Body fluid proteome has been intensively studied as a primary source for diseasebiomarker discovery. Using advanced proteomics technologies, early researchsuccess has resulted in increasingly accumulated proteins detected in differentbody fluids, among which many are promising biomarkers. However, despite ahandful of small-scale and specific data resources, current research is clearlylacking effort compiling published body fluid proteins into a centralized andsustainable repository that can provide users with systematic analytic tools. Inthis study, we developed a new database of human body fluid proteome (HBFP) thatfocuses on experimentally validated proteome in 17 types of human body fluids.The current database archives 11 827 unique proteins reported by 164scientific publications, with a maximal false discovery rate of 0.01 on both thepeptide and protein levels since 2001, and enables users to query, analyze anddownload protein entries with respect to each body fluid. Three unique featuresof this new system include the following: (i) the protein annotation pageincludes detailed abundance information based on relative qualitative measuresof peptides reported in the original references, (ii) a new score is calculatedon each reported protein to indicate the discovery confidence and (iii) HBFPcatalogs 7354 proteins with at least two non-nested uniquely mapping peptides ofnine amino acids according to the Human Proteome Project Data InterpretationGuidelines, while the remaining 4473 proteins have more than two unique peptideswithout given sequence information. As an important resource for human proteinsecretome, we anticipate that this new HBFP database can be a powerful tool thatfacilitates research in clinical proteomics and biomarker discovery.Database URL:https://bmbl.bmi.osumc.edu/HBFP/ Human body fluids are thought to be rich resources of disease-associated proteinsthat are secreted or leaked from pathological tissues across the body, many of whichare commonly obtainable through non-invasive procedures , 2. DrivWith great effort by a few large consortiums, several community-based proteomicdatabases have been developed in the past decades. For example, in 2002, theinternational Human Proteome Organization initiated the Human Plasma ProteomeProject and reported human plasma and serum protein constituents in its onlinedatabases . Another Our team has recently conducted a systematical assessment of human proteomeidentified using quantitative proteomics tools such as MS and computationalpredictive models, as documented in a recent review article . To expaWe have manually collected proteins reported in 17 types of body fluids bycarefully reviewing 164 scientific references published since 2001 based on aPubMed search with FDR ≤1% on both the peptide and proteinlevels.https://biodbnet-abcc.ncifcrf.gov/) (https://www.UniProt.org/)to confidently convert different identifiers to UniProt accession numbers. Thecommon identifiers involved in this study include International Protein Index ID[hosted at European Bioinformatics Institute (EBI) (closed in 2011)], GI number(from Genbank database), RefSeq protein accession (from RefSeq database), Genename/symbol (from NCBI Gene database) and UniProt protein/entry name (fromUniProt database). The ID conversion process is shown in  In the HBFP database, each protein is assigned with a unique identifier ofUniProtKB/Swiss-Prot accession (UniProt release 2020_06) . Since drf.gov/)  and UniPThe interface of the HBFP database is constructed by PHP, while the databasesystem is based on MySQL. The main contents of the current database includequery and browse pages described as follows.As one of the most important functions, the querying page allows users tosearch for body fluid proteins based on different types of input includingprotein ID, gene name, and protein or gene sequence. When given a FASTAinput, BLASTp or BLASTn is used to translate sequence input to thebest-match protein entry. The top hit (the highest bit score) from the BLASTsearch is considered the best match of the query. The annotation of each protein contains the following information:Protein ID/name/entry nameGene nameAssociated body fluid type along with indicated discovery confidence(explained in the next section)References and protein abundance information where the protein isreportedExternal links to public databases including UniProt, PeptideAtlasand NeXtProt , MassIVEFunctional annotation based on the KEGG pathway  and GO This page provides an overview list of proteins associated with 17 types ofbody fluids and links to view and download selected proteins.When determining the inclusion of reported proteins, we applied the followingcriteria for credibility of the MS evidence. First, for papers that issuedpeptide sequence details, we remapped all those peptide sequences to neXtProt(release 2021-02-15) using the neXtProt peptide uniqueness checker to removeunreliable matches . Specifihttps://bmbl.bmi.osumc.edu/HBFP/.The overall statistics about the protein entries and references in terms of eachbody fluid are summarized in In order to provide users experimental evidence from the original study, thisdatabase also displays relatively abundant information from the correspondingliterature studies. General proteomics approaches using MS identify proteins bymatching identified peptides against predefined protein sequence databases. Thequalitative measures of protein reported in the original reference include thefollowing: (i) peptide information: most of cited studies provide explicitinformation about peptide sequence, the total number of peptides, MS counts orthe percent sequence coverage; (ii) differential expression informationincluding fold change , up- or down-regulatedexpression in case vs. control or  spectral counts and (iii) otherstatistical information including FDR, relative standard deviation and thenumber of times across different samples or experiments, as shown in N datasets with a substantial FDRand piling them all together, the overall FDR increases with the number ofdatasets. For example, for plasma, there are 38 papers with plasma proteinlists, each with a substantial FDR  or gene sequence (FASTA) (<50 items perquery)  is perfoHBFP allows users to browse the entire protein list in each body fluid, wherethe proteins are ordered based on descending confidence scores. Users cancheck and download all entries of the selected body fluid type in one go, asshown in Many proteins in the HBFP database have a broad distribution in terms of bodyfluid types. An internal comparative analysis across different fluids canprovide further information regarding the specificity of a proposed markerprotein. Of 11 827 identified proteins, 66.8% are identifiedin at least two body fluids .A totalTo take a closer look at this comparison, we focused on five body fluids thathave the most protein counts, including blood, urine, CSF, SF) and BALF. Aninteresting discovery is that urine shares large numbers of common proteinswith other fluids . A totalin silico analysis on human secretomes. We currently schedule aregular update every 6 months. The future plan is to include computationallyidentified proteins using statistical and machine learning approaches (The new HBFP database developed in this study represents the first of its kind as acomprehensive reference resource of HBFP. All data are available through anopen-access user-friendly Web platform. All protein entries were manually curated,which can be easily traced back to the original literature. Users can query anddownload proteins of interest to verify discovery in their own study or conduct anproaches .In the


Citrus tristeza virus (p20 and p25) that are known to function as RNA silencing suppressors, using an experimental system of transient expression in a model plant. The study was expanded by considering two different isolates of the virus, and some key interactions were confirmed by bimolecular fluorescence complementation assays. We found that p20 and p25 target a common set of plant proteins including chloroplastic proteins and translation factors. Moreover, we noted that even specific targets of each viral protein overlap in function. Notably, we identified argonaute proteins (key players in RNA silencing) as reliable targets of p20. Furthermore, we found that these viral proteins preferentially do not target hubs in the host protein interactome, but elements that can transfer information by bridging different parts of the interactome. Overall, our results demonstrate that two distinct proteins encoded in the same viral genome that overlap in function also overlap in their interactions with the cell proteome, thereby highlighting an overlooked connection from a degenerate viral system.As genomic architectures become more complex, they begin to accumulate degenerate and redundant elements. However, analyses of the molecular mechanisms underlying these genetic architecture features remain scarce, especially in compact but sufficiently complex genomes. In the present study, we followed a proteomic approach together with a computational network analysis to reveal molecular signatures of protein function degeneracy from a plant virus (as virus-host protein-protein interactions). We employed affinity purification coupled to mass spectrometry to detect several host factors interacting with two proteins of  i.e., the interactions that they establish with their host plants) is the cornerstone of modern molecular virology. In this work, a proteomic study was conducted to determine protein complexes that associate with two proteins of Citrus tristeza virus : p20 and p25. CTV is a plant virus of agronomic importance that also serves as a suitable model of viral complexity. p20 is a silencing suppressor that acts at both local and global levels, while p25 is the coat