Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design.

The COVID-19 pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates a profound understanding of the virus and its lifecycle. As an RNA virus with high mutation rates, SARS-CoV-2 exhibits genetic variability leading to the emergence of variants with potential implications. Among its key proteins, the RNA-dependent RNA polymerase (RdRp) is pivotal for viral replication. Notably, RdRp forms dimers via non-structural protein (nsp) subunits, particularly nsp7, crucial for efficient viral RNA copying. Similar to the main protease (Mpro) of SARS-CoV-2, there is a possibility that the nsp7 might also undergo mutational selection events to generate more stable and adaptable versions of nsp7 dimer during virus evolution. However, efforts to obtain such cohesive and comprehensive information are lacking. To address this, we performed this study focused on deciphering the molecular intricacies of nsp7 dimerization using a multifaceted approach. Leveraging computational protein design (CPD), machine learning (ML), AlphaFold v2.0-based structural analysis, and several related computational approaches, we aimed to identify critical residues and mutations influencing nsp7 dimer stability and adaptation. Our methodology involved identifying potential hotspot residues within the dimeric nsp7 interface using an interface-based CPD approach. Through Rosetta-based symmetrical protein design, we designed and modulated nsp7 dimerization, considering selected interface residues. Analysis of physicochemical features revealed acceptable structural changes and several structural and residue-specific insights emphasizing the intricate nature of such protein-protein complexes. Our ML models, particularly the random forest regressor (RFR), accurately predicted binding affinities and ML-guided sequence predictions corroborated CPD findings, elucidating potential nsp7 mutations and their impact on binding affinity. Validation against clinical sequencing data demonstrated the predictive accuracy of our approach. Moreover, AlphaFold v2.0 structural analyses validated optimal dimeric configurations of affinity-enhancing designs, affirming methodological precision. Affinity-enhancing designs exhibited favourable energetics and higher binding affinity as compared to their counterparts. The obtained physicochemical properties, molecular interactions, and sequence predictions advance our understanding of SARS-CoV-2 evolution and inform potential avenues for therapeutic intervention against COVID-19.

Long-timescale atomistic simulations uncover loss-of-function mechanisms of uncharacterized Angiogenin mutants associated with ALS.

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease characterized by progressive degeneration of motor neurons, resulting in respiratory failure and mortality within 3-5 years. Mutations in the Angiogenin (ANG) cause loss of ribonucleolytic and nuclear translocation activities, contributing to ALS pathogenesis. This study focused on investigating two uncharacterized ANG mutations, T11S and R122H, newly identified in the Project Mine consortium. Using extensive computational analysis, including structural modeling and microsecond-timescale molecular dynamics (MD) simulations, we observed conformational changes in the catalytic residue His114 of ANG induced by T11S and R122H mutations. These alterations impaired ribonucleolytic activity, as inferred through molecular docking and binding free energy calculations. Gibbs free energy landscape and residue-residue interaction network analysis further supported our findings, revealing the energetic states and allosteric pathway from the mutated site to His114. Additionally, we assessed the binding of NCI-65828, an inhibitor of ribonucleolytic activity of ANG, and found reduced effectiveness in binding to T11S and R122H mutants when His114 assumed a non-native conformation. This highlights the crucial role of His114 and its association with ALS. Elucidating the relationship between physical structure and functional dynamics of frequently mutated ANG mutants is essential for understanding ALS pathogenesis and developing more effective therapeutic interventions.

Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research.

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics.

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Delineating the Structure-Dynamics-Binding Differences among BA.1, BA.4/5, and BF.7 SARS-CoV-2 Variants through Atomistic Simulations: Correlation with Structural and Epidemiological Features.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus possessing a spike (S) protein that facilitates the entry of the virus into human cells. The emergence of highly transmissible and fit SARS-CoV-2 variants has been driven by the positive selection of mutations within the S-protein. Notable among these variants are alpha, beta, gamma, delta, and omicron (BA.1), with the latter contributing to significant global health challenges and impacting populations worldwide. Recently, a novel subvariant of BA.1, named BF.7, has surfaced, purportedly exhibiting elevated transmissibility and infectivity rates. In order to comprehend and compare the transmissibility and disease progression characteristics of distinct SARS-CoV-2 variants, we performed an extensive comparative analysis utilizing all-atom molecular dynamics (MD) simulations (in triplicate) to investigate the structural, dynamic, and binding features of BA.1, BA.4/5, and BF.7. Our simulation findings, energetic analysis, and assessment of physicochemical properties collectively illuminate the dominance of the BA.1 variant over the others, a trend that is further substantiated by the sustained global prevalence of BA.1 relative to BA.4/5 and BF.7. Additionally, our simulation results align well with the reported cryoelectron microscopy (cryo-EM) structural data and epidemiological characteristics obtained from the Global Initiative on Sharing All Influenza Data (GISAID). This study presents a comprehensive comparative elucidation of the critical structural, dynamic, and binding attributes of these variants, providing insights into the predominance of BA.1 and its propensity to continuously generate numerous novel subvariants.

