The aggregation of therapeutic proteins in solution has attracted significant interest, driving efforts to understand the relationship between microscopic structural changes and protein-protein interactions determining aggregation processes in solution. Additionally, there is substantial interest in being able to predict aggregation based on protein structure as part of molecular developability assessments. Molecular Dynamics provides theoretical tools to complement experimental studies and to interrogate and identify the microscopic mechanisms determining aggregation. Here we perform all-atom MD simulations to study the structure and inter-protein interaction of the Fab and Fc fragments of the monoclonal antibody (mAb) COE3. We unravel the role of ion-protein interactions in building the ionic double layer and determining effective inter-protein interaction. Further, we demonstrate, using various state-of-the-art force fields , that the protein solvation, ionic structure and protein-protein interaction depend significantly on the force field parameters. We perform SANS and Static Light Scattering experiments to assess the accuracy of the different forcefields. Comparison of the simulated and experimental results reveal significant differences in the forcefields' performance, particularly in their ability to predict the protein size in solution and inter-protein interactions quantified through the second virial coefficients. In addition, the performance of the forcefields is correlated with the protein hydration structure.  One of One of the most important variables determining the solution stability is the intrinsic aggregation propensity of the protein, which quantifies the tendency of proteins to form aggregates. Protein aggregation reduces antibody product quality, which could impact efficacy. Moreover, the presence of aggregates in some circumstances has been linked to increased immunogenicity, making it essential to monitor and control aggregation . High-coProteins are believed to aggregate through short-range interactions between hydrophobic and uncharged or charged regions of partially unfolded proteins. Quantifying the interaction between folded proteins as a function of distance can provide microscopic insight into the aggregation mechanism and help identify residues and regions which could potentially be modified to reduce aggregation. Specifically, protein aggregation takes place when aggregation-prone regions on the protein surface interact with each other. In solution, proteins undergo intermittent unfolding at the secondary and tertiary structure level, resulting in either transient or irreversible exposure of the aggregation hot spots at the protein surface. Undistorted proteins also form small reversible oligomeric clusters, which may precede subsequent unfolding of the protein, followed by aggregation .The kinetics of protein aggregation depend strongly on the composition of the protein formulation. Addition of  salt or change in the solution pH can modify the rate of aggregation . Increasff s) that can predict protein interactions, structure and dynamics as a function of the solution composition. Here we report all-atom MD simulations of the Fab and Fc fragments of the monoclonal antibody (mAb) COE3 [ff s . Our work highlights essential differences in the predictions obtained with different ff s, and provides insights into the microscopic mechanisms influencing the aggregation of these complex proteins.Molecular dynamics (MD) simulations have gained popularity as a tool to explain the molecular mechanism behind various biological processes. The increase in computational power has expanded the applicability of MD simulations to the study of large proteins  in atomiAb) COE3 . We inve2.2.1.We simulated the Fab and Fc fragments of mAb COE3. The sequence of the Fc fragment of COE3 is identical to the Fc fragment of the human IGG B12 (pdb id: 1HZH). The sequence of COE3 Fab fragment differs by 27 rom Ref. . mAb COEThe simulations were performed at conditions corresponding to pH=7. propKa3.1 ,19 was uff parameters used to describe the various inter-, and intra-molecular interactions in the simulations are listed in Table ff [ff [α-helical regions [ff , we considered two water models (wms): the spc [ff , and the spce model [ff [wm, and with tip3p [lj) interaction, whereas the tips3p model was parametrised by adding lj interactions with the hydrogens in the water molecule. These two models, tip3p and tips3p, have similar surface tension, dielectric constant and self-diffusion coefficient [ff [wm [wm on the protein solvation, we also used the spce wm. Table ff parameter combinations considered in this work, and the ion parameters are listed in Table The e − ion  in combie − ion  paramete-ildn ff . The gron ff [ff  is a modn ff [ff  ff. The  regions . For thi the spc ,27 modelce model , which podel [ff  were perth tip3p ,27 waterfficient . With thient [ff , we used2.2.ff-wm combinations, we performed three independent runs (for both Fab and Fc) resulting in a cumulative simulation time of 8.4 All the simulations were performed using the GROMACS(2021.3) package ,32. The ermostat   and two perpendicular axes that quantify the width (W) and height (H) of the protein   techniquThe initial conformation of two Fc fragments is shown in Figure -rescale  thermost-rescale .Figure 2.5.The Fab and Fc samples were provided by AstraZeneca as stock solutions in 25 mM Histidine buffer, 72.5.1.q. The momentum transfer, q, is determined by the neutron wavelength (λ) and scattering angle (θ) as q is the momentum transfer, and The radius of gyration  was used for holding the sample. All measurements were conducted at 25C. K is an optical constant, deduced by the light wavelength (633 nm) and Static light scattering (SLS) was used to determine the 2e method . Differe3.3.1.ff employed. Our results are comparable to the experimental value of 2.5 nm, determined from the SANS result using the Guinier approximation. The gromos ff features the largest deviation from the experimental data. The analysis of the probability distribution of Rff predicts two distinct peaks . This change is significant, considering ff,wm) to reproduce amino acid solvation free energies, is close to the results obtained with other state of the art ff s (charmm-tip3p), and with the value of 2.65 nm, obtained using SANS. We recall that unlike all-atom models, the gromos ff employed here does not include non-polar hydrogen atoms explicitly, and uses instead a united-atom representation for non-polar bond pairs. We performed additional simulations with the gromos ff and spce wm, as this model has been used in combination with the gromos ff in previous studies of proteins in solution [wm influences the protein structure to a significant extent, and therefore the combination of protein ff s with different wms must be exercised with care.We find a stronger dependence of the Rsolution . Furthersolution  and reprsolution  better. ff s against experimental data. The probability distribution of ff predicts much broader probability distributions for both Fab and Fc that are biased towards smaller values of Rff has been parameterised by fitting the solvation free energies of amino acid analogues to experiments. The solvation free energies determine the effective hydrophobicity of proteins. Previous gromos ff (53a6)-spce simulation of proteins at the water-vapour interface precdicted a strong protein adsorption. This behaviour has been attributed to the higher surface hydrophobicity of gromos proteins, which would be connected to the solvation free energy-matched gromos parameters [ff , pointed towards an enhanced protein aggregation propensity [The average rameters . Additioopensity , which wff for the Fc fragment, we analyzed the variation of To gain further insight into the origin of the broad ff s.Microscopically, the monotonic decrease of Rff s. Commonly used ff s are known to predict preferentially collapsed conformations for unstructured or flexible protein regions [ff s towards collapsed conformations of such flexible regions may result in a smaller average value of Rff s predict. The enhanced flexibility of the Fc fragment is consistent with earlier studies [Overall, our