pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring

Insight from atomistic molecular dynamics simulations into the supramolecular assembly of the aldo-keto reductase from Trypanosoma cruzi

CONTEXT

Currently, Chagas disease represents an important public health problem affecting more than 8 million people worldwide. The vector of this disease is the Trypanosoma cruzi (Tc) parasite. Our research specifically focuses on the structure and aggregation states of the enzyme aldo-keto reductase of Tc (TcAKR) reported in this parasite. TcAKR belongs to the aldo-keto reductase (AKR) superfamily, enzymes that catalyze redox reactions involved in crucial biological processes. While most AKRs are found in monomeric forms, some have been reported to form dimeric and tetrameric structures. This is the case for some TcAKR. To better understand how TcAKR multimers form and remain stable, we conducted a comprehensive computational analysis using molecular dynamics (MD) simulations. Our approach to elucidating the aggregation states of TcAKR involved two strategies. Initially, we explored the dynamic behaviour of pre-assembled TcAKR dimers. Subsequently, we examined the self-aggregation of eight monomers. This investigation led to the identification of crucial residues that contribute to the stabilization of protein-protein interactions. It was also found that TcAKRs can form stable supramolecular assemblies, with each monomer typically surrounded by three first neighbours. These findings align with experimental reports of tetrameric or more complex supramolecular structures. Our computational studies could guide further experimental investigations aiming at drug development and assist in designing strategies to modulate aggregation.

METHOD

Atomistic molecular dynamics simulations were carried out. The TcAKR 3D model structure was obtained by homology modelling using the Swiss Model for the TcAKR sequence (GenBank accession no. EU558869). Further, we checked the model with Alphafold2 and found a high degree of similarity between models. Several tools were used to build the dimers including CLUSPRO, GRAMM-Docking, Hdock, and Py-dock. Protein superstructures were built using the PACKMOL package. CHARMM-GUI was used to set up the simulation systems. GROMACS version 2020.5 was used to perform the simulations with the CHARMM36 force field for the protein and ions and the TIP3P model for water. Further analyses were performed using VMD, GROMACS, AMBER tools, MDLovoFit, bio3d, and in-house programs.

Epitopes screening and vaccine molecular design of PEDV S protein based on immunoinformatics

Porcine epidemic diarrhea virus (PEDV) is a serious disease that poses a significant threat to the pig industry. This study focused on analyzing the Spike protein of PEDV, which harbors crucial antigenic determinants, in identifying dominant epitopes. Immunoinformatics tools were used to screen for B-cell, CD4+ and CD8+ predominance epitopes. These epitopes were then connected to the N-terminal of ferritin to form a self-assembled nanoparticle vaccine. Various physical and chemical properties of the candidate vaccine were analyzed, including secondary structure prediction, tertiary structure modeling, molecular docking, immune response simulation and computer cloning. The results demonstrated that the candidate vaccine was antigenic, soluble, stable, non-allergic, and formed a stable complex with the target receptor TLR-3. Immune simulation analysis showed that the candidate vaccine effectively stimulated both cellular and humoral reactions, leading to increased related cytokines production. Furthermore, efficient and stable expression of the candidate vaccine was achieved through reverse translation in the Escherichia coli K12 expression system following codon optimization and in silico cloning. The developed nanoparticle candidate vaccine in this study holds promise as an effective PEDV vaccine candidate, offering a new approach for the research, development and improvement of vaccines targeting porcine enteric diarrhea coronavirus.

Phosphorylation of GCN2 by mTOR confers adaptation to conditions of hyper-mTOR activation under stress

Adaptation to the shortage in free amino acids (AA) is mediated by 2 pathways, the integrated stress response (ISR) and the mechanistic target of rapamycin (mTOR). In response to reduced levels, primarily of leucine or arginine, mTOR in its complex 1 configuration (mTORC1) is suppressed leading to a decrease in translation initiation and elongation. The eIF2α kinase general control nonderepressible 2 (GCN2) is activated by uncharged tRNAs, leading to induction of the ISR in response to a broader range of AA shortage. ISR confers a reduced translation initiation, while promoting the selective synthesis of stress proteins, such as ATF4. To efficiently adapt to AA starvation, the 2 pathways are cross-regulated at multiple levels. Here we identified a new mechanism of ISR/mTORC1 crosstalk that optimizes survival under AA starvation, when mTORC1 is forced to remain active. mTORC1 activation during acute AA shortage, augmented ATF4 expression in a GCN2-dependent manner. Under these conditions, enhanced GCN2 activity was not dependent on tRNA sensing, inferring a different activation mechanism. We identified a labile physical interaction between GCN2 and mTOR that results in a phosphorylation of GCN2 on serine 230 by mTOR, which promotes GCN2 activity. When examined under prolonged AA starvation, GCN2 phosphorylation by mTOR promoted survival. Our data unveils an adaptive mechanism to AA starvation, when mTORC1 evades inhibition.

Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches

Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.

Quantum Chemistry-Based Protein-Protein Docking without Empirical Parameters

This study developed a novel protein-protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein-protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.

Intermolecular Electrostatic Interactions in Cytochrome c Protein Monolayer on Montmorillonite Alumosilicate Surface: A Positive Cooperative Effect

Montmorillonite (MM) crystal nanoplates acquire anticancer properties when coated with the mitochondrial protein cytochrome c (cytC) due to the cancer cells' capability to phagocytize cytC-MM colloid particles. The introduced exogenous cytC initiates apoptosis: an irreversible cascade of biochemical reactions leading to cell death. In the present research, we investigate the organization of the cytC layer on the MM surface by employing physicochemical and computer methods-microelectrophoresis, static, and electric light scattering-to study cytC adsorption on the MM surface, and protein electrostatics and docking to calculate the local electric potential and Gibbs free energy of interacting protein globules. The found protein concentration dependence of the adsorbed cytC quantity is nonlinear, manifesting a positive cooperative effect that emerges when the adsorbed cytC globules occupy more than one-third of the MM surface. Computer analysis reveals that the cooperative effect is caused by the formation of protein associates in which the cytC globules are oriented with oppositely charged surfaces. The formation of dimers and trimers is accompanied by a strong reduction in the electrostatic component of the Gibbs free energy of protein association, while the van der Waals component plays a secondary role.

