Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.  The drug discovery field is constantly evolving to yield safe and potent drugs in the most time-, cost- and labor-efficient way. Traditionally, wet-lab high-throughput screening (HTS) was performed with libraries of compounds tested against a target of interest  to identify potential drug candidates . The draFor instance, virtual screening helps to narrow down the score of compounds for experimental validation from large chemically diverse databases, thereby saving time and resources ,7. The mSeveral previous studies have leveraged DL-based models to predict drug–target interaction (DTI) and interaction binding affinity . An effiHowever, complex patterns of protein–ligand interactions can only be captured with more realistic 3D structures and 3D and higher-order descriptors that have been in development over several decades by many groups around the world , includiWhile these approaches yield excellent predictive results, grids with atomic voids within the structure makes them susceptible to sparsity, memory bottleneck, and inefficient computations. Furthermore, CNNs are sensitive to rotation and orientation. While this may be useful for different conformers of ligands, proteins are typically considered as fixed rigid bodies for binding. To overcome this expensive computation of 3DCNN for large protein structures, alternative rotation invariant representations, namely Graph Neural Networks (GNN), have been investigated.In contrast to CNNs, graphs provide efficient and succinct representations of 3D structures. Several studies have reported superior performance on binding affinity prediction by representing proteins as a network of their amino acid residues ,31,32,33These methods report exceptional results on the affinity prediction task on PDBBind or CSAR-HiQ . HoweverSimilar to our work, GEFA  fuses twTo alleviate these limitations and challenges of classical docking as well as previous deep-learning approaches, we introduce a graph-Siamese-like network with attention-based graph pooling. Benefitting from the advantages of protein crystallographic structures, the developed method applies a graph attention network to all the protein structures to predict several measures of ligand–protein binding affinities . The graOur main contributions in this paper are(i)We propose an effective graph-Siamese-like network, PSG-BAR, to simultaneously operate on structural graphs of proteins and molecular graphs of drugs for binding affinity prediction.(ii)We introduce an attention-based readout strategy to generate graph-level embedding for proteins. These attention scores are shown to correlate with some known physical properties of binding sites. We call this learning component the interaction attention module.This paper is organized as follows: In In this work, we used four publicly available datasets on protein–ligand binding affinity including BindingDB , PDBBindIn addition to those values, we also retrieved data from the KIBA dataset  which coWe also evaluated the proposed model on the DAVIS dataset . containTo further evaluate the applicability of our method for the SARS coronavirus (SARS-CoV-2), we attempted to predict inhibitors of the SARS-CoV-2 3C-like protease (3CLpro), also known as the main protease (Mpro). The bioassay record (AID 1706)  by ScripFor each of the datasets, certain targets and ligands were dropped on account of the unavailability of the crystal structures and un-processability by RDKit, respectively. The intricate 3D structure of proteins that develop as a result of complex folding largely determine the functions and properties of protein molecules. Geometric deep learning provides an efficient way to represent such 3D structures as translation and rotation invariant graphs. These protein graphs can be created at the residual or atomic level. We conduct all our experiments on residual-level graphs where each amino acid corresponds to a unique attributed node in the protein graph beddings , and expbeddings . Furtherbeddings .To define molecular graphs for ligands . (2016) : atom tyPSG-BAR receives two attributed graphs ectively . The conpx)||pseqx] and equences . xp′ = [approach . The decTo further enhance the predictive capability of our model and to learn from the relationship between protein and ligand interaction, we propose an interaction attention module based on the cross attention mechanism . Cross aIn all our experiments we employed a base model with three stacked GAT layers as encoder . LeakyReEvaluation metrics: Our models were evaluated for regression using the Pearson coefficient and mean squared error (MSE). Additionally, for activity classification on SARS-CoV datasets, we report cross entropy loss and area under the curve of receiver operating characteristic curve.We benchmark PSG-BAR against several state-of-the-art methods  that conWarm setting: No splitting restriction imposed on proteins and ligands. Any protein (or ligand) may be repeated in training and test split; however, interactions are not duplicated across the two splits.Cold protein setting: Each unique protein in the dataset has restricted membership to either the training or the test set. No restrictions on ligands.Cold ligand setting: Each unique drug in the dataset has restricted membership to either the training or the test set. No restrictions on proteins.Cold protein–ligand setting: Any protein or ligand seen during training is absent in the test set. This is the most stringent measure for our model.To perform a binary classification on the SARS-CoV Mpro inhibitor and to overcome the problem of class imbalance between actives and inactives, we oversampled the actives and randomly subsampled inactives to construct a class-balanced dataset of 26,400 interactions. With a train–test split of 80–20% and five-fold CV, our method yielded 0.72 ROC-AUC A. The spTo validate the utility of PSGBAR method, we additionally perform testing on the novel coronavirus, SARS-CoV-2, the causative agent of the ongoing COVID-19 global pandemic. We rank PSG-BAR scores against docking screen results for the Mpro target. This docking screen  was perfThe predicted score distribution for hits from docking is right skewed, meaning that the model highly ranks most compounds in agreement with the docking program’s ranking.The flat, near-zero left tail indicates that the tested set has almost no compounds which are predicted to be extremely poor binders by our method. This aligns with our a priori knowledge that these tested compounds are expert-selected following docking screen.A set of decoys of a random sample of 20,000 poorly ranked compounds by docking yields low score on our model too. Thus far, we have evaluated the performance of our model as a whole on several datasets and use cases. We are further interested in investigating the interpretability of the method’s predictions and understanding the cause of its superior performance. The relevance of the attention scores was evaluated on a randomly selected sample of 50 proteins from six protein families, namely, protease, kinase, polymerase, transferase, phosphodiesterase, and hydrolase, from the PDBBind dataset. We focus on the amino acid residues with the highest attention scores, which should be relevant for predicting binding. PSG-BAR operates as a protein target agnostic method for ligand binding prediction. As such, it could be utilized to study the off-target effects of drug compounds on a multitude of human proteins. To this end, we evaluate the promiscuity of the hit compounds discovered in To evaluate effects on ADR proteins, we chose 19 compounds ranked within the top 25th percentile by both PSG-BAR and docking screen. These compounds are predicted to be the most likely binders to Mpro by our pipeline. We averaged scores across all proteins to assess the general promiscuity of a molecule, i.e., the likelihood of binding to many proteins. The average score ranged from −3 to −7, where a higher score means a more promiscuous compound B. We exaWe critically analyze the contribution of key components of our model towards predictive performance. These studies were conducted on the PDBBind dataset under warm setting, train/validation split of 0.8/0.2 ratio, and with a fixed random seed to impose fair uniformity across experiments. We first study the effect of the skip connections in GATs. The protein and ligand encoders are equipped with a varying number of GAT layers while iteratively ablating the skip connections for each constituent layer.We observe that stacking graph layers have diminishing returns A. This iWe further investigated the effect of the pre-trained amino acid embeddings on the overall performance. In the model without amino acid embeddings, we observed a 3.47% decrease in the Pearson correlation and 4.93% increase in the MSE on the PDBBind dataset. This is attributed to the fact that protein homology is driven by sequence similarity and a well-trained embedding on large amounts of amino acid sequences effectively captures such protein similarity.As described in the dataset section, utilization of any protein–ligand interaction in the training  process was contingent upon the availability of the crystallographic structure of the corresponding protein target. For the KIBA dataset we supplemented the proteins with missing PDB with their corresponding structures from AlphaFold. Consequently, the additional interaction samples led to a 43.4% increase in available proteins in the usable dataset and a 19.67% increase