Terminal residues of protein chains are charged and more flexible than other residues since they are constrained only on one side. Do they play a particular role in protein-protein and protein-DNA interfaces? To answer this question, we considered large sets of non-redundant protein-protein and protein-DNA complexes and analyzed the status of terminal residues and their involvement in interfaces. In protein-protein complexes, we found that more than half of terminal residues (62%) are either modified by attachment of a tag peptide (10%) or have missing coordinates in the analyzed structures (52%). Terminal residues are almost exclusively located at the surface of proteins (94%). Contrary to charged residues, they are not over or under-represented in protein-protein interfaces, but strongly prefer the peripheral region of interfaces when present at the interface . The almost exclusive location of terminal residues at the surface of the proteins or in the rim regions of interfaces explains that experimental methods relying on tail hybridization can be successfully applied without disrupting the complexes under study. Concerning conformational rearrangement in protein-protein complexes, despite their expected flexibility, terminal residues adopt similar locations between the free and bound forms of the docking benchmark. In protein-DNA complexes, N-terminal residues are twice more frequent than C-terminal residues at interfaces. Both N-terminal and C-terminal residues are under-represented in interfaces, in contrast to positively charged residues, which are strongly favored. When located in protein-DNA interfaces, terminal residues prefer the periphery. N-terminal and C-terminal residues thus have particular properties with regard to interfaces, which cannot be reduced to their charged nature. Interactions between proteins or proteins and DNA are a generic process underlying many biological processes. Thanks to the growing number of complexes available in the Protein Data Bank (PDB ), generivia a two-step mechanism  have their terminal secondary structure elements in contact [In this anatomic description of protein binding sites, nothing has been said concerning the terminal residues of protein chains, which are often hybridized in biochemical techniques aimed at detecting protein-protein interactions. Being the first and last residues of the chain, terminal residues have peculiar properties: they are charged and presumably highly flexible, because they are connected to the rest of the peptidic chain only on one side. These properties have been studied in the context of isolated proteins. It has been shown that terminal residues display some sequence and conformation preferences , and res contact . This bi contact . It also contact ,17. A su contact . It thusIn protein-DNA complexes, protein tails are thought to play a major role in the recognition  and, in In this paper we specifically study terminal residues in the context of protein-protein and protein-DNA complexes. On a large non-redundant set of dimeric protein-protein interfaces, we quantify their implication in interfaces and its significance compared to random, in different classes of complexes. We also assess whether terminal residues are displaced upon interaction, compared to their location is isolated structures. This is done on a set of protein-protein complexes for which the structures of isolated monomers are available. We then investigate the link between implication at the interface and biochemical hybridization. Finally, we analyze the implication of terminal residues in protein-DNA complexes.We took into account the presence and integrity of terminal residues in structures, and computed their frequency at protein-protein interfaces and specific regions of interfaces, in a large dataset of 17,658 binary complexes, non-redundant at the interface level, termed DIMER70, and a filtered dataset of 5,203 proteins, non-redundant at the monomer level, termed MONOMER25. We analyzed their conformational rearrangement upon complexation using the Docking Benchmark , the corIn order to study the location of terminal residues in protein-protein complexes, we have to take into account the presence of expression tags in the resolved structures, and missing coordinates. Both factors could interfere with our analyses: expression tags add artefactual residues to native protein tails, and missing coordinates prevent any structural analysis.Terminal residues were thus classified into three categories: modified, missing or genuine, as illustrated in The classification of all the terminal residues in the DIMER70 dataset is available in In order to study terminal residues in their native structural context, we restrict the rest of our analyses to the genuine class .We computed the relative accessible surface area of terminal residues in the context of isolated monomers. Using a 25% of relative accessible area cutoff as in , we founversus K-mers. This is the direct consequence of a size effect, as explained in the next section.We classified residues into different regions  according to their exposed surface area, in isolated monomers and in complexes, as described in the Materials and Methods section. The amino-acid compositions in different regions are shown in Figure C in C-terminal residues are slightly more frequent than N-terminal residues in homo-interfaces, with the exception of non-obligate K-mers. In hetero-interfaces the situation is reversed: the rate is higher for N-terminal residues, again with the exception of non-obligate K-mers. In the next paragraph, we assess the significance of these frequencies, compared to random expectations due to protein size.In this section, we assess whether the rate of terminal residues in interfaces is different from what is expected by chance. For this, we simulate 1000 data sets with a random model based on hypergeometric laws for each protein, as explained in Materials and Methods. This model takes into account the size of each protein, and the fact that different categories of complexes display different size distributions , and hence, a higher chance to involve a terminal residue at the interface solely by chance. On the contrary, bigger proteins have smaller (relative) interfaces, and hence, lower probability to have terminal residues involved at the interface.How does this contrast with the behavior of charged residues? To answer this question, we computed the fraction of charged residues in interfaces (without consideration of their location in the protein chain) and simulated data sets using the same protocol based on hypergeometric laws see . Lysine,In conclusion, terminal residues are not significantly preferred or avoided at protein-protein interfaces, unlike charged residues.−3), indicating that terminal residues have a preference for rim regions. This preference is confirmed on the MONOMER25 dataset . This confirms a previous observation that tails tend to be close to each other [We found that the co-occurrence of terminal residues from a same chain is favored , although this preference is modest  or fusion domains (such as two-hybrid), and those detected using other methods. We thus had to map from InterEvol chains, through PDB chains, to Uniprot chains. We then searched IntAct for the experimental methods used to detect the interactions. In order to get meaningful results, we restricted our analysis to biological interfaces and genuine residues. Out of the 7079 complexes considered, IntAct provides information for 849 complexes (12%), with a better coverage for hetero-complexes  than for homo-complexes .Hybridization-based techniques require the modification of terminal residues by attachment of a peptide or a protein domain. We hypothesized that this modification could perturb the formation of a complex when terminal residues are in interfaces. Such binary interactions could be more difficult to detect by hybridization-based techniques. In this case, we would expect fewer interactions detected by hybridization-based techniques when terminal residues are in interfaces. Contrary to our expectation, we did not find any such bias . Dividing the data set between homo-complexes and hetero-complexes yielded the same result. Let us note that all complexes with a terminal residue in the interface are treated in the same way, since the side on which the hybridization takes place is not reported in IntAct. This factor could weaken the signal, if any. However, we observe here no signal at all. We postulate that this is due to the particular location of terminal residues in interfaces. As we have shown in this study, terminal residues, when involved in interfaces, are preferentially located in the rim. This preferred location presumably could allow the hybridization of tag peptides or fusion domains without disrupting the complex.versus 6% of genuine N-terminal and 3% of the genuine C-terminal in the other DNA-binding proteins. Thus, N-terminal residues seem more frequent in protein-DNA interfaces than C-terminal ones, both in transcription factors and DNA-binding proteins with other functions. In these data sets, no filtering was carried out to reduce the redundancy between protein sequences.We analyzed the location of terminal residues in two datasets prepared by us: 156 transcription factors complexed with DNA and 265 DNA-binding proteins with other functions. We found that 16% of the genuine N-terminal and 4% of the genuine C-terminal residues were involved in protein-DNA interfaces in transcription factors, In order to get statistics on non-redundant data, we analyzed the location of terminal residues in a dataset prepared by  composedWe compared the trend seen for terminal residues to the trend of charged residues, see In conclusion, our analysis shows that more than half of the terminal residues in protein-protein complex structures from the PDB are either modified or missing. It is thus mandatory to take it into account to perform a fair analysis of terminal residues. Terminal residues do not behave strictly as charged residues. In protein-protein interfaces, they are globally not preferred or avoided in interfaces, while charged residues are clearly under-represented  or over-represented (aspartate). When involved in interfaces, terminal residues prefer the rim region, but their overwhelming presence in the rim cannot be explained only by their charged nature. The particular location of terminal residues in the rim explains why high-throughput techniques that rely on tail hybridization can be successfully applied without abrogating the interaction under study.The preference of terminal residues for surface has been explained by an advantage in folding and stability , suggestIn protein-DNA complexes, we also observed a contrast between terminal residues and