results show that the simulated R regions ,49. The  studies ,51 of imff , leading to shorter protein widths. Generally, the results for Fc show more variability with respect to the PDB reference data, reflecting the larger flexibility of Fc in solution.While Rsee e.g. ,53). We ff s used in this study predict Rff which shows clear deviations when the simulations are performed with the spce wm. For the Fc fragment, we find a larger variability in the ff – spce predicting again larger deviations form the experiments followed by the amber ff . In case of the gromos ff , the observed deviations can be attributed to the enhanced amino acid hydrophobicity of this forcefield. The opls-tip4p and charmm-tips3p combinations predict Rff s also predict protein dimensions similar to those extracted from PDB crystallographic structures.The results discussed above indicate that most of the 3.2.3.2.1.gmx rdf -surf included in the gromacs package. This code calculates the RDF by assigning each atom belonging to water to a bin corresponding to the minimum distance between the atom and the protein surface. The number of atoms in each distance bin is then divided by the bin-width. The integral of this RDF upto a distance r is, thus, the number of atomic pairs (protein-water) within distance r of the protein surface. We show the RDFs in Figure S4 of SI. To facilitate comparison between results obtained for different systems, we normalised the original RDFs by performing a linear fitting in the interval We analyzed the hydration structure around the protein by computing the un-normalised water-protein Radial Distribution functions (RDFs) using the code ff-wm combination. The gromos ff , in combination with both spce and spc wms, for instance, leads to a much weaker hydration of the protein surface as compared to other systems. This difference cannot be explained in terms of the wm only. Indeed, the hydration structure using spce and the opls ff, is much stronger, and similar to that obtained with other state of the art ff s, such as charmm with the tips3p water model. The weaker hydration is therefore connected to the weaker protein-water interactions and the fact that the amino acids modelled with the gromos ff are more hydrophobic. The larger hydrophobicity explains the tendency of the gromos proteins to adopt a more compact structure in water, which is consistent with the reduction in the level of hydration reported in Figure As discussed above, the charge of Fab (+11e) and Fc (+2e) fragments at pH=7 is rather different. However, the hydration structure is very similar, (c.f. panels (a) and (b) in Figure t layers  or hydrot layers . The hyd3.2.2.ff combinations. The RDFs reveal apparent differences between the ionic distribution around the Fab and Fc. Firstly, the ionic double layer structure is weaker around the Fc fragment, as indicated by the larger accumulation of ClIon-protein interactions play an essential role in regulating the protein-protein interactions, e.g. by adsorbing selectively at specific regions on the protein surface. Differences in the ionic environment around the proteins can lead to aggregation. Figures ff predicts very strong ionic adsorption at the protein surface, as shown by the height of the first peak of the RDFs . The number of ions within a given distance r of the protein surface was obtained from the integral of the RDFs shown in Figures r, divided by the bin width. Thus, the cumulative number of ions up to a distance r around the protein is given by:ff s predict significant charge enhancement at the protein surface, revealing strong co-ion adsorption. This result shows that simple electrostatic arguments do not explain ion adsorption, and as noted above, dispersion interactions play an important role in determining the ion-protein interactions. Among all the ff s studies here, the charmm27-tip3p model leads to substantial charge enhancement, resulting in an effective surface charge The results discussed above show that the ionic environment around the proteins depends significantly on the ζ) for the different ff s investigated above. The decay of the net charge in Figure ff predicts the shortest ζ and the charmm ff the longest, with the values ranging from 0.62 nm to 0.85 nm. For the Fc fragment, we obtain a larger variability in the values of ζ, with values between 0.28 nm and 1.07 nm. This variability reflects deviations from the exponential decay of Qζ, is problematic. We report in Table 5 the values for this decay length as they might be helpful to inform future electrostatic models.To quantify the decay length of the electrostatic interactions associated with the protein charge and the ion adsorption layers, we calculated the Debye length . Our data for Fab roughly follow the dependence predicted by Equation  theory. The PB Debye length for monovalent 1:1 ions is defined by:ζ=, see e.g. ,58. The 35 g/mol , for theff s in reproducing the interaction of therapeutic proteins in solution. For Fc fragment, we calculated BWe computed the Fc-Fc potential of mean force using charmm-tips3p and gromos-spc parameters . Our results reveal a clear correlation between hydration level and inter-protein interaction.Figure ff predicts very large virial coefficients, ff used here over-predicts the amino acid interaction, resulting from an enhanced protein hydrophobicity and consequently, strong protein-protein interactions, which is reflected in the large negative virial coefficient, We calculated the virial coefficient using the Equations -6) for  for 6) folutions . The Grolysozyme , and henff parameters.The umbrella sampling trajectory generated with the charmm forcefield predicts a Fab-Fab PMF that is mostly repulsive  and molecular dynamics simulations.We have investigated the hydration structure, ion-protein interaction, and protein-protein interaction of the Fab and Fc fragments of mAb COE3, using state-of-the-art atomistic ff s predict similar radii of gyration for the Fab fragment. We find that this fragment features small flexibility; therefore, the average structure in the solution is very well-defined. In contrast, the Fc fragment is highly flexible, especially near the hinge region, and the radii of gyration predicted by the different ff s differ from each other. charmm and opls are closer in agreement with experiments, while the gromos ff , and to some extent, the amber ff underestimate the protein size. The smaller size predicted by the gromos ff can be traced back to the enhanced hydrophobicity of the proteins, leading to more compact proteins. The protein size is correlated with the hydration strength, and ff s that over-predict hydrophobicity feature significantly weaker solvation shells.All the e.g. using neutron reflectivity. Fab and Fc are anisotropic proteins whose shape can be described by three perpendicular axes that quantify protein anisotropy. Our estimates of the protein dimensions in solution agree reasonably well with the results employed in previous experiments, which were based on crystal structure data. For most ff s, the deviations vary between 5–10%, with the gromos ff showing more significant differences, up to 15%, from the crystal data. Computationally derived protein dimensions consider the protein's fluctuation in solution. These predictions might provide an approach to improve the models needed to deconvolute the information obtained from neutron experiments.The MD simulation trajectories provide information that can be helpful to interpret experimental results obtained, ff s might predict compact structures, particularly in the disordered protein regions. We found that the protein compactness can also depend on the wm employed. Specifically, we find that the combination of the gromos and spce ff results in very compact proteins. This ff combination is popular in the simulation community. Our results indicate that such an approach should be used carefully when modelling flexible proteins, such as the Fc fragment.It is known that state-of-the-art ff and tip3p or tips3p wms. The enhancement emerges from the accumulation of co-ions  resembles the structure expected based on simple mean field electrostatic theories. Namely, a build-up of counterions in the neighbourhood of the