Epitope screening and self-assembled nanovaccine molecule design of PDCoV-S protein based on immunoinformatics

Based on the whole virus or spike protein of pigs, δ coronavirus (PDCoV) as an immunogen may have unrelated antigenic epitope interference. Therefore, it is essential for screening and identifying advantageous protective antigen epitopes. In addition, immunoinformatic tools are described as an important aid in determining protective antigenic epitopes. In this study, the primary, secondary, and tertiary structures of vaccines were measured using ExPASy, PSIPRED 4.0, and trRosetta servers. Meanwhile, the molecular docking analysis and vector of the candidate nanovaccine were constructed. The immune response of the candidate vaccine was simulated and predicted using the C-ImmSim server. This experiment screened B cell epitopes with strong immunogenicity and high conservation, CTL epitopes, and Th epitopes with IFN-γ and IL-4 positive spike proteins. Ferritin is used as a self-assembled nanoparticle element for designing candidate nanovaccine. After analysis, it has been found to be soluble, stable, non-allergenic, and has a high affinity for its target receptor, TLR-3. The preliminary simulation analysis results show that the candidate nanovaccine has the ability to induce a humoral and cellular immune response. Therefore, it may provide a new theoretical basis for research on coronavirus self-assembled nanovaccines. It may be an effective candidate vaccine for controlling and preventing PDCoV.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Computational Insights into the Interaction of the Conserved Cysteine-Noose Domain of the Human Respiratory Syncytial Virus G Protein with the Canonical Fractalkine Binding site of Transmembrane Receptor CX3CR1 Isoforms

The human Respiratory Syncytial Virus (hRSV) stands as one of the most common causes of acute respiratory diseases. The infectivity of this virus is intricately linked to its membrane proteins, notably the attachment glycoprotein (G protein). The latter plays a key role in facilitating the attachment of hRSV to respiratory tract epithelial cells, thereby initiating the infection process. The present study aimed to characterize the interaction of the conserved cysteine-noose domain of hRSV G protein (cndG) with the transmembrane CX3C motif chemokine receptor 1 (CX3CR1) isoforms using computational tools of molecular modeling, docking, molecular dynamics simulations, and binding free energy calculations. From MD simulations of the molecular system embedded in the POPC lipid bilayer, we showed a stable interaction of cndG with the canonical fractalkine binding site in the N-terminal cavity of the CX3CR1 isoforms and identified that residues in the extracellular loop 2 (ECL2) region and Glu279 of this receptor are pivotal for the stabilization of CX3CR1/cndG binding, corroborating what was reported for the interaction of the chemokine fractalkine with CX3CR1 and its structure homolog US28. Therefore, the results presented here contribute by revealing key structural points for the CX3CR1/G interaction, allowing us to better understand the biology of hRSV from its attachment process and to develop new strategies to combat it.

Insights into energy quenching mechanisms and carotenoid uptake by orange carotenoid protein homologs: HCP4 and CTDH

Photodamage to the photosynthetic apparatus by excessive light radiation has led to the evolution of a variety of energy dissipation mechanisms. A mechanism that exists in some cyanobacterial species, enables non-photochemical quenching of excitation energy within the phycobilisome (PBS) antenna complex by the Orange Carotenoid Protein (OCP). The OCP contains an active N-terminal domain (NTD) and a regulatory C-terminal domain (CTD). Some cyanobacteria also have genes encoding for homologs to both the CTD (CTDH) and the NTD (referred to as helical carotenoid proteins, HCP). The CTDH facilitates uptake of carotenoids from the thylakoid membranes to be transferred to the HCPs. Holo-HCPs exhibit diverse functionalities such as carotenoid carriers, singlet oxygen quenchers, and in the case of HCP4, constitutive OCP-like energy quenching. Here, we present the first crystal structure of the holo-HCP4 binding canthaxanthin molecule and an improved structure of the apo-CTDH from Anabaena sp. PCC 7120. We propose here models of the binding of the HCP4 to the PBS and the associated energy quenching mechanism. Our results show that the presence of the carotenoid is essential for fluorescence quenching. We also examined interactions within OCP-like species, including HCP4 and CTDH, providing the basis for mechanisms of carotenoid transfer from CTDH to HCPs.

Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning

Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2. Over 48,960,000 variants were predicted. Eight prospective variants that could surface in the future underwent modeling and molecular dynamics simulations. The study forecasts that the latest variant, ISOY2P5O1, may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days. The ISOY8P5O2 variant displayed an increased binding capacity in the dry assay, with a total predicted binding energy of -110.306 kcal/mol. This represents an 8.25% enhancement in total binding energy compared to the original SARS-CoV-2 strain discovered in Wuhan (-101.892 kcal/mol). Reverse research confirmed the structural significance of mutation sites using ML models, particularly in the context of protein folding. The study validated regression methods (SVR, RF, and PLS) with different data structures. This study investigates the effectiveness of the "ML-Guided Design Correctly Predicts Combinatorial Effects Strategy" compared to the "ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy". To enhance machine learning, we created a timestamping algorithm and two auxiliary programs using advanced techniques to rapidly process extensive data, surpassing batch sequencing capabilities. This study not only advances machine learning in guiding protein evolution but also holds potential for forecasting future viruses and vaccine development.