in total interactions. The model performance also rose by 1.72% in terms of Pearson correlation and 11.1% for MSE .We hypothesized that additional protein structure descriptors derived from the DSSP module will help to ameliorate the definition of binding pockets. Indeed, Stank et al. highlighted examples where secondary and tertiary features aided the classification of various protein pockets . FurtherIt should be noted that drug discovery practitioners tend to care more for prediction concerning good binders rather than for the overall performance of computational approaches. To this extend, we stratify the affinity score range into four intervals as ≤10, 10–12, 12–14, and >14 for the KIBA dataset . A signiIt should be mentioned, however, that these interactions are of relatively low significance to the design and discovery of drugs. Strong binders (score > 14) are unarguably the interval of highest interest and difficulty to predict. This, coupled with their sparsity in the evaluated subset of the data, leads to a mediocre result for strong binders . However, further elaborating the KIBA dataset with AlphaFold structures shows a marked improvement of 57.9% in MSE to bring MSE down to 0.50. This is due to the presence of more structurally similar proteins that enable the model to learn complex binding features, central to strong protein–ligand binding.In this study we report the development of PSG-BAR, a ligand-binding affinity prediction method leveraging protein 3D structures, 2D graph representations of a drug molecule, and attention-based interaction scoring. The integration of protein structures helps to achieve better predictive results on protein–ligand binding. This is mainly because 3D structures contain relevant information on the actual configuration of the binding pockets, which have immediate implications for the ligand binding. Many reported studies concerning the protein–ligand interaction fail to consider the complex folded 3D structure of the proteins and employ just the primary protein sequence. Yet others that consider protein 3D structure fall short of considering the relevance of active sites of the protein and view all the protein residues as equally important. Some other studies utilizing protein–ligand complex information rely on computationally expensive methods to determine binding sites a priori and hence restrict their applicability to limited datasets.In this work, we considered the residue-level graphs of the 3D structures of proteins in conjunction with molecular topological graphs of the ligands. Our Siamese-like architecture encodes both these graphs simultaneously. Additionally, we also proposed an interaction attention module that learns attention scores between the ligand’s latent representation and nodes (amino acids) of the protein structural graph. This enabled our model to ascribe higher weights to residues critical concerning binding. Through a comparative analysis, we demonstrate that the learning from the proposed interaction attention module concurs with domain knowledge about SAA. The learned critical residues had higher SAA than average SAA of the protein for most frequent protein families in the PDBBind dataset.Unlike most previous studies, PSG-BAR does not rely on expensive precomputation of binding sites and does not expect protein–ligand complex information explicitly. This interaction inference is conducted implicitly through the attention mechanism. As a result, it may be applied to many diverse and extensive binding datasets. Our experiments yielded state-of-the-art results on BindingDB, KIBA, and DAVIS datasets and comparable to state-of-the-art results on the PDBBind dataset.This applicability of our method on diverse datasets enables its use as an “off-target detection tool”. The training on the abundance of diverse proteins makes the model highly generalizable across different protein families. Thus, the model could be applied for predictions across a set of selected proteins such as ADR-relevant targets. We evaluated the selected Mpro lead compounds using our model built for prediction across ADR-related proteins. We found that the molecules range in their predicted promiscuity. Thus, these predictions might help to guide future lead optimization of the drug candidates.We acknowledge other works with better-reported MSE, especially on the PDBBind dataset (KDeep). This gain is largely attributed to the utilization of protein–ligand complexes in their model, indicating that the binding pockets (active sites) of the proteins are most critical towards this downstream prediction. However, obtaining such a protein–ligand complex is expensive and hence limits the applicability of many of these methods to smaller datasets with selective protein targets. Similarly, conventional docking approaches depend on computationally expensive preparation of binding sites. Our attention-based method learns surface residues without direct supervision while simultaneously predicting binding affinity. This identification of key binding residues diminishes the need for expensive binding site preparation and makes our model accessible to minimally preprocessed protein structures.In conclusion, our work represents a step in the direction of alleviating the problem of a priori knowledge of a binding site which is an expensive prerequisite to all protein–ligand interaction studies. The road to explainable AI, such as in attention-based visual question answering, is expected to reform deep learning for DTI. We expect more protein–ligand complexes to be experimentally resolved, so the next generation of deep models will be able to learn such attention scores more accurately. In fact, with soft-supervision these attention scores may even lead to reliable identification of binding sites. In this study, we work under the rigidity assumption of protein and ligand (by using only 2D molecular structures). Further research should investigate 3D ligand conformers in conjunction with flexible protein 3D structures.


Robust characterization of the protein corona—the layer of proteins that spontaneously forms on the surface of nanoparticles immersed in biological fluids—is vital for prediction of the safety, biodistribution, and diagnostic/therapeutic efficacy of nanomedicines. Protein corona identity and abundance characterization is entirely dependent on liquid chromatography coupled to mass spectroscopy (LC-MS/MS), though the variability of this technique for the purpose of protein corona characterization remains poorly understood. Here we investigate the variability of LC-MS/MS workflows in analysis of identical aliquots of protein coronas by sending them to different proteomics core-facilities and analyzing the retrieved datasets. While the shared data between the cores correlate well, there is considerable heterogeneity in the data retrieved from different cores. Specifically, out of 4022 identified unique proteins, only 73 (1.8%) are shared across the core facilities providing semiquantitative analysis. These findings suggest that protein corona datasets cannot be easily compared across independent studies and more broadly compromise the interpretation of protein corona research, with implications in biomarker discovery as well as the safety and efficacy of our nanoscale biotechnologies. Analysis of the protein corona formed around nanoparticles is important for multiple applications in nanomedicine, the methods used at core facilities used for analysis can impact the results. Here, the authors report on a study into the variability of the results obtained from 17 different core facilities and the implications of this. To date, liquid chromatography coupled to mass spectroscopy (LC-MS/MS) remains the dominant methodology to characterize the protein corona in terms of the number of unique proteins and their relative abundances. However, the role of mass spectroscopy in causing technical variations in assessing the composition of the protein corona, and to what extent such proteomics outcomes can affect the interpretation of the nanobio interfaces, remains largely unknown.Many technical factors contribute to poor reproducibility in nanomedicine, but the complexity inherent in the nanobio interface, of which the most heavily studied component is the protein corona, poses a major challenge3. This coating, known as the protein corona3 4, defines the biological, chemical, and physical identities of NPs. Robust and accurate characterization of the protein corona is therefore essential in predicting interactions between NPs and biosystems, which define the safety and efficacy outcomes of the biotechnologies built from those NPs8. Robust protein corona characterization plays a vital role in (i) the safe and efficient development and in vivo translation of nanobiotechnologies, (ii) the improvement of our fundamental understanding of the role of the protein corona in determining the biological fate of nanobiotechnologies, (iii) biomarker discovery and overcoming current critical issues in mass spectroscopy-based proteomics analysis of human plasma, and (iv) enabling comparison of independent protein corona studies.It is now well understood that the surface of nanoparticles (NPs), due to high surface free energy, becomes covered by a complex layer of biomolecules and mainly proteins, upon exposure to biological fluids11. The protein corona undergoes specific interactions during cell association followed by receptor-mediated membrane adhesion and subsequent cellular uptake12. It was recently