Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases.We proposed a technique called RWPCN  for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes.Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations. Uncovering the associations between the genetic diseases and their causative genes is a fundamental objective of human genetics In recent years, there has been an increase in the number of genes confirmed as causative genes to diseases et al.Recent studies have revealed that similar phenotypes are often caused by functionally related genes The observation that phenotypically similar diseases are often caused by functionally related genes also suggests a modular organization of the genetic landscape of human diseases. Many specific examples have shown that individual genes that cause a given phenotype tend to be linked at the biological levels as components of a multi-protein complex et al.Both the Vanunu and Li methods mentioned above did not make use of protein complexes to aid in their inference of gene-phenotype associations. In an earlier work, Lage Our proposed method is different from the existing methods as our network propagation algorithm is operated at the complex-level instead of the protein level. We used reliable human protein complexes from the Comprehensive Resource of Mammalian protein complexes (CORUM) In this section, we will first describe the experimental data we have used. Then, we will introduce the overall network structure for our RWPCN algorithm, including the phenotype network, protein complex network, protein interaction network, as well as gene-phenotype associations. Finally, we describe the construction of the phenotype network and protein complex network. With these, we then present our RWPCN algorithm for prioritizing disease-related genes.Human PPI data were downloaded from the Human Protein Reference database (HPRD) coimmunoprecipitation, cosedimentation, and ion exchange chromatography. We only considered those human protein complexes that include at least one gene in HPRD human protein interaction data. This resulted in 379 human protein complexes with an average size (the average number of proteins) of 3.83. This set of protein complexes contains a total of 918 human genes, covering 10.5% of human genes in our PPI network. Note that we have also filtered out giant complexes if they covered a number of smaller complexes.Human protein complexes data were downloaded from the Comprehensive Resource of Mammalian protein complexes (CORUM) database Recall that we also view those proteins which are not included in any CORUM protein complexes as individual protein complexes. There were 7838 of these, and 3964 of these individual protein complexes directly interact with CORUM complexes in our resulting protein complex network.Gene-phenotype associations are assembled from the OMIM database The protein complex network in the middle layer is where phenotypically-related protein complexes are connected. Within the protein complex networks, the links are marked with gray lines, with the thicker lines indicating stronger linkage strengths between the two corresponding protein complexes. We will describe how to compute the protein complexes' linkage strengths later. The links between the phenotypes and complexes capture the known gene-phenotype associations, denoted by dashed red lines.At the bottom level is the PPI network. Two proteins are connected if they were reported to be interacting to each other. Across the networks, each protein complex in the middle level links with all its component proteins (yellow nodes) in the PPI network.Given a query disease phenotype (a query node in the top level), our objective is to predict disease genes for this phenotype in the bottom level PPI network, guided by the protein complex relationships in the middle level. Our proposed RWPCN algorithm will traverse between the three networks and exploit the structural relationships accordingly.Biologists already have a detailed knowledge of the phenotypes that are associated with each other. These phenotype associations have been used to prioritize candidate disease genes as well as to discover functional relations between genes and proteins MeSH) controlled vocabulary as standardized phenotypic feature terms. A phenotype pt∈PT  is represented as a feature vector pt =  where each dimension represents a vocabulary of Medical Subject Headings. A dimension i (1< = i< = l) in the feature vector pt represents a MeSH concept which provides a standardized way to retrieve information to refer to the concept. Each feature value ix is the weight of ithMeSH concept, which is determined by the concept relevance and document frequency in As in the method by van Driel ipt = , jpt = , we measure the phenotypic similarity between two vectors by the cosine similarity between the normalized vectors, i.e.,ipt and jpt using a logical function c and d, namely c = -15 and d = log(9999) respectively.Given two k-NN model (k-Nearest Neighbor). That is, for each phenotype ipt, we compute its top k most similar phenotypic neighbors (i.e. having the k highest phenotypic similarities with ipt) to link to it. We experimentally test the effects of different values of k on the performance of our proposed algorithm, and we set k = 10 as the default value.We construct our phenotype network using PPIG = , where PPIV is the set of nodes (proteins) and E = {| u,v∈PPIV} is the set of edges (protein interactions). To construct protein complex network in the middle level, we need to collect known protein complex data or use some computational methods to predict protein complexes. In this paper, we use the known protein complex database Comprehensive Resource of Mammalian protein complexes (CORUM) COM consists of a set of multi-protein complexes from CORUM (set MC) as well as a set of individual complexes (set CI) — namely those individual proteins that are not involved in any of the current CORUM complexes. As such, we have the following:A PPI network (in the bottom level) is an undirected graph COM, we define the protein complex network as a directed super graph COMG = , where the super node set COMV = COM denotes a set of protein complexes and COME = {| Ac,Bc∈COMV} represents the set of links between protein complexes. Note that a link ∈COME can be categorized into one of three types depending on the nature of complexes Ac and Bc, namely, CEC2 (C2C links between two multi-protein complexes), IEI2 , and IEC2 . Next, we describe how to assign weight for these three types of links.Given the protein complex set Ac ∈MC is a super node that can be represented as a graph Ac =  where the set cAV represents all the proteins in the complex Ac, and the set cAE represents the protein-protein interactions among the proteins in cAV. Given two complexes Ac =  and Bc = , Ac,Bc∈MC , a C2C link C2CE between Ac and Bc can be quantified as follows:Note that each complex CEC2  = CEC2 .Basically, Equation (5) evaluates how closely the protein members from different complexes interact with each other overall. It is the proportion number of interaction between the complexes among the number of all possible interactions. If there are a lot of physical interactions between the members from two complexes (non-overlapping proteins), then the two complexes are likely to be highly related as mutations of proteins in one of protein complexes could correspondingly disrupt the other complexes' functions, thereby producing similar disease phenotypes. Note that according to equation (5), it is easy to know that Ac∈MC and one individual protein complex AI ∈IC, then the C2I link CEI2  and the I2C link I2CE  can be defined as follows:In the case that we have one multi-protein complex individual protein complexes AI and BI, , then the I2I link IEI2  and the I2I link I2IE  are computed as follows:AI) is the number of neighbors of vertex AI.Finally, given two ipt, we aim to prioritize candidate disease genes based on known disease genes which are associated with ipt's similar phenotypic neighbors in the phenotype network.We are now ready to present our proposed algorithm. Given a query phenotype N(ipt) represents the k-NN phenotype neighbor set of the query phenotype ipt where each jpt∈N(ipt) is similar with ipt. Let dis(ipt) be the set of causative genes of the phenotype ipt. We define the seed disease gene set with respect to ipt as s∈ipt and the score for its seed gene set Ac as follows:F denotes initial score of the protein complex Ac with respect to ipt.Let G, EG)G = , quantifies the richness of edges within G and it is defined as shown in equation (10) density (G)≤1. If density (G) = 1, then G is a complete graph, which means every pair of distinct vertices in GV is connected by an edge. As each protein complex can be viewed as a graph, we apply A)density. The probability vector at step r, rF, can be calculated by equation 11,1F = 0F.Formally, let W' is the column normalized form transpose of adjacency matrix W which is the transition matrix of the whole protein complex interaction network. We construct matrix W based on the three different links between protein complexes. Recall that our protein complex set COM consists of both multi-protein complexes (MC) and individual complexes (IC). The matrix W is thus defined as:C2CAn*n)   ( and I2IAm*m)  ( represents the sub-network links between multiple-protein complexes (equation 5), C2IAn*m)  , and I2IAm*m)  respectively, where n = |MC| and I|m = |C are the numbers of multi-protein complexes and individual complexes respectively.rF and r-1F, represented as Dif = |rF−r-1F| (measured by L1 norm). When Dif = |rF−r-1F|< = 10−10, as suggested in Li et al.F is smooth over the whole protein complex-complex network, and each vertex complex is assigned a value to represent its association with the disease phenotype of interest.Note that in Equation 11, the parameter rF reaches a steady state, we obtain the final scores of protein complexes with respect to query phenotype. Recall that the final objective of our algorithm is to prioritize candidate disease genes amongst the genes in the PPIG. The final step is therefore to prioritize candidate disease genes based on their associations with protein complexes. Given a candidate gene g, its association with query phenotype ipt, denoted by S, is computed asAC is the set of complexes containing the gene g, rF denotes probability of complex AC associated with phenotype ipt when rF reaches a steady state. Because mutations on the genes shared by multiple protein complexes may lead to multiple similar phenotypes, scores of these shared genes should be the accumulated score of protein complexes that contain them.Once the vector In this section, we firstly introduce the experimental settings and evaluation metrics. Then, we present the experimental results compared with state-of-the-art techniques.g, p) is employed as one test case where the phenotype p is the query phenotype and the gene g is the test disease gene. In each round of cross-validation test, we will first intentionally remove the association  from our data. We then run our proposed algorithm to score the genes based on their associations with protein complexes with respect to the query phenotype p. If the test disease gene g is ranked as top 1, we will consider it as a successful prediction; otherwise it is a failed case. We use the number of overall successful predictions to evaluate the performance of different prediction methods. Depending on the genes involved in the ranking, we further categorize our evaluation metrics into the following two classes, namely, whole genome evaluation and ab initio evaluation Whole genome evaluation proposed by Ab initio prediction proposed by Wu p to all of its known causative genes and we can only use the other disease genes associated with p's neighbor phenotypes as the seed disease gene set. If one of the known causative genes (assuming p is associated with multiple disease genes) related to the phenotype p is ranked top 1, we consider it a successful prediction.Our objective is to uncover novel gene-phenotype relationships. In order to compare different techniques, we employ standard leave-one-out cross-validation in our experiments. Each known gene-phenotype association  whole genome evaluation and ab initio evaluation. whole genome evaluation , it was regarded as a successful prediction where N (N> = 1) denotes the number of known disease genes for the query phenotype. Using this less stringent evaluation metric, our method predicted 240 genes successfully while CIPHER-DN could only predict 157 genes in the ab initio evaluation.Note that in the original CIPHER-DN paper whole genome evaluation, and 247 (a sensitivity of 0.153) in terms of ab initio evaluation, indicating our method is certainly capable of detecting the novel knowledge which were absent in the older reference data.In the evaluations above, we have used the standard (but old) gene-phenotype association data which were also used in k in our RWPCN algorithm. The flow parameter k decides the number of related phenotypes with regard to the query phenotype. An unnecessarily large k will include many phenotypes which are not relevant while a smaller k will include lesser number of related phenotypes and may miss out some important relevant phenotypes as a result.Recall that we have two parameters k fixed as 10 using leave-one-out cross-validation. The performance of the algorithms is measured using whole genome evaluation and ab initio evaluation mentioned above, as shown in We first investigated how the flow parameter whole genome evaluation and ab initio evaluation. This is expected since the seed nodes in protein complex network are more likely to hold the information flows, thus few flows will be distributed to the distant neighbors in the network. Biologically, this is reasonable since the protein complexes (and the corresponding proteins in the complexes) that directly interact with the disease complexes/proteins are more likely to be disease/phenotype related. We observe that the performance of RWPCN with With increasing value of k that decides the number of related phenotypes, we ran RWPCN with k from 4 to 15 with whole genome and ab initio evaluations. Results are shown in k from 4 to 10, indicating that incorporating more related phenotypes is helpful for prioritizing target disease genes. However, if we further include more phenotypes (e.g. when k>10) with low phenotypic similarities, noisy and un-meaningful phenotypes will be included k in the range of k in the wide range k.To study the effect of the parameter We also applied our method for uncovering novel candidate genes on specific complex genetic diseases. We have chosen Breast Cancer (MIM: 114480) and Diabetes Mellitus type 2 (MIM: 125853) for our case studies here.k = 10 and novel disease genes instead of evaluating the performance using cross-validation. We ranked the resulting candidate genes over the whole genome and selected the top 20 ranked genes associating with target phenotypes (Breast Cancer and Diabetes Mellitus type 2).We ran our RWPCN algorithm  and Pex26-Pex6-Pex1 complex. For evaluation, a set of 248 disease protein complexes from Lage et al.Recall that we have assigned scores to the protein complexes to indicate the degree of association of the protein complexes to the query disease phenotypes. The higher the scores protein complexes were assigned, the higher probability protein complexes were associated with corresponding phenotypes. Based on the scores, we ranked the protein complexes and studied the two top-ranked complexes here: Sarcoglycan-sarcospan complex (SG-SPN complex (surrounded by green line) contained five human proteins: Q16586, Q16585, Q92629, Q13326, Q14714 and it was ranked at top 1 protein complex in predicting phenotype (MIM: 608099)-gene (SGCA) association by our RWPCN algorithm. We found that this SG-SPN complex had a large overlap (shared four proteins) with the disease complex No. 230 (surrounded by red dash line) in our benchmark set. We also found that all shared 4 proteins were known disease genes linked to phenotypes (Blue dash links), which had high phenotypic similarities among them. Noted that gene Q14714 (SSPN) in SG-SPN complex was associated with phenotype Fukuyama Congenital Muscular Dystrophy (FCMD) (MIM: 253800) SG-SPN complex could indeed be a valid disease complex.PBD-ZSD) and rhizomelic chondrodysplasia punctata type 1 (RCDP1). PBD-ZSD represented a continuum of disorders including infantile Refsum disease (MIM: 266510), neonatal adrenoleukodystrophy (MIM: 202370), and Zellweger syndrome (MIM: 214100). Noted that the Q7Z412 (PEX 26) protein in the our predicted disease complex was also a known disease gene associated with all the three phenotypes, suggesting that the mutations of proteins in the same CORUM protein complexes were likely to induce the same or similar phenotypes. It also showed that our highly ranked protein complexes were indeed disease related.Similarly, While great progress has been made in genomics and proteomics, discovering the associations between genes and phenotypes have remained challenging. In this paper, we constructed a novel human protein complex network by integrating HPRD protein interaction network We have verified the ability of our RWPCN algorithm to disclose gene-phenotype associations through extensive experiments. We first exploit known gene-phenotype associations to initialize the higher-level complex phenotype associations based on the modular nature of complex diseases. Then, we prioritize candidate disease genes for disease phenotypes using the network propagation technique on the complex interaction network. Our RWPCN algorithm was shown to outperform the existing methods RWRH It should be acknowledged that the proposed RWPCN algorithm can be improved further. As RWPCN relies on the human protein complex interaction network, the coverage of the protein complex data can affect the performance of prediction. Since the current protein complex data is by no means complete, predicted human protein complexes with high quality could be taken into consideration. Combining the predicted and experimental validated complex data into the prioritization process (e.g. using the method reviewed in


Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, the presence of protein domains or the membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software. Mass spectrometry-based proteomics can now deliver highly accurate data on hundreds of thousands of peptide features in a single biological project. AccuratThe ability to perform side-by-side large-scale quantitative profiling of the proteome ,transcriptome or genome raises the question which classes of gene products show concordant and which show discordant behavior between the different levels of gene expression. For instance, one general question in proteomics is how far absolute levels of expression changes correlate between the transcriptome and the proteome. In the hypothetical case of pure transcriptional regulation the correlation between these two levels would be near one, and would only be limited by the technical limitations and imperfections of the respective quantitative profiling technologies. Indeed, while early investigations found low or no correlation between proteome and transcriptome,7, recenWhile transcriptional regulation is generally a dominant aspect of the entire expression cascade, there are many known examples of posttranscriptional regulation like micro-RNA controlled inhibition of transcripts and direThe protein intensity data used in the explanation of the 1D annotation distribution in the Results section is taken from a label-free proteome study of mouse dendritic cells to a depth of 5,780 proteins. In thatThe yeast data used in the sub-section on 2D annotation enrichment is obtained from de Godoy et al. where haIn all cases peptides were analyzed on a nanoflow HPLC system connected to a hybrid LTQ-Orbitrap or Orbitrap-Velos mass spectrometer (Thermo Fisher Scientific). Human and mouse data were searched against International Protein Index (IPI) prWe start the description of the data analysis workflow at the point where protein abundances or protein expression ratios have already been calculated. While all examples show proteomics data obtained with the MaxQuant software,17 in coWhen reporting quantitative data for proteins from shotgun proteomics data, care has to be taken in the counting of independent protein identifications. The measured peptides may in some cases not be unique and map to several proteins, which is called the protein interference problem. Only prWhen using MaxQuant, if several quantitative experiments are combined or replicates were made these will all be projected onto the same protein grouping over all 'quantitative columns'. Therefore, the proteome data will be in a convenient matrix form already, even for very complex experimental designs. This will not be the case when one wants to compare the proteome data with transcriptome data, for instance. Several probe sets of an Affymetrix chip measure the same gene and there may be several genes belonging to the same protein group. For the matching we take a protein centric view. For each protein in the protein group we determine all probe sets that are annotated in the chip annotation file with a UniProt identifier. It is not trivial to decide which UniProt identifiers to use for a group of proteins that are indistinguishable by the measured peptides. A protein group consists of proteins from the list of protein sequences submitted to the search engine that cannot be quantified independently based on the set of identified peptides. In particular, if two proteins have identical sets of identified peptides they will be grouped together. Also if the set of identified peptides of one protein is completely contained in the set of identified peptides of another protein, these two proteins will be combined in a protein group as well. Proteins within a protein group are sorted by the number of identified peptides in descending order. For the remaining ambiguities we use the razor peptide or parsimony concept, which means that the peptide is assigned according to Occam's razor principle to the protein group that most plausibly explains its existence, which is the one which already has the most peptide identifications assigned to it.The number of probe sets matched in this way to every protein group can vary now from zero to several. If none is matching then no comparison can be made for this protein. If one is matching, then the quantitative information for this probe set will be used. If several probe sets match the point-wise median of their quantitative profiles is taken. Expression data from other microarray types can be matched in a similar way as long as the vendor provides UniProt or other protein identifiers for the hybridization probes. Deep RNA sequencing data is also easily matched, for instance in the form of RPKM values produced. Note that for the quantitative analysis of expression data (irrespective of which kind) it is usually advisable to take the logarithm before proceeding with further steps. This is true for ratios as well as for abundance data. Also here before averaging expression profiles this is advisable, even if the median is taken. This is because also for the median an averaging can take place between the two central numbers in case there is an even number of values. The need for taking logarithms becomes immediately apparent in the case of ratios. One would expect that the average of a two-fold up-regulation and a two-fold down-regulation should be no regulation. This is however not the case if the ratios are averaged (2 + 1/2)/2 = 1.25 ≠ 1. If logarithms (e.g. to the base two) are averaged the desired result is obtained: (log(2) + log(1/2))/2 = 0. The base of the logarithm does not matter in principle since it can be absorbed in an overall factor multiplied to all the data. However it is customary to use base two for ratio data and base two or ten for abundance data.We base the annotation of proteins on UniProt identifiers. Also heOne major source of annotation is the gene ontology (GO) whiThe 2D annotation enrichment algorithm works equally well for 1D data, such as any quantitative proteomics experiment. We first describe the principle of the 1D distribution analysis, which also serves as a preparation for the 2D algorithm. The input is a single column of numerical values assigning one numerical value to every protein. These values are typically protein ratios or absolute protein abundances. They could also be derived quantities, like average fold-changes between replicate groups or p-values or test statistic resulting from a test for significant changes in protein expression. If the column has missing values then the respective proteins will be ignored in further analysis.We wish to test for every annotation term (such as every protein complex or pathway) whether the corresponding numerical values have a preference to be systematically larger or smaller than the global distribution of the values for all proteins. In the schematic example displayed in Figure To be independent of the shape of the distribution we apply a non-parametric test which in particular does not assume a normal distribution of the numerical values. These properties single out the two-sided (two-sample) Wilcoxon-Mann-Whitney test as the method of choice, which we apply to all protein categorizations in a given set of terms.The Wilcoxon-Mann-Whitney test assesses whether one of two observation groups tends to have larger values than the other. The method works entirely with the ranking of the values and checks in our case if the proteins of interest tend to be ranked higher (or lower) as a group relative to the ranking of all proteins. The test statistic isn1 is the size of group 1 and R1 is the sum of ranks in group 1.Where Technically, the Mann-Whitney test assumes independence of the values, which is a good approximation in our case, in particular since every peptide is used in only one protein group for quantification. If non-unique peptides were used in several protein groups, the independence assumption would not hold.The number of terms and therefore also the number of hypotheses tested simultaneously can be quite large. For instance, there are 9,732 different terms among the GO molecular functions. This makes it important to adjust for multiple hypothesis testing. We apply the Benjamini-Hochberg method and base1 and R2 are the average ranks within the group under consideration and its complement , respectively and n is the total number of data points. It is a number between -1 and 1. A value near 1 indicates that the protein category is strongly concentrated at the high end of the numerical distribution while a value near -1 means that the values are all at the low end of the distribution. For significant terms it is not possible that s reaches zero exactly, but especially for larger categories that show a slight but consistent trend it is possible to have small absolute values of s. A moderately positive value of s for a category with many members, for instance, indicates that there is a significant collective shift towards larger values for this category which however is small in absolute terms and possibly not noticeable when looking at individual proteins. Note that the method's calculations are entirely based on information within the measured proteome. Often, enrichment calculations in proteomics against the whole genome are problematic. By construction these problems are completely circumvented here.where R-37 and the s-value is 0.85, indicating that ribosomal proteins are strongly enriched among the most abundant proteins.In the ribosome example of Figure When applied to ratios of protein abundances the method described here is similar to the quantile-based enrichment calculations introduced by Pan et al. There thFor the analysis of quantitative protein expression values together with other high throughput data we would like to generalize the method described above to the joint distribution of two numerical quantities. To be specific in the further discussion we will assume that the other high throughput data to be analyzed together with proteomic data is