protein surface and an approximately exponential decay of the charge density from the protein surface. However, we also find evidence for deviations from the simple electrostatic picture. These are more evident for Fc, which has a lower charge (+2e at pH=7). In this case, we found an enhancement of the protein charge modelled with the charmm ff parameters. The gromos ff predicts a strong, attractive interaction between Fc fragments . Regarding the Fab fragments, the charmm virial coefficient is positive, indicating repulsion, compatible with the high electrostatic charge of Fab (+11e) at pH=7. Our Static light scattering experiments show that Fc features a strong negative virial coefficient in the absence of buffer, indicating strong protein attraction. However, Fab features a positive virial coefficient indicating protein repulsion. This experimental behaviour is reproduced by the charmm ff , while gromos predicts strong attraction  for Fab and Fc fragments. These results support the idea that gromos proteins have a stronger hydrophobic character. We note, however, that the computation of virial coefficients for these complex proteins is challenging due to the configurational space determining the potential of mean force. To calculate our potentials of mean force, we chose similar reaction coordinates for the different systems to ensure a consistent comparison of the results obtained with different models. It will be interesting to expand our investigation by using e.g. enhanced sampling techniques to compute PMFs along other reaction coordinates, which should allow the quantification of the virial coefficients taking into account non-spherical protein geometries. Albeit more challenging, the PMF computations using the methods discussed in this work, might be extended to other fragment pairs like Fab-Fc, as well as full antibodies, taking advantage of more advanced enhanced sampling techniques.Regarding inter−protein interactions, we found that interaction strength depends considerably on the ff s predict significant differences in the solvation, ion adsorption (double layer structure) and inter-protein interaction. More experimental results are needed to develop accurate ff s to investigate the microscopic behavior of these complex therapeutic proteins.Overall, our results indicate that the simulation of large proteins, such as monoclonal antibodies, must be performed with care. As shown here, different state-of-the-art Click here for additional data file.


Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement. In 2020, DeepMind’s AlphaFold2 method  made a m4The core of the AlphaFold2 framework consists of transformer neural networks powered by the attention mechanism , a sequeIt is worth noting that both AlphaFold2 and RoseTTAFold rely on the availability of evolutionary information in the form of MSAs, which are not always as abundant as these methods demand, such as with orphan proteins or rapidly evolving proteins. Protein language models (PLMs) aim to overcome this limitation by pretraining a family of transformer-based models of protein sequences. For example, OmegaFold  introducDespite their methodological differences, the state-of-the-art methods for protein structure prediction have two things in common: 1) Their underlying neural network architectures use transformers as building blocks, and 2) their source codes are freely available with open-source license. That is, an open-source ecosystem of transformer-based neural networks is having a transformative effect in the field of protein structure prediction.Given these recent advances, a natural question arises: how far have we progressed in predicting protein structures since the breakthrough of AlphaFold2 in 2020? It is widely accepted by the structure prediction community that blind structure prediction tests are needed to objectively assess the performance of new protein structure prediction methods. Fortunately, the recently concluded CASP15 meeting provides an excellent testbed to evaluate these emerging methods over the same set of proteins. Here, we benchmark the predictive modeling performance of AlphaFold2, RoseTTAFold, ESMFold, and OmegaFold on a set of 69 CASP15 single-chain protein targets with available experimental coordinates. Our benchmarking differs from the CASP15 official assessment in multiple ways. First, we downloaded the open-source software implementations for all methods and employed the modeling pipeline in fully automated mode with default parameter settings, without any manual interventions. Second, we evaluated single-chain predictive modeling accuracies by directly comparing the full-length predictions against the experimental coordinates, without splitting them into domains. Additional domain-level analyses were performed on multidomain proteins to examine the relative accuracies of individual domains and their packing. Third, we used static databases, libraries, and model weights released before CASP15, without intermediate updates. Our study highlights the similarities and differences among contemporary methods, reveals the challenges, and emphasizes future areas of improvement.y axis) and the whole chain GDT-TS (x axis). The Grishin plots shown in Inset). Collectively, the results indicate that the source of the error in large multidomain proteins is often due to the misprediction of domain orientations even when the individual domains are accurately predicted. Clearly, there is room for improvement in the relative packing of individual domains.For the 19 multidomain proteins according to the official CASP15 domain definitions, we examined the relative accuracies of 45 individual domains and their packing by inspecting the Grishin plots , which aThree representative CASP15 proteins are shown in Inset).In terms of correct overall topology prediction, quantified by the percentage of template modeling score (TM-score) > 0.5 , all metHow does the stereochemistry of the predicted structural models compare with the experimental structures? The Ramachandran plots , capturiWith respect to the side-chain positioning, measured by the global distance calculation for side-chains (GDC-SC) metric , there iDespite the dramatic progress in protein structure prediction with transformer-based neural networks trailblazed by AlphaFold2, our study reveals two lingering issues. First, accurate prediction of large multidomain proteins with complex topology remains challenging. For some of such targets, none of the methods were able to predict even the correct overall topology with appropriate domain orientations. Adjusting the transformer model architecture by emulating the principles of interprotein interactions for modeling intraprotein domain–domain interactions may be necessary for large multidomain proteins. Second, the accuracy of side-chain positioning remains low for all methods. Structure prediction algorithms will benefit from accurate side-chain packing, possibly by incorporating side-chain optimization in the neural architecture.The rise of PLMs clearly offers some unique advantages by being applicable even for proteins that do not have identifiable homologous sequences in the current sequence databases and bypasses the computational overhead of MSA searching. However, the performance of ESMFold and OmegaFold in our benchmarking raises the question: have PLM-based methods already eliminated the need for evolutionary information? Probably not yet. But PLM can be effective for small proteins with simple topology. Combining the strengths of MSA and PLM may be a promising avenue for further progress, broadening the applicability of predictive protein modeling in structural biology.SI Appendix) and subsequently evaluated the predictive modeling performance using standard evaluation metrics including GDT-TS (SI Appendix).We considered 69 single-chain protein targets from CASP15 having length less than 3,000 residues with available experimental coordinates. We obtained the sequences of these target proteins from the CASP15 website and predicted their structures using publicly-available versions of AlphaFold2, RoseTTAFold, OmegaFold, and ESMFold Click here for additional data file.Dataset S01 (GZ)Click here for additional data file.


