The number of quaternary folds thus corresponds to the number of ways proteins physically interact with other proteins. This study focuses on answering two basic questions: Whether the number of protein-protein interactions is limited and, if yes, how many different quaternary folds exist in nature. By all-to-all sequence and structure comparisons, we grouped the protein complexes in the protein data bank (PDB) into 3,629 families and 1,761 folds. A statistical model was introduced to obtain the quantitative relation between the numbers of quaternary families and quaternary folds in nature. The total number of possible protein-protein interactions was estimated around 4,000, which indicates that the current protein repository contains only 42% of quaternary folds in nature and a full coverage needs approximately a quarter century of experimental effort. The results have important implications to the protein complex structural modeling and the structure genomics of protein-protein interactions. The protein universe refers to a collection of all proteins across all organisms in nature In contrast to the extensive studies of protein tertiary structural space, the quaternary structure space of protein-protein interactions is relatively unexplored. For example, the questions on whether the number of unique protein-protein complex structures is constrained and if yes, how many they are, have remained largely unanswered. Since most proteins perform their physiological functions via interaction with other protein molecules, the answers to these questions have practical applications in the understanding of protein-protein interaction specificity and protein-protein networks Exploration of the quaternary structure space has been mainly hampered by the relative dearth of protein-protein complex structures in the PDB library, and the lack of an unambiguous definition of protein quaternary structural folds and efficient methods to compare and categorize protein-protein complex structures. Among limited attempts, Aloy and Russell Here, we present a systemic study of a representative set of protein-protein complex structures in the PDB, with all structural pairs compared by a recently developed protein complex structural alignment algorithm, MM-align A non-redundant dimeric structure library was screened from DOCKGROUND 2 buried surface area. These procedures result in a total set of 7,616 non-redundant dimeric protein structures for our consideration (as of December 2011).For the counting of physically  meaningful protein-protein interactions, it is important to focus only on bona fide dimers in our dataset. For this purpose, DOCKGROUND has screened its complexes from the PDB Biological Unit files in order to ensure that crystallization artifacts are removed. Second, we eliminated all complexes with <30 interface residues and/or <250 ÅWe also attempted to apply the computational methods, including IPAC The pair-wise alignment of protein-protein complex structures is constructed by MM-align At the second step, a set of five initial alignments are constructed, including (1) an alignment of secondary structure (SS) elements; (2) gapless threading of two complex sequences; (3) an alignment based on the sum of the SS score and the distance score matrix from the second initial alignment; (4) a gapless threading of the longest continuous segments in the complexes; (5) a scan of superimpositions of five-residue fragment pairs.ijS=ijd is the distance of ith residue in the first complex and jth residue in the second. A modified Needleman-Wunsch dynamic programming ijS. Based on the new alignment, a new scoring matrix is derived, on which a fresh alignment is generated again by dynamic programming. This procedure is repeated till a converged alignment is reached. Finally, the alignment with the highest rTM-score is returned.At the third step, a residue-residue distance similarity matrix Lc is the total length of all chains in the target complex and Lali is the number of the aligned residue pairs in the two complexes. id is the distance of ith pair of Cα atoms after the superposition. The similarity of protein tertiary structures is often evaluated by TM-score rL and lL are lengths of the receptor and ligand, respectively; TM-scorer and TM-scorel are their TM-scores calculated based on the same rotation matrix of the complex superposition. Therefore, one flaw of TM-score, when used to compare complex structures, is that it becomes more sensitive to the tertiary structure of the monomers, due to the linear dependence of the monomer TM-scores. For example, for a pair of homodimers, if the structure of one chain is identical, the TM-score is at least 0.5 even if the orientation of the other chain is completely different (see e.g. l∼0), the rTM-score of the complex structure will be close to 0 even if the structure of another chain is identical (TM-scorer∼1). In other words, two complexes have a high rTM-score only when both the monomer tertiary structure and the relative orientation are similar.For complexes, TM-score in Eq. 1 can be factorized as two additive parts from two chains:see e.g. . For hetsee e.g. . To overr,l >0.5) according to Eq. 3.Quantitatively, for tertiary protein structures, it has been shown In MM-align was used to compare each protein complex in the non-redundant complex library to all other complex structures. It returned an rTM-score as the measure of the structural similarity of the complex pairs. SPICKER N and n, respectively. At the sequence level, protein complexes can be categorized into homologous families and we suppose the numbers of complex families in nature and in PDB are M and m, respectively. Apparently, a fold can contain multiple families since it is well-known that different sequentially homologous families can have the similar structural folds.Protein complexes solved in the PDB library is only a small subset of the complex universe in nature. Let’s suppose the numbers of complex folds in nature and in the PDB are m/M. Thus, the probability of a fold having Q families in nature to be included in the PDB as a fold with q families isq quaternary families in the PDB can be calculated using the equationQ is the total number of quaternary folds containing Q quaternary families in nature.If we suppose that the PDB structures are a random subset of nature, an assumption taken by many tertiary fold estimation models IG contains IX quaternary folds with sizes IK to IL (IL >IK) with each quaternary fold appearing with equal probability of I=1,2, … Imax. The observed quaternary folds in the PDB are grouped in a similar way, i.e. the group ig will include ix quaternary folds that comprise ik to il quaternary families. Thus, the expected number of observed quaternary folds in the PDB in the group ig can be written asie is replaced with the observed numbers in the PDB library, we get a linear equation for each of the ig groups that allow us to calculate IX, the number of quaternary folds of each group in nature. The total number of quaternary folds in nature, is thenFollowing the idea of the moment method of estimation IK and IL, which are needed for solving Eq. 6, we use the maximum probability principle method. Since quaternary folds consisting of more than 5 families represent a rare fraction (<5%) in the library, we divide the ensemble of quaternary folds in the PDB into 9 groups with ix>ix+1. The groups in nature were deduced according to the maximum probability principle, i.e. an observed quaternary fold with q quaternary families in the PDB should come from the quaternary fold with qT families in nature so that the probability P is maximal. Thus, the intervals for the values of IK and IL of the Groups 5 to 9 in nature can be set using following rules:Ik and Il are the boundaries of the corresponding groups in the PDB database. We note here that the indexes of groups are the same in the PDB and in nature. For the Groups 1 to 4, we haveIK and IL according to Eqs. 8–9 for each group in nature and selected the ones that fulfilled the conditionIX in each group is calculated, the total number of estimated quaternary folds in nature can be easily calculated by Eq. 7. From these values, the predicted total number of quaternary families is thenOnce the number of quaternary folds Pfam is a standard database for protein domain families where each family is represented by a multiple sequence alignment (MSA) as searched by Hidden Markov Model (HMM) Following this procedure, 7,523 