Within the Human Proteome Project initiative framework for creating functional annotations of uPE1 proteins, the neXt-CP50 Challenge was launched in 2018. In analogy with the missing-protein challenge, each command deciphers the functional features of the proteins in the chromosome-centric mode. However, the neXt-CP50 Challenge is more complicated than the missing-protein challenge: the approaches and methods for solving the problem are clear, but neither the concept of protein function nor specific experimental and/or bioinformatics protocols have been standardized to address it. We proposed using a retrospective analysis of the key HPP repository, the neXtProt database, to identify the most frequently used experimental and bioinformatic methods for analyzing protein functions, and the dynamics of accumulation of functional annotations. It has been shown that the dynamics of the increase in the number of proteins with known functions are greater than the progress made in the experimental confirmation of the existence of questionable proteins in the framework of the missing-protein challenge. At the same time, the functional annotation is based on the guilty-by-association postulate, according to which, based on large-scale experiments on API-MS and Y2H, proteins with unknown functions are most likely mapped through “handshakes” to biochemical processes. A clear understanding of a protein function is crucial for any systematic investigation of biological systems. Global DNA projects  with technological advances in high-throughput sequencing facilitated the explosion of sequencing data, which, unfortunately, do not scalably translate into information at the proteome level.The structures and sizes of the information space of genes and proteins differ significantly. Twenty thousand genes turn into millions of protein species, resulting from alternative splicing and carrying a unique profile of point mutations and post-translational modifications. These aberrations ,2 outsidThrough the efforts of the participants in the Human Proteome Project, some progress has been made over 10 years of hard work: to date, we have obtained experimental confirmation of the existence of protein products of 18,354 human genes [According to the present proteomic knowledge, most protein entries are functionally annotated in automatic mode . In partThe significant laboriousness, high cost, and complexity of experiments on the validation of protein functions  comparedThe complexity of functional annotation lies in the absence of a clear interpretation of the “function” and generally accepted standards for its definition. In a general sense, the function is everything that happens to or through a protein . In practice, experimental evidence is substituted with computational inference through data-intensive methods, based on the ambiguous expansion of the relatively small number of experimentally validated functions to large sets of uncharacterized proteins. One of the most popular tools for exploring genes and proteins functions is Gene Ontology (GO) . Gene OnThe accumulation of data on the proteome heterogeneity ,13 and tWe were intrigued by the understanding of protein function and used the neXtProt database  to invesOverall, 20,239 protein-coding genes have been predicted from the analysis of the human genome (neXtProt release 18 February 2021), and about 10% of them are still lacking functional annotation, either predicted by bioinformatics tools or captured from experimental reports. Despite technological progress, the pace of human protein characterization studies is still slow. It could be accelerated by better integrating existing knowledge resources and by initiating large collaborative projects involving specialists from different fields of biology. Traditionally, gene/protein functions are first identified by in vitro and in vivo experiments and recorded in biological databases via literature-based curation. However, wet-lab investigations and manual curation efforts are cumbersome and time-consuming. Thus, they cannot resolve the knowledge gap produced due to the continuous growth of biological sequence data [Sequence and structural analysis;Subcellular localization(s);Protein–protein interactions (Guilty by Association);Expression and coexpression;Phenotypes and diseases;Text mining (including GO annotation).The existing computational instruments for protein function annotation are mostly based on the following data and approaches : SequencProtein function prediction from sequence using the Gene Ontology classification is helpful in many biological problems. One of the common approaches is “Guilty by Association” (GAS) on STRING, used to predict protein function by exploiting protein–protein interaction networks without knowledge about sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and is located in the same cellular compartment. The Guilty by Association (GAS) on STRING tool predicts protein function by exploiting protein–protein interaction networks without sequence similarity and is used in many existing function prediction programs, e.g., NetGO and MetaGO.Speaking about ready-to-use instruments for protein function prediction, it is necessary to mention the following: Numerous sequence-based function prediction methods are based on structural motifs possibly associated with a known biochemical function . Today, GO functional annotation has become the standard tool in computationally based bioinformatics analyses. Due to this, the majority of method development in functional annotation is nowadays focused on GO classes, e.g., GOtcha , Argot , and BlaThree pipelines of function annotations  have been exploited by COFACTOR. Detailed analyses show that structure template detection based on low-resolution protein structure prediction makes a significant contribution to enhancing the sensitivity and precision of the annotation predictions, especially for cases that do not have sequence-level homologous templates .Jack London’s famous quote as applied to proteins might sound like this: “The proper function of a protein is to live, not to exist”. To date, 12% of the protein-coding fraction of the human genome encodes proteins with an unknown function. Additionally, suppose we adhere to the opinion about the nonwastefulness of the cell. In that case, all of these produced proteins with yet-unknown functions are here for a reason—they are doing something. In this study, we analyzed the terms used in neXtProt for a brief description of the protein functions to understand how and why the “profile” of the functional diversity of the human proteome has changed in recent years.To perform the study, neXtProt was used locally. The data accessible by the resource were downloaded from the official site as a document-oriented database (DB) in XML format. The data schema was also downloaded from the project site as an XSD file. The first neXtProt release available for download (8 August 2011), the current release (beginning of 2021), and interim releases (we selected the first releases of 2012–2020) were used in the study. The R language and some instruments for data processing and visualization of the results were used when comparing the versions of neXtProt . (1)https://CRAN.R-project.org/package=pheatmap (accessed on 11 March 2022);heatmap: Pretty Heatmaps, Raivo Kolde, 2019, R package version 1.0.12, (2)https://CRAN.R-project.org/package=wordcloud2 (accessed on 11 March 2022);wordcloud2: Create Word Cloud by “htmlwidget”, Dawei Lang and Guan-tin Chien, 2018, R package version 0.2.1, (3)https://CRAN.R-project.org/package=viridis (accessed on 11 March 2022);viridis: Default Color Maps from “matplotlib”, Simon Garnier, 2018, R package version 0.5.1, (4)https://CRAN.R-project.org/package=viridisLite (accessed on 11 March 2022);viridisLite: Default Color Maps from “matplotlib” (Lite Version), Simon Garnier, 2018, R package version 0.3.0, (5)https://CRAN.R-project.org/package=gridExtra (accessed on 11 March 2022);gridExtra: Miscellaneous Functions for “Grid” Graphics, Baptiste Auguie, 2017, R package version 2.3, (6)https://CRAN.R-project.org/package=RColorBrewer (accessed on 11 March 2022);RColorBrewer: ColorBrewer Palettes, Erich Neuwirth, 2014, R package version 1.1-2, (7)https://CRAN.R-project.org/package=forcats (accessed on 11 March 2022);forcats: Tools for Working with Categorical Variables (Factors), Hadley Wickham, 2020, R package version 0.5.0, (8)stringr: Simple, Consistent Wrappers for Common String Operations, Hadley Wickham, 2019, R;(9)https://CRAN.R-project.org/package=dplyr (accessed on 11 March 2022);dplyr: A Grammar of Data Manipulation, Hadley Wickham and Romain Francois and Lionel Henry and Kirill Muller, 2021, R package version 1.0.7, (10)https://CRAN.R-project.org/package=purrr (accessed on 11 March 2022).purrr: Functional Programming Tools, Lionel Henry and Hadley Wickham, 2020, R package version 0.3.4, The full list of packages tested and/or used in some way is as follows:In the comparison, the evidence for the existence of the protein was determined based on neXtProt data for 2021. Data on proteins and their functions were initially extracted from the latest available version, and data from earlier versions were mapped to neXtProt 2021 data by a protein identifier. In doing so, proteins not represented in earlier versions were considered separately.We selected proteins annotated with functions during the last five years at the further stage. We extracted all the publications associated with these functions and analyzed them manually. In total, we surveyed more than 600 publications. Based on their contents and keywords, we revealed the most common experimental practices for experimental protein annotation.We chose a few examples of proteins with isoforms and analyzed the availability of their functions in automatic and manual modes.We analyzed neXtProt releases to evaluate the increase in the number of identified human proteins and the change in the number of proteins with known functions. When comparing the versions, only records characterized by evidence of the existence of a protein (PE1) were considered.Most of the master proteins were known at the time of creating the first available version of neXtProt: in 2011, 17,375 proteins were known, and by 2019, there were data on another 319 proteins. The last three years have turned out to be the most fruitful in replenishing the base with new proteins, and most of the added entries relate to the products of genes of three chromosomes . Working with records to which no terms are assigned is nontrivial. To conclude, it is necessary to generalize heterogeneous functions, which requires deep knowledge of almost everything that happens with proteins