SARS-CoV-2, a deadly coronavirus sparked COVID-19 pandemic around the globe. With an increased mutation rate, this infectious agent is highly transmissible inducing an escalated rate of infections and death everywhere. Hence, the discovery of a viable antiviral therapy option is urgent. Computational approaches have offered a revolutionary framework to identify novel antimicrobial treatment regimens and allow a quicker, cost-effective, and productive conversion into the health center by evaluating preliminary and safety investigations. The primary purpose of this research was to find plausible plant-derived antiviral small molecules to halt the viral entrance into individuals by clogging the adherence of Spike protein with human ACE2 receptor and to suppress their genome replication by obstructing the activity of Nsp3  and 3CLpro (main protease). An in-house library of 1163 phytochemicals were selected from the NPASS and PubChem databases for downstream analysis. Preliminary analysis with SwissADME and pkCSM revealed 149 finest small molecules from the large dataset. Virtual screening using the molecular docking scoring and the MM-GBSA data analysis revealed that three candidate ligands CHEMBL503 (Lovastatin), CHEMBL490355 (Sulfuretin), and CHEMBL4216332 (Grayanoside A) successfully formed docked complex within the active site of human ACE2 receptor, Nsp3, and 3CLpro, respectively. Dual method molecular dynamics (MD) simulation and post-MD MM-GBSA further confirmed efficient binding and stable interaction between the ligands and target proteins. Furthermore, biological activity spectra and molecular target analysis revealed that all three preselected phytochemicals were biologically active and safe for human use. Throughout the adopted methodology, all three therapeutic candidates significantly outperformed the control drugs (Molnupiravir and Paxlovid). Finally, our research implies that these SARS-CoV-2 protein antagonists might be viable therapeutic options. At the same time, enough wet lab evaluations would be needed to ensure the therapeutic potency of the recommended drug candidates for SARS-CoV-2. Coronaviridae family within Nidovirales order [The World Health Organization (WHO) reported that the prevalence of SARS-CoV-2 is spreading at an alarming rate, posing severe health problems. The most recent outburst of second wave of SARS-CoV-2 has turned into a worldwide catastrophe. Following the coronavirus (CoV) epidemic in China in December 2019, WHO classified SARS-CoV-2, as the newest candidate of the es order . Accordies order . It has es order .SARS-CoV-2 is a membrane-encased positive-sense single-stranded RNA ((+) ssRNA) virus having a diameter ranging from 60 to 140 nanometers . The envThe viral disease is spreading at a surprising pace worldwide, and researchers are racing to develop effective drugs to use as therapeutic agents. The most promising choices appear to be natural compounds with substantial bioavailability and minimal cytotoxicity . ClinicaHumans have always relied on natural compounds, especially phytochemicals, to treat health problems since the dawn of time. Recently, Shawan et al. presented luteolin and abyssinone II as possible phytochemicals against SARS-CoV-2 . BesidesThe main focus of this in silico work was to utilize computational tools, i.e., molecular docking and MD simulation to examine the effective binding interactivity and affinities of repurposed antiviral phytochemicals with the human ACE2 receptor, Nsp3 macrodomain, and the main protease of the SARS-CoV-2 virus and identify the finest ligand hit . Among ahttp://bidd.group/NPASS/) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) servers as prospective ligands for SARS-CoV-2. The recently approved COVID-19 antiviral drugs Molnupiravir and Paxlovid were used as control drugs [Virtual screening of natural bioactive molecules has become the standard method in the present therapeutic development workflow . In thisA drug-like molecule can be considered a drug candidate by assessing its drug-like properties. The canonical SMILE sequence of the 1163 small molecules was fetched from the PubChem drug web server. The free accessible SwissADME was employed to compute the major physicochemical descriptors, pharmacokinetic properties, drug-like parameters, and associated factors . To analpkCSM is an online tool that employs graph-based structural signatures for determining and improving pharmacokinetic characteristics and toxicity in small molecules. To devise an ADMET prediction benchmark for in silico drug discovery, pkCSM applies a cut-off scanning strategy . The choAt pH 7.4, polar hydrogen atoms were introduced to the downloaded 3D molecular ligands in SDF  format using the build module of the Avogadro 1.2.0. The same program was then used to conduct geometry optimization and energy reduction employing the MMFF94 force field and steepest descent option. These structures were retained in the PDB  extensiohttps://www.rcsb.org/). Initially, the 3D structures were prepared in the PyMOL program [The preferred structures of SARS-CoV-2 main protease in complex with FSCU015 (PDB ID: 7NT3), Nsp3 macrodomain in complex with ADP-ribose (PDB ID: 7KQP), and inhibitor bound human ACE2-related carboxypeptidase (PDB ID: 1R4L) were taken from RCSB repository  was employed to detect the active region of target proteins [Finding a ligand-binding region on a protein is the basic strategy for the molecular docking technique . The posproteins . It appl3 and exhaustiveness 10, and ligands being flexible while receptors remained rigid [Virtual screening via docking studies is extensively used in computer-led pharmaceutical research to uncover promising drug-like substances. Initially, AutoDock vina was exploited to conduct rigid molecular docking among the proteins and selected compounds (ligands and control drugs), including a search area of 27,000 med rigid . AutoDocSchrodinger was employed to perform glide docking and MM-GBSA analyses . Previously screened ligands having higher affinity for target proteins than the reference drugs were explored in this step.The LigPrep module yields top hits of 3D configurations for small molecules, beginning from 1-dimensional/2-dimensional/3-dimensional structures in Maestro, Mol2, SMILES, or SD format . By intrThe protein structures  were loaded straight into the protein preparation wizard . ProteinThe grid region directs small molecules to the binding center of the protein, making it an important part of molecular docking research. The grid model was created with the standard options of a Van der Waals radius scaling marker of 1.0 and a charge threshold score of 0.25 in the receptor grid generation package. The attached ligands UQZ, AR6, and XX5 within the protein structures main protease, Nsp3, and human ACE2 receptor, respectively, were used to define the region for the grid map.Glide is a combined molecular docking technology that can facilitate both ligand and receptor flexibility . Glide Xa and b are coefficient constants for VdW and Coul, respectively, VdW is the Van der Waals energy, Coul is the Coulomb energy, H-bond is the hydrogen bonding with the receptor, Metal is the binding with metal, Lipo is the constant term for lipophilic, BuryP is the buried polar group penalty, RotB is the rotatable bond penalty, and Site is the active site polar interaction [Here, eraction .The binding free energy among the receptor and the collection of small molecules was measured using the prime MM-GBSA module. The binding energy of the ligand-protein constructs was estimated utilizing the OPLS3e force field, and the docked conformations were minimized utilizing Prime's native optimization tool. ΔEMM represents lowered energy deviations among the totality of the energies of the protein and ligand and protein-ligand complex. ΔGsolv displays the divergence in the GBSA solvation energy of the complex structure and the aggregate of the salvation energies for the ligand and protein. ΔGSA describes the deviation in the energies for the surface area of a complex and the total surface area of the ligand and protein complex [Here,  complex .https://simlab.uams.edu/) service was exploited to simulate