Isabelle Freire Tabosa Viana

With this status report, we aim to provide a timely snapshot of the protein engineering field as a broad and rapidly advancing discipline that integrates computational, molecular biology, structure-guided, evolutionary, and synthetic approaches to create new and improved proteins with tailored structures and useful functions. The report is organized into eight thematic areas spanning core methodologies and major application domains, including enzymes, therapeutics, detection, synthetic biology, and materials. Contributions from experts across these areas highlight both the historical foundations and recent advances in their respective fields, with particular emphasis on the growing influence of machine learning and artificial intelligence-based methods. Emerging from this broad overview is a central message: protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain. Looking ahead, the continued integration of computational and experimental strategies is poised to further accelerate the impact of protein engineering across an expanding range of economically and societally important sectors, from therapeutics and molecular imaging to diagnostics, plastic recycling, and industrial chemistry.

Targeting viral proteases is a well-established antiviral strategy and a promising approach that has been actively explored against SARS-CoV-2. The SARS-CoV-2 main protease (M^pro) is essential for viral replication and functions as a homodimer, making its dimerization interface an attractive therapeutic target. In this study, we report the rational design of HB3-Core25, a miniprotein computationally engineered to disrupt M^pro dimerization and inhibit its catalytic activity. In vitro production followed by biophysical characterization showed that HB3-Core25 folds into a compact trimeric helical bundle, exhibiting high solubility and thermal stability. Biophysical assays confirmed binding to M^pro with a dissociation constant (K_D) of 0.567 μM and the lowest IC50 reported to date for the dimer interface. Functional assays further demonstrated inhibition of M^pro catalytic activity, with 51.1%. These findings highlight HB3-Core25 as a stable inhibitor of M^pro activity by interfering with its dimerization, offering a complementary strategy to classical active-site inhibition in antiviral drug development.

The design of proteins capable effectively binding to specific protein targets is crucial for developing therapies, diagnostics, and vaccine candidates for viral infections. Here, we introduce a complementarity-determining region (CDR) grafting approach for designing nanobodies (Nbs) that target specific epitopes, with the aid of computer simulation and machine learning. As a proof-of-concept, we designed, evaluated, and characterized a high-affinity Nb against the spike protein of SARS-CoV-2, the causative agent of the COVID-19 pandemic. The designed Nb, referred to as Nb Ab.2, was synthesized and displayed high-affinity for both the purified receptor-binding domain protein and to the virus-like particle, demonstrating affinities of 9 nM and 60 nM, respectively, as measured with microscale thermophoresis. Circular dichroism showed the designed protein’s structural integrity and its proper folding, whereas molecular dynamics simulations provided insights into the internal dynamics of Nb Ab.2. This study shows that our computational pipeline can be used to efficiently design high-affinity Nbs with diagnostic and prophylactic potential, which can be tailored to tackle different viral targets.

Arthrospira platensis, an ancient cyanobacterium, is rich in bioactive compounds with therapeutic potential, supporting its use in studies for various health conditions, including infectious and chronic diseases. This study aimed to evaluate the antiparasitic, cytotoxic, and immunomodulatory activities of A. platensis compounds against Trypanosoma cruzi. Peripheral Blood Mononuclear Cells (PBMC) and T. cruzi trypomastigotes were cultured for cytotoxic and antiparasitic analyses. Cytotoxicity was assessed in PBMC treated with different concentrations of crude extract, obtained by mechanical agitation in 0.1 M TRIS-HCl buffer (pH 7.2), and purified protein by DEAE-Sephadex A-50 chromatography and FPLC. Immune response was analyzed in infected and uninfected PBMC by measuring cytokines (IFN-γ, TNF, IL-2, IL-6, and IL-10) after treatment with purified protein and benznidazole. In vitro experiments showed that both crude extract and a 15 kDa purified protein were toxic to trypomastigotes in a dose-dependent manner, eliminating over 80% of the parasite at 1000 and 200 μg/mL, respectively. Both the extract and protein were nontoxic to PBMC, with the protein (SI: 20.7) being more selective than benznidazole (SI: 11.5). Results indicated that the purified protein modulated the immune response in T. cruzi-infected individuals, inducing a protective Th1 response while controlling an excessive inflammatory response with appropriate IL-10 levels. The anti-T. cruzi activity of this protein, alone or in combination with the commercial drug, suggests it could be a low-cost, safer, and more tolerable therapy for Chagas disease treatment.

