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 (Mpro) 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 Mpro 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 Mpro with a dissociation constant (KD) of 0.567 μM and the lowest IC50 reported to date for the dimer interface. Functional assays further demonstrated inhibition of Mpro catalytic activity, with 51.1%. These findings highlight HB3-Core25 as a stable inhibitor of Mpro 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.

Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to ex vivo biotechnology, such as the development of therapeutic monoclonal antibodies, the engineering of enzymes for industrial biocatalysis, the development of biosensors for disease detection, and the assembly of artificial protein complexes for drug screening. Therefore, predicting the strength of their association allows for understanding the molecular mechanisms and ultimately controlling them. We devised a machine learning ensemble model that uses Rosetta-based quantities to predict binding free energies of protein-protein complexes with accuracy rivaling both computationally demanding methods and currently available ML/DL tools. The method was encoded into an application Python pipeline named PBEE, which stands for Protein Binding Energy Estimator, allowing a rapid calculation of the absolute binding free energies of protein complexes from their PDB coordinates.

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the “Zika-only” beads identified 40 candidate molecular structures. A lead candidate small molecule “CZV1-1” was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.

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.

Coronaviruses are large RNA viruses that can infect and spread among humans and animals. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 2019, has evolved since its first detection in December 2019. Deletions are a common occurrence in SARS-CoV-2 evolution, particularly in specific genomic sites, and may be associated with the emergence of highly competent lineages. While deletions typically have a negative impact on viral fitness, some persist and become fixed in viral populations, indicating that they may confer advantageous benefits for the virus's adaptive evolution. This work presents a literature review and data analysis on structural losses in the SARS-CoV-2 genome and the potential relevance of specific signatures for enhanced viral fitness and spread.

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol-1 to 2.45 kcal mol-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Predicting the strength of protein-protein association allows for understanding the molecular mechanisms, and ultimately controlling it. We devised a novel model to predict protein-protein binding free energies. A machine learning ensemble algorithm uses Rosetta-based quantities to predict binding free energies with unprecedented accuracy, rivaling the most computationally demanding methods available.

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

Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.

Arboviral infections such as Zika, chikungunya, dengue, and yellow fever pose significant health problems globally. The population at risk is expanding with the geographical distribution of the main transmission vector of these viruses, the Aedes aegypti mosquito. The global spreading of this mosquito is driven by human migration, urbanization, climate change, and the ecological plasticity of the species. Currently, there are no specific treatments for Aedes-borne infections. One strategy to combat different mosquito-borne arboviruses is to design molecules that can specifically inhibit a critical host protein. We obtained the crystal structure of 3-hydroxykynurenine transaminase (AeHKT) from A. aegypti, an essential detoxification enzyme of the tryptophan metabolism pathway. Since AeHKT is found exclusively in mosquitoes, it provides the ideal molecular target for the development of inhibitors. Therefore, we determined and compared the free binding energy of the inhibitors 4-(2-aminophenyl)-4-oxobutyric acid (4OB) and sodium 4-(3-phenyl-1,2,4-oxadiazol-5-yl)butanoate (OXA) to AeHKT and AgHKT from Anopheles gambiae, the only crystal structure of this enzyme previously known. The cocrystallized inhibitor 4OB binds to AgHKT with K i of 300 μM. We showed that OXA binds to both AeHKT and AgHKT enzymes with binding energies 2-fold more favorable than the crystallographic inhibitor 4OB and displayed a 2-fold greater residence time τ upon binding to AeHKT than 4OB. These findings indicate that the 1,2,4-oxadiazole derivatives are inhibitors of the HKT enzyme not only from A. aegypti but also from A. gambiae.

