Native or Non-Native Protein-Protein Docking Models? Molecular Dynamics to the Rescue

Investigating the mechanism of corilagin interfering with HSV-2 replication: an in vitro and in silico analysis of the cGAS-STING pathway

Herpes simplex virus type 2 (HSV-2) represents a significant etiological agent of recurrent and symptomatic genital herpes, which poses considerable risks to public health and the global economy. The cGAS (cyclic GMP-AMP synthase) protein, a pivotal component in the cGAS/STING DNA-sensing pathway, is an appealing target for pharmacological intervention due to its essential function in the immune response against DNA viruses. Recent investigations have indicated that corilagin, a polyphenolic compound derived from plants, exhibits a wide range of antiviral properties. In this study, we utilized molecular docking, molecular dynamics simulations, MM-PBSA analysis and in vitro experiments to explore the binding sites and interaction dynamics of corilagin with the cGAS protein. Our findings illustrated that corilagin formed a greater number of intramolecular hydrogen bonds with the cGAS protein and displayed lower binding energy relative to the original ligand found in the Protein Data Bank (PDB), thereby suggesting its enhanced potency. In vitro assays confirmed that corilagin effectively mitigated the overactivation of the cGAS-STING pathway, alleviated inflammation and inhibited apoptosis in HaCaT cells, thereby demonstrating a therapeutic potential against HSV-2 infection. In summary, corilagin may act as a structural template for further modifications aimed at developing more effective cGAS inhibitors, thereby advancing the treatment of viral infectious diseases.

Binding of Selected Ligands to Human Protein Disulfide Isomerase and Microsomal Triglyceride Transfer Protein Complex and the Associated Conformational Changes: A Computational Molecular Modelling Study

Human protein disulfide isomerase (PDI) is a multifunctional protein, and also serves as the β subunit of the human microsomal triglyceride transfer protein (MTP) complex, a lipid transfer machinery. Dysfunction of the MTP complex is associated with certain disease conditions such as abetalipoproteinemia and cardiovascular diseases. It is known that the functions of PDI or the MTP complex can be regulated by the binding of a small-molecule ligand to either of these two proteins. In the present study, the conformational changes of the MTP complex upon the binding of three selected small-molecule ligands (17β-estradiol, lomitapide and a phospholipid) are investigated based on the available biochemical and structural information by using the protein-ligand docking method and molecular dynamics (MD) simulation. The ligand-binding sites, the binding poses and binding strengths, the key binding site residues, and the ligand binding-induced conformational changes in the MTP complex are analyzed based on the MD trajectories. The open-to-closed or closed-to-open transitions of PDI is found to occur in both reduced and oxidized states of PDI and also independent of the presence or absence of small-molecule ligands. It is predicted that lomitapide and 1,2-diacyl-sn-glycero-3-phosphocholine (a phospholipid) can bind inside the lipid-binding pocket in the MTP complex with high affinities, whereas 17β-estradiol interacts with the lipid-binding pocket in addition to its binding to the interface region of the MTP complex. Additionally, lomitapide can bind to the b' domain of PDI as reported earlier for E2. Key residues for the ligand-binding interactions are identified in this study. It will be of interest to further explore whether the binding of small molecules can facilitate the conformational transitions of PDI in the future. The molecular and structural insights gained from the present work are of value for understanding some of the important biological functions of PDI and the MTP complex.

Quantum Mechanics-Based Ranking of Predicted Proteolysis Targeting Chimeras-Mediated Ternary Complexes

Targeted protein degradation has become the most pursued alternative modality to small-molecule inhibition over the past decade. The traditional strategy of blocking protein activity by tightly binding to a functional substrate pocket has progressed toward proteolysis-targeting chimeras (PROTACs), bivalent molecules that induce the knockdown of targeted proteins. Herein, a combined protocol is described for modeling ternary complexes via well-established approaches. We performed local protein-protein docking using Rosetta protocol and sampled the conformational landscape of a specific PROTAC molecule that was compatible with the generated protein-protein docking poses, followed by double and independent single-linkage/nearest-neighbor clustering for representative selection. Subsequently, we combined the fragment molecular orbital and density functional tight-binding methods to facilitate fast quantum mechanics-based energy calculations of the clustered ternary complexes. Finally, the computed energy values were utilized to score and select the best ternary poses, achieving good agreement with available crystallographic data.

Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

Identification of potential MMP-8 inhibitors through virtual screening of natural product databases

Matrix metalloproteinase-8 (MMP-8), a type II collagenase, is a key enzyme in the degradation of collagens and is implicated in various pathological processes, making it a promising target for drug discovery. Despite advancements in the development of MMP-8 inhibitors, concerns over potential adverse effects persist. This study aims to address these concerns by focusing on the development of novel compounds with improved safety profiles while maintaining efficacy. In this study, we employed a computational approach to screen potent and safe inhibitors of MMP-8 from the Natural Product Activity and Species Source Database (NPASS). Initially, we constructed a pharmacophore model based on the crystal structure of the MMP-8-FIN complex (PDB ID: 4EY6) utilizing the Pharmit tool. This model then guided the selection of 44 promising molecules from NPASS, setting the stage for further analysis and evaluation. We comprehensively evaluated their drug-likeness and toxicity profiles. Molecules 21, 4, and 44 were identified as potentially effective MMP-8 inhibitors through a robust pipeline that included ADMET profiling, molecular docking, and molecular dynamics simulations. Notably, molecule 21 stood out for its low toxicity, high binding stability, and favorable ADMET profile, while molecule 44 demonstrated excellent affinity. These compounds offer structural novelty compared to known MMP-8 inhibitors. These computational results can be combined with in vitro experiments in the future to validate their activity and safety. These findings provide an important reference for drug design of MMP-8 inhibitors.

Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

Guiding Competitive Binding Assays Using Protein-Protein Interaction Prediction: The HER2-Affitin Use Case

Affitins are a class of small artificial proteins, designed as alternatives to antibodies for therapeutic, diagnostic, and biotechnological applications. Recent patents by Bracco Imaging S.p.A have demonstrated the potential of two engineered affitins for designing imaging probes to detect and monitor human epidermal growth-factor receptor 2 (HER2) levels in vivo. Targeting HER2 is critical, as its overexpression is linked to poor prognosis of several cancer diseases, making it a key marker for treatment strategies and diagnostic tools. Interestingly, these affitins do not compete with the commonly used monoclonal antibodies trastuzumab and pertuzumab for HER2 binding sites, allowing their concurrent use in vivo and making them suitable for imaging or diagnostic purposes. Since these two affitins compete for the same yet unidentified binding site on HER2, structural insights into these interactions are essential for facilitating the design and development of more effective diagnostic tools and treatments. In this study, we used protein-protein docking and molecular dynamics simulations to model the binding of these affitins to HER2. The stability of the predicted complexes was quantified by using the DockQ parameter, a widely used metric for evaluating protein-protein docking predictions. The docking poses were then compared with HER2 sites likely to interact with a protein partner, as predicted by the matrix of local coupling energies method. The combination of these two computational methods allowed for the identification of the most likely docking poses. Comparative analysis with HER2-protein complexes from the Protein Data Bank suggests that both affitins may bind HER2 at the same epitopes as an antibody fragment and an affibody. These findings indicate that targeted competitive binding assays could efficiently reduce the experimental efforts to map the HER2-affitin interactions. The computational approach proposed in this study not only provides insights into this specific case but also establishes a robust framework applicable for facilitating the structural modeling and interaction prediction of other affitin-protein systems.

Binding Interaction Between Two Mutant Myocilin Olfactomedin Domain Monomers in a Homodimer

In myocilin-associated glaucoma, pathogenic missense mutations accumulate mainly in the olfactomedin domain (mOLF) of myocilin. This makes the protein susceptible to aggregation, where mOLF-mOLF dimerization is possibly an initial stage. Nevertheless, there are no molecular level studies that have probed the nature of interactions occurring between two mOLF domains and the key characteristics of the resulting dimer complex. In this work, we used AlphaFold2 to obtain an I477N mutant mOLF structure with high quality followed by a stable I477N mOLF-mOLF homodimer model using molecular docking combined with molecular dynamics simulations. Moreover, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods coupled with per-residue energy decomposition studies are carried out to identify the key residues involved in the binding interaction. Based on these results, we provide insights into the molecular level understanding of the intermolecular interaction between two mOLF domains in an I477N homodimer. Hydrogen bonds, salt bridges, and favorable van der Waals interactions are observed in the binding interface of the homodimer. Additionally, our results suggest that I477N mutant mOLF aggregation could be a multistep process, beginning with an initial mOLF-mOLF dimerization mainly mediated by residues such as Asp395 and Arg681. Also, the peptides P1 (residues 326-337) and P3 (residues 426-442) of the mOLF domain, previously identified as pertinent for myocilin aggregation, could potentially contribute to a subsequent stage of myocilin aggregation, the first step being mOLF-mOLF dimerization.