in the human body. In addition, the results of such work will be a priori controversial since the terms introduced do not belong to a controlled list approved by the scientific community. Therefore, when performing this part of the study, records of the function without controlled terms were not considered, and records with any other annotation flaws were also deleted, as a result of which 1392 proteins remained. Their annotations are visualized in An exception was made for the term “protein binding”, which was found much more often than others. We excluded it from further analysis. However, the term “protein binding” is not specific and does not contribute to understanding the protein behavior in complex systems. Protein binding is a highly generalized function description, does not provide more detailed information about the actual function of a protein, and in many cases may indicate a nonfunctional and nonspecific binding. If it is the only annotation gained by a protein, then it is hardly an advance in our understanding of that protein .It is noteworthy that the detection frequency of most functions has remained unchanged throughout the existence of neXtProt. At the same time, one small subset of the protein functions began to be detected more frequently and another subset began to be detected less frequently than the average over the entire lifetime of the resource. Some functions began to be detected much more often in the last five years. They are all directly or indirectly associated with the immune response. This fact correlates well with the interests of researchers in corresponding fields of knowledge, including immuno-oncology.Several interpretations of this fact can be assumed. Concerning functions that began to be detected less, we simply “run out” of proteins with those functions, and the interest of researchers has shifted from one type of function to another.The current results of the analysis of protein functions are consistent with the trends observed in biological research and serve as an indirect confirmation of their fruitfulness: a change in the priority area of interest leads to the accumulation of results in a new field of research. A total of 1392 proteins previously selected were reviewed for available evidence of the existence of the function. For the listed proteins, 8604 pieces of evidence were extracted from neXtProt21, of which 44 pieces of evidence did not indicate the presence of a function but rather its absence. In further study, such negative evidence was not taken into account.In addition to the data obtained during the experiments, the results of calculations  and the conclusions of the curators and authors of the original publications, which are not accompanied by direct experimental confirmation, also appear as evidence.Such data make it possible to preliminarily distinguish between the functions of proteins that can currently be used in further practical work and those that should be further investigated since solid experimental evidence is still lacking.In total, at the current stage of the study, 3411 pieces of evidence were revealed, which are based on published experimental data.On average, a publication confirms the functions of 3.81 proteins.In 135 publications, data are provided on the function of only one  protein.On average, there are 1.5 publications per protein with confirmation of its functions.Overall, 611 proteins have a confirmation of function in only one publication.In all, 58% of all the functions shown were binding to other proteins (“protein binding”).In the framework of this study, we performed an analysis of publications describing experiments in which the functions of proteins were first discovered in the period from 2016 to 2021. Experimental evidence and the corresponding publications (353 articles from 2016 to 2021) were found only for 708 proteins:In the final step, a comparison was made of the dates of the detection of the function of binding other proteins and other functions. A summary of the results of the comparison is shown in It follows from In The publication dates for the candidate articles under consideration were extracted from neXtProt and, if necessary, supplemented .The list of publications, evidence, annotations, and proteins was sorted by publication date and protein identifier.Multiple records of protein function were deleted, except for the one relying on the oldest publication for the period under review.In this way, records were deleted regarding all the functions, except for one. Several protein functions could be determined; the missing data were then reattached by the coincidence of the identifiers of the protein and publication.The study results show that over the past five years, more than 600 proteins have received functional annotation, many of which have been assigned several functions. Of particular interest are the studies and the methods used in them, in which the presence of function was determined for the protein for the first time. Such publications were identified as follows:Moreover, in one publication , the funThe authors of the study, relying on data from proteomic studies, selected pairs of proteins previously not shown experimentally as interacting and, using the two-hybrid yeast system, tested the presence of interaction between them. This method is based on the ability of two domains of the yeast protein GAL4 (transcription activator) to maintain functional activity in the composition of chimeric proteins, which, due to the interaction between chimeric proteins, exhibit GAL4 activity and activate the transcription of the reporter gene, confirming the presence of an interaction between proteins of which they are fragments. The authors validated the results using alternative methods for determining protein–protein interactions. Thanks to the efforts made by the authors to select pairs of proteins for testing interactions, they validated the method and statistically processed the results using a relatively simple procedure. They succeeded in determining most of the protein functions first discovered in the last five years.The next article  (by the AP-MS: affinity purification of labeled (3xFLAG) and interacting proteins with subsequent mass spectrometric identification of the components of the complex.LUMIER: a method based on copurification and luminescence of complexes: the gene of one of the potential participants of the complex was fused with a sequence encoding luciferase and stably expressed in 293T cell lines; genes of other potential partners with 3xFLAG incorporation were transfected into cells of the reporter line. Cell lysates were incubated together in the presence of antibodies for the label (3xFLAG), and after washing, the formation of a stable complex was determined by the presence of luminescence.In this study, the authors identified partners using methods relating to protein–protein interaction:Some of the complexes were determined by both methods, while some of the complexes were known from the literature. Thus, the AP-MS method was validated as part of the objective and showed high reliability in the determination of complexes after appropriate statistical processing of raw data.The next article   or by mass spectrometric analysis of the contents of the cell; subsequent analysis of data on multiple protein partners allows us to move from binding data to substantiated hypotheses about the role of the protein in the life of the cell as a whole and in individual events.The above shows that neXtProt is a powerful source of protein function information, corresponding to current guidelines ,35,36,37It is well known that alternatively spliced proteoforms of one gene can have different, sometimes opposed functions, as happened in the case of two splice variants of the Bcl-x gene. A long version Bcl-xL protein inhibits programmed cell death; it is antiapoptotic. Alternatively, short form Bcl-xS antagonizes the inhibitory functions of Bcl-xL. We observe a similar situation with the example of point amino acid substitutions. There is a vivid example of the “butterfly effect” of functional heterogeneity produced by an aberrant protein PIK3CA:H1047R. This protein  is responsible for cell growth, proliferation, and survival in healthy cell line MCF-10A. Single amino acid H1047R substitution induces the transformation of the expression profile of a healthy cell into a profile typical of a malignant tumor. Extensive cellular reorganization associated with this substitution far exceeds basic activities of PI3K and affects structural protein levels, the DNA repair machinery, and metabolism.These and many other cases prompted us to think about the importance of finding out the function for each proteoform and using reliable tools for these tasks, including bioinformatics and classical laboratory ones.Comparing versions of neXtProt demonstrates that annotating proteins with new functions is a more fruitful process than the detection of “missing” proteins due to the domination of bioinformatical approaches, which are less expensive than the experimental methods required to validate the existence of “missing” proteins. The proportion of proteins with known functions is higher for medium and small chromosomes. The most commonly determined protein functions change little over time: the binding protein function is often determined. However, in the last five years, protein functions associated with immunity have become much more common to discover than functions associated with transcriptional regulation and phosphorylation. The publication of information on protein function most often begins with data on the protein binding to other proteins. The methods that make the most significant contribution to identifying protein–protein interactions are various options for mass spectrometry and the use of a two-hybrid yeast system.


The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each. The method increases precision and Positive Predictive Value, and decreases Root Mean Square Error, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for  We suggest a model  in which total error totalσ is given by:n, the total error would converge to systematicσ – the error due to the perturbative assumption, crystallization artifacts, biases in the underlying force field, etc. The RMSE qualitatively appears to have this convergence, though the data admit other models.All data points are over the same set of mutants, namely single- and multiple-substitutions mutants, where 4 or more structures are available, N = 511. For all mutants, we randomly selected n = 1, 2, 3, and 4 of the available structures and computed RMSE; we repeated this five times. A maximum of (TIF)Click here for additional data file.