reported that changing the NP soft corona to hard corona—thereby enabling turning of the dynamic nature of protein corona dissociation—showed a role in cellular uptake. It was found that certain “caretaker” proteins that comprise the hard corona mediate NP-cell interactions, which allows for higher-affinity interactions between the cell and NPs, resulting in different internalization routes13. Moreover, protein corona-aided targeting offers unique opportunities for specific drug delivery by manipulation of interaction modes of plasma proteins on the surface of NPs14. For instance, liposomes functionalized with short nontoxic amyloid beta (Aβ) peptides, can specifically interact with the lipid-binding domain of exchangeable apolipoproteins, consequently exposing the receptor-binding domain of apolipoproteins to achieve brain-targeted delivery15. In addition, the dynamic NP protein corona composition regulates the interaction of NPs with the physiological environment. It is shown that the formation of protein corona on the surface of liposomes considerably reduces their capture by circulating leukocytes in whole blood and may be an effective strategy to enable prolonged circulation in vivo10.The protein corona plays critical roles in the cellular uptake of NPs, recognition of functional moieties  on the surface of NPs, and their interactions with the immune system8. Despite efforts to minimize variability in protein corona studies, the effect of the “workhorse” technique LC-MS/MS on characterizing the protein corona remains understudied. This limited understanding of the role of LC-MS/MS in the variability of proteomics datasets characterizing the protein corona may lead to misinterpretation of the safety, diagnostic, and therapeutic efficacy of nanobiotechnologies and nanomedicines8.Many biological and methodological factors can affect NP protein corona information in terms of protein type and abundance16. The broad dynamic range of protein abundance in plasma is perhaps the main analytical difficulty17. Seven proteins constitute 85% of the total protein mass in plasma, with albumin alone making up 55%18. We also know that 22 proteins comprise 99% of plasma proteins by weight19. Peptides from such abundant proteins crowd the mass spectra and hamper the comprehensive coverage and in-depth analysis of plasma proteomes, especially for proteins with lower abundance. For this reason, several plasma depletion strategies have been introduced that exploit immunodepletion spin columns, immunodepletion-LC, magnetic beads, and even NPs themselves to deplete abundant or moderately abundant proteins from plasma21. However, such methods are unsuitable for use in large cohorts due to high cost, increased handling requirements (causing lower reproducibility and underlying lower throughput), as well as carry-over concerns when processing multiple samples22. Another complication with the depletion of albumin is that many proteins are bound to albumin and can be co-depleted23. Apart from the dynamic range, another analytical difficulty is the presence of both known and unknown protein isoforms in plasma. Indeed, one of the emerging applications of NP protein corona is to reduce plasma proteome complexity in biomarker discovery25.Generally, mass spectrometry-based proteomics provides robust and reproducible data, and any major differences between various experiments performed in different labs would be related to proteome coverage, i.e., the number of proteins quantified in a given sample or a set of samples. While such variations in the depth of analysis can introduce bias in data interpretation , the bias can be minimized by increasing the proteome coverage. However, plasma proteomics is much more challenging than the analysis of most other biological materials, and the depth of analysis can vary significantly based on the workflow used26. Different studies have identified thousands of proteins with state-of-the-art MS-based plasma proteomics28. Advances in plasma proteomics are turning mass spectrometry into the tool of choice for biomarker discovery in large cohorts29. For example, in a recent study, 596 plasma samples were analyzed within 3 weeks to identify noninvasive proteomic biomarkers for alcohol-related liver disease30. In a recent paper, Ignjatovic et al.16 provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, MS data acquisition, data processing, and bioinformatic analysis.In spite of these challenges, according to the human plasma proteome draft published in 2017, a compilation of data from 178 individual experiments shows that 3509 proteins have been reliably quantified in plasma, with a protein-level false discovery rate (FDR) of 1%16. Furthermore, inevitably, different labs have access to different instrumentation, equipment, and software, which can also affect data processing workflows and relative protein quantification. The biological interpretation of data, especially in protein corona analysis, is highly dependent on the proteins detected or quantified; therefore, variabilities in the sheer number of quantified proteins and variations in data quality can impede the biological interpretation of data. Large variations in the analysis output in plasma proteomics can stem from the choice of sample preparation protocols, analytical columns, LC and MS systems, as well as the method settings and duration of the analysis. Small variations can also result from the choice of platform for the search and the sequence databases, and subsequent data processing. It would be highly desirable to identify the most important parameters contributing to the bias introduced by LC-MS in protein corona analysis, and to assess their effect on data interpretation.Protein corona analysis and plasma proteomics face similar challenges, as similar workflows are used for LC-MS/MS analysis. Since plasma proteomics is a field under continuous development, there are still no unified or streamlined protocols and workflowsIn this work, to investigate the contribution of LC-MS/MS to the variations in the analysis and interpretation of protein corona data, we submit 17 identical aliquots of fully characterized protein corona samples to 17 proteomics core facilities in the United States and provide a comprehensive comparative analysis of the retrieved data. The results highlight the critical effect of LC-MS/MS workflow details  on protein corona results, which can create bias in interpretation of protein corona applications, including biomarker discovery and assessing the biological fate of nanomedicine technologies.n = 17) were characterized using dynamic light scattering (DLS), zeta potential measurements, and transmission electron microscopy (TEM) to investigate the homogeneity/heterogeneity of the protein corona-coated NPs in each vial and to ensure the NPs had similar physicochemical properties  with proteins from human plasma Fig. e, f. As Qualitatively, no differences in protein corona formation were observed in TEM-imaged samples.We next submitted the 17 identical aliquots to 17 core facilities across the USA for analysis .We also plotted dynamic range (defined as the ratio of most intense protein divided by least intense protein in data from each core) vs. protein count to determine whether there were any correlations between the two parameters  helped reduce CV of measurements among the laboratories involved. However, it should be noted that measurement of a single parameter such as NP size involves far fewer steps than the steps involved in a LC-MS/MS workflow. It would be very difficult, if not impossible, to perform comparative proteomics analysis in a similarly controlled manner, as not all labs have access to the same instruments, equipment, and commercial software.The protein corona forms spontaneously on NPs intended for use in biological tissues and fluids. Given the known role of the protein corona in affecting the outcome of bionanotechnologies and nanoscale medicines, much effort has been focused on characterizing the protein corona in terms of protein identity and abundance. Despite the numerous reports available in the literature concerning the protein corona, efforts to reconcile independent studies and consolidate protein corona datasets toward predicting protein corona composition and NP biocompatibility outcomes remain limited40. For example, a recent paper demonstrated that biological variations in different stocks of cultured human HeLa cells can lead to huge heterogeneity in proteomics analysis and even partially determine the phenotypic response of different cell lines to Salmonella infection41.Despite the central role of LC-MS/MS in providing proteomics datasets for NP protein corona studies, the variabilities generated by LC-MS/MS itself remain understudied. Biological and technical variations can significantly compromise the reproducibility of proteomics analyses. The community is becoming more aware of the parameters influencing the detectability of proteins, affecting data outcomes42. The sample under study was a set of standard peptides with serial dilutions spiked into HEK293 cell extract. In this study, all sites used the same SCIEX TripleTOF 5600/5600+ mass spectrometer systems, while the nanoLCs consisted of various models from the same vendor (SCIEX), but sometimes different chromatographic columns were used, although they had the same dimensions (30 cm × 75 μm). In the aggregated data analysis, where the database search and FDR control was performed in a centralized way, a core set of 4077 proteins were