Comprehensive analysis of liquid-liquid phase separation propensities of HSV-1 proteins and their interaction with host factors.

In recent years, it has been shown that the liquid-liquid phase separation (LLPS) of virus proteins plays a crucial role in their life cycle. It promotes the formation of viral replication organelles, concentrating viral components for efficient replication and facilitates the assembly of viral particles. LLPS has emerged as a crucial process in the replication and assembly of herpes simplex virus-1 (HSV-1). Recent studies have identified several HSV-1 proteins involved in LLPS, including the myristylated tegument protein UL11 and infected cell protein 4; however, a complete proteome-level understanding of the LLPS-prone HSV-1 proteins is not available. We provide a comprehensive analysis of the HSV-1 proteome and explore the potential of its proteins to undergo LLPS. By integrating sequence analysis, prediction algorithms and an array of tools and servers, we identified 10 HSV-1 proteins that exhibit high LLPS potential. By analysing the amino acid sequences of the LLPS-prone proteins, we identified specific sequence motifs and enriched amino acid residues commonly found in LLPS-prone regions. Our findings reveal a diverse range of LLPS-prone proteins within the HSV-1, which are involved in critical viral processes such as replication, transcriptional regulation and assembly of viral particles. This suggests that LLPS might play a crucial role in facilitating the formation of specialized viral replication compartments and the assembly of HSV-1 virion. The identification of LLPS-prone proteins in HSV-1 opens up new avenues for understanding the molecular mechanisms underlying viral pathogenesis. Our work provides valuable insights into the LLPS landscape of HSV-1, highlighting potential targets for further experimental validation and enhancing our understanding of viral replication and pathogenesis.

Impaired tissue homing by the Ikzf3N159S variant is mediated by interfering with Ikaros function.

AIOLOS, encoded by IKZF3, is a member of the IKZF family of proteins that plays an important role in regulating late B-cell differentiation. Human individuals heterozygous for the AIOLOS p.N160S variant displayed impaired humoral immune responses as well as impaired B and T cell development. We have previously reported that a mouse strain harboring an Ikzf3N159S allele that corresponds to human IKZF3N160S recapitulated immune-deficient phenotypes, such as impaired B cell development and loss of CD23 expression. In this study, we investigated the effect of the Ikzf3N159S variant and found that B1a cell development was impaired in Ikzf3N159S/N159S mice. In addition, CD62L expression was severely decreased in both B and T lymphocytes by the Ikzf3N159S mutation, in a dose-dependent manner. Mixed bone marrow chimera experiments have revealed that most immunodeficient phenotypes, including low CD62L expression, occur in intrinsic cells. Interestingly, while Ikzf3N159S/N159S lymphocytes were still present in the spleen, they were completely outcompeted by control cells in the lymph nodes, suggesting that the capacity for homing or retention in the lymph nodes was lost due to the Ikzf3N159S mutation. The homing assay confirmed severely decreased homing abilities to lymph nodes of Ikzf3N159S/N159S B and T lymphocytes but selective enrichment of CD62L expressing Ikzf3N159S/N159S lymphocytes in lymph nodes. This finding suggests that impaired CD62L expression is the major reason for the impaired homing capacity caused by the Ikzf3N159S mutation. Interestingly, an excess amount of Ikaros, but not Aiolos, restored CD62L expression in Ikzf3N159S/N159S B cells. Together with the loss of CD62L expression due to Ikaros deficiency, the AiolosN159S mutant protein likely interferes with Ikaros function through heterodimerization, at least in activating the Sell gene encoding CD62L expression. Thus, our results revealed that AiolosN159S causes some immunodeficient phenotypes via the pathogenesis referred to as the heterodimeric interference as observed for AiolosG158R variant.

Multi-pathogen based chimeric vaccine to fight against COVID-19 and concomitant coinfections.

BACKGROUND: COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity. METHODS AND RESULTS: In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor. CONCLUSION: The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.

Immunoinformatics Protocol to Design Multi-Epitope Subunit Vaccines.

