RNA viruses cause a wide range of human diseases that are associated with high mortality and morbidity. In the past decades, the rise of genetic-based screening methods and high-throughput sequencing approaches allowed the uncovering of unique and elusive aspects of RNA virus replication and pathogenesis at an unprecedented scale. However, viruses often hijack critical host functions or trigger pathological dysfunctions, perturbing cellular proteostasis, macromolecular complex organization or stoichiometry, and post-translational modifications. Such effects require the monitoring of proteins and proteoforms both on a global scale and at the structural level. Mass spectrometry (MS) has recently emerged as an important component of the RNA virus biology toolbox, with its potential to shed light on critical aspects of virus–host perturbations and streamline the identification of antiviral targets. Moreover, multiple novel MS tools are available to study the structure of large protein complexes, providing detailed information on the exact stoichiometry of cellular and viral protein complexes and critical mechanistic insights into their functions. Here, we review top-down and bottom-up mass spectrometry-based approaches in RNA virus biology with a special focus on the most recent developments in characterizing host responses, and their translational implications to identify novel tractable antiviral targets. In recent years, technical advances in mass spectrometry (MS) analysis coupled with the availability of reliable MS workflows and user-friendly data analysis software triggered multiple studies investigating persistent modulation of protein abundances, post-translational modifications, and protein–protein interactions on a global cellular scale, with unprecedented sensitivity and quantitation ,2,3,4. SSince the introduction of mass spectrometry (MS)-based methods in proteomics a few decades ago, the technology has seen a tremendous increase in popularity. These approaches reached a level of maturity that now makes them accessible for researchers outside the mass spectrometry community, paving the way for a golden era of MS-based methods in virology. Amongst others, MS helped to elucidate elusive aspects in replication mechanisms using global or interaction proteomics, revealing the importance of post-translational modifications (PTMs) ,10,11. FTwo workflows have been traditionally established in MS-based proteomics: “bottom-up” and “top-down” proteomics. In bottom-up-based methods, proteins are proteolytically digested into peptides prior to their injection into the mass spectrometer, allowing an analysis of complex specimens from different biological material. Conversely, in top-down methods intact proteins are analyzed, making these workflows particularly suited for the analysis of different, co-existing post-translational modifications (PTMs) of a protein or protein complex ,12. Herem/z can be selected for fragmentation  resulting in specific fragmentation spectra. From these, the modifications can be located and determined. Another approach to separate species in samples with microheterogeneity is charge reduction mass spectrometry. Here, alkylated ammonia or alkali metal acetate salts are being utilized. This furthermore prevents the undesired denaturation of labile proteins in the gas phase, as well as the loss of non-covalent interactions in native MS approaches, which could otherwise result in the loss of information on the protein structure [Top-down approaches are often combined with tandem MS methods to distinguish between protein species. By connecting multiple mass analyzers in a series, ion bundles with specific tructure ,14,15.In recent years, MS methods have gained more attention in virology studies. In 2019, Dülfer et al.  gave an m/z and the charge are simultaneously detected for a single particle ion allowing for direct mass calculation [By implementing ion mobility (IM) separation in the MS analysis, additional information about the 3D structure of proteins and protein complexes can be obtained ,16,17. Wculation . This, oculation . The ongThere are a few examples of protein complexes of RNA viruses being analyzed with native MS showing the potential of this method. The complex formation between viral RNA and proteins is widely researched in virology. Hagan et al.  analyzedCoronaviridae family), containing important co-factors of the main enzymes, was investigated. Native MS enabled simultaneous analysis of the processing and coordinated the release of non-structural protein (nsp) monomers by the viral protease and subsequent protein complexation. The order of processing was identified from the intermediate polyprotein products nps7–8 and nsp7–9, which provided much higher precision than e.g., SDS-PAGE (sodium dodecyl sulphate–polyacrylamide gel electrophoresis) performed for comparison. In a follow-up [Besides RNA–protein complexes, the interaction between different proteins is also of enormous importance. Big protein “machineries” are usually heterogeneous protein complexes, with each of the protein subunits serving a different role. In a seminal study , the dynollow-up , nsp7+8 ollow-up . To furtollow-up . This meIn MS, there are several techniques for labeling proteins in order to obtain structural information. The idea in covalent labeling mass spectrometry (CL-MS) is that the applied reagent labels only solvent-accessible amino acids, as buried residues are protected. Then, the labeled residues can be compared in different conditions, e.g., upon binding to another protein, inducing a conformational change. There are a variety of labeling agents with different chemical properties offering different opportunities to pursue a scientific question ,28. One Luteoviridae by using bottom-up XL-MS only [Crosslinking mass spectrometry (XL-MS) is a specific CL-MS approach to study protein complexes, in which crosslinking agents link nearby sidechains. Based on the length of the crosslinker as a constraint, 3D models can be created from distance maps ,31. Ther-MS only ,42.Most of the studies quoted here used the bottom-up approach in their experiments, though such experiments can be done in a top-down approach as well. The chemical labeling with a selected cross-linker precedes MS. In order to derive constraints or in order to get a distance map it is important to obtain peptides and sequence information cf. . Using aOne of the recent developments of XL-MS is In Vivo chemical crosslinking, facilitating PTM analysis or the analysis of large proteins/protein complexes that are difficult to purify. Strikingly, a more comprehensive overview of protein interaction networks can be obtained, including the identification of novel virus–host interactions  cf. Fig. DeBlasiIn general, XL-MS techniques are widely used but little has been done yet in the context of ssRNA viruses. Due to the current pandemic, SARS-CoV-2 studies are evoking increased interest, giving rise to chemical crosslinking datasets, especially in the context of protein-RNA binding and host-pathogen association unravelling a more detailed picture in COVID-19 disease response ,48. JackChemical labeling can also distort the picture of protein structures when the experiments are not carefully conducted In Vivo. There are less perturbing strategies using the same principle, namely small labels, such as in the reversible exchange of hydrogen to deuterium (HDX) . HDX couIn conclusion, HDX and XL-MS/CL-MS are able to provide valuable structural data on proteins and protein assemblies. In the context of research on viruses, In Vivo XL-MS is especially valuable to elucidate host–pathogen associations and unknown involved proteins. Due to the fact that HDX is a gentler technique, it is particularly well-suited for the investigation of PTMs and the binding of ligands. For further reading, there are reviews that give a profound insight into the techniques and application of HDX  and XL-MTobamovirus) [Picornaviridae) [Mass spectrometry is a powerful tool to not just analyze small molecules like metabolites or macromolecular interactions, but even to understand the high level structures of protein complexes like virus capsids. Beyond that, it has been used to investigate the architecture of various viruses  includinmovirus) , foot anmovirus) , and rhiviridae) . To undeviridae) ,69,70,71viridae) . AnalyziBromoviridae family [An approach to this method was performed by Snijder et al. in 2013  to undere family . Obtainie family . By rapie family . After aCaliciviridae family. Experiments were done on stability, particle size, and the particle composition of the norovirus-like particles (NVLP) [T = 3) and a closely related strain were intact at physiological pH, and higher pH triggered disassembly [T = 1 particles. The truncation of the N-terminus of the major capsid protein VP1 results in exclusively smaller capsid sizes [Additionally, a lot of structural work was performed on different noroviruses belonging to the s (NVLP)  of diffes (NVLP) ,79, the s (NVLP) ,81,82, as (NVLP) . A varieassembly , whereasid sizes ,78,79. WDrug-target identification often starts in academia, where protein pathways, protein functions, and their molecular mechanisms are investigated. In Vivo XL-MS showed its potential to discover virus–host associations and pathways and, thus, can find potential interfaces for drug design ,84 or deIn drug discovery, NMR and X-ray crystallography are commonly the structural methods of choice in a hit-to-lead or lead optimization strategy. The other is cell-based platforms requiring the development of primary and secondary assays and screening workflows . Here, nConsidering drug discovery for ssRNA viruses, this technique has not yet been the first choice. In the current pandemic, native MS was deployed as a supportive method to find potential inhibitors against the viral main protease ,90. In 2Moreover, Mehaffey et al.  used natAlternatively, HDX can be used in the hit-to-lead or lead optimization stage in early drug development. The investigation of the binding mode, e.g., epitope-mapping, plays a role in optimizing substances. Puchades et al.  showed pIn addition to top-down and structural approaches, bottom-up proteomics methods have also seen remarkable progresses in the past few years, reaching unprecedented analytical depth, sensitivity, and throughput (reviewed in ). For thCurrently available state-of-the-art LC-MS/MS set-ups allow for the reliable identification and quantification of more than 5000 proteins in very short gradients . This ofThese improvements have been the coherent result of the development of more robust and high-throughput chromatographic systems , the conIn the following paragraphs, we provide a few recent examples of bottom-up-based methods for the study of (+)RNA virus interactions.Viruses often perturb global proteostasis or dynamically modulate host protein expression to degrade cellular proteins restricting virus replication, enhance the expression of host proteins critically required for productive infection, or inhibit/activate specific cellular pathways. Similarly, virus-induced host responses often entail the degradation of viral proteins or the turnover of certain cellular proteins required for effective counter defense mechanisms. Altogether, these cross talks influence viral replication fitness, viral tropism, and the cross-species transmission of zoonotic RNA viruses, and can be driven by the modulation of viral and/or cellular protein abundance.13C or 15N-labeled arginine or lysine  and the natural isotopes  into the cell culture medium that metabolically incorporate into proteins after several cell division cycles. This approach allows for highly accurate relative quantification based on the intensity ratio of isotope-labeled peptides to unlabeled peptides [With respect to sample preparation, label-free or label-based methods, such as stable isotope labeling by amino acids in cell culture (SILAC) and tandem mass tag (TMT), have been widely employed to study RNA viruses. Label-free proteomics is relatively inexpensive and less time-consuming compared to label-based methods, and became more popular in recent years due to the development of user-friendly software featuring reliable inter-sample normalization steps  and the peptides  RNA viruses such as hepatitis C virus (HCV), HIV, and DENV ,116,117.