consistently detected in >80% of all samples analyzed across 11 sites. A fraction of proteins was not detected in all sites. Furthermore, when the analysis and FDR control were carried out independently on a site-by-site basis, there was a reduction in consistently detected proteins among 11 sites. One important consideration is that there was no sample preparation involved in the above study and therefore, large batch effects were not expected. This study shows that if sample preparation is centrally performed, and instrumental parameters are kept largely constant, it is possible to achieve reproducible and comparable quantification of the majority of proteins in a complex proteomics dataset.A few studies have also investigated technical variations in the analysis of proteome samples among laboratories, albeit not involving NP-based protein corona formation. In one such study, the reproducibility of Sequential Window Acquisition of all THeoretical fragment ion spectra (SWATH) MS data acquisition was evaluated among 11 sites worldwide43. In DDA, when the number of precursor selection cycles are exceeded by the number of precursor ions44, selection of precursors occurs stochastically, leading to lower repeatability of peptide sampling45.In our study, all experimental steps involving NP-protein corona formation were performed identically, such that samples supplied to mass spectrometry centers were identical. However, once received, the protein corona samples were prepared for subsequent mass spectrometry analysis on-site, no standardized instrumentation parameters or protocols were requested/provided, and the data extraction and analysis were not aggregated. Furthermore, as stated earlier, plasma-based samples are generally more challenging to analyze than cell or tissue samples using MS. Last but not least, the cores in our study had to employ data-dependent acquisition (DDA). Generally, more variability is expected in the data-dependent acquisition, since unlike the data-independent acquisition method used in the above study (SWATH), data-dependent acquisition involves stochastic fragment ion (MS2) sampling46. Interestingly, it was found that sample preparation was 12% less reproducible than MS between the two laboratories. It is noteworthy that the study was carried out over a period of 3 years, during which time the authors studied and defined experimental parameters, exchanged protocols, chose the appropriate reagents, and used consumables with the same lot numbers. Furthermore, the scientists involved were trained to perform the workflows. Similar interlaboratory studies on other technologies such as RNAseq and microarrays have also shown that the analysis platforms and data analysis pipelines can result in large differences in data output48.Another study between two centers has shown that given a standardized affinity purification–mass spectrometry (AP-MS) workflow for 32 kinases, high interlaboratory reproducibility (81%) can be achieved , in spite of differences in MS configurations and subtle variations in sample preparation protocols51, delays in plasma preparation53, multiple freeze-thawing cycles53, postcentrifugation delay in time and temperature54, hemolysis55 and carry-over of platelets and coagulation55, centrifugation force52, and differences in proteomics of EDTA-plasma vs. heparin-plasma56. Furthermore, in 2016, the Association of Biomolecular Resource Facilities (ABRF) Proteome Informatics Research Group (iPRG) investigated variability in proteoform inference and FDR estimation from bottom-up proteomics data among different research labs57.Due to the resemblance of protein corona samples to plasma, lessons can be learned from studies on plasma proteomics. Various studies have assessed the effects of preanalytical sample processing and storage on the integrity of plasma proteome for analysis, such as the effects of biobanking conditions58. Results such as the latter highlight the importance of consistency across proteomics centers not just for high-abundance proteins but for lower-abundance proteins as well. Another consideration is that, since tubes or other materials used for sample processing can enhance or deplete the presence of specific proteins , negative or mock samples without NPs can be included in the workflow and analyzed in parallel to rule out proteins that do not necessarily belong to protein corona, or that conversely have been artificially depleted due to sample handling protocols.Focusing on the technical variations that may be introduced by sample preparation protocols and proteomics analysis, here we present substantial data heterogeneity in protein corona analysis of an identical sample performed by 17 different core facilities and discuss how bias may be introduced in the biological interpretation of protein corona studies . Data provided by different cores (we discuss semiquantitative data from only 12 cores) differ with regard to the number of quantified proteins (ranging from 467 to 1430), number of quantified peptides (ranging from 1541 to 7565), median CV , and median protein sequence coverage (ranging from 4 to 19%). Across 12 cores, only 1.8% of the proteins were shared in identical samples. Such a high variation across LC-MS/MS centers compromises interpretation of independent NP protein corona studies, and even calls into question inter-study dataset comparisons, especially if those datasets were not analyzed by the same LC-MS/MS facility. For instance, there are numerous studies claiming differences in NP protein corona compositions as a function of NP size, shape, charge, surface chemistry, etc., yet these sample comparisons may hold true only if LC-MS/MS dataset integrity can be confirmed. Furthermore, interpretation of protein corona datasets as they pertain to the biological fate of NPs may be questionable, particularly for analyses based on comparisons of independent studies. It should however be stated that the proteins shared among the centers usually have a higher abundance than other detected proteins in the corona. It is possible that more abundant proteins play the main roles in masking functional moieties and in affecting NP uptake, transport, or their interactions with cells, which benefits from the relative consistency across centers in quantifying the more abundant proteins in the NP corona. However, recent studies have also shown that NPs often show preferential protein adsorption or depletion in their coronas, such that even high-abundance plasma proteins such as albumin are so strongly depleted in the NP corona they can just barely be measured with mass spectrometry43. Some of these variabilities can arise from different strategies used in protein recovery from NPs, as previous research has shown that using on-particle, in-solution and in-gel digestion of the protein corona can affect both the class of proteins recovered and their abundances measured in protein corona59. While during on-bead digestion, protease cleavage sites may not be accessible due to the orientation of proteins at the NP surface, during in-gel digestion, proteins and peptides can be lost through the isolation process59. As such, these mentioned technical variabilities produce different depths of analysis among the cores, which obviously affects the biological interpretation of any given study. This is especially important in protein corona research, as each protein could serve as a biomarker of safety, diagnosis, or prognosis. Therefore, using better protocols and optimizing instrumental parameters would ensure higher coverage of the proteome, better representing the underlying biology and bringing interpretations closer to reality. It is noteworthy that the proteomics of other biological samples such as cell and tissue extracts is much less biased, as high coverage of the proteome can be reached due to lower protein dynamic range and less challenging nature of such samples. One could argue that we could request a deeper analysis of the protein corona samples, but since nanomedicine labs submit their corona samples requesting proteomics data using the discretion and/or commonly used protocols of the given core facility, we opted to do the same. Also, deeper analysis would usually require labeling, multiplexing and prefractionation of samples before LC-MS/MS analysis, introducing more steps in sample preparation and further complicating the comparative analysis of the results. We also acknowledge that the results obtained by different core facilities would have been more homogenous if we requested the core facilities to follow the same methodology using similar instrumentation and protocols. However, such a study would not recapitulate the heterogeneity of NP protein corona datasets currently in existence.There is an acceptable correlation between the proteins quantified across 12 cores, showing that proteomics methodology is not the main factor behind variability in the analysis results; instead, it is different sample preparation routines, instrumental settings, raw data processing and a range of other confounding factors that significantly affect the proteome coverage and impose a bias. For example, as mentioned above, in DDA, there are also inevitable variabilities introduced by stochastic MS2 sampling26. Reanalyzing the data collected from 178 individual experiments from 2005 till 2017, only 50% of the studies reported the 500 most abundant plasma proteins26.While these data should not be generalized to plasma proteomics, our results partly recapitulate those obtained