protein of the virus and also functions as a systemic silencing suppressor. Computational analyses revealed that CTV evolved different silencing suppressors that can establish convergent virus-host interactions, which is a mark of protein function degeneracy.Determining the mode of action of plant viruses ( It wo ,4. Beyon ,6, recen –10. The   or error . Even th , so the  . A plausiciently . HoweverCitrus tristeza virus  as a model system to depict molecular signatures of protein function degeneracy from a virus. Notably, CTV is a plant virus of agronomic importance [Closteroviridae, CTV is one of the largest monopartite RNA viruses found in nature (the largest in plants), with a genome of approximately 20 kilobases that contains 12 open reading frames  [Cucumber mosaic virus because it blocks intercellular (systemic) and intracellular RNA silencing and is also responsible for the formation of inclusion bodies during infection [i.e., to determine which proteins they interact with in the host plant). Notably, this problem remains largely unaddressed in the case of CTV. We chose these two proteins because they can suppress RNA silencing from a long distance and also for simpler comparative analysis. Broadly, we aimed to determine whether an overlap in function within a natural degenerate system is accomplished through an overlap in molecular interactions or through entirely different modes of action . To expand the analysis, we considered two CTV isolates that differ in their induced symptomatology in citrus and infectivity in Nicotiana benthamiana [In this work, we considered portance . Belongi . More in . In termnfection ,17. p23 nfection ,18. p25 nfection ,19. Consthamiana  and the thamiana  were conN. benthamiana plants using agroinfiltration . Although this is a non-natural host of CTV, it is suitable for controlled experimental developments and also susceptible to be systemically infected by this virus (isolate T36) showing characteristic disease symptoms [S1 Table displays an alignment between the proteins of both isolates (EMBL-EBI web services), showing that the two p20s differ in five amino acids, while the two p25s differ in nine amino acids. The tagged viral proteins and the host plant proteins that formed complexes with them were purified by affinity chromatography, and the resulting samples were analyzed by mass spectrometry to obtain lists of interacting proteins . The expression of the tagged viral proteins in planta was confirmed by western blot analysis . Moreover, we provide experimental evidence of the tagged viral proteins exhibiting silencing suppression activity in N. benthamiana plants similarly to their native counterparts .We began with the transient expression of the four viral proteins of interest (the two p20s and two p25s from the CTV isolates T36 and T318A) in Viridiplantae (green plants) to proteins of the model plant Arabidopsis thaliana. It is presumed that ortholog proteins between plants behave similarly. Although CTV does not infect A. thaliana, this mapping allowed us to perform successive functional and network analyses from a systems biology perspective  while taking advantage of all available annotations [i.e., by reading the genomes of the source and target organisms and performing on the fly different sequence alignments; S2 Fig). Here, the source organisms given by the MS search engine were from Viridiplantae, and the target organism was always A. thaliana. S1 Data contains the different protein lists . Notably, since AP-MS detects protein complexes, we could expect that some of the identified host factors interacted with the viral proteins through third parties.Affinity purification coupled to mass spectrometry (AP-MS) is a powerful proteomic technique that has been successfully exploited to decipher protein-protein interactions between plant or animal viruses and their respective hosts ,24, as wotations . To thisFig 2; see also S2 Data). This corresponds to a merged picture of the identified interactions from the AP-MS analysis having used the variants from two isolates. Among them, we identified 86 plant proteins that interact with both viral proteins. This is a substantial set of common interactors that illustrates, given that p20 and p25 share a function, a partial overlap in terms of their interaction with the host, which presumably results in an overlap of the molecular mechanisms followed by both proteins to achieve that shared function. This contributes to supporting the notion of protein function degeneracy  in CTV . Interestingly, most of the shared interactors are associated with both isolates , which highlights that such overlap diverged little from one isolate to another. Among this common set, we found 41 chloroplastic proteins  and four translation factors . Interestingly, some viral proteins have been shown to interact with chloroplastic proteins to modulate the plant immune system during infection [We found that p20 putatively interacts with 288 plant proteins and p25 interacts with 197 .To give further support to the reported overlap between p20 and p25, we considered the putative interactors of ous work . VPg is ous work . We founP < 0.0001). In particular, we found RAN GTPase 3 , a plant protein involved in import movement into the nucleus, to interact with p25 (from isolate T36), which agrees with recent results showing that this viral protein localizes in the nucleus [P < 0.0001). However, we observed two significant interactions related to its main function: p20 (from both isolates) targets argonaute 1  and argonaute 2 , which are key components of the RNA silencing machinery that bind small interfering RNAs (siRNAs) to form the RNA-induced silencing complex (RISC) and degrade the genomic RNA of the virus [In addition, we observed that most of the specific targets of p25  were detected only upon expression of the T36 variant . Broadly, we observed three different modules that were congruent with the two viral proteins. The first corresponds to metabolism, while the second corresponds to response to stress, and the third corresponds to transport and localization. This suggests that both viral proteins touch host proteins that have very similar functions in the cell despite not necessarily being the same molecule. For example, p20 (from both isolates) targets argonaute 4 , which is a key component of the RNA silencing machinery for the plant DNA methylation [e.g., the chloroplast heat-shock protein 70–2; locus AT5G49910). However, the network associated with p20 contains more GO terms , which indicates that p20 introduces a greater perturbation on plant physiology. In particular, 24 terms of response to stress were recognized as perturbed upon p20 expression, while only 12 were recognized upon p25 expression. Furthermore, we observed that nearly all functional terms participated by the proteins targeted by p20, especially those related to metabolism and response to stress, were associated with both isolates. This suggests that p20 slightly diverged in function from one isolate to another. Nevertheless, this is not the case for the functional network of the proteins targeted by p25. While nearly all metabolic terms are only associated with isolate T36 in this case, the response to stress seems to be shared. Consequently, it is possible that p25 greatly diverged in function (perhaps by losing some dispensable subfunctions) from T36 to T318A.To further analyze the mode of action of the two viral proteins under study, we performed a computational analysis of the function of their targets. To achieve this, we applied gene ontology (GO) over the identified host targets to assess the enrichment in different biological processes . This wahylation ,35. Yet,hylation ,37. Intehylation . AdditioA. thaliana , which was essentially developed by yeast two-hybrid experiments [i.e., not necessarily hubs) [U-tests, P > 0.05; boxplots in Fig 4). In contrast, differences were significant in terms of average shortest path length  for both p20 and p25. In particular, the host targets connect on average in 3.85–3.95 steps (depending on the CTV protein and isolate considered) with any other protein of the interactome, while randomly chosen proteins connect in 4.30 steps. This suggests that the local perturbations induced by these viral proteins  might easily have a more global impact. Although AGO1 is not a hub in the interactome, it does interact with highly connected proteins (neighborhood connectivity of 352). This adds to the fact that protein function degeneracy is presumed to enhance information transfer throughout the entire pathosystem [Next, we contextualized the identified host targets on the protein interactome of eriments ,40. Thiseriments . Previoueriments ,43, whicly hubs) . Here, whosystem –10.N. benthamiana plants using a split yellow fluorescent protein (YFP) [Fig 5). These latter interactions escaped our AP-MS approach, which may indicate that these argonaute proteins interact more weakly with p25 than with p20. As a control experiment, we used BiFC assays to assess the interaction of CTV with thioredoxin H-type 9 , an element involved in oxidative stress and cell-cell movement [S4A Fig) and also verified that our results are not a consequence of spurious interactions . These results illustrate how viruses (through their respective silencing suppressors) have multiple means to disrupt the different RISC-mediated channels in the host that evolved to deploy a sophisticated antiviral response.According to our AP-MS results, AGO1 and AGO4 are specific interactors of p20 (from both isolates). Intriguingly, the family of argonaute proteins, with up to 10 different elements, constitutes a degenerate defense system in plants, although some of them have reached a high level of specialization ,45. In iin (YFP)  to gain movement . Accordie.g., virion formation