out of the 7,616 complexes could be assigned to a Pfam family with an E-value <0.001, which results in 3,536 distinct quaternary Pfam families. The remaining 93 complexes did not return any Pfam hits for either subunit. These complexes shared a sequence identity <20% to each other and to all other complexes in the library, and hence were considered orphan families. Thus, 3,629 homologous families are obtained in total from the 7,616 protein-protein complexes based on evolutionary and sequence comparisons.The structural types of protein-protein interactions is specified by rTM-score, a scoring function designed to simultaneously assess the similarity between the individual subunits as well as their relative orientation of two complexes (see Eq. 3). Given a pair of complexes, rTM-score was calculated by an extended version of MM-align Using an rTM-score cutoff 0.5, the 3,629 quaternary families could be clustered by SPICKER Proteins of similar structure usually have similar function. Not surprisingly, it is observed that the proteins in each of our clusters present considerable convergence of function, although the sequence identity of protein complexes in one cluster can be as low as 9.9%. For instance, the biggest cluster consists of 47 members with the average and the lowest pair-wise sequence identity 41.6% and 17.7%, respectively. Despite the low sequence identity, all 47 structures are part of the RNA polymerase superfamily from different species. The second biggest cluster contains 34 complexes with the average and the lowest sequence identity 34.8% and 21.3%, respectively; all 34 are enzyme-inhibitor complexes where the enzyme is trypsin, thrombin or their derivatives . These complexes contain also the same GO term for “Tissue factor”. The third largest cluster consists of 32 coiled-coil complexes of the “mainly alpha” class of proteins. A graphical representation of all complex structures in our dataset has been shown in Another intriguing trend that was observed is that the cluster size distribution follows a power-law dependence as shown in h(S) is the histogram of the structure clusters which have S complex members.A similar power-law distribution has been extensively observed in the clustering of tertiary structures of protein domains M). Using the SwissProt database with a sequence identity cutoff of 30%, Orengo et al. estimated the number of protein tertiary families as 23,100 We use a statistical model (as outlined in METHODS) to estimate the number of quaternary folds, which assumes that the current PDB library is a random subset of the complex universe. First, to calculate the probability of a quaternary family to be included in the PDB, we need an estimation of the number of complex homologous families in nature  are homodimers; the number of quaternary families in these proteins is identical to that of tertiary families. For heterodimers, if all component chains were non-homologous to each other, the number of quaternary families should have been half of that of the involved tertiary structural families. Actually, in the 1,523 heterodimer families in PDB, around 11% consist of homologous monomers with a sequence identity >70% and around 50% have monomers with sequence identity >30%. Thus, the number of quaternary families in heterodimers should be considerably higher than half of the tertiary families they contain. There are also some proteins which may not participate in any interactions but the number of these proteins should be neglectable since most proteins perform functions through interactions with other proteins N) using Eqs. 7–10 for a range of M values from 4,000 to 25,000. As observed in N and M show a clear logarithmic dependence:In et al. and suppose the number of quaternary sequence families is the same as that of monomers, the probability for a quaternary family to be included in the PDB is λ=0.16  and the number of quaternary folds in nature should be 4,149. If taking the maximum Pfam number as the number of quaternary families , the number of expected quaternary folds is 4,782.N, as an examination of self-consistence of our calculations, we estimated the number of quaternary families (M’) assuming that the range of N predicted in the previous step are actual values existing in nature. In Column 3 of M’ calculated by Eq. 13, which is in close agreement with the arbitrary values of M, with a Pearson correlation coefficient=0.999.Since the number of the quaternary families can also be derived independently for any given To cross-validate our results as well as to verify the consistency of our definition of quaternary family, instead of using the Pfam family definition, the calculations were repeated by defining quaternary families based on pair-wise sequence comparisons, i.e. two complexes are in the same family if both chains have a sequence identity >30%. The cutoff of 30% in sequence identity has been extensively used as the homologous family cutoff for monomer proteins The above analysis showed that the current PDB library accounts for <50% of total folds in nature. While the number of tertiary folds in PDB is approaching its completeness Protein quaternary structure universe dictates possible ways that proteins interact with each other. Despite the extensive analysis on the universe of protein tertiary folds for which a general consensus exists, very few such studies were conducted on protein-protein complexes. Using a non-redundant set of protein dimeric structures in the PDB and the complex structural alignment tool MM-align, this study proposed a quantitative estimate of quaternary folds possibly existing in nature.First, a new scoring function, rTM-score, was introduced to measure the “similarity” between complex structures, which accounts for both chain orientation and monomer structural similarity into a single sensitive parameter. All non-redundant complex structures in the PDB screened at a sequence identity cutoff 90% were classified into “quaternary families” by mapping both sequences of each dimer onto the Pfam database. The 3,629 unique quaternary families were thereby clustered by rTM-score into 1,761 quaternary folds, with the largest cluster comprising 47 complex structures. About 60% of the structures were found to be structural orphans, indicating that the protein complex structure library is largely incomplete. A power-law dependence was observed between the cluster size and the number of clusters, which may implicate a cascade mechanism in the evolution of the protein-protein complexes Based on the maximum probability principle, the number of possible quaternary structure folds in nature was estimated. The number of folds in our estimation varies when the number of quaternary families changes which follows a strict logarithmic dependence. If we assume that the number of quaternary families in nature is similar to the number of monomeric protein families, it was estimated that the number of expected quaternary folds in nature is approximately 4,000–5,000. This number is about two times lower than the previous estimation There are several uncertainties in our model which can be improved in the future studies. First, quaternary structural folds are defined by rTM-score >0.5, which only accounts for global topology and corresponds to the complexes of the same chain-orientation and similar monomer fold (TM-score >0.5). A statistical study similar to TM-score Overall, despite the possible uncertainties, based on the simplicity and robustness of the model calculations and the comprehensive analysis of structural and sequence databases, our data provide the first quantitative estimation of the number of protein-protein interaction types in nature on a structural base of global topology. Given the fact that the possible phase space of protein complexes is almost infinite, it is striking that there are only several thousand possible quaternary folds, which demonstrates that the protein-protein interactions are highly specific. For instance, millions of antibodies interact with similar amount of antigens through the similar CDR locations, with the structural complexes sharing only a few unique conformations Figure S1TM-score versus rTM-score of complex structures.(TIF)Click here for additional data file.Figure S2Illustrative examples to highlight difference between TM-score and rTM-score values.(TIF)Click here for additional data file.Text S1Correlation between TM-score and rTM-score.(DOC)Click here for additional data file.


Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro.By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA–protein pair. This score can be used to measure the interactions between an RNA–protein pair. This method effectively discriminates interacting and non-interacting RNA–protein pairs and predicts RNA–protein interactions within a given complex. Applying this method on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins.Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process. Recent studies show that only a small part of the human transcriptome is involved in the protein-coding process . Long noIn general, lncRNAs function with their binding proteins. Thus, in order to understand the molecular mechanisms underlying the functions of lncRNAs, the binding proteins of lncRNAs need to be identified. Several experimental approaches such as RNA immunoprecipitation followed by mass spectrometry analysis have been developed to identify the lncRNA binding proteins. Recently, Bellucci et al.  developehttp://cmbi.bjmu.edu.cn/lncpro) has been developed for lncPro.This study aims to explore a new method, lncPro, for predicting lncRNA–protein interactions. lncPro yields a score using amino acid and nucleotide sequences. This score can be used to measure the interaction between a pair of lncRNA and protein. Fisher’s linear discriminant method was used to compute the matrix directly. Based on the mathematical model being used, the result was found to be theoretically optimal in the sense of discriminant. Applying lncPro on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins. A convenient online server . RNA–protein pairs were extracted from these complexes. Although the number of pairs is large, many sequences from the same complex are identical according to PDB. These repeated pairs were deleted. Take complex 2FTC as an example. 2FTC has 18 chains, but chain E and chain F are identical. Thus, we included 17 chains (one RNA sequence and 16 protein sequences) in the training set. Following this procedure, a training set containing 1761 pairs was obtained from 44 complexes  database (http://weizhong-lab.ucsd.edu/cdhit_suite/cgi-bin) was used to compute sequence similarity for both protein and RNA sequences. Pairs that share both the protein sequence and the RNA sequence are considered to be similar, and thus removed from the training set. The similarity cutoff was set at 90% for both protein and RNA. After redundancy removal, a training set containing 649 protein-RNA pairs, including 322 interactive pairs and 327 non-interactive pairs, was obtained. This set will be called the non-redundant training set.After collecting RNA–protein pairs, a criterion is needed to determine whether a pair is interactive or non-interactive. The “least atom distance” was used as the criterion : assume This section will focus on encoding sequences into numerical feature vectors. Information from the secondary structure, the hydrogen-bonding propensities, and the Van der Waals’ propensities was used. The encoding procedure is presented in Figure n possible forms of secondary structure with the lowest free energy. Different selections of n may lead to differences in performance. The Discriminative Power (Details of discriminative power can be found in the “Measuring the performance of the method” section) is used as an indicator of the performance. The Discriminative Power does not change significantly when n is set at values of 5, 6, 7, or 8 . Since a larger n value brings more computation, n was set at six. When an RNA sequence was given, six results were obtained in the form of dots and brackets. Each bracket was replaced with “1”, and each dot was replaced with “0”. Thereafter, six binary sequences were added to obtain a new feature vector with integers between zero and six.RNAsubopt from the Vienna Package  was usedPredator  softwarePurine and pyrimidine contact information from a set of 41 RNA–protein complexes  was usedFor the proteins, hydrogen bonding feature vectors were encoded using Grantham’s propensities  and ZimmM to conduct the computation. Therefore, the vectors need to be transformed in order to unify the dimension.Each protein sequence and each RNA sequence were transformed into five and three numerical feature vectors, respectively. However, these vectors cannot be used for direct computations because the dimension of each vector depends on the length of the corresponding RNA or protein sequence, which makes it impossible to find a fixed matrix The Fourier series was used to solve the problem. The formula is presented as follows:L is the length of the original feature vector.Where Here, the first 10 terms of the Fourier series were used as the new numerical feature vector. Dimension 10 was selected because the results did not improve significantly with a higher dimension and the computation is faster when the dimension is set lower. When the dimension was set at 10, 15, and 20, we acquired Discriminative Power (Details of discriminative power can be found in the “Measuring the performance of the method” section) values 90.3%, 89.8%, and 91.9% respectively. After transforming each RNA–protein pair into eight 10-dimensional numerical feature vectors (three for RNA and five for protein), the feature vector encoding process was completed.r and p , we want to train a matrix M and use the score <p|M|r > to measure the interaction between r and p. M will be a 100-D matrix because the dimension of vectors was set at 10. If we unsystematically search the matrix in the 100-D Euclidean space, the efficiency and accuracy would be low. The efficiency and accuracy will be further degraded when a higher dimension is used.For each pair of feature vectors p|M|r>. Without loss of generality, the situation of dimension two is used to clarify the idea:Let us analyze the expansion of <p = , r = T,Assuming that p|M|r > as follows:Equivalently, we can write <k = , x = T.Here, p and r, then x will be fixed. Therefore, the idea of finding the matrix M is equivalent to finding the vector k. The score <p|M|r > is actually the inner product of vectors x and k. This inner product score is expected to discriminate the data into two groups. Thus, according to the theory of Fisher’s linear discriminant method, the best vector k is actually the direction k to optimize the Fisher criterion function:When given mi represents the mean of each category. Here, In the ten-dimensional case, the whole procedure is similar. We can obtain the following:k =  and x = T; both are 100-dimensional vectors. After transforming each pair of r and p in the training set into T, Fisher’s linear discriminant method was applied to these 100-dimensional vectors. Subsequently, the optimal direction k was computed directly.Where M in the 100-dimensional Euclidean space was transformed into another equivalent problem of finding a vector k. Using Fisher’s linear discriminant method, k was computed directly from known data, thereby simplifying the process. Based on the mathematical model being used, the obtained result was found to be theoretically optimal.Thus, the problem of finding a matrix p > = |p1 > + |p2 > + |p3 > + |p4 > + |p5 >, |r > = |r1 > + |r2 > + |r3 >. Thereafter, the computation can be performed directly with this new feature vector.Information on the secondary structure, hydrogen bonding propensities, and Van der Waals’ interaction has to be integrated. We can simply add the different feature vectors into a new vector that contains information from different aspects, |p1|M|r2 >. Assuming that p1 indicates the protein secondary structure information and that r2 indicates the RNA hydrogen bonding information, then the computation of <p1|M|r2 > is nonsensical because combining different kinds of information is theoretically meaningless. Thus, we selected another combining method. We computed five scores using each feature vector pairs, respectively, which included: the protein and RNA secondary structures, protein Grantham’s propensities and RNA hydrogen bonding, protein Zimmerman’s propensities and RNA hydrogen bonding, protein Kyte-Doolittle propensities and RNA Van der Waals’ interaction, and protein Bull-Breese propensities and RNA Van der Waals’ interaction. Here the Grantham and Zimmerman scores are both characterizing the protein hydrogen bonds, the Kyte-Doolittle and Bull-Breese scores are both characterizing the protein Van der Waals’ interaction.However, Equation (6) will lead to cross terms such as <The five scores must be combined to generate a final score. Given that the encoding methods of feature vectors are different, their magnitudes are also different. As a result, the five scores cannot be combined directly. Thus, we transform each score into open interval . This is realized by a 1–1 map from the real line to :X is the raw score, Y is the transformed score, c1 = k * m1, c2=k * m2, and c = (c1+ c2)/2 is the mean of c1 and c2. In c1=k*m1, and c2=k*m2, m1 and m2 are the mean vectors of positive and negative sets, respectively. c = (c1+ c2)/2 can be considered as the separate point of interactive pairs and non-interactive pairs. If X> >c, then Y is near 100. If X < <c, then Y is near zero. If X = c, then Y = 50. Thus, the cutoff will be decided at 50, naturally.Where According to this formula, we can transform all five scores into a scale ranging from 0 to 100. We considered two options to combine the five scores. The first is the arithmetic mean of the five scores. The second is to set weights at 1/3, 1/6, 1/6, 1/6, and 1/6, respectively, considering the Grantham and Zimmerman scores are both characterizing hydrogen bonds and the Kyte-Doolittle and Bull-Breese scores are both characterizing the Van der Waals’ interaction. We observed the prediction of different cases. The former method is more accurate than the latter. Therefore, the arithmetic mean of the five scores was used as the final score.The definition of Discriminative Power (DP) was adopted from the study of Bellucci et al. .