A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens.

Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen's protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein-drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.

Machine learning and protein allostery.

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD).

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

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.

Hotspot residues and resistance mutations in the nirmatrelvir-binding site of SARS-CoV-2 main protease: Design, identification, and correlation with globally circulating viral genomes.

Shortly after the onset of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has acquired numerous variations in its intracellular proteins to adapt quickly, become more infectious, and ultimately develop drug resistance by mutating certain hotspot residues. To keep the emerging variants at bay, including Omicron and subvariants, FDA has approved the antiviral nirmatrelvir for mild-to-moderate and high-risk COVID-19 cases. Like other viruses, SARS-CoV-2 could acquire mutations in its main protease (Mpro) to adapt and develop resistance against nirmatrelvir. Employing a unique high-throughput protein design technique, the hotspot residues, and signatures of adaptation of Mpro having the highest probability of mutating and rendering nirmatrelvir ineffective were identified. Our results show that ∼40% of the designed mutations in Mpro already exist in the globally circulating SARS-CoV-2 lineages and several predicted mutations. Moreover, several high-frequency, designed mutations were found to be in corroboration with the experimentally reported nirmatrelvir-resistant mutants and are naturally occurring. Our work on the targeted design of the nirmatrelvir-binding site offers a comprehensive picture of potential hotspot sites and resistance mutations in Mpro and is thus crucial in comprehending viral adaptation, robust antiviral design, and surveillance of evolving Mpro variations.

Increasing Computational Protein Design Literacy through Cohort-Based Learning for Undergraduate Students

Undergraduate research experiences can improve student success in graduate education and STEM careers. During the COVID-19 pandemic, undergraduate researchers at our institution and many others lost their work- study research positions due to interruption of in-person research activities. This imposed a financial burden on the students and eliminated an important learning opportunity. To address these challenges, we created a paid, fully remote, cohort based research curriculum in computational protein design. Our curriculum used existing protein design methods as a platform to first educate and train undergraduate students and then to test research hypotheses. In the first phase, students learned computational methods to assess the stability of designed protein assemblies. In the second phase, students used a larger data set to identify factors that could improve the accuracy of current protein design algorithms. This cohort based program created valuable new research opportunities for undergraduates at our institute and enhanced the undergraduates' feeling of connection with the lab. Students learned transferable and useful skills such as literature review, programming basics, data analysis, hypothesis testing, and scientific communication. Our program provides a model of structured computational research training opportunities for undergraduate researchers in any field for organizations looking to expand educational access.

Computational Design of Miniprotein Binders.

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

Computational Design of Potential Binder Protein for SARS-CoV-2 Spike RBD through A Novel Deep Neural Network Based-Protein Outpainting Algorithm

COVID-19 caused by SARS-CoV-2 is seriously endangering the health of all human beings. There is an urgent need for drugs that can inhibit the replication and propagation of the virus. Traditional macromolecular drugs have long discovery and development cycles and high experimental costs, which can't give rapid response to new viruses. Through computational protein design method, scientists have designed binder proteins with high affinity for the RBD of SARS-CoV-2 spike protein which can effectively inhibit virus replication. However, traditional computational protein design methods rely heavily on human experience and domain knowledge of protein design, and the protein design workflow is too complicated to be widely accepted and used in academia and industry. Based on previous work in the field of deep neural network protein structure prediction and protein design, we developed a novel protein outpainting method that can generate the remaining part of the protein based on a given hot spot motif and complete the entire protein. This method can generate stable protein scaffold which can support the functional hot spot motif, resulting in a protein with excellent thermal stability and developability. We tested this method in a drug discovery project with the aim of designing new SARS-CoV-2 inhibitors. Several proteins are obtained which are predicted to be stable and may have high affinity for the RBD of the SARS-CoV-2 spike protein. Although they have not been verified by wet-lab experiments, we believe that these proteins have great potential to be developed into effective drugs for the treatment of COVID-19. The protein outpainting algorithm proposed in this paper has great advantages over traditional protein design methods. It can be applied to many fields that require the design of functional proteins, such as protein drug design, enzyme de novo design, vaccine design, etc. The method will play an important role in reducing the cost of experiments, shortening the research and development period, and improving the successful rate of biological research and development. © 2021 ACM.

Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution.

BACKGROUND: Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints. RESULTS: We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods. CONCLUSION: Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

Propedia: a database for protein-peptide identification based on a hybrid clustering algorithm.

BACKGROUND: Protein-peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein-peptide databases and tools that allow the retrieval, characterization and understanding of protein-peptide recognition and consequently support peptide design. RESULTS: We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein-peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein-peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation. CONCLUSIONS: Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein-peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia.

