Structure and Computation in Immunoreagent Design: From Diagnostics to Vaccines

Chagasin from Trypanosoma cruzi as a molecular scaffold to express epitopes of TSA-1 as soluble recombinant chimeras

Trypanosoma cruzi is the causative agent of Chagas disease, a global public health problem. New therapeutic drugs and biologics are needed. The TSA-1 recombinant protein of T. cruzi is one such promising antigen for developing a therapeutic vaccine. However, it is overexpressed in E. coli as inclusion bodies, requiring an additional refolding step. As an alternative, in this study, we propose the endogenous cysteine protease inhibitor chagasin as a molecular scaffold to generate chimeric proteins. These proteins will contain combinations of two of the five conserved epitopes (E1 to E5) of TSA-1 in the L4 and L6 chagasin loops. Twenty chimeras (Q1-Q20) were designed, and their solubility was predicted using bioinformatics tools. Nine chimeras with different degrees of solubility were selected and expressed in E. coli BL21 (DE3). Western blot assays with anti-6x-His and anti-chagasin antibodies confirmed the expression of soluble recombinant chimeras. Both theoretically and experimentally, the Q12 (E5-E3) chimera was the most soluble, and the Q20 (E4-E5) the most insoluble protein. Q4 (E5-E1) and Q8 (E5-E2) chimeras were classified as proteins with medium solubility that exhibited the highest yield in the soluble fraction. Notably, Q4 has a yield of 239 mg/L, well above the yield of recombinant chagasin (16.5 mg/L) expressed in a soluble form. The expression of the Q4 chimera was scaled up to a 7 L fermenter obtaining a yield of 490 mg/L. These data show that chagasin can serve as a molecular scaffold for the expression of TSA-1 epitopes in the form of soluble chimeras.

Computational Epitope Prediction and Design for Antibody Development and Detection

The design of optimized protein antigens is a fundamental step in the development of new vaccine candidates and in the detection of therapeutic antibodies. A fundamental prerequisite is the identification of antigenic regions that are most prone to interact with antibodies, namely, B-cell epitopes. Here, we describe an efficient structure-based computational method for epitope prediction, called MLCE. In this approach, all that is required is the 3D structure of the antigen of interest. MLCE can be applied to glycosylated proteins, facilitating the identification of immunoreactive versus immune-shielding carbohydrates.

COVID-19 Vaccines: Computational tools and Development

The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Elucidating the 3D Structure of a Surface Membrane Antigen from Trypanosoma cruzi as a Serodiagnostic Biomarker of Chagas Disease

Chagas disease (CD) is a vector-borne parasitosis, caused by the protozoan parasite Trypanosoma cruzi, that affects millions of people worldwide. Although endemic in South America, CD is emerging throughout the world due to climate change and increased immigratory flux of infected people to non-endemic regions. Containing of the diffusion of CD is challenged by the asymptomatic nature of the disease in early infection stages and by the lack of a rapid and effective diagnostic test. With the aim of designing new serodiagnostic molecules to be implemented in a microarray-based diagnostic set-up for early screening of CD, herein, we report the recombinant production of the extracellular domain of a surface membrane antigen from T. cruzi (TcSMP) and confirm its ability to detect plasma antibodies from infected patients. Moreover, we describe its high-resolution (1.62 Å) crystal structure, to which in silico epitope predictions were applied in order to locate the most immunoreactive regions of TcSMP in order to guide the design of epitopes that may be used as an alternative to the full-length antigen for CD diagnosis. Two putative, linear epitopes, belonging to the same immunogenic region, were synthesized as free peptides, and their immunological properties were tested in vitro. Although both peptides were shown to adopt a structural conformation that allowed their recognition by polyclonal antibodies raised against the recombinant protein, they were not serodiagnostic for T. cruzi infections. Nevertheless, they represent good starting points for further iterative structure-based (re)design cycles.

SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: A Computational Model of Epitope Loss in Variants of Concern

The SARS-CoV-2 spike (S) protein is exposed on the viral surface and is the first point of contact between the virus and the host. For these reasons it represents the prime target for Covid-19 vaccines. In recent months, variants of this protein have started to emerge. Their ability to reduce or evade recognition by S-targeting antibodies poses a threat to immunological treatments and raises concerns for their consequences on vaccine efficacy. To develop a model able to predict the potential impact of S-protein mutations on antibody binding sites, we performed unbiased multi-microsecond molecular dynamics of several glycosylated S-protein variants and applied a straightforward structure-dynamics-energy based strategy to predict potential changes in immunogenic regions on each variant. We recover known epitopes on the reference D614G sequence. By comparing our results, obtained on isolated S-proteins in solution, to recently published data on antibody binding and reactivity in new S variants, we directly show that modifications in the S-protein consistently translate into the loss of potentially immunoreactive regions. Our findings can thus be qualitatively reconnected to the experimentally characterized decreased ability of some of the Abs elicited against the dominant S-sequence to recognize variants. While based on the study of SARS-CoV-2 spike variants, our computational epitope-prediction strategy is portable and could be applied to study immunoreactivity in mutants of proteins of interest whose structures have been characterized, helping the development/selection of vaccines and antibodies able to control emerging variants.

Identification of a specific surface epitope of OmpC for Escherichia coli O157:H7 with protein topology facilitated affinity mass spectrometry

Abstract

The goal of this work was to identify the target protein and epitope of a previously reported Escherichia coli O157:H7 (ECO157)–specific monoclonal antibody (mAb) 2G12. mAb 2G12 has shown high specificity for the recovery and detection of ECO157. To achieve this goal, the target protein was first separated by two-dimensional gel electrophoresis (2-DE) and located by Western blot (WB). The protein spots were identified to be the outer membrane protein (Omp) C by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF–MS). After that, the target protein was purified by immunoaffinity chromatography (IAC) and subjected to in situ enzymatic cleavage of the vulnerable peptides. Eight eluted peptides of OmpC identified by liquid chromatography–tandem mass spectrometry (LC–MS/MS) were further mapped onto the homologous protein structure of E. coli OmpC (2IXX). The topology of OmpC showed that three peptides had extracellular loops. Epitope mapping with overlapping peptide library and sequence homology analysis revealed that the epitope consisted of a specific peptide, “LGVING,” and an adjacent conservative peptide, “TQTYNATRVGSLG.” Both peptides loop around the overall structure of the epitope. To test the availability of the epitope when ECO157 was grown under different osmolarity, pH, and nutrition levels, the binding efficacy of mAb 2G12 with ECO157 grown in these conditions was evaluated. Results further demonstrated the good stability of this epitope under potential stressful environmental conditions. In summary, this study revealed that mAb 2G12 targeted one specific and one conservative extracellular loop (peptide) of the OmpC present on ECO157, and the epitope was stable and accessible on ECO157 cells grown in different environment.

Key points

• OmpC is the target of a recently identified ECO157-specific mAb 2G12.

• Eight peptides were identified from the OmpC by using LC–MS/MS.

• The specificity of mAb 2G12 is mainly determined by the “LGVING” peptide.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11511-8.

Structure, Immunoreactivity, and In Silico Epitope Determination of SmSPI S. mansoni Serpin for Immunodiagnostic Application

The human parasitic disease Schistosomiasis is caused by the Schistosoma trematode flatworm that infects freshwaters in tropical regions of the world, particularly in Sub-Saharan Africa, South America, and the Far-East. It has also been observed as an emerging disease in Europe, due to increased immigration. In addition to improved therapeutic strategies, it is imperative to develop novel, rapid, and sensitive diagnostic tests that can detect the Schistosoma parasite, allowing timely treatment. Present diagnosis is difficult and involves microscopy-based detection of Schistosoma eggs in the feces. In this context, we present the 3.22 Å resolution crystal structure of the circulating antigen Serine protease inhibitor from S. mansoni (SmSPI), and we describe it as a potential serodiagnostic marker. Moreover, we identify three potential immunoreactive epitopes using in silico-based epitope mapping methods. Here, we confirm effective immune sera reactivity of the recombinant antigen, suggesting the further investigation of the protein and/or its predicted epitopes as serodiagnostic Schistosomiasis biomarkers.