Computational design and engineering of self-assembling multivalent microproteins with therapeutic potential against SARS-CoV-2

Multivalent drugs targeting homo-oligomeric viral surface proteins, such as the SARS-CoV-2 trimeric spike (S) protein, have the potential to elicit more potent and broad-spectrum therapeutic responses than monovalent drugs by synergistically engaging multiple binding sites on viral targets. However, rational design and engineering of nanoscale multivalent protein drugs are still lacking. Here, we developed a computational approach to engineer self-assembling trivalent microproteins that simultaneously bind to the three receptor binding domains (RBDs) of the S protein. This approach involves four steps: structure-guided linker design, molecular simulation evaluation of self-assembly, experimental validation of self-assembly state, and functional testing. Using this approach, we first designed trivalent constructs of the microprotein miniACE2 (MP) with different trimerization scaffolds and linkers, and found that one of the constructs (MP-5ff) showed high trimerization efficiency, good conformational homogeneity, and strong antiviral neutralizing activity. With its trimerization unit (5ff), we then engineered a trivalent nanobody (Tr67) that exhibited potent and broad neutralizing activity against the dominant Omicron variants, including XBB.1 and XBB.1.5. Cryo-EM complex structure confirmed that Tr67 stably binds to all three RBDs of the Omicron S protein in a synergistic form, locking them in the "3-RBD-up" conformation that could block human receptor (ACE2) binding and potentially facilitate immune clearance. Therefore, our approach provides an effective strategy for engineering potent protein drugs against SARS-CoV-2 and other deadly coronaviruses.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Structural and molecular investigation of the impact of S30L and D88N substitutions in G9R protein on coupling with E4R from Monkeypox virus (MPXV)

Understanding the pathogenesis mechanism of the Monkeypox virus (MPXV) is essential to guide therapeutic development against the Monkeypox virus. In the current study, we investigated the impact of the only two reported substitutions, S30L, D88N, and S30L-D88N on the G9R of the replication complex in 2022 with E4R using structural modeling, simulation, and free energy calculation methods. From the molecular docking and dissociation constant (KD) results, it was observed that the binding affinity did not increase in the mutants, but the interaction paradigm was altered by these substitutions. Molecular simulation data revealed that these mutations are responsible for destabilization, changes in protein packing, and internal residue fluctuations, which can cause functional variance. Additionally, hydrogen bonding analysis revealed that the estimated number of hydrogen bonds are almost equal among the wild-type G9R and each mutant. The total binding free energy for the wild-type G9R with E4R was -85.00 kcal/mol while for the mutants the TBE was -42.75 kcal/mol, -43.68 kcal/mol, and -48.65 kcal/mol respectively. This shows that there is no direct impact of these two reported mutations on the binding with E4R, or it may affect the whole replication complex or any other mechanism involved in pathogenesis. To explore these variations further, we conducted PCA and FEL analyses. Based on our findings, we speculate that within the context of interaction with E4R, the mutations in the G9R protein might be benign, potentially leading to functional diversity associated with other proteins.Communicated by Ramaswamy H. Sarma.

Docking Foundations: From Rigid to Flexible Docking

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

GRAMM Web Server for Protein Docking

Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein-protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

In vitro and in silico evaluation of the serrapeptase effect on biofilm and amyloids of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an emerging threat for hospitalized and cystic fibrosis patients. Biofilm, a microbial community embedded in extracellular polymeric substance, fortifies bacteria against the immune system. In biofilms, the expression of functional amyloids is linked with highly aggregative, multi-resistant strains, and chronic infections. Serrapeptase (SPT), a protease possessing similar or superior anti-microbial properties with many antibiotics, presents anti-amyloid potential. However, studies on the employment of SPT against Pseudomonas biofilms and Fap amyloid, or the possible mechanisms of action are scarce. Here, SPT inhibited biofilm formation of P. aeruginosa ATCC 27853 on both plastic and glass surfaces, with an IC50 of 11.26 µg/mL and 0.27 µg/mL, respectively. The inhibitory effect of SPT on biofilm was also verified with optical microscopy of crystal violet-stained biofilms and with confocal microscopy. Additionally, SPT caused a dose-dependent decrease of bacterial viability (IC50 of 3.07 µg/mL) as demonstrated by MTT assay. Reduction of bacterial functional amyloids was also demonstrated, employing both fluorescence microscopy with thioflavin T and photometrical determination of Congo-red-positive compounds. Both viability and functional amyloids correlated significantly with biofilm inhibition. Finally, in silico molecular docking studies provided a mechanistic insight into the interaction of SPT with FapC or FapD, proving that both peptides are possible targets of SPT. These results offer new insights into the biofilm formation of P. aeruginosa and potentiate the involvement of SPT in the prevention and eradication of Pseudomonas biofilms. KEY POINTS: • Serrapeptase inhibits biofilm formation of P. aeruginosa on plastic and glass. • Biofilm inhibition correlated with reduced viability and functional amyloid levels. • In silico studies indicated that serrapeptase may target FapC and FapD peptides.

Identification of the ATPase alpha subunit of Trypanosoma cruzi as a potential biomarker for the diagnosis of Chagas disease

BACKGROUND

Chagas disease (CD) is considered by the World Health Organisation (WHO) a neglected disease endemic to the Americas, but it has spread throughout the world due to migrations. The disease is almost 100% curable if detected in time. Still, the lack of rapid diagnostic tests with sufficient sensitivity and specificity leads to a chronic phase with a mortality of about 50,000 people worldwide per year.

METHODS

Using the total proteins extracted from serum samples of patients confirmed with chronic phase CD; we performed the Bio-SELEX strategy. The best aptamers were selected using next-generation sequencing (NGS) based on their most abundant sequences (reads and rpm). Then, selected aptamers were used to isolate potential biomarkers directly from serum samples of patients with chronic phase CD using pull-down and mass spectrometry experiments.

RESULTS

CH1 aptamer was the aptamer selected after the NGS results analysis. The pull-down and mass spectrometry experiments identified the presence of the ATPase alpha subunit of T. cruzi circulating in serum samples of patients with chronic phase CD.

CONCLUSIONS

We report the ATPase alpha subunit of T. cruzi as a potential biomarker for chronic phase CD and CH1 aptamer as a potential tool for diagnosing CD.