Pairwise Relative Expressions with Random walks). PRER captures the dysregulation patterns of proteins based on the neighborhood of a protein in the PPI network. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s expression level with other proteins’ levels that are within its neighborhood. The neighborhood of the source protein is derived by biased random-walk strategy on the network. We test PRER’s performance in survival prediction task in 10 different cancers using random forest survival models. PRER yields a statistically significant predictive performance in 9 out of 10 cancers when compared to the same model trained with features based on individual protein expressions. Furthermore, we identified the pairs of proteins that their interactions are predictive of patient survival but their individual expression levels are not. The set of identified relations provides a valuable collection of protein biomarkers with high prognostic value. PRER can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER.Changes in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER ( Cancer remains to be one of the most prevalent and challenging diseases to treat. Cancer is a complex disease with several disrupted molecular mechanisms at play. The protein expression level is a fundamental indicator of how the molecular mechanisms are altered in each tumor. Predicting patient survival based on the changes is essential for understanding the cancer mechanisms and arriving at patient-specific treatment plans. For this task, existing machine learning models are used, such as random survival forest, which requires a feature-based representation of each patient based on her tumors. Most of these models use the individual molecular quantities of the tumors. However, cancer is a complex disease in which molecular mechanisms are dysregulated in various ways. In this work, we present a new patient representation scheme in which we integrate each tumor’s protein expression levels with their neighboring proteins’ expression levels in a protein-protein interaction network to capture patient-specific dysregulation patterns. Our results suggest that proteins’ relative expressions are more predictive than their individual expressions. We also analyze which of the protein interactions are more predictive of patient survival. The identified set of important protein interactions can be potentially used for cancer prognosis. PLOS Computational Biology Methods paper.This is a With the advances in sequencing technologies, large-scale molecular profiling of patients has become possible. The comprehensive profiling of cancer patients, along with their clinical data, presents an opportunity to gain deeper insights into cancer and develop prediction tools for disease outcome. Machine learning has been an instrumental tool in various studies to realize this aim. In these studies, patients are often represented with their molecular profiles, such as protein or gene expressions. For example, Yuan et al.  assess tGenes and proteins interact to carry out their functional roles in the cell. Phenotypes arise from these functional interactions. Based on this basic principle, alternative approaches where the patient molecular data are integrated with cataloged molecular interactions, based on prior research, have been proposed (reviewed in  and 6]))6]). IncFew studies use the pairwise comparisons of molecular measurements instead of the aggregation of expression levels. Geman et al.  proposesIn this work, we explore a method that combines the two aspects discussed above; network integration and pairwise comparison of expression levels. Pairwise Rank Expressions with Random walks (PRER) is a novel molecular representation method that considers the relative expression of a protein within its neighborhood on the PPI network. A given protein’s neighborhood is defined based on the biased random walk search on the PPI network. Pairwise relations within the known neighborhood of molecules offer a direct interpretation of molecular dysregulation patterns in the context of known protein interactions. Additionally, we present a method to analyze pairs that are predictive because of their pairwise comparisons.We compute PRER representation using protein expression data obtained from patient tumors and used it for survival prediction in ten cancers cataloged in the Cancer Genome Atlas (TCGA) project . When coPRER constructs a vector-based patient representation by integrating the patients’ molecular expression profiles and the PPI network. In this work, we use protein expression data to calculate PRER.G =  be a given PPI network, where V is the set of vertices representing the proteins, and E is the set of edges that exist between proteins if they are known to interact. Let U ⊆ V be the proteins that are measured in the data set. The nodes with the protein expression data, U, represent the source proteins. Given G, U, and patient expression data over U, the output of PRER for a patient k is a feature vector, xk) is the transition probability on edge , where the current node is v, the next node to visit is x. The transition probability depends on the function π, and it is defined as:wvx is the edge weight between nodes v and x; t is the previous node visited. In this work, we use an unweighted PPI network and, thus, we set wvx = 1. αpq is the random walk bias which is defined by p and q and the shortest path distance between nodes t and x, dtx:Here, p and q adjust random-walk strategy between BFS and DFS. With a high q value, sampled nodes in the random walk are aligned with BFS and get a local view over the source node. A small q value aligns random walk with DFS to explore a global view of the network. p controls the chance of revisiting the nodes. A high value of p decreases the probability of revisiting the already visited nodes, whereas a small value of p steers the random walk towards the source node.This bias controls the different search strategies to sample the next visited nodes. We use two different search methods: depth-first sampling (DFS) and breadth-first sampling (BFS), as in . BFS saml and (ii) the number of random walks r. We select these parameters based on the parameter sensitivity analysis at node2vec [p and q are used as p = 0.25, q = 0.25 in our random walk generation. p = 1, q = 1 leads uniform random walks are generated without any bias as stated in [q value is used to bias the random walks to capture the network’s global view, while a small p value is used to capture the community around the source node u. With these given values, random walks are inclined to capture the communities inside the network. By using fixed-length (l = 100) random walks, we sample a neighborhood for a given source node, u. Multiple random walks per source node are generated so that different neighborhoods are sampled for each node. We sampled random walks 18 times, and these are stored in WB  walk length node2vec . The partated in . A smalln WB see . The frei, which we denote as Ni. Some neighbors lack measurements, and we define the subset of neighbor proteins with accompanying measurements as Mi ∈ Ni ∩ U. Next, for a protein i, we generate pairwise rank features with every protein i ∈ Mi as follows.At the end of step one, we arrive at the neighborhood of the protein i and j for patient k. Protein i is the source protein, and protein j is a protein in the neighborhood of i. The pairwise rank expression representations (PRER) for this patient is defined as:i is more upregulated with respect to molecule j for this patient, whereas i in U and every j in Mi, we define a pairwise rank order for the protein pair. If the protein i’s phosphorylated state or states are measured, their comparison with i is also included. Since the current PPIs do not account for the phosphorylated state  when we create features for the phosphorylated state of the protein, we use the neighbors of the unphosphorylated  node in the PPI network.Let This representation constitutes a nonlinear interaction feature mapping among original features that capture expression dysregulations among interacting proteins. This representation is a nonparametric rank statistics. The rank-based statistics are widely used to obtain a robust statistical analysis. For example, Kendall tau is a robust correlation measure based on pairwise orderings . Since tn is the number of patients. For each patient, x is the derived feature vector from protein expression data, S is the overall survival time, and δ denotes censoring. We use random survival forests for the problem. Random Survival Forest(RSF) [rfsrc package [We apply the PRER representation to the survival prediction problem. For each cancer type, the data is of the form, est(RSF)  is a non package , where t package , 21.https://genome-cancer.ucsc.edu) . The protein expressions are quantified by reverse-phase protein array (RPPA) with a panel of 131 proteins some of which are phosphorylated. For example, RPPA data include STAT3 and STAT3PY705, where STAT3 is Signal Transducer And Activator Of Transcription 3 protein, and STAT3PY705 is the phosphorylation of STAT3 at tyrosine 705 residue. We are able to map all proteins to the PPI network. The obtained RPPA data was already normalized with the replicate-based normalization method [We test PRER on ten different cancer types in TCGA: ovarian adenocarcinoma (OV), breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), bladder urothelial carcinoma (BLCA), colon adenocarcinoma (COAD), uterine corpus endometrial carcinoma (UCEC). The number of patients for each cancer type ranges from 112 to 841, in total, it is 3253. Details for each cancer are provided in n method . Since PWe obtained the protein-protein interaction (PPI) network from the InBioMap platform . InBioMap specifies a confidence score for each edge, representing the support of the interaction in the literature. Interactions that their confidence scores are lower than 0.1 are filtered out, leading a final network that consists of 17,653 proteins and 625,641 interactions.To assess if PRER representation captures the molecular expression profiles better than the individual protein expression values, we use these representations for survival prediction. We first build two sets of survival prediction models for the 10 cancer types. In building these two sets of models, only the feature representations differ. In the first one, we use the protein expression values as input, which is the typical approach taken in survival prediction. In contrast, in the second one, we use the proposed PRER representation.α to 0.5 and run the RSF model with default parameters. As the second method, we compare PRER with Reweighted RSF (RRSF) method, proposed by Wang and Liu [Next, we compare our model with two network-based competitor methods from the literature. The first model is by Hofree et al. , which u and Liu . RRSF weFor all the models trained, we randomly split the samples into 80% of samples as the training set and 20% as the test set. We repeat this process 100 times leading to 100 different train-test splits and 100 different models. In training each model, we perform a univariate feature selection based on the hazard ratio of the Cox model  except fp-values are listed in Table C in UCEC, BRCA, KIRC and OV.We first compare PRER with the reference models trained with individual features. Next, we compare PRER with two other competitive, network propagation by Hofree et al.  and RRSFTo understand the effect of PPI network used, we repeat the experiments on 10 cancers using another network: a PPI network by the IntAct database . This tip and q. To see their influence on the output of PRER, we conduct runs with various choices of these parameters. Figs C-L in p and q does not drastically change the performance. These hyperparameters can be tuned for each cancer separately for larger patient cohorts. We provide an analysis of each parameter for different cancers in Figs C-L in In PRER, we define the neighborhood of a protein using random walks. There are several input parameters for the random walk technique which we use: the number of walks, walk length, PRER representation assigns a binary value to a specific protein pair, either 1 or −1 based on pairwise comparison of the protein expression levels. We experiment with an alternative representation, for which we assign the feature value 0 if the difference between the expression values is less than 10% of the compared neighbor. Otherwise, we assign 1 and -1 based on the comparison. We call this representation as ternary PRER. We compare this ternary feature representation model against the binary feature representation model. We observe no improvements in eight cancer types, but we observe improvement in GBM  and UCEC . The detailed results are presented in Table D in We seek to determine the features ranked as significant in the RSF models trained with PRER features. Note that in these models, pairs of proteins constitute the features. A particular feature’s importance is quantified by the performance difference between the models trained with the original feature vector and the case where the feature vector values are permuted . A signiAs shown in Another important protein that participates in important features is Caveolin-1 (CAV1). CAV1 takes on critical roles in cell survival, cell proliferation, cell migration and programmed cell death . An earlWe list the top-ranked PRER pairs for each cancer in We should note that many of the proteins reported in the RPPA assay in the TCGA study are selected due to their relevance to cancer. Thus, these important genes are likely to exhibit the individual importance of PRER partners. Therefore, we suggest an alternative way to exclusively analyze those features which emerge as important in the next section.fi,j denotes the features’ importance score of the protein pair i and j. We calculate the individual feature importance score for molecule i as follows:Ni