Advanced strategies for development of vaccines against human bacterial pathogens

Infectious diseases are one of the main grounds of death and disabilities in human beings globally. Lack of effective treatment and immunization for many deadly infectious diseases and emerging drug resistance in pathogens underlines the need to either develop new vaccines or sufficiently improve the effectiveness of currently available drugs and vaccines. In this review, we discuss the application of advanced tools like bioinformatics, genomics, proteomics and associated techniques for a rational vaccine design.

Construction and Immunological Evaluation of an Adenoviral Vector-Based Vaccine Candidate for Lassa Fever

Lassa virus (LASV) is a rodent-borne arenavirus circulating in West African regions that causes Lassa fever (LF). LF is normally asymptomatic at the initial infection stage, but can progress to severe disease with multiorgan collapse and hemorrhagic fever. To date, the therapeutic choices are limited, and there is no approved vaccine for avoiding LASV infection. Adenoviral vector-based vaccines represent an effective countermeasure against LASV because of their safety and adequate immunogenicity, as demonstrated in use against other emerging viral infections. Here, we constructed and characterized a novel Ad5 (E1-, E3-) vectored vaccine containing the glycoprotein precursor (GPC) of LASV. Ad5-GPC_LASV elicited both humoral and cellular immune responses in BALB/c mice. Moreover, a bioluminescent imaging-based BALB/c mouse model infected with GPC-bearing and luciferase-expressing replication-incompetent LASV pseudovirus was utilized to evaluate the vaccine efficacy. The bioluminescence intensity of immunized mice was significantly lower than that of control mice after being inoculated with LASV pseudovirus. This study suggests that Ad5-GPC_LASV represents a potential vaccine candidate against LF.

The Answer Lies in the Energy: How Simple Atomistic Molecular Dynamics Simulations May Hold the Key to Epitope Prediction on the Fully Glycosylated SARS-CoV-2 Spike Protein

SARS-CoV-2 is a health threat with dire socioeconomical consequences. As the crucial mediator of infection, the viral glycosylated spike protein (S) has attracted the most attention and is at the center of efforts to develop therapeutics and diagnostics. Herein, we use an original decomposition approach to identify energetically uncoupled substructures as antibody binding sites on the fully glycosylated S. Crucially, all that is required are unbiased MD simulations; no prior knowledge of binding properties or ad hoc parameter combinations is needed. Our results are validated by experimentally confirmed structures of S in complex with anti- or nanobodies. We identify poorly coupled subdomains that are poised to host (several) epitopes and potentially involved in large functional conformational transitions. Moreover, we detect two distinct behaviors for glycans: those with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes, and those with weaker coupling could themselves be prone to antibody recognition.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.

Advances in the Development of Anti-Toxoplasma gondii Vaccines: Challenges, Opportunities, and Perspectives

Important progress has been made in understanding how immunity is elicited against Toxoplasma gondii - a complex pathogen with multiple mechanisms of immune evasion. Many vaccine candidates have been tested using various strategies in animal models. However, none of these strategies has delivered as yet, and important challenges remain in the development of vaccines that can eliminate the tissue cysts and/or fully block vertical transmission. In this review, we provide an overview of the current understanding of the host immune response to T. gondii infection and summarize the key limitations for the development of an effective, safe, and durable toxoplasmosis vaccine. We also discuss how the successes and failures in developing and testing vaccine candidates have provided a roadmap for future vaccine development.

Vaccine Target Discovery

With the rise in novel infectious agents and disease pandemics, a new era of vaccine discovery is necessary. To address this, the new field of immunomics is described, which is synergistically powered by integrating bioinformatics methodologies with technological advances in biology and high-throughput instrumentation. By incorporating biological data from immunology and molecular biology with current genomics and proteomics, immunomics is geared to deliver an insight into immune function, optimal stimulation of immune responses and precise mapping and rational selection of immune targets that cover antigenic diversity. These efforts are expected to contribute towards the development of new generation of vaccines, tailored to both the genetic make-up of the human population and of the pathogen. Vaccine technologies are also being explored for prevention or control of non-communicable diseases.

