Identifying protein complexes plays an important role for understanding cellular organization and functional mechanisms. As plenty of evidences have indicated that dense sub-networks in dynamic protein-protein interaction network (DPIN) usually correspond to protein complexes, identifying protein complexes is formulated as density-based clustering.In this paper, a new approach named iOPTICS-GSO is developed, which is the improved Ordering Points to Identify the Clustering Structure (OPTICS) algorithm with Glowworm swarm optimization algorithm (GSO) to optimize the parameters in OPTICS when finding dense sub-networks. In our iOPTICS-GSO, the concept of core node is redefined and the Euclidean distance in OPTICS is replaced with the improved similarity between the nodes in the PPI network according to their interaction strength, and dense sub-networks are considered as protein complexes.f-measure and p-value on four DPINs, which are from the DIP, Krogan, MIPS and Gavin datasets. In addition, our predicted protein complexes have a small p-value and thus are highly likely to be true protein complexes.The experiment results have shown that our iOPTICS-GSO outperforms of algorithms such as DBSCAN, CFinder, MCODE, CMC, COACH, ClusterOne MCL and OPTICS_PSO in terms of The proposed iOPTICS-GSO gains optimal clustering results by adopting GSO algorithm to optimize the parameters in OPTICS, and the result on four datasets shows superior performance. What’s more, the results provided clues for biologists to verify and find new protein complexes. Proteins are the indispensable components in various types of cells and tissues, and the executors of the biological functions. At the same time, each protein in the cell does not exist in isolation, and the occurrence of every life process must involve more than one protein . ProteinThe discovery of protein complexes is equivalent to find subsets of function-related proteins from a data set. Clustering is an effective method, which can find subsets that have some common attributes from the database . TherefoIn real life, many optimization problems require not only to calculate the extremum, but also obtain their optimal values. This kind of problem is a serious challenge to the traditional algorithm. In this case, a growing number of swarm intelligence algorithms are successively put forward, such as Genetic Algorithm (GA) , Particlp-value for function enrichment analysis. The experiment results illustrated that iOPTICS-GSO achieved better performance compared with other competing algorithms.OPTICS algorithm does not produce cluster for a data set explicitly; but instead creates an augmented ordering queue representing its density-based clustering structure. Then we need to deal with cluster-ordering and get clustering results. For each network clustering, different parameters settings produce different results. In this study, we put forward the algorithm named iOPTICS-GSO which is the improved OPTICS algorithm by using GSO to optimize the parameters in OPTICS. In order to investigate its performance, iOPTICS-GSO with other seven computing methods including DBSCAN , CFinderThe outline of this paper is as follows. In Section 2, after reviewing the GSO algorithm, basic OPTICS and our iOPTICS-GSO are presented. In Section 3, experimental results and analysis are described and discussed, and the conclusions are in Section 4.In the GSO algorithm, glowworms with higher fluorescein are more attractive to other glowworms, and thus a group of glowworms move towards the glowworms with high fluorescein. Each glowworm in its dynamic decision domain radius chooses a glowworm whose fluorescein value is higher than its own fluorescein value to move towards and updates its dynamic decision-making domain. Then some glowworms are selected according to probability to update the position from dynamic decision-making domain. Finally, the decision domain updated. GSO algorithm has two important phases as follows.i (t) represent the location of the i-th glowworm in the t-th generation, J(xi(t)) represent the fitness function of the i-th glowworm in the t-th generation. The fluorescein value li(t) of the i-th glowworm in the t-th generation is calculated as follows:ρ and γ are two parameters with the values between 0 and 1.The fluorescein value of each glowworm is related to the value of previous generation of fluorescein and the current fitness function. Let xS is the update step length of the glowworms, S0 is the initial step length, and tmax is the largest number of iterations. Here, we adopt the method of linear regressive instead of fixed step length [Each new position of the glowworms is a small movement from the original position, which is calculated as follows:p length , in ordeIn the GSO, each glowworm is looking for the neighborhood within its field of vision, and then moves to a brighter glowworm. Each time the moving direction depends on the neighborhood selection. In addition, the glowworm decision domain radius size is influenced by the number of glowworms in different neighborhoods, when the number of glowworms is too small, glowworms will increase their decisions radius in order to find more glowworms; On the contrary, they will reduce their own decision-making radius. At the end, the GSO makes most of the glowworms gathered in a better position.MinPts), which is the cardinality of the neighborhood. The condition Card(Nε(q)) ≥ MinPts is called the “core object condition”. If this condition holds for an object p, then we call p a “core object”. Only from core objects, can other objects be directly density-reachable.The key idea of density-based clustering such as OPTICS is that for each object in a cluster the neighborhood within a given radius has to contain at least a minimum number of objects .p be a protein in a PPI network, DistanceMinPts (p) be the MinPts-th maximum distance from node p to all the other nodes. Then, the core-distance of p is defined as follows:Let Definition 2: (Distancereachability of node p).p and o be two proteins in a PPI network, let N(o) be the set which contains neighbors of node o. Then, the Distancereachability is defined as follows:op is the distance from node p to node o. As can be seen above, the reachability distance of a node cannot be smaller than the core distance of node o. Thus OPTICS creates an ordering queue of all nodes, and stores the core distance as well as a suitable reachability distance for each node.Let nodes Calculating the distance in a PPI networkIn this section, we elaborate the proposed iOPTICS-GSO how to identify protein complexes. The following four subsections describe the calculation of distance between proteins, clustering PPI networks, iOPTICS-GSO algorithm and its time complexity analysis, respectively.Xi =  indicates the interactions between protein i and other proteins, then we calculate the number of common neighbor(CN) between proteins i and j by the equation: CNij = |Ni∩Nj|. Here Ni and Nj expresses the neighbor that proteins i and j have, respectively. Therefore, if CNij ≠ 0, the similarity between proteins i and j is calculated as follows [In a PPI network, we use the similarity between two proteins to measure their distance. As we know, the fewer the number of same neighbors between two proteins is, the less the similarity of two proteins is, and the smaller the probability that they belong to the same protein complex is. On the contrary, the higher the similarity of the two proteins is, the more likely they belong to the same protein complex . Therefo follows :6\documeS as follows:Considering in the PPI network, the two nodes which have no common neighbor also have connection, and there have multiple protein complexes which only contains two proteins in standard complexes. we redefined the similarity The greater the similarity between two proteins, the smaller the distance between them is. Then the distance can be calculated as follows:Dij to replace the Euclidean distance in OPTICS for measuring the distance between two proteins in a PPI network.2.Clustering PPI network.We use the MinPts to be 4, and then from Fig. o. For obtaining the core distance of o, we calculate all distances between core o and its neighbors according to Eq. (8). From the definition, we get the value Distancereachability  = 0.64. In the same manner, we obtain a sequence of values of all nodes.Fig. We can now improve the algorithm to preserve the track of all the reachability distance values and use them to save the expensive operations identified above. We can obtain an augmented ordering queue from OPTICS, and convert the ordering queue into a reachability-plot. Fig. 3.iOPTICS-GSO Algorithm.For example, in Fig. Although the OPTICS algorithm can find all clusters, the dynamic PPI network has more than one sub-network, and the size and topological structure of these sub-networks are quite different. For example, when we apply OPTICS to dynamic PPI network with 12 sub-networks, 12 reachability-plots are obtained; and each reachability-plot is different from others. The optimal parameters and the corresponding performance of each sub-network are shown in Table It is well known that the GSO algorithm has less parameters, simple operation and good stability, etc. GSO algorithm simulates the characteristic of glowworms glow in nature, by comparing the size of the fluorescein value to achieve the purpose of communication, so as to