the protein-ligand conformations, and the GROMOS96 43a1 force field was employed to produce the topological data of the complex constructs [http://davapc1.bioch.dundee.ac.uk/cgi-bin/prodrg) Server was employed to render small molecule topology and coordinate information [The molecular dynamics program simulates the movements of a protein molecule utilizing the interaction potential to compute interatomic energies and equations of motion that regulate the machinery's dynamics in the drug design study. It illustrates the stability and flexibility data of ligand binding to a flexible target protein. GROMACS  to determine the human body receptors for small compounds that had previously been identified by molecular docking and shown stability via MD simulation investigations [The anticipation of a molecular target for a small-molecule is vital for drug research and development. These studies are essential for assessing the potential for adverse reactions or cross-reactivity in igations . The canhttp://www.pharmaexpert.ru/passonline/) was employed to the prediction of biological activity scales for Lovastatin, Sulfuretin, and Grayanoside A [The PASS-Way2Drug webserver , the number of hydrogen bond donors , the number of hydrogen bond acceptors (standard range: ≤10), lipophilicity , and molar refractivity (preferable range: 40–130). Additionally, the ligands were screened based on the criteria of Ghose, Veber, Egan, and Muegge's rule. Subsequently, 497 out of 1163 compounds were shortlisted for the following evaluation , and Paxlovid  . LovastaGlide incorporates high-throughput virtual screening (HVS), estimating protein-ligand interacting sites and grading ligands using experimental score systems. Out of the 149 small compounds, 120 small molecules exhibited greater binding energy for SARS-CoV-2 main protease  are cytochrome p450, oxidoreductase, kinase, family A of G protein-coupled receptor, enzyme, and membrane receptor and the possibility of binding, respectively, are 16%, 12%, 8%, 8%, 8%, and 4% respectively. For Sulfuretin, they they may bind with kinase (52%), enzyme (24%), and membrane receptor (4%); and for Grayanoside A, they they may bind with protease (20%), kinase (20%), surface antigen (4%), enzyme (12%), and family A of G protein-coupled receptor (4%). Control drug Paxlovid provides the binding possibility in target sites are protease (60%), enzyme (16%), family A of G protein-coupled receptor (8%), membrane receptor (4%), and surface antigen (4%). The prediction tool did not show any human target for Molnupiravir .Using the identified compounds, prediction of activity spectra for substances (PASS) was carried out and is shown in Supplementary Tables https://covid19.who.int/). Surprisingly, currently, a limited amount of effective anti-SARS-CoV-2 therapeutics are available, and most of them are under investigation [In recent years, pandemics and epidemics caused by viruses have become one of the most prevalent reasons for infections and mortality worldwide. SARS-CoV-2, the updated variant of coronaviruses, has led to a catastrophe, with 665,518,891 and 6,714,212 confirmed cases and fatalities, respectively , became a viable therapeutic focus due to its involvement in the development of replication-translation mechanisms. Furthermore, given the accessibility of high-resolution protein structures, these proteins have a highly conserved sequence and no homology with human proteins . Nsp3 isThis research was divided into three sections, namely, virtual screening (VS) of the physicochemical and pharmacokinetic features of drug-like compounds, virtual screening by molecular docking of proteins and ligands, and simulation of the best hit complexes. In the initial stage, we studied the drug-like characteristics of the ligands utilizing the five principles of Lipinski. We stuck to the established guidelines, i.e., hydrogen bond donors ≤ 5, hydrogen bond acceptors ≤ 10, molecular mass < 500, and logP < 5. The molecular weight of a small molecule can influence its absorption, bile excretion ratio, blood-brain barrier passage, and engagements involving receptors . LikewisThe importance of virtual screening employing molecular docking has grown significantly in the field of drug development over time. According to the study, 24 small molecules had a greater binding affinity against the main protease (7NT3) than the reference compounds: Molnupiravir and Paxlovid . The MM-GBSA approach is recognized for its impressive precision in estimating the free binding energy of small molecules to target proteins. Both analyses indicated that Lovastatin (CHEMBL503) had a higher glide score and binding-free energy value of -6.01 kcal/mol and -52.85 kcal/mol, respectively. Recently, Mashraqi et al. found fenoterol had a glide score and MM-GBSA values of −7.14 and -38.733 kcal/mol . We founα and RMSF of the amino acids, also the ligand-protein H-bonding association, the solvent-accessible surface area (SASA), and the radius of gyration (Rg), were assessed to determine the steadiness of the complex structures [MD simulations offer a plethora of energetic data on protein and ligand binding, as well as a wealth of structural figures on biological macromolecules. This type of knowledge is crucial for comprehending the structural and functional correlation of the receptor and the basis of protein-ligand association, and also for steering the therapeutic research . During ructures . The RMSructures . Thus, aructures . The SASIn the context of Nsp3, the ligand Sulfuretin did not produce conformational changes to the protein. Firstly, the RMSD value revealed that Nsp3_Sulfuretin was consistently stable compared to the reference complexes. Throughout the simulation, the Sulfuretin molecule remained relatively stable. Upon binding of the Sulfuretin molecule, the Nsp3_Sulfuretin complex displayed lesser fluctuation in comparison to the reference complexes. According to the Rg value of the Nsp3_Sulfuretin complex, it remained steady during the simulation timeframe, suggesting the compactness of the protein following inhibitor binding. Nsp3_Sulfuretin complex displayed a similar amount of hydrogen interactions as the reference complexes demonstrating excellent protein-ligand stability. Similarly, the SASA value revealed that the Nsp3_Sulfuretin complex remained unchanged throughout the simulation, supporting earlier findings. The simulation results for human ACE2 protein showed that the binding of the Grayanoside A molecule caused a small consequence on the structure of human ACE2 protein. The RMSD graph of protein-ligand complexes and ligands implied that the ligand (CHEMBL1909923) might not destabilize the protein. The RMSF results revealed that there was a similar fluctuation, suggesting the identical nature of the binding of the three ligands . The plots of Rg, hydrogen bond, and SASA value also confirmed the previous viewpoint, indicating that the Grayanoside A molecule's attachment did not impair the stability of human ACE2 protein. In case of Desmond, we found similar results that further validate our findings. The RMSD values suggested that Lovastatin, Sulfuretin, and Grayanoside A were firmly bound to the proteins. The RMSF plots implied that the main protease (3CLpro), Nsp3, and human ACE2 receptor were structurally stable while bound with respective ligands. The protein-ligand attachment maps continuously showed that the proposed ligands-maintained contact with active site residues. Lastly, the postsimulation MM-GBSA results stated that Lovastatin, Sulfuretin, and Grayanoside A had a higher free-binding affinity for their respective proteins.Previous structure-based computational research yielded similar findings, demanding wet-lab investigation. According to study lead by Gurung et al., bonducellpin D was found as a potential inhibitor for SARS-CoV-2 3CLpro protein . In anotRepurposing drug-like phytochemicals is a secure means of building new therapeutics, with the main benefit of lowering the cost and duration of preclinical trials for novel candidates. The COVID-19 infectious disease induced by the SARS-CoV-2 outbreak has caused a worldwide medical catastrophe and finding a suitable cure for the