+ T cells identified more than 600 HIV-modulated cellular proteins with functions in amino acid transports, DNA repair, and lymphocyte activation, providing a framework to study the pathophysiology of HIV-infected primary human CD4+ T cells at the cellular level [TMT-based proteomics can also be used to generate quantitative and functional proteomic atlases from primary cell cultures. For instance, a recent study on HIV-infected primary human CD4ar level . In thisar level . Interesar level . NotablyLabel-free methods can additionally be used to provide insights into the mechanisms of action of novel antiviral inhibitors. For instance, Nitazoxanide (NTZ) has been shown to exert broad antiviral activity against several RNA viruses such as HCV, Middle East Respiratory Syndrome coronavirus (MERS-CoV), and SARS-CoV-2 ,124,125;In addition to top-down proteomics, bottom-up based approaches can also be used to systematically identify protein–protein interactions and characterize macromolecular complexes. While the first approach provides invaluable information on relative stoichiometry, 3D spatial organization, or ligand-induced rearrangements, it requires multiple optimization steps and the expression of crude or purified protein complexes prior to the analysis. Conversely, bottom-up-based methods have usually lower biochemical requirements, as protein complexes can be enriched using antibodies specific to the endogenous protein of interest, or against epitopes fused to the target of choice, in the context of intact cellular backgrounds. In this case, affinity purification (AP) usually precedes sample preparation, and can be easily multiplexed for thousands of proteins  or combiRecently, global efforts to systematically profile cellular targets of viral proteins have intensified exponentially, greatly expanding the virus–host interaction landscape. Using ectopically expressed viral proteins as baits, multiple studies profiled the interactome of entire viral species including DENV, ZIKV, Ebola or Influenza virus (reviewed in ). For inPhleboviruses, identified guanine nucleotide exchange factor 1 (GBF1) amongst the 39 cellular proteins specifically interacting with the two envelope glycoproteins Gn and Gc [n/Gc-GBF1 interaction in supporting a post-entry step of UUKV identifying a GBF1-specific inhibitor with broad antiviral activity against UUKV and several other important RNA viruses, including chikungunya virus (CHKV), HCV, and human coronavirus 229E [Coronaviridae, identifying several conserved interactors among SARS-CoV, SARS-CoV-2, and MERS-CoV [MS-driven discovery of virus-host protein–protein interactions may also streamline the identification of novel host-directed antivirals. For instance, a systematic investigation of the DENV non-structural protein 1 (NS1) cellular interactome in virus-infected cells identified RACK1 and oligosaccharyltransferase (OST) complex as conserved cellular binding partners critically required for viral RNA replication in multiple cellular backgrounds and for multiple other flaviviruses including ZIKV and West Nile virus (WNV) . Pharmacn and Gc . Functiorus 229E . More reMERS-CoV . This syMERS-CoV .In addition to traditional affinity purification-based methods providing reproducible snapshots of stable and relatively persistent macromolecular complexes, alternative approaches such as proximity-dependent biotinylation identification (BioID) have been successfully coupled to MS to identify relatively weak, transient, or indirect viral-host protein interactions B. BioID In addition to global proteostasis and macromolecular complex composition, RNA viruses often deregulate post-translational modifications (PTMs) to hijack entire signaling pathways or interfere with the activity of specific effector proteins. The most commonly targeted PTMs by RNA viruses include glycosylation, ubiquitination, SUMOylation, and phosphorylation (reviewed in ). Irresp2), or antibody-based immunoaffinity purification , and mitogen-activated protein kinases (MAPK) were identified in SARS-CoV-2 infected cells when compared to the uninfected counterpart. Phosphorylation of CK2 is likely to support viral egress or cell-to-cell spread through actin remodeling, while MAPK phosphorylation may play a role in the disease pathophysiology triggering cell cycle arrest [2-based enrichment methods were used to systematically study the impact of IAV infections to the phosphoproteome of macrophages and lung epithelial cells [2-based phosphopeptide enrichment has also been used to profile infection by neurovirulent flaviviruses such as WNV and Japanese encephalitis (JEV) [In recent years, phosphoproteomics has seen the most dramatic technical improvements, achieving near-to-complete phosphosite coverage while requiring minute amounts of starting material. The most widely used methods for phosphopeptide enrichment involve the use of immobilized metal ion affinity chromatography (IMAC), metal oxide affinity chromatography (MOAC), titanium dioxide beads (TiOiewed in ). Recentiewed in . Analogoiewed in . Interesiewed in ,147. Amoe arrest ,149. More arrest . IMAC- aal cells ,151,152.al cells . Notablyal cells ,154 restal cells ,152. TiOis (JEV) ,154. In is (JEV) . AnalysiIn addition to phosphorylation, the ubiquitination of specific cellular targets and viral proteins has often been associated with RNA virus infections in vitro and In Vivo ,156. Forβ signaling, suggesting a potential role in virulence and pathogenesis [An emerging PTM still vastly understudied in the context of RNA virus replication and virulence is protein acetylation. Until recently, no specific enrichment methods have been developed to systematically increase the relative abundance of acetylated lysine residues prior to MS, hence their identification mostly relied on the traditional AP of target proteins (or protein complexes) and targeted spectral searches. For instance, a recent study focusing on the IAV viral nucleoprotein (NP) identified multiple acetylation sites including K77, K113, and K229, playing an important role in multiple steps of the viral replication cycle . In addiogenesis . Similarogenesis . In thisogenesis .Additional PTMs playing critical roles in the replication cycle of multiple RNA viruses, including viral RNA replication, virus assembly, and virus entry and attachment are glycosylations. These have been extensively studied by multiple biochemical methods, including MS-based bottom-up approaches, and have been recently comprehensively reviewed elsewhere ,162.Top-down and bottom-up proteomics have entered an era of exciting discoveries. The global SARS-CoV-2 pandemic has recently shown the great potential of MS-based research in speeding up our understanding of viral replication mechanisms, identifying the perturbation of critical host factors driving viral pathogenesis, as well as streamlining the identification of host targets for drug repurposing and enabling high-resolution structural analysis by cryo-EM. Indeed, in the near future, MS will likely assist sequencing-based methods to profile emerging virus variants providing a rapid response toolkit in research and clinical settings. The recent optimization of robust high-throughput chromatographic methods and fast sample preparation protocols has already shown great promise in different fields, and will likely also enter diagnostic and prognostic pipelines in the clinical setting for infectious diseases. So far, top-down and structural MS approaches have focused on purified assemblies, showing the potential for deducing the determinants of viral protein complex formation . MoreoveThe improvement in sensitivity and reproducibility allowing single cell proteomics (SCP) has just emerged, and will likely also introduce new paradigms in our understanding of virus–host interactions (reviewed in ). Holist


Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms.2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome.POur tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies.2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.The unified protein-centric interface presented in P High-throughput sequencing is increasingly applied in clinical settings to establish precise genomic diagnoses, and in research, either to understand mechanisms of diagnostic, actionable, or pathogenic variants, or for better understanding opportunities for intervention. However, most variants have limited utility in these applications since they lack functional characterization. For lack of characterization, they are classified as variants of uncertain significance (VUS). Though guidelines,,,,Assembling data for each variant is challenging and time-consuming; relevant literature and databases are typically queried manually, which can result in data being overlooked or underutilized. Tools have been developed for this purpose and generally fit into one of three categories (i) protein-level databases of pathogenicity, functional sites2T2 as a platform for understanding proteins and protein-coding genetic variation. It is based on an interactive viewer populated using rich genome-wide and proteome-wide functional data including potential phenotypic effects, post-translational modifications, domains, motifs, structure availabilities, literature knowledge derived from NLP-mining, and paralog mappings. We additionally provide mappings to experimentally derived structures, and those of homologs, significantly expanding the ability to identify opportunities to enhance genomic information with 3D structure, which we recently demonstrated is not captured by genomic resources.2T2 as a service, but also a platform which can be customized to each lab or workflow’s needs. P2T2 is also searchable using Human Genome Variation Society (HGVS) syntax, which is standard nomenclature in clinical genetics reports. Thus, our platform fills an important and currently vacant niche for facilitating the interpretation of human genetic variants and enhancing the information available to research and genetics workflows.Existing tools either require the user to translate effects from a DNA or RNA view into an understanding of their