πi and πn represent the scores of interactive and non-interactive RNA–protein pairs, respectively. The function θ is defined as follows:Where After all pairs were reordered with the score provided by our method, DP = 1 if all the interactive pairs are ordered before the non-interactive ones. If all the non-interactive pairs are ordered before interactive ones, then DP = 0. Thus, DP can be used to determine whether the method could discriminate the positive and negative sets well.Furthermore, each pair was given a score between 0 and 100 using the method above. The cutoff is naturally decided as 50 according to the formula we used. Thus, a pair is interactive if it has a score over 50; otherwise, the pair is non-interactive. Using this cutoff, we can calculate the accuracy of the prediction.Besides the DP value defined above, Matthews correlation coefficients (MCC) can also be used to measure the quality of classification:TP is the number of true positives; FP is the number of false positives; TN is the number of true negatives; FN is the number of false negatives.Here, First, we used cross-validation to test the classification ability of the method on the training set. Then the ncRNA–Protein Interaction (NPInter) database and several complexes were used to test the ability of the prediction. Finally, we used the method on all human proteins.Since the CD-HIT tool has been used to delete similar sequences, a 4-fold cross-validation could be performed on the non-redundant training set. The mean DP was taken as the final result. Our method obtained a DP value 90.3% on the non-redundant set. Since there are some similar pairs in the redundant set, a higher discriminative power value 94.3% was obtained on the redundant set. Compared with the results of CatRAPID (78% on the non-redundant set and 90% on the redundant set), this method showed a better ability to discriminate interactive and non-interactive pairs. The Matthews Correlation Coefficient (MCC) was also computed. We obtained an MCC value of 0.74 on the non-redundant set and a higher MCC value of 0.83 on the redundant set. This is consistent with the previous DP results.CatRAPID used 7 Å as the cutoff between interacting and non-interacting RNA-protein pairs and RNA sequences shorter than 100 bp were also kept in the training set. To compare with CatRAPID, the method was also tested on the other set when distance cutoff was set at 7 Å and RNA sequences shorter than 100 bp were kept in the training set. A 4 fold cross-validation was performed. We obtained DP values of 90.2% and 91.6% on the non-redundant set and the redundant set, respectively, which were more accurate than CatRAPID. MCC values were 0.77 on the non-redundant set and 0.78 on the redundant set.Next, we checked if the results were improved when five scores were combined. If the combining results were less accurate than the prediction using any single score, then the combination of different information would be meaningless. We considered the five scores separately. The DP for each score is presented in Table We further checked whether a strict sequence similarity cutoff for the non-redundant training set would influence the prediction performance dramatically. When a strict sequence similarity cutoff was used (0.3 for protein and 0.8 for RNA), 21 pairs were deleted from the non-redundant training set. A DP value of 87.1% was then obtained. The result was still more accurate than CatRAPID (78% on the non-redundant set).http://bioinfo.ibp.ac.cn/NPInter/) contains many ncRNA–protein pairs from different species including Homo sapiens, Mus musculus, Escherichia coli, Drosophila melanogaster and Saccharomyces cerevisiae. These ncRNA–protein pairs can be separated into eight groups [In the following section, we used the whole non-redundant training set to train the method, and tested the method’s performance on another database. The NPInter database . We considered groups 1, 5, and 6 to have direct evidence of interaction. The rest of the groups were considered to have indirect evidence. Since the RNAsubopt software will only process the first 4095 nucleotides if the input RNA is too long, sequences longer than 4095 were deleted. Finally, 74 pairs with direct evidence of interaction and 41 pairs with indirect evidence were obtained. The CD-HIT tool was used to check if there is significant similarity between the training set and the pairs from NPInter. When both cutoffs were set at 0.9 for protein and RNA sequences, CD-HIT showed that there was no overlapping. Among the 74 pairs with direct evidence, 48 pairs have scores over 50, suggesting that 65% of the interactive pairs were predicted. Among the 41 pairs with indirect evidence, we predicted 27 pairs, which accounted for 66%.Both pairs with direct and indirect evidence (115 pairs) were used as the positive set. Then we used another 115 pairs randomly from the non-redundant negative training set as the negative set. A DP value of 91.7% was obtained, which was higher than the result of CatRAPID (85%). The MCC value was 0.64, which also indicates acceptable classification ability.By shuffling pairs in the non-redundant negative training set, we built a randomized set containing 327 pairs. The mean value of predicted interaction scores on this set was 32.71 and the distribution of the predicted interaction scores is displayed in Additional file http://www.ncrna.org/frnadb). The sequence of HOTAIR and MEG3 were downloaded from the National Center for Biotechnology Information database (http://www.ncbi.nlm.nih.gov/). The protein sequences were obtained from the Uniprot database (http://www.uniprot.org/).We also studied the performance of the method on several complexes. First, we took MRP, RNase P, PRC-2, and LSD1/CoREST/REST complex into consideration. The RNA sequences of MRP and RNase P were obtained from the Functional RNA Database  we used to train the model exist in ribosome, which could potentially lead to a biased prediction model. Therefore, in order to validate the method, lncPro needs to be tested on non-ribosome complexes. The PCR-2 complex contains four protein units: Ezh2, Eed, Suz12, and RBBP4. Their Uniprot IDs are Q15910, O75530, Q15022, and Q09028, respectively. All four proteins were predicted as interactive with HOTAIR (has a score above 50). Ezh2 was predicted to be the main RNA-binding unit. These results are in agreement with known experimental results . The inthttp://www.noncode.org) and five protein sequences . It is known that both roX1 and roX2 have interactions with MSL1, MSL2, MSL3, and MLE [Next, lncPro was tested on non-ribosome complexes of other species besides human. The MSL complex of Drosophila melanogaster contains two RNA sequences , and deleted those sequences longer than 3000 due to the low computation speed of RNAsubopt software when dealing with long RNA sequences. Finally, 72 human lncRNAs were obtained. With the cutoff set at 0.05, 79.1% (57 out of 72) of these lncRNAs have significant p-values . Among these 20224 protein sequences, 5575 were annotated as the nuclear protein according to the Cellular Component of Gene Ontology . The ID lists of 20224 overall proteins and 5575 nuclear proteins were presented in Additional file Following a similar procedure, we also compared the CDF of the RNA-binding protein scores to that of the overall protein scores for each given lncRNA. Among the 20224 protein sequences of Homo sapiens we collected, 639 were annotated as the RNA-binding proteins according to the Cellular Component of Gene Ontology (GO:0003723, RNA binding, downloaded from BioMart, Additional file M. This matrix was also found to be theoretically optimal based on the model being used. Our method is computational friendly and does not lead to nonsensical cross terms. The comparison results with CatRAPID also show that our method has enhanced abilities of predicting the associations between lncRNAs and proteins. Specifically, we found the human lncRNAs we collected tend to interact with nuclear proteins and RNA-binding proteins.In this study, we introduced a new method lncPro for the prediction of protein associations with lncRNAs. Compared with existing methods, our method shortened the time for training matrix However, the process of finding proteins that directly interact with a given lncRNA is still unsatisfactory because of the large number of proteins. Only when the complex is provided can the prediction of interaction within the complex be more accurate. Also, most RNA-protein pairs in our training set exist in the ribosome, so the training data might not be general enough. Though we tested the method on some non-ribosome complexes and it performed well. We should still be aware of the limited range of cases being used. To conduct a more accurate prediction, further work needs to be performed and more information should be considered. The use of Fisher’s linear discriminant method provides direction on how to incorporate different information into the prediction process.Another thing is that lncPro meets some computational issues when dealing with very long non-coding RNAs. This is limited by the computational ability of RNA secondary structure prediction program. We will update lncPro when new software is available. When we study a long RNA sequence, sometimes we are only interested in certain sections of this sequence. lncPro can be applied if the particular section is not too long.The authors have declared that no competing interests exist.QL carried out the bioinformatics analysis and drafted the manuscript. SR helped to carry out the bioinformatics analysis and drafted the manuscript. ML built the online server. YZ, DZ and XZ participated in its design and helped to draft the manuscript. TL conceived the study and participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.The number of sequences contained in 44 complexes.Click here for fileThe number of sequences in the remaining 18 complexes.Click here for fileDistribution of Interaction Score. The distribution of predicted interaction scores for the shuffled set. The shuffled set was got by randomizing all pairs in the non-redundant negative training set.Click here for fileID list of proteins.Click here for fileTest result of nuclear proteins.Click here for fileTest result of RNA-binding proteins.Click here for file