constituted by mRNA expression levels. One may for instance be interested in the enrichments of annotations in the plane spanned by protein abundances and mRNA abundances. Similarly one may wish to plot protein abundance ratios (e.g. from isotopic labeling experiments) against mRNA abundance ratios between the same samples. Figure Also for the two-dimensional case, we want to avoid the normality assumption and therefore wish to use a non-parametric testing strategy. What is needed for the generalization to two numerical dimensions is a replacement of the Wilcoxon-Mann-Whitney test that works with two-dimensional input data. All the remaining strategy can then be taken over from the one-dimensional case. The concept of rank sums that is used in the definition of the test statistic for the Wilcoxon-Mann-Whitney test at first appears to be tied to the one-dimensional case since only in the one-dimensional case is it possible to define an order of the data points in a meaningful way. For points in a two-dimensional plane, in contrast, a natural order relationship does not exist. The situation is different for parametric tests, like Student t-test or analysis of variance (ANOVA) where the generalization to the multivariate case is straightforward and known as multivariate analysis of variance (MANOVA) (see e.g. reference), which The test statistic for the MANOVA test for two groups in two dimensions are given here for reference. It is proportional towhereare the differences of the group means between group 1 and 2 in the x and y coordinartes, respectively,are the summed squares of the deviations from the group means for x, y and mixed coordinates,are the means of groups 1 and 2 in x and y coordinates,are the sizes of groups 1 and 2 andare the ranked values for x and y dimensions, separated into group 1 and 2.We define the resulting MANOVA test result as the 2D annotation enrichment p-value. The FDR of this approach can be controlled with the Benjamini-Hochberg method in the same way as was done for the one-dimensional case.x, sy), the coordinate-wise difference of average ranks used in the one-dimensional case. It is confined to the square -1 ≤ sx ≤ 1 and -1 ≤ sy ≤ 1. The point  =  corresponds to annotation terms that are not distributed differently from the overall distribution of value pairs. The significance cutoff creates an empty region around the origin. The remaining parts of the rectangle can be subdivided into eight regions corresponding to correlating, non-correlating and anti-correlating regions (see Figure After determining which annotation terms show a significantly deviating protein/mRNA level distribution, we calculate an s-score in analogy to the one-dimensional case. Now the score is a number pair (sFigure Another example is shown in Figure The 2D enrichment analysis is integrated into the Perseus software package which will be described elsewhere. Perseus is freely available and can be downloaded from www.biochem.mpg.de/mann/tools/. All necessary preprocessing and normalization steps can be found in the 'Processing' menu in Perseus. The 2D enrichment analysis is located in the main menu under 'Processing → Annotation → 2D annotation enrichment'. Figure While replicates within one technology are usually best done as 'biological' as possible to ensure that the findings are robust and reproducible, for cross technology comparisons it is more desirable to have the equivalent of a 'technical' replicate. For instance, the cell populations from which the transcriptome and the proteome are measured should be as similar as possible, ideally aliquots from the same sample so that one is sure that one samples the same cellular state on different levels of expression. If desired, the whole measurement including the proteome and the transcriptome can be repeated as 'biological' replicates. In the majority of cases, however, the available data has not been recorded in this optimal way. Of course the data analysis described here can still be applied to this situation as well.Often enrichment analysis in proteomics is performed by calculating a p-value corresponding to a test if a certain annotation term is enriched in a certain set of proteins relative to all genes in the genome. The results of this kind of calculations have to be taken with caution, especially in cases where the proteome coverage is far away from saturation or completeness since apart from the effect under investigation they are biased by which proteins are measurable at all by the employed mass spectrometric technology. This maAnother issue of interest is the potential application of corrections when multiple related terms are used for statistical comparisons. For example, terms in GO are mutually dependent. In principle correction methods like this can be applied to 1D and 2D annotation enrichment as well and we might do so in the future. Note, however that by not taking the hierarchy and relatedness of terms into account the significant findings reported after multiple hypothesis correction are on the conservative side, since the number of effectively independent tests is lower than the total number of terms which is used in the multiple testing correction. Therefore there is no danger of over-reporting. On the contrary, at fixed FDR one might miss a few significant terms which one would have obtained with a method taking the relatedness into account.Many other tools for enrichment analysis already exist. In Hauang et al. the authThe authors declare that they have no competing interests.


Those proteins with short half-lives are involved in various regulatory processes. To better understand the regulation of cell process, it is important to study the key sequence-derived factors affecting short-lived protein degradation. Until now, most of protein half-lives are still unknown due to the difficulties of traditional experimental methods in measuring protein half-lives in human cells. To investigate the molecular determinants that affect short-lived proteins, a computational method was proposed in this work to recognize short-lived proteins based on sequence-derived features in human cells. In this study, we have systematically analyzed many features that perhaps correlated with short-lived protein degradation. It is found that a large fraction of proteins with signal peptides and transmembrane regions in human cells are of short half-lives. We have constructed an SVM-based classifier to recognize short-lived proteins, due to the fact that short-lived proteins play pivotal roles in the control of various cellular processes. By employing the SVM model on human dataset, we achieved 80.8% average sensitivity and 79.8% average specificity, respectively, on ten testing dataset (TE1-TE10). We also obtained 89.9%, 99% and 83.9% of average accuracy on an independent validation datasets iTE1, iTE2 and iTE3 respectively. The approach proposed in this paper provides a valuable alternative for recognizing the short-lived proteins in human cells, and is more accurate than the traditional N-end rule. Furthermore, the web server SProtP ( Proteins are the chief actors within the cell. All proteins in mammal cells are continually being degraded and replaced. Some cytosolic enzymes have half-lives as short as 10 minutes, whereas others last for days. The fluctuations in their expression are fundamental for metabolism, cell cycle control and communication between a cell and environment. The cell's proteolytic machinery must be highly selective and tightly regulated, since the accelerated destruction of an essential protein or the failure to degrade a short-lived regulatory protein could drastically alter cell function The continual destruction of cell proteins may appear to be wasteful, but it serves several important homeostatic functions The study of factors affecting those short-lived proteins has begun in the last century. The amino acid sequence composition was shown to be closely related to protein half-lives. In 1986, Rogers et al. proposed the PEST hypothesis that proteins with PEST sequence tend to undergo rapid intracellular degradation Most of above studies for protein degradation were previously performed on only a few or individual proteins due to the technical difficulty in global profiling. While in 2006, Archana et al. performed profiling of over 3,000 protein stability in yeast Short-lived proteins are usually regulatory proteins. Most of the studies for short-lived proteins have been performed on individual protein in human. And there is still lack of the characteristics profile analysis for these short-lived proteins. During our study, Huang, et al. published a paper in which they employed Nearest Neighbor Algorithm to identify four-type half-life protein based on some features The datasets of human protein half-lives used in this paper were downloaded from literature in Science The training dataset was obtained by randomly selecting 80% of the positive samples and the same number of negative samples in order to maintain data balance. Thus, the training dataset contains 408 positive and 408 negative samples, and the remaining proteins including 102 positive and 3920 negative samples were employed as the testing dataset. To better assess the variance of the estimation, this procedure was repeated 10 times resulting in 10 training datasets (denoted as TR1-TR10) and 10 corresponding testing datasets (denoted as TE1-TE10), respectively.The first independent validation