Enhancing Antibody Serodiagnosis Using a Controlled Peptide Coimmobilization Strategy

Antigen immunoreactivity is often determined by surface regions defined by the 3D juxtapositions of amino acids stretches that are not continuous in the linear sequence. As such, mimicking an antigen immunoreactivity by means of putative linear peptide epitopes for diagnostic purposes is not trivial. Here we present a straightforward and robust method to extend the reach of immune-diagnostic probes design by copresenting peptides belonging to the same antigenic surface. In this case study focused on a computationally predicted Zika virus NS1 protein putative antigenic region, we reached a diagnostic confidence by the oriented and spatially controlled coimmobilization of peptide sequences found adjacent within the protein fold, that cooperatively interacted to provide enhanced immunoreactivity with respect to single linear epitopes. Through our method, we were able to differentiate Zika infected individuals from healthy controls. Remarkably, our strategy fits well with the requirements to build high-throughput screening platforms of linear and mixed peptide libraries, and it could possibly facilitate the rapid identification of conformational immunoreactive regions.

Rational Drug Design Using Integrative Structural Biology

Modern drug discovery and design approaches rely heavily on high-throughput methods and state-of-the-art infrastructures with robotic facilities and sophisticated platforms. However, the anticipated research output that would eventually lead to new drugs with minimal or no side effects to the market has not been achieved. Despite the vast amount of information generated, very little is converted to knowledge and even less is capitalized for cross-discipline research actions. Therefore, the need for re-launching rational approaches has become apparent. Here we present an overview of the new trends in rational drug design using integrative structural biology with emphasis on X-ray protein crystallography and small molecules as ligands. With the aim to increase researchers' awareness on the available possibilities to perform front line research, we also underline the benefits and enhanced prospects offered to the scientific community, through access to research infrastructures.

Comparative Pan-Genome Analysis of Piscirickettsia salmonis Reveals Genomic Divergences within Genogroups

Piscirickettsia salmonis is the etiological agent of salmonid rickettsial septicemia, a disease that seriously affects the salmonid industry. Despite efforts to genomically characterize P. salmonis, functional information on the life cycle, pathogenesis mechanisms, diagnosis, treatment, and control of this fish pathogen remain lacking. To address this knowledge gap, the present study conducted an in silico pan-genome analysis of 19 P. salmonis strains from distinct geographic locations and genogroups. Results revealed an expected open pan-genome of 3,463 genes and a core-genome of 1,732 genes. Two marked genogroups were identified, as confirmed by phylogenetic and phylogenomic relationships to the LF-89 and EM-90 reference strains, as well as by assessments of genomic structures. Different structural configurations were found for the six identified copies of the ribosomal operon in the P. salmonis genome, indicating translocation throughout the genetic material. Chromosomal divergences in genomic localization and quantity of genetic cassettes were also found for the Dot/Icm type IVB secretion system. To determine divergences between core-genomes, additional pan-genome descriptions were compiled for the so-termed LF and EM genogroups. Open pan-genomes composed of 2,924 and 2,778 genes and core-genomes composed of 2,170 and 2,228 genes were respectively found for the LF and EM genogroups. The core-genomes were functionally annotated using the Gene Ontology, KEGG, and Virulence Factor databases, revealing the presence of several shared groups of genes related to basic function of intracellular survival and bacterial pathogenesis. Additionally, the specific pan-genomes for the LF and EM genogroups were defined, resulting in the identification of 148 and 273 exclusive proteins, respectively. Notably, specific virulence factors linked to adherence, colonization, invasion factors, and endotoxins were established. The obtained data suggest that these genes could be directly associated with inter-genogroup differences in pathogenesis and host-pathogen interactions, information that could be useful in designing novel strategies for diagnosing and controlling P. salmonis infection.