by the 2017 human plasma proteome draftWe acknowledge that comparing across institutions is challenging. This is because data analysis is not standardized yet, and most institutions are able to report only relative abundance rather than semiquantitative abundance. The latter type of data can be more easily compared. Nevertheless, in our study, the quality of analyses performed by cores 2, 3, 5, 9, and 11 was higher than others with regards to the number of detected proteins and peptides as well as CV of technical replicates and protein sequence coverage. While we have blinded the core names in the current paper, the associated protocols can be found in 60, similar to guidelines suggested for plasma proteomics57.Human blood and blood-derived products (such as plasma and NP protein corona) are viable proxies for the identification of biomarkers and translation of research results into the clinic. With continuously emerging applications of protein corona analysis in the assessment of NPs’ safety and predicting their biological fate as well as biomarker discovery for disease detection, based on these data, as a community we need to pursue paths to minimize and report these differences so that we can better understand the underlying bias in proteomics data for protein corona experiments. This study can, therefore, serve as the first step in unifying and streamlining a standardized mass spectroscopy approach involving sample preparation protocols and instrumental settings for protein corona analysisIn the meantime, we recommend precise documentation of protocols when reporting protein corona data. We would like to raise awareness of the challenges to be addressed and hope that the current study will fuel community discussions on how experimental data from protein corona proteomics should be analyzed, reported, and interpreted. Such efforts will further enhance the validity of studies in protein corona proteomics, which, in turn, could significantly improve the clinical translation of both diagnostic and therapeutic nanomedicine products.The results of each study can be generally trusted, as long as all the samples  are analyzed in the same core. Our findings do not undermine the results obtained by any core facility, but rather highlight that the analysis of a given sample by different cores can result in different data interpretations. For example, with low depth of analysis, some important biomarkers might be missed, or biomarkers with less importance come into focus instead. Furthermore, minimizing technical noise is critical in identifying biologically significant changes, especially those of small size.In summary, our pilot study demonstrates that the characterization of protein corona formed on NP surfaces is subject to high variability originating from the analysis and compilation of MS data. Broadening the comparison to similar protein descriptions could provide a better understanding of the differences between proteomics core facilities, while relying on MS data being provided in a format amenable to descriptive and statistical comparison. Nonetheless, these results underscore the variability of protein corona data originating from proteomics core facilities alone and emphasize the fact that this degree of variability is larger than the effect size of most published NP/protein corona literature. Therefore, particularly when protein corona data are compared using analysis from multiple proteomics core facilities, the interpretation of effect size is more likely to originate from core-to-core variability rather than biological effect size. Regardless, experimental and analytical validation should be an integral part of protein corona studies. Our study will hopefully pave the way for developing best practice and quality control measures in NP protein corona research by developing gold standard protocols and workflows in LC-MS/MS sample preparation and analysis.www.innov-research.com) and diluted to a final concentration of 55% using phosphate buffer solution . Plain PSNPs (~˂100 nm) were provided by Polysciences. (www.polysciences.com).Healthy human plasma protein was obtained from Innovative Research 61 with guaranteed next-morning delivery. It is noteworthy that the units had 48 h of cooling time at 4 °C and safe arrival of all samples was confirmed by each core facility.For protein corona formation, NPs were incubated with 55% plasma (with NP concentration of 200 µg/ml) for 1 h at 37 °C at a constant agitation . To remove unbound and plasma proteins only loosely attached to the surface of NPs, protein-NP complexes were then centrifuged at 14,000×DLS and zeta potential analyses were performed to measure the size distribution and surface charge of the NPs before and after protein corona formation using a Zetasizer nano series DLS instrument . A Helium Neon laser with a wavelength of 632 nm was used for size distribution measurement at room temperature. Protein corona profiles at the surface of the NPs were studied by sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE).TEM was carried out using a JEM-2200FS (JEOL Ltd.) operated at 200 kV. The instrument was equipped with an in-column energy filter and an Oxford X-ray energy dispersive spectroscopy (EDS) system. In total, 20 μl of the bare PSNPs was deposited onto a copper grid and used for imaging. For protein corona-coated NPs, 20 μl of the sample was negatively stained using 20 μl uranyl acetate 1%, washed with DI water, deposited onto a copper grid, and used for imaging at the same day. Details of the cryo-TEM protocols are available in ref. Details of sample preparation, LC-MS/MS analysis, and data processing protocols are included in First, for each core, data were normalized by total protein intensity in each technical replicate. CVs were calculated based on normalized intensities between technical replicates for each protein. To unify protein IDs, for some cores, the various protein IDs used were converted to UniProt IDs. The data among the cores were combined by UniProt IDs. Data analysis was performed using R project version 3.6.1.All centers performed a triplicate analysis of the given aliquot.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Protein assembly is a highly dynamic process and proteins can interact in different ways and stoichiometries within a complex. The importance of maintaining protein stoichiometry for complex function and avoiding aggregation of orphan subunits has been demonstrated. However, how exactly the organization of proteins into complexes constrains differential protein abundance in extreme cellular conditions like cancer, where a lot of protein abundance changes occur, has not been systematically investigated. To study this, we collected proteomic data made available by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to quantify proteomic changes during carcinogenesis and systematically tested five interaction types in complexes to investigate which of these features impact on protein abundance correlation patterns in cancer. We found that higher than expected fraction of protein complex subunits does not show changes in their abundances compared to those in the normal samples. Furthermore, we found that the way proteins interact in complexes indeed constrains their co-abundance patterns. Our results highlight the role of the interactions between the proteins and the need of cancer cells to deal with aberrant changes in protein abundance. Cancer is a system that is characterized by a large number of somatic molecular alterations. Many of these alterations appear on the level of the transcriptome and proteome of tumor cells. Among other factors, copy number alterations (CNAs), gain or losses in DNA copies, can cause these imbalances in mRNA expression and protein abundances . HoweverProteins are interacting with each other as complexes to govern cellular functions. Protein complexes are not static units but assemble and disassemble dynamically. This process relies on cellular abundances and localization of proteins, and binding affinities between proteins . For exaHere, we performed two related tasks: 1) developing a definition and systematic characterization of protein interaction types in complexes; 2) asking how those different interaction types result in (co-)abundance rules between the complex subunits in particular in cellular conditions like cancer that are characterized by dysregulation of gene expression. To answer these questions, we first systematically categorized protein interactions by using structural data, proteomics measurements in different cellular conditions, interaction data, and literature information and then, with the integration of cancer proteomics data, tested how each category impacts on protein abundance correlation patterns in cancer. The outcomes emphasize the role of the interactions between the proteins and their role in complexes in constraining differential protein abundances in cancer.https://github.com/SengerG/PPIs_Data-Code.Code for data analysis is provided as part of the PPIs_Data-Code repository and can be found at Proteomics data used in this publication were generated by the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH). TMT-based log-transformed proteomics data for the following CPTAC cohorts  were obtained: Colon (COAD) from Proteomics data for the available The Cancer Genome Atlas (TCGA) projects were directly obtained from the CPTAC consortium, comprising 3 cohorts, spectral counts for colorectal (COREAD) , and relp-values were subjected to multiple