Novel peptide inhibitors targeting CD40 and CD40L interaction: A potential for atherosclerosis therapy

Atherosclerosis is a chronic inflammatory disease characterized by plaque build-up in the arteries, leading to the obstruction of blood flow. Macrophages are the primary immune cells found in the atherosclerotic lesions and are directly involved in atherosclerosis progression. Macrophages are derived from extravasating blood monocytes. The monocytic CD40 receptor is important for monocyte recruitment on the endothelium expressing the CD40 ligand (CD40L). Thus, targeting monocyte/macrophage interaction with the endothelium by inhibiting CD40-CD40L interaction may be a promising strategy for attenuating atherosclerosis. Monoclonal antibodies have been used against this target but shows various complications. We used an array of computer-aided drug discovery tools and molecular docking approaches to design a therapeutic inhibitory peptide that could efficiently bind to the critical residues (82Y, 84D, and 86N) on the CD40 receptor essential for the receptor's binding to CD40L. The initial screen identified a parent peptide with a high binding affinity to CD40, but the peptide exhibited a positive hepatotoxicity score. We then designed several novel peptidomimetic derivatives with higher binding affinities to CD40, good physicochemical properties, and negative hepatotoxicity as compared to the parent peptide. Furthermore, we conducted molecular dynamics simulations for both the apo and complexed forms of the receptor with ligand, and screened peptides to evaluate their stability. The designed peptidomimetic derivatives are promising therapeutics targeting the CD40-CD40L interaction and may potentially be used to attenuate atherosclerosis.

Unraveling the Aurora kinase A and Epstein-Barr nuclear antigen 1 axis in Epstein Barr virus associated gastric cancer

Aurora kinase A (AURKA) is one of the crucial cell cycle regulators associated with gastric cancer. Here, we explored Epstein Barr Virus-induced gastric cancer progression through EBV protein EBNA1 with AURKA. We found that EBV infection enhanced cell proliferation and migration of AGS cells and upregulation of AURKA levels. AURKA knockdown markedly reduced the proliferation and migration of the AGS cells even with EBV infection. Moreover, MD-simulation data deciphered the probable connection between EBNA1 and AURKA. The in-vitro analysis through the transcript and protein expression showed that AURKA knockdown reduces the expression of EBNA1. Moreover, EBNA1 alone can enhance AURKA protein expression in AGS cells. Co-immunoprecipitation and NMR analysis between AURKA and EBNA1 depicts the interaction between two proteins. In addition, AURKA knockdown promotes apoptosis in EBV-infected AGS cells through cleavage of Caspase-3, -9, and PARP1. This study demonstrates that EBV oncogenic modulators EBNA1 possibly modulate AURKA in EBV-mediated gastric cancer progression.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2

The COVID-19 pandemic has created an urgent need for effective therapeutic and diagnostic strategies to manage the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the emergence of numerous variants of concern (VOCs) has made it challenging to develop targeted therapies that are broadly specific in neutralizing the virus. In this study, we aimed to develop neutralizing nanobodies (Nbs) using computational techniques that can effectively neutralize the receptor-binding domain (RBD) of SARS-CoV-2 VOCs. We evaluated the performance of different protein-protein docking programs and identified HDOCK as the most suitable program for Nb/RBD docking with high accuracy. Using this approach, we designed 14 novel Nbs with high binding affinity to the VOC RBDs. The Nbs were engineered with mutated amino acids that interacted with key amino acids of the RBDs, resulting in higher binding affinity than human angiotensin-converting enzyme 2 (ACE2) and other viral RBDs or haemagglutinins (HAs). The successful development of these Nbs demonstrates the potential of molecular modeling as a low-cost and time-efficient method for engineering effective Nbs against SARS-CoV-2. The engineered Nbs have the potential to be employed in RBD-neutralizing assays, facilitating the identification of novel treatment, prevention, and diagnostic strategies against SARS-CoV-2.

Flagellin, a plant-defense-activating protein identified from Xanthomonas axonopodis pv. Dieffenbachiae invokes defense response in tobacco

BACKGROUND

Secretome analysis is a valuable tool to study host-pathogen protein interactions and to identify new proteins that are important for plant health. Microbial signatures elicit defense responses in plants, and by that, the plant immune system gets triggered prior to pathogen infection. Functional properties of secretory proteins from Xanthomonas axonopodis pv. dieffenbachiae (Xad1) involved in priming plant immunity was evaluated.

RESULTS

In this study, the secretome of Xad1 was analyzed under host plant extract-induced conditions, and mass spectroscopic analysis of differentially expressed protein was identified as plant-defense-activating protein viz., flagellin C (FliC). The flagellin and Flg22 peptides both elicited hypersensitive reaction (HR) in non-host tobacco, activated reactive oxygen species (ROS) scavenging enzymes, and increased pathogenesis-related (PR) gene expression viz., NPR1, PR1, and down-regulation of PR2 (β-1,3-glucanase). Protein docking studies revealed the Flg22 epitope of Xad1, a 22 amino acid peptide region in FliC that recognizes plant receptor FLS2 to initiate downstream defense signaling.

CONCLUSION

The flagellin or the Flg22 peptide from Xad1 was efficient in eliciting an HR in tobacco via salicylic acid (SA)-mediated defense signaling that subsequently triggers systemic immune response epigenetically. The insights from this study can be used for the development of bio-based products (small PAMPs) for plant immunity and health.

Understanding the Molecular Regulation of Serotonin Receptor 5-HTR1B-β-Arrestin1 Complex in Stress and Anxiety Disorders

The serotonin receptor subtype 5-HTR1B is widely distributed in the brain with an important role in various behavioral implications including neurological conditions and psychiatric disorders. The neuromodulatory action of 5-HTR1B largely depends upon its arrestin mediated signaling pathway. In this study, we tried to investigate the role of unusually long intracellular loop 3 (ICL3) region of the serotonin receptor 5-HTR1B in interaction with β-arrestin1 (Arr2) to compensate for the absence of the long cytoplasmic tail. Molecular modeling and docking tools were employed to obtain a suitable molecular conformation of the ICL3 region in complex with Arr2 which dictates the specific complex formation of 5-HTR1B with Arr2. This reveals the novel molecular mechanism of phosphorylated ICL3 mediated GPCR-arrestin interaction in the absence of the long cytoplasmic tail. The in-cell disulfide cross-linking experiments and molecular dynamics simulations of the complex further validate the model of 5-HTR1B-ICL3-Arr2 complex. Two serine residues (Ser281 and Ser295) within the 5-HTR1B-ICL3 region were found to be occupying the electropositive pocket of Arr2 in our model and might be crucial for phosphorylation and specific Arr2 binding. The alignment studies of these residues showed them to be conserved only across 5-HTR1B mammalian species. Thus, our studies were able to predict a molecular conformation of 5-HTR1B-Arr2 and identify the role of long ICL3 in the signaling process which might be crucial in designing targeted drugs (biased agonists) that promote GPCR-Arr2 signaling to deter the effects of stress and anxiety-like disorders.

