Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers. Existing visualizations of large PPI networks continue to be difficult to use. Some proteins interact with many hundreds or thousands of others. Often referred to as ‘PPI hairballs’, such hubs are in the way of understanding large data sets. Many ways have been proposed to resolve such hairballs through the addition of biologically meaningful dimensions such as pathways2 or time3.Many tools visualize different aspects of protein-protein interaction (PPI) networks; the most prominent might be Cytoscape4. Combining PPI networks with protein location provide an intuitive way of laying out PPI networks on a graphical representation of the cell, and might reduce the clutter from PPI hairballs. This decade-old solution4 no longer copes with today’s data, in terms of scalability nor of customizability and in terms of ease-of-use.Another dimension was first introduced a decade ago, namely the overlay of PPIs with subcellular localization5 and PPI data from the Human Integrated Protein-Protein Interaction rEference (HIPPIE) resource6 is available athttp://cell.dallago.us.CellMap, the prototype introduced here, takes up on the idea of PPI visualization constrained by protein location, and provides a simple visual interface for users to explore protein location inside a cell. It presents this information in a graphically pleasant way and offers several customization features. The framework has been optimized to simplify future developments, such as the addition of further data dimensions (e.g. inclusion of protein trafficking). An instance of the tool with localization data from a previous publication that includes protein localizations of the human proteomehttps://nodejs.org) for the backend and vanilla JavaScript for the frontend. The portal is deployed to the public through a Docker container. Docker is a technology that allows shipping of packaged services such as web applications to customers and users without the need to install dependencies other than the Docker engine (available through:https://www.docker.com). For the representation of cell images as maps, the Leaflet framework is used. Leaflet is a JavaScript-based tool used to represent maps (http://leafletjs.com).The CellMap prototype is an integrated portal that exposes API calls to retrieve images (representing cells) and protein information, as well as a frontend to visualize protein location and PPI data. The portal is fully written in JavaScript, namely in the JavaScript interpreter node.js (http://mongodb.com) database. All information about the interaction partners and the subcellular localization of a protein is stored in a single JSON document, making the data structure simple to understand for non-experts and enabling them to deploy prototypes using their own data.http://cell.dallago.us/api/proteins/search/Q99943).Data about proteins are stored as JSON documents in a Mongo . They can modify the location of regions of interest (ROIs) for a selected map , and visTo maintain a consistent coloring scheme for different cellular compartments throughout a set of different images, each compartment is assigned a unique color through the hash of the compartment’s name , and the protein subcellular location viewer . The two viewers can load the same images of cells (maps) and collect localization data from the same source, in the publicly available instance by7 into the viewer  in different compartments. The objective was to showcase how CellMap can simplify PPI hairballs. We visualize the same PPI network using CellMap  with many interaction partners , instead of drawing nodes in positions based on edge weight (force layout of Cytoscape), some aspects of the protein and its partners become obvious at first glance, e.g. that HSPA4 interacts with many nuclear and cytoplasmic proteins, as well as with proteins that are secreted (extra-cellular) and located in the Endoplasmic Reticulum ER,. This maOne disadvantage of CellMap over the Cytoscape.js view is that the protein identifiers are not visible at all on the static image (protein identifiers become visible through mouse-over events in CellMap). However, in the image shown  the Cyto8 is a recent tool that also connects localization with PPI networks. It emphasizes the way PPI networks are laid out through the adaptation of a so-called force-directed layout (using the tool While). Although CellMap and CellNetVis are founded on a similar idea, user experience and focus differ importantly. For instance, CellMap can be driven by data from users that define the number of compartments on a map, and provide localizations. In contrast, CellNetVis uses a fixed subset of compartments and an ad hoc diagram for the cell. Additionally, CellMap comes with out of the box data for the human proteome and allows the community to grow the tool by enriching datasets , whereas CellNetVis has a per-use approach, allowing to visualize networks stored in specialized XGMML files. Another unique aspect of CellMap is the openness to introduce further biologically meaningful dimensions (beyond location such as time or pathways) that increase the usefulness of PPI visualization tools to create new testable hypotheses.CellNetVis5 and protein-protein interaction data from HIPPIE6 (http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie) is running athttp://cell.dallago.us. The visualization tool is written in JavaScript, thereby tapping into a very large user base for customized extensions and modifications. With the release of the prototype, we aim at creating a user base and awareness of the tool, ultimately collecting precious feedback from experimentalists and technical users alike.CellMap is a prototype providing a portal exploring the idea of using protein subcellular location as the basis to construct more complete visualizations of biological data, such as protein-protein interactions (PPIs). Using this paradigm, we claim that additional information, such as pathways, can be layered on top of the current visualization of subcellular location to potentially generate meaningful biological insights. The source code for the portal is publicly available and an instance of the portal with location data from a previous publication about the subcellular localization of the human proteome2D: two dimensions, API: Application Program Interface, ID: identifier, JSON: JavaScript Object Notation, PPI: protein-protein interaction, ROI: region of interest.https://github.com/sacdallago/cellmap. Archived source code as at the time of publication is available athttps://doi.org/10.5281/zenodo.9043249. An example of use with protein localization data from a recent publication5 and from the HIPPIE database of protein-protein interactions6 is available athttp://cell.dallago.us.The CellMap prototype is released as open source software under the GNU General Public License v3.0. Documentation, source code and viewer are available at The authors have addressed my concerns and I have no further comments to add.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. It would be good if the names of the used databases was stated in the last paragraph of the introduction.The tool would be more intuitive for new users, if it provided descriptions the various colors used on the site with the same explanation as in the paper. For example, the colored boxes that represent localizations in the search results and the dot colors used for the protein visualization on the cell map.It is unclear from the paper all the types of interactions might be shown in the represented networks.Also, it is unclear from the paper, what happens to the network visualization in the cases where the identified proteins are present in multiple locations.The tool provides an interesting feature to help declutter visualizations of biological networks using localization information. Some comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Dear Dr. Luna,Thank you very much for your input on our work.points one and four of your comment.We have submitted a new version of the manuscript, which should addresssecond point: we have created a new feature item for our next release that displays a button on the map viewer to display a modal with the legend. As of now: a legend is available by scrolling down to the second half of the page in the map or ppi viewers  and expanding the "Legend" tab. We understand that this can be overseen and improved, therefore we thank you for the input.As to thethird point: in this manuscript, we focus on discussing the software implementation and visualization abilities of CellMap, rather than the data sources used in the example deployment hosted onhttp://cell.dallago.us. More information about the types of interactions reported by the HIPPIE data source can be found inthe latest paper describing HIPPIE  and directly on theHIPPIE information page (http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/information.php#sources).As to thePlease, feel free to suggest any other changes to both our manuscript and tool.Best regards,Christian Dallago. http://cell.dallago.us link gave me a MongoDB error. I have therefore had to review the paper without being able to view the tool. This is not satisfactory. I was unable to test the conclusions about the tool and its findings.I have twice tried to review this paper and both times theThe tool uses a static interaction compilation database (HIPPIE) as the source of PPIs. Did the authors not consider using the PSICQUIC web service, which gives the users considerably more options as to where to source their PPI data from, and also allows the visualisation of protein-small molecule interactions and also potentially the site of action of protein-drug interactions, also available via PSICQUIC. It would also allow the data to be as up to date as the latest release of each database, which will be more frequent than releases of HIPPIE.I am not clear where the subcellular location data comes from. This may be obvious to regular users of CellMap but not to me, an should be stated in the paper for other user who do not know this.The authors describe a tool for visualising PPI networks in the context of the subcellular localisation of the searched protein.