testing correction by using the Bonferroni correction. Log2 fold change (log2FC) for proteins was calculated as the median difference of log2 transformed TMT-values between tumor and normal samples. Proteins with absolute log2FC greater than 1 and adjusted p-value less than or equal to 0.05 were considered differentially abundant. Associations between the differentially abundant proteins and protein complex subunits, obtained from the CORUM database . The difference between the distributions of correlations between members of the same complex and members of different complexes was tested by Wilcoxon test. To this end, correlations from different cohorts for each protein pair were pooled.Stoichiometry information for proteins was retrieved from the structural data available in the Protein Data Bank (PDB)   in Marchn = 2,916) and their subunits  were obtained from the CORUM database    coveringbinding)  as a meaTransient and permanent PPIs were obtained from For each comparison between groups in each class of PPIs, Wilcoxon test was applied.p-value < 0.05, linear regression model) were considered for the comparison of slopes between protein pairs with even and uneven stoichiometric ratio. Wilcoxon test was used for the comparison.For each protein pair covered by the structural data obtained from the PDB , we testp-value ≤ 0.05, Wilcoxon test, and absolute log2FC > 1); 457 out of 6,554 proteins, 481 out of 6,478 proteins and 3,971 out of 10,316 proteins for COAD, HCC, and LUAD, respectively. The majority of those differentially abundant proteins were down-regulated in tumor samples   .p < 0.0001, chi-square test)  . In ordere test) .p = 6.28e-17, chi-square test) while a significant depletion was observed (consistent with the other cohorts) in the overlap between the down-regulated proteins and complex subunits   . The ovep < 0.0001, t-test)   . We ther t-test) , OV (The t-test)  and BRCA t-test)  cohorts.on test) .Together these observations suggest that protein complex organization constrains protein abundance changes in cancer, which is characterized by dysregulation of proteins, and has an impact on the strength of co-abundance patterns.To further understand how the dynamics of protein complex formation and the interaction between their components affect co-regulation of abundance changes of complex subunits, we first systematically categorized interaction types in complexes into five classes based on protein structure, proteomics measurements in different cellular conditions, biochemical properties of the binding interfaces, and literature information: i) stoichiometric ratio between proteins; ii) co-occurrence frequency of proteins; iii) context-specific vs. general interactions; iv) competitive vs. cooperative interactions; and v) transient vs. permanent interactions . Next, wn = 8,388) and calculated the stoichiometric ratio between the proteins within the same complex. Then we grouped protein pairs as the ones with even stoichiometric ratio  and those with uneven stoichiometric ratio  and compared protein level correlations between these two groups in each cohort. We observed that protein pairs involved in complexes in an even stoichiometric ratio have significantly stronger correlations than other protein pairs  in all cohorts , that positively correlated protein pairs with even stoichiometric ratio are associated with higher slopes  while their negatively correlated counterparts are associated with lower slopes  (Proteins interact with each other in varying stoichiometric ratios in protein complexes . To test cohorts . We nexton test) . These op-value < 0.0001, Wilcoxon test)  . This obp-value < 0.0001, Wilcoxon test)  . The resp-value < 0.05, Wilcoxon test)  interaction sites . To asseon test) . This caon test) . This obp-value < 0.05, Wilcoxon test)  . This ison test) .Cancer is characterized by many alterations including transcriptome and proteome dysregulation. There seems to be a larger number of dysregulated transcripts as compared to proteins . This suIn most yet not in every case, our observations matched our expectations. However, in some comparisons we observed different degrees of the expected trend in different cohorts: e.g., for the observation that permanent interactions are associated with higher correlations . This vain vitro experiments. The proteins classified as competing could have different localization or be expressed in different cells/tissues in vivo and in reality never meet.For proteins binding to the same proteins through overlapping interaction sites, competition is expected. We hypothesized that proteins competing with each other to bind their common partners will show weaker correlations in their protein abundances. However, we were surprised that the results were in the opposite direction of our expectation . Those rThe purpose of this study was to provide a proof of concept that interaction types within complexes affect co-abundance patterns in cancer. To this end, we picked only a limited set of studies to classify interactions or quantify protein abundances. Hence, the primary limitation of our study is the small number of representative examples for several types of interactions such as permanent vs. transient interactions where mining the literature revealed only a few instances. Additionally, for the class of context-specific vs. general interactions, we considered the proteomics measurements of two different cell lines. Thus, integrating a larger number of cell-line specific proteomics interaction datasets will potentially provide a more comprehensive understanding in the future.


Structural genomics began as a global effort in the 1990s to determine the tertiary structures of all protein families as a response to large-scale genome sequencing projects. The immediate outcome was an influx of tens of thousands of protein structures, many of which had unknown functions. At the time, the value of structural genomics was controversial. However, the structures themselves were only the most obvious output. In addition, these newly solved structures motivated the emergence of huge data science and infrastructure efforts, which, together with advances in Deep Learning, have brought about a revolution in computational molecular biology. Here, we review some of the computational research carried out at the Protein Data Bank Japan (PDBj) during the Protein 3000 project under the leadership of Haruki Nakamura, much of which continues to flourish today. Finally, because each of the wwPDB member sites developed their own structure analysis tools, PDB researchers played an important role in fairly assessing the output of structural genomics efforts.Within the structural genomics movement, perhaps the most audacious plans were made in Japan: The stated goal of the Protein 3000 project was to solve at least 3000 new structures in 5 years. In the end, 3199 PDB entries were generated. According to an assessment by one of us (D.M.S) and Haruki Nakamura, structures for 1192 unique protein families , Zc3h12a has been found to be essential for regulating the activation of both innate and adaptive immune cells  and HHpred (a protein structure prediction method based on MSAs) significantly degraded the resulting model quality Fig. , right. 18) of possible antibody sequence combinations (Briney et al. Many, if not most, protein structural modeling problems fit nicely into the MSA-Deep Learning workflow described above. However, antibody-antigen interactions are an important exception. It is now well established that the extension of AlphaFold to protein complexes, AlphaFold multimer, does not work well for antibody-antigen complexes (Evans et al. How, then, do we tackle this problem? One approach is to make use of antibody high-throughput sequencing. The number of antibody sequences deposited in public databases has grown dramatically in recent years. For example, we have recently collected over 200 million heavy chain sequences in the InterClone database (InterClone Large-scale science initiatives like genome sequencing and structural genomics have required huge funds and extensive coordination between scientists working in different disciplines. Such efforts also depend on prominent scientists to champion goals that may not be realized for years or decades. In the early 2000s, efforts to develop structural genomics and to expand protein structural data centers were promoted by a dedicated group of scientists with a shared vision. In Japan, Haruki Nakamura led the structural bioinformatics efforts that grew into the present-day PDBj. At the time these efforts made practical sense, but when we consider the recent transformation of computational structural biology, the development of protein sequence and structure databases seems visionary. Moreover, the combined successes of experimental and computational biology have brought us to the point where functional genomics is now possible.In the present era, large scientific budgets are being allocated for focused research on human health. As the COVID-19 pandemic showed, we do need to overhaul our ability to rapidly roll out new vaccines and therapeutics. However, one concern is that the benefits of these large-scale projects will be assessed on a narrow timeframe, such as the immediate impact of publications. The lessons of the Protein 3000 project and PDBj are that the data itself has great value. To see the full potential of this data realized, we must give it time to evolve, just as the concept of structural genomics itself has evolved.