Interface-based design of the favipiravir-binding site in SARS-CoV-2 RNA-dependent RNA polymerase reveals mutations conferring resistance to chain termination.

Favipiravir is a broad-spectrum inhibitor of viral RNA-dependent RNA polymerase (RdRp) currently being used to manage COVID-19. Accumulation of mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RdRp may facilitate antigenic drift, generating favipiravir resistance. Focussing on the chain-termination mechanism utilized by favipiravir, we used high-throughput interface-based protein design to generate > 100 000 designs of the favipiravir-binding site of RdRp and identify mutational hotspots. We identified several single-point mutants and designs having a sequence identity of 97%-98% with wild-type RdRp, suggesting that SARS-CoV-2 can develop favipiravir resistance with few mutations. Out of 134 mutations documented in the CoV-GLUE database, 63 specific mutations were already predicted as resistant in our calculations, thus attaining ˜ 47% correlation with the sequencing data. These findings improve our understanding of the potential signatures of adaptation in SARS-CoV-2 against favipiravir.

Stabilization of the SARS-CoV-2 Spike Receptor-Binding Domain Using Deep Mutational Scanning and Structure-Based Design.

The unprecedented global demand for SARS-CoV-2 vaccines has demonstrated the need for highly effective vaccine candidates that are thermostable and amenable to large-scale manufacturing. Nanoparticle immunogens presenting the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein (S) in repetitive arrays are being advanced as second-generation vaccine candidates, as they feature robust manufacturing characteristics and have shown promising immunogenicity in preclinical models. Here, we used previously reported deep mutational scanning (DMS) data to guide the design of stabilized variants of the RBD. The selected mutations fill a cavity in the RBD that has been identified as a linoleic acid binding pocket. Screening of several designs led to the selection of two lead candidates that expressed at higher yields than the wild-type RBD. These stabilized RBDs possess enhanced thermal stability and resistance to aggregation, particularly when incorporated into an icosahedral nanoparticle immunogen that maintained its integrity and antigenicity for 28 days at 35-40°C, while corresponding immunogens displaying the wild-type RBD experienced aggregation and loss of antigenicity. The stabilized immunogens preserved the potent immunogenicity of the original nanoparticle immunogen, which is currently being evaluated in a Phase I/II clinical trial. Our findings may improve the scalability and stability of RBD-based coronavirus vaccines in any format and more generally highlight the utility of comprehensive DMS data in guiding vaccine design.

Targeting Viral Surface Proteins through Structure-Based Design.

The emergence of novel viral infections of zoonotic origin and mutations of existing human pathogenic viruses represent a serious concern for public health. It warrants the establishment of better interventions and protective therapies to combat the virus and prevent its spread. Surface glycoproteins catalyzing the fusion of viral particles and host cells have proven to be an excellent target for antivirals as well as vaccines. This review focuses on recent advances for computational structure-based design of antivirals and vaccines targeting viral fusion machinery to control seasonal and emerging respiratory viruses.

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors.

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBDSpike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBDSpike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBDSpike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBDSpike-ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBDSpike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently "reaction-prone." The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic "hydrophobic ↔ polar" mutations serve as a potential breakthrough.

Computational design and modeling of nanobodies toward SARS-CoV-2 receptor binding domain.

The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health concern and pose a serious threat to humanity. There is an urgent need for developing therapeutic drugs and (or) biologics to prevent the spread of the virus. The life cycle of SARS-CoV-2 shows that the virus enters host cells by first binding to angiotensin-converting enzyme 2 (ACE2) through its spike protein receptor-binding domain (RBD). Therefore, blocking the binding between of ACE2 and SARS-CoV-2 RBD can inhibit the virus infection in the host cells. In this study, by grafting the complementarity-determining regions (CDRs) of developed SARS-CoV, MERS-CoVs specific neutralizing antibodies (nAbs) include monoclonal antibodies (mAbs) as well as SARS-CoV-2 mAbs onto a known stable nanobody (Nb) scaffold, and a total of 16 Nbs sequences were designed. Five Nbs, namely CS01, CS02, CS03, CS10, and CS16, were selected based on the free energy landscape of protein docking verified by the recently reported Nb-RBD cocrystal structures. CS01, CS02, and CS03 occupied the ACE2 binding site of RBD, while CS10 and CS16 were proposed to inhibit the interaction between RBD and ACE2 through an allosteric mechanism. Based on the structures of the five Nbs in complex with RBD, seven brand-new Nbs with enhanced binding affinities (CS02_RD01, CS03_RD01, CS03_RD02, CS03_RD03, CS03_RD04, CS16_RD01, and CS16_RD02) were generated by redesign of residues on the interface of the five Nbs contact with SARS-CoV-2 RBD. In addition, the identified "hot spots" on the interface of each complex provide useful information to understand the binding mechanism of designed Nbs to SARS-CoV-2 RBD. In sum, the predicted stabilities and high binding affinities of the 11 (re)designed Nbs indicating the potential of the developed computational framework in this work to design effective agents to block the infection of SARS-CoV-2.