Developing a universal multi-epitope protein vaccine candidate for enhanced borna virus pandemic preparedness

Introduction

Borna disease virus 1 (BoDV-1) is an emerging zoonotic RNA virus that can cause severe acute encephalitis with high mortality. Currently, there are no effective countermeasures, and the potential risk of a future outbreak requires urgent attention. To address this challenge, the complete genome sequence of BoDV-1 was utilized, and immunoinformatics was applied to identify antigenic peptides suitable for vaccine development.

Methods

Immunoinformatics and antigenicity-focused protein screening were employed to predict B-cell linear epitopes, B-cell conformational epitopes, and cytotoxic T lymphocyte (CTL) epitopes. Only overlapping epitopes with antigenicity greater than 1 and non-toxic, non-allergenic properties were selected for subsequent vaccine construction. The epitopes were linked using GPGPG linkers, incorporating β-defensins at the N-terminus to enhance immune response, and incorporating Hit-6 at the C-terminus to improve protein solubility and aid in protein purification. Computational tools were used to predict the immunogenicity, physicochemical properties, and structural stability of the vaccine. Molecular docking was performed to predict the stability and dynamics of the vaccine in complex with Toll-like receptor 4 (TLR-4) and major histocompatibility complex I (MHC I) receptors. The vaccine construct was cloned through in silico restriction to create a plasmid for expression in a suitable host.

Results

Among the six BoDV-1 proteins analyzed, five exhibited high antigenicity scores. From these, eight non-toxic, non-allergenic overlapping epitopes with antigenicity scores greater than 1 were selected for vaccine development. Computational predictions indicated favorable immunogenicity, physicochemical properties, and structural stability. Molecular docking analysis showed that the vaccine remained stable in complex with TLR-4 and MHC I receptors, suggesting strong potential for immune recognition. A plasmid construct was successfully generated, providing a foundation for the experimental validation of vaccines in future pandemic scenarios.

Discussion

These findings demonstrate the potential of the immunoinformatics-designed multi-epitope vaccines for the prevention and treatment of BoDV-1. Relevant preparations were made in advance for possible future outbreaks and could be quickly utilized for experimental verification.

Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence

Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction

In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.

Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Directly visualizing individual polyorganophosphazenes and their single-chain complexes with proteins

Polyorganophosphazenes are water-soluble macromolecules with immunoadjuvant activity that self-assemble with proteins to enable biological functionality. Direct imaging by cryogenic electron microscopy uncovers the coil structure of those highly charged macromolecules. The successful visualization of individual polymer chains within the vitrified state is achieved in the absence of additives for contrast enhancement and is attributed to the high mass contrast of the inorganic backbone. Upon assembly with proteins, multiple protein copies bind at the single polymer chain level resulting in structures reminiscent of compact spherical complexes or stiffened coils. The outcome depends on protein characteristics and cannot be deduced by commonly used characterization techniques, such as light scattering, thus revealing direct morphological insights crucial for understanding biological activity. Atomic force microscopy supports the morphology outcomes while advanced analytical techniques confirm protein-polymer binding. The chain visualization methodology provides tools for gaining insights into the processes of supramolecular assembly and mechanistic aspects of polymer enabled vaccine delivery.

Galectin-3 - A novel ligand of complement protein C1q

In the present study we report a novel interaction of human C1q, a primary activator of the Complement system, with human Galectin-3 (Gal-3). We investigated the potential recognition between C1q and Gal-3 on a solid hydrophobic surface by ELISA, by fluorescence spectroscopy, molecular docking and molecular dynamics (MD). The data showed that C1q and Gal-3 had a pronounced affinity for protein-protein interaction and supramolecular binding, locating the binding sites within the globular domains of C1q (gC1q) and on the backside of the carbohydrate recognition domain (CRD) of Gal-3. Fluorescence spectroscopy gave quantitative assessment of the recognition with KD value of 0.04 μM. MD analysis showed that when the active AAs of the two proteins interacted, electrostatic attraction, aided by a large number of hydrogen bonds, was dominant for the stabilization of the complex. When the contact of C1q and Gal-3 was not limited to active residues, the complex between them was stabilized mainly by Van der Waals interactions and smaller in number but stronger hydrogen bonds. This is the first report analyzing the interaction of Gal-3 with C1q, which could open the way to new applications of this protein-protein complex.