BACKGROUND:The four Dengue viruses (DENV) serotypes were re-introduced in Brazil's Northeast region in a couple of decades, between 1980's and 2010's, where the DENV1 was the first detected serotype and DENV4 the latest. Zika (ZIKV) and Chikungunya (CHIKV) viruses were introduced in Recife around 2014 and led to large outbreaks in 2015 and 2016, respectively. However, the true extent of the ZIKV and CHIKV outbreaks, as well as the risk factors associated with exposure to these viruses remain vague. METHODS:We conducted a stratified multistage household serosurvey among residents aged between 5 and 65 years in the city of Recife, Northeast Brazil, from August 2018 to February 2019. The city neighborhoods were stratified and divided into high, intermediate, and low socioeconomic strata (SES). Previous ZIKV, DENV and CHIKV infections were detected by IgG-based enzyme linked immunosorbent assays (ELISA). Recent ZIKV and CHIKV infections were assessed through IgG3 and IgM ELISA, respectively. Design-adjusted seroprevalence were estimated by age group, sex, and SES. The ZIKV seroprevalence was adjusted to account for the cross-reactivity with dengue. Individual and household-related risk factors were analyzed through regression models to calculate the force of infection. Odds Ratio (OR) were estimated as measure of effect. PRINCIPAL FINDINGS:A total of 2,070 residents' samples were collected and analyzed. The force of viral infection for high SES were lower as compared to low and intermediate SES. DENV seroprevalence was 88.7% (CI95%:87.0-90.4), and ranged from 81.2% (CI95%:76.9-85.6) in the high SES to 90.7% (CI95%:88.3-93.2) in the low SES. The overall adjusted ZIKV seroprevalence was 34.6% (CI95%:20.0-50.9), and ranged from 47.4% (CI95%:31.8-61.5) in the low SES to 23.4% (CI95%:12.2-33.8) in the high SES. The overall CHIKV seroprevalence was 35.7% (CI95%:32.6-38.9), and ranged from 38.6% (CI95%:33.6-43.6) in the low SES to 22.3% (CI95%:15.8-28.8) in the high SES. Surprisingly, ZIKV seroprevalence rapidly increased with age in the low and intermediate SES, while exhibited only a small increase with age in high SES. CHIKV seroprevalence according to age was stable in all SES. The prevalence of serological markers of ZIKV and CHIKV recent infections were 1.5% (CI95%:0.1-3.7) and 3.5% (CI95%:2.7-4.2), respectively. CONCLUSIONS:Our results confirmed continued DENV transmission and intense ZIKV and CHIKV transmission during the 2015/2016 epidemics followed by ongoing low-level transmission. The study also highlights that a significant proportion of the population is still susceptible to be infected by ZIKV and CHIKV. The reasons underlying a ceasing of the ZIKV epidemic in 2017/18 and the impact of antibody decay in susceptibility to future DENV and ZIKV infections may be related to the interplay between disease transmission mechanism and actual exposure in the different SES.

Neonates have a limited adaptive response of plasma cells, germinal center (GC) B cells, and T follicular helper cells (TFH). As neonatal vaccination can be an important tool for AIDS prevention, these limitations need to be overcome. Chimeric DNA vaccine encoding p55Gag HIV-1 protein conjugated with lysosomal-associated membrane protein 1 (LAMP-1) has been described as immunogenic in the neonate period. Herein, we investigated the immunologic mechanisms involved in neonatal immunization with a LAMP-1/p55Gag (LAMP/Gag) DNA vaccine in a C57BL/6 mouse background. Neonatal LAMP/Gag vaccination induced strong Gag-specific T-cell response until adulthood and elevated levels of anti-Gag IgG antibodies. We also demonstrated for the first time that the immunogenicity of the neonatal period with LAMP/Gag is due to the induction of high-affinity anti-p24 IgG antibodies and long-term plasma cells. Together with that, there is the generation of early TFH cells and the formation of GC sites with the upregulation of activation-induced cytidine deaminase (AID) enzyme mRNA and protein expression in draining lymph nodes after neonatal LAMP/Gag vaccination. These findings underscore that the LAMP-1 strategy in the chimeric vaccine could be useful to enhance antibody production even in the face of neonatal immaturity, and they contribute to the development of new vaccine approaches for other emerging pathogens at an early stage of life.

At cell surface gangliosides might associate with signal transducers proteins, grown factor receptors, integrins, small G-proteins and tetraspanins establishing microdomains, which play important role in cell adhesion, cell activation, motility, and growth. Previously, we reported that GM2 and GM3 form a heterodimer that interacts with the tetraspanin CD82, controlling epithelial cell mobility by inhibiting integrin-hepatocyte growth factor-induced cMet tyrosine kinase signaling. By using molecular dynamics simulations to study the molecular basis of GM2/GM3 interaction we demonstrate, here, that intracellular levels of Ca2+ mediate GM2/GM3 complexation via electrostatic interaction with their carboxyl groups, while hydrogen bonds between the ceramide groups likely aid stabilizing the complex. The presence of GM2/GM3 complex alters localization of CD82 on cell surface and therefore downstream signalization. These data contribute for the knowledge of how glycosylation may control signal transduction and phenotypic changes.

The continuous evolution of SARS-CoV-2 is an expected phenomenon that will continue to happen due to the high number of cases worldwide. The present study analyzed how a Variant of Concern (VOC) could still circulate in a population hardly affected by two COVID-19 waves and with vaccination in progress.

Human papillomavirus (HPV) is recognized as the causative agent of cervical cancer in women, and it is associated with other anogenital and head/neck cancers. More than 120 types of HPV have been identified and many classified as high- or low-risk according to their oncogenic potential. One of its proteins, E6, has evolved to overcome the oncosuppressor functions of p53 by targeting this protein for degradation via interaction with the human ubiquitin-ligase E6AP. This study evaluates the correlation between the association strength of 40 HPV E6 types to the E6AP/p53 complex and the HPV oncogenesis risk using molecular simulations and machine and deep learning (ML/DL). In addition, a ML/DL-driven prediction is proposed for the HPV unclassified oncogenic risk type. The results indicate that thermodynamics play a pivotal role in the establishment of HPV-associated cancer and highlight the need to include some viral types in the HPV-related cancer surveillance and prevention strategies.