is the set of all pairwise ranking representations that include molecule i. si represents the average importance of molecule i concerning the expression levels of other proteins in its neighborhood. We get the each protein’s rank order based on si, and a lower rank indicates that the protein is important. Let rp be the protein’s rank in the models with PRER representation and let rq be the rank order in the models trained with individual protein expressions. To find the proteins whose ranks are low in the models trained with protein expression but are highly ranked in the PRER models, we measure the differences of feature ranks, rq − rp. rq − rp difference. We provide the full list of the ranks and differences in Since many of the proteins that are in the protein expression data are cancer-related, it is not surprising that they are found to be relevant to cancer. However, proteins that emerge as important in the PRER representation but are not highly ranked in the models trained with individual protein expression values would be interesting. These sets of proteins will reveal proteins whose relative expression states to their neighbors are important as opposed to the expression level being up or down-regulated. To identify these proteins, we first assign a feature importance score to each protein in the PRER representation. As the features are pairs of proteins in the PRER, we calculate the feature importance of a protein by averaging the importance of all corresponding PRER feature importance in which this protein contributes. Let th most important feature for ovarian cancer in the models trained with PRER, while it is ranked as the least significant one in the models trained with individual expressions only. Similarly, for GBM, EGFR is ranked as the least significant protein in individual expression models, while it is ranked as the 13th most significant feature in PRER. Thus, the PRER models actually highlight that the dysregulation of EGFR expression with respect to its neighbors is an important feature. Below we mention other interesting observations in We analyze a subset of the proteins in STAT3PY705 (STAT3 phosphorylation at tyrosine 705), phosphorylated state of STAT3  protein, and STAT5ALPHA  also appear in multiple cancer types. While we observe STAT3PY705 as significant in LUAD, STAT5ALPHA appears in BRCA and COAD in YAPPS127 and YAP proteins, which are encoded with the YAP1 (Yes-associated protein 1) gene, are found important in BRCA, HNSC, LUAD, and LUSC cancer types in The upregulation of STATHMIN is linked with poor survival for primary HNSC , and KouYB1 and its phosphorylated state YB1PS102 show correlation with many genes that have functions such as resistance to drugs, transcription and translation of cancerous cells . AlthougPredicting patient survival using omics profiles still remains to be a challenge for cancer. If achieved, it can guide the decision-making process for choosing optimal treatment and surveillance strategies among alternative options. Typically, clinical or pathological features such as the patient’s age, tumor stage or grade are employed to predict the clinical outcomes. With the advent of high-throughput technologies, molecular descriptions of the tumors for a large number of patients across many cancer types have become available. However, it remains a significant challenge to use this data due to the high level of genomic heterogeneity among patients. This study proposes a novel patient representation method, PRER. PRER is based on a pairwise comparison of a protein’s expression values with the other proteins in its neighborhood on the PPI network. In this way, the relative expression level patterns with respect to the proteins in their neighborhood can be captured.We showcase PRER in survival prediction for ten different cancer types. PRER with Random Survival Forest (RSF) model achieves significant improvements compared to the models with individual expression values in 9 of the 10 cancers. The only cancer type that PRER underperforms is LUAD. It is the only cancer type for which the number of patients available is small and the ratio of the censored patient to deceased patient is high Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Protein–protein interactions (PPIs) are the core of protein function, which provide an effective means to understand the function at cell level. Identification of PPIs is the crucial foundation of predicting drug-target interactions. Although traditional biological experiments of identifying PPIs are becoming available, these experiments remain to be extremely time-consuming and expensive. Therefore, various computational models have been introduced to identify PPIs. In protein-protein interaction network (PPIN), Hub protein, as a highly connected node, can coordinate PPIs and play biological functions. Detecting hot regions on Hub protein interaction interfaces is an issue worthy of discussing.Two clustering methods, LCSD and RCNOIK are used to detect the hot regions on Hub protein interaction interfaces in this paper. In order to improve the efficiency of K-means clustering algorithm, the best k value is selected by calculating the distance square sum and the average silhouette coefficients. Then, the optimization of residue coordination number strategy is used to calculate the average coordination number. In addition, the pair potentials and relative ASA (PPRA) strategy is also used to optimize the predicted results.DataHub dataset and PartyHub dataset were used to train two clustering models respectively. Experiments show that LCSD and RCNOIK have the same coverage with Hub protein datasets, and RCNOIK is slightly higher than LCSD in Precision. The predicted hot regions are closer to the standard hot regions.This paper optimizes two clustering methods based on PPRA strategy. Compared our methods for hot regions prediction against the well-known approaches, our improved methods have the higher reliability and are effective for predicting hot regions on Hub protein interaction interfaces. Proteins perform their various biological functions by interacting with other proteins, nucleic acids or small molecules , 2. RecoFor many years, a lot of new technologies have emerged to identify PPIs –9. TheseEarly studies on the interaction network of yeast protein revealed that PPIN belongs to the scale-free topology, with a small number of highly connected proteins  and a large number of low-connectivity proteins . In thesOver the years, many researchers have studied the relationship between hub protein and topological centrality in PPIN. Jeong et al.  studied In PPIs, the hot spot residues , 21 are The essence of hot region identification is to find out the most stable structural conformation in the interface. Its physical properties determine the spatial layout of residues, including spatial potential energy and spatial distance. And its biological properties determine the pairing tendency between residues. Therefore, the calculation method can be used to simulate the biological structure of hot region more comprehensively, and then the machine learning algorithm can be used to speed up performance. Therefore, combining the three-dimensional structure of proteins and the machine learning technology to analyze the physicochemical characteristics of protein residues, some works have been carried out to detect the hot regions in PPIs . The hotBecause published data about hot spots of Hub proteins are fewer, we first used common hot spot datasets in published literature for better evaluating the performance of our methods. There are different definitions of hot spots, different datasets, and even different evaluation criteria in many researches. To more effectively evaluate our method, we first performed experiments used the same datasets as –26, whicThen, the Hub protein datasets were used to test, which were obtained from Ekman’s PPIN . In thisWhen the number of features exceeds a threshold for the limited training set, the accuracy will decrease with the increase of features to result in over-fitting. In addition, the number of features is proportional to training time of the model. Redundancy features will take more training time, and the most important is to reduce generalization ability on datasets. Therefore, the appropriate feature subset is help to construct the training model and can also increase the interpretability of the model. Protein residues have many chemical and physical characteristics. To effectively identify hot regions on Hub protein interaction interface, the feature selection method should be carried out to select the stable and optimum feature set. We have used several feature selection methods, such as the improved mRMR method  and SVM-When predicting hot regions on the protein interface, more natural hot spots and fewer false hot spots  should be identified. Therefore, to obtain better prediction results, it is necessary to create a strong classifier for predicting hot spots. We have used some algorithms, such as gradient boosting  and randHot spot: The residues with binding free energy ot spots . The hotHot region: Supposing that each residue is regarded as a ball. If the distance between two hot spot residues is less than the sum of radius and a harmonic distance 2Å, the two hot spot residues are located in the same community. Three or more hot spots in the same community can form a hot region . The closer the value is to 1, the better the clustering is efficient.The effect of clustering depends on the similarity of objects within clusters and the dissimilarity between clusters. That is, the larger the distance between clusters, the better the clustering effect. And the smaller the distance between objects in the same cluster, the better the clustering result. Considering the aggregation degree of objects within clusters and the separation degree between clusters, the silhouette coefficient  of each d is the distance between two i and residue j.i can be defined asThere are several distance-based measurements in the spatial structure of proteins, such as the contact number of residues, the number of neighboring residues, and the coordination number of residues. The contact residues are the basis of the interactions between protein interfaces. The neighborhood residues are important in the structure of aggregation clusters, which provide the support for constructing the protein interfaces. The coordination number of residues is the total number of neighborhood residues. Due to the hot spot residues tend to aggregate at one or more same regions in the protein interfaces, the average coordination number of hot spot residues is higher. Therefore, the average coordination number of residues can be used as the optimization condition. Two residues are supposed to contact if the distance between their The biological properties and physical properties of protein residues are diverse, such as hydrophobicity, ASA, distribution potential, solvents and so on. Tuncbag  proposedi is defined as:i, j) denotes the potential energy of the connection between residue i and residue j.i in the complex is defined as:Potential energy often occurs in the space folding and the interaction interface. The potential energy of residue mentclass2pt{minimSeveral common measures are used to analyze the performance of prediction methods.Frist, we performed experiments used the common ASEdb dataset. Table Next, we introduced the execution process of the RCNOIK method. First, the optimal number of clusters k can be obtained by the average silhouette width (ASW). Figure Figure In the experiments, we found that the different feature combinations have great influence on clustering results. The optimum feature combination are composed of nine features, namely: BsRASA, BsASA, BsmDI, BnRASA, BminPI, BpRASA, BmaxPI, BpASA and BnASA. The accuracy and kappa coefficient of these nine features are shown in Fig. Two clustering methods LCSD and RCNOIK were also carried out with DateHub and PartyHub datasets. Table To further improve the performance of prediction results, we optimized the prediction methods by using PPRA. Table To further analyze the predicted performance, Tables To observe predicted hot regions more intuitively, the three-dimensional spatial structures of 1A0A and 1E9G are given. As shown in Fig. In PPIN, a minority of Hub proteins have many interacting partners. Obviously, the connectivity of proteins is related to protein function. However, the characteristics of Hub protein have not been fully understood. There are still many important and meaningful problems need to be solved about Hub protein interaction interfaces. In this paper, two clustering methods were optimized to predict hot regions on Hub protein interaction interfaces. First, LCSD method and RCNOIK method were used to identify the hot region structures of Hub protein interaction interfaces. Then, PPRA optimization strategy was used to optimized the predicted hot regions. The experimental results show that the improved methods are effective in predicting hot regions of Hub protein interaction interfaces, and have better performance than other methods. In addition, compared with the classification results, the clustering method adopted can obtain essentially identical results with those of classification methods, which further illustrates that the improved methods are feasible for predicting hot regions.Combining with the previous works, the following aspects need to be further explored. We will continue to study the characteristics of different Hub protein interfaces and explore which types of Hub protein interface are more likely to form hot region structures. In addition, the structural domain can reflect the mechanism of PPIs, which is helpful to understanding the cell function. Next, we will also continue to study the function of domain on Hub protein interaction interfaces, and analyze the importance of Hub protein for predicting drug-target interactions.