A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations.

The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions

Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org/.

Activation of insulin-like growth factor-1 receptor (IGF-1R) promotes growth of colorectal cancer through triggering the MEX3A-mediated degradation of RIG-I

Insulin-like growth factor-1 receptor (IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers. However, targeting it has often produced the disappointing results as the role played by cross talk with numerous downstream signalings. Here, we report a disobliging IGF-1R signaling which promotes growth of cancer through triggering the E3 ubiquitin ligase MEX3A-mediated degradation of RIG-I. The active β-arrestin-2 scaffolds this disobliging signaling to talk with MEX3A. In response to ligands, IGF-1Rβ activated the basal βarr2 into its active state by phosphorylating the interdomain domain on Tyr64 and Tyr250, opening the middle loop (Leu130‒Cys141) to the RING domain of MEX3A through the conformational changes of βarr2. The models of βarr2/IGF-1Rβ and βarr2/MEX3A could interpret the mechanism of the activated-IGF-1R in triggering degradation of RIG-I. The assay of the mutants βarr2^Y64A and βarr2^Y250A further confirmed the role of these two Tyr residues of the interlobe in mediating the talk between IGF-1Rβ and the RING domain of MEX3A. The truncated-βarr2 and the peptide ATQAIRIF, which mimicked the RING domain of MEX3A could prevent the formation of βarr2/IGF-1Rβ and βarr2/MEX3A complexes, thus blocking the IGF-1R-triggered RIG-I degradation. Degradation of RIG-I resulted in the suppression of the IFN-I-associated immune cells in the TME due to the blockade of the RIG-I-MAVS-IFN-I pathway. Poly(I:C) could reverse anti-PD-L1 insensitivity by recovery of RIG-I. In summary, we revealed a disobliging IGF-1R signaling by which IGF-1Rβ promoted cancer growth through triggering the MEX3A-mediated degradation of RIG-I.

N-(2-ozoazepan-3-yl)-pyrrolidine-2-carboxamide, a novel Octopus vulgaris ink-derived metabolite, exhibits a pro-apoptotic effect on A549 cancer cell line and inhibits pro-inflammatory markers

This research aimed to chemically synthesize and evaluate the antiproliferative and anti-inflammatory potential of ozopromide (OPC), a novel compound recently isolated from O. vulgaris ink. After chemical synthesis, OPC structural characterization was confirmed by COSY2D, FTIR, and C-/H-NMR. OPC inhibited the growth of human breast (MDA-MB-231), prostate (22Rv1), cervix (HeLa), and lung (A549) cancerous cells, being the highest effect on the latter (IC₅₀: 53.70 μM). As confirmed by flow cytometry, OPC induced typical apoptosis-derived morphological features on A549 cells, mostly at early and late apoptosis stages. OPC generated a dose-dependent effect inhibiting IL-6 and IL-8 on LPS-stimulated peripheral mononuclear cells (PBMCs). A major affinity of OPC to Akt-1 and Bcl-2 proteins in silico agreed with the observed pro-apoptotic mechanisms. Results suggested that OPC has the potential to alleviate inflammation and be further studied for anticancer activity. Marine-derived food products such as ink contains bioactive metabolites exhibiting potential health benefits.

Dorzolamide suppresses PKCδ -TIRAP-p38 MAPK signaling axis to dampen the inflammatory response

Background: Sepsis is a syndrome due to microbial infection causing impaired multiorgan function. Its underlying cause is immune dysfunction and macrophages play an essential role. Methods: TIRAP interaction with PKCδ in macrophage was studied, revealing downstream signaling by Western blot and quantitative reverse transcriptase PCR. Dorzolamide (DZD) disrupting TIRAP-PKCδ interaction was identified by virtual screening and validated in vitro and in septic mice. Results: The study highlights the indispensable role of TIRAP-PKCδ in p38 MAPK-activation, NF-κB- and AP-1-mediated proinflammatory cytokines expression, whereas DZD significantly attenuated the signaling. Conclusion: Targeting TIRAP-PKCδ interaction by DZD is a novel therapeutic approach for treating sepsis.

WFS1 autosomal dominant variants linked with hearing loss: update on structural analysis and cochlear implant outcome

BACKGROUND

Wolfram syndrome type 1 gene (WFS1), which encodes a transmembrane structural protein (wolframin), is essential for several biological processes, including proper inner ear function. Unlike the recessively inherited Wolfram syndrome, WFS1 heterozygous variants cause DFNA6/14/38 and wolfram-like syndrome, characterized by autosomal dominant nonsyndromic hearing loss, optic atrophy, and diabetes mellitus. Here, we identified two WFS1 heterozygous variants in three DFNA6/14/38 families using exome sequencing. We reveal the pathogenicity of the WFS1 variants based on three-dimensional (3D) modeling and structural analysis. Furthermore, we present cochlear implantation (CI) outcomes in WFS1-associated DFNA6/14/38 and suggest a genotype-phenotype correlation based on our results and a systematic review.

METHODS

We performed molecular genetic test and evaluated clinical phenotypes of three WFS1-associated DFNA6/14/38 families. A putative WFS1-NCS1 interaction model was generated, and the impacts of WFS1 variants on stability were predicted by comparing intramolecular interactions. A total of 62 WFS1 variants associated with DFNA6/14/38 were included in a systematic review.

RESULTS

One variant is a known mutational hotspot variant in the endoplasmic reticulum (ER)-luminal domain WFS1(NM_006005.3) (c.2051 C > T:p.Ala684Val), and the other is a novel frameshift variant in transmembrane domain 6 (c.1544_1545insA:p.Phe515LeufsTer28). The two variants were pathogenic, based on the ACMG/AMP guidelines. Three-dimensional modeling and structural analysis show that non-polar, hydrophobic substitution of Ala684 (p.Ala684Val) destabilizes the alpha helix and contributes to the loss of WFS1-NCS1 interaction. Also, the p.Phe515LeufsTer28 variant truncates transmembrane domain 7-9 and the ER-luminal domain, possibly impairing membrane localization and C-terminal signal transduction. The systematic review demonstrates favorable outcomes of CI. Remarkably, p.Ala684Val in WFS1 is associated with early-onset severe-to-profound deafness, revealing a strong candidate variant for CI.