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

The in-vitro development of novel enzyme-based chemo-mechanical caries removal agents

OBJECTIVES

Bromelain is a potent proteolytic enzyme that has a unique functionality makes it valuable for various therapeutic purposes. This study aimed to develop three novel formulations based on bromelain to be used as chemomechanical caries removal agents.

METHODS

The novel agents were prepared using different concentrations of bromelain (10-40 wt. %), with and without 0.1-0.3 wt. % chloramine T or 0.5-1.5 wt. % chlorhexidine (CHX). Based on the enzymatic activity test, three formulations were selected; 30 % bromelain (F1), 30 % bromelain-0.1 % chloramine (F2) and 30 % bromelain-1.5 % CHX (F3). The assessments included molecular docking, Fourier-transform infrared spectroscopy (FTIR), viscosity and pH measurements. The efficiency of caries removal was assessed by DIAGNOdent pen, measuring the excavation time and number of applications, followed by a morphological evaluation of the remaining dentine using scanning electron microscopy (SEM). The results were compared to Brix 3000 as a control.

RESULTS

The chloramine and chlorhexidine were chemically compatible with bromelain without compromising the enzyme activity. All experimental formulations showed higher viscosity and pH in comparison to Brix 3000. The DIAGNOdent readings were <20 in all groups, and the lowest readings were observed in F2. The excavation time and number of applications were lowest in F2 and F1. Both F2 and F3 produced smooth dentine surfaces with less tissue debris, but more patent dentine tubules were observed in F1 and F2.

CONCLUSIONS

The bromelain-contained formulations showed a potential to be used as chemomechanical caries removal agents in vitro. Further laboratory and clinical studies are needed to validate this claim.

CLINICAL SIGNIFICANCE

The bromelain from pineapple stem has broad specificity for cleavage the peptide bonds in denatured protein to facilitate their removal. The study proved the efficiency of this enzyme to remove the dental caries chemomechanically when used alone or conjugated with chloramine and/or chlorhexidine to enhance the disinfecting and cleansing properties.

Statistical Improvement of rGILCC 1 and rPOXA 1B Laccases Activity Assay Conditions Supported by Molecular Dynamics

Laccases (E.C. 1.10.3.2) are glycoproteins widely distributed in nature. Their structural conformation includes three copper sites in their catalytic center, which are responsible for facilitating substrate oxidation, leading to the generation of H2O instead of H2O2. The measurement of laccase activity (UL-1) results may vary depending on the type of laccase, buffer, redox mediators, and substrates employed. The aim was to select the best conditions for rGILCC 1 and rPOXA 1B laccases activity assay. After sequential statistical assays, the molecular dynamics proved to support this process, and we aimed to accumulate valuable insights into the potential application of these enzymes for the degradation of novel substrates with negative environmental implications. Citrate buffer treatment T2 (CB T2) (pH 3.0 ± 0.2; λ420nm, 2 mM ABTS) had the most favorable results, with 7.315 ± 0.131 UL-1 for rGILCC 1 and 5291.665 ± 45.83 UL-1 for rPOXA 1B. The use of citrate buffer increased the enzyme affinity for ABTS since lower Km values occurred for both enzymes (1.49 × 10-2 mM for rGILCC 1 and 3.72 × 10-2 mM for rPOXA 1B) compared to those obtained in acetate buffer (5.36 × 10-2 mM for rGILCC 1 and 1.72 mM for rPOXA 1B). The molecular dynamics of GILCC 1-ABTS and POXA 1B-ABTS showed stable behavior, with root mean square deviation (RMSD) values not exceeding 2.0 Å. Enzyme activities (rGILCC 1 and rPOXA 1B) and 3D model-ABTS interactions (GILCC 1-ABTS and POXA 1B-ABTS) were under the strong influence of pH, wavelength, ions, and ABTS concentration, supported by computational studies identifying the stabilizing residues and interactions. Integration of the experimental and computational approaches yielded a comprehensive understanding of enzyme-substrate interactions, offering potential applications in environmental substrate treatments.

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors

The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.