realize the optimization of the problem. So we introduce the GSO algorithm to optimize the parameters of OPTICS, in order to obtain optimal results. Algorithm2 describes the details of iOPTICS-GSO. After several circulations iterative process, a glowworm constantly updates its position and iteratively approaches to the best position. At last, the glowworm finds the best position.The corresponding relationships between GSO and OPTICS are showed in Fig. 4.Time complexity analysis of iOPTICS-GSO algorithmIn Algorithm: iOPTICS-GSO, firstly, the fluorescein values, the decision domain radius and the positions of glowworms are initialized. Secondly, GSO algorithm is used to optimize the parameter ɛ in OPTICS. In this part, one position of a glowworm is one parameter value. Then OPTICS is run by using this parameter value. For each value (position), a corresponding clustering result is obtained. Next the clustering performance is evaluated for each value (position). Next the fluorescein value is updated and the glowworms move accordingly. After iterations, the new positions of glowworms are found. The maximum fitnessvalue is selected as the optimal position.maxiter be the maximal iterations of external loop in iOPTICS-GSO algorithm, num be the number of proteins in sub-works and PopSize be the number of glowworms. The time complexity is analyzed below:O (num2).The time complexity of OPTICS algorithm is O (PopSize * O (num2).The time complexity of computing the fitness of glowworms is O (PopSize2).The time complexity of glowworms moving process is O is (PopSize).The time complexity for updating the position The time complexity is used to estimate the efficiency of the iOPTICS-GSO algorithm. Let O (maxiter * (num2 + PopSize * num2+ PopSize2 + PopSize)). Finally, the time complexity of this algorithm is O (maxiter * PopSize * num2).In summary, the time complexity of iOPTICS-GSO is In this study, we used four static PPI networks for yeast, including DIP , Krogan In study, we construct DPINs similar to Ref.  by integprecision, recall and f-measure [f-measure is used to evaluate the closeness between the known protein complexes and the identified protein complexes. Precision, recall and f-measure are calculated as follows:X is the set of proteins in an identified protein complexes and F is the set of known complexes in the standard dataset. |pc| is the number of proteins in the identified protein complex and |kc| is number of proteins in the known protein complex. The overlapping score (OS) evaluates how many proteins in the true protein complexes can be recovered by the identified protein complexes [p-value to evaluate the statistical and biological significance of the identified protein complexes [k proteins in a true protein complex C with a biological function shared by an identified proteins complex F from a total set V of proteins, the p-value is defined as:p-value of an identified protein complex means the collective occurrence of these proteins belongs to the same complex not by chance, yet with a high statistical significance. That is to say, the lower the p-value of a protein complex is, the stronger biological significance the protein complex possesses, while the protein complex with p-value greater than 0.01 is considered to be insignificant. In the experiments, p-value was calculated on biological process ontologies.In order to evaluate the clustering results, we have adopted three kinds of commonly used statistical metrics: -measure . Precisiomplexes , 33. Usuomplexes . We alsoomplexes . In detay chance . A low pMinPts. According to the topological properties of PPI networks, if the value of MinPts is too large, there would be no meaningful cluster that can be identified by the algorithm. For example, when we set MinPts to 10, there is no meaningful cluster that can be identified from the DPIN network. On the contrary, if the value of MinPts is too small, it will be too many proteins in the same cluster and the number of identified protein complexes will be few. In this study, the value of MinPts is set according to Fig. f-measure. Each value of parameter corresponds to a value of f-measure,a set of values form the line chart, as shown in Fig. In iOPTICS-GSO algorithm, there is one parameter to be preset, which is the value of MinPts on f-measure is not very big, and this also confirms that the reachability-plot is rather insensitive to the input parameter of the method. We observe that the value of f-measure increases initially as the value of MinPts increases and decreases after reaching the maximum. Then we chose the value of MinPts at which the f-measure reaches the maximum in iOPTICS-GSO. As a result, we find that the optimal values of MinPts are 3, 2, 2 and 4 for DIP, Krogan, MIPS and Gavin, respectively.In Fig. In order to directly validate its performance, the iOPTICS-GSO is compared with other seven competing algorithms, DBSCAN , CFinderFrom Table precision, recall, f-measure of each algorithm on four datasets. From Fig. precision and f-measure than other competing algorithms. After combining OPTICS with GSO algorithm, the iOPTICS-GSO algorithm can produce the clustering results based on the optimal parameters. Therefore, it obtains a much better performance than the OPTICS algorithm. From the last green and blue column in Fig. precision and f-measure than other competing algorithms.Fig. p-value of the identified protein complexes on Biological Process ontologies based on four datasets by using the tool SGD’s GO: TermFinder (http://www.yeastgenome.org/cgi-bin/GO/goTermFinder.pl). We calculate the p-value of the protein complexes identified by six algorithms, COACH, MCL, MCODE, ClusterOne, OPTICS and OPTICS_PSO, whose size are greater than or equal to 3. The comparison results are showed in Table p-value < E-15 in predicted complexes by iOPTICS-GSO was the highest. It accounted for 8.70%, 12.22% and 26.25%, respectively. In MIPS data, the percentage of complexes with p-value < E-15 in protein complexes identified by iOPTICS-GSO was the highest. It accounted for 20.00%. As for the comparison with OPTICS_PSO, the percentage of complexes which are significant identified by iOPTICS-GSO was the higher on DIP data and Krogan data. In MIPS data and Gavin data, the percentage of complexes with p-value < E-10 in protein complexes identified by iOPTICS-GSO was the higher. In general, the statistical results in Table To evaluate the biological significance and functional enrichment of the complexes identified by our algorithm, we calculated the OS is low), but both have low p-value of GO terms. The p-value of the identified protein complexes is calculated on Molecular Function. In each row, the proteins in bold have well matched some known protein complex in benchmark complex dataset, and the additional proteins probably share the similar functions with other proteins. For example, 5 proteins do not matches the known protein complex in the first predicted protein complex, while 4 proteins of which  share the similar annotations—DNA-directed 5′-3′ RNA polymerase activity—with the true protein complex. We visualize this protein complex shown in Fig. OS but with small p-value are highly likely to be true protein complexes. Therefore, the results provided clues for biologists to verify and find new protein complexes.We list some identified protein complexes in Gavin data shown in Table f-measure and p-value. It means the results from iOPTICS-GSO are more biologically meaningful than others for identifying significant proteins complexes. However we also found that the number of clustering modules is relatively small and the recall of clustering results is lower than other algorithms in iOPTICS-GSO results. The reason may be that each protein only can belong to one cluster in iOPTICS-GSO, which causes that other clustering modules are small. Therefore, it would be our focus to discover the effective strategy to improve the result and detect more protein complexes in the future.Protein complexes are not only the basis of normal biological processes, but also play an important role in the pathological process. Therefore, identifying protein complexes play an important role in understanding the cellular organizations and functional mechanisms. In this study, we have put forward the algorithm named iOPTICS-GSO, which is the improved OPTICS algorithm by using GSO to optimize the parameter in OPTICS, and we changed the concept of core node and redefine the similarity which makes more accord with the actual situation of PPI network. As different parameter setting have different results on each sub-network of DPIN, we have used GSO algorithm to optimize these parameters, and finally checked the quality of every cluster and gained the optimal cluster results. The experiment results have shown that our iOPTICS-GSO outperforms competing algorithms in terms of