virus continues to be a primary concern. The findings of our work indicated that using a structure-based strategy such as molecular docking and MD simulations, novel therapeutic candidates may be developed that selectively address the nonstructural protein 3, the main protease from SARS-CoV-2, and the human ACE2 protein. A preliminary screening of 1163 small phytochemicals combining drug-likeness and ADMET characteristics resulted in the identification of 149 of them. The degree of binding interaction and energy between the filtered compounds and the main protease, nonstructural protein 3, and human ACE2 receptor was estimated utilizing the docking procedure on the AutoDock vina and Schrodinger Suites. Compounds Lovastatin, Sulfuretin, and Grayanoside A outperformed Molnupiravir and Paxlovid in terms of binding score and hydrogen bond numbers against the main protease, Nsp3, and human ACE2 receptor, respectively. Eventually, 100 ns MD simulation studies of 3CLpro_ligand, Nsp3_ligand, Grayanoside A_ligand complexes were completed to evaluate and improve our proposed design. This investigation is aimed at determining the promising inhibitors and devise protocols for continual improvement of COVID-19 medications. To summarize, all the repurposed compounds suggested here may provide a holistic understanding of structure-based drug development for SARS-CoV-2 given that they continue to remain potent in further drug development processes.


Arabidopsis thaliana. Employing this system, we successfully modified the Photosystem II (PSII) complex by ectopically attaching an extrinsic subunit of PSII, PsbTn1, to CP26—a component of the antenna system of PSII. Surprisingly, this mandatory interaction between CP26 and PsbTn1 in plants impairs the efficiency of electron transport in PSII and unexpectedly results in noticeable defects in leaf development. Our study not only offers a general strategy to modify multiprotein complexes embedded in thylakoid membranes but it also sheds light on the possible interplay between two proteins without native interaction.A promising approach for the genetic engineering of multiprotein complexes in living cells involves designing and reconstructing the interaction between two proteins that lack native affinity. Thylakoid-embedded multiprotein complexes execute the light reaction of plant photosynthesis, but their engineering remains challenging, likely due to difficulties in accurately targeting heterologous membrane-bound proteins to various sub-compartments of thylakoids. In this study, we developed a ubiquitin-based module (Nub–Cub) capable of directing interactions in vivo between two chloroplast proteins lacking native affinities. We applied this module to genetically modify thylakoid multiprotein complexes. We demonstrated the functionality of the Nub–Cub module in the model organism  In both eukaryotic and prokaryotic cells, the majority of proteins do not act in isolation, but instead engage in interactions with one or more other proteins to create more-extensive protein complexes. Protein–protein interaction has consistently remained a fundamental focus in biological science, leading to the development of numerous methods for its investigation . The utiSynthetic biology offers a crucial opportunity in higher plants to reengineer the light reactions of photosynthesis, which could lead to a significant improvement in the efficiency of these reactions. Such an improvement has the potential to increase biomass and crop yields ,9. MoreoArabidopsis by ectopically attaching one of the extrinsic subunits of the PSII core, PsbTn1, to CP26, a component of the PSII antenna system. Surprisingly, this mandatory interaction of CP26 and PsbTn1 had a noticeable impact on the photosynthetic electron transport rate (ETR) of PSII. In addition, this manipulation resulted in unexpected defects in the leaf development of Arabidopsis. Our study offers not only a general strategy to modify multiprotein complexes embedded in membranes, such as thylakoid multiprotein complexes and mitochondrial respiratory chain complexes but also a route to explore possible interactions between two proteins that lack inherent affinity. This module has the potential to be a versatile tool for studying and manipulating protein–protein interactions in the chloroplasts of plant cells. It will offer new insights into various aspects of chloroplast biogenesis and opens up avenues for enhancing photosynthesis and crop yield through synthetic biology approaches.Ubiquitin is a conserved protein of 76 amino acids that is usually attached to the N-terminal of proteins as a signal for their degradation. It was found that an N-terminal part  has a high affinity for a C-terminal part of ubiquitin , and they can spontaneously assemble into a split-ubiquitin heterodimer when expressed in the same yeast cell . BuildinArabidopsis  to the N-terminal of the split YFP proteins fused with Cub and Nub. Our expectation was that the TP sequence would guide the fused proteins into chloroplasts. We again observed prominent YFP signals that merged well with chlorophyll autofluorescence  assay, to investigate the protein–protein interaction facilitated by Nub and Cub in tobacco leaves. By using CP26 and TROL as our experimental subjects, the LCI assay effectively demonstrated the viability of protein–protein interaction facilitated by Nub and Cub C.LHCa1-TROL/Tic56/Toc33-TP-Cub-EYFP). We then compared the localization of these fusion proteins to that of corresponding native proteins . However, the underlying mechanism remains unknown.In proteins A. Observproteins A, indicaNext, we exchanged the Nub and Cub modules in Our next objective was to investigate whether the Nub–Cub module can direct protein interactions not only transiently but also in plants. In this assay, we selected CP26 along with PsbTn1, a 5 kDa subunit of PSII ,31. Accocp26-com)  A,B follom plants A,C. In tproteins A. The co protein D, indicatibodies E. If thetibodies E. Our reLHCa1-Cub-PsbTn1-TP-GFP was retarded and disorderly to varying degrees , which leads to the cleavage of the protein, as reviewed by Varshavsky , one couWe observed in our study that when CP26 is bound to Tic56 or Toc33, the YFP fluorescence signal is localized to the chloroplast envelope rather than the thylakoid membrane A. FurtheOnce the native thylakoid proteins encoded by the nucleus are imported into the chloroplasts from the cytosol, they are targeted to their correct sub-compartments through various sorting pathways ,37. In tArabidopsis acd1 mutant displays lesions caused by ROS, which mainly start at the tip of the leaf and then spread towards the leaf blade ) and photographed. Trypan blue staining for cell death was performed as described [The Heviously . The seeescribed .The comigration assay was performed according to the method described by Peng et al. . BrieflyArabidopsis genes: TROL (AT4G01050), Tic56 (AT5G01590), Toc33 (AT1G02280), ClpC1 (AT5G50920), CP26 (AT4G10340), and psbTn1 (AT3G21055).Sequence data from this article can be found under the following accession numbers in the TAIR library for Arabidopsis, further exploration is needed to assess its potential applicability in other organelles or species. Future research should focus on a deeper understanding of the underlying mechanisms and the broader utility of this method in various biological processes.The ubiquitin-based module developed in this study can direct interactions in vivo between two chloroplast proteins without native affinities. In addition, this system opens a new avenue to generate unique phenotypes rarely observed in traditional genetic modification approaches, such as knockout mutants or overexpression lines. While the system has demonstrated its efficacy in In theory, this module has the ability to facilitate the physical interaction of any known/unknown chloroplast proteins, which can be used to modify the components of protein complexes in chloroplasts. Hence, it is anticipated that this module will be extensively utilized in manipulating various aspects of the photosynthesis process, particularly the light reaction within thylakoids, with the goal of enhancing photosynthesis and crop yield.