charged residues: terminal residues are all under-represented in protein-DNA interfaces, while charged residues are clearly discriminated according to their charge (over-representation of lysine and arginine and under-representation of aspartate and glutamate). When involved in interfaces, terminal residues also clearly prefer the rim region. Terminal residues thus have specific properties with regard to protein-protein and protein-DNA interfaces that cannot be reduced only to their charge.http://biodev.cea.fr/interevol/downloads.html). This list is non-redundant at the dimer level, at the 70% threshold. We termed this list DIMER70. In this list, interfaces are already annotated in terms of:An initial list of 17,658 binary interfaces was retrieved from the InterEvol resource  ,biological interfaces or crystal contacts (predicted by NOXclass).We further made the distinction between interfaces extracted from complexes with two chains (dimers) and those extracted from higher order complexes (K-mers), from information available in the PDB.http://dunbrack.fccc.edu/PISCES.php) [We filtered the DIMER70 list in order to reduce the redundancy between monomers to 25%, using PISCES (CES.php)  with thehttps://zlab.umassmed.edu/benchmark/) [The docking benchmark version 5.0 was used (chmark/) . Complexchmark/)  in the cProtein-DNA complex structures solved by X-ray crystallography at a resolution better than 2.60 Å and a maximum R free value of 0.4. were obtained from the PDB. The complexes were defined as any structures containing at least one protein chain, and a double-stranded DNA longer than six base pairs. All entries containing significantly modified DNA were discarded. The list was then split between 156 transcription factors and 265 enzymes.Another list of protein-DNA complexes with various functions were retrieved from , and filhttp://bioinf.org.uk/pdbsws/) [We retrieved interaction data from the IntAct resource . The xmlpdbsws/) .Missing coordinates and Tag residues were identified thanks to the mmCIF files from the PDB .The software NACCESS  was usedFollowing the definition of Levy , residue○Surface residues (RASAm>25%)○Interior residues (RASAm <25%)Non-interface residues (RASAm = RASAc):○Rim (RASAc>25%)○Core (RASAc <25% and RASAm>25%)○Support (RASAc <25% and RASAm <25%)Interface residues (RASAm> RASAc):This cutoff of 25% allows an optimal separation between surface and interior amino-acids, in terms of composition .All statistical analyses are made using the R statistical environment .We considered a random model based on the hypergeometric law to assess the significance of the involvement of terminal residues at the interfaces and rim regions.A hypergeometric law H  describes the number of successes in a trial experiment, where one draws, without replacement, k individuals from a population of size N containing K successes. We used this law to simulate the presence of terminal residues at the interfaces and the rim regions of interfaces. When simulating the presence of a terminal residue at the protein-protein interface, we simulated hypergeometric variables for each protein with parameters set to k = number of interface residues (only rim and core residues), K = 1 , and N = number of surface residues when the monomer is isolated.To simulate the appearance of a terminal residue at the rim of an interface, we used parameters k = 1, K = number of residues in the rim regions, and N = number of residues at the interface (only rim and core residues).The reason for excluding support and interior residues is that we have found that almost all terminal residues are exposed to the solvent when monomers are considered alone.Thus, it was possible, for each protein, to simulate the appearance of terminal residues in interfaces, and, for those proteins with terminal residues in interfaces, their appearance in the rim region. We simulated separate distributions for N-terminal and C-terminal residues, because their statistics are based on different protein subsets .The same protocol was used to study the preference of charged residues, without consideration of their location in the chains . To be consistent with the terminal residue analysis, buried residues (support and interior regions) were excluded from the analysis. For example, when simulating the frequency of arginine residues in interfaces, parameters were set to k = number of interface residues (core and rim), K = number of exposed arginine residues  and N = number of exposed residues, and for simulating the frequency in rim regions, k = number of rim residues, K = number of interface arginines (core and rim) and N = number of interface residues (core and rim).−4.The use of such random models to simulate data sets results in empirical p-values: when simulating 1000 values, an observed value outside of the simulated range corresponds to an empirical p-value lower than 10S1 FileFigures A to K and Tables A and B.(PDF)Click here for additional data file.S2 File(TXT)Click here for additional data file.


Recently, researchers have tried to integrate various dynamic information with static protein-protein interaction (PPI) networks to construct dynamic PPI networks. The shift from static PPI networks to dynamic PPI networks is essential to reveal the cellular function and organization. However, it is still impossible to construct an absolutely reliable dynamic PPI networks due to the noise and incompletion of high-throughput experimental data.To deal with uncertain data, some uncertain graph models and theories have been proposed to analyze social networks, electrical networks and biological networks. In this paper, we construct the dynamic uncertain PPI networks to integrate the dynamic information of gene expression and the topology information of high-throughput PPI data. The dynamic uncertain PPI networks can not only provide the dynamic properties of PPI, which are neglected by static PPI networks, but also distinguish the reliability of each protein and PPI by the existence probability. Then, we use the uncertain model to identify dynamic protein complexes in the dynamic uncertain PPI networks.We use gene expression data and different high-throughput PPI data to construct three dynamic uncertain PPI networks. Our approach can achieve the state-of-the-art performance in all three dynamic uncertain PPI networks. The experimental results show that our approach can effectively deal with the uncertain data in dynamic uncertain PPI networks, and improve the performance for protein complex identification.The online version of this article (10.1186/s12864-017-4131-6) contains supplementary material, which is available to authorized users. Over the past decade, yeast two-hybrid, mass spectrometry and other high-throughput experimental have generated a mass of protein-protein interaction (PPI) data. Such PPI data construct the large-scale PPI networks for many organisms. Great efforts have been made to understand organizational principles underlying PPI networks. Many cellular principles have been uncovered by analysis of these networks, such as the scale-free topology , disassop-values for all pairs of proteins. Similarly, Wu et al.  is the function that assigns a probability of existence to each protein and PTiE: ETi →  is the function that assigns a probability of existence to each PPI at Ti time point.Definition 1 (Uncertain PPI network) An uncertain PPI network at DG = {UGT1, UGT2, …, UGTk}, is defined over a set of uncertain PPI networks. In Fig. UGT1, UGT2, UGT3}.Definition 2 (Dynamic uncertain PPI network) A DUPN G’j =  denote an instantiation of an uncertain PPI network UGTi = , where V’j ⊆ VTi and E’j ⊆ ETi∩(V’j × V’j). The instantiation is a deterministic network with an observing probability. We denote the relationship between G’j and UGTi as UGTi⟹ G’j. The probability of Pr(G’j) is given as follows:To deal with uncertain data, some uncertain graph models and theories , 23, 24 UGTi at Ti time point. Based on the Eq.  denote an uncertain PPI network at Ti time point. PGTi = {G’1, G’2,…, G’n} is a set of possible instantiations of UGTi, where G’j = . Pr(G’j) is the probability associated with instantiation G’j∈PGTi. Given a set of protein vertices in UGTi, VS ⊆ VTi, the expected density of VS is defined as follow:hj is the number of PPIs among the proteins of VS in the instantiation G’j.Definition 3 (Expected Density) Let UGTi =  denote an uncertain PPI network at Ti time point. PGTi = {G’1, G’2,…, G’n} is a set of possible instantiations of UGTi, where G’j = . Given a set of protein vertices VS ⊂ VTi, a protein vertex va∈VTi and va∉VS, the attached score between va and VS in the UGTi, is given as follows:Definition 4 (Attached Score) Let mj is the number of PPIs between va and VS in the instantiation G’j.where As the uncertain graph model, an uncertain PPI network can generate a large amount of different possible instantiation. According to the Eqs.  and 8),,8), the Thus, based on the uncertain graph model, we can use the Eqs.  and 10)10) to efSome studies has revealed the complex core-attachment organization . A proteUGTi∈DG in turn at line 1–31. The candidate complexes are added into Candidate_complex set. Then, CDUN removes the highly overlapped protein complexes from Candidate_complex at line 32–44, based on their ED value.CDUN algorithm consists of two phases. CDUN firstly detects candidate protein complexes from all UGTi based on Eq.  denotes the expected density of the edge between u and v. The edge will be added into Seed_set, if its expected density is not less than Core_thresh that is a predefined threshold parameter. The effect of Core_thresh is discussed in ED value of the core structure is not less than Core_thresh, CDUN will add the neighbor protein p into the core structure at line 25–28. We use the same parameter (Core_thresh) in lines 7 and 16 to keep the expected density of both the seeds and the core structures are not less than the Core_thresh. Finally, CDUN detects the attachment proteins for each core structure based on the AS score that is calculated by Eq. ) at line 35. The candidate complex with highest ED value in will be removed from Candidate_list and added into Complex_set. CDUN checks the overlapped degree between cci ∈Candidate_list and cc1. CDUN will removes cci from Candidate_list at line 39–42, if the overlapped degree is larger than the Overlap_thresh. In our experiments, we set the Overlap_thresh as 2/3. The above steps will be repeated until Candidate_list is empty and the final complex set Complex_set is generated.The candidate protein complexes in The PPI datasets used in our experiments are the DIP , MIPS 2 and STRIWe download the gene expression data GSE3431  from GenTo evaluate the protein complexes identified by our method, the gold standard data are CYC2008 . The