in the case of p25) [Cucumber mosaic virus has been shown to target AGO1 and disrupt its cleavage ability [i.e., interactions that are established between different viral proteins and the same host protein).To achieve at least partial systemic RNA silencing suppression, it would be expected that a viral protein must touch several elements of the host since this mechanism involves multiple interlinked routes for the biogenesis, amplification, and transport of siRNAs ,49. Our  of p25) . Remarka of p25) . Notably ability . Furthere.g., HC-Pro is involved in genome replication and silencing suppression in potyviruses, while p25 is the coat protein and a silencing suppressor in CTV) [i.e., one protein can be somewhat dispensable). In this regard, the aforementioned situations are quite different since HC-Pro cannot replicate the genome by itself  [Besides, it is important to note the difference between degeneracy and multifunctionality . Viral p in CTV) . In prinymerase) , while pymerase) ,19. MoreDengue virus, a duplication of an RNA motif in the 3’ untranslated region was shown to provide host specificity [Interestingly, the occurrence of degeneracy in viral genomes, as it is the case of p20, p23, and p25 in CTV, might contribute to enlarging the host range and codifying host specificity. In recent work with cificity . In CTV,cificity . Alternacificity . Future N. benthamiana [e.g., ADF2, locus AT3G46000), which might subvert the actin cytoskeleton to promote the viral spread [Moreover, we might speculate that the divergence between T36 and T318A is at least partially responsible for the differential infectivity observed between both isolates in thamiana . Only isthamiana . Accordil spread . Isolatel spread . The intIn addition, the symptoms induced in citrus by these two isolates are different. A quick decline in tree health upon infection with isolate T36 might be aligned with the fact that p25 from such isolate targets a substantially greater number of metabolic pathways when compared to p25 from T318A. In turn, yellow leaf disorder upon infection with both isolates would be the result of targeting a series of chloroplastic proteins.A. thaliana is not a natural host of CTV. We mapped the AP-MS hits to A. thaliana orthologs to perform subsequent computational analyses by exploiting functional annotations and protein networks for this model plant [The present study presents nonetheless certain limitations. One limitation is that el plant ,39. ThusS2 Table; genomic sequence of isolate T36 at GenBank AY170468 and of isolate T318A at GenBank DQ151548). PCR products were then digested and cloned into a pBluescript vector. The Twin-Strep-tag  was fused to the CTV proteins at the C-terminal. The new constructs were finally cloned into the pMOG binary vector. The empty vector, which only expresses the TST tag as a peptide, was used as a negative control. The resulting plasmids were named pMOG-p25-T36-tag, pMOG-p25-T318A-tag, pMOG-p20-T36-tag, pMOG-p20-T318A-tag, and pMOG-tag (S3 Table).To perform the AP-MS study, CTV genes p20 and p25 were amplified by polymerase chain reaction (PCR) from the full-length T36 or T318A complementary DNA (cDNA) infectious clones [Cauliflower mosaic virus 35S promoter and terminator, while the 5’ and 3’ untranslated regions are derived from the Cowpea mosaic virus RNA-2 [S3 Table). Plasmids used to express the green fluorescent protein (GFP) and the p19 RNA silencing suppressor of Tomato bushy stunt virus (TBSV) were previously described [S3 Table).To perform the silencing suppression assays, the cDNAs of CTV p20 and p25 corresponding to the viral isolates T36 and T318A were inserted into a binary expression vector derived from pCLEAN-G181  with andus RNA-2 . The resescribed  and nameS2 Table). The coding sequence of AGO1 from N. benthamiana was amplified by PCR from the pROK-AGO1 vector [N. benthamiana were amplified by reverse transcription PCR from a total RNA preparation of wild-type plants. Subsequent PCRs allowed the addition of the external attb markers (S2 Table). Reverse transcriptions were performed using SuperScript retrotranscriptase (Thermo) and PCRs with Phusion high-fidelity DNA polymerase (Thermo). PCR products were cloned into the binary vectors pSPYNE or pPSYCE using Gateway technology (Invitrogen) for further BiFC assays. These empty vectors were used as negative controls. The resulting plasmids were named pSPYNE-p25-T36, pSPYNE-p25-T318A, pSPYNE-p20-T36, pSPYNE-p20-T318A, pSPYCE-AGO1, pSPYCE-AGO4, pSPYCE-TRX, and pSPYNE (S3 Table).To perform the BiFC assays, the coding sequences of p20 and p25 (without the stop codon and the tag sequence) were amplified by PCR from the aforementioned pBluescript plasmids by using appropriate primers  in chambers maintained at a temperature of 25°C and a light/dark photoperiod of 16/8 h. The leaves of eight plants (three batches) were agroinfiltrated (3- to 4-week-old plants). The corresponding leaves were collected 3 days post-inoculation.Plasmids pMOG-p25-T36-tag, pMOG-p25-T318A-tag, pMOG-p20-T36-tag, pMOG-p20-T318A-tag, and pMOG-tag were electroporated into N. benthamiana agroinfiltrated leaves by affinity chromatography run in native conditions by using 1 mL Strep-Tactin superflow columns (IBA), as previously performed [Protein complexes containing the tagged viral proteins (p20 and p25) were purified from 15 g of each batch of erformed . BrieflyProtein preparations were separated by sodium dodecyl sulfate-based polyacrylamide gel electrophoresis . The bands corresponding to each sample were excised, cut into pieces, in-gel digested with sequencing-grade trypsin (Promega), and the resulting peptides were eluted. Then, liquid chromatography and tandem mass spectrometry (LC-MS/MS) was performed, and the peptides were analyzed in a mass spectrometer nanoESI qQTOF , as previously performed . FinallyA. tumefaciens GV3101 was individually transformed with the plasmids pMOG-GFP (to express GFP), p235CPMVZ-p25-T36, p235CPMVZ-p25-T318A, p235CPMVZ-p20-T36, p235CPMVZ-p20-T318A, p235CPMVZ-p25-T36-tag, p235CPMVZ-p25-T318A-tag, p235CPMVZ-p20-T36-tag, p235CPMVZ-p20-T318A-tag (to express the different versions of CTV p20 and p25), pDGB-p19 (to express TBSV p19 and serve as a positive control), and an empty plasmid (to serve as a negative control). The resulting strains were induced for 2 h at 28°C in 10 mM MES-NaOH (pH 5.6), 10 mM MgCl2, and 150 μM acetosyringone at an optical density of 0.5 (600 nm). For local silencing analysis, N. benthamiana leaves were infiltrated with a mixture of A. tumefaciens cultures (1:1) to express GFP and a viral suppressor. In general, transient GFP expression declines after some days due to RNA silencing, and the presence of a viral suppressor restrains this decline. Plants were cultivated at 25°C in a growth chamber and the infiltrated tissues were harvested after 6 days for GFP analysis. For systemic silencing analysis, the upper leaves of the same plants were infiltrated with A. tumefaciens to express only GFP at an optical density of 0.5 three days after the initial infiltration. In this case, GFP was subject to both local and systemic silencing [ilencing  and the A. thaliana genes. To achieve this, we used The Arabidopsis Information Resource (TAIR) release 10. By using the exponentially modified protein abundance index (emPAI) for the estimation of absolute protein amount in our samples [A. thaliana. Using the GI identifier, the corresponding amino acid sequence was retrieved from the NCBI database in FASTA format using the Entrez module of Biopython. For each hit, the tool then performed a protein alignment to obtain the precursor gene in A. thaliana and provided genes and their functional descriptions as outputs. Finally, only the alignments with e-values of < 0.0001 were considered, and repeated elements were removed (S1 Data). Open source software deposited in https://github.com/silsgs/protein2gene.A general bioinformatic pipeline (implemented in Biopython)  was deve samples , our too samples  to obtaiP < 0.05), only considering GO terms with five or more mapping entries. Using these identified biological processes, a functional network was constructed by using REVIGO [S3 Data).With both lists of p20- and p25-targeted host proteins (both isolates), two functional analyses were performed by using agriGO . These sg REVIGO  in whichA. thaliana interactome was used to contextualize the host proteins identified as targets for the viral proteins. This interactome was constructed by accounting for all known interactions with experimental evidence [A. thaliana interactome was represented by using Cytoscape [k), average shortest path (l), betweenness centrality (b), and clustering coefficient (C) for each node in the network. For each list of virus-targeted proteins , the distributions of those topological properties were represented as boxplots. Random lists of proteins were also generated to obtain control distributions of topological properties (we repeated this process 10 times). These control distributions, as well as the distributions corresponding to the total proteins of the interactome, were also represented as boxplots. The statistical significance of the results was assessed using Mann-Whitney’s U-tests (P < 0.05), by comparing the resulting distribution (of a given property) for each list of virus-targeted proteins against the distribution of the entire interactome or the one corresponding to a random list. This calculation was performed in MATLAB (MathWorks).The evidence ,40. The ytoscape  and an aN , knowing that N-terminal p25 fusions were not functional in planta. On the other hand, the pSPYCE vector was used to generate the fusions between the plant proteins  and YFPC . The resulting constructs (see S3 Table) were transiently expressed in planta with A. tumefaciens. The inocula consisted of a combination of three cultures (in a 1:1:0.5 ratio): one carrying the viral protein of interest  in the pSPYNE vector, another with the host targeted protein in the pSPYCE vector, and another carrying the silencing suppressor p19 from TBSV to enhance transient expression [The pSPYNE and pSPYCE vector system  was used . On the pression . Leaves S1 FigN. benthamiana plants to analyze the effects of the different viral suppressors on local and systemic RNA silencing . *Statistical significance . B) Representative images of plant leaves expressing GFP together with a silencing suppressor . Scale bar, 5 mm.A) Experimental results of GFP expression in (TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 FigA) Comparison in case of a combined list of interactors from both CTV isolates. B) Comparison in case of isolate T36. C) Comparison in case of isolate T318A.