The prolific applicability of nanomaterials has made them a common citizen in biological systems, where they interact with proteins forming a biological corona complex. These complexes drive the interaction of nanomaterials with and within the cells, bringing forward numerous potential applications in nanobiomedicine, but also arising toxicological issues and concerns. Proper characterization of the protein corona complex is a great challenge typically handled with the combination of several techniques. Surprisingly, despite inductively coupled plasma mass spectrometry (ICP-MS) being a powerful quantitative technique whose application in nanomaterials characterization and quantification has been consolidated in the last decade, its application to nanoparticle–protein corona studies is scarce. Furthermore, in the last decades, ICP-MS has experienced a turning point in its capabilities for protein quantification through sulfur detection, hence becoming a generic quantitative detector. In this regard, we would like to introduce the potential of ICP-MS in the nanoparticle protein corona complex characterization and quantification complementary to current methods and protocols. Nanotechnology has experienced enormous growth in recent decades, bringing forward the surging use of engineered nanoparticles as advantageous novel materials in numerous fields such as biomedicine, cosmetics, pharmacology, food, or agriculture ,2. Its uUpon introduction in a biological fluid, the process of protein adsorption is an almost instantaneous event given the higher binding energy of the nanoparticle surface compared to the surrounding biological environment (Gibbs free energy drives the complex stability) ,10. The Nanomaterial interactions with proteins can be controlled by different strategies, nanoparticle surface functionalization being the most common one . This isThe interactions between nanoparticles and proteins have significant biological consequences to the original nanoparticle and to the native proteic environment. On the one hand, the formation of the protein corona on the nanoparticle surface changes the nanoparticle physicochemical properties. It changes the size, shape, and even the aggregation state of the nanoparticle, which in turn affects how proteins are oriented and presented to the biological targets . On the Upon entrance in the biological media, nanoparticles are involved in a myriad of biological processes. The formation of the protein corona creates a different structure that will drive and influence the behavior and interaction of the nanoparticles with biological systems. Thus, the protein corona can alter the interactions of the nanoparticle with the cell surface moieties , promoting or inhibiting the uptake of the nanoparticle by the cell . For insThe biological impact of the nanoparticle interactions with proteins is particularly relevant in the development and use of the NPPC in biomedicine. The small size of nanoparticles gives them the ability to efficiently access cell compartments, which has important biomedical implications, leading to important developments in the field of nanomedicine ,21. The Protein–nanoparticle interactions are highly determined, not only by the environment composition and dynamics, but also by the nature and structure of the nanoparticles. Therefore, characterization of the starting nanoparticle is a key issue that must be investigated and understood before affording the study of the protein corona.Nanoparticles’ physicochemical properties control the formation and composition of the protein corona. Whereas the extent of the impact of each property on the protein corona composition and disposition is not completely understood, there is an extended consensus that no nanoparticle property alone drives the formation of the corona, but all of them together . NanoparThe hydrophobicity of a nanoparticle’s surface is, on the contrary, a significant factor limiting the amount and identity of the proteins bound to the nanoparticle. In this regard, for instance, Lynch et al. found that when plasma is incubated with low hydrophobic copolymer particles, virtually no protein is retrieved, while significant amounts of protein are consistently bound to hydrophobic particles . Nanopar2, SiO2, and Zn nanoparticles; TiO2 nanorods; and TiO2 nanotubes, despite these nanoparticles having similar surface charges [Interestingly, several works have found that nanoparticle size seems to be more relevant than their functionalization in the formation of the protein corona. Tenzer et al., for instance, determined nanoparticle size to be dominant in the formation of the protein corona over surface functionalization, which in turn was dominant over exposure time . When co charges .In this regard, common approaches to study and determine nanoparticle size and size distribution include dynamic light scattering (DLS) and differential centrifugal sedimentation (DCS) . DLS is In contrast to DLS, DCS allows the resolution of multiple peaks in polydisperse samples as well as the possibility to carry out the analysis even in the presence of large biomolecules, as in the case of protein corona studies . TransmiWhen nanoparticles enter biological media, the number of proteins they may get in contact with is in the thousands. However, they cannot all fit onto the nanoparticle surface: typically, less than 100 proteins form the hard corona in nanoparticles of >10 nm ,35, resuIt should be stressed that identification of the different proteins is not enough to obtain a comprehensive characterization of the protein corona. The dynamism in the protein corona composition (particularly the soft corona), as well as the reasonable assumption that the most abundant proteins will define the biological roles of the NPPC, demands quantitative data in order to achieve a comprehensive characterization. Additionally, considering that protein corona composition is driven by the affinity and interactions between nanoparticle and proteins, there is not necessarily a correlation in protein abundance in the media and in the corona ,18. BecaQuantification of proteins forming the corona can be performed through standard approaches such as colorimetric assays, such as BCA, Bradford or Lowry . These tIn a typical workflow , before The biological relevance of protein–nanoparticle interaction in the formation of the protein corona and the relevance and need for a proper characterization of such interactions, which are yet not completely understood, has already been remarked . These iThe common applicability of DLS or TEM to the characterization of nanomaterials can also be extended to characterize the NPPC. In the case of DLS, the measurement of the hydrodynamic diameter of nanoparticles before and after the protein corona formation provides an estimation of the corona thickness . NeverthThe study of nanoparticle–protein interactions can also be addressed with separation techniques such as size exclusion chromatography (SEC) or asymmetric flow field-flow fractionation (AF4). Nanoparticles are too large to be retained in the SEC phase pores, whereas proteins are not, and their elution time will depend on their molecular weight. Then, if proteins are associated with the nanoparticles, their