E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI).Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs . The method is also validated for PPIs in influenza and  Proteins are essential molecules in all biological systems in a cell, with most proteins requiring protein-protein interactions (PPIs) to function effectively. For example, transport proteins interact with structural proteins and hormone peptides interact with receptors. Some proteins form structural complexes, and the interactions among different protein complexes are necessary for cell functions. Protein interactions are fundamentally characterized as stable or transient, and both types of interactions can be either strong or weak. If two protein interact via physical contact and the affinity is strong, the strong interaction can be detected using in-vitro biochemical experiments such as pulling-down and co-immunoprecipitation assays. However, biochemical experiments for PPIs are time-consuming and expensive, making it difficult to study complete protein interaction networks within a genome .Recently, computational methods for detecting PPIs based on coevolution analysis have distinguished themselves from biochemical experiments and other computational methods , 3. ProtTo address the problems in MSA based coevolution method, several new feature encoding and extraction methods in PPI predictions have been developed. Converse vectors encoding of protein sequence pairs based on k-mer scheme can improve the accuracy of PPI prediction . The geoWe present here a novel alignment-free method for coevolution analysis. The method is based on biochemical properties of amino acids, instead of using sequence alignments. Comparison of sequence similarity adopts discrete Fourier transform (DFT) as an analysis method. Using coevolution analysis, we apply this DFT method to investigate the interactions of all seven proteins in Ebola virus.Ebola virus is a filamentous, nonsegmented, negative-strand RNA virus. Ebola virus infects both primates and humans and leads to severe hemorrhagic fever, with high mortality rates. Understanding the PPIs of Ebola virus will advance the development of effective vaccines. Ebola virus genome encodes seven proteins: glycoprotein (GP), nucleoprotein (NP), RNA polymerase (L), VP24, VP30, VP35, and VP40 . GlycoprWe perform a case study on the effectiveness of the DFT method on PPI predication. The study on PPIs in Ebola virus and influenza virus using DFT based coevolution analysis indicates that our method is accurate, effective and thus outperforms the coevolution methods that are based on sequence alignments. Our approach significantly contributes to the PPI analysis of proteins in various genomes.We present an effective computational method to identify PPIs based on the coevolution model. The method employs chemical properties of proteins in phylogenetic analysis. The method uses the Euclidean distance of Fourier transform of proteins as dissimilarity measure for pairwise protein interactions. To predict if two proteins A and B interact in a species by coevolution analysis, we first collect the protein sequence mutations in a collection of species. The two proteins may undergo different mutations in these genomes. We compute the distance matrix of protein A using DFT distance measure. Similarly, we get the distance matrix of protein B in the same set of genomes. We then compute the correlation of two distance matrices A and B. If two proteins A and B interact, the distance matrices A and B have high correlation, otherwise, the correlation is low. Finally, we use multidimensional scaling (MDS) to visualize the correlation between the matrices as a measure for the interaction distance.The existing methods in coevolution analysis for PPI are based on multiple sequence alignments by sequence characters. Yet character based similarity cannot provide insight into the structural aspects of a protein. The biological function of a protein is the direct consequence of its sequence and is determined by the chemical properties of the sequence. There are hundreds of physico-chemical properties in 20 amino acids of proteins . ExampleComparison of protein sequences for PPI shall consider the contributions of physicochemical properties, but none of the physico-chemical properties has been employed in existing PPI method by coevolution analysis. To understand the conservation of residues in protein sequences during coevolution, it is important to qualitatively and quantitatively measure the differences among residues using physico-chemical properties of amino acids. The functions of a protein depend on how the protein folds into 3D structure which most importantly depends on the hydrophobicity properties of the proteins. Therefore, effective PPI prediction methods need to capture hydrophobicity properties of the proteins. In this study, we use the hydrophobicity values to represent protein sequences .Artemisiaannua, which contains α-helix structures (α-helix structures in the protein sequence (α-helix structure [β-sheet structures (β-sheet structures in the protein sequence (β-sheet structure [X(k) be the DFT of time series x(n) of length N, and X(k) is defined as [Distance between protein sequences can be measured through Fourier transform of protein hydrophobicity profiles. Discrete Fourier transform (DFT), a broadly used digital signal processing approach, transforms data from time space to frequency space and reveals periodicities that are hidden in time space. Fourier transform gives a unique representation of the original underlying signal in frequency domain. The frequency domain vector contains all the information about signal in time domain. To analyze the distribution of hydrophobicity along protein sequence positions, a sliding window is used to extract different length protein sequence segment and then apply DFT on each windowed protein sequence. we performed Fourier transform of the sequence of sesquiterpene synthases from ructures . The prosequence . The pertructure . The othructures . The DFTsequence . The pertructure . These ttructure , 16. Mortructure , 18, andtructure –21. Thusfined as X(k)=∑n=a and b by the corresponding Fourier transform coefficients A and B as followsR(A(i)) and I(A(i)) are the real part and the imaginary part of complex number A(i), respectively.We can infer the information content in protein sequences from the distribution of Fourier coefficients because the original sequence can be recovered from the Fourier coefficients by inverse Fourier transform. The relationship between original sequence and its Fourier transform is one-to-one, therefore, the DFT method can be efficient and effective in comparing similarities of protein sequence mutations in coevolution analysis. We define the distance between two time series x(t) and y(t) are of different lengths, the DFT of the two sequences have different lengths, the Euclidean distance of DFT power spectrum or DFT coefficients of protein sequences of unequal lengths cannot be directly computed. Some solutions to this problem in the signal processing are to extend the shorter series yt, by padding zeros or even scaling method to extend power spectra of short lengths to the longest one [When two protein sequences gest one . We use After Fourier transform of numerical sequences of proteins from different species, we can get pairwise distance matrix of different species based on a specific protein and construct the phylogenetic tree of these species. Our algorithm for computing the pairwise distance of two genomes from a protein is as follow.Algorithm 1: Computing pairwise Euclidean distances of genomes by their specific protein sequences in Fourier frequency domain.Data: a set of genome, protein XResult: distance of genome a and bStepsConvert protein sequences X in genome a and b into numerical series using hydrophobicity values of amino acids sequences.For all the numerical sequences, pad zeros to extend numerical sequences the longest length in the genome set.Apply DFT to numerical sequences to frequency domain and get Fourier transform of the sequence.Construct distance matrix from the pairwise Euclidean distances.Construct phylogenetic tree of the genome set from the distance matrix.Our algorithm for identifying interaction between proteins X and Y is as follows.Algorithm 2: Identifying interaction between proteins X and Y.Data: Protein X and Y, a set of genomesResult: Co-evolution distance of protein X and YStepsConstruct distance matrix X of the genomes by protein X (Algorithm 1).Construct distance matrix Y of the genomes by protein Y (Algorithm 1).Compute correlation coefficients of distance matrices X and Y.Visualize the interaction distance of protein X and Y from distance correlations by multidimensional scaling analysis (MDS).cor of two distance matrices as follows.n is the number of elements of the upper triangular of the distance matrices, X and Y, respectively. For a set of N proteins, these pairwise Pearson correlations form a correlation matrix C ∈ RN × N.To detect if two proteins X and Y interact with each other, we first collect protein sequences X and Y from the same set of genomes, we then use the proposed DFT method to construct the distance matrices for protein X and Y, respectively. The correlation between two distance matrices is used to indicate if two protein X and Y interact. The correlation of protein mutations in an interaction pair is measured by the Pearson correlation To increase the specificity of PPI method, the genomes with a pair of normal proteins are excluded in the coevolution analysis. In details, if each of two proteins X and Y has no mutation in genomes A and B, the DFT distance of protein sequence X (or Y) between two genomes A and B is zero. These two genomes A and B are excluded in coefficient computation.http://www.ebi.ac.uk/Tools/msa/) [http://csbg.cnb.csic.es/mtserver/) [One widely used computational method for inferring PPI is coevolution analysis