While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species.MLH1, PMS2, EPHB4 ) and could confirm more than 73% of them based on evidence in the literature.We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage. Elucidating protein function is still one of the major challenges in the post-genomic era ,2. Even The approaches that use PPI for function prediction can be classified into two main classes:1. Link-based methods predict novel functions for a protein by transferring known functions from directly or indirectly interacting proteins. This may be achieved by studying the set of neighbors ,19,26,272. Module-based methods assign functions to proteins by first computing clusters (or modules) within the protein network . Based oBoth approaches have their benefits and their drawbacks. PPI-based prediction methods provide a better coverage but are sensitive to the high level of false-positives ,35 and fIn this paper, we describe a novel algorithm for protein function prediction that combines link-based and module-based prediction with orthology, thus overcoming the respective limitations of each individual approach. The key to our method is to analyze proteins within modules that are defined by evolutionary conserved processes. To this end, we first compute PPIs that are highly conserved within a given set of species. These so-called interologs  are asseMLH1, PMS2 and EPHB4, which received 14, 16, and 15 novel annotations through our method. Detailed literature analysis indicates that at least 73% of the novel functions actually are true predictions.We apply our function prediction strategy to different sets of species, ranging from species pairs to groups of up to four species. We show that our approach reaches very high prediction precision, especially for three and four species. Especially due to the combination of different sources of evidence for functional similarity between proteins, our method is able to predict many functions even for uncharacterized or only weakly characterized proteins. These functions are not reflected in the recall since these functions are novel, i.e., counted as FP in the comparison against a gold standard. For instance, when combining the novel predictions from different species combinations, we suggest 7,500 new functional annotations for 1,973 human proteins that previously had only zero or one function annotated. Overall, our method produces 12,300 novel annotations for human with an estimated precision of ~76% and 5,246 for mouse with ~81% precision. These numbers by far outreach that of comparable methods. It is also remarkable that our predictions are rather specific, which is reflected in a mean GO-depth of 8 for humans and 7 for mice. To confirm our estimated precision values, we manually verified a number of predictions in the context of colon cancer. Specifically, we studied the gene products Neighbor Counting Neighbor veraging . We showWe devise an algorithm for predicting functional annotations of proteins using Gene Ontology (GO)  terms. OS. cerevisiae, D. melanogaster and C. elegans, and the mammals R. norvegicus, M. musculus and H. sapiens. Corresponding PPI data were obtained from the major public PPI databases DIP [We use interaction data of the model organisms ases DIP , IntAct ases DIP , BIND [4ases DIP , MIPS-MPases DIP , HPRD [4ases DIP , MINT [4ases DIP  and BioGases DIP . Since tases DIP ,48, we iases DIP  to enablBesides the interaction data we utilize protein sequences and protein domain information  from UniWe compare protein interaction networks across different species to detect subgraphs that are evolutionary conserved and likely represent functional modules. Figure (1) Orthology is a strong indicator for functional conservation. However, the presence of large protein families, typical for mammals and higher eukaryotes in general, makes it hard to distinguish between true orthologs, in-paralogs and paralogs . We dete2) For comparing protein networks across species, we consider all ortholog groups that comprise at least one protein of each species under consideration. We then use an adaption of an algorithm for frequent subgraph discovery  to assem For compCCS are conserved subgraphs of interacting proteins and therefore a strong indicator for functional similarity of proteins within a CCS even across species. However, not all detected CCS are good candidates for function prediction due to the noise and incompleteness within the existing PPI and annotation data sets. Therefore, we first filter for CCS that are too heterogeneous or simply too small to be used for function prediction. We then use different methods for predicting functional annotations for all proteins in a CCS, namely transfer of annotations from other species along orthology relationships and transfer within species from all PPI neighbors. In both cases, only proteins within the same CCS are considered. Finally, special care has to be taken for the processing of large CCS which, due to their sheer size, usually are functionally heterogeneous. In the following, we give details for each of these steps.et al. [neigh Sim- similarity between neighbors) and across the species (ortho Sim- similarity between orthologs). The formal definitions of both similarity measures are provided in the Additional File We first test all detected CCS for functional coherence using a functional similarity measure proposed by Couto et al.  that is ortho Simor neighSim, exceeds a given threshold. We applied three different thresholds  to study the performance of our method for different levels of functional coherence. This scheme is applied separately for each subontology of GO (molecular function (MF), biological process (BP), cellular component (CC)).We further only consider CCS which have (a) more than two proteins and (b) whose similarity score, either <0.01) we transfer all known protein annotations to poorly annotated or uncharacterized orthologs. Note that an orthologous protein group might consist of more than one protein per species . Although all proteins within such a group in theory should be functionally highly similar, this is, probably due to missing or wrong annotations, not always reflected in the data (see Results). We define the consensus annotation of all proteins of one species in an orthologous group to be the set of all GO terms that are associated to more than half of the annotated proteins of that species in that group. When considering more than two species we combine the species-specific sets of consensus annotations and transfer them to the other proteins in the same group.For inferring protein function from orthology relationships within a CCS, we determine orthologous groups that differ significantly in their individual functional similarity from the similarity score of the CCS by computing the standardized z-score (see Eq. S9). In groups with significant differences , we also consider the pairwise functional relation between interaction partners, assuming that a high functional similarity between indirectly linked partners should also hold for the protein itself. Again, if the pairwise similarity scores exceed the threshold We combine the two different methods to predict protein functions within a CCS  often results in very large CCS with up to several hundreds of proteins. However, biological processes typically involve only between 5 and 25 proteins . ConsequP and recall R are defined as:We use a leave-one-out cross-validation to estimate the expected precision and recall of function prediction using (a) only orthology within CCS, (b) only neighbors within CCS, and (c) the combination of both methods. Precision where TP and FP denote true and false positives, respectively, and FN denotes false negatives.ppR) we consider only proteins p that are part of a CCS:For cross-validation we 'hide' selected annotations before applying our algorithm. Predicted terms are then compared to the held out annotations. We count a GO term as correctly predicted if the proposed term was an ancestor of the original term on the path to the root or the term itself  corresponds to the number of annotations that are originally associated with the protein p. To also give an idea of the per-species performance, we always complement precision and recall values with the coverage measure, which simply counts the total number of predictions.where TPKeep in mind that, as always when comparing to an incomplete gold standard, cross-validation inherently considers any new annotations as false, although new annotations are the primary target of function prediction. Therefore, we also performed an extensive literature evaluation to judge the correctness of selected new annotations.We compare our approach against a number of different techniques.First, we use two baseline methods: The orthology baseline purely considers orthology ignoring structural network conservation. We randomly select one third of the orthologous protein groups, remove annotations from one protein in the group and predict their functions using only its orthologs. The link-based baseline takes only direct interaction partners into account, again independently of conservation of interactions. For each species we randomly choose one third of the proteins