CYCF-score. The other type are sensitivity (Sn), positive predictive value (PPV) and accuracy.Overall, most of the complexes identification methods use two type of evaluation metrics to evaluate the performance of complexes prediction . One typP be an identified complex and B be a known complex. The neighborhood affinity score NA between P and B is defined as follows:Let P is considered as matching the B if NA is larger than 0.2 [NA.In most studies of complex prediction, the than 0.2 . In our F-score are used to evaluate of our experimental results, which are defined as follows:Precision, recall and Nci and Ncb are the number of detected protein known complexes by our method, respectively. Identified_Set and Benchmark_Set denote the set of complexes identified by our method and gold standard dataset, respectively. In additional, we also report Sn, PPV and accuracy in our experiments. The definitions of Sn, PPV and accuracy are described in the study [where he study .Core_thresh on the performance of CDUN. The Core_thresh determines not only the number of the seeds in the Seed_set, but also the expected density of the core structures generated from the seeds.In this experiment, we evaluate the effect of the threshold parameter Core_thresh. Table Core_thresh ranged from 0 to 1. It can be seen that when Core_thresh takes from 0 to 1, the number of complexes identified by our method decreases constantly. When Core_thresh = 0, CDUN can identify 763 protein complexes on the DUPN_DIP. It indicates that too many seeds are generated due to the value Core_thresh is too small. When Core_thresh = 1.0, CDUN cannot identify any complexes on the DUPN_DIP. It indicates that no seeds can be generated due to the value Core_thresh is too large. Overall, with the increase of Core_thresh, the precision and PPV are increased, and the recall, Sn and Accuracy are. The F-score of CDUN ranges from 0.246 to 0.575. When Core_thresh is set as 0.4, the major metrics F-score achieves the highest value of 0.575.We use DUPN_DIP to evaluate the effect of Then, we compare CDUN with other complex identification methods: CSO , ClusterF-score of 0.575, 0.553 and 0.486, respectively, which significantly outperforms other methods. Both CSO and COAN exploit the GO data, which contain much valuable information related to protein complexes curated by experts. However, CDUN can achieve the highest F-score of 0.575 without integrating GO annotation data. HUNTER achieves the highest precision of 0.852. TransClust achieves the highest recall of 0.674, Sn of 0.622, PPV of 0.725 and accuracy of 0.672, respectively. But the precision of TransClust is only 0.13, which leads to a low F-score of 0.218.Firstly, we use DIP dataset to compare the performance of complex detection methods. From Table F-score of 0.377, which are superior to other methods. HUNTER achieves the highest precision of 0.538. TransClust achieves the highest recall of 0.623, Sn of 0.544, PPV of 0.71 and accuracy of 0.621, respectively.Secondly, we use MIPS dataset to compare these methods. On MIPS dataset, CDUN achieves the highest F-score of 0.537, respectively. COACH achieves the highest Sn of 0.963. TransClust achieve the highest PPV of 0.528 and accuracy of 0.667, respectively. Furthermore, it can be seen that the F-score of other compared methods is much lower on STRING dataset than other two datasets. For instance, Cluster ONE achieves a very low F-score of 0.188 on STRING dataset, which is much lower than on other datasets. This is mainly because the STRING PPI network is much more complex than the PPI networks constructed by other datasets. In addition, STRING dataset integrates PPIs not only from high-throughput experiments, but also from biomedical literatures, co-expression data, genomic context data. The multiple source data generally lead to more noise data in STRING dataset. These noise data also have impact on the performance of protein complex identification methods. Compared with other methods, CDUN integrates gene expression data and STRING dataset to construct DUPN_STRING which consists of 12 uncertain PPI subnetworks, {UGT1, UGT2, …, UGT12. Then, CDUN identify the complexes from such uncertain PPI subnetworks. Eventually, CDUN achieve a high F-score of 0.537 on STRING dataset.Thirdly, we use STRING dataset to compare these methods. STRING dataset is much larger than other two datasets. This makes more difficult for protein complex identification on STRING dataset than other two datasets. From Table We also note that CDUN does not achieve high recall and accuracy in some cases. For instance, CDUN only achieve accuracy of 0.526 and 0.387 on DIP and MIPS dataset, respectively. In the future work, we will try to improve the recall and accuracy of our method further.F-score than DCU on DIP dataset.In additional, we compare CDUN with DCU  on the Dp-value of an identified complex with respect to GO data in our experiment. If the p-value is less than 0.01, we consider the identified complex to be statistically significant. In Table In this experiment, we use GO data to evaluate biological significance of the identified complexes. The GO classifies gene product functions along biological process, molecular function and cellular component. SGD’s GO::TermFinder  is used Our method can identify many protein complexes, as well as their active time points. The cellular systems are highly dynamic and responsive to cues from the environment. These dynamic complexes results are more valuable to reveal the cellular function and organization than the static complexes results. In Fig. Cdc28-cyclin complexes are a series of cyclin-dependent protein kinase holoenzyme complexes, which had been validated by , 35. CdcT2, T9, T10 and T11. For instance, YBR160W, YGR108W, YDL155W, YMR199W and YLR210W are active at T2 together. Then, our method constructs DUPN_MIPS based on PPI networks and protein dynamic information. Eventually, Cdc28-cyclin complex 1, 2 and 3 are identified from UGT2, UGT9, UGT10 andUGT11 by CDUN, which all matched in CYC2008 dataset.The PPI networks including the 10 proteins have extracted from MIPS dataset in Fig. T2 and T9, respectively. Cdc28-cyclin complex 3 is associated with T10 and T11. The experimental results reveal the dynamic property of Cdc28-cyclin complexes in the cellular systems. Firstly, the kinase catalytic subunit, YBR160W, associated with YGR108W, YDL155W, YMR199W and YLR210W to construct the Cdc28-cyclin complex 1 at T2. Then, the kinase catalytic subunit, YBR160W, associated with YAL040C to construct the Cdc28-cyclin complex 2 at T9. Finally, YBR160W associated with YPL256C and YMR199W to construct the Cdc28-cyclin complex 3 at T10 and T11.From Fig. In this paper, we firstly exploite gene expression data to calculate dynamic information of PPI networks. Then, we give a novel method to construct DUPN by integrating gene expression and PPI data based on uncertain graph theory. Next, we propose a new CDUN algorithm to detect complexes on DUPN. It is encouraging to see that our approach achieves the state-of-the-art PPI performance on different yeast PPI datasets. Furthermore, the framework of DUPN can be applied to other similar applications.As a future study, we will collaborate with medical experts, and further analyze the dynamic property of the protein complexes identified by CDUN. We note that the recall and accuracy of our method cannot improve significantly. In the future, we will focus on this issue and try to improve the recall and accuracy of our method. In addition, we will attempt to integrate other resources, such as the TAP dataset to improve the performance of protein complex identification.Additional file 1:GSE3431 gene expression data. (TXT 4397 kb)Additional file 2:DIP PPI data. (TXT 290 kb)Additional file 3:MIPS PPI data. (TXT 184 kb)Additional file 4:STRING PPI data. (TXT 3624 kb)


Identifying deleterious mutations remains a challenge in cancer genome sequencing projects, reflecting the vast number of candidate mutations per tumour and the existence of interpatient heterogeneity. Based on a 3D protein interaction network profiled via large-scale cross-linking mass spectrometry, we propose a weighted average formula involving the combination of three types of information into a ‘meta-score’. We assume that a single amino acid polymorphism (SAP) may have a deleterious effect if the mutation rarely occurs naturally during evolution, if it inhibits binding between a pair of interacting proteins when located at their interface, or if it plays an important role in a protein interaction (PPI) network. Cross-validation indicated that this new method presents an AUC value of 0.93 and outperforms other widely used tools. The application of this method to the CPTAC colorectal cancer dataset enabled the accurate identification of validated deleterious mutations and yielded insights into their potential pathogenesis. Survival analysis showed that the accumulation of deleterious SAPs is significantly associated with a poor prognosis. The new method provides an alternative method to identifying and ranking deleterious cancer SAPs based on a 3D PPI network and will contribute to the understanding of pathogenesis and the discovery of prognostic biomarkers. Recent advances in high-throughput sequencing technologies have promoted the identification of many somatic mutations by ongoing initiatives, such as The Cancer Genome Atlas  and the International Cancer Genome Consortium 5. These initiatives have shown that cancer genomes often contain hundreds or thousands of mutations; however, not all of these mutations appear to play a functional role in tumour development. In fact, among the 2,000,000 coding mutations described in COSMIC (version 70), most mutations have no effect on disease development6, and only a few of these changes are closely associated with or lead to cancer. These changes are referred to as deleterious mutations or, at the protein level, deleterious single amino acid polymorphisms (SAPs)8. Deleterious mutations in cancers are closely associated with early diagnosis, personal therapy and prognostic prediction11.The accumulation of DNA mutations can cause cancer12. LogRE  predicts the effect of a SAP by evaluating the sequences of Pfam domains between wild type and mutant alleles14. In addition to the sequence information, protein structural information is also helpful. PolyPhen-2 is a prominent tool that uses both sequence- and structure-based features in a naïve Bayes classification16. As a cancer-specific tool, CHASM (cancer-specific high-throughput annotation of somatic mutations) is a major machine-learning approach employing a random forest algorithm17 and was trained using 49 predictive features, including conservation exon information, UniProt annotations and the frequency of missense