of MirrorTree from MSA , 23. To ls/msa/) , and theserver/) .C is first transformed to 1 − C as dissimilarity distance matrix D, which represents relative interactions of proteins.We employ MDS method to visualize the relative interaction distance of PPIs in two-dimensions. Multidimensional scaling (MDS) projects a distance matrix into a set of coordinates such that the Euclidean distances of these coordinates approximate the original distances . Since tD. The MDS analysis for visualization is as follows. From the distance matrix D of N protein samples, we construct the Gram matrix B as I ∈ RN × N is the identity matrix and e ∈ RN × N is a vector of all ones. The matrix B has maximum r non-zeros eigenvalues and B = VΛVT, if we take r largest eigenvalues, then X is an N × r matrix with the coordinates xi as its rows. Thus the coordinates in an r-dimensional can be recovered from the distance matrix D. In coevolution analysis, the distance matrix D derived from the correlation coefficients of pairwise proteins is projected onto two dimensions (r = 2) by MDS for visualizing the relative interactions of proteins.The MDS method is then applied to the dissimilarity distance matrix To demonstrate the effectiveness of the DFT measure of protein sequences, we apply the phylogenetic analysis of Ebola virus using NP protein by the DFT method and multiple sequence alignment (MSA). The result shows that the phylogenetic trees from the DFT method contain rich hierarchical information about different virus species . The DFTTo assess our proposed DFT method in detecting PPIs, we use the DFT method to investigate the interactions of all seven proteins in Ebola virus. The Pearson correlation of the distance matrices of seven proteins in coevolution analysis by the DFT method is listed in After the correlation matrix for pair-wise proteins is obtained from the DFT method , relativNP protein plays a central role in protein interaction network. The proteins NP, VP24, VP30 and VP40 have a close relationship. Previous experimental study suggested that VP40 appears to physically interact with NP by detection of NP in VP40-containing VLPs . The coeIt shall be noted that from co-immunoprecipitation study, NP protein interacts with both VP24 and VP35, the four proteins  are necessary and sufficient to mediate assembly of an NC with structure , but ourThe VP24 protein of Ebola virus is a secondary matrix protein and minor component of virions. VP24 is critical matrix protein in Ebola virus . PreviouThe membrane-associated matrix protein VP40 of Ebola virus is the most abundant virion protein and plays a key role in virus assembly and budding in the form of virus-like particles (VLPs) . Our coeEbola virus VP30 is an essential transcription activator, and RNA binding protein of viral transcription. In viral particles, VP30 is closely associated with the nucleocapsid complex, but how VP30 activates transcription is still unclear. Phosphorylation of VP30 regulates viral transcription and replication by modulating interaction with the nucleocapsid proteins VP35 and NP . Our coeOur coevolution analysis indicates VP35 does not have strong interactions with other proteins. VP35 acts as RNA polymerase cofactor in the transcription and replication complex and plays an essential role in viral RNA synthesis and interacts with the viral nucleoprotein . Three pIt is noted that the Ebola RNA polymerase (L) does not have strong coevolution with other proteins. It is reasonable because RNA polymerase works as making reliable copies of virus genomes. Dramatic mutation in RNA polymerase may produce instable virus genomes. Another possible reason for the loose coevolution between RNA polymerase and other 6 proteins is that a virus with high mutation in RNA polymerase may not survive.The glycoprotein (GP) is the only viral protein on the surface and is therefore responsible for receptor binding and membrane fusion, mediating attachment and entry of the virus into host cells . The coeWe compare our DFT method with the state-of-art MirrorTree method in PPI prediction , 25. TheInfluenza A viruses belong to the Orthomyxoviridae family of negative-sense, single-stranded RNA viruses. The virus genome is composed of 8 segments, encoding for 11 proteins: HA (hemagglutinin), NA (neuraminidase), NP (nucleoprotein), M1 (matrix protein), M2, NS1 , NEP (nuclear export protein), PA (Polymerase acidic protein), PA-X, PB1 (polymerase basic 1), and PB2 , 40. HA We assess the DFT coevolution method in identifying the protein-protein interactions among these 11 proteins in influenza A virus. The coevolution analysis is performed on the 60 influenza virus genomes. The GenBank access numbers of the influenza virus genomes are listed in supplementary materials . The Peain vivo genetic study [We verify these interactions by curating literatures of experimental studies. In virus, HA attaches to sialic acid on host cell surface to initiate virus infection ; NA remoic study . NS1 proic study . Since Nic study , 58, NEPic study , 47. TheE.coli. Examining the phenotypes resulting from pairs of mutations may offer a way to understand how these genes intersects [To test proposed DFT method on coevolution analysis in large scale, we evaluate the relationship of Pearson correlation by the DFT method and the genetic interaction score (GI) of protein pairs in tersects . GeneticE.coli [E.coli genomes are listed in supplementary materials , which are genetic contributions to complex diseases, such as cancer . Most ofIn this study, we develop and extend a quantitative method that identifies interacting proteins by coevolution analysis. Our method employs DFT analysis of chemical properties of amino acids in position context of protein-protein interactions. The method offers evidences of coevolution for protein interactions in Ebola and influenza viruses. Most of the interactions identified by this coevolution study are in agreement with previous studies. We also find that there is strong coevolution between two matrix proteins VP24 and VP40, between matrix proteins VP24/VP40 and transcription factor VP30.S1 File(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(PDF)Click here for additional data file.


Background: Long non-coding RNAs (lncRNAs), are being reported to be extensively involved in diverse regulatory roles and have exhibited numerous disease associations. LncRNAs modulate their function through interaction with other biomolecules in the cell including DNA, RNA, and proteins. The availability of genome-scale experimental datasets of RNA binding proteins (RBP) motivated us to understand the role of lncRNAs in terms of its interactions with these proteins. In the current report, we demonstrate a comprehensive study of interactions between RBP and lncRNAs at a transcriptome scale through extensive analysis of the crosslinking and immunoprecipitation (CLIP) experimental datasets available for 70 RNA binding proteins.Results: Our analysis suggests that density of interaction sites for these proteins was significantly higher for specific sub-classes of lncRNAs when compared to protein-coding transcripts. We also observe a positional preference of these RBPs across lncRNA and protein coding transcripts in addition to a significant co-occurrence of RBPs having similar functions, suggesting a modular organization of these elements across lncRNAs.Conclusion: The significant enrichment of RBP sites across some lncRNA classes is suggestive that these interactions might be important in understanding the functional role of lncRNA. We observed a significant enrichment of RBPs which are involved in functional roles such as silencing, splicing, mRNA processing, and transport, indicating the potential participation of lncRNAs in such processes. The recent years have seen the discovery of a large number of novel transcripts which belong to the long non-coding RNA (lncRNA) class in humans and other model organisms  for these RNA binding protein sites at a genome-scale from doRiNA   (Table 2). The RBP binding sites were downloaded from 3 databases namely: starBase v2.0 , HITS-CLIP-seq (High Throughput sequencing of RNA isolated by crosslinking immunoprecipitation), RIP-seq (RNA immunoprecipitation), iCLIP , PAR-iCLIP  and CLASH (cross-linking ligation and sequencing of hybrids) followed by sequencing of the pull-down fraction of RNA. The sequenced RNA is further used to identify exact or probable binding site using various bioinformatic approaches. In case of ClipDB, the peak calling and identification were done using PARalyzer, CIMS, Piranha, and CITS software tools. Hence, we stored each of files derived from all databases in form of peaks as separate files for downstream analysis. Details of all the techniques and methodologies used to process the data used in our analysis is given in Table All these RNA binding sites were liftover to hg38/GRCh38 assembly using the CrossMap-0.2.2 tool    dataset due to lack of strand orientation information. Bespoke shell scripts were used to identify RBP sites which co-occurred with each other and were therefore clubbed together.The coordinates for each RBP peak dataset were intersected separately with both the lncRNA and protein coding exons using BEDtools. These intersecting coordinates were then used to calculate the number of bases which were shared between each of the protein datasets to examine their co-occurrence. The values were further