from the corresponding interaction network and exploit their direct neighbors for deriving new functions. We repeat this procedure 100 times for each baseline and compute average and standard deviation across all runs.et al. is a local prediction approach that derives new annotations for a protein based on the frequency of annotations within its direct interaction partners [2 algorithm from Hishigaki et al. extended this idea by also considering the background frequency of a functional term [et al., a weighted averaging method to predict the function of a protein based on its direct and indirect interaction partner [et al. demonstrate in [et al. that implements these three methods , mouse (mmu), human (hsa), fly (dme), worm (cel) and yeast (sce) from several public databases to generate species-specific PPI networks .We first show the performance of our two baseline methods, orthology and link-based, for function prediction. Precision for predictions based solely on orthology relationships varies between 3% and 11% , although it is less steep than observed for orthology-only. This increase has mostly only minor influence on precision and recall. Precision reaches similar levels and the recall increases slightly. Note, for few combinations the combined method yields the same results as link-based-only because no predictions could be inferred through orthology relationships.Comparing the combined method and CCS link-based only . Precision is affected the least for the highest similarity threshold (0.7) fostering the most reliable precisions.hsa-dme-sce, the respective numbers are shown as Venn diagrams in Additional File hsa-dme-sce, the respective numbers are shown as Venn diagrams in Additional File We combined orthology- and link-based function prediction within CCS to benefit from the strengths of both methods. To study whether the predictions of the individual methods result in the same or complementary sets of predictions we determined the overlap of GO terms predicted by either strategy. For This behavior is also observable when predictions are analyzed separately per species . The impact on precision and recall is shown in Additional File hsa-dme-sce at a similar or even better precision. When comparing split and non-split results from hsa-dme-cel-sce the precision decreases for human along a five-fold increase of the number of predictions, but increases for all the other species (at 0.7).Large CCS naturally encompass various biological functions. In consequence, their functional homogeneity is often too low which excludes the entire CCS from function prediction. However, large CCS actually are strong indicators for conserved functions. For instance, Figure Neighbor Counting (NC) [2 statistics [FS-Weighted Averaging (FS-WA) [NC and χ2 statistics. Precision and recall obtained from the latter two are very low and even below our baselines. This also holds for yeast and fly  , χ2 statatistics  and FS-W (FS-WA)  considerFS-WA results with our approach, CCS-based function prediction performs consistently as well or better. Depending on species and subontology we achieve either higher precision at a similar recall or an improved precision and recall. Especially, when considering molecular function and biological process in human  Orthology restricts prediction to proteins that have at least one orthologous protein with known function and exhibits a very low precision. (2) Considering only protein-protein interactions disregards the power of comparative genomics, leading to low coverage in organisms where PPI data is not available in abundance. (3) Using only functional modules within protein networks yields high precision, but strongly affects recall on a species basis, as only highly conserved functions performed by dense protein clusters can be predicted. We showed that combining these methods leads to high precision predictions with very good coverage. Essentially, we achieve high precision by looking only at subgraphs conserved in multiple species without restricting them to dense modules. Furthermore, we achieve high coverage when considering multiple species, by using a relaxed definition of interologs, and by transferring function from PPI neighbors and from orthologous proteins. Altogether, our method predicts thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision .Compared to other methods presented in the literature, our method has also the important property that it is not limited to so-called "informative" GO terms . Many prCompared to the orthology baseline, including CCS yields precisions up to 10-times higher, confirming that information on conserved interactions is a very effective filter for avoiding false positive predictions across orthology relationships. Compared to the neighbor baseline, considering CCS also leads to a clear and significant increase in precision. This effect can be explained by the fact that using interologs (strict or relaxed) instead of single interactions largely improves reliability of PPI data , since fOur results show that combining various evidences into a single and comprehensive method leads to improved results. Evidently, the predictions made by different methods using information from different species complement each other quite well instead of only predicting the same functions again and again. However, the concrete approach has to be chosen with care. We showed that good results can only be achieved when using a proper definition of interaction conservation and when treating large CCS in an adequate manner. Failing to do so either restricts coverage of the method or leads to a higher false positive rates.2 statistics and FS-Weighted Averaging (see Methods). Our combined CCS-based approach significantly outperforms Neighbor Counting and χ2 statistics, especially in terms of precision. Moreover, we perform comparably well or better against FS-Weighted Averaging, mostly achieving much higher precision at higher recall. Notably, our method achieves better results especially for species without comprehensive PPI coverage, such as human. However, precision for Neighbor Counting and χ2 is significantly lower  and 20% (χ2) for yeast and 20% (NC) and 16% (χ2) for fly. The second point concerns the amount of interaction data. For example, results from [2. However, functions predicted for non-annotated proteins are necessarily counted as false positives although these are truly novel findings. Thus, disregarding such proteins results in higher precisions.We compared precision and recall of our approach to Neighbor Counting, χe et al. , which alts from  are baselts from ,26,27,62E presents the edges and V denotes the nodes within a CCS. As expected, filtering CCS according to their density considerably improves precision  any protein which has at most two terms assigned a-priori in our data. For WAP, we count annotations as new if they are more specific than the existing ones or if they belong to another sub-branch in the subontology. Note that such annotations are counted as false positives in our evaluation as they cannot be validated from our gold standard data.hsa-dme-sce are shown in Figure Results from comparing In the following, we discuss specific predictions for proteins that are relevant for colorectal cancer. Note that these predictions were counted as false positives in our evaluation because they are not contained in the Gene Ontology annotations at all or only marked as putative . However, we show that many predictions already have strong experimental support in the literature. Thus, the group of novel predictions falls into two classes - those that, given the current literature, can be considered as true but have not yet made it into the annotation databases and those for which we could not find conclusive evidence in the literature. We argue that, given the large amount of predictions that fall in the first class, predictions from the second class should be considered as promising candidates for further studies.MLH1, PMS2 and EPHB4, all of which have an established importance for colorectal cancer [We discuss predicted functions for the gene products of l cancer ,68. OverMLH1 and PMS2 from UniProt and EntrezGene and newly inferred functions are listed in Additional File The DNA mismatch repair protein MLH1 and the mismatch repair endonuclease PMS2 belong to the main components of the post-replicative DNA mismatch repair (MMR) system  is directly related to the functionality of the MutLα complex which is formed by MLH1 and PMS2. Rich supporting evidence can be found from the respective orthologs in yeast and mouse. For instance, PMS1, the PMS2 ortholog in yeast, contributes to dinucleotide insertion or deletion binding, loop DNA binding [Mlh1, the mouse ortholog of MLH1, is annotated to guanine/thymine mispair binding [four-way junction DNA binding) [MLH1 and PMS1 in yeast [ATP binding and ATPase activity predictions for their human orthologs [PMS2 contains a conserved metal-binding motif that constitutes part of the active site for the endonuclease activity of the protein and might enable magnesium ion binding [protein homodimerization activity, the dysregulated gene expression of PMS2, either as a monomer or homodimer, can disrupt MMR function in mammalian cells [The majority of our predictions  . High anin yeast  which surthologs . Moreove binding . Considean cells . Note thguanine/thymine mispair binding, single guanine and thymine insertion binding or oxidized DNA binding. Moreover, we associate both proteins to base-excision repair as well as postreplication repair and MLH1 to maintenance of DNA repeat elements. These are interesting hypotheses supported by recent findings from Erdeniz et al. who suggested that the endonuclease activity of PMS2 in MutLα is not only important in MMR-dependent mutation avoidance but also for suppression of homologous