potential effect on the encoded protein, do not provide up-to-date information from literature such as derived from NLP, cannot share information across related proteins , or lack a unified view of diverse annotations. For example, UniProt feature viewer provides some of these layers, but the provenance of alleles, and therefore their disease context, is difficult to ascertain, and the ability to view broad data about an individual protein is separated from the ability to look across isoforms and proteins. The Ensembl variant table has clearer data provenance but lacks NLP resources, and again the per-transcript and pan-transcript views are separate. Therefore, we present PAmino acid sequences of nonfragment isoforms for all human proteins were downloaded from the SwissProt section of the UniProt Knowledgebase,Protein annotations were integrated from a diverse set of resources including population allele frequencies via gnomAD,Hmmer3 alignmentAnnotation data was compiled using custom code from the R programming language (version 3.2.0)2T2, an interactive web-based tool designed to assist in the interpretation of protein coding variants by presenting multiple annotation and data types in a unified view . Queries that represent nonspecific genomic or protein entities such as gene symbols will be mapped to the protein product encoded by the canonical transcript. Unrecognized queries are mapped to close linguistic matches. Once a protein is selected, P2T2 provides an interactive interface which can be zoomed by clicking and dragging, and amino acid positions can be “marked” either by entering the position number in a search bar in the header menu, or by shift clicking within the interface. Marking a site creates a mini-report section to the right of the main UI, of all annotations at that position. Thus, P2T2 facilitates rapid access to protein annotations, for enhancing the interpretation of variants.We present herein our Protein Panoramic annoTation Tool, Pied view . Rather ,2T2 the first automated process for PAA of the human proteome. We believe the combination of annotations available within P2T2 and the implementation of PAA will help researchers generate novel hypotheses and interpret the effects of missense variants.Annotation resources used in the default instance of our tool include allele frequencies from gnomAD, variants from ClinVar, and Uniprot, PTMs from PTMCode2, phosphorylation sites from PhosphositePlus, NLP-mined PubMed disease-gene-variant associations from DoCM,2T2 can be useful in hypothesis generation and variant interpretation (2T2, amino acid position M41 is shown to be a site for post-translational methionine oxidation, which can lead to protein misfolding and regulation2T2 enables connections such as these to be made rapidly, maximizing the utility of variant annotations and facilitating hypothesis generation.The interface presented with UBA1 selected illustrates how the integrated protein-centric annotations presented by Pretation . Mutatio2T2 harmonizes diverse information for the interpretation of novel missense variants discovered from next-generation sequencing and clinical genetic testing. The annotations are visually organized to help researchers efficiently identify existing knowledge for specific amino acids, in order to better understand their potential functional effects on the translated exome. Information from primary sequence, domain, motif, and structural levels are presented in a simple format, combining the information available from other tools into PAA across the human proteome. PAA can be used for variant interpretation in the following way: if a novel variant is identified in one protein and the analogous residue in a closely related protein has a similar variant that is known to be pathogenic, this is suggestive of a similar effect in the target protein. The process of leveraging homology to identify sites or regions that are critical for protein functions is well established,,2T2 combines the annotation of each human protein with annotated MSAs, making this information available systematically and accessible to a wide audience.P2T2 indicates statistically significant relationships to homologous protein structures using sequence profiles, enabling a fuller view of current experimental data and potential for structural modeling. The three-dimensional context provided by these structures can imply a variant’s role in protein function, especially when used in conjunction with the other annotations presented by P2T2. Thus, we believe our tool will support multiple clinical and research workflows by linking the genomics to 3D experimental and computed structural models, for enhancing interpretation of genetic variation.In addition to PAA, structural biology is another research process that is not frequently used in the clinical assessment of the potential functional effects of novel variants, or hypothesis generation about their underlying mechanisms. Structural biology and computational biophysics can provide strong indications about the molecular effects of variants.2T2, but our approach has several that are unique and valuable to the field of genetics. Existing tools such as from UniProt aggregate data, but the source and therefore context for most alleles is unclear. Ensembl’s variant table is clear about the source of alleles, but users cannot view the data alongside transcript and paralog tables. Explicit consideration of all transcripts is recommended in the genomic guidelines,,2T2 is the first protein-centric tool searchable using genomic HGVS nomenclature. Thus, our tool can be used in automated and manual workflows, with much greater ease and interconnectivity, compared to existing tools. Across the above features and more, P2T2 provides unique functions that help researchers to interpret the effects of missense genetic variation.Tools have been developed with some features of P2T2 is a flexible platform for a truly protein-centric understanding of genomics. It provides a rich environment for understanding each position of a protein by combining annotations of disease-association and functional investigation across all human proteins at amino acid resolution. Our tool can be used to ease the challenge of manual database query and literature review in clinical and research genomics interpretation workflows. Linking the data across gene isoforms and human paralogs enhances how it can be used for interpreting novel genetic variations. Thus, we believe P2T2 fills a critical role in the expanding repertoire of data science tools for genomics.PM.Z., R.U., and E.K. formulated the concept of the study. E.D., R.Z., and M.Z. contributed software, formal analysis, implementation, and investigation. G.O., P.B., M.C., N.B., and J.K. contributed to data curation and reviewing the written works. J.K., R.U., E.K., and M.Z. supervised aspects of the study. M.Z. completed project administration. J.K., R.U., and E.K. completed funding acquisition.


Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database , which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.The house mouse or  Mus musculus, has emerged as a leading model of human biology and disease [Because of its close genetic and physiological similarity to human, the ease of the manipulation and analysis of its genome, the convenience of its breeding in the laboratory, the house mouse,  disease , 2. Geno disease –5. These disease , 6. HoweIn addition to databases integrated with interactions from experiments, studies of potential interaction prediction based on high-throughput technology have also been a focus area, including MouseNet , mentha The process of attaching biological information to a set of simultaneously changed genes  is knowns as functional annotation, which is a frequent component of bioinformatics analysis in omics research . As the The enrichment-based strategy summarizes the observed GDEs to established biological concepts. This strategy is successful in many cases. However, when there is no established annotation term that can accurately describe these changes, enrichment-based approaches frequently report terms that are conceptually very general  or simply report no term. These results provided limited help for investigators to formulate further hypotheses and design studies to elucidate the mechanism underlying the observed GDE. On the other hand, even in cases that no established biological concept is available to accurately describe what these GDEs are, we may still use established biological concepts to describe what potential functional impacts may be collaboratively exerted by these GDEs. For instance, the observed GDEs may collectively interfere with the function of GO:1903393 (positive regulation of adherens junction organization), even when the GDEs themselves are not enriched with this term (an example is provided in Discussion).To interpret the potential functional impacts of observed GDEs, we developed a web tool, gene set linkage analysis (GSLA), which complements the existing enrichment-based approaches, and are available for human and Arabidopsis transcriptome interpretations , 22. TheIn this work, we developed a high-quality functional gene association network, the mouse interactome database (MID), for searching potential functional gene associations in mice. We also constructed the GSLA web tool for interpretation of mice transcriptomes. To infer high-quality functional associations between mice protein-coding genes, MID integrates six types of evidence from 9 public databases. All evidence used for inference predate the end of 2018. Newly reported experimentally confirmed protein-protein interactions (after 2018) were used to assess the inference accuracy. The current version of MID includes 155,887 gene associations. These gene associations are expected to cover 19.32% of the protein-protein interactions in mice, and 26.02% of the gene associations may represent protein interactions. The web interface for MID is available for users to investigate the functional associations among the protein-coding genes, and provides a GSLA web tool for interpretation of the collective functional impacts of mice GDEs. In the end, two case studies are provided to illustrate the use of the MID/GSLA system.Protein-protein interactions are considered evidence of strong functional associations. A total of 32,997 experimentally reported unique protein-protein interactions between mouse protein-coding genes were collected from BioGRID , and IntA. thaliana, C. elegans, H. sapiens, D. melanogaster, R. Norvegicus, S. cerevisiae and S. pombe to compute interologs [http://mouse.biomedtzc.cn./#/help/feature).Apart from the experimentally confirmed protein-protein interactions, we also collected six types of functional association evidence from 7 public databases with the year set to before 2018. These evidence data include 17,738 expression profiles (COXPRESdb) , 402,516terologs . 