changes in the COSMIC database18. These tools primarily rely on the characteristics of and evolutionary information for an individual protein sequence and ignore the effect of the mutation on protein interactions and topology in the protein-protein interaction (PPI) network. Indeed, cellular processes and biological functions are rarely attributed to the activity of a single protein. Instead, proteins act in functional modules, such as macromolecular complexes or signal transduction networks20. Since aberrant PPIs can have drastic effects on biochemical activities that are essential to the homeostasis, growth, and proliferation of cells, leading to various human diseases, determination of the proximity of a mutation to known disease-related proteins in a PPI network can aid in the detection of important proteins or deleterious SAPs21. For example, the loss of key novel interactors that promote ΔF508 CFTR channel function in primary cystic fibrosis epithelia and proteins critical for CFTR biogenesis was recently identified by identifying the CFTR mutation-specific interactome22. In addition, Yu et al. predicted a 3D protein interactome network with structural resolution and found that disease-associated mutations are significantly enriched at protein interaction interfaces24. Notably, cross-linking mass spectrometry has recently emerged as a powerful technology for identifying both the interactions and interaction interfaces between proteins on a large scale in vivo26. Several follow-up studies have profiled thousands of in vivo PPIs with interface structures in living human cells using the most recent cross-linking technologies29. These analyses offer the alluring opportunity to study the relationships between protein functions and interaction structures.Identifying deleterious SAPs in a cohort of tumours is a key challenge in cancer omics studies. Many strategies for predicting the effects of SAPs on protein function have been developed. Among these strategies, SIFT (Sorting Intolerant From Tolerant) is a reliable and widely used method for predicting deleterious or tolerated SAPs30. For some deleterious SAPs predicted using NIPS, we conducted a network-based analysis and molecular dynamics simulation of the interaction structure. In addition, we used the predicted deleterious SAPs to classify 86 colorectal samples. The results showed that accumulating deleterious SAPs were significantly associated with a poor survival rate, while the neutral SAPs showed no correlation. These results confirm the reliability of NIPS and increase the current understanding of the pathogenesis of known deleterious SAPs. Users can discover new deleterious SAPs and markers related to the prognosis of cancer using NIPS.Here, we describe a new method, referred to as NIPS, that integrates 3D interface interactions, network topology and information on sequence evolution to determine which mutations identified in cancer genomes are likely to be deleterious. The cross-validation revealed that as an integrative method, NIPS shows better performance than methods based on individual information, also outperforms other widely used tools. The area under the receiver operating characteristic (ROC) curve (AUC) of NIPS reached 0.93, indicating that this method is highly accurate. We applied this method to 796 somatic SAPs previously detected in 95 colorectal cancer samples using RNA-Seq and mass spectrometrynetwork-integrated risk predictor of somatic SAPs (NIPS) tool, we integrated a 3D PPI network interface, network topology, and information on sequence evolution. First, we identified SAPs located at the interface between pairs of interacting proteins identified based on cross-linking experiments and INstruct data, as these mutations may disrupt protein interactions (generating the I-score). Next, the ratio of the average shortest paths to cancer nodes and non-cancer nodes in the protein-protein interaction network (the T-score) was used to measure the proximity of a mutated node to known cancer nodes. This score is based on the assumption that when a mutated node is closer to a known cancer-related node in the network, the more likely it is that the mutation is deleterious. We also used the SIFT method to evaluate the potential deleteriousness of mutations using protein sequence evolution information (to generate the S-score). Finally, we combined these three normalized individual scores (0 to 1) into a weighted average ‘meta-score’ to evaluate the risk of a SAP. The workflow of the development of our network-based predictor is shown in Fig. To generate the 3D res 0 to  into a wpROC package for R31, the p-value is 1.5e-13). This finding suggests that the integration of three different types of data sources can improve the accuracy of the identification of deleterious SAPs. We further performed comparisons with widely used tools, including SIFT (S-score), LogRE, PolyPhen-2 and CHASM, using the same test dataset. As shown in Fig. Using the training dataset described in the Methods section, we evaluated the performance of the meta-score of NIPS in cross-validation and the S-score, T-score and I-score individually Fig. . Althoug30, we identified 85 deleterious SAPs with a false positive rate of less than 10%, for which the meta-score was above the cutoff score of 0.9. Among these deleterious SAPs identified by NIPS, only 21 were predicted by SIFT too. Among the deleterious SAPs predicted exclusively using the NIPS method, many of which have been reported in other tumour type, or reported for their association with the progression of colorectal cancer, pancreatic cancer and gastric cancer, such as G12S in KRAS, W383G in CTNNB1, R517K in COL4A2, and V44I in LASP139.We applied NIPS to the colorectal cancer proteome dataset from CPTAC. Among the 795 candidate SAPs identified from 95 colorectal tumour samples7. NIPS classified these mutations as deleterious, reflecting their high I-scores. Based on a previous study22, we hypothesized that mutations at the interface could result in impairment or loss of the corresponding interactome. Thus, we investigated how these deleterious SAPs affect the CTNNB1 and KRAS interactome. As shown in Fig. 40. As an interaction pair in colorectal cancer pathway and Wnt signalling pathway, genomic alterations in the pair of APC and β-catenin (CTNNB1) significantly associate with reductions in DFS  in patients with prostate cancer, It’s interesting that mutations in APC are mutually exclusive from those occurring in β-catenin in both colon cancer and prostate cancer41. KRAS and its three interaction partners 6. It was identified as a deleterious mutation by NIPS due to high I-score while being classified as a neutral one using SIFT. More recently, Ortiz-Cuaran et al. reported that KRAS G12S mutation is significantly related to acquired drug resistance in cancer. Furthermore, introduction of KRAS G12S resulted in increased KRAS expression and sustained ERK phosphorylation under treatment with drug AZD9291, which provide clinical evidence for a possible role of MAPK pathway activation in the context of acquired resistance to third-generation EGFR inhibitors44. In our analysis, the NIPS identified the deleteriousness of KRAS G12S mutation accurately. More importantly, it provided insight to its possible mechanism and the link between the mutation and the downstream MAPK pathway  to −42.26 kcal/mol (mutant)  regulates the onset and progression of prostate cancerner Fig. , which wnt) Fig. . The roo51. A meta-analysis of 15 studies involving 4,932 lung cancer patients and 6,555 cancer-free controls found that in an Asian population, carriers of APEX1 D148E exhibited an increased risk of developing lung cancer52. Moreover, the presence of this mutation increased the risk of gastric cancer and affected the survival of patients with urothelial carcinoma of the bladder in a Chinese population53. The high T-score of the D148E mutation in APEX1 suggests that the mutant protein is closer to cancer-related nodes than neutral nodes in the protein interaction network. We randomly sampled 1,000 nodes in the background network and calculated the shortest path from APEX1 to each node. We found that the average length of shortest paths of APEX1 to cancer-related nodes was 3.6, which was less than the average distance to non-cancer-related nodes of 4 . All of the interaction partners of APEX1 within a maximum of two steps are displayed in Fig. Some SAPs were classified as deleterious by NIPS because of high T-scores. For example, the D148E mutation of APEX1 received an S-score of 0 and a T-score of 0.93. In addition to its role in DNA repair, APEX1  is a transcriptional regulatorThe availability of the TCGA survival data enabled the investigation of the relationship between the accumulation of the deleterious SAPs and the overall survival of patients. We investigated the correlation between the accumulation of deleterious SAPs and survival in 84 of the 95 colorectal tumour samples with available survival information. Based on the summation of the meta-scores of the top 30 SAPs in each sample, these patients were classified into two groups: G1 (high-risk group), in which the sum of the meta-scores of each sample was higher than the mean value 2.99, and G0 (low-risk group), in which the sum of the meta-scores of each sample was below the mean value. As shown in Fig. 54 and ranks SAPs from the local candidate list. Based on the S-score, I-score, T-score, and meta-score, any new SAP identified via human cancer genome sequencing can be evaluated and ranked using the NIPS server. The 3D PPI network and the training datasets can also be downloaded.The results and data obtained in this study are available for download at lilab.life.sjtu.edu.cn:8080/nips. Users can search all of the known SAPs annotated in the CanProVar database, which stores single amino acid alterations in the human cancer proteome55, including APEX1, SERPINA1, and CASP7. More importantly, the NIPS method provides a novel insight into the understanding of the complex relationship between the occurrence of SAPs and disease at a view of structural protein interaction network. Some mutations are deleterious, primarily because these mutations rarely occur during evolution, disrupt interactions in the 3D protein structure, or induce changes in topology and signal transmission in the PPI network.In the present study, we developed an integrative approach referred to as NIPS, employing a meta-score to evaluate the risk of SAPs computationally and identify deleterious SAPs in cancer by combing information on PPI 3D structure (I-score), network topology (T-score), and sequence conservation (S-score). NIPS can be used to identify new deleterious SAPs in cancer genome or proteome data, which would be helpful for early detection or