recombination, DNA damage signaling, and damage response functions [PMS1 with meiotic mismatch repair and DNA recombination [MutLα complex [MutLα complex is a clearly sensible refinement of the existing annotation nucleus and only seven others genes are annotated to this term, which emphasizes the specificity of our method.Our algorithm also generates a number of predictions that are not as clearly supported by the existing literature, such as unctions . Associabination  further  complex , an annoEphrin type-B receptor 4 is a transmembrane receptor for the ephrin-B family. It belongs to the family of receptor tyrosine kinase (RTK) and is usually expressed in endothelial and neuronal cells. Known and predicted functional annotations are displayed in Additional File protein, enzyme and ATP binding, SH3/SH2 adaptor and enzyme regulator activity and protein amino acid phosphorylation, derived both from conserved interactions and orthology, are evidently consistent with the characteristics of receptor tyrosine kinases.Several predicted functions, such as transmembrane-ephrin receptor activity and transmembrane receptor protein tyrosine kinase signaling pathways. Both are supported by annotations from highly related receptors, such as Ephb1 in mouse and EPHB2 in human [cell-cell signaling [cell migration [angiogenesis and behavior [EPHB4. EPHB4 participates in the axon guidance pathway and in this context predictions like axon guidance or axon guidance receptor activity can be integrated [Two functions inferred by orthology are in human ,81. Lessignaling , cell miigration , angiogebehavior . These ftegrated -87.2 statistics, and FS-Weighted Averaging, our CCS-based prediction strategy performs comparably well or significantly better, especially in terms of precision.Elucidating protein function is a major challenge in the post-genomic era. We developed a method for predicting protein function based on the structural and functional conservation of PPI subnetworks in multiple species. Our approach integrates three different sources of evidences for inferring functional similarity. Altogether, we employ orthology relationships, evolutionary conservation of functional modules in protein networks, and direct and indirect protein-protein interactions for deriving novel functions for uncharacterized proteins. Using our method we derive thousands of protein functions for every species in our study at varying, yet high levels of precision. Thus, combining orthology relationships, functional modules and PPI neighborhood into a single, comprehensive prediction method yields high-quality predictions with very good coverage. In comparison against three other function prediction approaches, Neighbor Counting, Additionally, we predict a large amount of novel functions for a number of poorly or non-annotated proteins that can not be validated directly. However, this shows that our method also generates novel functional knowledge rather than only reproducing known functions for well-characterized proteins. The manual curation of predictions for three selected proteins confirms their high quality and precision as many predictions already have strong experimental support in the literature.Apart from the promising results of our prediction approach, our method currently only provides lists of yes/no predictions. This binary behavior is implicit in the way we compute CCS and how we determine predicted terms and targets of prediction. For further improvement and applicability we plan to derive confidence scores for each prediction based on the multiple biological evidences. Predictions ranked by reliability will provide a method of selection for focusing experimental resources on hypotheses (predictions) that are more likely to be true. This is essential for experimental biologists to decide which proteins and predictions should be investigated further, e.g. in follow-up experiments.The authors declare that they have no competing interests.SJ: developed the methods to identify conserved protein interaction subgraphs and to predict protein functions, carried out the studies described in this paper and contributed to the manuscript. CS: contributed to the manuscript. UL: conceived the study and contributed to the manuscript. All authors read and approved the final manuscript.Supplementary Material. The Supplementary Material includes supplementary figures and tables as well as additional analysis.Click here for fileComplete results of the strict and relaxed network comparisons. This files contains the complete results of the strict and relaxed network comparisons for pairs of species and three, four, five and six species combinations. The number of OrthoMCL groups, interologs from strict and relaxed definition as well as the total number of CCS and the size of the largest CCS are given.Click here for fileComplete results of the CCS-based function prediction approach. This file contains the complete results of the combined CCS-based function prediction approach for pairs of species and three, four, five and six species combinations. CCS from strict and relaxed network comparison are used depending on the species combinations.Click here for file


Currently a huge amount of protein-protein interaction data is available therefore extracting meaningful ones are a challenging task. In a protein-protein interaction network, hubs are considered as key proteins maintaining function and stability of the network. Therefore, studying protein-protein complexes from a structural perspective provides valuable information for predicted interactions.In this study, we have predicted by comparative modelling and docking methods protein-protein complexes of hubs of human NR-RTK network inferred from our earlier study. We found that some interactions are mutually excluded while others could occur simultaneously. This study revealed by structural analysis the key role played by Estrogen receptor (ESR1) in mediating the signal transduction between human Receptor Tyrosine kinases (RTKs) and nuclear receptors (NRs).Although the methods require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. This adds a fourth dimension to interaction network, that of time, and can assist in obtaining concrete predictions consistent with experiments.This article was reviewed by Dr. Anthony Almudevar, Prof. James Faeder and Prof. Eugene Koonin. For the full reviews, please go to the Reviewers' comments. Protein-protein interactions are the major mechanism that controls biological processes and their studies have recently become very attractive not only for understanding cellular functions but also for therapeutic reasons. With the tremendous increase in human protein interaction data, network approaches are used to understand molecular mechanisms of disease  particulProtein-protein interactions are usually shown as an interaction network where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. Many biological networks are known as scale-free networks and are characterized by a power-law degree distribution . This meIt is clear that hubs in protein-protein network are able to recognize and bind to many other proteins. Interactions in proteins are mediated by the recognition of distinct binding regions by the protein on the surface of its interaction partner. Such molecular recognition must be specific enough and of sufficient affinity for the interaction to take place. In order to recognize and bind several other proteins, it is imperative for hubs to have some structural characteristics  and specCurrently, the number of protein-protein interactions derived from high throughput experimental methods and prediction approaches has dramatically increased in protein interaction databases. Therefore, in extracting meaningful information from this interaction data set there is a strong need to avoid the huge amount of false positives.The prediction of the structure of a protein-protein complex by docking methods is one of the major challenges in current computational structural biology -9. AccurIn this study, we predict protein-protein interaction complexes of previously identified protein hubs of NR-RTK network and their interactions through docking of their molecular structures.in vivo or in vitro assays but no experimental complex structure are available in PDB  with the SH2 domain of c-Src . MoreoveThese theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of such protein complexes considered as potential cancer therapeutic targets. More interestingly, including microarray data can help in determining if two proteins bind to ESR1 at the same time by looking at the correlation of their expression patterns. If their expression is correlated, most likely these two interactions can occur simultaneously.This work has taken the approach of predicting protein-protein complex interactions of previously predicted hub proteins of NR-RTK network through docking of their molecular structures. Since studied complexes are not available in PDB, we have relied upon comparative modelling and docking methods. This methodology has the advantage that it can also identify interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. We believe that the correctness of our results depends on experimental validation which is an important task.We previously determined potential protein hubs of NR-RTK network . Out of http://zhanglab.ccmb.med.umich.edu/I-TASSER/), which gave the best protein models at the Critical Assessment of Structure Prediction (CASP 7 and CASP 8), a community-wide, worldwide experiment designed to obtain an objective assessment of the state-of-the-art in structure prediction [To predict an interaction complex or predict a new interaction, we require the protein structures of both interacting proteins. We used comparative modelling approaches because the studied protein structures are not