(TIFF)Click here for additional data file.S4 Figin planta using a split YFP system. B) Control BiFC assays.A) Validation of additional protein-protein interactions by BiFC (TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 DataN. benthamiana and A. thaliana is also presented.The mapping between (XLSX)Click here for additional data file.S2 DataThe emPAI values are presented. In addition, classified lists of targeted proteins by specific or common interactors of p20 and p25, also indicating the isolate from which they were identified. In the case of common interactors, both isolates are indicated if at least they are associated with one contact.(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.


Solubility is a fundamental protein property that has important connotations for therapeutics and use in diagnosis. Solubility of many proteins is low and affect heterologous overexpression of proteins, formulation of products and their stability. Two processes are related to soluble and solid phase relations. Solubility refers to the process where proteins have correctly folded structure, whereas aggregation is related to the formation of fibrils, oligomers or amorphous particles. Both processes are related to some diseases. Amyloid fibril formation is one of the characteristic features in several neurodegenerative diseases, but it is related to many other diseases, including cancers. Severe complex V deficiency and cataract are examples of diseases due to reduced protein solubility. Methods and approaches are described for prediction of protein solubility and aggregation, as well as predictions of consequences of amino acid substitutions. Finally, protein engineering solutions are discussed. Protein solubility can be increased, although such alterations are relatively rare and can lead to trade-off with some other properties. The aggregation prediction methods mainly aim to detect aggregation-prone sequence patches and then making them more soluble. The solubility predictors utilize a wide spectrum of features. Caenorhabditis elegans indicated that about 75% of proteins appear in cells in abundances close to their solubility limits . Bot. Bot5]. Many factors affect solubility and aggregation, including intrinsic properties of the protein, solvent and additives as well as physical conditions. List of relevant protein factors is long and could be started with amino acid sequence and composition, three-dimensional structure and exposure of residues, intramolecular interactions within the protein , and protonation status. Solvent properties  and constituents  and their concentrations have significant contributions to solubility. Further, factors like pH and temperature affect both the protein and solvent.Proteins are large and relatively fragile molecules, thus not necessarily ideal as drugs. Lots of research has been devoted to find smaller structures having (some) functions of the biological proteins. Miniproteins or protein scaffolds have been tested as biologics . Some fuQuite successful solubility prediction methods have been developed for small molecules that are most common as drugs (see chapters in this volume). These methods, however, do not work for proteins. There are several reasons. Proteins are much larger, they have lots of groups and protein folding affects the solvent accessibility of the groups. Further, the protein structures are flexible and have a large number of slightly different conformations. Therefore, different approaches are needed for protein solubility prediction. Here, methods for protein solubility and aggregation prediction are introduced. Further, methods to investigate the effects on solubility due to amino acid substitutions (variations) are discussed. This topic is important for the design of changes for protein engineering e.g. to increase protein solubility, production etc.Protein solubility is discussed here from the perspective of predictions for solubility, aggregation and for variation effects. The principles of the methods are discussed along with performances of the tools. It is apparent that the performances of the methods are not very high, which is due to the complexity of the phenomenon and thereby difficulty in finding good features that would reliably distinguish between the solubility states. One limiting factor has been the small number of experimentally verified cases. However, the field is making progress and there are some rather good methods available.During the last decade, several computational methods have been developed to predict protein solubility, especially in the context of heterologous protein overexpression . These mBased on examples with known solubility, computer tools have been trained (see e.g. ). Proteien masse. Their Structural Biology Knowledgebase [http://tanpaku.org/tp-esol/index.php?lang=en) for almost all Esherichia coli proteins [Saccharomyces cerevisiae protein solubility details [Most methods are trained on one of two major datasets. Protein Structure Initiative was a large project to determine protein structures edgebase  containsproteins  has been details . The rem details  or not tE. coli were soluble [Although many proteins are poorly soluble their solubility is biologically sufficient as many proteins have very low abundance in cells . Data ex soluble .DeepSol is a deep learning method, ccSOL omics is a support vector machine solution, PaRSnIP utilizes gradient boosting, SOLart random forest, SWI and Protein-Sol are based on weighted scores. Details for the algorithm used in SODA have not been released.As with any prediction task, the choice of the tool(s) is important. The best comparisons are independent benchmark studies , 21, howAnalysis of predictions for 57 UDP-dependent glycosyltransferases with 11 predictors  is interAggregation is largely mediated by short sequence stretches of consecutive residues, these regions are typically 15 residues or longer . Almost Aggregation predictors can be grouped to two major categories: sequence-based and three-dimensional structure-based . The strAmylPred and MetAmyl are metapredictors, i.e. use predictions from other tools. RFamyloid is machine learning-based method, Waltz has a position specific scoring matrix, the others are based on physicochemical propensities and other features including information about secondary structural elements, amino acid composition, structural features, physicochemical propensities of amino acids, packing density, hydrogen bonds etc. Neural network tool for amyloid aggregation rate prediction is a related application . Some ofSystematic independent method performance assessments are missing. Some recent studies contain comparisons of several methods. PASTA 2.0 was compared to eight methods, showing the best performance (MCC 0.24) along with AMYLPRED 2 (0.22) and MetAmyl (0.19) . HoweverThe tools described above are for predictions on entire proteins or polypeptides. Much less effort has been put on predicting the effect of variations on solubility or aggregation. We are looking at tools predicting consequences of amino acid substitutions as there is not enough data for other types of variations. Single amino acid alterations can have profound effects of solubility and lead to diseases, including severe complex V deficiency  and cataPredictors for solubility affecting variants include CamSol , OptSolMThese methods can be used for numerous purposes including identification of disease related amino acid substitutions, predictions of solubility of heterologous recombinant protein expression and enhanced crystallizability. Of the aggregation prediction tools, PASTA 2.0 can predict also effects of amino acid substitutions. SoluBis has a somewhat different application, optimization of multiple variants to increase protein solubility . It deteProtein properties have complex relations. Several approaches have been tried to improve solubility or prevent aggregation. Electrostatic interactions have been a starting point for one approach , stabiliStructural changes designed by four tools have been reviewed in relation to structure-based predictions . The disE. coli levoglucosan kinase, indicated trade-offs between fitness and solubility [Systematic performance assessments have not been made to these methods. Massively parallel reporter assay (MPRA) of two proteins, TEM-1 β-lactamase, a common antibiotic resistance protein in Gram positive bacteria, and lubility . SolubilComputational methods available for the prediction of protein solubility and aggregation were discussed along with tools for engineering solubility or aggregation by introducing amino acid substitutions. Many features and different algorithms have been applied to the available solutions. Although systematic performance assessments have not been performed, it is evident that the methods have widely varying performances. Solubility and avoidance of aggregation are crucial properties for any protein to be used for diagnosis or therapy.


Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure – including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome. People with hemophilia A (PwHA) have endogenous defective copies of the coagulation factor VIII gene, which in turn dramatically affects the blood coagulation cascade. As a result, depending on the type of mutation on the F8 gene, disease symptoms may vary from mild , to moderate , and severe .Hemophilia A (HA) is an X-linked heritable disease affecting approximately 1 in every 5000–10,000 live male births2 to recombinant proteins3 and a recent monoclonal antibody4. These so-called replacement therapies aim to replace the defective FVIII protein through constant supplementation of one of these products.Although it is a relatively rare disorder, the coagulation pathway is well-characterized and treatment options are improving since the 1950s, evolving from blood-derived FVIII concentrates5, current treatment options still have issues to be addressed; for instance, it is necessary to improve the half-life of recombinant FVIII proteins , as well as its immunogenic profiles to avoid the development of neutralizing antibodies3. Finally, it is also important to develop recombinant proteins suitable for both prophylaxis and treatment of severe bleeding episodes6.Although the life expectancy and the quality of life of PwHA improved considerably in the last decades12. However, the lack of strict data curation and the analysis of each property in isolation prevented these methods from predicting and mechanistically understanding the occurrence of mild, moderate, and severe phenotypes.To overcome these drawbacks, a thorough understanding of the FVIII protein structure is essential. Reported mutations include deletions and inversions of large parts of its encoding gene, as well as single-nucleotide polymorphisms that do not change the reading frame, but replace an amino acid and generate a defective protein. Using gene information and protein structure properties, previous studies revealed fundamental aspects of single amino acid changes and their relation to severe or mild forms of HATherefore, we created a different representation of the FVIII protein structure and used machine learning (ML) methods to analyze all protein properties in conjunction. We named this framework Hema-Class Fig. . To uncoFinally, we used Hema-Class to make predictions of the severity of all possible FVIII mutations—including those not yet reported in the medical literature. We grounded the reliability of these predictions in the close agreement between Hema-Class and in vitro mutagenesis assays, as well as hundreds of clinical reports of FVIII mutations.In summary, the contributions of this study are twofold: first, we established an ML approach based on limited clinical and molecular data, and second, we used this system to advance the understanding of the FVIII protein by indicating which mutations are neutral or detrimental to its function. We anticipate that this study will enable other rare diseases to benefit from ML and will contribute to the engineering of better recombinant FVIII therapeutics.2. In this delicate interaction with multiple molecular partners, even single nucleotide changes can destabilize the FVIII protein structure and compromise its function. Therefore, we collected several properties of all amino acids of the FVIII structure13 (Protein Data Bank (PDB) accession 2R7E), including the surface exposure area, hydrophobicity and torsion angles of the amino acids, and searched for clinical reports that described HA cases caused by mutations at each of those residues. In addition, we verified that the properties of the structure we used in this study strongly correlated to the properties of another structure published recently14 , coagulation factor X (FX), and the phospholipid membrane of plateletsAs it happens with rare diseases, the size of the input data often hampers its use for robust statistical analyses; we circumvented this issue by a careful and strict data sanitation strategy (“Methods”). We designed an input set with 443 non-synonymous mutations at 364 different FVIII positions , maximizing the amount of information without biasing the dataset toward any particular variable Fig. .11, we found that mutations at positions with low solvent-accessible and low solvent-excluded surface areas, as well as in highly conserved residues are related to more severe phenotypes .Next, we created a residue interaction network (RIN) of the FVIII protein. In this network, each amino acid corresponds to a ure Fig. . This ty21 values are good indicators of the disease phenotype. These centrality measures quantify respectively the number of connections a node has, and how central or isolated a node is in the RIN  are related to more severe phenotypes. For instance, among the top 1% most connected amino acids in the RIN network, 34 out of 44 have reported mutations associated with HA (77%); on the other end, among the 1% least connected amino acids, only 21 out of 74 have cases reported (28%).24. On the other hand, substitutions of amino acids that are peripheral in the residue network tend to cause mild or moderate symptoms. The mutations Ala394Ser, Thr454Ile, and Val578Ala in the A2 domain result in mild HA phenotypes26. A comprehensive characterization of the FVIII RIN is presented in a separate study .Residues Phe447 and Lys444 are among the most central residues in the FVIII RIN, and upon mutation to Ser and Arg/Thr/Asn, respectively, a severe form of HA ensuesTaken together, our results indicate that in addition to the properties inferred directly from the protein structure, properties derived from the RIN offer a distinct perspective on the importance of the residues in FVIII and their relation to HA phenotypes.27). Therefore, we studied not only protein properties in isolation but how they jointly determine the severity of HA.Although the study of individual characteristics of the FVIII protein helps to understand the disease severity caused by mutations, these properties are all interrelated  , and a cross-validation procedure (“Methods”), we compared six classification algorithms with and without data augmentationNext, we posed the first challenge to all classifiers: to predict experimental mutations on the FVIII protein that were not used during the training phase.31. The A2 domain is important for the FVIII activity because in addition to its role in stabilizing the protein structure32, it contains a binding site for FIXa (ref. 33). In a similar fashion, the C2 domain is critical because it contains hydrophobic residues involved in interactions both with the von Willebrand factor as well as with the phosphatidyl-l-serine containing membrane34. Importantly, due to the amino acid composition of these domains, the binding sites to coagulation factors also form epitopes targeted by the different inhibitor antibodies developed by patients during treatment36.We used an in vitro mutagenesis screening where almost all of the residues of the A2 and the C2 domains were mutated to alanine31.In those alanine mutation assays, 344 mutant FVIII constructs were expressed in COS-1 cells, and the products were subject to a chromogenic assay to measure their level of thrombin production. The ELISA assay was used to assess the efficiency of FVIII mutant constructs expression and secretion  immobilized on the ELISA plate. Two commercial kits were used to apply the “sandwich” method, where the antigen (FVIII construct) is captured between two layers of antibodiesWe used the 6 algorithms to predict the effects of the alanine mutations on each residue and found that two of the algorithms successfully identified mutants that did not impair the production of thrombin and its secretion (>50% wild type) Fig. . In mostHema-Class.Interestingly, we verified that the two classifiers that performed better in this challenge outputted different Severity Scores for the same instances Fig. . Thus, w38, ensembles of classifiers closely approximate predictions to the real values of instances and avoid the biases that all algorithms inevitably have.We found that Hema-Class predictions had less variance than either algorithm alone Fig. c, d. As Along these lines, one aspect that we did not address in the present study are the so-called cross-reactive material (CRM+) mutations, where the mutant protein exhibits relatively high antigen binding, but low chromogenic activity . As more data becomes available, Hema-Class can be retrained using CRM+ and CRM− input sets separately.Nevertheless, our results indicate that even with limited input data—as is often the case for rare diseases—it was possible to build a ML framework that had a close agreement with in vitro and clinical data. Finally, Hema-Class succeed in one of the critical tasks of ML, namely, to be trained using one type of input data , and to predict another (in vitro mutagenesis).With the confidence that Hema-Class effectively generalized to unseen FVIII mutations, we decided to make predictions about the HA severity that ensues upon mutating all FVIII positions to all 19 remaining amino acids. The most up-to-date database contains point mutations in only ~730 positions ; therefore, predictions of unseen amino acid substitutions are relevant to understand which regions and mutations are detrimental to the FVIII activity.We created a dataset with mutations in all of the wild-type FVIII positions to each of the remaining 19 amino acids. In total, this dataset had more than 25,000 instances—more than 50 times the size of our training set. Different from our previous analyses, the values of the properties derived from the 3D structure remained constant for the same amino acid, and only the distance between the wild-type residue and the new amino acid varied. For example, the instances with mutations Leu26Arg and Leu26Pro have the same surface exposure values, degree, and Burt’s constraint (of the leucine at position 26), the only difference between them is the distance between arginine and leucine, and proline to leucine (we describe the distance measure in the Methods section); this setting , is a major challenge for ML classifiers because it tests the sensibility of the classifiers to small changes in a single variable.We used Hema-Class to predict the Severity Score of 530 point-mutations described in the literature but