Large interfacial relocation in RBD-ACE2 complex may explain fast-spreading property of Omicron

The Omicron variant of SARS-CoV-2 emerged in South African in late 2021. This variant has a large number of mutations, and regarded as fastest-spreading Covid variant. The spike RBD region of SARS-CoV-2 and its interaction with human ACE2 play fundamental role in viral infection and transmission. To explore the reason of fast-spreading properties of Omicron variant, we have modeled the interactions of Omicron RBD and human ACE2 using docking and molecular dynamics simulations. Results show that RBD-ACE2 binding site may drastically relocate with an enlarged interface. The predicted interface has large negative binding energies and shows stable conformation in molecular dynamics simulations. It was found that the interfacial area in Omicron RBD-ACE2 complex is increased up to 40% in comparison to wild-type Sars-Cov-2. Moreover, the number of hydrogen bonds significantly increased up to 80%. The key interacting residues become also very different in Omicron variant. The new binding interface can significantly accommodate R403, as a key RBD residue, near ACE2 surface which leads to two new strong salt bridges. The exploration of the new binding interface can help to understand the reasons of high transmission rate of Omicron.

Evaluation of docking procedures reliability in affitins-partners interactions

Affitins constitute a class of small proteins belonging to Sul7d family, which, in microorganisms such as Sulfolobus acidocaldarius, bind DNA preventing its denaturation. Thanks to their stability and small size (60–66 residues in length) they have been considered as ideal candidates for engineering and have been used for more than 10 years now, for different applications. The individuation of a mutant able to recognize a specific target does not imply the knowledge of the binding geometry between the two proteins. However, its identification is of undoubted importance but not always experimentally accessible. For this reason, computational approaches such as protein-protein docking can be helpful for an initial structural characterization of the complex. This method, which produces tens of putative binding geometries ordered according to a binding score, needs to be followed by a further reranking procedure for finding the most plausible one. In the present paper, we use the server ClusPro for generating docking models of affitins with different protein partners whose experimental structures are available in the Protein Data Bank. Then, we apply two protocols for reranking the docking models. The first one investigates their stability by means of Molecular Dynamics simulations; the second one, instead, compares the docking models with the interacting residues predicted by the Matrix of Local Coupling Energies method. Results show that the more efficient way to deal with the reranking problem is to consider the information given by the two protocols together, i.e. employing a consensus approach.

Legionella pneumophila PPIase Mip Interacts with the Bacterial Proteins SspB, Lpc2061, and FlaA and Promotes Flagellation

The peptidyl-prolyl- cis/trans -isomerase (PPIase) macrophage infectivity potentiator (Mip) contributes to the pathogenicity and fitness of L. pneumophila , the causative agent of Legionnaires’ disease. Here, we identified the stringent starvation protein SspB, hypothetical protein Lpc2061, and flagellin FlaA as bacterial interaction partners of Mip.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Computational epitope binning reveals functional equivalence of sequence-divergent paratopes

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Epitope binning groups target-specific protein binders recognizing the same binding region.

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The “Epibin” method utilizes docking models to computationally predict competition and identify bins.

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Epibin recapitulated binding competition of repebody variants as determined by immunoassays.

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In addition, Epibin enabled identification of ‘paratope-equivalent’ residues in sequence-dissimilar variants.

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Computational epitope binning can scale to allow characterization of entire antigen-specific antibody repertoires.

The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in in vitro library display screening and next-generation sequencing have enabled accelerated development of strong binders, yet identifying their binding sites still remains a major challenge. The differentiation, or “binning”, of binders into different groups that recognize distinct binding sites on their target is a promising approach that facilitates high-throughput screening of binders that may show different biological activity. Here we study the extent to which the information contained in the amino acid sequences comprising a set of target-specific binders can be leveraged to bin them, inferring functional equivalence of their binding regions, or paratopes, based directly on comparison of the sequences, their modeled structures, or their modeled interactions. Using a leucine-rich repeat binding scaffold known as a “repebody” as the source of diversity in recognition against interleukin-6 (IL-6), we show that the “Epibin” approach introduced here effectively utilized structural modelling and docking to extract specificity information encoded in the repebody amino acid sequences and thereby successfully recapitulate IL-6 binding competition observed in immunoassays. Furthermore, our computational binning provided a basis for designing in vitro mutagenesis experiments to pinpoint specificity-determining residues. Finally, we demonstrate that the Epibin approach can extend to antibodies, retrospectively comparing its predictions to results from antigen-specific antibody competition studies. The study thus demonstrates the utility of modeling structure and binding from the amino acid sequences of different binders against the same target, and paves the way for larger-scale binning and analysis of entire repertoires.