CONCLUSIONS

We expanded the genotypic spectrum of WFS1 heterozygous variants underlying DFNA6/14/38 and revealed the pathogenicity of mutant WFS1, providing a theoretical basis for WFS1-NCS1 interactions. We presented a range of phenotypic traits for WFS1 heterozygous variants and demonstrated favorable functional CI outcomes, proposing p.Ala684Val a strong potential marker for CI candidates.

Riboflavin kinase and pyridoxine 5′-phosphate oxidase complex formation envisages transient interactions for FMN cofactor delivery

Enzymes catalysing sequential reactions have developed different mechanisms to control the transport and flux of reactants and intermediates along metabolic pathways, which usually involve direct transfer of metabolites from an enzyme to the next one in a cascade reaction. Despite the fact that metabolite or substrate channelling has been widely studied for reactant molecules, such information is seldom available for cofactors in general, and for flavins in particular. Flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN) act as cofactors in flavoproteins and flavoenzymes involved in a wide range of physiologically relevant processes in all type of organisms. Homo sapiens riboflavin kinase (RFK) catalyses the biosynthesis of the flavin mononucleotide cofactor, and might directly interplay with its flavin client apo-proteins prior to the cofactor transfer. Non-etheless, none of such complexes has been characterized at molecular or atomic level so far. Here, we particularly evaluate the interaction of riboflavin kinase with one of its potential FMN clients, pyridoxine-5′-phosphate oxidase (PNPOx). The interaction capacity of both proteins is assessed by using isothermal titration calorimetry, a methodology that allows to determine dissociation constants for interaction in the micromolar range (in agreement with the expected transient nature of the interaction). Moreover, we show that; i) both proteins become thermally stabilized upon mutual interaction, ii) the tightly bound FMN product can be transferred from RFK to the apo-form of PNPOx producing an efficient enzyme, and iii) the presence of the apo-form of PNPOx slightly enhances RFK catalytic efficiency. Finally, we also show a computational study to predict likely RFK-PNPOx binding modes that can envisage coupling between the FMN binding cavities of both proteins for the potential transfer of FMN.

Epitopes screening and vaccine molecular design of SADS-CoV based on immunoinformatics

The regional outbreak of the Swine acute diarrhea syndrome coronavirus (SADS-CoV) has seriously threatened the swine industry. There is an urgent need to discover safe and effective vaccines to contain them quickly. The coronavirus spike protein mediates virus entry into host cells, one of the most important antigenic determinants and a potential vaccine target. Therefore, this study aims to conduct a predictive analysis of the epitope of S protein B cells and T cells (MHC class I and class II) by immunoinformatics methods by screening and identifying protective antigenic epitopes that induce major neutralized antibodies and activate immune responses to construct epitope vaccines. The study explored primary, secondary, and tertiary structures, disulfide bonds, protein docking, immune response simulation, and seamless cloning of epitope vaccines. The results show that the spike protein dominant epitope of the screening has a high conservativeness and coverage of IFN-γ, IL-4-positive Th epitope, and CTL epitope. The constructed epitope vaccine interacts stably with TLR-3 receptors, and the immune response simulation shows good immunogenicity, which could effectively activate humoral and cellular immunity. After codon optimization, it was highly likely to be efficiently and stably expressed in the Escherichia coli K12 expression system. Therefore, the constructed epitope vaccine will provide a new theoretical basis for the design of SADS-CoV antiviral drugs and related research on coronaviruses such as SARS-CoV-2.

α7 Nicotinic acetylcholine receptors regulate translocation of HIF-1α to the cell nucleus and mitochondria upon hypoxia

Nicotinic acetylcholine receptors (nAChRs), initially characterized as ligand-gated ion channels mediating fast synaptic transmission, are now found in many non-excitable cells and mitochondria where they function in ion-independent manner and regulate vital cellular processes like apoptosis, proliferation, cytokine secretion. Here we show that the nAChRs of α7 subtype are present in the nuclei of liver cells and astrocytoma U373 cell line. As shown by lectin ELISA, the nuclear α7 nAChRs are mature glycoproteins that follow the standard rout of post-translational modifications in Golgi; however, their glycosylation profile is non-identical to that of mitochondrial nAChRs. They are exposed on the outer nuclear membrane and are found in combination with lamin B1. The nuclear α7 nAChRs are up-regulated in liver within 1 h after partial hepatectomy and in H₂O₂-treated U373 cells. As shown both in silico and experimentally, the α7 nAChR interacts with hypoxia-inducible factor HIF-1α and this interaction is impaired by α7-selective agonists PNU282987 and choline or type 2 positive allosteric modulator PNU120596, which prevent HIF-1α accumulation in the nuclei. Similarly, HIF-1α interacts with mitochondrial α7 nAChRs in U373 cells treated with dimethyloxalylglycine. It is concluded that functional α7 nAChRs influence HIF-1α translocation into the nucleus and mitochondria upon hypoxia.

VGLL4 and MENIN function as TEAD1 corepressors to block pancreatic β cell proliferation

TEAD1 and the mammalian Hippo pathway regulate cellular proliferation and function, though their regulatory function in β cells remains poorly characterized. In this study, we demonstrate that while β cell-specific TEAD1 deletion results in a cell-autonomous increase of β cell proliferation, β cell-specific deletion of its canonical coactivators, YAP and TAZ, does not affect proliferation, suggesting the involvement of other cofactors. Using an improved split-GFP system and yeast two-hybrid platform, we identify VGLL4 and MENIN as TEAD1 corepressors in β cells. We show that VGLL4 and MENIN bind to TEAD1 and repress the expression of target genes, including FZD7 and CCN2, which leads to an inhibition of β cell proliferation. In conclusion, we demonstrate that TEAD1 plays a critical role in β cell proliferation and identify VGLL4 and MENIN as TEAD1 corepressors in β cells. We propose that these could be targeted to augment proliferation in β cells for reversing diabetes.