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Dear Dr. Orchard,Thank you for your valuable input on the tool!With regard to point 1: we apologize for the broken database connection, unfortunately, the deployment system missed that flag and thus didn’t restart the service. We have fixed the issue and the website is now running. Up until now, I have not identified any other issues that could prevent the web server to run properly.With regard to point 2: the software presented in this paper has a dual-purpose. On the one hand, we want to give the ability to discover protein localization and protein-protein interaction from two known sources ). On the other hand, we want to propose a system that can be reused on user-defined data  and be integrated as JavaScript visualization tool in different portals. For now, we would like to avoid having a direct integration of the portal with external tools via, for example, API calls. In an upcoming version of the portal, we will offer scripts to populate the database from different sources for the two data entities .PSICQUIC generates interaction data on-demand, which can later be downloaded. Obtaining the data requires some time: a user input one specific protein identifier, selects the databases to use to collect interaction data, submits a cluster job and finally gets access to the data. Searching for protein P45381 identified 80 interactions in all online databases. After several hours, the job was not finished, so we decided to lower the number of databases to fetch information from. Reducing the number of databases produced results quickly. The results page of PSICQUIC presents a table of interactions and visualizes a graph, which we could not load due to lack of compatibility with the Chrome browser. We believe it would be interesting to present CellMap at the level of this resource and will contact the authors of the tool to discuss what the best idea in this regard would be. Fetching the data from PSICQUIC as it is now and putting it into the portal requires to also normalize the PSICQUIC data and map it to protein localization data. Writing a parser for the PSI-MITAB tables is straightforward, the normalization and mapping of identifiers should occur externally to CellMap. We will create a guide on how this can be done in the next days and put it on the landing page of CellMap.Integrating protein-molecule data and displaying these entities meaningfully is an interesting idea for the future development of the CellMap tool.With regard to point 3: the data about protein localization stems from a publication of our group (http://doi.org/10.1038/ncomms8866). The data on protein subcellular localization for humans published through this paper was the starting point for the development of CellMap. In the current manuscript, we focused more on describing the visualization tool, rather than going into detail about how the localization data was retrieved  and predicted localisations for 6 subcellular compartments). This is again because we didn’t want to develop a tool around this specific data source, but rather offer the possibility to change the origin for the localization data in the future.We appreciate the suggestions for further data sources and data entities that can be used and integrated into CellMap. In upcoming releases, we will make sure to offer a bigger variety of data sources and scripts to populate and update the information on protein subcellular localization, and protein-protein interaction data used by the visualization tool. Additionally, we will contact the authors of PSICQUIC to discuss if it would be possible to integrate CellMap in the results page of a cluster job.Best regards,Christian Dallago, Tatyana Goldberg & Burkhard Rost.


Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques.An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80–90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free.The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy Table . The pro Comparison of protein structures is an important for understanding structural, functional and evolutionary relationship among protein specially in case of novel proteins . In addiIn the last two decades, research in the area of protein structure comparison has gained momentum but the problem of finding optimal alignment having significant role in biological context still continues . Number Algorithms of two protein 3D structures comparison approaches can be broadly classified into two categories, i.e., (1) is based on rigid body alignment by super positioning protein structures heuristically with scaling, rotation, transformation and then super-positioning  and (2) 3, for representing the protein structures. The alignment of two protein structures is being defined as the alignment of the two curves derived from backbone atoms of two structures i.e., one from each protein. Each of these parameterized curve is represented by a special function called square root velocity function (SRVF). Further, shapes comparison has been done after removing all shape preserving transformations from these curves. It has been pointed out that this comparison can be improved further by using higher dimensional composite curves by concatenating the geometric (3D) coordinates with primary and secondary structures as auxiliary coordinates  between the curves, where The same procedure as given in step 3 is used to calculate a new change point parameter The proposed algorithm has been implemented in R software. In order to evaluate the performance of the proposed algorithm for protein 3D structure comparison with existing algorithms i.e., (1) CE, (2) jFATCAT and (3) ESA, the benchmark data was collected from the literature . Furtherhttp://www.backwin.cabgrid.res.in:8080/ProtSComp. In this web server, R package serves in back-end execution, Java Server Pages (JSP) as server side scripting language, and Cascading Style Sheets (CSS), HTML and Javascript as client side programming language.The proposed algorithm for comparing protein 3D structures has been developed as an R package . R packaTwo datasets of protein structures from structural classification of proteins (SCOP) , 7 databThe distance matrix of size N×N for N protein structures were computed for all four algorithms i.e.,, (1) CE, (2) jFATCAT, (3) ESA and (4) proposed algorithm, The distance matrices for first three existing algorithms are based on 3D coordinates of backbone atoms, however the proposed method also incorporates auxiliary information along with these 3D coordinates. In order to make this distance matrix uniform, a sigmoid function has been used for conversion of values of geodesic distance and RMSD to common similarity measures between 0 and 1.In order to compare the proposed algorithm with commonly used existing algorithms for 3D protein structures, number of clustering techniques such as K-Means, C-Means, Spectral K-Means clustering techniques have been used. It is noted that the results of clustering is not unique as it depends on clustering algorithms used for the analysis. In case of large datasets having known number of classes, the non-hierarchical clustering performs better than the hierarchical clustering. Therefore, the above clustering techniques are likely to perform best in a given situation. The performance of these algorithms for each of these clustering techniques was evaluated based on rand index (RI), recall, precision and F-measure. These evaluation measures have been calculated based on confusion matrix Table . The perMii where i = j is the number of true positives for ith class, i.e., pair of proteins that are classified correctly as per the SCOP database classes; Mji where Mij where Mij where i = j is the number of true negatives for ith class, i.e., pair of proteins that are classified correctly as incorrect identified and also rejected. Based on these values, RI, recall, precision and f-measure are calculated as followsIn Table α (ESA-CA) along with dihedral angles as geometric property of molecular structure. Further, in order to improve the alignment molecular auxiliary information such as hydrophobicity (ESA-MC-BB + HP or ESA-CA + HP), polarity (ESA-MC-BB + POL or ESA-CA + POL), mass of residues, functional group type and number of side-chain atoms along with back bone atoms have been considered. In order to compare the effect of auxiliary information on classification accuracy and computational time, different combinations of molecular auxiliary information has been included through the proposed algorithm and analysis was done on the first and second datasets using different clustering techniques. It was observed that either the classification accuracy has increased or there is substantial reduction in computational time of comparison of two protein structures through proposed algorithm. The performance measures such as RI, precision, recall and f-measure are shown in Table In earlier study for comparing two protein structures based on ESA using only backbone atoms resulted with classification accuracy of 80.73 and 92.10 % for the first and second dataset of proteins respectively . The proThe proposed algorithm was evaluated with existing algorithms based on computational time Table . It is on as each amino acid is being represented by three atoms. The rest of the criterion i.e., (2) and (3) are based on n number of centroid 3D coordinates, five molecular properties and three geometric properties as dihedral angles. These dihedral angles are phi, psi and omega for each amino acid, and five molecular properties as mentioned above. Hence, the proposed algorithm is faster than the existing ESA [It has been reported earlier  that oriting ESA  as the pThe performance of Spectral K-Means clustering is better for comparison of various algorithms in terms of precision followed by Fuzzy C-Means clustering. However, results obtained by K-Means clustering techniques are not satisfactorily in terms of precision.The performance of CE in terms of recall, F-measure and RI is much better in comparison to all existing methods i.e. jFATCAT, original ESA etc. However, in case of proposed algorithm (ESA-MC-BB), recall and RI are comparable with CE through Spectral K-Mean clustering. It may be noted that computational time for CE is 126.18 h whereas proposed algorithm takes around 2.20 h for same task.α atoms and hydrophobicity as the auxiliary information. This may be due to the fact that the proposed algorithm used only single coordinate for each residue as centroid of backbone atoms or Cα with dihedral angles . These dihedral angles are indirectly using all coordinates by single data point with three more additional parameters. The proposed algorithm also included molecular properties of each residue and hence the results of proposed algorithm are comparable with ESA of all backbone atoms.In terms of RI, the accuracy for the first and second set of proteins increased up to 88.72 and 95.57 %, respectively when hydrophobicity was included as auxiliary information. It was also observed that the RI of the protein structures of second set shows 94.11 % accuracy when distance was calculated using 3D coordinates of CIn another experiment, the computing time of the different methods of protein structure comparison , viz., cThe proposed method performed better in terms of classification accuracy due to the inclusion of side chain/amino acid properties. This is due to the fact that inclusion side chain/amino acid properties provides more appropriate representations of protein structures as per elastic shape analysis. Further, hydrophobicity plays important role in the folding of protein structures as the hydrophobic residues tend to moves towards inner structure of the protein whereas, hydrophilic atoms moves towards the protein surface during protein folding . TherefoIn order to use the proposed algorithm, a web based tool (ProtSComp) has been developed. In this tool, two proteins can be compared. The number of residues for the comparison is based on selection of model and chain. The user can upload PDB file(s) or give the PDB ID(s) Fig. , select With the advent of high-throughput methods, the availability of structural information of proteins is increasing at a much accelerated pace. There is a requirement of automatic annotation and classification of proteins in order to save resources in terms of time. Therefore, the fast and efficient algorithm is developed that will find the best alignment between two protein structures.In this study, a computationally efficient algorithm has been developed in terms of run time for comparing protein structures based on ESA approach. The 3D coordinates of protein backbone atoms using different criteria have been used including the auxiliary information based on side-chain properties residue wise. The proposed algorithm has been developed using R.The proposed algorithm performed equally well in terms of accuracy with respect to existing techniques due to the inclusion of side chain and amino acid properties. Inclusion of hydrophobicity as auxiliary information shows better result since it plays important role in the folding of protein structures. Incorporation of molecular properties as auxiliary information led to the improvement in comparison of two protein 3D structures. The proposed algorithm is faster in terms of computational time than the existing algorithm since it is based on n number of data instead of 3n in data size employed by existing algorithms.