36 featterologs . DetaileTo characterize the functional associations between mouse protein-coding genes, 36 feature values were selected for computation . The 36 Ninteractome is the estimated number of mouse protein interactions; Npredict is the size of the predicted functional gene interactome; Nall-pairs is the number of all protein-coding gene pairs in mice; and the sensitivity and specificity measure the accuracies of the prediction model to predict the newly published (after 2018) protein interactions and random gene pairs.To calculate the fraction of protein interactions that were covered by these putative functional gene interactions, we used the following equation.To evaluate the power of our selected 36 feature values to indicate functional associations, we used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. For the computation of protein-protein interaction predictions, each feature value will produce a series of sensitivities and specificities based on different cut-offs with the training dataset (collected before 2018). The sensitivity and specificity pairs of the ROC curve  were plotted corresponding to different cut-offs. In this study, the feature values with an AUC greater than 0.6 were considered informative to indicate functional associations . A totalTo train and infer functional gene associations, we used the LIBSVM software package  with theTo train the SVM prediction model, we used the soft-margin Gaussian kernel algorithm. A 5-fold cross-validation method was implemented to evaluate the sensitivity and specificity, the optimal harmonic mean of which was targeted by the kernel width parameter σ and soft margin parameter C. The optimized σ and C were used to train the prediction model, which was then validated by the experimentally reported protein-protein interactions published after 2018 and the randomly generated negative examples. Finally, our optimized model reported 155,887 functional associations with a sensitivity of 19.32% and a specificity of 99.95%. 5. Based on the estimated interactome size (1.95 x 105) and the estimated sensitivity , the predicted interactions in MID is expected to include 144,477 rue protein interactions. Therefore, 26.02% of the MID functional interactions  are expected to represent protein interactions.Applying this model to all mouse protein-coding gene pairs produced 144,477 inferred functional associations. These inferred functional interactions together with the 11,410 known protein interactions make the MID dataset, which consists of 155,887 interactions. Solving this equation that described in the methods section, we obtained the estimated mouse protein interactome size of 1.95 x 10The LNMP system is an integrated system that was used to deploy the online database. The LNMP system includes Linux, Nginx, MySQL, and PHP. We used the MySQL database to store data. The web interface of the online database was developed using the Laravel framework using PHP. The front-end of the online database was implemented with the Vue.js script library, which implements single page application (SPA). Vue.js is an open source JavaScript library designed for SPA web interface creation. Cytoscape  was usedOlfm4-knockout mice in the prostate tissue [Olfm4 (+/+) or Olfm4-knockout (-/-) prostate RNA extracted from five individual mice. The RNA-seq dataset GSE135282 showed 560 genes were up-regulated and 297 genes were down-regulated in Piezo1-knockout (Piezo1fl/fl) mice relative to the wild type. Four biological repeats were performed for both the wild type and Piezo1-knockout mice.From the GEO database , 41, we e tissue . Five biIn this study, we used the online tool GEO2R –45 to reTo evaluate the quality of the predicted functional gene association network of MID, we measured its capacity to group functionally related genes together. This capacity is evaluated as the accuracy of using a gene’s network neighbours to predict the gene’s function, i.e., the “guilt-by-association” prediction of gene functions. We evaluated the newly inferred mouse interactome (MID) together with five other available interactomes, including MouseNet , mentha The data used to predict functional gene interactions in MID were collected before 2018 . A total of 7,935 genes with new annotations  were collected from the GO database , 47 to eWe used the precision-recall curve to measure the overall accuracy of new annotation prediction across six interactomes. Here, precision means the proportion of annotations predicted by PANTHER that were consistent with the total 327,092 annotations. Recall means the proportion of PANTHER-reported annotations that were successfully covered 40,949 newly added annotations. Each PANTHER-predicted annotation has an enrichment significance . Therefore, when different cut-offs on P-values were applied, the number of annotations predicted by PANTHER will change accordingly. More reported annotations would result from a higher cut-off, which would lead to higher recall but lower precision. In contrast, if a lower cut-off was used, fewer annotations would be predicted, leading to more reliable predictions and higher precision. In general, the precision-recall curve is a cut-off-independent approach that shows the advantage of providing a more comprehensive view of the capability of an interactome to predict new gene annotations. An interactome with a higher AUC may be better to support “guilt-by-association” prediction of gene function.The precision-recall curves of the six interactomes are shown in We provide two search modes in the MID website: single gene search and multiple gene search . The sinPreviously, we developed the GSLA tool as a transcriptomic analysis tool for potential functional impact predictions of Arabidopsis based on the observed GDEs . The strThe default significance cut-offs for GSLA to report a gene set interaction are density>0.01 (Q1) and p<0.001 (Q2).The GSLA web service is provided on the MID website and is based on GSLA, which is used to interpret the potential functional impacts of the observed GDEs in the mouse transcriptomic experiments. The main website interface of GSLA is presented in Olfm4 in murine tissues [Olfm4-knockout, and the loss of Olfm4 promoted progression of prostatic neoplasms. Li et al. also found that OLFM4 protein interacts with sonic hedgehog protein [Prostate cancer is common in males and is the second leading cause of cancer-related death in men in the United States . The rol tissues . They di protein . These d protein , 50–52. Olfm4-knockout mice (GEO database: GSE39989) [Olfm4-knockout, as stated in the original publication [Three gene set annotation tools, MID/GSLA, DAVID , and GO SE39989) . DAVID, lication . In contlication ) is assolication . Apart flication . In thisThroughout the lifetime of a mammal, natural bone is constantly renewed and remodeled. This complex process involves both the osteoblasts mediated bone formation phase and osteoclasts mediated bone resorption phase –57. The Piezo1-knockout mice and discovered that Piezo1-deficiency in osteoblasts cells lead to decreased bone mass, increased bone resorption, and spontaneous fractures after weight bearing [Piezo1 deficiency mice displayed a resistance to further bone loss and osteoclast accumulation, suggesting that the PIEZO1 mediated osteoblast-osteoclast crosslink responses to mechanical loads. Mechanistically, Piezo1 deficiency impaired the production of COL2 and COL9 through decreasing YAP nuclear translocation, which in turn regulates a number of bone matrix proteins including collagens. Their study also suggested that integrins may be a candidate that mediates matrix bridging and osteoclast regulation. Wang et al. performed RNA-Seq of the tibial and femoral cortical bones of the WT and Piezo1-knockout mice  [Piezo1-knockout cortical bones.To understand the mechanism of how mechanical loading coordinates bone remodeling, Wang et al. generated  bearing . In addiE135282) . Among aIn this study, the top 250 transcriptionally changed genes were chosen for analysis by DAVID, PANTHER and MID/GSLA. As shown in Mus musculus. To facilitate the hypothesis formulation for molecular investigators, a comprehensive and accurate reference interactome is needed that can serve as a framework to summarize individual gene changes as high-level biological process changes. To date, many mouse interactome databases have been developed. Some of them contain experimentally reported molecular interactions, such as BioGRID [in-vitro interactions do not have in-vivo significance, for example, they are not from the same subcellular compartments in normal physiology.As an important disease model, many studies have focused on building the molecular interaction network of  BioGRID  and IntA BioGRID . Others  BioGRID  and MIST BioGRID . In geneThe negatively correlated accuracy measurements, sensitivity and specificity, are used to evaluate the quality of predicted functional gene associations. An inferred interactome cannot improve its sensitivity and specificity at the same time. Low sensitivity leads to less effective capturing of the true functional interactome, and therefore an insufficient basis for functional annotation of the observed GDE. In contrast, low specificity results in high level of noise in the interactome, leading to a high level of false positive annotations in interactome-based functional annotation of GDEs. Therefore, a high-quality functional gene interactome requires balanced sensitivity and specificity.On the other hand, available predicted interactions show different sensitivity-specificity characteristics. STRING is a widely used predicted interactome. It has 9,536,624 predicted mouse interactions that are expected to cover a large proportion of mouse protein interactome (52.17%). The fraction of these interactions representing true protein interactions is expected to be low, only 1.06%, as shown in the results section. In contrast, the MID interactome showed balanced coverage and reliability (19.32% coverage and 26.02% reliability if assessed as a protein interaction network), if compared to other existing mice interactomes. Therefore, MID complements existing resources and provides a suitable basis for GSLA annotation of GDEs in mice.To this date, a variety of tools have been developed for omics data interpretation, including PANTHER , KEGG 1, and DAVTo meet this challenge, we developed the gene set linkage analysis (GSLA) method, which relies on a functional association network to evaluate whether an observed omics change will collectively interfere with functions of known biological processes. Even when an omics change itself cannot be accurately described by an existing concept; its functional impact may still be described by well-established concepts. The creation of MID enables the application of GSLA for functional impact predictions in mice. The density of functional gene interactions between the component genes in two gene sets can be evaluated by GSLA, which is able to identify significant functional associations between two gene sets. Based on this strategy, we required a high-quality reference interactome in mice with balanced coverage and reliability. The previously developed interactomes cannot serve this purpose, as we discussed above. In this study, two case studies included a mice microarray dataset and a mice RNA-seq dataset were analysed based on the MID . CompariS1 FigThe 15 features with areas under the curve above 0.6 were selected for use in functional gene association prediction.(JPG)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.