dataset (denoted as iTE1) were downloaded from literature We have examined a number of features based on protein sequences and structures that are possibly relevant to the classification of short-lived proteins. Some features are involved because they are known to be relevant to short-lived proteins, while others are taken into account because of their statistical relevance to our classification problem.As we have known, the short-lived protein stability in a cell can be somewhat determined by its structure and sequence information, and the amino acid sequence is the basis to investigate the protein function. Therefore, we analyzed the amino acid components and distribution of the protein sequence. Firstly we statistically analyzed single peptide and di-peptide composition yielding a feature vector of 420 dimensions. Due to the polypeptide sequences variation in evolutionary process, analyzing the composition of classified amino acids will be more reasonable Structure-related factors are also important for the degradation of short-lived proteins. In this work, we considered the protein secondary structure components such as alpha-helix, beta-strand and coil contents, which were predicted by PSIPRED http://bioinfo2.weizmann.ac.il/~danag/d-box/form.html. The D-box identification is implemented by comparing the query sequence with blocks representing the D-box motifs from cyclin A, cyclin B, securin, and geminin protein families. We took the existence of these four kinds of blocks in the protein as a set of features to describe the stability of short-lived proteins. The KEN box was determined by a sequence search for the KENxxxN/D motif. Therefore, we obtained 5 features representing degradation motifs.Other classical degradation motifs such as short sequence motifs (destruction box and Ken box) are signals for a more specific degradation mechanism that serves primarily to regulate the function of proteins involved in the cell cycle. Destruction-box content was estimated by the “Destruction Box Motif (D-box) Finder” algorithm proposed by Dana Reichmann and his co-workers, which is available at Moreover, we took into consideration about the low complexity region (LCR) as an important feature related to short-lived protein. The LCR is a protein region consisting of a very small variety of residues. We examined the numbers of LCR, the length of maximum LCR and the total length of LCR in every sequence by the program of “SEG” The N-terminal residue has been expected to be significantly correlated with the protein half-life PEST regions are generally accepted to be associated with short-lived proteins Disordered region in a protein is another important signal for short-lived protein degradation. Some degradation motifs such as D-box tend to fall into the locally disordered regions In this study, signal peptides and transmembrane regions are found to be highly related to protein stability, thus we took the existence of signal peptides, the existence of transmembrane regions and the total length of transmembrane regions in a protein as three features in the initial feature list.Post-translational modification (PTM), which is the chemical modification of a protein after its translation, is closely correlated with the protein function. Thus we investigated possible relationships between kinds of PTMs and protein degradation. In general, PTMs includes phosphorylation, glycosylation, N-acetylation, ubiquitination, and so on. In this study, features focusing on protein phosphorylation and glycosylation were considered including the number of phosphorylation site in serine and threonine respectively, the number of C-glycosylation site, N-glycosylation site and G-glycosylation site.In the end, we totally obtained 776 features as displayed in We employed F-value We defined the propensity score of feature in short-lived or long-lived proteins as follows,In which To screen out discriminative features and remove the irrelevant ones, we first removed the redundant features based on Pearson correlation coefficient. We found that 160 pairs of attributes are strongly and positively correlated ranging 0.50–0.99 with P<0.001 in the human training datasets. Therefore, we removed attributes with lower F-values among these correlated features, and obtained 616 features to construct the SVM model. Then we ranked the initial features based on their F-values and selected the ones with F-value not less than 0.1. After that, we applied analysis of variance (ANOVA) to evaluate their discriminative power, and further removed the features with p-value more than 0.001. Finally, we obtained the 211∼254 distinctive features in 10 human training dataset (TR1-TR10) respectively to construct the SVM calssifier Therefore, we established 10 SVM-based classifier models with 10 human training datasets (TR1-TR10) to distinguish the positive(short-lived proteins) from the negative training data (long-lived proteins), using the program of libsvm-2.88. with RBF kernel function.To obtain the best model, there are two parameters c and g to be optimized. Using the automatic parameter selection script grid.py supplied by LIBSVM By using the feature selection method described in Protein degradation is a regulated process, and it may involve recognition of certain motif or degradation signal in short-lived proteins Employing the selected features which would have strong relevance to protein degradation see , we estaTo obtain an optimal set of features, we further reduced the number of features for the “short”-“medium/long/extra-long” classifier, and evaluated the prediction performance of each classifier. We found that the number of features can be further reduced from 616 to 11 without decrease the performance see . Using tAnalysis of the 11 feature components showed four classes of features: hydrophobic features , signal peptide, protein length, and an acidic amino acid or short sequence motif containing an charged amino acid see . The proIt is interesting that signal peptide can affect protein half life see . Of the Hydrophobic features are strongly enriched in short-lived proteins see . In contIt is possible that hydrophobic interactions are an important mechanism for regulated degradation. It has been known that hydrophobic character of the tyrosine in IκBα is important for its rapid turnover Transmembrane proteins can be degraded by specific pathways. In our localization analysis, we also found significant enrichment of fast degrading proteins in cytoplasmic membrane (Fisher's exact test: p = 1.78E-14). Cytoplasmic membrane is not in a static state, accompanied with cyclic generation and membrane movement. Endocytosis is the degradative rout to lysosomes followed by extracellular material and also by certain plasma membrane proteins which will not be recycled to the plasma membrane for re-usage In addition, an independent datasets  as introduced in ROC curve of the proposed SProtP was also plotted in http://www.expasy.ch/tools/protparam.html). To compare our approach with ProtParam tool, we predicted short-lived proteins in human testing datasets (TE1-TE10). The results showed that ProtParam achieved an overall prediction accuracy 57.0% in human  KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. Some of the characters such as KEGG enrichment scores and the number of protein complexes the protein involved are difficult to accurately calculate for less studied proteins. But SProtP Human does not involve this problem, and is easier to be implemented. It can achieve an accuracy of 79.8%. It will provide a rich resource for biologists the study of protein turnover, evaluation of the efficiency of RNAi on protein level.Based on the “short”-“medium/long/extra-long” classifier using 11 feature components from protein sequence, we developed software Short-lived Protein Prediction in Human (SProtP Human). And it was made freely accessable academically via With the availability of the human protein half-life data, we found that sequence-derived features of protein could globally influence their half-lives in mammalian cells. The approach presented in this paper provide a valuable alternative for recognizing short-lived proteins at proteome scale in human cells. However, its accuracy may be affected by many factors such as cell types or stress conditions. It should be noted that protein degradation is very complex in eukaryotic cells. For example, E3 ubiquitin protein ligase family, which provide selectivity to the proteasome-ubiquitin degradation pathway, may number in hundreds in humans By investigating the sequence-based features related to protein degradation, a new approach was developed to recognize the short-lived proteins in human cells which are important for cell cycle progression, signal transduction, and differentiation. The average sensitivity and specificity on 10 human testing dataset were 80.8% and 79.8%, respectively. We also obtained 89.9%, 99% and 83.9% of accuracy in an independent datasets iTE1, iTE2 and iTE3, respectively.http://reprod.njmu.edu.cn/sprotp.As a conventional prediction method for protein half-lives, N-end rule has been employed by researchers for a long time Table S1Evaluation on 10 human testing datasets using corresponding training model.(PDF)Click here for additional data file.Table S2Feature List.(PDF)Click here for additional data file.Table S3Feature selection in 10 human training dataset.(PDF)Click here for additional data file.


CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs.Recent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7  were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations.By comparing the results of all seven prediction tools, three nsSNPs  were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins.This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.Cell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. These approaches were based on the biochemical severity of the amino acid substitution, as well as the protein sequence and/or structural information, which can provide a more feasible method for phenotype prediction.Cyclin-dependent kinase 7 (Cdk7), a regulatory enzyme for the initiation of cell cycle progression, was initially identified from a search for cDNA encoding protein kinase(s) related to Cdk1 ,3. Subsein silico algorithms were developed to predict the impact of amino-acid substitutions on protein structure and function. Some of the variation tolerance methods follow a similar procedure, in which a missense variant is first labeled with properties, related to the damage it may cause to the protein structure or function [in silico methods can provide a feasible and the high-throughput way to determine the impact of large numbers of nsSNPs on protein function. To understand the atomistic level changes and the dynamic behavior of the molecule with respect to the potential mutations, we conducted molecular dynamics (MD) simulations analysis. MD simulations can help us understand the effects of mutation on protein structure, which allow exploring how one amino acid substitution can create a ripple effect throughout the protein structure. Offman et al. found a strong correlation between MD analysis and the experimental work on the molecular basis of the most common protein upon N370S mutation in causing Gaucher's disease [Recently, more sophisticated function . Howeverfunction . The ult disease ,27. ThusCDK7 gene have received considerable attention from experimental biologists, the functional consequence of most of the nsSNPs in CDK7 at the structural level is still unknown. The main goal of this in silico analysis is to determine the most deleterious variants in CDK7 gene. In this context, publicly available in silico tools such as Sorting Intolerant From Tolerant  [CDK7 gene. As a next step, we subjected MD simulation study in the native and mutant models of CDK7 proteins using GROMACS 4.5.3 package [Although deleterious nsSNPs of le, USA) , Polymorle, USA) , PANTHERle, USA) , I-Mutanle, USA) , SNPs&GOle, USA) , predictle, USA) , and scrle, USA)  were use package ,36. MD s package -39. The CDK7 gene was retrieved from dbSNP [Dataset for the evaluation of potential nsSNPs in om dbSNP  and Swisom dbSNP  databaseom dbSNP , PubMed,om dbSNP  were obtom dbSNP  respectiom dbSNP .in silico algorithms. Here, we analyzed 14 nsSNPs of CDK7 gene with seven different in silico tools specifically SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and SNAP to determine the protein structural and functional significance. Table CDK7 gene with the corresponding amino acid substitution. SIFT makes inferences from sequence similarity using mathematical operations. SIFT constructs a multiple sequence alignment (MSA) and considers the position of the missense variants. Based on the amino acids appearing at each position in the MSA, SIFT calculates the probability and classifies a missense variant ‘tolerated’. SIFT can be applied not only to naturally occurring nsSNPs but also to identify artificial missense mutations. Among the 14 nsSNSPs analyzed by SIFT, six were identified as deleterious which obtained a SIFT score ≤0.05. PolyPhen2 utilizes a combination of sequence and structure-based attributes for the description of an amino acid substitution, and the effect of mutation is predicted by a native Bayesian classifier. The sequence-based features include position specific independent count (PSIC) scores, MSA properties, and position of mutation in relation to domain boundaries as defined by Pfam [CDK7 gene, 13 nsSNPs showed negative DDG values, were considered to be less stable and deleterious. The remaining one nsSNP showed a positive DDG value and classified as non-deleterious. SNPs&GO is an support vector machines (SVM) classifier based on variation type and sequence environment information, sequence profiles taken from MSAs, predictions from the program PANTHER, and a function-based log-odd score describing information about protein function defined by Gene Ontology (GO) terms. SNPs&GO predicted four nsSNPs of CDK7 gene, which are related to a disease condition. SNAP is a neural network-based method that uses in silico derived protein information  in order to make predictions regarding functionality of the mutated proteins. The network takes protein sequences and lists of mutants as input, returning a score for each substitution. These scores can then be translated into binary predictions of effect (present/absent) and reliability indices. SNAP screened four nsSNPs of CDK7 gene as non-neutral, and it may cause phenotypic changes. PhD-SNP is a prediction method based on single sequence profile-based SVM, trained on Swiss-Prot variants. The single sequence SVM classifies the missense variant to be pathogenic or neutral, based on the nature of substitution and properties of the neighboring sequence environment. PhD-SNP classified four nsSNPs as deleterious. Comparing the results of all seven prediction tools, three nsSNPs at corresponding amino acid position I63R, H135R, and T285M with a highest SIFT tolerance index of 0.00 and PSIC score difference 1.0 were selected for structural analysis.Identifying the deleterious nsSNPs has become possible with the aid of improved  by Pfam . The strStructural information could play a vital role in unveiling the molecular mechanisms leading to a disease. Based on this, we proposed modeled structures for all the three mutants  of CDK7 protein using PyMOL  . SubstitIn vitro studies of flavopiridol showed inhibitory activity towards CDK7 protein and lead to programmed cell death in cancerous cells [in silico docking tool, Autodock4 and PatchDock [us cells . SubstitatchDock . Before atchDock  and WorlatchDock . In theiatchDock  in theirIn order to determine the consistency in docking analysis, in addition to Autodock4 analysis, we performed docking in PatchDock . DockingMolecular dynamic simulations studies were carried out to unravel the atomic level changes in the CDK7 protein with respect to the time scale. The overall protein stability changes upon mutation were evaluated by root mean square deviation (RMSD) values. We calculated the backbone RMSD for all the atoms from the initial structure, and this is considered to be a primary criterion to measure the convergence of the protein system concerned. The backbone RMSD was calculated for both the native and mutant models from the appropriate trajectory files Figure . We obseHydrogen bonds and salt bridges are the key parameters in determining the stability of protein ,54. Non-Salt bridge distances of CDK7 protein in both native and mutant proteins were calculated from the 10 ns trajectory data and shown in Additional file in silico programs and statistical methods may be used to predict the effects caused by mutations and elucidate the underlying biological mechanisms. However, in silico tools can be used to examine the potentially deleterious nsSNPs that might affect important drug targets before further investigation by wet laboratory techniques. Previously, our group also identified and analyzed the effects of deleterious nsSNPs in several proteins at structural and functional level and drug binding capability using various in silico tools [in silico analysis to determine the potential deleterious and functional nsSNPs in CDK7 protein along with molecular dynamics and docking study. To determine the possible effects of nsSNPs in CDK7 gene, we employed seven widely used in silico tools specifically SIFT, PloyPhen2, I-Mutant3, PANTHER, SNPs&GO, SNAP, and PhD-SNP. SIFT predicted 6 (42.85%) nsSNPs as deleterious, PolyPhen2 identified 9 (64.28%) substitutions which affect protein structure and function, I-Mutant3 identified 13 (92.85%) substitutions which affected the stability of protein, PANTHER predicted 7 (50%) nsSNPs to be deleterious, SNPs&GO, SNAP, and PhD-SNP identified 4 (28.57%) nsSNPs related to the disease condition. The basis for the predicting impact of nsSNPs in these seven algorithms was different, and we would expect the outcomes to occur in some ways, dissimilar. However, the positive predictions that overlap all these seven in silico tools would provide high reliability to behave similarly. The difference in their predictions might be due to the difference in features utilized by the methods or the training dataset. Comparing the prediction of all the seven methods, three nsSNPs  were identified as highly deleterious and selected for further structure and functional investigations. To gain insight knowledge on the protein structure and what kind of harmful modulation these mutations give rise, the CDK7 protein was analyzed by MD approach. In the 10 ns simulation trajectory, different parameters were applied to analyze the level of structural changes. Molecular stability and flexibility changes were observed by RMSD and RMSF analyses. Stability is a fundamental property affecting the bimolecular function, activity, and regulation. Protein stability analysis results inferred that the stabilities of I63R and H135R RMSD are less deviated than those of the native and T285M protein. High or less deviation implies increase or decrease in the stability of protein. Hence, we believe that reduction in the stability of I63R and H135R models could affect the CDK7 protein structure. From the fluctuation analysis, we observed a decrease in the flexibility for all the three mutant models in first half of the residues (approximately 25 to 150) and increase in flexibility for the mutant model I63R in the rest of the residues (about 150 to 260). Increase in the flexibility could make the protein more flexible, and decrease in the flexibility makes protein more rigid. Conformational changes are required for many protein functions [in silico tools can indicate possible deleterious nsSNPs in CDK7 protein. Then, MD studies support the structural and conformational changes for the CDK7 deleterious nsSNP incorporated model. Finally, the