LncRNA-protein interactions play important roles in many important cellular processes including signaling, transcriptional regulation, and even the generation and progression of complex diseases. However, experimental methods for determining proteins bound by a specific lncRNA remain expensive, difficult and time-consuming, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. In this study, we developed a novel matrix factorization computational approach to uncover lncRNA-protein relationships, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF). Moreover, it is a semi-supervised and does not need negative samples. As a result, new model obtained reliable performance in the leave-one-out cross validation (the AUC of 0.9025 and AUPR of 0.6924), which significantly improved the prediction performance of previous models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is anticipated that LPI-NRLMF could serve as a useful resource for potential lncRNA-protein association identification. Long non-coding RNA (lncRNA) is a class of endogenous single-stranded polynucleotides with a length of more than 200 nucleotides. And it is the largest proportion of all newly found non-coding RNAs. It is widely presented in the transcription of the eukaryotic genome and can regulate gene expression at various levels. Since the first lncRNA was found 20 years ago, an increasing amount of lncRNAs were found by analyzing the chromatin state map -3. At prIn fact, there have been many computational methods for lncRNA-disease interactions in recent studies -15 and cPrevious studies were found to have the following limitations: (1) most of the machine learning models predicted ncRNA-protein interactions by using interaction data between RNAs and proteins instead of using ncRNA-protein interaction data, which would bias the predictions. (2) Most of the experimental data comes from the NPInter database, which is currently the best database for storing lncRNA and protein data, but the latest NPInter only provides gene-protein interactions corresponding to relevant lncRNAs rather than direct lncRNA-protein interactions. Gene-protein data was directly used in the above models to predict ncRNA-protein interactions and did not revealed true lncRNA-protein interactions. (3) Finally, lncRNA features and protein features are difficult to select in machine learning models. Moreover, negative sample is lacking for lncRNA-protein interactions prediction. To solve these problems, we introduced a novel matrix factorization computational approach in this study, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF) to predict the potential lncRNA-protein associations. Different from the traditional machine learning model, LPI-NRLMF adopts a semi-supervised learning strategy, which deduces unknown data mainly by known interactions and their similarities, so negative samples are not needed. Considering the validity of the classical method with comprehensive similarity, we combined the similarity of the Gaussian interaction profile with the modified matrix to obtain more accurate prediction results. The method involved lncRNA-protein pairs into feature vectors, then constructed matrices, and finally scored the matrix through a series of calculations. The proposed method focused on predicting the probability that a lncRNA would be associated with a protein by mapping a lncRNA and a protein to a shared low dimensional latent space as two latent vectors. Additionally, we also studied the local structure of the association data for higher prediction accuracy by exploiting the influences of the neighbors which were from the most similar lncRNAs and most similar proteins. Moreover, the proposed approach assigned higher importance level to the nearest neighbors for avoiding noisy information. Furthermore, leave-one-out cross validation (LOOCV) was introduced to evaluate the effectiveness of LPI-NRLMF and the AUC of 0.9025 was achieved. We also predicted the lncRNA-protein interactions of “Mus musculus” based on predictive scores rank in comparation with other methods in the case study. The results showed that the method is effective in predicting lncRNA–protein interactions.We performed a cross validation of known experimental lncRNA-protein scores to assess the performance of LPI-NRLMF. The performance of LPI-NRLMF is evaluated by the following parameter indicators: ACC , SEN (Sensitivity), PRE (Precision) and F1 (F1 score), which are widely used in computational biology and are expressed as , 26:ACC=In addition to the above indicators, we also used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to evaluate the performance of the LPI-NRLMF. The AUC of the perfect classifier is 1 and the AUC of the random classifier is 0.5. AUPR is the area under the accuracy-recall curve. Due to the large number of unknown label data in the data set, we used the AUPR to reduce the impact of false positive data on the model predictive performance. AUC and AUPR can better reflect the merits of model performance, the greater the two values, the better the performance of the model.We evaluated the performance of different models by conducting the LOOCV experiment , which has been increasingly adopted by researchers to examine the quality of various computational models , 28. In Adjusting the threshold in the experiment can increase the predictive specificity at the expense of sensitivity. The corresponding trade-off between true positive and false positive rates can be seen from ROC curve. As shown in the Figure Table Furthermore, we proposed the case study to assess the practical capabilities of our approach in predicting the interaction of unknown lncRNA-protein. We predicted the new lncRNA-protein interaction based on foregone interactions of “Mus musculus” in the NPInter v3.0 data set. The first 10 lncRNA-protein interaction pairs were listed in Table The flexibility and complexity of gene expression regulation have been greatly enhanced by lncRNA-protein interactions. A great deal of experiments was used to explore the associations between lncRNA and protein. However, a lot of material and human resources were required in the experimental study of lncRNA-protein interaction. Therefore, computational methods to predict the potential interactions of lncRNA-protein were imperative for the study of lncRNA, which plays an increasingly important role in the regulation of epigenetics. Network analysis as a method of simulating biological systems is also simple and effective. In a network, a node or a vertex of a network represents a biomolecule (gene or protein), while an edge or link represents its physical or functional interaction. Recent studies have shown how PIK3CA mutations interact with other mutations in a breast cancer survival network . In thishttp://www.bioinfo.org/NPInter/) is a database that collects a great number of experimental interactions between noncoding RNAs (ncRNAs) and other biomolecules. In this work, we downloaded the known ncRNA-protein interaction dataset from the NPInter v2.0 [With the development of bioinformatics and experimental technology, the global lncRNA-protein interaction database was created. NPInter  is defined by the following function:The similarity matrix of the lncRNA sequence was calculated by the lncRNA sequence information. We screened 990 reliable lncRNAs and their sequence information from the above database. After that, the lncRNA sequence similarity matrix (LSM) was constructed, the normalization of LSM  is expressed as the sequence similarity between the proteins pi and pj. The proteins sequence similarity matrix is normalized as follows:We derived PPI data from STRING 10.5 database and sequence information from the Uniprot database , 36, whiThe entire work flow for the LPI-NRLMF model is shown in Figure i and protein pj can be described by two potential vectors ui and vj, respectively. Then, we modeled the interaction probability pij of the lncRNA-protein pair  by the following logic function:Matrix factorization techniques have been successfully applied to previous studies of RNA and proteins. In this study, we created a LPI prediction model based on Logical Matrix factorization (LMF)  and provNext, we denoted the potential vectors of all lncRNAs and all proteins with U and V, respectively.Here, we used the zero-mean spherical Gaussian priors to generate lncRNA and protein latent vectors:I represents the identity matrix. In the LPI prediction study, the known lncRNA-protein interactions are more reliable than the unknowns because they are experimentally validated. Thus we proposed to give