target-therapy. For instance, the 70 proteins containing deleterious SAPs identified in the colon cancer samples, 11 are currently targets of FDA-approved drugs or drugs in clinical trialsThe AUC value of each of individual score Fig.  revealedIn comparisons across multiple methods, NIPS and CHASM showed significantly better performances than the other methods. LogRE and SIFT, which use individual protein domains or protein sequences, displayed a lower accuracy. PolyPhen-2 performed slightly better than SIFT, though its strategy combines sequence and structural information. The poor accuracy of these three methods in predicting deleterious SAPs in cancer might reflect the lack of consideration of the specificity of the cancer genome in their algorithms. In contrast, the training systems of NIPS and CHASM use the known cancer-related genes and the frequency of missense mutations in the cancer somatic mutation database, respectively. The results suggest that specificity should be addressed to improve prediction accuracy. The performance of NIPS was similar to that of CHASM and showed higher overall accuracy and sensitivity but lower specificity. In CHASM, the model was trained using 49 features based on information on exon conservation, UniProt annotation and the frequency of missense mutations from a large-scale cancer genome project. In NIPS, only three feature scores, based on sequence conservation, protein interaction structure and interaction network topology, were employed. Moreover, NIPS provides the prioritization of deleterious SAPs and allows explanation of the results with respect to the 3D protein interaction or interaction network. Thus, NIPS could represent a good alternative and complementary method to existing methods for the prediction of deleterious SAPs. Additionally, this model can be extended to other diseases, but only if disease-specific training data are used.et al.22 has demonstrated that the presence of a protein mutation might lead to derailment of the entire protein interaction network, directly resulting in the disease phenotype. In order to elucidate the dynamic impact of deleterious SAP in the network, the differential expression and modification profiles of all of downstream targets can be considered in the further study.The molecular dynamics simulations in this study showed that a deleterious SAP could alter the structure of the protein complex, thereby affecting the molecular function of the protein complex. A recent study of Yates 58. Here we have shown the good performance of the NIPS algorithm for ranking deleterious SAPs in the cross-validations. We also identified 85 deleterious SAPs in the colorectal cancer cohort using NIPS, of which the accumulation of the top deleterious SAPs was significantly associated with a higher risk of prognosis. However, it should be noted that the follow-up wet-lab validations are essential for the novel driver SAPs predicted by in silico method before making a real application. Moreover, the association analysis between the top deleterious SAPs and prognosis was conducted in the TCGA colorectal cancer (CRC) samples only. Further studies in independent cohorts can be carried out by considering of clinical stages and subtypes, which will likely facilitate more effective prognostication efforts.Recent studies involving the dynamic mathematic modelling of human tumour initiation and progression indicate that most somatic mutations observed in common tumours do not play any causal role, and only driver mutations are effectualet al. were used as the positive training dataset12. According to this previous report, 2,682 somatic mutations were found in at least two tumour samples from the COSMIC database were defined as deleterious mutations. A total of 7,170 variants with a minor allele frequency of at least 0.25 in dbSNP (Build ID 135) were used as the negative training dataset, as relatively frequent mutations are unlikely to be deleterious59.The deleterious SAPs identified by Gnad et al. identified 796 non-duplicated single amino acid variants  from 95 colorectal cancer samples via RNA-Seq and shotgun proteomics30. These SAPs were used in the application of the new method developed in the present study.In the CPTAC project, Zhang 60. The data in HINT were collected from BioGrid, DIP, HPRD, IntAct, iRefWeb, MINT, MIPS, and VisAnt67. Low-quality interactions were filtered and systematically and manually removed; thus, only confident physical interactions remained. The new interactions detected via cross-linking were also added. Self-interactions and duplicates were removed, leaving 5,585 edges with 3,280 proteins in the background PPI network.The high-quality HINT interactome was used as a background network to measure network topology29. To improve coverage, we added a three-dimensional (3D) protein interactome with structural resolution using INstruct24. INstruct employs iPfam and 3 did to identify the interface of two interacting proteins by mapping the proteins to known atomic-resolution 3D structures in the Protein Data Bank (PDB)70.As shown in Table We scanned the 3D network to determine whether a SAP was located at the interface of two interacting proteins. If so, the SAP received an I-score of 1; otherwise, the SAP received a score of 0. If a protein was not present in the 3D-network, then it received a score of 0. SAPs located at the interaction interface were considered likely deleterious SAPs.71, there are 451 cancer-related nodes in the network, which are defined as deleterious in cancer, whereas the other nodes are considered neutral. Next, we calculated the average length of the shortest path from each node to cancer-related nodes and neutral nodes.We first calculated the shortest path between each node (protein) in the background PPI network, and subsequently compared these paths with the positive dataset described above. According to the Cancer Gene Census database3. We obtained SIFT 5.1.1 from http://sift.bii.a-star.edu.sg, and used the UniProt database from EMBL (ftp://ftp.ebi.ac.uk/pub/databases/ fastafiles/uniprot/) as a reference sequence database, with a default cutoff score (0.05). SIFT depends on PSI-BLAST72; therefore, blast-2.2.26 was downloaded from ftp://ftp.ncbi.nlm.nih.gov/ blast/executables/. We defined the S-score as one minus the SIFT score; therefore, when the S-score is larger, the more likely the SAP will be deleterious.SIFT first performs multiple sequence alignment of homologous proteins and identifies conserved protein residues based on the probability of each of the 19 amino acid changes being tolerated, relative to the most frequent residue. Less conserved protein changes are considered neutral, and more highly conserved protein changes are considered deleterious73.We combined these three normalized scores into a weighted average score, referred to as the ‘meta-score,’ using the previously published Condel methodet al.73. If a protein does not receive a score from an individual method, the corresponding weight is set to 0. The cutoff of the meta-score (0.9) was chosen based on the score distributions in the training dataset  was used to align wild type and mutant protein sequences against Pfam protein domain models74. LogRE scores were then calculated using E-values from HMMER according to the strategy of LogRE13.The software HMMER 3.0 (http://www.cravat.us) and is easy to apply online.This tool is a web-based application within CRAVAT  mode can indicate the binding affinity of proteins77. We used MMPBSA in AMBER12 to calculate the binding free energy in GB mode for both types78.We performed molecular dynamics simulations (50 ns) in AMBER12 for the wild-type and mutant protein sequences to validate the influence of each mutation on protein binding affinity30. We summed the meta-scores of the top 30 deleterious SAPs predicted using NIPS, and divided the samples into two groups (above or below the median). Then, survival analysis and Cox proportional hazards regression were conducted using the survival package in R.Clinical prognosis information was available for a total of 86 patients among the 95 colorectal cancer samples in CPTACSupplementary Information


Proteins are highly dynamic entities. Their myriad functions require specific structures, but proteins’ dynamic nature ranges all the way from the local mobility of their amino acid constituents to mobility within and well beyond single cells. A truly comprehensive view of the dynamic structural proteome includes: (i) alternative sequences, (ii) alternative conformations, (iii) alternative interactions with a range of biomolecules, (iv) cellular localizations, (v) alternative behaviors in different cell types. While these aspects have traditionally been explored one protein at a time, we highlight recently emerging global approaches that accelerate comprehensive insights into these facets of the dynamic nature of protein structure. Computational tools that integrate and expand on multiple orthogonal data types promise to enable the transition from a disjointed list of static snapshots to a structurally explicit understanding of the dynamics of cellular mechanisms. This expands the available space of alternative structures Switchable alternative protein–protein interactions lead to yet more diversity Protein turnover. The half‐lives of eukaryotic proteins range from on the order of minutes to decades Multiple subcellular locations enable the same protein to exert different functions in different parts of the cell Proteins involved in transducing intrinsic and extrinsic signaling exist within spatially restricted concentration gradients. For example, Wnt signaling gradients control asymmetric cell divisions during early development and later in the life of complex organisms maintain tissue organization. Spatial organization enables the formation of complex tissues and organs up to the highly interconnected human brain.The human genome sequence has a smaller number of genes than expected: ∼19 000 compared to 6.7 million genes in earlier estimates http://viewpoint.proteomics‐journal.deCorrespondence concerning this and other Viewpoint articles can be accessed on the journals' home page at: Correspondence for posting on these pages is welcome and can also be submitted at this site.The term “proteoform” has been recently proposed as an umbrella term to summarize all possible alternative protein sequences for a given protein including genetic sequence variants, PTMs, splice variants, proteolysis variants Compared to extensive insights into multiple aspects of proteoforms, much less is known about higher‐order structural proteome dynamics that enable cellular complexity Fig. . We focu2Not all protein interactions fit the classical lock and key model of molecular recognition achieved by docking of rigid components. Fine‐tuning