G protein–coupled receptors (GPCRs) are important pharmaceutical targets for the treatment of a broad spectrum of diseases. Upon ligand binding, GPCRs initiate intracellular signaling pathways by interacting with partner proteins. Assays that quantify the interplay between ligand binding and initiation of downstream signaling cascades are critical in the early stages of drug development. We have developed a high-throughput mass spectrometry method to unravel GPCR–protein complex interplay and demonstrated its use with three GPCRs to provide quantitative information about ligand-modulated coupling selectivity. This method provides insights into the molecular details of GPCR interactions and could serve as an approach for discovery of drugs that initiate specific cell-signaling pathways. iβγ and β-arrestin-1 and showed that carvedilol induces an increase in coupling of β-arrestin-1 and Gαiβγ to β1AR. A normalization strategy allows us to quantitatively measure the binding affinities of GPCRs to partner proteins. We anticipate that this methodology will find broad use in screening and characterization of GPCR-targeting drugs.G protein–coupled receptors (GPCRs) are important pharmaceutical targets for the treatment of a broad spectrum of diseases. Although there are structures of GPCRs in their active conformation with bound ligands and G proteins, the detailed molecular interplay between the receptors and their signaling partners remains challenging to decipher. To address this, we developed a high-sensitivity, high-throughput matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) method to interrogate the first stage of signal transduction. GPCR–G protein complex formation is detected as a proxy for the effect of ligands on GPCR conformation and on coupling selectivity. Over 70 ligand–GPCR–partner protein combinations were studied using as little as 1.25 pmol protein per sample. We determined the selectivity profile and binding affinities of three GPCRs  to engineered Gα-proteins  and nanobody 80 (Nb80). We found that GPCRs in the absence of ligand can bind mGo, and that the role of the G protein C terminus in GPCR recognition is receptor-specific. We exemplified our quantification method using β1AR and demonstrated the allosteric effect of Nb80 binding in assisting displacement of nadolol to isoprenaline. We also quantified complex formation with wild-type heterotrimeric Gα Each Gα-subunit is involved in a specific signal transduction pathway (3G protein–coupled receptors (GPCRs) are the largest family of membrane receptors in humans and play essential roles in physiology and disease . Their ppathway 3, much repathway 3, 8.Investigation of the interplay between GPCRs, ligands, and intracellular binding partners is challenging due to the complexity of their interactions. The functional outcome of GPCR activity depends on a still poorly understood network of protein interactions. To date, there are no high-throughput methods to study every G protein and its ability to couple to a given receptor under a standard set of conditions. Many GPCR assays use radio/fluorescence-labeled ligand binding or measurement of second-messenger molecules. More recent methods involve cell-based biosensors, including dynamic mass redistribution and cellular dielectric spectroscopy, that display an overall cellular response and translate GPCR signaling into distinct optical or impedance readouts, respectively , 10. HowHere, we developed a quantitative high-mass matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) strategy that combines chemical cross-linking and quantification based on an internal standard to assay the interplay between receptors, ligands, and interacting proteins. Our versatile method enables us to 1) elucidate the selectivity profile of G proteins to GPCRs; 2) dissect the molecular details of complex formation and probe the conformational regulation of GPCRs; and 3) determine binding constant values and characterize ligand–ligand and protein–protein competitions. This method has a much higher tolerance to buffers, salts, detergents, or lipids than ESI-MS . MoreoveThe combination of cross-linking and mass spectrometry is a rapidly emerging approach to provide information on the structure and interaction networks of proteins , 14. TheSI Appendix, Fig. S1). Based on this, we used PEGylated bis(sulfosuccinimidyl)suberate [BS(PEG)9], a bifunctional amine-reactive reagent with a spacer arm length of 38.5 Å , to cross-link interacting proteins via lysine residues. After reaction, samples will contain intramolecular cross-links, monolinks, and, most importantly, intermolecular cross-links (o-subunit) . We found that even short (≤1-min) preincubation with BS(PEG)9 prevents the association between Rho and mGo , probably due to quick reaction of the cross-linker with lysine residues near the binding interfaces of Rho and mGo, precluding assembly of the complex. Using an optimized experimental procedure, we estimated that in all of the Gα-proteins or their truncated versions tested, six to nine lysine residues react with BS(PEG)9 , resulting in the formation of approximately two intermolecular cross-links in each complex .Lysine residues are present at the G protein–interacting interfaces of GPCRs (ss-links A that stsubunit)  . With this sensitivity, we were able to even detect picomole quantities of protein.GPCRs are extremely challenging integral membrane proteins to work with as they are unstable in detergent solution and require the use of an appropriate condition for their extraction from membranes. Since they are available in low quantity only, a sensitive detection method will therefore help reduce protein sample consumption. Thus, we optimized the MALDI sandwich spotting method by trial and error by testing various chemicals and the number of layers in the sandwiching method, and found that addition of a third layer of saturated sinapinic acid considerably improved the signal level of GPCR proteins by MALDI detection and thus improved sensitivity . The GPCRs studied were a constitutively active mutant of bovine Rho, thermostabilized turkey beta-1 adrenergic receptor (β1AR), and the F117W mutant of mouse angiotensin II type 1 receptor (AT1R) .Using our optimized cross-linking protocol, we first showcase our method by examining the coupling ability of three class A GPCRs to a panel of mini–Gα-proteins  hereaft in the p9, and the resulting complexes and remaining unbound partners in the sample are detected by high-mass MALDI-MS by monitoring the peak intensities of each species. Examples of measured spectra are shown in SI Appendix, Fig. S4. Our method allows us to detect conformational changes and ensembles of the receptor by following receptor–complex formation, which can be read out directly from the mass spectra.Detection and analysis of multicomponent protein complexes (such as GPCRs with their heterotrimeric G proteins) by any biophysical method are challenging. We therefore established our method by using mGα-proteins, which are simplified versions of their full-length counterparts (Gα) containing the GTPase domain but lacking the α-helical domain, and are widely used in biochemical, biophysical, cellular, and structural biology studies for studying GPCR–G protein interactions and GPCR activation mechanisms , 19, 20.i/o-family, mGo and mGi, both in the apo (apo-Rho) and agonist-bound (atr-Rho) forms , inactivating (inverse agonists), and neutral (antagonists). Our assay largely displays the expected GPCR–G protein recognition patterns. The constitutively active Rho mutant couples to the two members of the Gαo) forms . This wao) forms , 22, 23.o) forms . It has families  and, indfamilies . Apo-β1Afamilies . This cofamilies . As mGi families . Azilsarfamilies . We expefamilies . These dSI Appendix, Fig. S4). Native Go protein is highly expressed in the central and peripheral nervous systems, endocrine cells, and cardiomyocytes, being the most abundant G protein subtype in neurons , which is stable in its monomeric form  and does not interfere with the analytes of the sample . This allowed us to calculate the concentrations of each species at equilibrium  and the corresponding dissociation constants (Kds) of the complexes between GPCRs and their partner proteins  as a reference protein . Literature Kd values are scarce because such measurements are challenging. A comparison of the MALDI-based Kd data with the literature and a microscale thermophoresis measurement showed good agreement . We observed that mGo generally had a higher affinity to the GPCRs compared with other partner proteins (Kd of mGo (0.25 μM) was hardly influenced by the ligands  and was considerably lower than that of mGs (0.35 μM), mGq (1.24 μM), and mGi (1.62 μM). Among the receptors, β1AR generally has higher affinities to the test partner proteins. For AT1R, binding to mGo is twice as strong as to mGq . We quantitatively elucidated the interaction strength between the protein–protein complexes. These interactions are the key determinant of information transmission within a signaling network.The measured proteins . For β1ASI Appendix, Fig. S13). However, truncation of mGo affected coupling to Rho and AT1R, but not to β1AR, which still bound mGo_Δ5 with similar affinities to mGo in both the apo (0.28 μM) and agonist-bound (0.23 μM) states. This indicates that the last five residues of G protein are not always the main determinant for receptor recognition and other regions can mediate high-affinity binding . In the absence of ligand, β1AR binds only to mGo due to its precoupling ability (Kd of 0.25 µM)  in the presence of the competitor Nb80 at equimolar amounts (0.25 µM) , indicat0.25 µM) .SI Appendix, Fig. S14C), and calculated the inhibitory constant (Ki) of Nb80 to mGo (1.57 ± 0.24 μM) . We also measured the effects of isoprenaline on the competition between mGo and Nb80 and, as expected, the competitiveness of Nb80 increased with rising isoprenaline concentration . These results show that when multiple partner proteins coexist, while GPCRs prefer to couple with partners of higher affinity, changes in ligand and partner concentrations can alter this coupling selectivity. We can substantiate that the promiscuous binding of mGo is specific for the two following reasons: First, we were able to displace mGo binding to AT1R in the presence of the inverse agonist azilsartan, showing that mGo binding can be allosterically modulated by ligands . All antagonists tested had the same effect on the coupling ability of β1AR, which binds only to mGo in their presence . At these concentrations, isoprenaline cannot displace propranolol or carvedilol from the receptor, and propranolol/carvedilol-bound β1AR still only recruits mGo, but it can displace s32212 and recovers coupling to mGs, Nb80, and, partially, mGq. Interestingly, in nadolol-bound β1AR, isoprenaline only partially recovers its recruiting ability with Nb80, but not with mGs and mGq .We also investigated the allosteric conformational regulation of GPCR–G protein complexes by several ligands . s32212 behaves as a surmountable competitive antagonist, as raising the isoprenaline concentration recovers near-maximal formation of the β1AR–mGs complex (80%); the Ki of s32212 was determined to be 3.56 ± 0.26 µM . On the contrary, propranolol behaves as an insurmountable competitive antagonist, as isoprenaline (at any concentration) cannot