a higher weight than the unknown for known and interactive pairs [Where ve pairs , 39. In Where C is simply a constant term, independent of the other parameters of the formula. The model parameters can then be determined by the following functions: and Where i, we denoted the nearest neighbor set with N (li) ∈ L\li, where N (li) contains K1 proteins that are most similar to li. Then we constructed a set N (pj) ∈ P\ pj consisting of pj and K1 most similar objects. To make the model more efficient, K1 was set to 5 in experiment.The LMF model can effectively estimate the global structure of LPI data by mapping lncRNAs and proteins to latent vectors. However, the strong association between a handful of closely related lncRNAs or proteins was ignored. Therefore, we proposed to use the nearest area of the lncRNA and the protein for further improvement of the prediction accuracy in predicting lncRNA-protein interactions. For protein piμ are defined as follows:In this work, the adjacency matrix A is used to represent the lncRNA neighborhood information, where the  elements ajν are defined as follows:Similarly, the adjacency matrix B represent the protein neighborhood information, where the  elements bi and its nearest neighbor N (li). It can be obtained by the following functions:The main idea of using LPI to predict lncRNA neighborhood information is to minimize the distance between lWhere tr (·) is the trace of a matrix, In addition, we also used LPI to predict neighborhood information for the protein by minimizing the following objective functions:Where in, The final LPI prediction model was constructed by using lncRNA-protein interactions and the neighborhood of lncRNAs and proteins. By inserting Eqs m × n, in which the element pij, as shown by Eq i and Pj. U and V are randomly initialized using a Gaussian distribution of standard deviation We use the alternating gradient rise to optimize the equation − of lncRNAs and the negative data set P− of proteins, which may have a potentially positive effect on LPI in these negative observations. For the lncRNA li ∈ L−, we denoted the K2 nearest neighbors in L+ as N+ (li). Similarly, for the protein pj ∈ p−, we noted the K2 nearest neighbors in +P as +N (pi). It should be noted that +N (li) and +N (pj) are constructed using the same standard as used to construct neighborhoods during the training process. Then, the prediction of the probability of interaction of the lncRNA-protein pair  is modified to:We can predict the unknown LncRNA-protein pair by Eq 2 in our model is empirically set to 5 in the repeated experiment.Here, Eq


New insight into the nature of protein crystal lattices is proposed. Until recently, the occurrence of conformational entropy in protein crystal contacts was considered to be a very unlikely event. A study based on the most accurately refined protein structures demonstrated that side-chain conformational entropy and static disorder might be common in protein crystal lattices. The present investigation uses structures refined using ensemble refinement to show that although paradoxical, conformational entropy is likely to be the major factor in the emergence and integrity of the protein condensed phase. This study reveals that the role of shape entropy and local entropic forces expands beyond the onset of crystallization. For the first time, the complete pattern of intermolecular interactions by protein atoms in crystal lattices is presented, which shows that van der Waals interactions dominate in crystal formation. MD simulation programs predict the movement of the individual atoms in a protein molecule and provide information about the dynamic behavior of the molecule that is needed to understand processes related to the mechanism of catalysis, protein–protein interactions, drug design and ligand binding. A recent refinement technique  and isotropic (space group P212121). We calculated all symmetry-related molecules using CONTACT . Stereochemical analysis of the protein structures and symmetry-related partners was performed with Coot . However, in a nonpolar cage, where the three orthogonal screw axes provide an identical crystallographic environment along all three of the crystallographic axes, the flexible loops A66–A69 and B66–B69 and the termini are restricted in movement . Furthermore, analysis of the entropy in five randomly selected ensemble members from both molecules did not find any role for the symmetry operator in the intermolecular contacts .In a polar cage, one would expect degrees of freedom along the twofold screw axis, because if there were symmetry operators perpendicular to the polar axis they would provide an identical environment in all crystallographic directions. Indeed, the flexible loops  Tables 3 and 4 ▸.a), 2b), 2c) and 2d) the oscillations of a particular symmetry-related pair of residues demonstrate that the pair is found in four states: forming a salt bridge , forming an electrostatic interaction , making an electrostatic interaction and a van der Waals contact  and, finally, with a lack of contact . Crystal contacts were compared in pairs of ensemble members that represent the structures of HIV protease and the transcriptional antiterminator , and a random distribution of cohesive and noncohesive constellations between side chains in the contact area was found. Here, we argue that the terms ‘contact residue’ and ‘contact area’ in protein crystal lattices are relative by nature owing to the flexibility of the surface and are dynamic. A time-averaged crystal structure may not identify all possible contact residues. Fig. 3a) and Supplementary Figs. S2, S3 and S4 vividly demonstrate a range of oscillations of a dynamic protein model that go far beyond static disorder in the time-averaged models. The data for the ranges of disagreement between the time-averaged model and selected ensemble members are presented in Table 5et al., 2002A snapshot encompassing all 600 ensembles in the structure of HIV hydrolase  shows that some of the ensemble members are too distant to interact with a symmetry-related molecule. In Figs. 2ge Fig. 2a, formion Fig. 2b, makinct Fig. 2c and, fct Fig. 2d. Cryst3.2.et al., 2014It has previously been found  in crystal contacts of 105 protein structures refined at high resolution , 1b), 1c) and 1d) present oscillations in the secondary structure that can be rationalized as a rigid-body movement described by the symmetry operator. The ensemble members that are shown in Supplementary Fig. S2 and in Figs. 2a), 2b) and 2c) are traditionally believed to be different conformations of the side chains within the volume of the crystals, characterized quantitatively by an occupancy factor. In studies that report the structures of the transcriptional antiterminator and HIV hydrolase this is exactly the case because the data were collected at cryogenic temperatures from crystals in a vitreous state. At ambient temperature, the conformations presented in Figs. 2a), 2b) and 2c) are states governed by the symmetry operator because they occur as a result of interaction with the ordered phase. The conformations shown in Fig. 2d) and some of those in Supplementary Fig. S2, however, are transient because they are in contact with the dynamic phases (the hydration shell or the bulk solvent) and will explore the conformational space on a timescale that is experimentally measurable with the use of advanced structural methods based on X-ray free-electron lasers. Wall et al. , 2b) and 2c) demonstrate that all types of interactions that are known to exist in protein molecules also occur in the crystal contacts. We took this one step further and quantified the occurrence of interactions by type in the crystal lattices of two protein molecules: one with experimentally determined positions of H atoms  and dynamic (15N relaxation rates). The comparison showed that the ensemble yields better cross-validation statistics.Lindorff-Larsen  al. 2005 comparedet al. (2013Rfree factors independent of the accuracy of the data (Levin et al., 2007et al., 2012It is well known that dynamic disorder is not within the reach of single-model refinement. Liebschner  al. 2013 first qu al. 2013. For thi4.We show here, through the analysis of conformational entropy in two protein crystal lattices that belong to polar and nonpolar space groups, that the intermolecular contacts in the protein condensed phase are dynamic by nature.Based on 20 ensemble-refined structures and corroborated by 105 structures that have been analyzed by us before, we have shown that the role of shape entropy and local entropic forces expands above the onset of crystallization, and we have formulated a principle that determines the behavior of colloidal particles in a crowded environment.Static disorder is defined in the time-averaged models as the distribution of side-chain conformations within the volume of the protein crystal determined by the occupancy factor. Actually, at ambient temperature, it represents the ensemble of conformational states.Analysis of the intermolecular interactions in the crystals of two native protein molecules containing experimentally determined H atoms or theoretically assigned H atoms vividly demonstrates that hydrogen-mediated van der Waals interactions are the dominant force that maintains the integrity of protein crystal lattices. ‘Stripping’ protein molecules of H atoms leads to an artificially elevated role for hydrogen bonds and electrostatic interactions and masks the real interrelationships in the protein contact area.10.1107/S2052252517017833/mf5019sup1.pdfSupplementary Figures S1 and S2.. DOI:


Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available. Proteins physically associate into complexes in order to carry out the essential functions of life. Knowing how proteins are physically arranged three dimensionally in these complexes provides clues towards how they work. In principle, the associations between proteins in large-scale proteomics datasets should often reflect direct physical contacts between proteins in each complex. Here, we describe a statistical method to discover which subunits within complexes directly contact each other based on their co-purification behavior in published co-fractionation mass spectrometry datasets. Within our predictions, we recover many known protein-protein contacts, serving to validate our method, as well as unknown contacts that can inform future studies of these complexes. Specifically, we observe confident contacts between subunits within the exocyst and tRNA multi-synthetase complexes, two complexes that have incomplete structural information. Using our method, we further provide structural information for a previously missing subunit of the EKC/KEOPS complex. We anticipate that this method and the associated predictions will help to better inform our understanding of the functions and structures of diverse protein complexes. PLOS Computational Biology Methods paper.This is a Many proteins assemble into large macromolecular complexes with essential cellular functions. The three dimensional arrangement of proteins in a complex is vital to the complex’s function and knowledge of this arrangement would be highly valuable in understanding the mechanism of function. Conserved protein complexes are estimated to number in the thousands but the vast majority of these are structurally elusive by traditional structural biology techniques. Advances in proteomics technologies have allowed for the high throughput identification of protein complexes across the tree of life including large-scale affinity purification mass spectrometry (AP-MS) datasets –3 as welIn the CF-MS approach, cellular lysate is biochemically fractionated by multiple, non-denaturing chromatographic methods and then complexes are inferred bioinformatically in a machine-learning framework using correlations of the resulting protein elution profiles as a prominent feature. Although this approach has primarily been used to identify component subunits of complexes, we previously observed that the correlation structure of the protein elution profiles also revealed structural information about the complexes . This alProtein sub-complexes are valuable in understanding the three dimensional arrangement of proteins in a complex but correlation often convolutes specific physical interactions between proteins with indirect interactions and non-physical relationships. Removal of these spurious interactions from the correlation network is crucial to identifying which specific proteins directly contact each other. A classical statistical approach to remove such interactions can be achieved with graphical models . Graphicλ which balances model complexity and model fit. We additionally characterize our method’s performance finding better predictions for well-observed complexes and validate our predictions with a whole cell lysate crosslinking dataset where we observe enriched overlap. We therefore believe, in principle, these measures of conditional dependence could also be applied to additional proteomic datasets such as AP-MS as well as used in conjunction with other features of direct protein-protein contacts in supervised machine learning frameworks to further improve predictive performance.Here, we apply a graphical model to identify direct protein interactions  from oneWe highlight predictions made for the 26S proteasome complex and demonstrate agreement with the true set of contacts. We show new predictions for complexes without known structures, specifically the exocyst and tRNA multi-synthetase complex, to illustrate the utility of our approach. Finally, in our predicted set of directly contacting proteins we show support for direct contact of a recently identified component of the human EKC/KEOPS complex. Our results suggest that our predicted direct protein interaction edges will be a valuable constraint that can be used in structurally modeling the thousands of stable protein complexes in the human proteome inaccessible to current structure determination techniques, as we demonstrate with an improved 3D model of the EKC/KEOPS complex.In order to identify direct physical interactions between proteins, we first organized a large, published dataset of human CF-MS experiments . CF-MS ed = 15,964 protein elution profiles each consisting of a vector of n = 2,989 protein abundance values. Each protein abundance value is derived from 28 fractionation experiments using multiple, distinct biochemical separation techniques, including ion exchange chromatography, isoelectric focusing and sucrose gradients, analyzing native protein extracts isolated from HeLa cells (17 experiments), HEK293 cells (8), glioma stem cells (2) and neural stem cells (1). Fractionation experiments consist of a series of collected fractions along a biochemical gradient of the applied chromatography method. The number of fractions ranges between ~10 to ~200 per experiment depending on the method. Each fraction of extract is then subjected to proteomic analysis using mass spectrometry producing observed protein abundances. We use the pipeline described in Wan et al. 2015 d×d. Standard StARS selects λ where the normalized sum of variances of the Pij in the corresponding P(λ) drops below β = 0.1. It has been shown in . Cland Gon7 ), TPRKB and Gon7 , 61, andand Gon7 , 64.Fig 8B) strongly supported direct binding of human TP53RK with TPRKB, consistent with expectation from the yeast and archeal crystal structures [in vitro and GST-C14ORF142 was shown to bind directly to the OSGEP-LAGE3 sub-complex validating our prediction.We found that the conditionally dependent interactions . The resulting 3D model accounts for most of the OSGEP, TP53RK, and TPRKB amino acid sequences, but leaves the C-terminal region of C14ORF142 and the N-terminal region of LAGE3 unmodeled, pointing to additional aspects of this complex still yet to be described. Importantly, the model faithfully recapitulates the known functional and interaction data from the literature, the direct contact predictions from the co-fractionation / mass spectrometry datasets, and the newly recognized C14ORF142/Gon7 structural homology, and thus serves to integrate a large body of data into a single model to help guide future mechanistic studies of this ancient human protein complex.Taking advantage of our predicted direct contacts of C14ORF142 with OSGEP and LAGE3, we constructed a 3D model of the human EKC/KEOPS complex by homology modeling the human proteins onto their yeast orthologs of known 3D structure, including modeling C14ORF142 on the known Gon7 structure  a PDB entry and 2) > 4 subunits. True = pairs of proteins with >0.0 Å(XLS)Click here for additional data file.S2 TableS1 Table.Complete list of contacting and non-contacting protein pairs derived from PDB entries in (XLS)Click here for additional data file.S3 TableList of direct contact predictions with probability score. Predictions were restricted to only pairs that are co-complex in hu.MAP .(XLS)Click here for additional data file.S1 FilePDB formatted file consisting of the structural homology models of TPRKB, TP53RK, OSGEP, LAGE3 and C14ORF142 interacting in complex.(PDB)Click here for additional data file.