available in PDB. To predict the structure of the protein, we have relied upon Zhang's I-TASSER server ,16  returns the top models based on energy and cluster size. We select one of the returned models after considering the energy and the size of the cluster preferring lower energies and larger cluster sizes.After having both structures of an interacting pair we used docking to predict the protein complex formed in a protein-protein interaction. We used the Cluspro server ,20 for dEGFR: Epidermal growth factor receptor; ESR1: Estrogen Receptor1; Erbb2: Receptor tyrosine-kinase Erbb2; IGF1-R: Insulin-like growth factor 1; NR: Nuclear Receptor; PGR: Progesterone receptor; RTK: Receptor Tyrosine KinaseMC did the comparative modelling and docking. She also wrote the manuscript. AR supervised the work and corrected the manuscript. All authors read and approved the final manuscript.Anthony Almudevar, Department of Biostatistics and Computational Biology University of Rochester Medical Center, Rochester, NYThe authors propose the use of structural analysis for the validation of PPI networks compiled using high-throughput data. The object is to reduce false positives, as well as to introduce additional structure, for example, determination of interactions which are mutually exclusive due to shared binding sites. Published software applications  are used to predict structure, then to predict binding sites of protein complexes. The method is demonstrated using a PPI network compiled by the authors in an earlier paper (Choura and Rebai (2010) Biology Direct).The paper is interesting, but application of the method is limited to a small subset of a PPI network. Additionally, interactions among the exemplary proteins are already described in literature cited by the authors, so that validity is more likely to occur than for a randomly selected interaction. Thus, it is difficult to assess the value of the method with respect to the reduction of false positives. Would it be possible to systematically apply the method over a larger portion of the network?A report on the resulting concordance would be interesting.Author's response: We agree with the reviewer. We think that this approach could not be systematically applied because of the lengthy computational time and it requires human judgement. Nonetheless, many such cases can be investigated and the results can provide new information.Minor PointsPage 4: "Results of the docking of these interactions are shown in Figure Page 6: Is it possible to give more interpretation of the c-score?Page 6 "select the model "repeated.Table Author's response: We have corrected these points accordingly.Quality of written English: Needs some language corrections before being publishedAuthor's response: We have corrected the manuscript. .The paper is an interesting contribution to PPI network reconstruction, and I think would be of interest to readers of Biology Direct. The only concern I have at this point is in the reference on page 3 to the motivation of false positive control. It is difficult to evaluate the methodology from this point of view given the limited application demonstrated. The authors, in their earlier response, point out that this is difficult due to the need for human judgement, and because of the lengthy computation time. Some comment on the required computational burden should therefore be provided. More generally, it would be good for the authors to predict, for example, with how much certainty their validation method will detect a false positive selected from a high-throughput screening.Author's response: At least homology modelling takes one day for one protein, similarly for docking of a pair wise interaction. Regarding the false positives estimation, at this time it is difficult to give a certainty value for false positive detection because studied complexes are not validated experimentally.minor correctionspage 2 - commas around 'that of time'page 3 - 'target' -> 'targets'page 3 - 'Therefore, extract' -> 'Therefore, in extracting'page 3 - 'set is a strong need' -> 'set there is a strong need'page 4 - 'only by nuclear' -> 'only of nuclear'Quality of written English: Needs some language corrections before being publishedAuthor's response: doneThe comments in my second review still apply.Author's response: We thank very much the reviewer. We agree with his comments that we will consider carefully in our upcoming work.Quality of written English: AcceptableProf James Faeder, Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, USA.The goal of this study is to use structure prediction methods to determine which of a possible set of complexes that can form between a set of proteins based on their known interactions can actually form. In particular a hub protein, ESR1, that has been shown to form a hub linking growth factor and estrogen signalling networks has been studied in detail. Its potential interactions with four different receptor proteins have been studied. It is found that because some of the interaction pairs have overlapping sites, a number of the interactions are mutually exclusive, with the result that ESR1 is at most able to form ternary complexes of the involved proteins. This is an interesting and to my knowledge novel finding that could be of interest both to experimentalists and modelers studying these networks. However, not enough detail about either the methods or the results is provided to give sufficient confidence that obtained results are valid. Specifically:-No evidence is provide that the combination of structure prediction methods for proteins with unknown or partially known structures and docking can be used to accurately predict the structure of complexes, particularly complexes involving potentially three or more proteins. No evidence has been provided that the predictions from this approach have been validated. For example, can docking of KNOWN structures be used to accurately predict ternary complexes and to rule out possible complexes?Author's response: The following articles illustrate some examples of docking application to accurate prediction of protein complexes:* Prediction of multimolecular assemblies by multiple docking (PMID: 15890207).* Pushing structure by high throughput experiments (PMID: 19714207).-How are the interfaces between the involved proteins determined and how reliable are those predictions likely to be?Author's response: the interfaces between proteins are studied in our paper currently under preparation.-Structures of both individual proteins and complexes should be provided as supplemental material. It is also not clear what portions of the receptor protein were used in study - presumably the cytosolic portions. Large portions of these regions are known to be disordered how is that handled? How many docked structures were obtained and how prevalent were the final structures reported in the docking runs?Author's response: All pdb files obtained in this work are deposited as supplemental files.Portions of receptors used correspond to extracellular domain for EGFR and IGF1R (receptor tyrosine kinases) and ligand binding domain for the other receptors (nuclear receptors).As Cluspro server implements rigid body docking, when a partner protein in a complex is structurally flexible Cluspro is not so able to account for his flexibility.-Figures Author's response: Figures A few minor comments- The abstract should give more concrete details about the methods being employed.Author's response: done.http://www.sciencemag.org/content/314/5807/1938.abstract) and determining interaction interfaces involving hub proteins in yeast signaling networks may be relevant to the current study.-The work of Gerstein and co-workers (Author's response: We thank very much the reviewer for this relevant reference.Quality of written English: Needs some language corrections before being publishedIt is difficult to tell from the response letter or the revised manuscript what changes were made in response to my critique or that of Reviewer 1. My impression is that only a few revisions were made. In the future, it would be very helpful to the Reviewers to highlight any changes. The main new item in the revision is the set of pdb files of the docked structures, which are now provided as Supplemental information. While these are helpful, they are not really a substitute for further clarification of the methodology employed or validation of the procedures. I will leave it to readers to judge whether the obtained structures are meaningful.Quality of written English: AcceptableAuthor's response: We thank the reviewer for his comments.I have no additional comments.Quality of written English: AcceptableProf Eugene Koonin, National Center for Biotechnology Information, NIH, BethesdaMaryland, USA.This reviewer provided no comments for publicationQuality of written English: acceptable.EGFR. EGFR structure.Click here for fileESR1. ESR1 structure.Click here for filePGR. PGR structure.Click here for fileIGF1R. IGF1R structure.Click here for fileErbb2. Erbb2 structure.Click here for fileESR1-EGFR. ESR1-EGFR complex structure.Click here for fileESR1-Erbb2. ESR1-Erbb2 complex structure.Click here for fileESR1-PGR. ESR1-PGR complex structure.Click here for fileESR1-IGF1-R. ESR1-IGF1-R complex structure.Click here for fileESR1-IGF1R-PGR. ESR1-IGF1-R-PGR complex structure.Click here for fileESR1-Erbb2-PGR. ESR1-Erbb2-PGR complex structure.Click here for fileESR1-Erbb2-IGF1R. ESR1-Erbb2-IGF1R complex structure.Click here for fileESR1-IGF1R-EGFR. ESR1-IGF1R-EGFR complex structure.Click here for fileESR1-EGFR-PGR. ESR1-EGFR-PGR complex structure.Click here for fileESR1-EGFR-Erbb2. ESR1-EGFR-Erbb2 complex structure.Click here for fileESR1-EGFR-Erbb2-IGF1R. ESR1-EGFR-Erbb2-IGF1R complex structure.Click here for fileESR1-EGFR-Erbb2-PGR. ESR1-EGFR-Erbb2-PGR complex structure.Click here for file