Several pathogens that spread through the air are highly contagious, and related infectious diseases are more easily transmitted through airborne transmission under indoor conditions, as observed during the COVID-19 pandemic. Indoor air contaminated by microorganisms, including viruses, bacteria, and fungi, or by derived pathogenic substances, can endanger human health. Thus, identifying and analyzing the potential pathogens residing in the air are crucial to preventing disease and maintaining indoor air quality. Here, we applied deep learning technology to analyze and predict the toxicity of bacteria in indoor air. We trained the ProtBert model on toxic bacterial and virulence factor proteins and applied them to predict the potential toxicity of some bacterial species by analyzing their protein sequences. The results reflect the results of the in vitro analysis of their toxicity in human cells. The in silico-based simulation and the obtained results demonstrated that it is plausible to find possible toxic sequences in unknown protein sequences. Humans spend over 90 percent of their time indoors, whether at home or at work . Upon enBacteria and fungi are the most common airborne pathogens. These organisms have specific ecological niches and can adapt to the environment. Living organisms produce proteins derived from a chain of peptides, which act as building blocks in many organisms. The roles of proteins include the creation of hormones that affect various parts of the organism for different purposes, such as reproduction and heart rate control. Proteins also participate in chemical reactions within an organism as catalysts in the form of enzymes responsible for speeding metabolism . AlthougDue to globalization and advancements in transportation, the number of people moving from one place to another, often from country to country, is steadily increasing. These factors facilitate the spread of highly contagious and deadly diseases. Diseases often produce unpredictable outcomes, including spontaneous mutations, in which some variants become more contagious or deadly ,7. ThereStudies in bioinformatics have revealed that incorporating deep-learning techniques to analyze genome and amino acid sequence data is often helpful in many subtasks. For instance, finding DNA-protein-interacting areas using reinforcement learning  and predLanguage models developed for natural language processing have yielded promising results over the past few years. The transformer model suggested by Vaswani has outperformed the previous state-of-the-art models, with fewer required computations and higher bilingual evaluation understudy (BLEU) scores . Newer aIn this study, we propose the use of a fine-tuned ProtBert model to predict bacterial proteins that may act as virulence factors. We first tested the model on existing toxic protein datasets to determine whether it could outperform previous methods for toxic protein classification. We then trained the model on a new dataset, where we labeled virulence factors as toxic protein sequences, to determine whether the toxic-protein-prediction performance would improve when compared to using only toxic protein sequences for training. Finally, we applied the model to random protein sequences of four common bacteria found in indoor conditions.Although our study aimed to uncover potential toxic proteins of bacteria, we tested our method to compare it with other existing toxic-protein-prediction models to determine the validity of our method. Three independent datasets were used to evaluate the performance of the proposed method. The first dataset consisting of known animal toxins was collected from a previous study . It consThe second dataset was collected from another study  to prediFor the final dataset, we combined two separate datasets, each containing different types of protein sequences. The VFDB  dataset,For an unbiased evaluation of our model on toxic bacterial proteins, we downloaded 18,194 bacteria from SwissProt , from whIn the toxDL dataset, proteins composed of more than 1002 amino acid residues were truncated for implementation . HoweverStaphylococcus aureus, Staphylococcus epidermidis, Bacillus subtilis, and Micrococcus luteus. All bacteria were heat-inactivated before cytotoxicity assay. The MTT  assay, a representative method for measuring cellular metabolic activity and evaluating cytotoxicity caused by toxic agents [To verify whether our method can find potential toxicity-related sequences in unlabeled protein sequences, we chose four well-known bacterial species that are commonly found in indoor air. The selected species were c agents , was empc agents , HeLa  at a density of 2 × 10⁴ cells per well (MRC5 and YD38) or 1 × 10⁴ cells per well (Hela), and cultured in a COTo collect the protein sequences of all four species, we searched GenBank  and seleThe Hugging Face  implemenx is the feature vector produced in the previous layer, and the prediction results of tanh range from −1 to 1. A positive number indicates a toxic protein and a negative number indicates the absence of toxicity.The equation for tanh is as follows:To maximize the performance of the model, each dataset was pretrained for 20 epochs. After choosing the second lowest loss within the pretrained results, the model was fine-tuned using different learning rates and dropouts for the ToxDL data, BTXpred data, and combined data.From 1.F1 score:2.MCC:M. luteus on MRC5 and HeLa cells, and their cytotoxic effects were increased in proportion to bacterial burden. The results can be viewed in 10 CFU/mL), S. aureus, S. epidermidis, and B. subtilis reduced cell viabilities to 69%, 73%, 62% in MRC5 cells, 60%, 77%, 58% in HeLa cells, and 66%, 65%, 33% in YD 38 cells, respectively. M. luteus was cytotoxic only to YD38 cells. This result demonstrates that bacterial contents from these bacterial species can be cytotoxic in a dose- and cell type- dependent manner, proposing a considerable method to evaluate in vitro cellular toxicity of indoor air bacteria.To test the reliability of our in silico prediction program, we investigated the effects of four bacterial species on cell viabilities of MRC5, HeLa, and YD38 cells. Results showed cytotoxic effects of bacterial samples on all human cell lines used in this study, except The results of the final experiment, in which we trained the model on the combined dataset ,22, are M. luteus, S. epidermidis, and B. subtilis were almost the same for both models, whereas the model trained using the combined dataset could identify more sequences as toxic for S. aureus. We suggest this is because the model trained with the combined dataset has more complex knowledge of possible virulence factors than the model trained with BTXpred data.The in vitro toxicity experiments revealed that all four species cause hazardous effects. From this, we presumed that a substantial number of proteins that can be labeled as toxic would be found in the collected protein dataset. In this study, we proposed the use of ProtBert for the prediction of toxic bacterial proteins. We tested our model on two public datasets and showed that it yields similar results as previous methods for animal toxic protein prediction and toxic bacterial protein prediction. We also trained the model using bacterial virulence factors to further investigate whether the model performance would improve when trained with much broader data. The results showed that our model could correctly classify toxic bacterial protein sequences. The in vitro experiments on unlabeled protein sequences revealed the possibility of finding new toxic protein sequences, and that the in silico method can capture possible toxic protein sequences.It is noteworthy, however, that even though we could identify possible toxic proteins that may act as virulence factors, we can only presume that these proteins are responsible for hazardous reactions in the in vitro experiments. Hence, we intend to further investigate the link between the identified protein sequences and virulence data through more thorough in vitro experiments.To strengthen the performance of the in silico protein toxicity prediction, we hope to add other features to the training of the model, such as evolutionary and protein chemical compositions, which are known to create harmful effects.