We have recently presented Schizophrenia Interactome, i.e., the network of protein-protein interactions (PPIs) of schizophrenia associated genes. PPIs predicted by our High-precision Protein-Protein Interaction Prediction model (HiPPIP) which uses machine learning to classify features of protein-pairs such as colocalization, coexpression, common molecular functions and biological processes could explain the apparent discordance between modern and historical genetic basis of Schizophrenia (published in npj Schizophrenia), and also were instrumental in discovering that OASL interacts DDX58 to activate the RIG-I immunity pathway during viral infection. These novel predicted PPIs were found to be highly accurate based on computational and experimental validations, and gave insights into possible functions of SZ genes that previously had no-known functional information. Even a single novel PPI can have enormous impact on advancement of biology, when translated effectively. How can we ensure that 500+ of these novel PPIs of schizophrenia interactome are translated effectively?We developed a platform for Massive Open Online Discovery for Schizophrenia Research, or “MOOD for Schizophrenia Research”, that brings together trained biologists and bioinformaticians including those who are currently not affiliated with research labs , as well as scientists who are active researchers, to hypothesize, discuss and prioritize the novel PPIs. Hypotheses are written as nanopublications with authorship credit. We are developing a number of features on the portal that allow and encourage scientists to create knowledge around the predicted PPIs and be recognized and given credit.The first version of our website that disseminates the Schizophrenia Interactome is receiving hundreds of unique users each month. We have since developed MOOD for Schizophrenia Research and will present the key features of the website in this work. Each PPI can be viewed, researched on and written about, by participating scientists. Comprehensive information about the proteins in the PPI regarding their known functions, pathways, diseases and drug associations, is made readily available to scientists, allowing them to hypothesize the importance of the specific PPI. We present methods that we employ to promote collaboration in this work. Initially, the portal starts with a few members and it grows through referral webs (i.e. current members invite new members). The portal has a number of features that recognize and thus entice users to participate. We will also present the user feedback and participation.PPIs are central to cellular systems. Yet less than 10% of estimated PPIs are known today. Thus, the computationally predicted PPIs which are deemed accurate, can accelerate advancement of schizophrenia biology research. The time is ripe to benefit from computer science and information technologies methods for not only discovering aspects of computational biology but to create new mechanisms to promote online collaboration to achieve big things as a summation of nanocontributions. The knowledge potential generated through this system would aid various principal investigators in well established (but ill funded) research labs by giving them access to bioinformaticians and biologists around the world who are eager be recognized for their contributions. We present here, not merely a website but a novel approach to promote collaborative research between people with heterogeneous skills and commitments by benefiting from the untapped talent of researchers around the world.


Escherichia coli, no common methodology exists to integrate these datasets and extract reliable modules reflecting the existing biological process and protein complexes. Naïve Bayesian formula is the highly accepted method to integrate different PPI datasets into a single weighted PPI network, but detecting proper weights in such network is still a major problem.Although different protein-protein physical interaction (PPI) datasets exist for In this paper, we proposed a new methodology to integrate various physical PPI datasets into a single weighted PPI network in a way that the detected modules in PPI network exhibit the highest similarity to available functional modules. We used the co-expression modules as functional modules, and we shown that direct functional modules detected from Gene Ontology terms could be used as an alternative dataset. After running this integrating methodology over six different physical PPI datasets, orthologous high-confidence interactions from a related organism and two AP-MS PPI datasets gained high weights in the integrated networks, while the weights for one AP-MS PPI dataset and two other datasets derived from public databases have converged to zero. The majority of detected modules shaped around one or few hub protein(s). Still, a large number of highly interacting protein modules were detected which are functionally relevant and are likely to construct protein complexes.We provided a new high confidence protein complex prediction method supported by functional studies and literature mining.The online version of this article (doi:10.1186/s12859-016-1422-x) contains supplementary material, which is available to authorized users. E. coli three large datasets of pair-wise protein-protein interactions are available which are measured by high-throughput tandem affinity purification followed by mass spectrometry (AP-MS) [E. coli [With the advent of new technologies and computational methods, large set of pair-wise physical protein-protein interactions and protein complexes have became available. The pair-wise interactions can be obtained through high throughput interactions –4, publi (AP-MS) , 10, 11.[E. coli . Further[E. coli . Availab[E. coli . MCL [14[E. coli , 16, and[E. coli  are amonIn naïve Bayesian classifier, a confidence score is usually assigned to each protein pair through the naïve Bayesian formula. Through the naïve Bayesian procedure, a confidence score is assigned to each of the used datasets. This procedure needs to preset gold standard positive and negative sets. Positive sets consist of functionally related and probably experimentally validated interacting proteins. Determining negative set is less straight-forward. To build negative set protein pairs with unrelated function , 4, 5, lDue to the mentioned problems, some studies just focused on high-confidence protein-protein interactions data which are small set of all possible interactions and this data was further validated with functional data sources such as co-expression and literature mining . PPI netE. coli into a single weighted PPI network and we detected functionally relevant PPI modules which lead to new predictions in protein complexes.In this study, we introduce a new methodology to integrate different protein-protein interaction datasets into a reliable weighted PPI network. Here, the weight of each protein pair is calculated through the highly used naïve Bayesian formula and frequently used MCL clustering method is employed afterward to determine protein modules from this weighted network. The similarity between the detected modules and provided functional modules are used as the criteria for optimization. Modules deleted from PPI network can be compared with the other functional module sets using the Normalized Mutual Information (NMI) measure. The main idea is that characterizing the best confidence score for protein-protein interaction datasets will maximize the similarity of detected modules from the PPI network with the other functional modules. This obviates the usage of problematic gold standard. We characterized the best confidence score of dataset using a global optimization method referred to as Harmony search. Using this methodology we integrated a large set of physical protein-protein interaction datasets of Mycoplasma Pneumonia were retrieved from the original paper [E. coli, derived from co-complex database [E. coli were downloaded from EcoCyc database [E. coli [Three AP-MS high-throughput experiments of protein-protein interactions datasets were downloaded from the original papers , 10, 11.al paper  and theidatabase , were usdatabase  and all [E. coli  was downCo-expression, Co-function, and Co-regulation modules are derived as explained in Additional file To integrate PPI data sources into a single weighted PPI network, we used the confidence score for each pair of proteins through the confidence score of each data source and the naïve Bayesian formula as follows: pi stands for the ith protein and Sp shows the confidence score (weight) of the pth dataset. This score is between 0 and 1. Therefore, Sp is zero if the interaction was not predicted in the pth dataset, and it is equal to Sp otherwise. Consequently, the similarity between two proteins is zero if their interaction is not predicted by any dataset. This similarity increases proportional to the confidence of each dataset for which the interaction was predicted. In addition, as more datasets predict a certain interaction, the corresponding similarity value increases. We just optimized the confidence scores Sp for each PPI dataset by a global optimization method in such a way that the detected modules in the integrated PPI network show the highest similarity with the co-expression, co-function, or co-regulation modules (see the next part). For this aim, we considered the weights between co-complex proteins or interacting proteins in DIP dataset as 1, and the remaining weights were optimized.where MCL method  was usedmemory size =100, number of variables =6, memory considering rate =0.8, pitch adjusting rate =0.3 and bandwidth range =0.5. The optimization was performed for 10000 iterations to increase the chance of reaching to the global optimality.The Harmony search is a metaheuristic optimization algorithm, inspired by the underlying principles of the musicians’ improvisation of the harmony. There are usually three possible choices for a musician to compose a harmony: 1) play any famous piece of music exactly from their memory; 2) play something similar to a known piece (thus adjusting the pitch slightly); or 3) compose new or random notes. The usage of harmony ensures that the best harmonies will be carried over to the new harmony memory, which corresponds to the choice of the best-fit individuals. Pitch adjustment means to change the frequencies, which corresponds to generate a slightly different solution. The randomization is then