PARK7 may result in the onset and progression of a few neurodegenerative disorders such as Parkinson’s disease. This study has analyzed the non-synonymous single nucleotide polymorphisms (nsSNPs) resulting in single amino acid substitutions in PARK7 to explore its disease-causing variants and their structural dysfunctions. Initially, we retrieved the mutational dataset of PARK7 from the Ensembl database and performed detailed analyses using sequence-based and structure-based approaches. The pathogenicity of the PARK7 was then performed to distinguish the destabilizing/deleterious variants. Aggregation propensity, noncovalent interactions, packing density, and solvent accessible surface area analyses were carried out on the selected pathogenic mutations. The SODA study suggested that mutations in PARK7 result in aggregation, inducing disordered helix and altering the strand propensity. The effect of mutations alters the number of hydrogen bonds and hydrophobic interactions in PARK7, as calculated from the Arpeggio server. The study indicated that the alteration in the hydrophobic contacts and frustration of the protein could alter the stability of the missense variants of the PARK7, which might result in disease progression. This study provides a detailed understanding of the destabilizing effects of single amino acid substitutions in PARK7.Parkinsonism-associated deglycase-PARK7/DJ-1 (PARK7) is a multifunctional protein having significant roles in inflammatory and immune disorders and cell protection against oxidative stress. Mutations in  Parkinsonism-associated deglycase-PARK7/DJ-1 (PARK7) is a multifunctional protein that plays a crucial role in inflammatory diseases, immune disorders, and cell protection against oxidative stress. It is localized in the nucleus, cytoplasm and mitochondria . It has PARK7 has been involved in various complexities such as cancer and infertility [PARK7 gene [PARK7 is found in the cytoplasm and nucleus, whose expression has been seen in many tissues, including brain, eye, and endocrine tissues [PARK7 mRNA may be crucial for basal ganglia function [PARK7 is mainly traced in the nucleus and the cytoplasm, whereas its lesser amount exists in mitochondria under stress conditions [PARK7 in the inter-membrane space and mitochondrial matrix was also shown by subcellular fractionation and immunogold electron microscopy [Apart from Parkinson’s disease, ertility ,7. A stuRK7 gene . PARK7 i tissues . In the function . On overnditions . In the croscopy .PARK7 gene is found at chromosome 1p36, which encodes a 189 amino acid residue long protein that shows structural similarity to the THiJ and PfpI bacterial proteins involved in protease activity and thiamine synthesis, respectively [PARK7 structure has been analyzed in detail, and the crystal structure of both the monomer and dimer was reported by an independent report [PARK7 as a monomer comprises 8 alpha-helix, and 11 beta-strands ordered asymmetrically in a helix–strand–helix sandwich similar to the Rossman fold [PARK7 [In humans, the PARK7 loses its functionality when it becomes mutated, and it is related to mitochondria dysfunction, resulting in the early onset of Parkinson’s disease [PARK7 mutations [PARK7 is related to the mitochondria’s abnormal morphology and dynamics, alteration in calcium homeostasis, and increased sensitivity to oxidative stress [PARK7 was recognized as a part of the novel glyoxalase family examined as detoxifying proteins [PARK7 acts as both a causative and carcinogenic gene and plays a crucial role in oxidative stress [PARK7 cause various complex diseases, we studied several variants of PARK7 using state-of-the-art computational approaches [PARK7 and their possible implication in Parkinson’s disease. disease ,11. Therutations . Mutatioe stress . PARK7 wproteins . PARK7 ae stress . Becauseproaches ,14,15,16PARK7 was downloaded from the UniProt database (ID: Q99497). A list of nonsynonymous single nucleotide polymorphisms (nsSNPs) was prepared from the Ensembl [PARK7 protein structure was downloaded from the RCSB Protein Data Bank (PDB ID: 1P5F). We used multiple tools for sequence-based and structure-based predictions to enhance the confidence score of the predicted results [PARK7 is illustrated in The protein sequence of  Ensembl , dbSNP [ Ensembl , HGMD [1 Ensembl , and Cli Ensembl  database results ,22,23,24http://sift.jcvi.org/, accessed on 1 December 2021) tool is used to examine whether a mutation in a protein is deleterious or not based on the physical characteristics of the amino acid. It also considers the sequence homology of a protein. If the SIFT score is less than or equal to 0.05, then the mutation is predicted as deleterious. The SIFT tool predicts the effect of these missense variants on the protein. A total of 152 missense variants were examined for PARK7 and categorized as deleterious/neutral or with unknown significance.The SIFT  is another sequence-based tool that predicts the damaging probability of mutation by considering the physical and comparative properties of the sequences. It provides the PSIC (Position-Specific Independent Count) score for the missense variants and then calculates the score divergence with the wild-type.PolyPhen-2  also predicts the deleterious impact in a protein. Here, if the calculated score is less than −2.5 for a mutation, it is considered damaging, whereas a mutation with scores greater than −2.5 is considered neutral.PROVEAN  is another sequence-based tool used to analyze the effect of missense mutations in a protein. It is based on evolutionarily conserved residues and a multiple sequence alignment approach. This tool accepts UniProt accession ID as input for protein sequence. It categories the missense mutations as neutral, low, or medium for damaging effects. It provides an FI score for each mutation. If the FI score is greater than 2.00, the missense mutation is considered damaging.Mutation Assessor  is another sequence-based approach that predicts pathogenic missense mutations using a machine learning technique. This tool categorizes the missense variants of a protein into unknown, neutral, and pathogenic categories. It gives results in less amount of time. It uses physical properties, evolutionary sequence conservation, and biochemical properties of the protein. The missense variants data to PON-P2 can be submitted in different file formats.PON-P2  is a structure-based tool that estimates the change in protein stability between the wild-type and the mutant. It accepts the PDB file format as input. The SDM2 server predicts the OSP (residue-occluded packing density), RSA (relative side-chain solvent accessibility), and residue depth for the mutant and wild-type protein. As a score, if the ΔΔG is >0 for a given mutant, SDM2 predicts it destabilizing.SDM2  is a web server used to predict destabilizing mutations of a protein using a graph-based approach. This tool provides better insights into the missense mutations associated with different disorders. A missense mutation is considered as destabilizing if the mCSM score (ΔΔG) < 0.mCSM  is a structure-based tool that differentiates disease-causing nsSNPs from neutral. This is an SVM-based tool that depends on the local environment of substitution for prediction. PhD-SNP accepts FASTA file format or PDB ID of the protein as an input. This tool classifies mutation into disease or neutral. Its prediction is based on sequence-based, profile-based, and hybrid methods.PhD-SNP  is a web server that predicts the mutant protein’s sequence conservation and structural properties. Rhapsody predicts residue-averaged pathogenicities of the missense mutations better than EVmutation and PolyPhen-2. Rhapsody accepts the PDB ID of a protein as an input. This tool can submit the batch query  of missense variants, i.e., less time-consuming.Rhapsody  is a sequence- and structure-based approach for the prediction of the solubility of a protein. This tool estimates the protein’s aggregation, disorder, helix, and strand propensity, which rise due to the missense mutations. SODA accepts both FASTA as well as PDB file format for input.SODA  is a web-based tool that calculates the number of interatomic interactions like van der Waal interactions, hydrogen bonds, aromatic interactions, and hydrophobic interactions of a protein structure. Arpeggio can estimate about 15 types of interatomic interactions. This tool accepts PDB file format as input. It provides a downloadable list of the number of different types of interactions.Arpeggio server  measures the degree of conservation of the sequence. We have used ConSurf-DB, which has pre-calculated evolutionary profiles of various proteins with known structures.Conservation plays a vibrant role in the structure and function of any protein. Conservation analyses can be performed using multiple sequence alignments of similar proteins. A web tool named ConSurf  was used to examine the residual frustration in PARK7 and its mutants. It evaluates the energy of a protein structure and compares it to the energy of a set of ‘decoy’ states. The single and configurational residual indexes for all three systems were computed. In this calculation, contact is highly frustrated/destabilizing if the value of the Z-score is <0.78. In contrast, contact is minimally frustrated/stabilizing if the value of the Z-score is >0.78. At the same time, if the energy lies in between these two values, the contact is considered neutral.The Frustratometer web tool  database. This study is based on the sequence- and structure-based analyses of these mutations retrieved from the Ensembl. A multilevel approach was operated to predict the functional and structural effect of mutations on the PARK7 protein. Sequence-based and structural-based approaches have been operated to obtain the high confidence diseased mutations [PARK7 protein were taken in the structure-based analysis. Then, the stability of the selected mutations was predicted using SDM2, mCSM, and MUpro. The input file format in all of these structure-based stability prediction tools was the PBD coordinates file of the protein. For further study, the mutations whose stability decreased were selected for the disease phenotype analysis using Rhapsody, PMut, PhD-SNP, and MutPred2. The packing density and accessible surface area, degree of solubility, and aggregation propensity were also studied using different approaches. The PARK7 nsSNPs retrieved from the Ensembl database were categorized into four types, graphed in At the beginning of the study, a total of 152 reported mutations were retrieved from the Ensembl . We comprehensively explored the local frustration ion indices in all three systems  were selected and explored in detail. The study suggested that L166P and L172Q mutations can alter the structure and function of PARK7, which might be responsible for the disease’s progression. The detailed understanding of PARK7 mutations will help make therapeutic strategies for associated diseases, including Parkinson’s disease.SNPs are examined as the most successive hereditary variations related to various human diseases. Broad investigation of SNPs can offer understandings to comprehend the disease-causing component and help discover successful therapies for various complex diseases. In the current study, we examined various mutations in PARK7. The sequence-based and structural-based approaches showed that out of 152 mutations in the