In Vitro, In Vivo, and In Silico Analyses of Molecular Anti-Pigmentation Mechanisms of Selected Thai Rejuvenating Remedy and Bioactive Metabolites

Thai rejuvenating remedies are mixed herbal formulas promoting longevity. Due to the complexity, the biological activities of these remedies are minimal. Therefore, in this study, the authors evaluated the anti-pigmentation effect at the molecular level of the selected Thai rejuvenating remedy to fulfill the knowledge gap. First, the authors found that the selected remedy showed promising activity against the tyrosinase enzyme with an IC₅₀ value of 9.41 µg/mL. In the comparison, kojic acid (positive control) exhibited an IC₅₀ value of 3.92 µg/mL against the same enzyme. Later, the authors identified glabridin as a bioactive molecule against tyrosinase with an IC₅₀ value of 0.08 µg/mL. However, ethyl p-methoxycinnamate was the most abundant metabolite found in the remedy. The authors also found that the selected remedy and glabridin reduced the melanin content in the cell-based assay (B16F1) but not in the zebrafish larvae experiment. Finally, the authors conducted a computational investigation through molecular docking proposing a theoretical molecular interplay between glabridin, ethyl p-methoxycinnamate, and target proteins (tyrosinase and melanocortin-1 receptor, MC1R). Hence, in this study, the authors reported the molecular anti-pigmentation mechanism of the selected Thai rejuvenating remedy for the first time by combining the results from in silico, in vitro, and in vivo experiments.

Prediction of antibody binding to SARS-CoV-2 RBDs

Summary

The ability to predict antibody-antigen binding is essential for computational models of antibody affinity maturation and protein design. While most models aim to predict binding for arbitrary antigens and antibodies, the global impact of SARS-CoV-2 on public health and the availability of associated data suggest that a SARS-CoV-2-specific model would be highly beneficial. In this work, we present a neural network model, trained on ∼315 000 datapoints from deep mutational scanning experiments, that predicts escape fractions of SARS-CoV-2 RBDs binding to arbitrary antibodies. The antibody embeddings within the model constitute an effective sequence space, which correlates with the Hamming distance, suggesting that these embeddings may be useful for downstream tasks such as binding prediction. Indeed, the model achieves Spearman correlation coefficients of 0.46 and 0.52 on two held-out test sets. By comparison, correlation coefficients calculated using existing structure and sequence-based models do not exceed 0.28. The correlation coefficient against dissociation constants of antibodies binding to SARS-CoV-2 RBD variants is 0.46. Additionally, the residue-level escapes are highest in the antibody epitope, correlating well with experimentally measured escapes. We further study the effect of antibody chain use, embedding dimension size and feed-forward and convolutional architectures on the model results. Lastly, we find that the inference time of our model is significantly faster than previous models, suggesting that it could be a useful tool for the accurate and rapid prediction of antibodies binding to SARS-CoV-2 RBDs.

Availability and implementation

The model and associated code are available for download at https://github.com/ericzwang/RBD_AB.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Genome-Wide Subtraction Analysis and Reverse Vaccinology to Detect Novel Drug Targets and Potential Vaccine Candidates Against Ehrlichia chaffeensis

Human monocytotropic ehrlichiosis is an emerging tick-borne infection caused by the obligate intracellular pathogen, Ehrlichia chaffeensis. The non-specific symptoms can range from a self-limiting fever to a fatal septic-like syndrome and may be misdiagnosed. The limited treatment choices including doxycycline are effective only in the initiation phase of the infection. It seems that novel therapeutic targets and new vaccine strategies could be effective to control this pathogen. This study is comprised of two major phases. First, the common proteins retrieved through subtractive analysis and potential drug targets were evaluated by subcellular localization, homology prediction, metabolic pathways, druggability, essentiality, protein-protein interaction networks, and protein data bank availability. In the second phase, surface-exposed proteins were assessed based on antigenicity, allergenicity, physiochemical properties, B cell and T cell epitopes, conserved domains, and protein-protein interaction networks. A multi-epitope vaccine was designed and characterized using molecular dockings and immune simulation analysis. Six proteins including WP_011452818.1, WP_011452723.1, WP_006010413.1, WP_006010278.1, WP_011452938.1, and WP_006010644.1 were detected. They belong to unique metabolic pathways of E. chaffeensis that are considered as new essential drug targets. Based on the reverse vaccinology, WP_011452702.1, WP_044193405.1, WP_044170604.1, and WP_006010191.1 proteins were potential vaccine candidates. Finally, four B cell epitopes, including SINNQDRNC, FESVSSYNI, SGKKEISVQSN, and QSSAKRKST, were used to generate the multi-epitope vaccine based on LCL platform. The vaccine showed strong interactions with toll-like receptors and acceptable immune-reactivity by immune simulation analysis. The findings of this study may represent a turning point in developing an effective drug and vaccine against E. chaffeensis. However, further experimental analyses have remained.

Modeling of Protein Complexes

The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.

HDOCK update for modeling protein-RNA/DNA complex structures

Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.

Subtractive genomic analysis for computational identification of putative immunogenic targets against clinical Enterobacter cloacae complex

Background

Enterobacter is a major nosocomial genus of Enterobacteriaceae responsible for a variety of nosocomial infections, particularly in prolonged hospitalized patients in the intensive care units. Since current antibiotics have failed treating colistin- and carbapenem-resistant Enterobacteriaceae, efforts are underway to find suitable alternative strategies. Therefore, this study conducted a reverse vaccinology (RV) to identify novel and putative immunogenic targets using core proteome of 20 different sequence types (STs) of clinical Enterobacter spp. Moreover, we introduced a structural-based approach for exploration of potential vaccine candidates against the Enterobacteriaceae family using their conserved domain analysis.