normalized by dividing it with the total number of unique bases of individual RBP datasets which were intersecting with lncRNA and protein coding exons. The mapping percentage in protein coding transcripts provided the baseline for co-occurrence frequency of the binding sites. These co-occurrence frequencies were calculated independently for all the RBP across six datasets.We also examined the positional preference of the RNA binding protein interaction sites across the length of lncRNA transcript. As the length of the transcripts varied considerably in our analysis therefore, we briefly define the length of the transcripts as divided into three equal parts. The length of long non-coding transcripts were normalized to 100 nucleotides and arbitrary divided into three equal parts viz., 5 prime end, the middle region, and 3 prime end for comparisons. The notation 5 prime, middle region, and 3 prime denote the positions of the three equal fragments and have no bearing with 5 prime and 3 prime UTRs. Except for datasets analyzed using Piranha, which did not have strand information of the called RBP peaks, all other datasets were used to check for their positional preference. The unique number of bases intersecting with each of the three lncRNA segments was calculated for each dataset. These were further normalized by dividing these values with the unique number of bases in the respective lncRNA segment. Percentage preference was calculated for each segment and the positional location of RNA protein-binding sites were enumerated and plotted as heatmaps.Additionally, we also plotted the counts of the RNA binding protein interactions sites in protein coding transcripts derived from GENCODE annotation file and the mappings were divided into 3 regions: 5 prime UTR, coding exons, and 3 prime UTR of the coding genes. The CLIPdb-Piranha-non-stranded dataset were not used for the analysis due to the lack of strand information of the peaks.We analyzed publicly available datasets for 70 RNA binding proteins derived from seven datasets encompassing five technologies viz. PAR-CLIP, HITS-CLIP, iCLIP, RIP-seq, and CLASH. The experimental datasets were downloaded for RNA binding proteins from three databases  for RBP sites as opposed to protein coding transcripts in some or the other RBP dataset.All proteins showed distinct frequency distribution across both protein-coding and long non-coding transcripts. In general, RBP binding was higher in protein coding transcripts when compared to long non-coding transcripts. But when we looked closely, few of RBPs showed higher enrichment for lncRNA subclass when compared to protein coding transcripts. We tested the significance of the enrichment of RBP sites across lncRNA subtypes as opposed to protein coding transcripts using paired We plotted the binding frequencies of RBPs in lncRNAs and protein coding transcripts for each of the seven datasets as separate graphs. Those datasets and biotypes which had a significantly higher binding for RBPs have been plotted Figure , 2. The We additionally chose a random set of 1,000,000 (1 million) genomic loci as a control set with an average length of 240 bases and mapped the RBP sites across this control set. The frequencies of protein binding sites across these random genomic loci, lncRNA, and protein coding transcripts of randomly chosen RBPs from each of the six datasets have been depicted in the Supplementary Figure The seven datasets considered in this study were observed to map onto lncRNA transcripts as well as protein-coding transcripts. To understand whether they map to common subset of loci in the respective transcripts, we evaluated the positional overlaps of the binding sites for each protein from these seven datasets individually. The counts of overlaps were measured as proportion of the total number of independent occurrences of binding sites for each protein. The overlaps were counted separately for all positions in the protein coding transcripts and in lncRNAs. The mapping in protein coding transcripts served as the control set which provided a fair idea of the general overlap in the genomic scale.Four proteins from the CLIPdb-CITS dataset CSTF2, HNRNPC, TARDB, and TIA1 showed maximum co-occurrence with their respective set of proteins both in protein coding and lncRNAs transcripts while CSTF2, HNRNPC, and TIAL1 co-occurred with each other as well. Our analysis revealed that similar functioning proteins have significantly higher overlapping binding sites with each other, as expected, while EZH2 was an exception in this dataset  proteins co-occurred with AGO proteins from CLIPdb-Piranha-stranded, Clipdb-PARalyzer, and starBase datasets, from previous observations it is has been seen that functionally related proteins co-occur as in case of TNRC6 with Argonautes, as they have shown to be to play important roles in microRNA mediated regulation of transcripts , a well-studied lncRNA with intricate roles in the pathophysiology of cancer Metastases is one of such candidate  from seven datasets processed through standardized computational pipeline for accurate assessment of protein-RNA interaction sites . This allowed us to compare the frequencies of the protein binding sites in systematic fashion. It has not escaped our attention that the datasets encompass a diverse set of experiments; cell line, and experimental protocols, nevertheless; our findings hold true despite these differences available in public domain as part of this analysis encompassing six experimental databases of RNA binding proteins. For instance, one of the most studied RBP, the Argonaute datasets showed similar trends regardless of the diverse experimental protocols  and analysis methodologies employed.The RBPs considered in our study are known to be involved in varied types of functional roles including silencing, splicing, stability, mRNA processing, and transport. In the current study, we observed RBPs enriched for specific lncRNA biotypes are involved in diverse functions, suggesting their probable functional mechanism of action. RBPs such as AGO, DGCR8, EWSR1, TNRC6A/B/C, and FUS, involved in maintenance of the stability of RNA, were having significant enrichment for the lincRNA, miscRNA, retained intron subclasses suggesting they might be acting as either transporters or as sponges for these RBPs. Another set of RBPs such as CPSF complex, FBL, TAF15, and HNRNP family, playing a role in mRNA processing were shown to be enriched in lncRNA subclasses, signifying that lncRNAs inturn might be acting as guides. These proteins might be also involved in mechanism of lncRNA biogenesis. Enrichment was also observed for proteins such ATXN2, C17ORF85, and HNRNPs which predominantly are involved in the export and transporting of RNA moieties, in addition to proteins such as EIF4A3, FOX2, PTBP1, QKI, SFRS1, SRRM4 among others which are predominantly involved in splicing. Hence our analysis suggests that interaction of lncRNA with such types of RBPs surely provide hints about the possible functional roles lncRNAs might be playing which can be validated by experimental approaches.We also highlight the localization of lncRNAs and RBPs within a cell. We classified the RBPs based on their known localization within the cells and overlapped it with MALAT1, which is an established nuclear enriched lncRNA. The results indicated that the intensity of nuclear localized RBPs were higher for MALAT1 across all the seven datasets. This further strengthened the fact that these bindings were not an arbitrary event and are indeed interacting with the co-localized lncRNAs.The present analysis reveals a set of interesting characteristics of protein-RNA interaction in the context of lncRNAs: (1) high frequency of RNA-protein interaction sites in lncRNAs subclasses; (2) co-occurrence of RNA binding protein interaction sites; and (3) positional preference for the binding sites across the transcript length. This analysis, to our best of knowledge is the most comprehensive analysis of RNA binding protein interaction sites in lncRNAs, and provides the basis for further analysis on the functional consequences of these patterns. It has also not escaped our attention that targeting protein-interaction sites and thus the functionalities could be in the future therapeutically explored. Recent reports from other laboratories have explored the possibility of targeting RNA structures using small molecules (Jamal et al., The interactions between proteins and RNA molecules can provide the essential insights into the functioning of the lncRNAs. In this study, we highlight the enrichment of RBP sites across some of the lncRNA transcript classes in comparison with protein coding transcripts. We have systematically demonstrated that proteins having similar functional roles showed a higher co-occurrence across both lncRNA and protein coding transcripts. Also, the positional preference of most of RBPs agreed with their possible functional roles. Our study gives a compendium of lncRNA and RBP interactions suggesting a large number of functional roles which they can play including silencing, splicing, mRNA processing, export or transport.VS conceptualized the analysis. Data analysis was performed by SJ and SG. SJ prepared the data summaries and visualization. SJ and SG wrote the manuscript. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer NA and handling Editor declared their shared affiliation.


Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world. A large number of structural bioinformatics applications rely on extracting structural features from a protein’s sequence. This is traditionally done by performing multiple sequence alignments (MSAs) of homologues. MSAs have been used as input to predict features such as secondary structure, torsion and bond angles, solvent accessibility, disorder regions, and domain boundaries. The main limitation of most of these descriptors such as predicted secondary structure is that, although often highly accurate, they provide information only about a protein’s local conformation. For instance, they may tell us how a set of residues comprise an alpha-helix, but they do not provide any information as to how different alpha-helices are oriented with respect to one another. Techniques based on co-evolution go a step further by extracting non-local structural information from MSAs. These techniques are based on the notion that two residues which mutate in a correlated fashion, so that a mutation in one is often compensated by a mutation in the other, can be considered to be co-evolving. Co-evolution is interpreted as functional dependence, i.e. if two residues are co-evolving, there is a cost in fitness for mutating only one of these residues. Although these techniques were originally conceived and applied to protein structure prediction, they are now established tools with a diverse set of applications in structural bioinformatics.6. To quantify the precision of different methods, protein contacts  were considered as true positives. These early attempts presented low precision and therefore limited usefulness. Methods based on calculating the Mutual Information (MI) between MSA columns were able to extend the applicability of these approaches9, but predictions were still not precise enough to be useful for most cases10.Initial attempts at identifying co-evolving residues were implemented by calculating the correlation between columns in an MSA14. Direct Coupling Analysis (DCA) techniques are based on a generalised Ising model and, unlike MI and previous approaches, addressed the problem of transitivity by considering the correlation amongst all columns in the MSA as background to establish if two residues are co-evolving. Subsequent implementations based on similar ideas attempted to relax some of the assumptions of the original model and yielded progressively better results18. Although close in conception, these methods managed to produce a significant number of non-overlapping predicted correlations19. Meta-predictors were then developed to combine the non-overlapping set of predictions to produce a consensus21, further improving the precision of co-evolution inference. A large-scale comparative study  has shown that the most precise of these methods, metaPSICOV22, achieved a precision greater than 50% for its top L predictions, where L is the protein length, for over 68% of test cases23. Other methods were developed with specific applicability, such as inferring co-evolving residues in membrane proteins26 or between β-sheets27. The precision of predicted correlated mutations has continued to increase with improved methods using physicochemical information28 and ultra-deep learning29.The exponential growth in the number of protein sequences combined with the application of existing statistical techniques that solved the inverse statistical problem to infer evolutionary couplings have allowed the development of methods with a precision range that has proved useful for many applicationsde novo structure prediction for both soluble30 and transmembrane proteins32 when sufficient sequence information is available. Recent results from the critical assessment of methods of protein structure prediction33 have shown that in the presence of a sufficiently accurate number of predictions, topology prediction can be performed consistently and accurately. However, a few challenges remain regarding the identification and assignment of domain boundaries, longer proteins, and, most importantly, for cases where the number of available sequences is insufficient for accurate co-evolution inference. This latter problem is the main limitation; without enough diverse sequence information, accurate evolutionary coupling inference is currently impossible. When considering the results of the Critical Assessment of methods for protein Structure Prediction (CASP), a blind community-wide experiment that evaluates different prediction methods, protein structure prediction has been applied to a large number of cases where the target structure was unknown, providing reliable large-scale information about unknown folds34.The implementation of DCA led to consistent and accurate35. A subsequent study has shown that when the number of paired sequences exceeds the average length of the proteins in the complex, most of the co-evolving residues are in contact at the protein–protein interface36.Co-evolution analysis of paired sequences from interacting proteins has been shown to be effective in identifying pairs of residues involved in complex formation38. Co-evolution has also been used to identify protein–protein interactions and was shown to predict the only two experimentally known interactions of the trp operon39. The main limitation that co-evolution techniques encounter when used to infer protein–protein interactions is that these methods require a large number of pairs of protein sequences of the same organism, which currently restricts its applicability to a small number of cases. Furthermore, pairing of same-organism sequences is particularly difficult in the presence of paralogues, and methods have been proposed to address this problem41.Co-evolution has also been shown to assist in protein–protein docking during the rounds 28–35 of the critical assessment of prediction of interactions. A potential based on co-evolution inference called InterEVScore was used in conjunction with ZDock, SOAP-PP, and Rosetta refinement to produce correct predictions for 10 out of 18 targetsThe concept of using co-evolution to predict protein-protein interactions can be further extended to include protein multimerisation. This offsets the limitation of dependence on paired sequences. However, multimerisation prediction is more challenging than the identification of co-evolving residues in a protein–protein interaction interface, since it is necessary to discriminate between multimeric contacts and intra-monomer contacts.42 and to validate a suggested dimeric interface between two Hsp70 molecules in the DnaK crystal43. This success in multimeric prediction suggests that the existing quality assessment of co-evolution techniques may be underestimating their precision. This is because of the fact that pairs of residues interacting in the multimeric conformation would not necessarily share spatial proximity when considering the monomeric protein chain and thus would be incorrectly considered as false positives.Co-evolution techniques were used to correctly identify multimeric contacts for 18 dimeric complexes44. Protein contacts have been used for automatic domain boundary assignment and prediction by means of minimising the inter-domain contacts whilst maximising the number of intra-domain contacts46. However, these contact-based methods depend on an existing structure for the target sequence and are therefore not applicable when predicting new structures. This limitation, however, can potentially be overcome by using co-evolution inference to predict protein contacts. Correlated mutations output by MI led to the successful prediction of domain boundaries47. A more precise co-evolution inference method has also been used for domain prediction. It was shown to produce better results for 368 targets compared to sequence-based methods and comparable results to homology-based methods48.Domain boundary identification is particularly useful for, but not restricted to, protein structure prediction, and it has been reported as one of the main challenges encountered in the free-modelling category of CASP49. They have also been used to identify distinct functional conformational states suggested to be observed between apo and holo conformations53. In another study, co-evolution was used to identify a framework for allostery for the MutS DNA mismatch repair protein54 by means of Statistical Coupling Analysis (SCA). This approach differs from the traditional DCA, as it aims to construct a network of co-evolving residues as opposed to performing the correlation assessment on a pairwise level.Co-evolution provides a way of assessing the biological relevance of different conformations observed in coarse-grained structural-based models or molecular dynamics simulations. Co-evolving residues have been used to guide coarse-grained simulations either towards the native conformation or to explore conformational ensembles that are supported by evolutionIdentification of alternative conformations and allostery using experimental techniques is challenging, suggesting co-evolution techniques may be a powerful tool for exploring and targeting conformational dynamics. The success of co-evolution approaches suggests that co-evolving residues can be in contact only in a subset of a protein’s conformations. Once again, this highlights that the precision of co-evolution methods may be underestimated if they are tested against a single protein structure.ab initio can be used to assist in crystallographic protein structure determination, particularly when no other structural information is available. In these scenarios,ab initio models are used in molecular replacement protocols to solve the phasing problem. However, this is limited by the quality and reliability of the input models. Up until the advent of more precise co-evolution methods,ab initio protein structure prediction led to poor modelling results for a large number of cases, including longer and/or multi-domain proteins. Co-evolution has broadened the applicability of models produced in the absence of a template, leading to more consistent and reliable predictions. Models generatedab initio in conjunction with co-evolution constraints have been shown to improve the success of molecular replacement56. Co-evolution has also been used to characterise the order in which macromolecular complexes self-assemble, complementing existing experimental data for those complexes57.Structural models produced30. Insufficient sequence information constitutes the main limitation for co-evolution techniques. In the absence of a minimal number of non-redundant sequences, the inferred evolutionary couplings are unlikely to suit any of the purposes mentioned thus far. The usefulness of the predictions is therefore restricted to protein families for which a sufficient number of non-redundant sequences is available. It was previously reported that approximately 25% of the protein families on Pfam58 would have a sufficient number of sequences for reliable co-evolution inference17. Metagenomics data have been used as a source for additional sequences, thus expanding the applicability of co-evolution59. Ovchinnikov and colleagues used metagenomics to increase the number of MSA sequences and subsequently to predict the protein structure of an additional 614 protein families, 140 with no members with known structure. Metagenomics provides a wealth of sequence information that is yet to be explored in other applications of co-evolution techniques, such as protein–protein interaction prediction and functional characterisation.The precision of co-evolution methods is known to be dependent on the number of non-redundant sequences used in the MSA60. Though the method can be generalised for any organism, it was tested for 34 cases to identify deleterious mutations in humans, showing comparable results to state-of-the-art supervised methods.A common application in bioinformatics is to predict the effect of a particular mutation on a phenotype. Given that co-evolution aims to capture correlated mutations, it can be used to quantify how likely a mutation is to be compensated for by a second mutation in another residue. This, in turn, provides a means of estimating the fitness cost for a particular mutation considering its effect based on co-evolving residues. A recent method, EVmutation, uses co-evolution to quantify the effects of multiple mutations on the phenotype63. These methods can estimate an energy for a target sequence that can be interpreted as the compatibility of this sequence to the fitness landscape of its family.Maximum entropy models, which serve as a basis for several co-evolution methods, can also provide insights on the fitness landscape of a particular protein family64. As an example, co-evolving site mutagenesis was used to improve protein thermostability of alpha-amylase65. There are also examples where mutations in co-evolving positions were shown to be de-stabilising64. Evolutionary couplings can also highlight residue interactions that are not known either because a structure is unavailable or because such relationships are not evident from structural data (e.g. unresolved residues). This provides additional insights into protein folding, stability, and function that can be explored by synthetic biology/bioengineering.Another application of co-evolution-based fitness estimation relates to bioengineering. Co-evolution can be used to identify pairs of residues which, if mutated, can alter a protein’s stability and/or function. This is particularly important when selecting hotspots for enzyme engineeringThe advent of precise methods for the identification of co-evolving residues has led to progress in many areas of structural bioinformatics. The limited applicability of these methods, usually constrained by the amount of sequence information available, may be offset by metagenomics efforts and the exponential growth in sequence information. This paves the way for co-evolution to become as pivotal to bioinformatics analyses as sequence alignments themselves. The functional relationships that can be derived from these predictions provide a source of additional data that goes beyond the realm of structural prediction, translating an abundant source of information (sequence) into biological signal. Though still in their infancy, many of the alternative applications of co-evolution show great promise, and we can expect to see many advances and new techniques in these areas over the coming years.


Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds. Understanding how proteins adopt their 3D structure remains one of the greatest challenges in science. Elucidation of this process would greatly impact various fields of biology and medicine, as well as the rational design of new functional proteins and drug molecules. Determination of the fold category of a protein is crucial as it reveals the 3D structure of proteins. Classification of a protein of unknown structure under a fold category is called fold recognition, which is a fundamental step in the determination of the tertiary structure of a protein.In the early years, determination of protein structure relies on traditional experimental methods, such as X-ray crystallography and nuclear magnetic resonance spectroscopy. In the post-genomic era, numerous sequences are generated by next-generation sequencing techniques. Although an increasing number of sequences are structurally characterized using experimental methods, the gap between structurally determined sequences and uncharacterized sequences is constantly increasing. Therefore, developing computational methods for fast and accurate determination of protein structures is urgently needed. Accurate computational prediction of protein folds has recently emerged as alternative approach to the labor intensive and expensive experimental methods. Computational methods for protein fold recognition can be generally categorized into three classes: (1) de novo modeling methods; (2) template-based methods; and (3) template-free methods. Many efforts have focused on the development of methods under classes (2) and (3) because the de novo approach (class 1) has two limitations. First, it requires long computational time and numerous sources, and second, it can only be successfully applied in small proteins.Z-score and E-value are the two commonly used scoring functions. The accuracy of the alignment is tremendously important in model building. Fourth, 3D structure models based on template atom coordinates and optimal query-template alignments are built. Last, the optimal structure models are determined from the model candidates through further structure optimization. The commonly used structural optimization methods include energy minimization and loop modeling.Template-based methods used to determine protein structures are based on the evolutionary relationships of proteins. The procedure for template-based methods can be summarized as follows: First, proteins of known structures retrieved from public protein structure databases ) are used as template proteins for a query protein sequence. To make template-based prediction fast and reliable, a simplified database is usually employed, in which the sequence similarity is less than 50%–70%. Second, distant evolutionary relationships between a target sequence and proteins of known structure are detected. In this step, multi-alignment algorithms are adopted to exploit evolutionary information by encoding amino acid sequences into profiles. Third, to determine the optimal alignments, scoring functions are usually used as measures to evaluate the similarity between the profiles derived from a query protein and those of template proteins with known structures. http://predictioncenter.org/.A series of template-based methods were developed in the last few decades. This series of approaches are regarded as the most successful methods in constructing theoretical models of protein structures. For instance, Jaroszewski et al.  developeAlthough much progress has been made in template-based methods, some problems still exist, as follows: First, we need to determine the structures of template proteins. The three-dimensional structures of many proteins remain to be determined. Second, template-based modelling largely relies on the homology between target and template proteins. When the target and template proteins display a sequence similarity of >30%, the use of sequence alignment methods , genetic algorithm, Artificial Neural Network, Support Vector Machines (SVMs), and ensemble classifiers. A key underlying assumption in employing machine learning-based methods for protein fold recognition is that the number of protein fold classes is limited . MachineMultiple database sources are often used in protein structure research. These databases include PDB ; UniversSCOP, proposed by Murzin et al. , is a hihttp://scop2.mrc-lmb.cam.ac.uk. Compared with SCOP, SCOP2 displays a more advanced framework for protein structure classification, wherein the best features of SCOP are retained and a novel approach for classification of protein structures is offered. In SCOP2, protein sequences and their structures are presented in a directed acyclic graph to form a network of many-to-many relationships.Murzin et al.  have recSimilar to SCOP, CATH is a hierarchical protein domain classification. In the CATH database, proteins and their structures are obtained from the PDB database. When proteins share a clear common evolutionary ancestor, they are clustered into a homologous superfamily  modIn the first phase (model building), query protein sequences are first submitted into a pipeline of feature representation, in which sequences of different lengths are encoded with fixed-length feature vectors by feature descriptors. The commonly used feature descriptors include Amino Acid Composition (AAC), Pseudo AAC, Functional Domain (FunD), Position Specific Scoring Matrix (PSSM)-based descriptors, Secondary Structure-based descriptors, and Autocross-covariance (ACC) transformation. When the resulting feature representations display some irrelevant features or redundant features, an alternative step is usually performed to select the optimal feature subsets, which can yield the best performance, from the resulting feature representations. Subsequently, the feature vectors are fed into a pre-selected classification algorithm to train a prediction model. Typical classification algorithms often used in model building include SVM, Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR). The first phase is completed in this step.In the second phase (prediction), uncharacterized query proteins are first submitted into the same pipeline of feature representation as in the first phase. Note that if feature optimization of the generated feature representation is performed in the first phase, feature optimization should also be performed in the second phase; otherwise, the resulting feature vectors are fed into the trained prediction model, wherein the protein fold class to which the query proteins belong is predicted.Machine learning-based methods can be further categorized into two classes according to the learning algorithms used in prediction models: (1) single classifier-based methods; and (2) ensemble classifier-based methods. Single classifier-based methods use a single specific learning algorithm to build prediction models, whereas ensemble classifier-based methods use an ensemble of multiple, either similar or different, learning algorithms to build prediction models. This section introduces the recent single classifier-based methods and ensemble classifier-based methods used in protein fold recognition, as follows:Most of the current single classifier methods used in protein fold recognition are based on SVM classifier probably because SVM, being a well-known classification algorithm, has been highly efficient in several fields of bioinformatics, such as in protein remote homology detection, protein structural classification, and DNA-binding protein prediction. SVM-based protein fold recognition methods include Shamim’s method , DamoulaChen et al.  recentlyLampros et al.  propose n different single basic classifiers, the first type of ensemble classifier-based methods use one specific feature descriptor to encode query proteins with feature representations  on the DD dataset. The compared 20 methods are first modeled by the training dataset of the DD dataset, and then they are tested on the testing dataset of the DD dataset. The prediction results are presented in We have systematically reviewed the recent progress in machine learning-based protein fold recognition methods. Compared with the traditional experimental methods, machine learning-based methods present three advantages. First, they demonstrate accurate, robust, and reliable performance. Second, they can be applied in large-scale protein fold recognition; this application is extremely important in the post-genomic era, wherein numerous proteins remain to be structurally characterized. Third, they can effectively address the intrinsic limitations of experimental methods, that is, their being time consuming and expensive. In the past decades, remarkable progress has been made in computational protein fold recognition. However, several challenges remain to be addressed.First, the benchmark dataset  used to evaluate the performance of predictors actually suffers some limitations. For instance, the DD dataset is imbalanced. Second, most of the existing methods, especially for those with online webservers, can only provide for the populated 27-fold class prediction. Although the sequences of the 27-fold classes cover the majority of the sequences in SCOP database, approximately 1800 protein fold classes actually exist in SCOP. Thus, developing adaptive multi-class protein fold predictors is desirable given that an increasing number of protein fold classes are being discovered.Third, constructing informative and effective prediction engines remains a great challenge. Well-established ensemble classifiers have demonstrated their classification power in protein fold recognition. The use of deep learning algorithms for classification tasks has been a recent research hotspot in the machine learning field. Deep learning networks have been successfully applied in protein fold recognition . CombiniIn general, machine learning-based methods can be successfully applied in protein fold recognition. In the future, machine learning methods will be extensively applied in other similar but unexplored fields, such as disease-causing amino acid change prediction ,59,60, p