recover maximal β1AR–mGs complex formation. Nadolol shows dual behavior in different complex systems: It is insurmountable in β1AR–mGs but surmountable in β1AR–Nb80  . Furthermore, we show that s32212 is a weaker antagonist for β1AR than nadolol, as shown by its less prominent inhibitory effect .We next explored in more detail the inhibitory ability of these antagonists on the formation of GPCR complexes. For that, we measured the formation of the β1AR–mGs and β1AR–Nb80 complexes in the presence of 1 or 25 µM antagonists at increasing concentrations of isoprenaline , while carvedilol-bound β1AR showed a higher affinity to Gαi and arrestin . We also tested the complex formation in an equimolar mixture of β1AR, Gαi, and arrestin. We found that both the β1AR–Gαi and β1AR–arrestin complexes were present, but that the former formed much more readily than the latter (four times higher intensity with apo- or iso-β1AR and three times higher intensity with car-β1AR). This also illustrates that Gαi possesses a higher binding affinity to β1AR than arrestin.Next, we expanded our method by using full-length wild-type protein partners—Gαrestin-1 . We firsomplexes B. We foui–Gβ–Gγ. We incubated Gαi with Gβ–Gγ at equimolar concentration and, as expected, we detected peaks for the cross-linked complexes Gβ–Gγ  and Gαi–Gβ–Gγ  . Following addition of β1AR, we observed the simultaneous presence of the cross-linked complexes Gαi–Gγ, Gαi–Gβ–Gγ, β1AR–Gαi , and β1AR–Gαi–Gβ–Gγ   C. Additi,900 Da) C. The prbind mGi , these dbind mGi , is the Several recent technological advances have enhanced our understanding of various aspects of GPCR activation mechanisms and signaling. For example, structural biology studies by NMR, X-ray crystallography, and cryo-EM have provided high-resolution structural insights, enabling the molecular characterization of different protein complexes. In addition, functional studies using biophysical and signaling assays have allowed the characterization of ligand properties and ligand-mediated cellular response. However, the characterization of the network of GPCR–protein interactions following receptor activation remains difficult to tackle. While the traditional view of GPCR signaling involves a more or less sequential course of events, it is now clear that receptors can adopt multiple active states and engage multiple intracellular binding partners in a complex interaction network. To better understand the network of ligand-mediated GPCR–G protein interactions, we developed a method to address this by directly monitoring the GPCR–protein complex formation. We demonstrated the use of our method by screening three class A GPCRs against a panel of engineered Gα-proteins and generated a selectivity profile for each ligand tested B. In agrSI Appendix, Fig. S16). Thus, our data enable us to observe the allosteric conformational regulation of GPCRs, which helps to explicate the plasticity of GPCR signal transduction.The selectivity profiles of our three GPCRs indicate that each ligand-free or ligand-bound receptor has its unique coupling profile B. ConcurThe development and application of efficient GPCR-binding assays are critical in the early stages of drug development. Current high-throughput technologies for assaying the function of GPCRs mainly depend on the measurement of second-messenger output, such as inositol phosphate, calcium, and cyclic adenosine monophosphate. These readouts are distant from the actual information of the GPCR–effector complex, and rely on cellular responses that can be modulated by several separate or even cross-talking signaling pathways. Therefore, the second-messenger output does not directly indicate the “recruiting” activity of a ligand and does not provide an accurate way to profile ligands according to this measure. Unraveling the relationships between ligand, receptor, and the coupling complexes (with G proteins and arrestins) that mediate downstream signaling events is the key to unscramble allosterism and biased signaling. We showed that our method can effectively be used to study the coupling of both G protein and arrestin  and thusi–Gβ–Gγ) (Investigating the pentameric complex system (ligand–β1AR–Gαi–Gβ–Gγ) C was mori–Gβ–Gγ) C. FurtheGPCRs are allosterically dynamic proteins. Multiple biophysical techniques are currently being used to fully understand how different ligands produce different signaling patterns. Complementary to previous techniques, our strategy represents a mass spectroscopy method that allows characterization of direct ligand-induced receptor–protein complex formation in detail. We developed a powerful all-in-one method, unraveling the G protein–coupling selectivity to GPCRs and receptor conformational regulation, to provide information regarding protein/analyte concentrations, their competition, affinity constants, molecular size, and structure. We therefore anticipate that our method will emerge as a valuable strategy for high-throughput screening and for unraveling the molecular details of ligand–GPCR–protein interaction.SI Appendix, Materials and Methods. This includes detailed information about materials used, methodology and experiment protocols, mass spectra and data analysis, microscale thermophoresis data, three-dimensional models of the tested proteins, tables of the number of intermolecular cross-links present in each complex, information on ligands, and amino acid sequences of the proteins.Detailed materials and methods are provided in Supplementary File


Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2’s most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2. COronaVIrus Disease 2019 (COVID-19) is currently a worldwide pandemic that was first reported in December 2019 in Wuhan, China, and, since then, led to more than 446 M infected people and over 6.00 M deaths . Since tAccording to the World Health Organization (WHO), there are Variants Of Interest (VOI), variants that have been recognized as being able to acquire community transmission causing clusters and being further identified in several countries, or assessed as a VOI by WHO’s SARS-CoV-2 Virus Evolution Group. On the other hand, Variants Of Concern (VOC) are variants that, adding to the characterization as VOI, are linked to increased transmissibility or virulence, and/or a decrease in the effectiveness of treatment, prevention, and diagnosis approaches currently used. VOI are distributed among clades G (lineages B.1.525 and B.1.617.1), GH (lineages B.1.427/B.1.429 and B.1.526), and GR . Moreover, VOC are distributed among clades G (lineage B.1.617.2), GH (lineage B.1.351), GR (lineage P.1), and GRY (lineage B.1.1.7).SARS-CoV-2 genes encode four major structural proteins: Spike (S) protein, Membrane (M) protein, Nucleocapsid (N) protein, and Envelope (E) protein. Along with these structural proteins, SARS-CoV-2 genes also encode sixteen non-structural proteins (nsp) and accessory proteins . One of M protein is constituted by 223 amino acids and has three major domains: a short- N-terminal ecto-domain, three TransMembrane Helices , and a long C-terminal endo-domain located on the cytoplasmic face of virions ,13,19,26Bioinformatic tools are well established methodologies that allow to attain a structural and functional characterization of relevant biomedical targets for viral infections and to predict new virus-host interactions ,30. In tDue to the lack of an M-protein experimental structure, we have used the AlphaFold predicted structure and subjected it to a MD simulation protocol. AlphaFold  algorithM protein is a membrane protein and the determination of its correct orientation in the lipid bilayer membrane is needed to understand its main interactions, and, therefore, its biological function. To this end, six different web-based resources for membrane orientation prediction were used: OPM , TMpred M protein Root-Mean-Square-Deviation (RMSD) results were obtained considering residues from the whole protein (monomer RMSD), TMH1, TMH2, C-terminal, and N-terminal, using the AlphaFold monomer structure as reference. M protein monomer predicted residue domains, after system equilibration by Molecular Dynamic (MD) simulations, were very similar for all membrane orientation predictions. Monomer RMSD values were 1.61 ± 0.26 Å for TMHMM, 1.38 ± 0.18 Å for CCTOP, 1.36 ± 0.22 Å for TMpred, 2.38 ± 0.59 Å for SACSMEMSAT, 1.83 ± 0.35 Å for OPM, and 2.95 ± 0.28 Å for PSIPRED predictions . For theOPM, TMpred, and TMHMM M monomers from the previous step were used to model dimer 3D structures using a well-established protein–protein docking software: HADDOCK . From 30The final dimer 3D structure  was subjRMSD results  showed tAfter dimer equilibration in an ER membrane used to mimic the expected biological environment, we showed that the dimer interface was composed of 38 residues, 17 from monomer A  and 21 residues from monomer B . These residues established 34 pairwise interactions, showing high proximity and high prevalence time (90% cut-off) . Carbon Interactions between monomer A and monomer B residues W59-L93, V66-V66, A69-V70, V70-A69, V70-V70, W75-Y71, I82-V70, W92-W92, L93-P59, F96-F96, F103-F103, and M109-F103 were prevalent interactions throughout 100% of MDs simulation time, with side chain distances lower than 5 Å . These rbinding) and the respective values by physico–chemical character of the analyzed mutation are illustrated in We retrieved 1271550 M protein sequences, submitted between 10 January 2020 and 3 May 2021 from 180 countries, from the Global Initiative on Sharing All Influenza Data (GISAID)  databaseTo assess the mutation effect at the dimer complex, we have focused on the reported mutations at the predicted interface. In these considered regions the vast majority of the mutations did not impact protein stability in a significant manner , Table 2SNP I82T, located at the TMH3 domain, was the most common SNP detected. This mutation led to the residue’s polarity modification from a non-polar residue into a polar one and occurred in 6316 (28.88%) sequences from our dataset. The second most frequent SNP was V70L, at the end of the TMH2 domain. This mutation did not change the type of polarity at that specific position and was detected in 6303 (28.82%) sequences. Physico–chemical properties of valine and leucine are similar, and V70L mutation was shown to be inconsequential for dimer stability. However, this is not true for isoleucine and threonine, two amino acids with different polarity, and I82T leads to a stability gain. Interestingly, these two residues, I82 and V70, are interacting strongly throughout the entire MDs simulation . These wbinding around 0 kcal/mol)   and A104V   . The seccal/mol) . A few mcal/mol) . G cladecal/mol) . A few dcal/mol) . GR cladcal/mol) . A few mcal/mol) . The remIn total, there were 8951 (40.93%) mutated sequences that were found in VOC and 2757 (12.61%) that were found in VOI. Out of VOC identified sequences, 8474 (94.67%) were contained in pango lineage B.1.1.7 and the most common mutation in this variant was V70L, represented in 6136 sequences (72.41%). In sequences identified as VOI, the most represented pango lineage was B.1.525 (72.59%) and the most frequent mutation for this variant was I82T, present in 2139 sequences (72.48%) .monomer, allows us to distinguish between residues with the same ΔSASA but with different solvent accessibility in the monomer. The most