Dengue is a common vector-borne disease in tropical countries caused by the Dengue virus. This virus may trigger a disease with several symptoms like fever, headache, nausea, vomiting, and muscle pain. Indeed, dengue illness may also present more severe and life-threatening conditions like hemorrhagic fever and dengue shock syndrome. The causes that lead hosts to develop severe infections are multifactorial and not fully understood. However, it is hypothesized that different viral genome signatures may partially contribute to the disease outcome. Therefore, it is plausible to suggest that deeper DENV genetic information analysis may bring new clues about genetic markers linked to severe illness.Pattern recognition in very long protein sequences is a challenge. To overcome this difficulty, we map protein chains onto matrix data structures that reveal patterns and allow us to classify dengue proteins associated with severe illness outcomes in human hosts. Our analysis uses co-occurrence of amino acids to build the matrices and Random Forests to classify them. We then interpret the classification model using SHAP Values to identify which amino acid co-occurrences increase the likelihood of severe outcomes.Tukey HSD statistical test. In four of five metrics, protein E was statistically different from proteins M, NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5, showing that E markers has a greater chance to be associated with severe dengue. Furthermore, the amino acid co-occurrence matrix highlight pairs of amino acids within Domain 1 of E protein that may be associated with the classification result.We trained ten binary classifiers, one for each dengue virus protein sequence. We assessed the classifier performance through five metrics: PR-AUC, ROC-AUC, F1-score, Precision and Recall. The highest score on all metrics corresponds to the protein E with a 95% confidence interval. We also compared the means of the classification metrics using the We show the co-occurrence patterns of amino acids present in the protein sequences that most correlate with severe dengue. This evidence, used by the classification model and verified by statistical tests, mainly associates the E protein with the severe outcome of dengue in human hosts. In addition, we present information suggesting that patterns associated with such severe cases can be found mostly in Domain 1, inside protein E. Altogether, our results may aid in developing new treatments and being the target of debate on new theories regarding the infection caused by dengue in human hosts.The online version contains supplementary material available at 10.1186/s12859-022-04597-y. Dengue is a viral infection that in most cases leads to a febrile syndrome without high clinical risk, accompanied by headaches, orbital, muscles and joints aches, nausea, vomiting and skin rashes. However, cases of severe dengue, like the dengue shock syndrome, occur with a certain frequency. Patients with severe dengue conditions may have difficulty breathing, severe bleeding, severe abdominal pain, frequent vomiting, fluid retention and fatigue. This combination of symptoms makes severe dengue potentially fatal . Early iAedes aegypti mosquito, the main vector of dengue [Aedes aegypti mosquito in 129 countries, suggesting a population of 3.9 billion people at risk for the infection [Dengue cases may be found in all continents, excluding Antarctica. However, the virus has established and persisted endemically in urban areas of tropical and subtropical, which are favorable for the maintenance of the f dengue , 5. Statf dengue . Analyzef dengue , 8. Althnfection , 9, 10.Dengue viruses are divided into four groups of antigenically distinct serotypes, this feature enables dengue reinfections through new serotypes for the host’s immune system . Despitesingle-stranded RNA—ssRNA) with approximately 10,200 nucleotides and can be represented by a sequence of characters taken from a specific alphabet. To preserve the biological functions of each protein, specific relationships between nucleotides occur in every coding RNA. Thus, the dengue RNA coding region is divided into three structural proteins that make up the virion: C, M and E and; in seven non-structural proteins used in viral replication: NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5. In the complete genome illustrated in Fig. The genetic material of the virus consists of single-stranded RNA . Finally, the instance-label associations learned by the classifier are interpreted locally using SHAP Values (SHapley Additive exPlanations), revealing the co-occurrence patterns of amino acids that increase the probability of severe dengue in the sample.Our results suggest that protein E has a better association with the degree of infection, with more relevant patterns for severity present in the region called Domain 1 of this protein. In addition to these results, the database of this work can be considered an additional contribution, as we provide data from protein-segmented dengue RNA samples containing information on the serotype and severity of the host-associated infection.The general objective of this research is to explore, through a machine learning (ML) explainability technique, the interaction between amino acids present in dengue proteins and how they generate patterns capable of associated the severity of dengue infection. For this, our framework is divided into 5 steps, namely: (1) viral RNA alignment and protein segmentation so that they can be explored independently; (2) sequence normalization and tokenization as steps to standardize and obtain protein amino acids; (3) generation of co-occurrence matrices of amino acids that will serve as training data for the classifier; (4) prediction of the degree of infection through the Random Forest (RF) algorithm and; (5) local explanation of the RF classification model for the training samples in order to extract sets of co-occurrences of significant amino acids for prediction of severe dengue.P be a protein such that, for any P can be mathematically represented by the series A is the alphabet and n is the number of amino acids in the protein.Proteins are chains of amino acids, such that amino acids are represented by characters taken from a specific alphabet known as IUPAC  . Let P bDespite the large amount of dengue genomes publicly available for research in gene sequence repositories, we found a great scarcity of samples labeled with the clinical picture of the infected patient. Therefore, we mine the NCBI  and NCBI Virus Variation repositories in search of dengue genomic sequences labeled with the patient’s clinical outcome. A total of 562 labeled samples were obtained. Of this total, 61 samples have the complete dengue genome encoding all 10 proteins. For each protein, we generate a separate data file in the following order: Additional file The labels found were: dengue fever (DF), dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Given the low amount of DHF and DSS samples and because they are severe cases of dengue, we performed the binary labeling of our database, where DF became “classic dengue” and DHF and DSS, “severe dengue”. All samples, with the exception of two samples collected from the spleen, were collected through blood material isolated from infected humans between 1985 and 2017. Data are from Brazil, Cambodia, Chile, China, Colombia, Cuba, Spain, Philippines, Ghana, India, Indonesia, Japan, Malaysia, Mexico, Paraguay, French Polynesia, Sri Lanka, Vietnam, Thailand and Taiwan (Republic of China).To avoid non-conformities in the classification and explanation of results steps, the protein sequences go through the steps of: alignment, normalization and tokenization, as illustrated in Fig. The sequences were aligned using the MUSCLE algorithm available in the UGENE  softwareSequence alignment allows for standardization of raw data samples, filling incomplete sequences with gaps so that they line up with 61 samples with complete genomes, allowing the creation of a database for each protein Fig. . The seqThe alignment, filling, selection and segmentation procedure ended up generating 10 databases, one for each protein. Furthermore, based on the hypothesis that identical samples could be used in several researches and that, moreover, duplicate samples do not add value to the learning of a ML classifier, identical sequences of the same coding protein were eliminated. After that, the final distribution of the bases can be seen in Table The normalization step consists of analyzing the nucleotides of the sequences, standardizing nucleotides without biological meaning, probably caused by sequencing errors. Therefore, in normalization, nucleotides that are not defined in the IUPAC nucleotide code are replaced by the pattern character I that represents indeterminacy.Tokenization consists of segmenting each sequence into smaller subsequences, obtaining an ordered list of these subsegments. In our experiments, codons are the sequence substructure used for tokenization. Codons consist of nucleotide triplets that can be transcribed to amino acids . Then, iCo-occurrence matrices have been used to collect statistics from varied data, especially image and text data –36. In mP be a sequence of amino acid and S a segment of P, the co-occurrence matrix X can be obtained by the formula: j was in the same segment as amino acid i. Thus, P, which represents the probability of occurrence of the terms i and j in the same segment.A amino acid co-occurrence is the occurrence of two amino acid in a protein segment. Let The segment, or context window, reflects on the type of information provided by the matrices, for example, large segments reflect the coverage of large areas of the genome, generating co-occurrences between distant amino acid and reflecting on the ability of the co-occurrence matrices to capture long-distance correlations. Similarly, small segments define a search for closer patterns within a small region.CAU, ICG, GGC, GCG, UGU, GAU, AIC} which allows us to generate the template co-occurrence matrix present in Fig. In order for the co-occurrence matrices of each sample of the same coding region to have identical dimensions, it was necessary to create a global dictionary containing all amino acids present in the samples. With possession of the global dictionary, it was possible to generate a template co-occurrence matrix that integrates all its co-occurrences. For example, let the samples be d is the size of the amino acid dictionary. The fact that the matrices are symmetric and interchangeable allows the resizing of the upper triangular matrix into a vector of dimension Based on the symmetry of the co-occurrence matrices, the first scaling step is to extract only elements of the upper triangular matrix. The generated co-occurrence matrices have dimensions In order to achieve maximum classifier performance by reducing problem complexity and eventually an overfitting, we eliminate co-occurrences that carry little or no information. For this, we use the Mutual Information (MI) algorithm that measures the dependence between two variables by calculating entropy using the k-nearest neighbors. In this context, two variables can be considered independent if, and only if, the MI coefficient between them is zero. In contrast, the greater the dependence between two variables, the greater their mutual information value , 42. Thedeep machine learning methods, yet they are still widely used in the field of bioinformatics [x is an input vector. After generating the trees, RF selects the most popular class among the trees for input x [The scarcity of publicly available samples with the clinical outcomes makes complex classification algorithms like CNN and LSTM have great difficulties in learning patterns in our