target recognition can require ‘conformability’ as in the case of bacterial Lac repressor protein, which assumes a fuzzy complex when sliding along non‐specific DNA sequence but a mostly structured state in the specific, tightly DNA‐bound complex once associated with its specific target sequence 33.1Traditionally, structural biology was rationalized by the dogma that biological function requires a rigid 3D protein structure. According to this dogma, it should be possible to understand biology by solving one minimal energy structure per protein. Greater than 100 000 structures of folded domains have been solved over the last decades and first near‐complete structural proteome models have been proposed based on homology modeling Despite its historical bias towards solving static structures, X‐ray crystallography has chiefly contributed to the birth of the IDP field Even fully disordered proteins are no longer outside of the reach of X‐ray crystallography. Several important c‐Myc structures have been solved in complex with specifically binding partner proteins 3.2In the last two decades, major technological breakthroughs in electron detection efficiency and image processing have culminated in a recent explosion of new Cryo‐EM structures, which is experiencing a higher average annual growth compared to x‐ray crystallography (with an average of 34 versus 9%). Cryo‐EM, like NMR spectroscopy, is capable of revealing local structural disorder. NMR peaks of disordered protein segments cluster together more closely because their more averaged chemical environments result in lower chemical shift dispersion 3.3Many aspects of protein conformational dynamics are either impractical or impossible to study using exclusively above‐mentioned high‐resolution structural methods. Biochemical methods including 1D SDS‐PAGE and proteolysis have been successfully used as valuable complementary methods to characterize protein folding and conformational heterogeneity in solution Limited proteolysis (LiP) has been used for many decades in structural biology and continues to be actively developed using a wide range of proteases and readout methods from low to high multiplexity 3.4A wide range of highly specific methods to study protein conformations depends on selective chemical protein labeling. Tryptophan‐free proteins can be selectively labeled using a single tryptophan substitution of a chemically similar aromatic residue like phenylalanine, which often does not perturb the biological behavior of the wild‐type protein 3.5Alternative methods based on physical principles increasingly complement chemical methods. One of the earliest physical methods to characterize protein unfolding is monitoring their soluble fraction at a range of temperatures. Analogous to egg‐white protein in boiled eggs, most proteins irreversibly precipitate above their unfolding temperature. Temperatures just slightly above the physiological growth optimum can cause dramatic reductions of proteome solubility in cells lacking the Hsp70 system that is an essential component of the cellular heat shock protection system by interacting with aggregation‐prone unfolded and partially folded proteins Interestingly, TPP can be also applied to many transmembrane proteins either before or after detergent solubilisation using a range of mild detergents 3.6Specific protein‐protein interactions (PPI) are widely considered as key to understanding cellular functions of proteins. One might intuitively expect most PPI to be high affinity as this ensures a high fraction of specifically bound complexes. Highest affinity can be reached with rigid proteins, but transient and biophysically weak interactions are at the hub of biological interaction networks 3.7Most large‐scale methods depend on short, disordered affinity‐tags How good are these methods for capturing weak yet potentially biologically important interactions? It is a priori not clear how these methods might bias against the detection of very transient binding events shorter than current affinity protocols or bias toward complexes that only form in vitro in dilute lysis and affinity purification buffers but would never form in the crowded intracellular environment in the presence of optimal concentrations of molecular chaperones. Clearly, orthogonal methods are needed to validate interactions and to discover additional interactions that are too transient or weak for detection by affinity‐enrichment methods.3.8While affinity methods readily provide large lists of specific interactors, it is generally difficult to derive predictions about proteoform‐specific dissociation constants, which would enable quantitative predictions for other protein concentrations. Direct biophysical high‐throughput quantification of binding strength of putative protein‐protein interactions has remained highly challenging. Single‐molecular‐interaction sequencing (SMI‐seq) enables high‐throughput quantification of up to hundreds of protein interactions in parallel covering a broad range of affinities by covalently crosslinking proteins to nucleotide‐barcodes for multiplexed sequencing in situ 3.9Comparison of in vitro and in vivo protein complexes is in principle possible by fixation of protein interactions using chemical crosslinking or using fluorescence correlation spectroscopy (FCS) 3.10Even the simplest known living cells are compartmentalized 3.11Efficient strategies are being developed to selectively label membrane‐associated protein complexes for subsequent MS detection. APEX2‐MS Aquifex aelicus) source and displays optimal activity far above 37°C Biotinylation is in principle also possible using more gentle enzymatic approaches as biotinylation is one of the most specific known PTMs 3.12Even within a single organelle, biomolecules are not homogenously mixed. Active sub‐organellar partitioning often involves ATP‐fuelled molecular machines, for instance dynein guiding cargo proteins along the cytoskeleton Given their large molecular size, the ribosome and other large cellular machines including the proteasome and chaperonins constitute nanoscopic cellular compartments in their own right. Based on RNA‐seq and isolation of translationally halted ribosomes, and one‐by‐one addition of chaperones, it is now becoming possible to selectively profile ribosomal complexes to unravel how molecular chaperones engage during the translation process. This “selective ribosome profiling” approach revealed that trigger factor (TF) engages in vivo only upon emergence of ∼100 nascent residues in contrast to the earlier suggestions based on in vitro work on TF that TF is waiting per default at the ribosomal exit tunnel Exciting open questions related to suborganellar cellular structures include: how is the timing of metabolic pathways tuned by subcellular structures? Are PTMs regulating their formation? How can we monitor systems‐wide perturbations of these structures by changing environments?3.13Combining state of the art mass spectrometry, partial separation of organelles in a density gradient, and statistical analysis of resulting patterns enabled first quantitative and nearly proteome‐wide maps of cellular localizations for eukaryotic cells, such methods include protein correlation profiling (PCP) Despite their current limitations to relatively small numbers of different proteins that can be observed simultaneously, it will be interesting to explore the complementary benefits of cryo‐electron tomography (cryo‐ET) 3.14Complex tissues and organs such as the human brain clearly require a high degree of spatial organization beyond single cells. Nearly 50 years ago, Francis Crick proposed diffusive “morphogen” gradients as minimal ingredient for spatial organization of cells during embryogenesis Wnt signaling as a whole is a perfect illustration of the importance of various levels of disorder in establishing multi‐cellular order. Many of its crucial signaling components including the scaffolds APC, Axin and WTX contain large IDRs up to some 2000 residues 3.15Acquiring all‐atom movies of the living organisms is clearly beyond experimental reach. Computational methods increasingly help to fill gaps in our understanding of structural biology. Efficient algorithms can predict secondary structure, IDRs and increasingly 3D structure can be predicted from readily available genomic sequences Computational approaches can amplify the attainable insight from highly complex multi‐dimensional proteomics experiments by efficient dimensionality reduction methods. PCA plots often capture most of the variation of highly dimensional data in visually intuitive two‐dimensional plots Fig.  136. Sig3.16Which weak or transient interactions are functionally important?How to quantitatively understand and predict in vivo versus in vitro protein interactions?How can we quantitatively link various “omics” from DNA to RNA and the higher‐order structure of proteins including their cellular trafficking?What are the underlying principles determining cellular protein structural dynamics and how to predict them from readily accessible genomic sequences?How can we improve the mutually enhancing efforts of experimentalists and theoretical scientists to tackle highly complex “multi‐omics” projects? It is a formidable challenge for computational biologists and mathematicians to glean sufficient breadth of data types from experimentalists, to discover overarching patterns in various “omics” datasets that are fundamentally connected by common cellular biology.How can we link different protein structural states to functional diversity? The decade‐old C‐value paradox states that genome sizes are not well‐correlated with organism complexity Can we use our improved understanding of spatiotemporal proteome dynamics to improve life of ageing and growing societies? 4Bottom‐up approaches have been very powerful in structural biology over many decades. DNA and RNA sequencing technologies have become highly robust and widely accessible technologies and rapid proteome‐wide protein sequencing is now possible for several organisms and transform our understanding of biology. Protein de novo folding simulations have reached near‐atomic precision for small folded domains and IDRs. Higher‐order structures are less readily predictable so far. Complicating factors are the intracellular and environmental fluctuations, which can be observed even in the most simple model systems Very recent breakthrough studies demonstrated the possibility of few‐protein spatiotemporal engineering of organisms to improve carbon fixation or accelerate the process of switching from reduced photosynthetic activity under low‐light conditions to full photosynthetic productivity once more light becomes available after clouds have passed Plenty of dynamics at the bottom of biology Fig. .