mutated residues such as I82, V70, and A69 showed higher ΔSASA and relSASA values, which indicates occlusion of these residues upon complex formation, with SASAcomplex values tending to zero (higher ΔSASA and relSASA closer to 1)  . Other fer to 1) .binding below −0.50 kcal/mol or over 0.50 kcal/mol are indicative of such cases. Mutations A69P, R107C, R107H, R107L, and R107S, all have ΔΔGbinding values over 0.50 kcal/mol. Despite the R107H relatively low mutation frequency, it appears in several VOCs as B1.1.7, B.1.351, P.1, and VOI B.1.617.1. On the other hand, mutations I82T, I82S, A69S, A104S, A69T, and A104T have ΔΔGbinding values below −0.50 kcal/mol meaning that they have a favorable impact on the mutated protein stability. Mutation I82T has been detected in several VOCs as B.1.617.2 and B.1.1.7, in higher frequency, but also in P.1.1 and B.1.351, and in VOI B.1.525. Mutation I82S has been detected in VOCs B1.1.7 and B.1.351 sparingly and in VOI B.1.617.1 more frequently. Mutation A69S has been detected in VOC B.1.1.7 more frequently than in VOC B.1.351 and in VOI B.1.526 much more infrequently, and in VOI P.2 just once. Mutation A69T is much less frequent than A69S but has also been detected in VOC B.1.1.7. Finally, mutations A104S and A104T have both been identified in VOC B.1.1.7 twice and three times, respectively.As some of these mutations may impact protein’s stability, we also investigate the identification of their presence in VOI and VOC key strains since it can lead to future drug discovery concerning the M protein. The mutations leading to ΔΔGIn order to understand if any of the previously mentioned mutation points were druggable residues, the production MD phase was clustered and subjected to FTMap ,58,59,60In the second cluster (garnet), probes interacted with predicted M protein dimer interface residues P59, V66, W92, L62, L67, A85, and L93 from Monomer A and B and I82 from Monomer B. Similarly, to the highest scoring cluster, the mutations on residues that interact with the second cluster are also mainly not impactful on protein stability or impact it in a positive manner. Even though some mutations are more frequent in these residues, all in all they are still very conserved . Probes Overall, the highest score cluster interacted with very conserved interfacial residues whereas the second highest cluster was in a region constituted by residues more prone to stabilizing mutations .At the moment, a few predictions for the M protein monomer were made as is the case for Heo and Feig , Zhang eDespite the M protein dimer being crucial for various biological functions such as SARS-CoV-2 virion assembly and shape formation , the typFeig’s laboratory has also proposed two possible configuration arrangements for the M protein homodimer , which wbinding higher than 0.50 kcal/mol, which means that these mutations can have a negative impact in the M protein dimer stability and 12.27% had a ΔΔGbinding lower than −0.50 kcal/mol, and, hence, could have a favorable impact in M protein dimer stability. Most mutations did not appear to influence M protein dimer interfacial stabilization, since about 85% showed ΔΔGbinding values between −0.50 kcal/mol and 0.50 kcal/mol. The ones that seem to lead to a gain of stabilization are presented in binding negative values, which endorses the maintenance or increase in stability as proposed. Mutations in homologous SARS-CoV experimentally interacting residues P59, W92, L93, and F96 were sparse and showed ΔΔGbinding values close to zero. Three exceptions were exposed: L93S and W92Q with ΔΔGbinding values lower than −0.5 kcal/mol, suggesting that these residues were also extremely important for M protein dimer interaction; and L93P ΔΔGbinding = 2.29 kcal/mol) value, the second highest, probably due to the destabilization caused by Proline in the TMH3 α-helix.Regarding mutation analysis, from the 127,1550 genomes analyzed, 21,868 sequences carried SNPs at M protein dimer predicted interaction residues. This represents only 1.7% of all retrieved genomes suggesting that the predicted interfacial region is extremely conserved . We idenbinding values of −0.49 ± 0.38 kcal/mol and −0.02 ± 0.22 kcal/mol, suggesting that I82T is the most favorable, high-prevalent mutation and should be further studied. A previous work that established structure changes caused by M protein mutations, suggested that I82T and V70L mutations will not lead to a significant impact in M protein monomer secondary structure [The most common mutations were I82T (28.88%) and V70L (28.82%), key residues for M monomers interaction as I82 and V70 interaction was conserved throughout the entire MDs simulation with a mean distance of 8.62 ± 0.65 Å for I82-V70 and 9.08 ± 0.66 Å for V70-I82 interactions (monomer A–monomer B). Both these residues (V70 and I82) had low RMSF values and were occluded from solvent upon complex formation , which protects the established interactions. I82T and V70L, showed ΔΔGtructure .Overall, most represented clades in our mutation study were GRY (36.69%), containing VOC and GH (21.25%), G (19.06%), and GR (17.27%), containing VOC and VOI. This could mean that SNPs in the interface region may impact SARS-CoV-2 life cycle, specifically regarding the M protein functions. Furthermore, these mutations are intrinsically related to known VOC and VOIs. For instance, V70L and I82T mutations appeared in 99.5 and 97.64% of clades sequences that contain VOC and VOI. The most common mutation in VOC was V70L, detected in 6137 VOC genomes, and 97.35% of the time this mutation was detected, it appeared in pango lineage B.1.1.7, a VOC in clade GRY.There were 25 co-occurring mutations on the GISAID data, 12 of which on interfacial residues involved in PPIs present throughout the entire MDs simulation. Even though SNP V70L only co-occurred with other mutations in nine cases, these sequences were from clade GRY, which contains several VOC. Overall, clades G (27.45%), GRY (23.53%), GH (23.53%), and GR (19.61%) were the most represented in our co-occurrence results, all containing VOC. V70L does not seem to be by itself relevant for homodimer formation but seems to be a catalyzer if co-occurring with other interfacial mutations as found in various VOCs. Clades GV (3.92%) and S (1.96%) also contained sequences with co-occurring mutations, and the remaining ones did not show any co-occurring mutations. It is possible to conclude that most co-occurring mutations were indeed in VOC and VOI containing clades.One of the new strategies in drug development has been to develop peptides to interrupt transmembrane interactions in dimers . As suchA computational docking approach recently proposed the M protein heterodimer interactions with E and S protein . In thisThis work can be split into three main steps: M protein monomer membrane orientation prediction, M protein dimer 3D structure prediction, and mutation effect assessment in the homodimer interface. The overall workflow to accomplish these goals is illustrated in As there are no experimentally resolved structures for SARS-CoV-2 M protein dimer or monomer, and protein homology to other known 3D structures is reduced, we used AlphaFold’s  team pro+ and Cl− ions and a bilayer membrane with POPC:POPE:PI:POPS:PSM:Cholesterol, in order to replicate human ER membrane [We used MD simulations for the M monomer initial minimization considering each membrane orientation obtained via OPM, TMpred and TMHMM, PSIPRED, CCTOP, and SACSMEMSAT. MDs were performed using GROMACS ,43 and tmembrane , as M prmembrane . Pressurmembrane . Long-ramembrane . RMSD anOPM, TMpred, and TMHMM protein monomers were selected from system equilibration results and subjected to M protein dimer prediction. To guide the protein–protein docking we used known information on SARS-CoV M protein that has a 90.5% sequence identity and 90% homology with SARS-CoV-2 M protein . Two equ–5 bar−1. Electrostatic interactions were performed with fast smooth Particle-Mesh Ewald, with a cutoff of 1.2 nm and Hydrogen bonds were constrained using the linear constraint solver.The final dimer system was built in a similar way as above-mentioned for M protein monomer MD simulations  Supplem. Three icomplex) and each monomer separately (SASAmonomerA and SASAmonomerB), and ΔSASA was calculated for each residue as SASAcomplex − (SASAmonomerA + SASAmonomerB). ΔSASA values provide another quantitative measure of conformational change upon protein coupling. To further understand the behavior upon complex formation, we also calculated relSASA for each residue that comes from the quotient between ΔSASA and SASAmonomer. To detect possible interacting residues, a structure was retrieved every 2 ns, totaling 100 structures from 300 ns until 500 ns, for each replica as further explored in Dimer system RMSD  and RMSF calculations were performed using Cα atoms with GROMACS package. CCA, which tracks the movements of two or more sets of time series data relative to one another, was performed using the Bio3D R package  based onhttps://www.gisaid.org. MicroGMT [Genome and protein sequences for this study were obtained from the GISAID  databaseMicroGMT , a pythoMicroGMT . This mebinding = ΔGmutant − ΔGWT) between mutant protein and reference protein, considering contributions from hydrophobic, polar, Van der Waals, hydrogen bonds, and electrostatic interactions [binding values close to zero as relevant for protein stability, we established a low  and high cut-off off . Results for this step were analyzed considering residue polarities, both for the WT (protein sequence YP_009724393.1) and mutated proteins, as well as splitting residues by aromaticity, as both these characteristics have a major impact on protein–protein interactions. Residues considered as polar were R, N, D, C, E, N, H, K, S, T, Q, and Y; residues considered as non-polar were A, G, I, L, M, F, P, W, and V. Residues F, W, and Y were considered as aromatic.To determine the impact of mutations in M protein dimer stability, Gibbs energy difference was calculated using FoldX , an empiractions . To avoiTo further identify ligand binding hotspots on SARS-CoV-2 M protein homodimer, an ensemble of representative structures was attained by clustering the production phase of the MD simulation. This clustering was performed by concatenating the trajectories and clustering with GROMACS using the gromos method with a cutoff of 0.25 nm. The 16 clusters were subjected to the FTMap ,58,59,60All presented structure images were produced with Protein Imager , ggplot2As M protein dimer has several important functions during SARS-CoV-2 life cycle, it was fundamental to understand its structure–function relationship. Herein, upon establishing a comprehensive and well detailed computational pipeline, we were able not only to assess mutation effects at this interface but also to understand the specificities of the behavior of this region and establish the consequences for dimer stability. This was the first time that SARS-CoV-2 M protein dimer structure, interactions and mutational effects were proposed and thoroughly studied either computationally or experimentally. M protein is overall well conserved, showing that key-residues F96, F103, S108, S111, and F112 are preserved and able to form important interactions in the dimer. These residues can now be assessed as regions of interest for new therapeutic solutions regarding SARS-CoV-2.