The design of proteins capable to effectively bind to specific protein targets is crucial for developing therapies, diagnostics, and vaccine candidates for viral infections. Here, we introduce a complementarity-determining regions (CDRs)-grafting approach for designing nanobodies (Nbs) that target specific epitopes, with the aid of computer simulation and machine learning. As a proof-of-concept, we designed, evaluated, and characterized a high-affinity Nb against the spike protein of SARS-CoV-2, the causative agent of the COVID-19 pandemic. The designed Nb, referred to as Nb Ab.2, was synthesized and displayed high-affinity for both the purified receptor-binding domain protein and to the virus-like particle, demonstrating affinities of 9 nM and 60 nM, respectively, as measured with microscale thermophoresis. Circular dichroism showed the designed protein’s structural integrity and its proper folding, whereas molecular dynamics simulations provided insights into the internal dynamics of Nb Ab.2. This study shows that our computational pipeline can be used to efficiently design high affinity Nbs with diagnostic and prophylactic potential, which can be tailored to tackle different viral targets. Author summary: In this study, we present a pipeline for designing a high-affinity nanobody (Nb) targeting the SARS-CoV-2 spike protein using enhanced sampling molecular dynamics simulations and CDR-grafting. To address the challenges of CDR grafting in Nbs, including the need for structural similarity between the CDR motif of interest and the scaffold region, we utilized the Nb scaffold cAbBCII10, known for its versatility in accommodating various CDRs. We generated a library based on the cAbBCII10 framework with diverse, unrelated CDRs and applied machine learning to identify the most promising candidates. Our approach enabled successful engineering of a Nb that binds to the SARS-CoV-2 spike protein with high affinity, demonstrating the effectiveness of our design pipeline for potential therapeutic applications.

[This corrects the article DOI: 10.3389/fimmu.2024.1307546.].

Zika virus (ZIKV) is a re-emerging pathogen with high morbidity associated to congenital infection. Despite the scientific advances since the last outbreak in the Americas, there are no approved specific treatment or vaccines. As the development of an effective prophylactic approach remains unaddressed, DNA vaccines surge as a powerful and attractive candidate due to the efficacy of sequence optimization in achieving strong immune response. In this study, we developed four DNA vaccine constructs encoding the ZIKV prM/M (pre-membrane/membrane) and E (envelope) proteins in conjunction with molecular adjuvants. The DNA vaccine candidate (called ZK_ΔSTP), where the entire membrane-anchoring regions were completely removed, was far more immunogenic compared to their counterparts. Furthermore, inclusion of the tPA-SP leader sequence led to high expression and secretion of the target vaccine antigens, therefore contributing to adequate B cell stimulation. The ZK_ΔSTP vaccine induced high cellular and humoral response in C57BL/6 adult mice, which included high neutralizing antibody titers and the generation of germinal center B cells. Administration of ZK-ΔSTP incorporating aluminum hydroxide (Alum) adjuvant led to sustained neutralizing response. In consistency with the high and long-term protective response, ZK_ΔSTP+Alum protected adult mice upon viral challenge. Collectively, the ZK_ΔSTP+Alum vaccine formulation advances the understanding of the requirements for a successful and protective vaccine against flaviviruses and is worthy of further translational studies.

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients’ care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ​±​ 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.

The development of therapeutics against Zika virus (ZIKV) requires the design of molecules capable of neutralising the virus and preventing cell infection.