Structural insights into recognition of chemokine receptors by Staphylococcus aureus leukotoxins

Staphylococcus aureus (SA) leukocidin ED (LukED) belongs to a family of bicomponent pore forming toxins that play important roles in SA immune evasion and nutrient acquisition. LukED targets specific G protein-coupled chemokine receptors to lyse human erythrocytes (red blood cells) and leukocytes (white blood cells). The first recognition step of receptors is critical for specific cell targeting and lysis. The structural and molecular bases for this mechanism are not well understood but could constitute essential information to guide antibiotic development. Here, we characterized the interaction of LukE with chemokine receptors ACKR1, CCR2, and CCR5 using a combination of structural, pharmacological, and computational approaches. First, crystal structures of LukE in complex with a small molecule mimicking sulfotyrosine side chain (p-cresyl sulfate) and with peptides containing sulfotyrosines issued from receptor sequences revealed the location of receptor sulfotyrosine binding sites in the toxins. Then, by combining previous and novel experimental data with protein docking, classical and accelerated weight histogram (AWH) molecular dynamics we propose models of the ACKR1-LukE and CCR5-LukE complexes. This work provides novel insights into chemokine receptor recognition by leukotoxins and suggests that the conserved sulfotyrosine binding pocket could be a target of choice for future drug development.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

A Computational Approach to Investigate TDP-43 RNA-Recognition Motif 2 C-Terminal Fragments Aggregation in Amyotrophic Lateral Sclerosis

Many of the molecular mechanisms underlying the pathological aggregation of proteins observed in neurodegenerative diseases are still not fully understood. Among the aggregate-associated diseases, Amyotrophic Lateral Sclerosis (ALS) is of relevant importance. In fact, although understanding the processes that cause the disease is still an open challenge, its relationship with protein aggregation is widely known. In particular, human TDP-43, an RNA/DNA binding protein, is a major component of the pathological cytoplasmic inclusions observed in ALS patients. Indeed, the deposition of the phosphorylated full-length TDP-43 in spinal cord cells has been widely studied. Moreover, it has also been shown that the brain cortex presents an accumulation of phosphorylated C-terminal fragments (CTFs). Even if it is debated whether the aggregation of CTFs represents a primary cause of ALS, it is a hallmark of TDP-43 related neurodegeneration in the brain. Here, we investigate the CTFs aggregation process, providing a computational model of interaction based on the evaluation of shape complementarity at the molecular interfaces. To this end, extensive Molecular Dynamics (MD) simulations were conducted for different types of protein fragments, with the aim of exploring the equilibrium conformations. Adopting a newly developed approach based on Zernike polynomials, able to find complementary regions in the molecular surface, we sampled a large set of solvent-exposed portions of CTFs structures as obtained from MD simulations. Our analysis proposes and assesses a set of possible association mechanisms between the CTFs, which could drive the aggregation process of the CTFs. To further evaluate the structural details of such associations, we perform molecular docking and additional MD simulations to propose possible complexes and assess their stability, focusing on complexes whose interacting regions are both characterized by a high shape complementarity and involve β3 and β5 strands at their interfaces.

Functional antibody characterization via direct structural analysis and information-driven protein-protein docking

Detailed description of the mechanism of action of the therapeutic antibodies is essential for the functional characterization and future optimization of potential clinical agents. We recently developed KD035, a fully human antibody targeting vascular endothelial growth factor receptor 2 (VEGFR2). KD035 blocked VEGF‐A, and VEGF‐C‐mediated VEGFR2 activation, as demonstrated by the in vitro binding and competition assays and functional cellular assays. Here, we report a computational model of the complex between the variable fragment of KD035 (KD035(Fv)) and the domains 2 and 3 of the extracellular portion of VEGFR2 (VEGFR2(D2‐3)). Our modeling was guided by a priori experimental information including the X‐ray structures of KD035 and related antibodies, binding assays, target domain mapping and comparison of KD035 affinity for VEGFR2 from different species. The accuracy of the model was assessed by molecular dynamics simulations, and subsequently validated by mutagenesis and binding analysis. Importantly, the steps followed during the generation of this model can set a precedent for future in silico efforts aimed at the accurate description of the antibody–antigen and more broadly protein–protein complexes.