Our PONDR® analysis found high levels of IDPRs in all five proteins with mutations linked to CM. The highest levels of IDPRs were in BRAF (45.95%), followed by PTEN (31.76%), NF1 (22.19%), c-KIT (21.82%), and NRAS (14.81%). Our STRING analysis found that each of these five proteins had more predicted interactions then expected . Our analysis demonstrates that the mutations linked to CM likely affected IDPRs and possibly altered their highly complex PPIs. Quantifying IDPRs in BRAF, NRAS, c-KIT, NF1, and PTEN and understanding these protein regions are important processes as IDPRs can be possible drug targets for novel targeted therapies for treating CM.In recent years, there has been tremendous enthusiasm with respect to detailing the genetic basis of many neoplasms, including conjunctival melanoma (CM). We aim to analyze five proteins associated with CM, namely BRAF, NRAS, c-KIT, NF1, and PTEN. We evaluated each protein for its intrinsically disordered protein regions (IDPRs) and its protein-protein interactions (PPI) with the Predictor of Natural Disordered Protein Regions (PONDR Conjunctival melanoma (CM) is an aggressive neoplasm of the ocular surface. While it is rare, the incidence of this neoplasm is on the rise ,2. The aWide excision with a “no touch technique” in combination with cryotherapy is the modern approach for managing CM ,9,10,11.In recent years, there has been a rise in precision medicine, and interest in the genetic basis of CM has blossomed. These molecular insights are crucial as they provide a potential understanding of the disease process, paving the road for novel medical therapeutics and targeted therapies. For example, patients with metastatic melanoma positive for the BRAF mutation can be treated with combined proto-oncogene B-Raf (BRAF) and mitogen-activated protein kinase kinase (MEK) inhibitors, and now they can also be treated with checkpoint inhibition through programmed cell-death 1 (PD-1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4) receptors for patients that show systemic metastasis ,19,20. LThe development of novel molecular-based medical therapies provide a potential new avenue to treat CM. Novel therapies may possibly limit treatment related ocular surface damage and reduce recurrence, metastasis, and mortality rates. In a 2020 paper, Gkiala and Palioura highlighted the key genetic changes implicated in CM, where specific mutations were localized to the mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-kinase protein kinase B (PI3K-akt) pathways . HoweverIn the interest of advancing the current management of CM, it is important to detail the unknown components of this neoplasm’s proteome. While there are many components of the proteome to consider, an area in the field that is becoming increasingly linked to neoplastic development is intrinsically disordered proteins (IDPs) and hybrid proteins containing ordered domains and IDP regions (IDPRs). IDPs and IDPRs are proteins that contain global or local levels of non-3D structural components and provide a protein with a highly flexible and functional conformational ensemble ,25,26. GThe impact that specific mutations in conjunctival melanoma have on IDPRs in proteins found in the MAPK and PI3K-akt pathways  has not been previously characterized. By utilizing a computational disorder-based structural analysis, we aim to bridge this gap. If intrinsically disordered proteins (IDPs) are present in proteins involved in the pathogenesis of CM, then they potentially provide a framework by which CM develops and a possible target for future drug development as IDPs have been successfully targeted to treat cancer . Our anaAn initial literature search with key terms “conjunctival melanoma” and “genetics” was performed to understand the current genetics applications for the treatment of CM. Articles used in the literature search were collected from the National Library of Medicine MEDLINE databases. All forms of published scientific articles were considered, including original research, meta-analyses, and systematic reviews. All articles were limited to publications in the English language. A disorder-based structural analysis that utilized publicly available databases was conducted following the literature search . https://www.genome.jp/kegg/ (accessed on 15 May 2021)) [The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database that houses high-throughput experimental biological data (available at: y 2021)) ,29,30. Ohttps://uniprot.org (accessed on 15 May 2021)) is a comprehensive database for protein data [The Universal Protein Resource ). The four proteins  were evaluated by AlphaFold2 [Structural propensity of each protein was analyzed. The structural propensity of each protein was analyzed. X-ray structures with the highest resolution (lowest Å) available on UniProt were evaluated for NF1 ) and IUPred2A platform (https://iupred2a.elte.hu/ (accessed on 10 June 2021)). Both platforms are publicly available and represent tools that input a protein’s amino acid sequence and output quantitative, disorder-based data. In this study, we used four per-residue PONDR® predictors including PONDR® VLXT [® VL3 [® VSL2 [® FIT [The five FASTA sequences used in this computational analysis  were run through the Predictor of Natural Disordered Protein Regions ) [The Search Tool for the Retrieval of Interacting Genes )  was usedThe MAPK signaling pathway , followed by PTEN (31.76%), NF1 (22.19%), c-KIT (21.82%), and NRAS (14.81%). Therefore, using the established criteria for the classification of proteins based on their PPIDR scores where proteins are considered as highly ordered (PPDR < 10%), moderately disordered (10% ≤ PPDR < 30%), and highly disordered (PPDR ≥ 30%) [Our disorder-based computational analysis was aimed at quantifying the intrinsically disordered protein region (IDPR) identified in the structural assessment of each gene product. To begin, the IUPred2 (Short (S)) and IUPred2 (Long (L)), PONDRanalysis  allowed analysis . Based oR ≥ 30%) , it was p-value < 10–16) as they vary greatly from their expected number of interactions. The next step in our investigation was focused on determining the significance of these structural findings. The presence IDPRs promotes conformational flexibility in each protein; therefore, these proteins likely do act as promiscuous binders and might have interaction partners outside of their well-defined roles in the MAPK and PI3K pathways. Our STRING analysis  demonstrp-value of 1.4 × 10−9.We also used STRING to investigate the intra-set interactions of human BRAF, NRAS, c-KIT, NF1, and PTEN. The analysis revealed that these proteins are heavily linked by binary protein-protein interactions. The intra-set networks of all five proteins were connected by 14 protein-protein interactions. Since the expected number of interactions among proteins in a similar size set of proteins randomly selected from human proteome is equal to two, this internal PPI network of human CM-related proteins has significantly more interactions than expected, being characterized by a PPI enrichment p-value < 1.0 × 10−16). These findings suggest that mutations associated with these proteins likely have an impact on protein interactions, are secondary to abhorrent regulation of IDPRs, and possibly promote oncogenesis and neoplastic development. This is consistent with findings that mutations can affect IDPRs and promote oncogenesis [BRAF, NRAS, c-KIT, NF1, and PTEN have specific mutations  that areogenesis . To our knowledge, this is the first study to analyze the mutations associated with the pathogenesis of CM, namely for their IDPRs and the PPI network of each of these proteins. In recent years, there has been a push to target intrinsically disordered protein regions involved in various cancers including adenocarcinoma, Ewing’s sarcoma, and lymphoma ,41,42. WThere are already several different drugs that are available for the extraocular melanomas, such as BRAF inhibitors and c-KIT inhibitors, and it is possible repurpose them to treat CM ,46,47. TOur analysis found high levels of IDPR’s in BRAF, NRAS, c-KIT, NF1, and PTEN and demonstrates the presence of highly complex PPIs, which may be possible future therapeutic targets. The presence of IDPRs is important as the solution space for potential drug targets is likely significantly larger than previously thought. CM’s mutational profile will likely consist of one or more the of these mutations. If more than one mutation is present, combination-based medical therapy may be warranted in order to target the neoplasm at multiple points in its protein interaction network. As with all studies, this study had limitations. The databases and tools utilized in this analysis do not provide a comprehensive nor exhaustive representation of the proteomics involved in CM. As with all bioinformatics investigations, translations to basic science and clinical outcomes are needed. The tools used herein revealed the molecular features of five key proteins related to CM. To our knowledge, this is the first study to identify IDPRs in five important proteins that have been linked to the pathogenesis of CM. There is evidence to suggest that targeting IDPRs play a role in cancer treatments  and the Neoplastic lesions are increasingly treated at a molecular level, and one method to address specific mutational proteomic variants is through targeted therapy. Potentially, drug therapies targeted at intrinsically disordered protein regions of BRAF, NRAS, c-KIT, NF1, and PTEN offer a possible avenue to new treatments that can aid in the management of this neoplasm. Ultimately, translating these findings to the clinical management of these patients will require an integrative omics approach. The hope is that small molecule inhibitors and targeted therapies aimed at IDPRs may assist in the management of conjunctival melanoma.