retention time will be decreased, depending on the duration of the interaction . This waThe need to improve or dispose of more accurate and robust NPPC production and standard nanoparticle characterization methods is clear, as they would significantly improve reliability, reproducibility, and transparency in biomedicine . The useThe current methods to characterize nanoparticles, including DLS, DCS, or TEM, are highly dependent on instrumental conditions and sample preparation and are seldom validated, risking the compromise of the results by uncontrolled processes . In receThe characterization of protein corona variable composition as a function of nanoparticle physicochemical properties  cannot be assessed without reproducible and upright LC-MS analyses and datasets. For instance, researchers examined the interactions between proteins and nanoparticles and the effect of small changes in surface chemistry on these interactions using SDS-PAGE and LC-MS for protein separation and quantification, respectively . The obtValidity of proteomics results depend on the robustness and reproducibility of preparation and characterization methods and can be compromised by biological and technical variations. Proteomics results are also dependent on the variability of identified proteins, which critically depends on the LC-MS instrumentation and data analysis approach . ProteinInductively coupled plasma mass spectrometry (ICP-MS) is a powerful elemental spectrometry technique that consists of the atomization and ionization of the sample in a high-energy argon plasma, which enables one to carry out multi-elemental and multi-isotope analysis. Th einorganic nanoparticles commonly used in bioanalytical applications are typically metal-based structures synthesized from metal-salt precursors. Thus, their metal atoms can be detected with ICP-MS to determine the multi-elemental content and absolute quantitative information on nanoparticle mass and number concentrations, as well as core/ligand ratios and elemental stoichiometries within the nanoparticle . Most ICThe use of separation techniques coupled to ICP-MS opens the door to the analysis of nanoparticles in polydisperse samples. This is the case with size exclusion liquid chromatography, which has been used to determine the size and shape of metallic nanoparticles and quantum dots ,54. AsymLastly, it is worth noting the increase in the use of the single particle-ICP-MS (spICP-MS) technique ,60. spICIt has already been discussed that determination of the protein corona thickness through the comparison of the nanoparticles and the NPPC size provides valuable information for studying the adsorption and interactions of the proteins with the nanoparticle. However, nanoparticle size determination with spICP-MS is approached through the measurement of the metal (or another detectable element) present in the nanoparticle core. In fact, size determination is based on the intensity of the detected events, and it is independent, as commented, of the nanoparticle environment. That is, for a certain nanoparticle, spICP-MS analysis would provide the core size exclusively and not the hydrodynamic size . spICP-MS is moreover limited in terms of nanoparticle size, as it cannot determine sizes below 10 nm. . AlternaICP-MS sensitivity and specificity to detect trace levels of metals and other elements in biological samples  makes it useful for measuring the concentration of the proteins present in NPPC and so study its dynamic changes over time, making it a valuable tool for understanding the interactions between nanoparticles and proteins. In contrast to common molecular MS approaches for protein quantification , ICP-MS ionization can be made species-independent. Once nebulization issues are under control , ICP-MS It is important to consider that the separation dimension provided by chromatography is a prerequisite to detect and quantify each protein individually to be able to unequivocally correlate the elemental signal to a specific biomolecule. An example of this is the work carried out by Matczuk et al., who used capillary electrophoresis coupled to ICP-MS to determine the number of protein molecules bound to AuNPs of different sizes (5–50 nm). The use of CE permitted separation of the conjugate between AuNPs and albumin from the free AuNPs (in excess) and thus estimate the number of albumin molecules bound per AuNP, relating the peak areas obtained from the complex AuNP:albumin and the free unbound AuNPs . LikewisRegardless of the large amount of potential information provided by ICP-MS, such as molar ratio NP/protein, absolute protein concentration, protein corona, or nanoparticle aggregation and/or size distribution, it is still necessary to combine this approach with previously described analytical methodologies for a better and complete characterization of the NPPC. For a better understanding of the different NP populations in protein corona studies, separation techniques should precede ICP analysis. Electrophoresis is commonly applied to determine binding kinetics and size exclusion, or hydrodynamic chromatography is suitable for species characterization ,78. AsymNowadays, it is unquestionable that the use of NPs in biological applications requires a precise knowledge of the NP–protein corona system (NPPC) that is formed when NPs enters biosystems. Consequently, NPs acquire a new biological identity through the formation of this protein corona, which affects their colloidal stability, biodistribution, interactions, toxicity, and clearance. The understanding of the fate of the NPPC in biological systems, the development of NPPCs with biomedical applications, or the understanding of the NPPC toxicological effect require accurate and reliable characterization of nanoparticle physicochemical properties, the protein corona composition, and the dynamics of the interaction between nanoparticles and the different proteins that constitute the corona. To this end, the combination of multiple analytical methodologies offering complementary information certainly seems to be necessary. Knowledge of nanoparticle protein corona composition commonly relies on approaches that lack standardization methods that guarantee or control the reproducibility and reliability of the results. In particular, important difficulties persist in the effective quantification of the different proteins from the corona. ICP-MS turns up as a complementary tool that could provide accuracy and robustness of quantitative results and workflows given its species-independent nature. In fact, thanks to the presence of ICP-detectable elements in widespread inorganic NPs  and proteins (S), ICP-MS can provide valuable information, including NP elemental composition, size, concentration, and populations on the one hand, and absolute protein amounts on the other.The combination of several state-of-the-art imaging techniques enable visualization of the individual biomolecules associated with the surfaces of NPs  and cleaIn summary, we believe that ICP-MS could fill some gaps in NPPC characterization that are clearly identified and currently demanded and could be established as a useful and resourceful complementary tool in the current challenge of protein corona assessment.