With the development of scientific technologies, the accessibility of genomic data, computational tools, software, databases, and machine learning, the field of immunoinformatics has emerged as an effective technique for immunologists to design potential vaccines in a short time. A large number of tools and databases are available to screen the genome sequences of parasites/pathogens and identify the highly immunogenic peptides or epitopes that can be used to design effective vaccines. In this chapter, we provide an easy-to-use protocol for the design of multi-epitope-based subunit vaccines. Though the computational immunoinformatics-based approaches have demonstrated their competency in designing potentially effective vaccine candidates quickly, their immunogenicity and safety must be evaluated in laboratory settings before they are tested in clinical trials.

Computational Protein Design for COVID-19 Research and Emerging Therapeutics.

As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies.

Functional expression, localization, and biochemical characterization of thioredoxin glutathione reductase from air-breathing magur catfish, Clarias magur.

The glutathione (GSH) and thioredoxin (Trx) systems regulate cellular redox homeostasis and maintain antioxidant defense in most eukaryotes. We earlier reported the absence of gene coding for the glutathione reductase (GR) enzyme of the GSH system in the facultative air-breathing catfish, Clarias magur. Here, we identified three thioredoxin reductase (TrxR) genes, one of which was later confirmed as a thioredoxin glutathione reductase (TGR). We then characterized the novel recombinant TGR enzyme of C. magur (CmTGR). The tissue-specific expression of the txnrd genes and the tissue-specific activity of the TrxR enzyme were analyzed. The recombinant CmTGR is a dimer of ~133 kDa. The protein showed TrxR activity with 5,5'-diothiobis (2-nitrobenzoic acid) reduction assay with a Km of 304.40 µM and GR activity with a Km of 58.91 µM. Phylogenetic analysis showed that the CmTGR was related to the TrxRs of fishes and distantly related to the TGRs of platyhelminth parasites. The structural analysis revealed the conserved glutaredoxin active site and FAD- and NADPH-binding sites. To our knowledge, this is the first report of the presence of a TGR in any fish. This unusual presence of TGR in C. magur is crucial as it helps maintain redox homeostasis under environmental stressors-induced oxidative stress.

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.

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.

Molecular insights into the differential dynamics of SARS-CoV-2 variants of concern.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has affected the lives and livelihood of millions of individuals around the world. It has mutated several times after its first inception, with an estimated two mutations occurring every month. Although we have been successful in developing vaccines against the virus, the emergence of variants has enabled it to escape therapy. Few of the generated variants are also reported to be more infectious than the wild-type (WT). In this study, we analyze the attributes of all RBD/ACE2 complexes for the reported VOCs, namely, Alpha, Beta, Gamma, and Delta through computer simulations. Results indicate differences in orientation and binding energies of the VOCs from the WT. Overall, it was observed that electrostatic interactions play a major role in the binding of the complexes. Detailed residue level energetics revealed that the most prominent changes in interaction energies were seen particularly at the mutated residues which were present at RBD/ACE2 interface. We found that the Delta variant is one of the most tightly bound variants of SARS-CoV-2 with dynamics similar to WT. The high binding affinity of RBD towards ACE2 is indicative of an increase in viral transmission and infectivity. The details presented in our study provide additional information for the design and development of effective therapeutic strategies for the emerging variants of the virus in the future.

High-throughput design of symmetrical dimeric SARS-CoV-2 main protease: structural and physical insights into hotspots for adaptation and therapeutics.

Dimerization of SARS-CoV-2 main protease (Mpro) is a prerequisite for its processing activity. With >2000 mutations already reported in Mpro, SARS-CoV-2 may accumulate mutations in the Mpro dimeric interface to stabilize it further. We employed high-throughput protein design strategies to design the symmetrical dimeric interface of Mpro (300 000 designs) to identify mutational hotspots that render the Mpro more stable. We found that ∼22% of designed mutations that yield stable Mpro dimers already exist in SARS-CoV-2 genomes and are currently circulating. Our multi-parametric analyses highlight potential Mpro mutations that SARS-CoV-2 may develop, providing a foundation for assessing viral adaptation and mutational surveillance.

Scanning the RBD-ACE2 molecular interactions in Omicron variant.

The emergence of new SARS-CoV-2 variants poses a threat to the human population where it is difficult to assess the severity of a particular variant of the virus. Spike protein and specifically its receptor binding domain (RBD) which makes direct interaction with the ACE2 receptor of the human has shown prominent amino acid substitutions in most of the Variants of Concern. Here, by using all-atom molecular dynamics simulations we compare the interaction of Wild-type RBD/ACE2 receptor complex with that of the latest Omicron variant of the virus. We observed a very interesting diversification of the charge, dynamics and energetics of the protein complex formed upon mutations. These results would help us in understanding the molecular basis of binding of the Omicron variant with that of SARS-CoV-2 Wild-type.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase.

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ ß-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes.

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson's r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody's structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.