Gossypium raimondii and G. arboreum” G. raimondii and G. arboreum SODs, the protein sequence used to generate 3D sructure of proteins and the template accession via SWISS-MODEL server, “SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information.” The data presented in this paper is supporting the research article “Genome-Wide Analysis of Superoxide Dismutase Gene Family in  Specifications TableValue of the data•Data on phylogenies separately estimated using Bayesian method of MrBayes enable researchers to examine how the topologies differ from each other.•SOD genes and its molecular evolution in general.Data on phylogenies of Gossypium SOD proteins enable researchers to infer the possible ranges of time frames in the divergence events of Gossypium •Data on RAMPAGE Ramachandran plot analysis of Gossypium SOD proteins enable researchers to evaluate the accuracy of the predicted models.1http://swissmodel.expasy.org/) http://zhanglab.ccmb.med.umich.edu/COACH/) http://mordred.bioc.cam.ac.uk/~rapper/rampage.php) The phylogenetic tree obtained using Maximum-Likelihood (ML) method of PhyML (version 20120412) 22.1G. raimondii (V1.0) and G. arboreum (V2.0) genomes and annotation files were downloaded from CottonGen (https://www.cottongen.org/data/genome). The latest version of the Arabidopsis (TAIR10) genome and annotation files were downloaded from the Joint Genome Institute (JGI) (http://www.phytozome.net).The latest versions of the 2.2http://www.girinst.org/repbase/index.html) were filtered out.We then filtered gene annotation results based on the following criteria 2.3G. raimondii and G. arboreum, we retrieved SOD protein sequences from the NCBI protein database (http://www.ncbi.nlm.nih.gov/protein/). These protein sequences from six species, including Arabidopsis , Theobroma cacao (XP_007030135.1 and XP_007038205.1), G. hirsutum , Po. trichocarpa (XP_002319589.1 and XP_002325843.1), Z. mays  and O. sativa , were used as query sequences to perform multiple database searches using BLAST for Proteins (BLASTP) To identify members of the SOD protein in 2.4Phylogenetic trees were constructed using the Bayesian analysis method. Bayesian trees were constructed using MrBayes (version 3.2.4) 2.5http://mordred.bioc.cam.ac.uk/~rapper/rampage.php) Structural evaluation and stereochemical analyses were assessed using RAMPAGE Ramachandran plot analysis (


How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate. Cellular functions are completed by protein complex formed by multiple proteins aggregating together, rather than by individual protein. Identifying protein complex has significant implications in revealing the important principle of protein organization within cell , 2. ProtIn proteomics, large-scale protein-protein interaction (PPI) data have being produced along with high-throughput techniques such as yeast two-hybrid (Y2H)  and affiPalla et al. proposed CPM (Clique Percolation Method) algorithm to detect overlapping dense groups of nodes as protein complexes by continuously merging maximal connected sub graphs containing k vertexes in PPI networks[Most of current methods are based on solely network clustering–10 or in networks. Review  networks, 18 provWe know that protein complex consists of highly connected proteins, but it is much more than that. Literature  indicateSaccharomyces cerevisiae show that our DCA method outperforms existing computational methods in terms of prediction accuracy, hF-measure and statistical significance in biology.In this paper, we propose a new algorithm, called DCA (Dynamic Core-Attachment), to identify protein complexes by integrating their inherent organizations into dynamic PPI network. Protein- complex cores are formed by continually expressed and highly connected proteins. We subsequently generate protein complexes by appending attachments into the protein-complex cores. The integration of core-attachment feature into the time-evolving PPI network is responsible for the superiority of our algorithm. Experimental results using two PPI data sets of To capture the dynamics of protein complex, time course gene expression data are integrated into the original static PPI network and generate the dynamic PPI network with three sigma method. In brieOur DCA algorithm operates in four phases based on the dynamic network. DCA first identifies protein-complex cores and then applies an outward growing strategy to produce protein complexes by including attachments into the protein-complex cores. We will first briefly introduce some basic terminologies and then describe in detail our proposed method for protein complex detection.ECC, as shown in Eq (AT) here is defined as the time span between the starting and ending time point of its active state. For example, suppose that one protein’s activity starts from time point 6 and it becomes inactive at time point 9, then its active time span (AT) of course is 3. To characterize effectively those biological nature of protein complex, we weight the PPI network by combining ECC and AT as shown in Eq , for anwn in Eq :ECCij=ZZij represents the number of common neighbors of the two interacting proteins i and j, while min equals to the theoretical maximum number of triangles containing the two nodes. ECC ranges from 0 to 1 and the greater value shows the closer relationship among the nodes and their neighbors. In Eq . Thirdly, for each protein-complex core we select reliable attachments cooperating with it to form a protein complex (lines 12~19). Due to the periodic properties of gene expression data, the identified protein complexes contain large number of approximate ones. The last step is the redundancy-filtering procedure (lines 20~23). The computational complexity of DCA algorithm is O(N2) under given parameters of α, β and γ, where N is the number of nodes in network.As shown in Our DCA algorithm operates on time-evolving PPI network and takes its time sequential feature into account. Besides, the predicted protein complexes may overlap with each other since the attachments typically participate multiple protein complexes to carry out specific biological functions.In order to verify the validity of the proposed DCA algorithm, we use two PPI networks of yeast: DIP  [pc and a known protein complex kc:Overlapping Score (OS) Eq 4) i iOS)[4) pc∩kc represents the number of the proteins involved in both complexes pc and kc; |pc| and |kc| represent the number of proteins involved in complex pc and complex kc respectively. Two protein complexes are considered to be matched if their overlapping score is greater than or equal to a given threshold, which is set to 0.2, the same as many other researches[OS = 1 indicates that the two complexes pc and kc match perfectly.Where esearches. ParticuSn) and Specificity (Sp). They are typically employed to evaluate the identification of protein complexes. Let true positives (TP) denote the number of predicted protein complexes that match with known complexes, false positives (FP) denote the number of unmatched ones, and false negatives (FN) denote the number of known protein complexes which match with none of the predicted ones, then Sn and Sp can be defined as Eq  active proteins at each time point. By mapping them into the static PPI networks, the number distribution of proteins and interactions in sub-networks is shown in α = 0.60, β = 0.55, γ = 1.4. Using two data sets, DIP and Krogan_extended, we have applied our DCA algorithm on two yeast dynamic PPI networks constructed with three sigma method to perfoSn, Sp and F-measure. On DIP data, the F-measure of DCA is 0.632, which is 23.2% higher than the next algorithm CAMSE on static network and 23.7% higher than that on dynamic network. Similarly, on Krogan_extended data, the F-measure of DCA is 0.683, which is 32.6% and 17.5% higher than the next algorithm CAMSE on static and dynamic network, respectively. Our DCA method can achieve the highest F-measure by providing the highest specificity and comparable sensitivity, which shows that our method can predict protein complexes very accurately.To substantiate the biological significance of predicted protein complexes, we calculate their p-values by the tool, SGD's GO::TermFinder. Table 3As shown in A. Separately, proteins in blocks B, C and D are the attachments of this complex under different time points. It’s GO annotation is “3'-5'-exoribonuclease activity” (GO:0000175) with p-value 6.93e-20 and hF-measureTb 0.64.F-measure than existing methods. Thus, our predicted complexes match very well with benchmark complexes. In addition, DCA also performs very well in terms of other metrics such as p-value and hF-measure, indicating that our new algorithm can predict protein complexes very accurately. Our identified complexes, therefore, could be probably the true complexes to help the biologists to get novel biological insights. Although the time sequential gene expression data have much help to explore dynamic protein complexes, many factors need to be considered for deep research on living system, such as living conditions and tissue specifics. The model integrated more dimensional biological data is very important to uncover the mystery of life.Protein complexes comprising of multiple highly related proteins are key molecular entities to perform specific cellular functions. The increasing amount of protein-protein interaction data have enabled us to identify protein complexes from PPI networks. However, current computational methods hardly take consideration of both the inherent organization and dynamics within protein complex. This paper presents a new algorithm named DCA for mining protein complex from dynamic PPI network. Its prominent advantage is combing the sequential feature of network with the characteristic of core-attachment structure in complex. The evaluation and analysis of our predictions demonstrate the following advantages of our DCA algorithm over the state-of-the-art completing approaches. First, our new method is fundamentally different from other approaches for its insight into the inherent dynamic organization of protein complexes, which is often neglected in existing algorithms. The consideration of dynamics in cell system made the model simulation more closely to reality. Second, DCA algorithm has achieved significantly higher