Targeted design of drug binding sites in the main protease of SARS-CoV-2 reveals potential signatures of adaptation.

Several existing drugs are currently being tested worldwide to treat COVID-19 patients. Recent data indicate that SARS-CoV-2 is rapidly evolving into more transmissible variants. It is therefore highly possible that SARS-CoV-2 can accumulate adaptive mutations modulating drug susceptibility and hampering viral antigenicity. Thus, it is vital to predict potential non-synonymous mutation sites and predict the evolution of protein structural modifications leading to drug tolerance. As two FDA-approved anti-hepatitis C virus (HCV) drugs, boceprevir, and telaprevir, have been shown to effectively inhibit SARS-CoV-2 by targeting the main protease (Mpro), here we used a high-throughput interface-based protein design strategy to identify mutational hotspots and potential signatures of adaptation in these drug binding sites of Mpro. Several mutants exhibited reduced binding affinity to these drugs, out of which hotspot residues having a strong tendency to undergo positive selection were identified. The data further indicated that these anti-HCV drugs have larger footprints in the mutational landscape of Mpro and hence encompass the highest potential for positive selection and adaptation. These findings are crucial in understanding the potential structural modifications in the drug binding sites of Mpro and thus its signatures of adaptation. Furthermore, the data could provide systemic strategies for robust antiviral design and discovery against COVID-19 in the future.

Elicitation of Potent Neutralizing Antibody Responses by Designed Protein Nanoparticle Vaccines for SARS-CoV-2.

A safe, effective, and scalable vaccine is needed to halt the ongoing SARS-CoV-2 pandemic. We describe the structure-based design of self-assembling protein nanoparticle immunogens that elicit potent and protective antibody responses against SARS-CoV-2 in mice. The nanoparticle vaccines display 60 SARS-CoV-2 spike receptor-binding domains (RBDs) in a highly immunogenic array and induce neutralizing antibody titers 10-fold higher than the prefusion-stabilized spike despite a 5-fold lower dose. Antibodies elicited by the RBD nanoparticles target multiple distinct epitopes, suggesting they may not be easily susceptible to escape mutations, and exhibit a lower binding:neutralizing ratio than convalescent human sera, which may minimize the risk of vaccine-associated enhanced respiratory disease. The high yield and stability of the assembled nanoparticles suggest that manufacture of the nanoparticle vaccines will be highly scalable. These results highlight the utility of robust antigen display platforms and have launched cGMP manufacturing efforts to advance the SARS-CoV-2-RBD nanoparticle vaccine into the clinic.

QTY Code-designed Water-soluble Fc-fusion Cytokine Receptors Bind to their Respective Ligands.

Cytokine release syndrome (CRS), or 'cytokine storm', is the leading side effect during chimeric antigen receptor (CAR)-T therapy that is potentially life-threatening. It also plays a critical role in viral infections such as Coronavirus Disease 2019 (COVID-19). Therefore, efficient removal of excessive cytokines is essential for treatment. We previously reported a novel protein modification tool called the QTY code, through which hydrophobic amino acids Leu, Ile, Val and Phe are replaced by Gln (Q), Thr (T) and Tyr (Y). Thus, the functional detergent-free equivalents of membrane proteins can be designed. Here, we report the application of the QTY code on six variants of cytokine receptors, including interleukin receptors IL4Rα and IL10Rα, chemokine receptors CCR9 and CXCR2, as well as interferon receptors IFNγR1 and IFNλR1. QTY-variant cytokine receptors exhibit physiological properties similar to those of native receptors without the presence of hydrophobic segments. The receptors were fused to the Fc region of immunoglobulin G (IgG) protein to form an antibody-like structure. These QTY code-designed Fc-fusion receptors were expressed in Escherichia coli and purified. The resulting water-soluble fusion receptors bind to their respective ligands with K d values affinity similar to isolated native receptors. Our cytokine receptor-Fc-fusion proteins potentially serve as an antibody-like decoy to dampen the excessive cytokine levels associated with CRS and COVID-19 infection.