previously unused in our study , lead to disruption of the protein structure due to side-chain clashes and separation of hydrogen and non-covalent bonds27. In the protein world conformation equals function, and it is a direct consequence that substitutions at these sites impair the FVIII activity.Next, we created a representation of the FVIII protein structure (RIN), and with this convenient and intuitive representation we visualized and quantified the “centrality” of all residues in the structure; we present a complete characterization of this network in a separate study . We verified that substitutions to the critical residues of the FVIII proteins  caused detrimental effects on the FVIII function Fig. . In geneWith the structural properties collected and evaluated, the next step was to use them in conjunction to make predictions about HA severity—and for this purpose, we built a small yet highly informative input dataset for our ML framework (the Hema-Class).31. These encouraging results gave us confidence that Hema-Class captured FVIII properties that were previously only observable using in vitro assays.After proper training and validation, we used Hema-Class to predict the severity of 344 alanine mutations in the A2 and C2 domains. We verified a close agreement between the in silico and in vitro results, as evidenced by the fact that the most dramatic reductions in the chromogenic and secretion activities of the FVIII protein were accompanied by high Severity Scores . Additionally, due to technical constraints at the time, the 2R7E structure13 was determined at a low-resolution (3.7 Å) and have regions that were not modeled or contain unfavorable torsion angles , and contacting the authors is infeasible, given that some studies were published decades ago. In addition, there are different methods to measure the FVIII co-factor activity , and although their results agree in most casesNevertheless, the Hemophilia community is actively working on these issues, and even with these adversities the Hema-Class framework uncovered meaningful patterns from clinical and molecular data; importantly, we designed the Hema-Class architecture in a way that it can be easily retrained as new data becomes available (see “Data availability”).In summary, we have established a ML framework to study and anticipate the severity of HA-based on the characteristics of the FVIII protein structure. The incremental difficulty of the challenges presented to Hema-Class posed a formidable challenge to its capacity and gave us the confidence in its predictions. We believe that the challenge of designing a recombinant FVIII with a longer half-life and better immunogenic profile remains; however, being able to mechanistically study mutations in silico brings us a step closer to this goal.13). We extracted the structural properties from this structure using Chimera version 1.14 (ref. 44). The measures obtained using Chimera were areaSAS, areaSES, kdHydrophobicity, PSI, PHI, and the bFactor (Supplementary Table We used the FVIII protein structure deposited in the PDB with the accession 2R7E ref. 3. We ext45) with the default parameters. We considered that two residues interacted if there was at least one edge between them, independently of the edge type. To analyze the RIN, we used R version 3.6.3 (https://www.R-project.org/) and the iGraph package, version 1.2.5 (ref. 46). With the iGraph package, we used the function simplify to remove redundant edges and self-interactions. Next, we calculated the degree, betweenness, closeness, Burt’s constraint21, Authority Score, Page Rank-like, KCore, and the Authority Score measures.Next, we transformed this protein in an undirected, unweighted graph using RINerator version 0.5.1 , and “Missense” (on variant effect) on the advanced search. It returned a total of 6051 rows. Next, we removed mutations on the signal peptide regions, or outside the mature form of the protein, as well as instances with 1-st/2-st FVIII:C > 100. We also removed non-numerical values on the FVIII:C column, substituted the values >5 for 5, <10 for 10, <11 for 11, “<1” or “<1” for “0”. We also removed instances with FVIII:C values that would lead to ambiguous diagnostics .We manually queried the EAHAD databaseWe substituted FVIII:C that contained ranges  to the average value . We removed instances without FVIII:C and without inhibitor information, and instances with discrepancies between “FVIII:C% (presumed 1-st)*” and “FVIII:C% (2-st/Chr)”, and one mutation encoding a stop-codon.Finally, we removed instances with ambiguously reported classifications .https://www.cdc.gov/ncbddd/hemophilia/champs.html), and considered only non-synonymous substitutions at single nucleotides, removed instances with ambiguously reported severities , removed duplicated instances as well as mutations that would lead to stop codons, as well as mutations outside the coding regions. After this data sanitations step, ~1000 unique reported mutations were left.For the ML validation step, we downloaded the CHAMP FVIII database (50) to obtain 544 numerical properties for each amino acid. Next, we used the package AMAP (ref. 51) to perform a principal component analysis of this set, and reduced the number of properties to 19 components, while retaining 99% of the information in the dataset. We calculated the Euclidean distance between all amino acids, considering all 19 component values. This gave us a 20 × 20 matrix which was the distance index used in our analyses (Supplementary Table We used the R package seqinR (ref. AP ref. 5 to perfof:χ→Ƴ of examples xi in some input space χ to class labels yi in the output space Ƴ (ref. 52). In the context of this work, input examples are composed of protein features and class labels are the HA severities. Here we describe the mapping strategy adopted by the ML algorithms that we used.Supervised learning is a subarea of ML focused on producing the best possible mapping (model) n = 152) and “Mild/Moderate” (n = 263). The Adaptive Synthetic Sampling Approach for Imbalanced Learning (ADASYN)29 was used to balance the dataset by employing a weighted distribution for different minority class examples according to their level of difficulty in learning. ADASYN yields more synthetic data for the minority class examples that were harder to be predicted, thus improving the learning process by reducing the bias introduced by the class imbalance, and adaptively shifting the classification decision boundary towards the more complex examples. After using ADASYN, our augmented dataset contained 296 examples of the Severe and 263 of Mild/Moderate classes.The experimental setup designed to create the Hema-Class followed the following steps: preprocessing, training, and testing. Besides the preprocessing tasks already mentioned in the Results section, we also normalized all attributes to make sure our framework is not biased by data scales. We also removed all examples where values in at least one attribute was missing. We employed the tenfold cross-validation method to reduce the chances of estimating overfitted models, and to ensure that the same sets of examples were considered by the different ML algorithms. This enabled a fair training and testing for all algorithms. Finally, before proceeding with model training, we used a strategy of data augmentation to balance the dataset classes, given it was composed of examples of the classes “Severe”  in a terminal node (leaf) was searched in the interval . Finally, the complexity parameter (cp) lied in range .28 was trained varying three parameters: a number of trees (ntree) in the interval ; the number of variables randomly sampled as candidates at each split (mtry) in the interval ; and minimum size of terminal nodes or leaves (nodesize) between .The Random Forest (RF)yω'x+c)d, given ω,x is two position vectors representing examples. For the radial kernel, we analyzed the following parameters y = {0.01, 0.02, …, 1.5}, while the polynomial kernel was assessed using c = {0.1, 0.15, …, 2}, c = {0.1, 0.15, …, 2}, d = {2, 3, …, 5}.The Support Vector Machine was assessed using the two best kernels according to a first empirical set of experiments: radial The model obtained with Naïve Bayes has no parameter estimation.53 was estimated by running a grid search on the following parameters: maximum depth of a tree in {1, …, 25}, y is the L2 regularization (Ridge Regression) term on weights in the range  to define the number of samples taken into consideration, ɳ∈ defining the learning rate by scaling the contribution of each tree, and obj is the loss function.Finally, the XGBoost method54.The best models obtained during the training phase with the tenfold cross-validation strategy were chosen by their relative performances in terms of the Kappa index and the Area Under the ROC Curve. The Kappa index measures the agreement between the predicted and expected values, thus emphasizing that the results were not obtained by chance. This coefficient subtracts the expected from the observed agreement to quantify the probability of correct classifications by chanceFurther information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Reporting Summary