Here, we show that crude cellular extracts of a eukaryotic thermophile, Chaetomium thermophilum, retain basic principles of cellular organization. Using a structural proteomics approach, we simultaneously characterized the abundance, interactions, and structure of a third of the C. thermophilum proteome within these extracts. We identified 27 distinct protein communities that include 108 interconnected complexes, which dynamically associate with each other and functionally benefit from being in close proximity in the cell. Furthermore, we investigated the structure of fatty acid synthase within these extracts by cryoEM and this revealed multiple, flexible states of the enzyme in adaptation to its association with other complexes, thus exemplifying the need for in situ studies. As the components of the captured protein communities are known—at both the protein and complex levels—this study constitutes another step forward toward a molecular understanding of subcellular organization.The arrangement of proteins into complexes is a key organizational principle for many cellular functions. Although the topology of many complexes has been systematically analyzed in isolation, their molecular sociology  Additional layers of functional organization, beyond free diffusion and random collision of functional biomolecules within organelles, are required to ensure, for example, the efficient transfer of substrates along enzymatic pathways  that forms a structurally characterized dimer in vitro  and true‐negative  interactions that we extracted from public sources after manual curation .We next used the protein elution profiles in conjunction with known functional associations to systematically define protein communities. Correlations between profiles can indicate membership of the same complex  by sequence alignments. As exemplified in This compendium of munities , which amunities . It not that is, correspond to distances between Ca atoms of cross‐linked lysine residues smaller than 33 Å (Lys(ca‐ca) < 33 Å). A comparison with all structurally known complexes (see above and a‐ca) distances effectively recapitulated the expected log‐normal distributions covered by the disuccinimidyl suberate (DSS) and BS3 cross‐linkers, which further validates the calibration method we employed  mapped interactions within single polypeptide chains and therefore probably define intramolecular contacts  to the fractions see . To captoyed Fig . A signicontacts . The remcontacts , which icontacts . Our anacontacts  and S12.Overall, the cross‐linking benchmarking methodology presented here suggests strict, but high‐quality, structural validation that may be applicable to any cross‐linking study on complex mixtures of proteins or complexes. For example, the XL‐MS dataset validates a community of heat‐shock complexes that elute with apparent molecular weights in the mega‐dalton range (i.e. much higher than known complexes). We mapped nine new interfaces within this community, based on XL‐MS data that suggest the existence of a complex interaction network or a chaperone community that comprises chaperones and co‐chaperone complexes . Our XL‐To demonstrate that crude cellular fractions are amenable to the structural characterization of protein communities, we examined the different fractions for recurring structural signatures using EM Fig B–D withoS. cerevisiae according to STRING). Consistent with this notion, additional electron optical densities, probably corresponding to associated protein complexes, are observed that sometimes form linear elongated arrangements , the effective transduction of signals, and the synthesis of proteins according to the local cellular needs. The concept goes beyond the classical linear representations of pathways that imply freely diffusing and randomly colliding biomolecules (bottom right panel). The assembly of protein communities sometimes require molecular scaffolds , and can be regulated by post‐translational modifications. Shapes 1–4 in the panel below show different enzymes of a sequential pathway. In contrast to free diffusion, these enzymes might also multimerize within a community to increase reaction efficiencies.Methods: The characterization of protein communities implies their retrieval from in vivo, cellular and physiological contexts, and the choice of a thermophilic organism is expected to minimize their disassembly. Their biochemical purification can be achieved via affinity purification coupled to mass spectrometry . The follow‐up analyses are also tuned to cope with the large size of the communities . The method described here is dedicated to the identification of protein communities, although of course other biomolecules such as nucleic acids or lipids might be part of the identified communities and contribute to their association. The combination with other identification strategies such as RNA sequencing and small molecule MS might further enlighten this aspect in the future. The broader applicability of cryoEM to non‐purified samples will be limited by the abundance and the stability of the protein communities during the lysis procedure. However, methods to improve the stability of these interactions, potentially with cross‐linking prior to fractionation or lysis, would allow discovery of further dynamic interactions and protein communities, and would allow further simplification of the protein mixtures for structural study using this pipeline. Advances in EM acquisition and data analysis methods might further improve the coverage and identification of protein communities in the future.in cellulo structural biology approaches, based on the electron tomographic analyses of entire cells, have already started to produce the next generation of “big data”  size exclusion chromatography (SEC) column from Phenomenex, Germany (see many see .Protein abundances were recorded from each SEC fraction by liquid chromatography–mass spectrometry (LC‐MS). Prediction of protein co‐elution was performed by Pearson correlation of protein abundance profiles. LC‐MS data were processed using the MaxQuant  was used to perform cross‐linking reactions as described previously  and searched against the Protein Data Bank . A threshold of 30% of sequence identity was assigned. A decision on the assembly was taken after back‐BLASTing the rest of the subunits, if any, of the PDB structure to the C. thermophilum proteome. All results are included in Each of the 1,176 proteins found in total in all three biological replicates was submitted to the NCBI BLAST server  water. Recording of data was performed with a side‐mounted 1K CCD Camera (SIS). After data acquisition (pixel size = 7.1 Å), E2BOXER was used for particle picking . Class averaging was performed using RELION 1.2  QuantifoilScheres, ,b. Cross®, type R2/1. A FEI Vitrobot® was used for plunge‐freezing. In short, humidity was set to 70%, blotting and drain time to 3 and 0.5 s, respectively. Sample volume applied was 3 μl and blot offset was set to −3 mm.ctFAS was ~50% enriched see  and over−/Å2, but the last frame was used only for particle picking. A total number of 13,419 micrographs were acquired in 21 h . Motion correction was applied to acquired micrographs  and enoyl reductase (ER) domains were generated using Modeller 9v2 and chosen structural homologs were selected from the yeast homolog with resolved densities for both (Leibundgut et al, et al, et al, et al (Models of l, et al .The primary datasets produced in this study are available in the following databases:http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-3757.Structural data: EMDB EMD‐3757 http://www.ebi.ac.uk/pride/archive/projects/PXD006660.Proteomics data: PRIDE PXD006660 http://www.ebi.ac.uk/pride/archive/projects/PXD006626.Cross‐linking data: PRIDE PXD006626 List of identified proteins: List of protein complexes: Results of network benchmarking: List of protein communities: Results from the cross‐linking experiments: Cytoscape file: A‐CG, MB, PB, RBR, and JR supervised and administered the project and secured funding. A‐CG, MB, PB, PLK, RBR, FJO'R, and TB wrote the manuscript. PLK designed and performed the electron microscopy experiments, carried out computation and modeling, analyzed the data, wrote the code, and solved the structure. FJO'R and TB designed and performed the SEC and the quantitative MS/MS and XL‐MS experiments. MZR, FJO'R and PLK designed and analyzed the network. KHB and WJH contributed to cryoEM data acquisition and analysis. NR computationally analyzed data. YL, KB, M‐TM, and MLH contributed to the SEC and MS experiments. MJB and RBR contributed the homologous interfaces for the network. All authors read and approved the final manuscript.The authors declare that they have no conflict of interest.AppendixClick 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.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Review Process FileClick here for additional data file.