used to increase the diversity of the solutions. The used parameters in our Harmony search method were E. coli was obtained from [The microarray compendium of ned from  and the PPI networks inside each module were visualized by Cytoscape software . To showCentral proteins interact with a large set of proteins inside a PPI module. We considered proteins with node degree larger than two times of average node degree of all constituting proteins of the modules as central proteins.E.coli K-12 strain (substrain MG1655) in normal condition. In addition to this dataset, EcoCyc database provides data for other conditions and other strains for each gene from other studies.EcoCyc database  was usedIn this section, we explain how we integrate different PPI datasets to construct a single weighted PPI network. First, we studied the average gene pairs co-expression of interacting proteins in each PPI dataset. Then we performed our proposed method to detect the weights of each dataset and integrate them through naïve Bayesian formula Fig. . FinallyM. Pneumonia high confidence interacting protein dataset. As it can be seen in Fig. The average gene pair co-expression in each dataset is shown in Fig. As mentioned above, the protein datasets were integrated through naïve Bayesian formula. The weights were optimized by a global optimization algorithm referred to as Harmony search algorithm . This alM. Pneumonia orthologous) clearly show higher confidence level.The final weights of different PPI datasets for naïve Bayesian formula are listed in Table Our proposed methodology not only determines the confidence level of the PPI datasets, but also generates high confidence modules similar to a given set of functional modules, which is co-expression modules here. To analyze modules, we studied a large number of PPI modules and complexes as well as their central/hub proteins. We also inspected how our result can accommodate recently published yeast two-hybrid PPI predictions .Most of the detected PPI modules contain a highly connected central protein. We picked a measure  to detect these central/hub proteins. Additional file We also studied several major large complexes such as Ribosome, RNA polymerase, DNA polymerase III, and primosomal complexes, as well as some interlaying membrane complexes. As the co-complex proteins have been connected by higher weights in the integrated PPI network, our prior expectation was to find all the large complexes or interlaying complexes in a single module, but it was not the case. Although most of the proteins constituting a certain protein complex were detected in the same module . Still omplexes . Integraomplexes .Large set of AP-MS and Y2H data sources will become available in near future. Based on this study, we propose that more stable interactions from AP-MS experiments provide a reliable dataset for module detection and protein complex prediction studies like this. On the other hand, less stable AP-MS and Y2H interactions provide a better dataset for studying hub proteins, post-translational modifications, protein chaperoning, and connections between different protein complexes. Integrating expression and functional dataset as well as PPI networks from related organisms is more useful for stable protein complex detection studies than more transient interactions studies such as post-translation modification studies. The reason is that the transient interactions may not remain conserved in the evolution and genes involved in these kinds of interactions may not exhibit high co-expression. On the other hand, more stable interactions and protein complexes are highly conserved in the evolution , 35 and


G′, termed Gaussian entanglement, is strongly correlated with the linking number. Based on 110 non redundant domain-swapped dimers, our analysis evidences a high fraction of chains with a significant intertwining, that is with |G′| > 1. We report that Nature promotes configurations with negative mutual entanglement and surprisingly, it seems to suppress intertwining in long protein dimers. Supported by numerical simulations of dimer dissociation, our results provide a novel topology-based classification of protein-swapped dimers together with some preliminary evidence of its impact on their physical and biological properties.The presence of knots has been observed in a small fraction of single-domain proteins and related to their thermodynamic and kinetic properties. The exchanging of identical structural elements, typical of domain-swapped proteins, makes such dimers suitable candidates to validate the possibility that mutual entanglement between chains may play a similar role for protein complexes. We suggest that such entanglement is captured by the linking number. This represents, for two closed curves, the number of times that each curve winds around the other. We show that closing the curves is not necessary, as a novel parameter  In biological systems, proteins rarely act as isolated monomers and association to dimers or higher oligomers is a commonly observed phenomenon12345671113141516Protein-protein interactions may occur between identical or non-identical chains (homo or hetero-oligomers) and the association can be permanent or transient19The mechanisms for the evolution of oligomeric interfaces and those for the assembly of oligomers during protein synthesis or refolding remain unclear. Different paradigms have been proposed for the evolution of protein oligomers, among which figures three-dimensional (3D) domain swapping21222324Three-dimensional domain swapping is a mechanism through which two or more protein molecules form a dimer or higher oligomer by exchanging an identical structural element see  and 2. S272829Domain-swapped proteins may assume rather complicated spatial structures, since the swapping arms in their rotation can wind up forming tightly compenetrated structures. Examples are shown in The apparent intertwining between proteins assembled in complexes is certainly a distinguishing characteristic of these systems. An interesting issue to explore is the possibility of introducing topology-based descriptors that can capture the entanglement in a robust and measurable way, and that can be related to either some physico/chemical properties or biological functionalities.For instance, for single chain globular proteins it has been observed that the backbones may entangle themselves into a physical knot31323334353839Although knots are mathematically defined only for closed loops434445nG, an integer index that quantifies the number of times that a curve winds around the other  during the process of dissociation of the dimer reveals additional information on the amount of intertwining of the two proteins.The short CPU time required to estimate nD = 110 non-redundant domain-swapped proteins and for each of them we have computed the average linking number G and the Gaussian entanglement G′ for open chains . The results are reported in G′. The table also indicates whether the dimer is human  or not, and reports the number N of Cα atoms of each protein forming the dimer.Within the protein databank we have found nG computed over the pair of loops obtained by closing randomly each protein Cα backbones on a sphere , namely the fraction of configurations that have a value at most equal to G′, see solid lines in G′ = 0.853  with time, in a Langevin simulation where only excluded volume effects play a role (besides of course the chain connectivities). This is equivalent to the unfolding at a sufficiently high temperature.Motivated by these observations, to complement G′(t) over many trajectories starts from the static value of the crystallographic structure , should increase with the chain length. This is expected for Rouse dynamics of polymers in generalv ≈ 3/5. It is not possible for us to assess if this scaling is respected, given the few cases analysed. However, these cases correctly display an almost monotonically increasing trend of τ with N  produces on average a linking number G that is strongly correlated with G′. This suggests that such procedure can be avoided in practice, one may just rely on the information from the open chains, encoded in G′.Mathematically, two curves are linked or not, in a rigorous sense, only if they are loops. Hence, it is not trivial to estimate the level of intertwining between two open chains. Yet, the mutual entanglement is a well-visible feature in the crystallographic structures of several domain-swapped dimers. Being interested in quantifying such entanglement, with Gauss double integrals over the backbones of the two proteins in the dimers we provide a simple and efficient indicator, the Gaussian entanglement We report that about 15% of the domain-swapped dimers have a significant G′, with many more cases with G′<1 than with G′>1, is another interesting feature emerging from our analysis. This asymmetry could be explained by the conjecture that the Gaussian entanglement, despite being a global feature, can be deduced from the local twisting of closely interacting swapped structural elements. In several cases the latter are β-strands within the same sheet  of the intertwining in the dimer, which complements the G′ of the crystallographic structure  in assessing the amount of linking in the dimer.Via numerical simulations of the dissociation of some dimers, we observe that the presence of a non-trivial mutual entanglement is a robust characteristic, which vanishes exponentially with time during the dimer unbinding. The exponential fit furnishes a characteristic disentanglement time Our new approach of classifying domain-swapped protein dimers according to their mutual entanglement will likely add novel insight on the crucial role played by the generic topological properties of linear polymer chains in the protein context. As already demonstrated in the case of knotted protein foldsG′|). A similar experiment was carried on very recently for single-domain protein knots, showing that the knotting topology of the unfolded state can be controlled by varying the pulling directionFinally, it is interesting to speculate on the possible outcome of a single-molecule experiment performed by pulling apart the two protein backbones of a domain-swapped dimer with significant entanglement , and the final estimate of linking is an average over Z closures up to How to cite this article: Baiesi, M. et al. Linking in domain-swapped protein dimers. Sci. Rep. 6, 33872; doi: 10.1038/srep33872 (2016).


Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them – with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID).We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins  contains supplementary material, which is available to authorized users. Virus-host Protein-protein interactions (VHPPIs) are essential to understand viral attack mechanisms. VHPPIs are used by viruses to disrupt or modulate host pathways in order to achieve goals like the evasion of the complement system , modulatA Short linear motif (SLiM)  is a short region of a protein, 3 to 12 residues long, with functions like controlling the assembly of protein complexes, marking proteolytic cleavage, tagging protein localization and enzyme recruiting , 10. SLiSLiMs might evolve rapidly in viral disordered regions through insertions, deletions and mutations . The newViruses use VHPPIs mediated by host-mimicked SLiMs to hijack cell regulation  and execExperimental determination of VHPPIs is expensive since the number of proteins for some host organisms is large, more than 30.000 in humans. There are many viral protein sequences available but few corresponding three-dimensional structures resolved to use structure-based interaction prediction methods. These are reasons for developing a general method for predicting mimetic host-virus PPIs based solely in sequence data. A bioinformatic approach to predict SLiM-mediated VHPPIs might be an inexpensive alternative to experimentation or can guide experimental design.SLiMs are represented computationally as regular expressions. A SLiM instance is a protein subsequence that matches the regular expression. For instance, a SLiM represented by the regular expression R.[RK]R. have several instances like RVRRE in Ebola virus  and RKRRIf a SLiM is conserved in a small viral genome it probably could be used to interact with a host protein. Evans et al. find that common SLiMs between HIV-1 and humans are significantly conserved in HIV-1 proteins . They prViral genomes have high mutation rates and are not too thermodynamically stable. This seems to favor protein structures with a small number of inter-residue interactions and a high number of polar residues that account for the abundance of disordered protein regions . SLiMs oHagai et al. also propose another criterion to filter SLiMs based on rarity in a big set of randomized proteins . A SLiM C), disordered (D) and rare (R). With sets C,D,R we form derived union and intersection sets: C∪D, C∪R, D∪R, C∪D∪R and C∩D, C∩R, D∩R, C∩R∩D. Each of these sets allow us to predict interactions between the viral protein containing the SLiM and the host proteins that interact with the SLiM.To our knowledge, there is no comparison of SLiM filtering methods in the literature. For that reason, we implement and compare the three criteria introduced above for SLiM filtering: conservation above a threshold of the available viral sequences, localization in a protein disordered region and rarity, or difficulty to form by pure chance. Each filtering method produces a set of SLiMs – conserved . It is the virus with more bioinformatic data available, with the NIAID databases for sequences and alignments  and for We download alignments for HIV-1 proteins env, gag, nef, pol, rev, vif, vpr, tat, vpu for the year 2014 and an alignment of Gag-Pol DNA sequences with years previous to 2015 from the NIAID HIV-1 sequence database . Gag-PolWe filter all protein sequences by HIV-1 subtypes B and C for their worldwide dominance and computationally cleave some of the alignments in the following manner: env into gp120, gp41, pol into pr, rt, rtp51, in and gag into ma, ca, p2, nc, p1, p6 . After tAmong several disorder prediction algorithms for proteins  we use IIUPred is enhanced with a sliding window addition proposed by Hagai et al. that allows to define disordered regions . Residuelong when calling IUPred, see Fig. As IUPred receives as input a Fasta file with only one sequence, we split Fasta files with multiple sequences, call IUPred on every split sequence-file, compute the sliding window based average values and give as output a list of disordered regions per protein sequence id. We set the parameter We randomize the HIV-1 proteins to create a big data set. For each sequence in a protein file we create 1000 shuffled versions randomizing the residues located in disordered regions of the sequence, as computed with IUPred. All disordered residues in a protein are joined together in a temporary list, shuffled with the modern Fisher–Yates algorithm, and put back in the disordered regions, leaving the ordered residues intact.C) and the number of SLiMs in disordered regions (set D).We download all the SLiMs, instances and interactions from the ELM database  and creaR).After randomizing as described above, we count the rare (scarce) SLiMs in these shuffled data set, i.e. the SLiMs that are found in 1% of the randomized sequences or less  obtained per HIV-1 protein we create VHPPIs based on the ELM database interactions and interacting domains. For each interaction reported in ELM we add the human protein interacting with the SLiM located in the viral protein. We also add the proteins that contain the domains listed as interacting with the SLiM. To map domains to human proteins we used the domain-protein mapping for the human proteome in the PFAM ftp server [For each SLiM set  and TRG (targeting sites) are less used in prokaryotes .The lists of predicted human-HIV-1 interactions that are not in the NIAID database are in the [Additional file The disordered regions for HIV-1 proteins are in the [Additional file We find that predicted disordered regions for HIV-1 proteins are relatively conserved. Perhaps the virus must keep flexibility in their proteins in order to interact with several partners.In Fig. The proteins that deviate the most from this tendency are vpr, vpu, gp41, in, and pr, with a percentage of conserved motifs that are located in disordered regions of 53.3, 52.9, 48.5, 32.5 and 0% respectively. The reason for this discrepancy lies in the few disordered regions predicted in the five proteins. Indeed, pr, in and gp41 are considered mostly ordered, while vpr and vpu are considered moderately disordered .A similar correlation between evolutionary conservation and location in disordered regions was found for the SLiMs that bind to SH2, SH3 and Ser/Thr Kinase domains .We use IUPred as disorder predictor only because its performance finding the disordered regions of the VIF protein is outstanding compared to other 18 disorder predictors . One proC∩R (conserved and rare) and C∩D∩R  were discarded for being almost empty for all proteins.The SLiM set sizes are in Additional file R,D,C,C∩D,D∩R. The criterion that filters the most is location in a disordered region and rarity. It is followed by location in a disordered region and conservation.Considering the sizes of SLiM sets we can rank them by the filtering strength; from low to high filtering. The obtained ranking is D∩R (SLiMs hard to form by pure chance and located in protein disordered regions) studied by Hagai et al. [C∩D, of SLiMs conserved and located in protein disordered regions, Fig. C∩R (conserved and rare) and C∩D∩R  are almost empty so they can be discarded as useful filtering criteria –data in Additional file The sets i et al. , tend toWe validate against two virus-host PPIs databases: NIAID  and LMPIThe validation of the predicted PPIs with the NIAID database is not the best way to gauge the proportion of SLiM-based interactions. This database contains PPIs of all kinds, not only SLiM-mediated ones. However, it is the most complete virus-host PPI dataset.A better validation set, conceptually, is constructed with pairs deemed to interact through a SLiM with the LMPID database. Nevertheless, this dataset is too small. We do the comparison with both databases, selecting the NIAID database to compare the sets prediction performance and check the statistical significance of the results.Although we are suggesting a partial ranking of SLiM-based predicted PPIs, another addition would be to rank totally the interactions with a score representing the probability that the interaction takes place based on experimental data  or otherIn Fig. p-values for the overlap between the PPIs predicted with base on each SLiM set and the PPIs in the NIAID database are in Table We use the hyper-geometric distribution to measure the statistical significance of the sets of interactions we found. The C,C∩D,C∩R,C∩D∩R capture half of the interactions in LMPID, while the sets C∪D,C∪R,D∪R,C∪D∪R,D∪R allow to infer all of them. All the interactions between HIV-1 and human extracted from LMPID are in Additional file Using the literature curated LMPID database , we findThe small number of human-HIV-1 interactions in this database (six), leaves open two possibilities: the number is really small, or the number is larger but few experiments have been performed to detect them. To estimate the number of human-HIV-1 SLiM-mediated PPIs more work is needed, perhaps an approach based on combining expert opinions .In Fig. D∪R,C∪R,C∪D,C∩D,D∩R. This ranking allow to present the PPIs predicted to researchers in a partial order: first the set of interactions deduced with D∪R –SLiMs located in disordered regions or hard to form by pure chance, then the set deduced with C –conserved SLiMs.The ranking of the sets we found by its capacity to infer real interactions is: C∩D of SLiMs conserved and located in disordered regions for their biological relevance to analyze their human counter domains. In Table We consider the set We develop a method to predict virus-host SLiM-mediated PPIs and rank them. It is applicable to any eukaryotic virus and host with available protein sequences. Using data for the most studied virus, HIV-1, we find a partial ordering of the PPIs obtained based on the set used to infer the interactions. This order is descending in the expected probability of inferring real interactions. We expect that the method gives interesting candidate interactions with other eukaryotic viruses and hosts. The call for using high-throughput methods to detect SLiM-mediated PPIs illustrates the benefits of a bioinformatic method that predicts SLiM-mediated PPIs and might guide experimental design .AlthoughIndeed, in a previous work we used the method with the viruses in the NCBI virus variation resource to predict interactions with the proteins from the human protein synthesis machinery . We founIn future work we could also incorporate structural information in the prediction and analysis of SLiM-mediated VHPPIs in order to create otter SLiM filtering methods and compare them with the filters obtained in this work. One possibility is the study of fuzziness and SLiM flanking regions , another