data, considering the large amount of samples that these algorithms require for parameter optimization. Therefore, we chose to use the Random-Forest (RF) classifier for our experiments. Overall, RF classifiers are significantly less complex than ormatics –47. RF  that will have the first condition verified, giving rise to two child nodes that will initiate branches to the left and right of the root node. The node generation process is repeated throughout the tree, determining rules that define the data flow through the tree’s branches and establish its decision making , 51. AllMany machine learning algorithms are considered functional black boxes because, given their complexity, it is almost impossible to understand their internal processes. However, in bioinformatics it is essential that there is a human domain over the classifier’s decisions. Given this issue, several explainability methods have been proposed to explore the decisions made by ML models by evaluating the influence of input variables on the prediction results –56.We can also mention other explanation techniques used in biological sequence classification problems through Deep Learning (DL) models –59, wherExplainability methods are divided into two classes: global methods that explain model results for all data inputs; and local methods that explain an individual input. Our interest in model explanation is to be able to understand what happens in the classification of severe dengue, making it possible to identify significant amino acids co-occurrences for classifier assign a sample to the severe dengue class. Therefore, in explaining the model we want to encode its learned patterns and decision-making into information explainable in human terms.f and g have approximate results for each instance. For this to occur, the condition f is the original predictive model, g is the interpreter model, and x that can be mapped to the original instance from a function h, such that f model, such that, S is the non-null subset of Therefore, we decided to use in our experiments the SHAP Values  method tf(x)|xS) .x in a tree. The complete methodology, as well as the algorithms that define the TreeExplainer, can be found at [TreeExplainer is a specific method for local explanations of tree-based models, providing fast and accurate results by calculating the SHAP values for each leaf of a tree. The algorithms estimate found at .Machine learning models internally perform multiple mathematical operations to obtain results. For example, to perform predictions, classifiers generate real values which in turn will be associated with labels. As described earlier, SHAP Values performs variable explanation from the conditional expectation function.f. Thus, the magnitude of the impact reflects the influence of the variable in the classification of the sample, such that positive impacts increase the probability of correct classification of the sample, while negative impacts have the opposite effect, suggesting that variables with positive impacts have a greater capacity to characterize the sample class [From there, the method assigns positive and negative impacts to the input instance variables so that the expected value of the interpreter le class . TherefoTo facilitate viewing the patterns provided by SHAP, we chose to generate a global explanation from multiple local explanations. For this purpose, after obtaining all the tables, the positive impact score of each co-occurrence is calculated, which consists of the number of times each co-occurrence had a positive impact divided by the number of times the co-occurrence appeared. Then, the average impact value of each of them is calculated. After that, each co-occurrence is ranked in descending order by the two metrics. Finally, we selected the resulting co-occurrences located in the first 20% ranking positions and the final 20%. That is, the 20% with the highest positive impact and the highest positive impact score and the 20% with the lowest positive impact and lowest positive impact score.Five stratified cross-validations were performed to observe the classifier’s response on different training and test sets. In view of the evident unbalance of classes in the bases presented in the Table It is possible to observe in the box-plots in Fig. Furthermore, the box-plots of the Precision, Recall and F1-score metrics in Fig. The data used in the ANOVA test must meet the assumption of homogeneity of variances, verified by the Levene test , as wellAfter obtaining the confirmations of the ANOVA test, we applied the Tukey test to verify the difference between the means of the metrics for each protein. The null hypothesis for Tukey’s test assumes that there is no statistically significant difference between the means of two samples, while the alternative hypothesis assumes the opposite. Protein pairs with statistically distinct means of metrics can be seen in Fig. TreeExplainer algorithm. The SHAP Values method generates individual explanations for each data sample. For our explanations we use force plots, which in turn show the impact of sample variables on the prediction [After being trained, the classifiers were interpreted using the SHAP Values method through the ediction . Then, fOf the 50 co-occurrences selected by the MI algorithm, the explanation graphs will be 20% of the most relevant co-occurrences in the classification of severe dengue and the 20% less relevant. Finally, the co-occurrence values will be compared with classic dengue samples. As stated earlier, explanations generate positive and negative impacts. Co-occurrences do not have a constant impact behavior for each sample, that is, the same co-occurrence may have positive impacts in certain samples and negative impacts in severe dengue samples.Protein E explanations reveal distinct characteristics between co-occurrences of significant amino acids for severe dengue compared to classic dengue. In general, as we can see in Fig. We can observe that for all cases the empirical distributions of significant co-occurrences for severe dengue are not graphically identical to those for classic dengue, although they are close in some cases. Again, it is important to emphasize that the co-occurrences present in Fig. Dengue E protein can be divided into four major regions, namely: Domain 1, Domain 2, Transmembrane 1 and Transmembrane 2. Each of the four dengue serotypes have specific RNA positions that mark the beginning and end of these regions –72. To iThe Domain 1 region of dengue E protein has the highest mean concentration of significant co-occurrences for the classification of severe dengue. With the exception of the co-occurrence  which is on average more present in Domain 2, all the others are more frequent in Domain 1, as we can see in Table In this article, we present a method capable of representing and classifying severe dengue according to the protein coding sequence of the virus. Furthermore, the method is focused on improving the extraction of significant patterns for the classifier. The procedure is based on the segmentation of dengue viral RNA in each of the ten protein coding sequences, transforming these protein segments into matrices of co-occurrence of amino acids within a context window that will be classified by a RF.The significant co-occurrences for severe dengue class were obtained through the SHAP Values explanation model, which employs a range of strategies to select variables that have greater weight in the classifier’s decision making, that is, co-occurrences that increase the probability of severe dengue. An important piece of information is that the context window is not automatically generated, this allows one to adjust the range of co-occurrences, allowing one to choose between performing local analyses, represented by patterns of co-occurrences conserved within the genome, or analyzes in large segments, allowing for co-occurrences between distant amino acids to be captured, increasing the chance of collecting long-distance correlations between amino acids.Another important point to highlight is that by applying a classifier with few hyper parameters for adjustment, we reduce the need to use large databases for classification. Therefore, our method is able to perform on small databases, however, this does not mean that additional strategies are excluded, in our problem, for example, it was necessary to binarize labels to reduce the negative effects of high unbalance of our base. One of the advantages of using an RF as a classifier is that, because it is a rule-based classifier, the significant patterns for classification obtained by the SHAP method tend to be more concrete, since this classifier does not employ transformations in the input data, as with the deep models CNN and LSTM .Finally, we emphasize that the focus of our approach is the exploratory analysis of the RNA sequences that produced a clinical outcome known as dengue severe, showing amino acid patterns that were related to this event. The presented methodology is flexible, as it would be possible to add metadata along with the co-occurrence vectors, such as mass, volume, polarity and charge of the protein segment. There are no limitations on the use of our method for classifying and interpreting other biological sequences.In this work, we described an ML method capable of identifying amino acid co-occurence patterns associated with severe dengue cases. In our analysis, precisely the same amino acids didn’t need to be found in all cases, but a signature of them. The biological basis of these results needs further evaluation, and other multifactorial aspects linked to dengue severe cases like secondary infection and host immunogenetics must not be ruled out. On the other hand, the method may be used as an interesting approach to identify patterns that may not be easily identified using other techniques.Moreover, the statistical analysis results do not support that the presented results occurred only by chance. Notwithstanding, the paucity of genomes with available outcome metadata may limit the robustness of some of the observed associations. Furthermore, we believe that the method described here may also be helpful for other studies with different viral agents.Additional file 1. C protein: A list of sequences belonging to Dengue virus protein C in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 2. M protein: A list of sequences belonging to Dengue virus protein M in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 3. E protein: A list of sequences belonging to Dengue virus protein E in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 4. NS1 protein: A list of sequences belonging to Dengue virus protein NS1 in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 5. NS2A protein: A list of sequences belonging to Dengue virus protein NS2A in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 6. NS2B protein: A list of sequences belonging to Dengue virus protein NS2B in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 7. NS3 protein: A list of sequences belonging to Dengue virus protein NS3 in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severityAdditional file 8. NS4A protein:A list of sequences belonging to Dengue virus protein NS4A in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 9. NS4B protein: A list of sequences belonging to Dengue virus protein NS4B in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.Additional file 10. NS5 protein: A list of sequences belonging to Dengue virus protein NS5 in csvformat. Each row is a sample of the amino acid chain labeledaccording to dengue severity.