Predicting protein complexes from protein-protein interaction (PPI) networks has been studied for decade. Various methods have been proposed to address some challenging issues of this problem, including overlapping clusters, high false positive/negative rates of PPI data and diverse complex structures. It is well known that most current methods can detect effectively only complexes of size ≥3, which account for only about half of the total existing complexes. Recently, a method was proposed specifically for finding small complexes (size = 2 and 3) from PPI networks. However, up to now there is no effective approach that can predict both small (size ≤ 3) and large (size >3) complexes from PPI networks.CPredictor2.0, that can detect both small and large complexes under a unified framework. Concretely, we first group proteins of similar functions. Then, the Markov clustering algorithm is employed to discover clusters in each group. Finally, we merge all discovered clusters that overlap with each other to a certain degree, and the merged clusters as well as the remaining clusters constitute the set of detected complexes. Extensive experiments have shown that the new method can more effectively predict both small and large complexes, in comparison with the state-of-the-art methods.In this paper, we propose a novel method, called The proposed method, CPredictor2.0, can be applied to accurately predict both small and large protein complexes. Most proteins perform biological functions by forming complexes through protein-protein interactions –4. The iprotein-protein interaction (PPI) data. In general, a PPI data set can be represented as a protein-protein interaction network (PIN) where nodes are proteins and edges signifies the interactions between pairs of proteins (nodes). Protein complexes can be detected from PINs by exploiting densely connected subgraphs using graph clustering methods. Up to now, a number of methods for detecting complexes from PINs have been developed.In recent years, high-throughput methods have provided us huge amounts of MCODE  is one oTo handle the high false positive and false negative rates of PPI data, some works detect clusters by exploiting additional information other than solely based on topological features. Due to the fact that interacting proteins are likely to have similar gene expression profiles, methods such as MATISSE , DMSP 1 and GFA core stands for a set of proteins that are densely connected and attachment stands for the proteins that own a few links to the core. Based on the core-attachment model, Wu et al. [Other than the densely connected subgraph assumption of protein complexes, Gavin et al.  proposedu et al.  and Leunu et al.  presenteQi et al.  observedCPredictor for protein complex detection. We first cluster proteins based on functional similarity calculated using Biology Process (BP) terms from Gene Ontology(GO) [from function to interaction strategy is better than previous methods when predicting large-size complexes.In our previous work , we intrlogy(GO) , then foThere are also some works on complex prediction in dynamic PINs. As they are not quite related to the work of this paper, we do not give more detail here. Readers interested in this topics can refer to a recent survey .In summary, existing methods have demonstrated their abilities to detect protein complexes from protein interaction networks. Yet, methods, which can accurately predict protein complexes of different sizes from PINs under a unified framework, have not been reported. In the protein complex dataset of MIPS , there aCPredictor [CPredictor2.0. Concretely, by using CYGD [In this paper, we propose a novel complex prediction method, which is an advanced version our previous work redictor . So we cing CYGD  functionCPreditor [CPredictor2.0 is more effective in grouping proteins to different clusters in terms of functions, and thus can predict more small protein complexes. Note that in CPreditor, the similarity between any two proteins is evaluated by GO terms, and with the calculated similarity values, all proteins are grouped into disjoint clusters. Obviously, CPredictor2.0 employs a finer clustering of proteins than CPreditor.Compared to Preditor , CPredicCPredictor2.0 can more effectively identify both small and large protein complexes.Experiments are conducted on three PPI datasets, and the predicted results are benchmarked with two ground truth datasets, MIPS and CYC2008. In comparison with several existing methods, CPredictor2.0.In this section, we first give a brief introduction to the functional annotation provided by CYGD , then prThe Comprehensive Yeast Genome Database (CYGD) at the Munich Information Center for Protein Sequences (MIPS)  providesThe workflow of CPredictor2.0 is shown in Fig. Algorithm 1 outlines the procedure of our method CPredictor2.0. Lines 1-2 preprocess functional annotations and cluster proteins of similar functions into groups. Lines 3-12 detect the preliminary clusters from the protein groups using PPI data. Thus, proteins in a cluster should first have similar functions, and then interact closely. Lines 13-23 merge highly-overlapping preliminary clusters and derive the final protein complexes.In what follows, we present the detail of each major step of CPredictor2.0.According to the Funcat scheme, protein functions are annotated by terms of various levels in a hierarchy.N levels in the hierarchy, where N is an input parameter. If N is larger than the height of the annotation hierarchy, we use all annotations. Then, proteins are grouped together if they have similar functional annotations.In order to evaluate the functional similarity among proteins and to group proteins, we first preprocess all function annotations. We extract the functional annotations specified by the terms of the first A has function “1.1.1” and protein B has function “1.1.2”. If we use the first two levels , then the two function terms are both shortened to “1.1”. Therefore, the two proteins are grouped together. However, if we use the first three levels , then their function terms are different, therefore the two proteins belong to different groups. In essence, grouping proteins is equivalent to cutting the annotation hierarchy, each resulting branch corresponds to a group, consisting of the proteins annotated by terms under this branch.Please note that, as a protein usually possesses multiple functions, therefore it may lie in multiple groups. For example, say protein We first build a network upon each protein group obtained in last step (Line 5 of Alg. 1). Node set represents proteins in the group, and edge set indicates interactions between proteins. Then in each network, Markov Clustering Algorithm (MCL)  is emploexpansion and inflation. The network is firstly treated as an adjacent matrix, where the elements indicate whether pairs of nodes are linked or not. At the expansion step, the matrix is updated by taking the power of itself using normal matrix product. At the inflation step, the matrix is normalized after taking the Hadamard power of itself. After a certain iterations of the procedure above, the derived matrix presents the probabilities of nodes belonging to different clusters.To get clusters, MCL simulates random walks in the network while repeating two steps called merge step. These are described in Lines 7-11 of Alg. 1.After obtaining clusters by using MCL, we discard these clusters containing only one protein. All the remaining clusters detected from different protein groups are collected together for the following To avoid redundancy, highly overlapping clusters are merged.overlapping graphGol is built to describe the overlapping rate between clusters. In Gol, nodes represent clusters detected in the previous step. For each pair of nodes (clusters), if the overlapping rate between them exceeds 0.8, then they are linked in Gol. The overlapping rate (olr) between two clusters C1 and C2 is calculated as We adopt a similar procedure like ClusterONE  to mergewhere |·| is the cardinality of a set.Gol, and each clique is composed of clusters that are highly overlapping with one another. For those nodes (preliminary clusters) not belonging to any clique, they are regarded as protein complexes without merging (Lines 15-19 of Alg. 1). For each detected clique composed of multiple preliminary clusters, all distinct proteins from these clusters make up a predicted complex (Lines 20-23 of Alg. 1).We detect cliques in recall, precision and F-measure to evaluate our approach. Let BC={bc1,bc2,⋯,bcm} and PC={pc1,pc2,⋯,pcn} be the sets of benchmark complexes and predicted complexes, respectively. We calculated the overlapping degreew of a real complex bci∈BC and a predicted complex pcj∈PC. If w≥0.2, we consider that the predicted complex matching with the real one.We used Mbc be the number of benchmark complexes matching at least one predicted complex, and Mpc be the number of predicted complexes matching at least one benchmark complex. Recall is defined as Let BC| stands for the size of benchmark set. Precision is defined as follows: where |PC| is the total number of predicted complexes.where |F-measure considering both recall and precision is defined as follows: The We used three PPI datasets of Saccharomyces cerevisiae, including Gavin et al. , Krogan In the dataset of Gavin et al. , socio-aIn the dataset of Krogan et al. , a machipurification enrichment (PE) score to analyze the raw data. In our study, we used the interactions with high confidence as suggested.The dataset of Collins et al.  combinedTable Protein complex datasets MIPS  and CYC2et al respectively. The performance is evaluated by recall and precision, which are calculated using MIPS and CYC2008 as ground truth. Results are shown in Figs. We first tested the effect of using different levels of functional annotations. The height of the lowest functional annotations in CYGD is 6 and the highest is 1. Protein complexes are detected from the three PPI datasets in Table recall and precision show almost similar trends in most cases. For example, in Fig. recall increases first and then becomes stable when three or more levels of functional annotations are used, while precision is relatively stable for all levels used. For large complexes, both recall and precision increase first and then become stable when three or more levels of functional annotations are used. Thus, three levels annotation is enough for predicting protein complexes. As more levels of functional annotations are used, functions of proteins can be described more specifically with those annotations, therefore the proteins can be well separated into different groups. In the following experiments, we use the most specific functional annotations by setting the level height to 6.From Figs. recall, precision and F-measure.We compared our method with several existing methods including MCODE , RNSC 77, DPClusF-measure. When detecting small complexes, all other methods shows obvious trade-off between recall and precision, while our method always achieves competitive and balanced recall and precision. As for large complexes, our method achieves the best recall and precision.Experimental results using MIPS dataset as benchmark are shown in Fig. F-measure in most cases, except when detecting large complexes from Krogan et al. and Collins et al, the F-measure of CPredictor2.0 is sightly lower than but comparable to that of CPredictor. Both recall and precision of our method are quite competitive and balanced, comparing to the existing methods.The results using CYC2008 as benchmark are illustrated in Fig. For comparison in detail, we present all results in Tables In this paper, we aimed at effectively detecting both small and large complexes from protein interaction networks. To this end, we first group proteins of similar functions according to their functional annotations. Upon each protein group, a network is built where nodes are proteins and edges are interactions between proteins. Then, we apply the MCL algorithm over each network to detect dense subgraphs, each of which is a protein cluster. Finally, we merge highly-overlapping clusters. The derived clusters are considered to be complexes.F-measure in detecting small complexes (size= 2 and 3) and large complexes (size ≥ 4) as well as all complexes as a whole. This result shows that our method is more effective in detecting complexes from PPI networks than the existing methods.Our method has been evaluated on three PPI datasets by taking MIPS and CYC2008 as benchmark datasets. Experimental results have shown that, comparing with several existing methods, in most cases our method achieves higher