We present NMR-based studies showing the stability of proteins lacking a hydrophobic core which in turn present hydrophobic surface clusters, such as plant defensins. Protein dynamics measurements by NMR are the key feature to understand these dynamic surface clusters. We contextualize the measurement of protein dynamics by nuclear relaxation and the information available at protein surfaces and water cavities. We also discuss the presence of hydrophobic surface clusters in multidomain proteins and their participation in transient interactions which may regulate the function of these proteins. In the end, we discuss how surface interaction regulates the reactivity of certain protein post-translational modifications, such as S-nitrosation.In this review, we briefly describe a theoretical discussion of protein folding, presenting the relative contribution of the hydrophobic effect versus the stabilization of proteins  The question of what are the major forces that stabilize the protein and control the folding pathway arose as fundamental. The balance between hydrophobicity and hydrophilicity is crucial for protein folding and also for its structural property. This balance is known to be essential for the folding of globular proteins. While in intrinsically disordered proteins (IDPs) the balance is shifted toward the hydrophilicity, displaying an excess of hydrophilic residues, for globular proteins, there is a hydrophobic collapse mediated by a solvent entropic effect. The entropy increases significantly when a hydrophobic solute is transferred from an aqueous solution to a nonpolar environment, such as the protein core. The explanation  protein dynamics in water cavities, ii) the structure and dynamics of core-less proteins, such as plant defensins and toxins, iii) the importance of surface forces in transient interactions in the modulation of multi-domain proteins dynamics and iv) how these forces may modulate reactivity at the surface.One of the methods to study protein solvation is x-ray crystallography, where immobile and symmetric crystal waters can be directly observed in the electron density map. This is important to give information on tightly bound waters, which frequently are involved in catalysis and biological function.−8 to 10−2 s, while surface water molecules are in the sub nanoseconds timescale. For this, NMR is a powerful technique to obtain information on the location and the residence time of individual hydration water molecules, being able to distinguish between tightly bound and unbound waters from the solvation layer. As mentioned before, it is an important mechanism involved in protein stability.The Nuclear Magnetic Resonance (NMR) spectroscopy can also be used, allowing the measurements of protein hydration in solution. The residence time obtained for the interior water molecules is in the range of 10The method is based on the dipolar coupling between the hydrogen nuclei spin of water and hydrogens of the protein. This is achieved by measuring the nuclear Overhauser effect at the laboratory frame (NOE) and the rotating frame (ROE). Otting and collaborators  have shoIt was verified later that non-local water molecules in fast exchange with the interacting water molecule also contribute to the negative ratio . TherefoThe measurement of protein hydration is not an easy task, once the quantity of water molecules in the solvent is extensive, and because of its fast motion and exchange. NMR experiments may provide the residence time and locality of water interactions through dipolar magnetization exchange of the hydrogens from protein-water interactions. By comparing the nuclear Overhauser effect at the NOE and ROE it can be verified the contributions of dipolar and chemical exchanges . Howevervia NMR experiments, Wang has proposed the idea of protein encapsulation through a reverse micelle. It provides a reduction of water molecules in the system, which consequently leads to a decrease in hydrogens exchange and the non-locality artifact. It is also possible to regulate the protein tumbling time using the solvent solution with low viscosity to improve the relaxation parameter and the signal/noise ratio.To minimize the difficulty in measure protein hydration by NOE and ROE 15N, 2H-ubiquitin encapsulated in reverse micelles in propane solution. It is remarkable the difference between the two spectra, ubiquitin in aqueous solution and the reverse micelle. The non-local effect and the long-range coupling to water were not present in the spectra, but just NOEs and ROEs with NOE distance from the surface were in the 1H planes of 13C-resolved NOESY and ROESY, which validating the method. The molecules of water in the protein interior have a residence time from about 10−8 to 10−2 s while the water molecules from the protein surface hydration in solution present residence time of sub-nanosecond . The ratio near -0.5 refers to protein-water interaction through the rotational correlation time and the ratio of 0 to interactions with times smaller than the rotational correlation time of the protein.Wang et al. used nosecond . By the The NOE and ROE measurements showed to be a powerful method to verify protein hydration through NMR. Protein hydration has been studied because of its role in macromolecule functions, as in the case of ubiquitin which is an important protein involved in interactions that regulated the protein degradation inside the cell. It has also been reported the involvement of water molecules in protein-protein interactions and the association of protein subunits.αβ fold (CSαβ) (There are a vast number of proteins that are stabilized by an extensive number of disulfide bridges. Plant defensins are a good example of these proteins and they share the same cysteine stabilized d (CSαβ) . Their pd (CSαβ) . Similard (CSαβ) . Differeβ1/α1 loop  used in the structure calculation, points toward the formation of surface clusters. Interestingly, the exposed hydrophobic residues form dynamic hydrophobic/hydrophilic surface clusters containing long and linear extended polar side chains, such as arginine, lysine, and glutamate. Prolines are also present in such clusters, conferring some hydrophilic/hydrophobic balance to the surface-clusters. These hydrophobic surface clusters showed in For plant defensins, long polar side chains interact with the surface hydrophobic amino acid side chains, minimizing their exposure to the solvent. It protects the hydrophobic amino acids from complete exposure to the solvent, making cysteine stabilized folds water-soluble and monomeric.15N relaxation parameters  enables the mapping of the residues in thermal conformational flexibility  and in conformational exchange (micro- to millisecond dynamics) at the core-less proteins, leading to the description of the backbone dynamics. The measurement of CPMG relaxation dispersion profiles in several temperatures enables the quantification of the micro- to millisecond conformational equilibrium and to understand the structural property of the first thermally accessible high energy conformational state. For Sd5  has been increasing. Due to the intrinsic hydrophilicity, IDPs and IDRs are highly hydrated, but they do not behave as a random coil. They are intrinsically disordered, but the remaining order is important for the protein functions, especially when acting as entropic linkers in multi-domain proteins . The IDP. Once formed, NO reacts with the cysteine side chain as well as peptides and proteins that possess cysteine residues in their sequence, forming a posttranslational modification named S-nitrosation. A range of diseases including Parkinson’s, Alzheimer’s, heart failure, arrhythmia, diabetes (type I and type II), asthma, and cancer correlates with dysregulated S-nitrosation of proteins (Nitric oxide (NO) is a free radical known as an important second messenger for signal transduction in cells. In mammals, three nitric oxide synthase isoforms are responsible for NO synthesis: neuronal (nNOS), inducible (iNOS), and endothelial (eNOS) . Once foproteins . Due to proteins . Than, wproteins .trans-S-nitrosation. The pathway of trans-S-nitrosation consists of a nucleophilic attack of a thiolate or thiol at the nitrogen atom from RSNO. Despite the recognized biological importance of RSNOs, the reactivity of these species deserves more investigation to understand how or what exactly controls it in vivo. Besides the mechanism of trans-S-nitrosation, RSNOs can perform an alternative mechanism of S-thiolation, which consists of a nucleophilic attack of the thiol at the S atom from the–SNO group. That second possible reaction leads to a disulfide formation and HNO release, an nitrogen species with great pharmacological potential , the products of S-nitrosation, play an important role due to their capacity to store, transport, and transfer NO to different targets, a biological event known as otential .trans-S-nitrosation and S-thiolation are possible due to RSNOs distinct electronic structure. −/NO+ ion pair (I) (The mechanisms of pair (I) .in vivo, the reactivity control of those biological RSNOs is not well understood. To investigate this phenomenon, Talipov et al. have used a range of models to computationally demonstrate that specific interactions of RSNOs with charged and polar residues in proteins can result in significant modification of RSNO characteristics, including their reactivity (Despite the great number of S-nitrosated proteins identified activity . These ractivity .The reactivity of cysteine residues to S-nitrosation is dependent on the protonation state of the thiol group. Thiolates are nucleophiles prompt to attack the RSNOs. When the nucleophilic attack is at the sulfur the product is a disulfide (thiolate reaction), conversely, when the attack is at the nitrogen the reaction is S-trans-nitrosation . The conHydrophobic collapse is considered the dominant driving force in protein folding in globular proteins. There is an increasing investigation on the contribution of surface direct forces to protein stability and folding. The balance between hydrophobicity and hydrophilicity is crucial for protein folding and also for its structural properties. There is no clear answer if the direct surface forces are underestimated. We discussed experimental results on the structure and dynamics of core-less proteins. They lack a hydrophobic core and are stabilized by disulfide bonds and the contacts between surface-exposed hydrophobic and hydrophilic residues. These proteins have many hydrophobic residues exposed to the solvent and yet, they are water-soluble and monomeric. They form locally stabilized hydrophobic surface clusters, in which the hydrophobic side chain is protected by the long linear side chains of the hydrophilic residues. Possibly, the hydration contributes to the cluster stabilization, by forming bridges between hydrophilic and hydrophobic side chains, as illustrated in Surface clusters are also present in globular proteins and may have an important role in regulating transient interaction in multi-domain proteins. CSP analysis through NMR revealed a hydrophobic surface patch in the co-chaperone Hsp40, which modulates inter-domain dynamics, regulates internal transient interaction and the interaction with Hsp70, being pivotal for the protein function.We also discussed the importance of the protein surface properties to modulate the reactivity of cysteines to the post-translational modification mediated by nitric oxide, forming S-nitrosated species. The methods revised here may be of extreme importance to fully understand the surface effect.


Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous state of the art high‐throughput protein complex resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 253 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface ( Integration of orthogonal high‐throughput mass spectrometry datasets using a machine learning framework reveals nearly 7,000 human protein assemblies and offers insights into novel protein function. Additionally, we see a drop in performance when we remove the WMM features from hu.MAP 2.0 further showing how important the WMM features are to performance improvements. Finally, we compare hu.MAP 2.0 to the HuRi dataset which encompasses a 17,500 × 17,500 all‐by‐all yeast 2‐hybrid screen  indicating a high degree of consistency between the orthogonal datasets.Recently, a co‐regulation map based on protein expression was shown to capture relationships among proteins that do not necessarily interact or co‐localize  among other metabolism‐related annotations , establish the utility of hu.MAP 2.0 for furthering our understanding of human protein functions.Herein, we describe the construction of the most accurate and comprehensive protein complex map to date which fills a large gap in our knowledge regarding the composition of functional complexes in the cell. We demonstrate the utility of our map by assigning functions for hundreds of completely uncharacterized proteins, providing testable hypotheses for their characterization. Additionally, we determine the prevalence of proteins that participate in multiple independent protein assemblies including ones with disparate functions suggesting moonlighting functions for the protein. Overall, our results, searchable with a simple web interface /(TP + TN + FP + FN) where TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives. Using accuracy as an evaluation metric is problematic in cases of large class imbalance such as protein interaction prediction. Specifically, there are orders of magnitude more negative interactions than positive interactions, and therefore, accuracy can be dominated by the true negatives resulting in a poor predictor of new interactions. In the hu.MAP 2.0 pipeline, we therefore remedied this by developing our own cross‐validation utility  which uses area under the precision‐recall curve (PR‐AUC) as a metric to identify optimal C and gamma SVM parameters. PR‐AUC does not rely on true negatives for its calculation and is preferred for evaluation of problems with large class imbalance   and individual terms based on the spoke model for where protein i is the bait (Sij) and where protein j is the bait (Sji). The matrix model term for the socioaffinity index was also used (Mij). Mass spectrometry data from  and the total number of experiments the pair of proteins is observed together .Our implementation of the WMM is based on presence or absence of proteins in individual experiments . Due to the nature of high‐throughput experiments, noise arises in the form of spurious identifications leading to a protein being erroneously called present in the experiment. To deal with this noise, we set arbitrary but sensible cutoffs of the quality of identification required for a protein to be considered present in the experiment. WMM for Bioplex2.0 was calculated only considering experiments for a given protein where the protein had > 2.0 Bioplex2.0 All features, precalculated from original publications and WMM, were combined into 17,564,755 protein pairs × 292 features matrix. Since not all features cover all protein pairs, missing values were filled with 0.0. The final feature matrix can be found in the et al,http://mips.helmholtz‐muenchen.de/corum/download/corum_2017_07_02.zip) and filtered out all non‐human proteins. Complexes were merged to eliminate redundancy, so no two complexes had > 0.6 Jaccard coefficient. Complexes were then randomly split into test and training sets. A complex was removed from the test or training sets if any pairs of proteins overlapped in the other set. Large complexes > 30 subunits were removed from the test and training complexes. Test and training sets were also generated for pairs of proteins for training the SVM classifier. A pair of proteins was labeled “positive” if both proteins were in the same complex. A pair was labeled “negative” if proteins were in separate complexes. All other pairs were left unlabeled. For test and training pairs, only 10% of pairs from large complexes were considered. Below is the command line used to generate the test and training sets:To create a test and training set of literature‐curated protein complexes, we downloaded the complete set of CORUM complexes  and gamma values . As an evaluation metric, we used Area Under the Precision‐Recall Curve (AUPRC) averaging across the five cross‐validation sets. We identified C = 512 and gamma = 001953125 with the highest cross‐validated AUPRC. We retrained a full model using all training data using these parameters. We used this final model to predict on all pairs in the feature matrix. The final result is a list of pairs with a corresponding score generated by model.We trained a SVM classifier using Libsvm , and weighted precision (P_weighted), which we described previously  to size n . A precision and recall value are calculated for all clique sizes. A weighted average is then calculated for both precision and recall across all clique sizes, weighted by the number of clusters with size >= to the clique size. This is to limit the bias effect that larger clusters will have on the final precision or recall value.To evaluate the clusterings as shown in Fig k‐cliques method, comparing to the training set of gold standard complexes. We selected five clusterings that optimize the trade‐off between precision and recall as shown in Fig We evaluated all clusterings using the k‐clique method by comparing to the leave‐out set of gold standard test complexes . The ‘subcomplex index’ is related to the Jaccard index but is normalized by the size of a single complex rather than the size of the union of both complexes as is done in the Jaccard index. We then calculated the subcomplex index for every complex compared to all other complexes. We then generated a set of complexes with limited overlap by selecting complexes that had SC < 0.5 to all other complexes. We then identified all proteins that participated in multiple complexes from this reduced set of complexes.To identify proteins which participate in multiple complexes, we first determined a set of complexes with limited overlap. To determine the degree with which one complex overlaps another, we developed a ‘subcomplex index’ (equation (2)) defined as:et al,Z‐scores for each age group were determined by comparing the number of promiscuous proteins to a background distribution. The background distribution was calculated by counting the number of randomly sampled non‐promiscuous proteins  in each age group.Protein ages were mapped using ‘modeAge’ in the main_HUMAN.csv file from  terms using gProfiler  (Uhlén Comparison of overall expression levels between promiscuous and non‐promiscuous proteins shown in Fig KD, JBW, and EMM conceived of the study. KD designed all experiments and analyzed data. KD wrote software including software pipeline and web resources. KD, JBW, and EMM discussed and interpreted the results, and wrote the manuscript.The authors declare that they have no conflict of interest.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Review Process FileClick here for additional data file.