binding ability of mutant model with the drug was validated to facilitate the study of new drug-targets and discovery of new drugs for CDK7 protein. In silico approaches reviewed here generated not only a considerable amount of valuable data but also the need for further validation by experimental methods such as in vitro binding/activity assays.The central objective in molecular biology and population genetics is to identify and characterize the nsSNPs that are functionally related from those that are not. This understanding not only provides insight into cancer biology but also highlights the anticancer therapeutic targets and diagnostic markers. NsSNPs in coding region can lead to amino acid change. This can lead to alterations in protein function and account for susceptibility to disease and altered drug response. Identification of deleterious nsSNPs from tolerant nsSNPs is ideal for analyzing individual susceptibility to disease, understanding the pathogenesis of disease, identifying molecular targets for drug treatment, and conducting individualized pharmacotherapy. Several experimental studies were carried out to analyze the relationship between nsSNPs and drug response in cancer treatments. Chambers et al.  reportedco tools -63. In ttified 9 .28% subsunctions , but theunctions . The fleCDK7 gene. Sequence evolutionary information-based methods  and the combination of protein structural and functional parameter-based methods  are some of the most reliable tools used for deleterious nsSNP prediction. SIFT, PANTHER, PhD-SNP, SNAP, SNPs&GO, and I-Mutant3 give results in two prediction categories, either tolerated or deleterious, while PolyPhen2 gives results in three categories: benign (probably lacking any phenotypic effect), possibly damaging, and probably damaging (should affect protein function). Sequence-based prediction includes all types of effect at the protein sequence level and can be applied to any human protein with known relatives. Structure-based approach is feasible to implement for proteins with 3D structures. Analyzing deleterious nsSNPs by both sequence and structure level has the added advantage of being able to assess the reliability of the generated prediction results by cross-referencing the results from both approaches. SIFT predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. A SIFT score ≤0.05 indicates that the amino acid substitution is intolerant or deleterious, whereas a score ≥0.05 is predicted as tolerant [The ability to distinguish pathogenic and benign variants from a pool of data is a daunting task. Recently, many computational algorithms have been developed for the feasible prediction of disease-associated variants. Some of the methods classify deleterious variants according to the predicted pathogenicity, and other methods predict the deleterious variants based on protein-stability changes upon mutation. We used both these approaches to identify deleterious variants in the tolerant ,69. PANTEbinding = Eintermolecular + Einternal + Etorsional − Eunbound. The unbound structure of ligand is the same as the bound state , so the Einternal is equal to Eunbound, and they do not contribute to the total energy. On the other hand, the Etorsional is calculated based only on the number of torsional bond in ligand, so this term remains the same in each complex. It is clear that there are significant differences between nine charge methods in the estimated binding free energies, so the difference should come from the Eintermolecular, including energies of dispersion/repulsion, hydrogen bonding (hbond), desolvation potential, and electrostatic interactions. The energy functions used in docking simulations attempt to account for the intermolecular energies between the protein and the ligand, as well as the intramolecular energies arising from the ligand conformation itself. AutoDock4 uses a grid-based approach to approximate the energy calculations used by the energy function. During the evaluation of a candidate conformation, the grids were used as lookup tables which store the values used in the calculation, thus making the overall docking simulation exceptionally fast. The Graphical User Interface program ‘Auto Dock Tools’ was used to prepare, run, and analyze the docking simulations. Kollman united atom charges, solvation parameters, and polar hydrogens were added into the receptor PDB information for the preparation of protein in docking simulation. Gasteiger charges were added in the ligand PDB file.Protein-ligand interaction study was performed between the native and mutant models of CDK7 protein with the inhibiting compound, flavopiridol. In order to carry out the docking analysis, we used the AutoDock4 suite as a molecular-docking tool. AutoDock4 is a suite of programs making it possible to predict how ligands bind to large macromolecules. In this docking simulation, we used semi-flexible docking protocols. Throughout the docking simulation, the target protein is kept rigid. The ligand being docked is usually flexible and, therefore, explores an arbitrary number of torsional degrees of freedom in addition to the six spatial degrees of freedom spanned by the translational and rotational parameters. AutoDock4 provides different optimization algorithms to search the space of possible protein-ligand combinations, such as simulated annealing, genetic algorithm (GA), and hybrid evolutionary algorithms EA termed Lamarckian GA (LGA) combining the GA with a local search strategy . The LamIn addition to the Autodock4 study, we used PatchDock for docking native and mutant CDK7 proteins with the drug flavopiridol. PatchDock performs docking based on molecular shape representation and surface patch matching plus filtering and scoring. PatchDock is more reliable because of its fast transformational search, which is driven by local feature matching rather than brute force searching for the six-dimensional transformation space. It further speeds up the computational processing time by utilizing advanced data structures and spatial pattern detection techniques, such as geometric hashing and pose clustering. Protein and the ligand molecule were given as input in performing the docking experiments with default root-mean-square deviation (RMSD) value (4.00 Å). It generated several complex structures based on docking scores. The complex structure file, with the best docking score was selected for further analysis. The geometry of both wild type and mutant type CDK7 structures were optimized through Steepest Descent method with 1000 steps each of GROMACS 4.5.3 package. Each minimization was carried out with GROMOS-96  43a1 for+ or Cl- ions. The temperature was kept constant by using a Berendsen algorithm [Molecular dynamics simulations for the native and mutant models were done with MD simulation package GROMACS 4.5.3 that adopts GROMOS96 43a1 force field parameter for energy minimizations. Energy minimized structures of the native CDK7 and three mutant models were used as a starting point for MD simulations. All the proteins were solvated in a cubic box with wall extending at least 0.9 nm from all atoms and filled with SPC  water molgorithm  with a clgorithm  was usedlgorithm . The SHAStructural properties of the native and mutant models of CDK7 protein were calculated from the trajectory files with the built-in functions of GROMACS 4.5.3 The trajectory files were analyzed through the use of g_rmsd and g_rmsf GROMACS utilities in order to obtain the RMSD and RMSF values. The number of distinct hydrogen bonds formed in the protein during the simulation was calculated using g_hbond utility. The number of hydrogen bond was determined on the basis of donor-acceptor distance less than 3.9 nm and of donor-hydrogen-acceptor angle larger than 90° . Salt brACE: Atomic contact energy; CDK7: Cyclin-dependent kinase 7; SNPs: Single Nucleotide Polymorphisms; nsSNPs: Nonsynonymous Single Nucleotide Polymorphisms; GRACE: Graphing Advanced Computation and Exploration; MSA: Multiple Sequence Alignment; RMSD: Root mean square deviation; RMSF: Root mean square fluctuation.The authors declare that they have no competing interests.NN, CGPD, CC, HZ, and LC were involved in the design of the study, drafting the manuscript, and the acquisition, analysis, and interpretation of the data. HZ, CC, and LC supervised the entire study. CGPD, CC, and HZ were involved in the final drafting of the manuscript. All authors read and approved the final manuscript.(A) Secondary structural elements of native CDK7 protein. (B) Secondary structural element changes in the mutant model I63R due to the substituted of arginine. (C) Substitution of histidine at position 135 showing the secondary structural changes in H135R mutant model. (D) Substitution of methionine at position 285 showing the secondary structural changes in T285M mutant model. All the substituted amino acids are indicated by green boxes. Secondary structural elements changes in mutant models of CDK7 protein. Click here for fileInvolvement of cation-Pi interaction in wild type and mutant structure of CDK7 protein.Click here for fileFlavopiridol interacting residues with CDK7 wild type and mutant type structures.Click here for fileA) Native complex showing high number of residues interacting with ligand. (B) LIGPLOT showing interaction between mutant model I64R and flavopiridol. (C) LIGPLOT showing interaction between mutant model H135R and flavopiridol. (D) LIGPLOT showing interaction between mutant model T285M and flavopiridol. LIGPLOT analysis of CDK7-flavopiridol complex in both native and mutant states.  and the abscissa is time (ps). Black, red, green, and blue lines indicate native, I63R, H135R, and T285M structures, respectively. Click here for file


Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques -4.ProteiTo resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups -8 have iIn this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction . The weiWeights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating. It is ali, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar.Semantic similarity has been used in Information Science to evaluate the similarity between two concepts in a taxonomy, and we i(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj|.C denotes the sets of GO terms, whose annotation includes x and y. |C| is the number of common GO terms which x and y both have.Suppose a protein x is annotated on m different GO terms. Ssem between x and y as follows:Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z.Ssem and topology weight to the edge between u and v as a weight.Considering the graph topology, we also involve the topology weight. For an input graph G = , we assign the topology weight of an edge  to be the number of neighbors shared by the vertices u and v. Then we assigned the sum of SWe define the weight of each vertex to be the sum of the weights of its incident edges. After all vertices are assigned weights, we also sort in non-increasing order the vertices by their weights and store them in a queue Sq (vertices of the same weight are ordered in terms of their degrees). The complexity of calculating edge weights and vertex weights is O(|V||E|), and the complexity of sorting all vertices by their weights is O(|V| log |V|).The notion that vertex weight is a good measure for selecting seeds has been adopted by DPClus  and MCODvk of a vertex v to a subgraph K, where v∉K, is defined byWe introduce a new concept to measure how strongly a vertex v is connected to a subgraph K: the interaction probability Evk is the sum of the weights of edges between the vertex v and K, and wk is the sum of weights of edges in K. We discuss the relationship between the parameter Evk and INvK introduced in the algorithm IPCA[vK is defined as vK is the number of edges between the vertex v and K, and nK is the number of vertices in K. By the expressions, our parameter Evk is similar to the parameter INvK. While our parameter considers with the biological weights, it have more biological meaning.Where eithm IPCA. Accordiithm IPCA, INvK isvk. This procedure is similar to the one proposed in IPCA [vk to judge the extending. So whether a high priority vertex v is added to the cluster is determined by the Extend-judgment test below.A cluster K is extended by adding vertices recursively from its neighbors according to the priority. The priority of a neighbor v of K is determined by the value E in IPCA , except in be a threshold ranging between 0 and 1, let d be a positive integer, and let K be a subgraph. SP is the shortest path. A vertex v∉K is added to the cluster if the following two conditions are satisfied (where K + v denotes the subgraph induced by K and v):Let Tvk≥Tin; and1. E2. The(SP(K+v)≤d)Only when the candidate vertex v is satisfied the conditions, can it be added to the cluster. Once the new vertex v is added to the cluster, the cluster is updated.Before we present the results of our comparative experiments, let us first introduce the various evaluation metrics that have been used to evaluate their computational methods for complex detection. We will then present the experimental results of comparing different state-of-the-art techniques using these evaluation metrics.Overall, there are three types of evaluation metrics used to evaluate the quality of the predicted complexes and compute the overall precision of the prediction methods.p,Ep ) and a real complex b = in the benchmark complex set, as defined in equation (3) below, can be used to determine whether they match with each other. If NA  ≥ ω ,they are considered to be matching . Let P and B denote the sets of complexes predicted by a computational method and real ones in the benchmark, respectively. Let Ncp be the number of predicted complexes which match at least one real complex and Ncb be the number of real complexes that match at least one predicted complex. Precision and Recall are then defined as follows: [Precision, recall and F-measure are commonly-used evaluation metrics in information retrieval and machine learning. For evaluating protein complex prediction, we need to define how well a predicted complex which consists of a set of protein members, matches an actual complex, which is another set of protein members. The neighborhood affinity score NA  between a predicted complex p =(Vfollows: (3)(4)(5)F-measure, or the harmonic mean of Precision and Recall, can then be used to evaluate the overall performanceij denote the number of proteins in common between ith benchmark complex and jth predicted complex. Sn and PPV are then defined as follows:Recently, sensitivity (Sn), positive predictive value (PPV) and accuracy (Acc) have also been proposed to evaluate the accuracy of the prediction methods ,29. Givei is the number of proteins in the ith benchmark complex.Here NGenerally, high Sn values indicate that the prediction has a good coverage of the proteins in the real complexes, while high PPV values indicate that the predicted complexes are likely to be true positives. As a summary metric, the accuracy of a prediction, Acc, can then be defined as the geometric average of sensitivity and positive predictive value,As we gained more and more biological knowledge about the proteins, we can associate a protein with (possibly multiple) functional annotations. The statistical significance of the occurrence of a protein cluster (predicted protein complex) with respect to a given functional annotation can be computed by the following hypergeometric distribution in equation (12) ,31:(12)Where a predicted complex C contains k proteins in the functional group F and the whole PPI network contains |V| proteins. The functional homogeneity of a predicted complex is the smallest p-value over all the possible functional groups. A predicted complex with a low functional homogeneity indicates it is enriched by proteins from the same function group and it is thus likely to be true protein complex. By setting a common threshold which specifies the acceptable level of statistical significance, the numbers of predicted complexes with functional homogeneity under this threshold for the various methods can then be used for evaluating their respective overall performance.in on clustering, compare the predicted clusters with the known complexes, evaluate the significance of the predicted clusters. We will also compare the algorithm OIIP to eight competing previous methods for their performance of identifying protein complexes. Since most proteins in the same complex have same or correlative function and involve in the same biological process, we employ biological annotation information, including Go cellular component annotation [The protein interaction database is downloaded from the Gavin database  and BioGnotation , GO Molenotation  and GO Bnotation  to assesin influences the outcome of the clustering, we generate 9 sets of clusters by using Tin = 0.1, 0.2,..., 0.9 from the Gavin PPI dataset. The effect on the predicted clusters with different Tin is given in Figure To understand how the value of Tin increases. However, there is a abrupt decrease at Tin = 0.5. This is probably caused by the Hub structures in the protein interaction network. When Tin = 0.5, these Hub structures are decomposed into complexes that consist of only 2 proteins. Figure in increases. With the increasing of Tin, the probability of neighbors added to the cluster is decreasing. Thus, the size of the predicted clusters is also decreasing.Figure in=0.5, which implies that the clusters generated by OIIP are reliable. The F-measure is about 0.45 when Tin=0.4, which represents overall performance of an algorithm. The Accuracy takes into account of both the sensitivity and PPV, and is determined by the larger one. In this experiment, the Accuracy is mostly influenced by the PPV. The PPV of the clusters generated by OIIP increases with the increasing of Tin. Especially, an obvious increase appears when Tin ≥ 0.5.As shown in Table in=04). Although Mcode has the highest precision, its recall is very low. The fact is that the predicted protein complexes are fewer than our method. Some predicted clusters which are not matched with criterion complexes are possible actual complexes which are undiscovered. So it is necessary to predict more clusters with high F-measure value.Since there have been protein complexes that were experimentally determined, a good protein complexes detecting algorithm should identify these known complexes as many as possible. Figure And we count the number of clusters with p-value less than 0.01, a threshold which represents significant biological sense and compute the proportion of clusters which achieve low p-value. The proportion of clusters from various methods with low p-value is shown in Table As show in Figure From the results of the experiment above we know that most of our predicted clusters have highly biological meanings. We give some examples of predicted clusters with detailed information which are matched with the benchmark complexes in Table Some of predicted clusters from our method are not matched with complex from criterion dataset. But we find that they have highly biologically significant and have high local density, so some of them may be real complexes which are still undiscovered. We also give some examples in Table It is believed that identification of protein complexes is useful to explain certain biological progress and to predict functions of proteins. In this paper, we developed an algorithm OIIP to identify protein complexes based on the new large weighted protein interaction networks. Experimentally generated protein-protein interaction data includes an enormous amount of false positives. So we introduced a semantic similarity method to measure the reliability of interactions. For this measurement, we use the annotations in Gene Ontology (GO), which provides the comprehensive functional information. When we implemented the OIIP algorithm with weighted networks, the overall F-measure and accuracy of complexes is substantially improved. This result strongly appeals the necessity of integrating of functional information for the analysis of protein-protein interaction data.The fact that biological properties are poor at the identification reveals that the higher-level structures  of proteins cannot be accurately represented by the primary structure under the current coding techniques. The experimentally determined protein interaction network has not been used in the research, and a possible future research could combine the experimentally determined protein interactions with the GO estimated interactions to further improve the identification.The authors declare that they have no competing interests.Please see sample text in BX identified all protein complexes and generated this manuscript; ZHY provided data information and edited this manuscript; HFL supervised all aspects of this manuscript. All authors read and approved the final manuscript.