Results

A number of 2616 core coding sequences (CDSs) were retrieved from 20 clinical strains of Enterobacter spp. with a similarity of ≥ 50%. Nine proteins with a score of ≥ 20 considered as the shortlisted proteins based on the quartile scoring method, including three TonB-dependent receptors, WP_008500981.1, WP_058690971.1 and WP_058679571.1; one YjbH domain-containing protein, WP_110108068.1; three flagellar proteins, WP_088207510.1, WP_033145204.1 and WP_058679632.1; one spore-coat U domain-containing protein, WP_039266612.1; and one DD-metalloendopeptidase family protein, WP_025912449.1. In this study, proteins WP_058690971.1 and WP_110108068.1 were detected as the top candidates with regard to immune stimulation and interactions with TLRs. However, their efficacy is remaining to be evaluated experimentally.

Conclusions

Our investigation introduced common ferrichrome porins with high sequence similarity as potential vaccine candidates against the Enterobacteriaceae family. These proteins belong to the iron acquisition system and possess all criteria of suitable vaccine targets. Therefore, they need to be specifically paid attention for vaccine development against clinically important members of Enterobacteriaceae family.

pyDockDNA: A new web server for energy-based protein-DNA docking and scoring

Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).

In silico discovery of biomarkers for the accurate and sensitive detection of Fusarium solani

Fusarium solani is worrisome because it severely threatens the agricultural productivity of certain crops such as tomatoes and peas, causing the general decline, wilting, and root necrosis. It has also been implicated in the infection of the human eye cornea. It is believed that early detection of the fungus could save these crops from the destructive activities of the fungus through early biocontrol measures. Therefore, the present work aimed to build a sensitive model of novel anti-Fusarium solani antimicrobial peptides (AMPs) against the fungal cutinase 1 (CUT1) protein for early, sensitive and accurate detection. Fusarium solani CUT1 receptor protein 2D secondary structure, model validation, and functional motifs were predicted. Subsequently, anti-Fusarium solani AMPs were retrieved, and the HMMER in silico algorithm was used to construct a model of the AMPs. After their structure predictions, the interaction analysis was analyzed for the Fusarium solani CUT1 protein and the generated AMPs. The putative anti-Fusarium solani AMPs bound the CUT1 protein very tightly, with OOB4 having the highest binding energy potential for HDock. The pyDockWeb generated high electrostatic, desolvation, and low van der Waals energies for all the AMPs against CUT1 protein, with OOB1 having the most significant interaction. The results suggested the utilization of AMPs for the timely intervention, control, and management of these crops, as mentioned earlier, to improve their agricultural productivity and reduce their economic loss and the use of HMMER for constructing models for disease detection.

ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP

Attention deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder. Single nucleotide polymorphisms (SNPs) in the Adhesion G Protein-Coupled Receptor L3 (ADGRL3) gene are associated with increased susceptibility to developing ADHD worldwide. However, the effect of ADGRL3 non-synonymous SNPs (nsSNPs) on the ADGRL3 protein function is vastly unknown. Using several bioinformatics tools to evaluate the impact of mutations, we found that nsSNPs rs35106420, rs61747658, and rs734644, previously reported to be associated and in linkage with ADHD in disparate populations from the world over, are predicted as pathogenic variants. Docking analysis of rs35106420, harbored in the ADGLR3-hormone receptor domain (HRM, a common extracellular domain of the secretin-like GPCRs family), showed that HRM interacts with the Glucose-dependent insulinotropic polypeptide (GIP), part of the incretin hormones family. GIP has been linked to the pathogenesis of diabetes mellitus, and our analyses suggest a potential link to ADHD. Overall, the comprehensive application of bioinformatics tools showed that functional mutations in the ADGLR3 gene disrupt the standard and wild ADGRL3 structure, most likely affecting its metabolic regulation. Further in vitro experiments are granted to evaluate these in silico predictions of the ADGRL3-GIP interaction and dissect the complexity underlying the development of ADHD.

Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation

The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.

Structural basis of ion uptake in copper-transporting P1B-type ATPases

Copper is essential for living cells, yet toxic at elevated concentrations. Class 1B P-type (P1B-) ATPases are present in all kingdoms of life, facilitating cellular export of transition metals including copper. P-type ATPases follow an alternating access mechanism, with inward-facing E1 and outward-facing E2 conformations. Nevertheless, no structural information on E1 states is available for P1B-ATPases, hampering mechanistic understanding. Here, we present structures that reach 2.7 Å resolution of a copper-specific P1B-ATPase in an E1 conformation, with complementing data and analyses. Our efforts reveal a domain arrangement that generates space for interaction with ion donating chaperones, and suggest a direct Cu+ transfer to the transmembrane core. A methionine serves a key role by assisting the release of the chaperone-bound ion and forming a cargo entry site together with the cysteines of the CPC signature motif. Collectively, the findings provide insights into P1B-mediated transport, likely applicable also to human P1B-members.

Reverse vaccinology approach to identify novel and immunogenic targets against Porphyromonas gingivalis: An in silico study

Porphyromonas gingivalis is a primary causative agent of chronic periodontitis. Moreover, it leads to several systemic diseases, including rheumatoid arthritis, cardiovascular, neurodegenerative, and Alzheimer’s diseases. It seems that the development of a vaccine against this bacterium is necessary. Thus, this study decided to identify novel immunogenic targets and developed multiple epitope-based vaccines against P. gingivalis. For this purpose, the pan/core-proteome of this bacterium was studied, and the suitable vaccine targets were selected based on different properties, including exposed localization of proteins, antigenicity, non-allergenicity, non-similarity to host proteome, stability, B-cell epitopes and MHC II binding sites, sequence conservation, molecular docking, and immune simulation. Through the quartile scoring method, 12 proteins with ≥ 20 scores were considered as suitable immunogenic targets. The results of the protein domain and functional class search showed that most of the immunogenic proteins were involved in the transport and metabolism of inorganic ions and lipids. In addition, two unknown function proteins, including WP_004584259.1 and WP_099780539.1 were detected as immunogenic targets. Three constructions carrying multi-epitopes were generated including Naked, LCL, and as chimeric structures. Among them, FliC chimeric protein had the strongest affinity to the human TLR2, 4, and 6, while the LCL platform represented the highest level of immune stimulation response. The obtained results from this study revealed new insights into prophylactic routes against P. gingivalis by introducing novel immunogenic targets. However, further investigations, including site-directed mutation and immunoassay are needed to confirm the pathogenic role and protectivity of these novel proteins.