Antibiotics are the commonly used drugs to tackle chlamydiae infections. However, when overused or wrongly used this may lead to strains’ resistance to antibiotics, this phenomenon represents a real health problem worldwide. Numerous studies showed the association of Chlamydia trachomatis resistance with mutations in different genes; these mutations could have a deleterious or neutral impacts on the encoded proteins. The aim of this study is to perform an in silico analysis of C. trachomatis rpoB-encoded proteins using numerous bioinformatics tools and to identify the functional and structural-related effects of the mutations and consequently their impact on the bacteria sensitivity to antibiotics.CrpoB-encoded proteins showed eight mutations: V136F, Q458K, V466A, A467T, H471N, H471Y, H471L, and I517M with big deleterious effects. Among them, six mutations, V136F, Q458K, V466A, A467T, H471N, and I517M, are located in a highly conserved regions decreasing the protein’s stability. Furthermore, the structures analysis showed that the mutations A467T, H471N, I517M, and V136F models had a high deviation compared to the wild type. Moreover, the prediction of protein-protein network indicated that rpoB wild type interacts strongly with 10 proteins of C. trachomatis, which are playing different roles at different levels.The analysis revealed that the prediction of the damaging impact related to the mutations in C. trachomatis resistance and enable managing the infection to avoid its complications. We recommend further investigations and perform deeper experimental analysis with collaboration between bioinformaticians, physicians, biologists, pharmacists, and chemistry and biochemistry scientists.As conclusion, the present study revealed that the changes observed in the encoded proteins can affect their functions and structures, in addition to their interactions with other proteins which impact the bacteria sensitivity to antibiotics. Consequently, the information revealed through this in silico analysis would be useful for deeper exploration to understand the mechanisms of  Chlamydia, 14 species [Candidatus (Ca.) species  [Chlamydia trachomatis (C. trachomatis) natural hosts [C. trachomatis bacteria is untreated, it may lead to severe complications. In females, pelvic inflammatory disease leads to tubal infertility [C. trachomatis can be transferred to the newborn from the infected mothers; they can develop ocular, respiratory, and gastrointestinal infections [C. trachomatis in both men and women infected with C. trachomatis. In another hand, C. trachomatis produces chronic ocular infections that can lead to trachoma, which is one of the leading causes of blindness worldwide [The family Chlamydiaceae comprises a group of obligate intracellular Gram-negative bacteria that are responsible for a broad range of infections in mammals, birds, and humans . Current species –4 and fotudinis) –8. Humanal hosts ; when C.ertility –13, ectofections . In men,fections , 17. In fections  and reacfections  are the orldwide , 21.The recommended treatment for chlamydia infections is antibiotics. However, the misuse or the overuse of these antibiotics may lead to antibiotic resistance. The acquired resistance occurs when the bacterium that has been sensitive to antibiotics develops resistance via mutation or via acquisition of new DNA .C. trachomatis may develop resistance to macrolides via mutations in the 23S rRNA gene, to fluoroquinolones via mutations in the gyrA gene, and to rifamycins via mutations in the rpoB gene [rpoB gene, impacting the inhibition of bacterial transcription related to interacting with beta-subunit of bacterial DNA-dependent RNA polymerase [Different studies showed that poB gene , 23–27. lymerase .All mutations within the genomic sequence can lead to alterations in the sequence of the encoded protein, which could have a deleterious or neutral impacts on the protein. Furthermore, it may ultimately affect alteration of protein charge, geometry, hydrophobicity dynamics, translation, and inter- or intra-protein interaction set cells in danger .C. trachomatis rpoB gene-encoded protein and identify the functional and structural-related effects of the protein’s variations, which consequently may impacting the bacteria sensitivity to antibiotics.The aim of the present study is to perform an in silico analysis of the retrieved amino acid variations in In the present study, we performed an in silico analysis using different machine learning algorithms, following the various steps described below and illustrated in the flowchart Fig. .Fig. 1StrpoB gene mutations linked to C. trachomatis resistance to antibiotics, we proceeded to extract all the rpoB mutations from various resources, which were gathered in our precedent investigations [To find the igations .http://loschmidt.chemi.muni.cz/predictsnp1/) [The damaging effects of the mutations on the protein were predicted using PredictSNP1.0 (ctsnp1/) ; this toctsnp1/) , PolyPhectsnp1/) , PolyPhectsnp1/) , MAPP [3ctsnp1/) , PhD-SNPctsnp1/) , SNAP [3ctsnp1/) , PANTHERctsnp1/) , Predictctsnp1/) , and nsSctsnp1/) . Most ofPredictSNP1.0 displays the confidence scores generated by each tool and a consensus prediction as percentages by using their observed accuracy values to simplify comparisons . We clasu); the sign of ΔΔG indicates if the mutation decreases (ΔΔGu < 0) or increases (ΔΔGu > 0) the protein stability [To predict the change on the protein stability, we performed the Sanavia et al. protocol, where the effects of the variants on the protein stability are quantified in terms of the Gibbs free energy of unfolding (ΔG), and the measure of interest is the difference of the unfolding free energy between the mutant and wild type proteins (ΔΔGtability .http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) is a support vector machine (SVM) and a web-based tool that provides the predicted change in Gibbs free energy (ΔΔG) [http://mupro.proteomics.ics.uci.edu/) is based on SVM [rpoB in FASTA format.We analyzed the different approaches available and selected I-Mutant 3.0 and MUpro to perform this analysis. I-Mutant 3.0 (gy (ΔΔG) . MUpro sd on SVM . We submhttps://consurf.tau.ac.il) [The evolutionary conservation analysis consists in estimating the degree of the amino acid conservation based on multiple sequence alignment using the ConSurf server (u.ac.il) . The degu.ac.il) , 43. In u.ac.il) , 45. In The grade ranges from 1 to 9 estimates the extent of conservation of the amino acid throughout evolution. Therefore, grade 9 represents the most highly conserved residue, and the numbers descend to 1 representing the least conserved region.rpoB-encoded protein and its selected mutants are designed with SWISS-MODEL (https://swissmodel.expasy.org/) [To understand the effect of each mutation on the protein structure, the 3D models of sy.org/) . Accordisy.org/) .https://zhanglab.ccmb.med.umich.edu/TM-align/), based on template modeling score (TM-score) and the root mean square deviation (RMSD) scores [To compare the native and mutated protein structures, structural similarities were calculated using TM-align tool  scores . Tm-alig) scores .The energy minimization is essential to determine the proper molecular arrangement in space; it used to eliminate high energies in the predicted model and achieve local minima that is closer to native structure . For thirpoB-encoded protein with various proteins, the STRING database was used (https://string-db.org) [Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions . To inve-db.org) . This da-db.org) .rpoB gene mutations associated with C. trachomatis resistance to antibiotics, we performed a literature search pertaining to the topic exhaustively, and a total of nine mutations were retrieved ; furthermore, all the models had template modeling score (TM-score) near to 1; these results signify that the status of protein folding is identical. In addition, the structural 3D models had high RMSD score which signify a high deviation between mutants and wild type  [Different studies revealed that the resistance to rifamycin was associated with mutations in the e (rpoB) , 25, 56;rpoB-encoded protein  have a deleterious effect on the encoded protein . Thus, we predict the 3D structure of wild type and mutants, by changing the six mutations into the native sequence. In addition, Ramachandran plot analysis was performed to validate these protein structures; all the structures had the residues in most favored regions more than 80%, which means that all the structures are valid (Fig. The wild protein 3D structures are essential to more understand the functional and structural effect of mutations; the lid Fig. . Hence, rpoB-encoded proteins with their receptors which may lead to resistance to antibiotics. Regarding the predictions of the mutations Q458K and V466A, the results showed a lowest RMSD score and their TM-score near to 1; these results signify that the status of the protein folding is identical.Additionally, the structural changes in the encoded proteins were analyzed using TM-Align to compute the RMSD and the TM-score by superimposing models of native and mutant proteins. The results showed that the models with the mutations V136F, A467T, H471N, and I517M had high RMSD values and their TM-score near to 1 (Table In another hand of the analysis, the results of the energy minimization revealed that the total energy of the mutants: V136F, A467T, H471N, and I517M were low compared to that of the wild type protein. Moreover, the mutants present the higher number of H-bonds. In opposition to the high total energy, the wild type structure had the lower number of H-bonds (Table rpoB interacts with 10 different bacteria’s proteins (Fig. rpoB-encoded protein structure and function might affect the bacteria sensitivity to antibiotics.At the final step of our in silico analysis, the prediction of the protein-protein interaction revealed that ins Fig. , which ains Fig. . In addirpoB gene which were revealed to be associated with C. trachomatis resistance to rifamycin and predicts their effects using various bioinformatics tools. The results revealed that the mutations, V136F, A467T, H471N, and I517M, had the most impact on both stability and RMSD, in addition to their location in the highly conserved regions; therefore, they can affect the protein’s function, structure, and their interaction with other proteins. All these changes can explain the observed resistance to antibiotics. Moreover, the study revealed that all mutations are not necessarily translated to strong phenotypic expression. Consequently, the revealed information through this in silico analysis would be useful for deeper exploration to understand the mechanisms of C. trachomatis resistance; this could enable managing the infection and avoid its complications. We recommend further investigations to perform deeper experimental analysis and explore alternative therapies and new drug design by molecular docking; this can be done in collaboration between bioinformaticians, physicians, biologists, pharmacists, and chemistry and biochemistry scientists.This study compiled the mutations in