folding mechanism and herein called nLuc-AFF, is paired with different DNA technologies to create a biosensor for specific nucleic acid sequences, sensors for serotonin and ATP, and a two-input logic gate. nLuc-AFF is a genetically encoded, ratiometric, blue/green-luminescent biosensor whose output can be quantified by a phone camera. nLuc-AFF retains ratiometric readout in 100% serum, making it suitable for analyzing crude samples in low-resource settings. This approach can be applied to other proteins and enzymes to convert them into DNA-activated switches.

Isolation, characterization, and structure-based engineering of a neutralizing nanobody against SARS-CoV-2.

SARS-CoV-2 engages with human cells through the binding of its Spike receptor-binding domain (S-RBD) to the receptor ACE2. Molecular blocking of this engagement represents a proven strategy to treat COVID-19. Here, we report a single-chain antibody (nanobody, DL4) isolated from immunized alpaca with picomolar affinity to RBD. DL4 neutralizes SARS-CoV-2 pseudoviruses with an IC50 of 0.101 µg mL-1 (6.2 nM). A crystal structure of the DL4-RBD complex at 1.75-Å resolution unveils the interaction detail and reveals a direct competition mechanism for DL4's ACE2-blocking and hence neutralizing activity. The structural information allows us to rationally design a mutant with higher potency. Our work adds diversity of neutralizing nanobodies against SARS-CoV-2 and should encourage protein engineering to improve antibody affinities in general.

Engineering Strategies to Overcome the Stability-Function Trade-Off in Proteins.

In addition to its biological function, the stability of a protein is a major determinant for its applicability. Unfortunately, engineering proteins for improved functionality usually results in destabilization of the protein. This so-called stability-function trade-off can be explained by the simple fact that the generation of a novel protein function─or the improvement of an existing one─necessitates the insertion of mutations, i.e., deviations from the evolutionarily optimized wild-type sequence. In fact, it was demonstrated that gain-of-function mutations are not more destabilizing than other random mutations. The stability-function trade-off is a universal phenomenon during protein evolution that has been observed with completely different types of proteins, including enzymes, antibodies, and engineered binding scaffolds. In this review, we discuss three types of strategies that have been successfully deployed to overcome this omnipresent obstacle in protein engineering approaches: (i) using highly stable parental proteins, (ii) minimizing the extent of destabilization during functional engineering (by library optimization and/or coselection for stability and function), and (iii) repairing damaged mutants through stability engineering. The implementation of these strategies in protein engineering campaigns will facilitate the efficient generation of protein variants that are not only functional but also stable and therefore better-suited for subsequent applications.

Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein.

The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequences that satisfy the desired properties from a large number of possible protein sequences. To develop general protein descriptors for computer-aided protein engineering tasks, we devised new protein descriptors, one sequence-based descriptor (PCgrades), and three structure-based descriptors (PCspairs, 3D-SPIEs_5.4 Å, and 3D-SPIEs_8Å). While the PCgrades and PCspairs include general and statistical information in physicochemical properties in single and pairwise amino acids respectively, the 3D-SPIEs include specific and quantum-mechanical information with parameterized quantum mechanical calculations (FMO2-DFTB3/D/PCM). To evaluate the protein descriptors, we made prediction models with the new descriptors and previously developed descriptors for diverse protein datasets including protein expression and binding affinity change in SARS-CoV-2 spike glycoprotein. As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance ( R 2 = 0.783 for protein expression and R 2 = 0.711 for binding affinity). As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance. Similar approaches with those descriptors would be promising and useful if the prediction models are trained with sufficient quantitative experimental data from high-throughput assays for industrial enzymes or protein drugs.

Deep mutational scanning for therapeutic antibody engineering.

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.