PEPscan is an old approach that has recently gained renewed interest for the identification of interfering peptides (IPs), i.e., peptides able to interfere with protein–protein interactions (PPIs). Its principle is to slice a protein sequence as a series of short overlapping peptides that are synthesized on a peptide array and tested for their ability to bind a partner, with positive spots corresponding to candidate IPs. PEPscan has been applied with a rather large success in various contexts, but the structural determinants underlying this success remain obscure. Here, we analyze the results of 14 PEPscan experiments, and confront the in vitro results with the available structural information. PEPscan identifies candidate IPs in limited numbers that in all cases correspond to solvent-accessible regions of the structures, their location at the protein–protein interface remaining to be further demonstrated. A strong point of PEPscan seems to be its ability to identify specific IPs. IPs identified from the same protein differ depending on the target PPI, and correspond to patches not frequently involved in the interactions seen in the 3D structures available. Overall, PEPscan seems to provide a cheap and rapid manner to identify candidate IPs, that also comes with room for improvement. Peptide arrays are a technology with a wide range of applications in basic and applied research, which is now nearly 40 years old and has been commercially available for about ten years. An array comprises hundreds to thousands of different peptides sequences immobilized in a solid support that can be tested simultaneously, offering many possibilities to analyse different signalling pathways between normal and pathological conditions, drug discovery, sequence dependent reactivity etc. Among the applications, one of the most common has been epitope mapping for antibodies , includiThe usage of peptide arrays differs depending on the choice of peptide synthesis, solid support, immobilization method, size of the peptides, overlapping size of the peptide, density of spots in the membrane and detection method. SPOT synthesis is the technique the most widely used to generate peptide arrays ,12,13,14Although recent publications have reported labeling-free methods to analyze the arrays, such as surface plasmon resonance and mass spectrometry , the majOne method derived specifically to identify an active region of a sequence of interest is the PEPscan approach . Its priL (unpublished results), (2) the phosphatase PP2A and the cysteine protease caspase-9, which is deregulated in apoptosis and tumoral transformation [L, for which the membranes are presented in Figure 1, the raw data correspond to those of the corresponding references.Given a target PPI between two proteins, two PEPscan experiments can be undertaken: either searching for partner 1 fragments able to bind partner 2, or scanning partner 2 fragments able to bind partner 1. In the former case, partner 1 is scanned as a series of peptides—on the membrane—whose ability to bind partner 2 is tested, while in the latter case, peptides on the membrane correspond to partner 2, and their ability to bind partner 1 one is tested. In the following, we note P1/P2 in an experiment where P1 is on the membrane that is hybridized using P2. In this study, we re-analyze the results of 14 PEPscan experiments targeting 8 PPIs for which structural data is available, at least for the two partners in isolation or in interaction. ormation ,37, (3) ormation ,39, (4) ormation , (5) theormation ,42 and  on an amino-derived cellulose membrane, as described ,12. The Peptides were synthesized in an automated multiple peptide synthesis with a solid-phase procedure and standard Fmoc chemistry. The purity and composition of the peptides were confirmed by reverse-phase high-performance liquid chromatography (HPLC) and mass spectrometry.For the analysis of the PEPscan membranes, a specific question arises from the partial redundancy of the consecutive spots that share the overlapping residues. Commonly noticed is that there is no standard number of consecutive reacting spots revealing a positive interaction. In general, it is accepted that a positive binding site requires the presence of at least 3 consecutive positive spots with a strong enough signal, sometimes with a difference in the intensity signal between the medium spot and the end spots. However, this is not a general rule, as illustrated in Figure 1. Occasionally, a ringspot effect is observed. This corresponds to spots for which a stronger signal of hybridization is observed on the ring of the spot compared to the center of the spot. This irregular signal can make the choice between binding and non-binding . One posSince the sequences of the consecutive spots overlap here by all but two, i.e., by 10 amino acids, the spots of a series of consecutive positive spots usually share only a few amino acids. For instance, for a series of 3 consecutive positive spots, 16 amino acids are involved, but the common part of the 3 sequences correspond to the central 8 amino acids. The fragments in common in the largest number of spots, are here referred to as Maximally Overlapping Fragments (MOFs), and the sequences resulting from the union of all the spots are here referred to as candidate IPs.−10 to identify close homologs of the proteins. We have then analyzed which PDB entries encompass several chains  and identified the chains in interaction with the query. Not to bias the results, we have discarded the interologs (complexes made of homologs) for which each chain has over 70% sequence identity with a previously identified complex. Doing so, we expect to obtain information about only the close homologs of the query, i.e., members of the same family or very close families, and obtain information on how specific the interaction of the associated peptide with a particular target could be, regarding the other structurally characterized interactions of this target, accepting close homologs should interact similarly. For instance, for PP2A, this procedure identifies complexes involving not only the PP2A catalytic chain, but also other phosphatases, such as PP1, PP5, etc. The propensity of a residue to be located at an interface was then simply set up as the number of times it was at an interface divided by the number of complexes identified. Z-scores were calculated on a per protein basis, using as reference the mean and standard deviations of the propensities over the whole ensemble of residues of the protein, and as observation the propensities averaged over the residues of the IPs. Positive values indicate a tendency to be located more frequently at PPI interface, while negative values indicate a tendency to be located less frequently at PPI interface.To analyze the specificity of the binding patches, we have searched for structures containing several chains including the proteins of It is important to note that the relationship between the signal intensity and the affinity of the peptide/ligand interaction is not straightforward. Several factors may influence the correlation between signal intensity and binding affinity; the synthesis can lead to different amounts of peptide in the spot and the type of detection assay used can also have an influence. In addition, the non-homogeneity of the cellulose membrane can influence the results ,47. In gSuccesses achieved using PEPscan for the identification of protein fragments able to interfere with protein–protein interactions raise numerous questions in terms of structure. First, the conformation adopted by the peptides synthesized using SPOT technology is largely unknown and, particularly, the impact of the linker binding to the support (cellulose or other). Second, the impact of splitting proteins into fragments on their conformational balance is also unknown. Third, the strategy to systematically consider all possible fragments, which is necessary when not knowing/considering the structure, also comes with numerous questions related to the behavior of the core hydrophobic fragments in terms of possibly non-specific binders. Finally, mechanisms for peptide interference with protein–protein interactions could also reveal greater complexity than the straight disruption of the 3D interaction patch. We now discuss these aspects from a series of results on interfering peptide identification.A first observation is that overall, one denotes the existence of a specific answer depending on the PPI studied. Indeed, the IPs identified for the same protein with two different partners are all different, with the exception of SET, for which M26 and M30 have a large overlap. A second observation is that the number of candidate IPs is, as expected, fully unrelated to the size of the protein and, for most of the cases, it is rather low (2.2 on average). It is more than four for only two cases. One such example corresponds to the K-Ras/Raf interaction, for which many spots appear positive, and one region (C-terminus) shows a stronger signal in experiment 2 . The secWe now turn to considering whether the IPs can target PPI interfaces. As shown in For the K-Ras/Raf interaction, one MOF (M13) is clearly located at the complex interface. Interestingly, however, M13 does not correspond to the IP showing some activity. In fact, it was not originally considered as a candidate by the experimenters and was consequently not tested. M18, which is not close to the interface, was instead tested and shows some activity, including in terms of competition, meaning it is able to interfere with the formation of the K-Ras/Raf complex . It is iFor the TEAD/YAP and TEAD/Taz interactions, two IPs, M22 and M3,1 are clearly located at PPI interaction sites in the 3D structures, which is not the case with M21, which is located at the opposite side of the structure . M22 is Overall, despite that these results tend to suggest that PEPscan is able to identify, with some fuzziness, peptides at the PPI interface, the cellular tests highlight that the biological reality could be much less straightforward.L and K-Ras-Raf), not considering the special case of TEAD. In A last aspect is the specificity of the putative interaction sites identified, and firstly the ability to identify IPs specific to a given interaction. In our dataset, IPs extracted from the same protein but targeting a different PPI are available in the cases of caspase-9 , PP2A , SET  and K-Ras , M10 (caspase-9), M13-14 (K-Ras) and M25 (SET). Among these, only M25 shows some activity  or correspond to foldamers. It is thus legitimate to expect that M5 can be involved in the interaction with K-Ras, but the specificity of its interaction could probably be questioned. Overall, among the nine MOFs that exhibit some activity and for which 3D coordinates are available, all but one  have negative z-scores, i.e., correspond to regions less frequently contacted by partners  does the number of candidate IPs exceeds five. The average and median number of IP are respectively 2.21 and 2, which is fairly low.Out of the 14 analyzed experiments, PEPscan was able to identify IPs showing some biological activity for all cases but two, i.e., a success rate in identifying IPs of close to 86%. The mechanisms underlying this activity are, however, less clear. If all the MOFs correspond to regions that are solvent-accessible, i.e., likely to be at the PPI interface, the analysis of the two examples for which structural information about the binding mode of the partners exists is limited in both cases by the lack of coordinates for large parts of the proteins. In some cases, IPs showing some activity are not located at the PPI interface of the regions of resolved structure. It is also noteworthy that for the K-Ras/Raf interaction, the fragment of Raf at the PPI interface did not show any signal in the PEPscan experiment. Reasons for this are unclear, and could come from the intrinsic properties of the associated sequence fragments. Looking at the aggregation propensities of the Raf sequence, various regions were predicted as aggregation-prone by the Walz server , but notA very promising aspect of this study is that our analyses suggest that when applied to IP identification, the PEPscan approach seems able to identify functional IPs that are specific to the target PPI. Indeed, functionally active IPs identified from one protein to address different PPIs correspond in all cases to different fragments of the protein. Of note, the only case of protein SET, with one IP identified for the PP2A/SET interaction, was largely overlapping with that identified for the caspase-9/SET interaction, but was not active. Conversely, for eleven out of fifteen (73%) of the functional IPs identified, the fragment corresponds to a region of the protein not frequently involved in the assemblies of 3D structure determined, available in the PDB. This last observation must be, however, taken with care, since the structural data available in the PDB are far from covering the complete set of interactions expected in a cell ,54.Overall, it seems obvious that SPOT peptide arrays should continue playing a key role in the above-described applications, as well as for new purposes. From our analyses, this approach combines the advantages of being rather successful while being cheap and quick to enforce. In addition, we have here analyzed the results of experiments obtained using a constant protocol, proven effective over years, but there is room for evolution concerning the solid support, the array density and the size of the immobilized peptides, as well as the size of the overlapping sequence. The object of the interactions could also vary. Presently limited to proteins, the binding of different classes of molecules could be foreseen, such as nucleic acids, lipids and small compounds, to cite some. Continuing on PPIs, many perspectives are still open, particularly for therapeutic applications. PEPscan seems of particular interest when the structure of the partners in interaction is not known, and when in silico analyses cannot reliably be conducted. This is the case in numerous pathologies, such as various cancers, for which identifying a peptide can benefit from the coupling with cell-penetrating peptides , and mor