Protein schematics are valuable for research, teaching and knowledge communication. However, the tools used to automate the process are challenging. The purpose of the drawProteins package is to enable the generation of schematics of proteins in an automated fashion that can integrate with the Bioconductor/R suite of tools for bioinformatics and statistical analysis. Using UniProt accession numbers, the package uses the UniProt API to get the features of the protein from the UniProt database. The features are assembled into a data frame and visualized using adaptations of the ggplot2 package. Visualizations can be customised in many ways including adding additional protein features information from other data frames, altering colors and protein names and adding extra layers using other ggplot2 functions. This can be completed within a script that makes the workflow reproducible and sharable. Thus, they represent one of the most common molecular visualizations shown and seen by researchers and students. Constructing protein schematics is often time consuming and is not performed in a reproducible and easily shared manner. The schematics frequently reflect domain expertise, but often also reflect the opinions of an individual researcher in a manner that is not obvious.Protein schematics are abundant in research papers, reviews, text books and on the internet2 that can be used for protein visualization. For visualization on the internet, there is also the BioJS solution, which can be used for proteins3. Both of these tools are useful but not easily integrated into the Bioconductor workflow. TheGenVisR package contains the option to produce highly customisable publication-quality graphics for genomic data4. The focus on genomic data reduces the usefulness of drawing protein schematics, particularly those illustrating multiple proteins and protein families.There are solutions in other languages: a Java and JavaScript tool6 as a resource of protein features and theggplot2 package7 as a basis for drawing the schematics. Multiple proteins can be drawn from similar or different families. The only limitation is the availability of UniProt entries.For these reasons, a protein visualization package was produced using R to allow compatibility with the Bio- conductor suite of bioinformatics packages. It uses the UniProt Proteins API8.Schematic customisation is possible. Protein chains, domains, regions, motifs or phosphorylation sites can be drawn separately or together. Colors can be altered and protein names (labels) can be changed. All of this can be done in a scripted manner that facilitates code sharing, visualization reproducibility and good practice in scientific computingThe Bioconductor pacakge, drawProteins, is designed to work with Bioconductor 3.7 and R version 3.5.This package has been created to allow the creation of protein schematics based on the data obtained from the Uniprot Protein Database.The basic workflow of drawProteins is:1. To provide one or more Uniprot IDs2. Get a list of feature from the Uniprot API3. Draw the chains of the proteins4. Add features as desiredggplot2 are then used to create the protein schematic.drawProteins uses the package httr to interact with the Uniprot API and extract a JSON object into R. The JSON object is used to create a data frame. Adaptations of the graphing packageGetting the data from Uniprot. Currently, drawProteins obtains the protein feature information from the UniProt Protein API6. At least one working Uniprot accession number must be provided. More than one can be provided but they must be in the same vector, separated by a space. The space is replaced to create a url that can be used to query the Uniprot API9.get_features function uses the Uniprot API to return the features of a protein - the chain, domain information and other annotated features such as “repeats” and “motifs”. Post-translational modifications, such as phosphorylations, are also provided by the UniProt API.Thehttr::content function is then used to extract the content. From theget_features function, this will provide lists of lists. The length of the parent lists corresponds to the number of accession numbers provided. Interestingly, the order is different to that of the UniProt accession numbers provided. The lists inside the parent list are a list of six, one for each protein, that contains names of the proteins and the features.TheRelA or NF-kappaB, p65, a well-studied transcription factor10.As an example, the script below will retrieve, from UniProt, the details of a the human version of a protein calledWith internet access, this can be retrieved from Uniprot with this code:library(drawProteins)library(ggplot2)# UniProt accession number for human rel A is Q04206rel_json <- drawProteins::get_features("Q04206")## [1] "Download has worked"Turning Uniprot data into a dataframe. The next step in the workflow is to convert the data from the Uniprot API into a dataframe that can be used with ggplot2.feature_to_dataframe function will convert the list of lists of six provided by theget_features function to a dataframe, which can then be used to plot the schematics.Thefeature_to_dataframe function will also add an “order” value to allow plotting. The order goes from the bottom in the manner of a graph.Therel_data <- drawProteins::feature_to_dataframe(rel_json)The rel_data object is a data frame with 9 variables and observations that include protein features. The variables are show below. A data frame of this type could be created independently of UniProt.str(rel_data)## ’data.frame’:    69 obs. of 9 variables:##  $ type       : chr  "CHAIN" "DOMAIN" "REGION" "MOTIF" ...##  $ description: chr  "Transcription factor p65" "RHD" "Activation domain" "Nuclear localization signal" ##  $ begin      : num  1 19 415 301 536 1 38 38 122 123 ...##  $ end        : num  551 306 459 304 544 1 38 38 122 123 ...##  $ length     : num  550 287 44 3 8 0 0 0 0 0 ...##  $ accession  : chr  "Q04206" "Q04206" "Q04206" "Q04206" ...##  $ entryName  : chr  "TF65_HUMAN" "TF65_HUMAN" "TF65_HUMAN" "TF65_HUMAN" ...##  $ taxid      : int  9606 9606 9606 9606 9606 9606 9606 9606 9606 9606 ...##  $ order      : int  1 1 1 1 1 1 1 1 1 1 ...Draw the canvas, protein chains and domains. The first step is to create the plot area with thedraw_canvas function. The x-axis of the canvas is based on the length of the protein (or the longest protein in the case of drawing multiple proteins). The y-axis is based on the number of proteins being drawn. Thedraw_canvas function requires a data frame.draw_chains function. This requires a ggplot2 object and a data frame in that order. The data frame does not have to be the same as that used fordraw_canvas but must contain the variablestype, description, begin, end, entryName, order.Usually, the next step is to draw the protein chains using thedraw_domains function, which also requires a ggplot2 object and a data frame in that order. Again, the data frame does not have to be the same as that obtained from UniProt but must contain the variablestype, description, begin, end, and order. Thus custom domains can be added with thedraw_domains function. Note that the chain and the domain are drawn to scale in terms of their number of amino acids (Protein domains can be added with theo acids .p <- draw_canvas(rel_data)p <- draw_chainsp <- draw_domainspTo show this visualization better, a white background helps, as does removing the y-axis and the grid . Changin# white background and remove y-axisp <- p + theme_bw(base_size = 10) + # white background    theme +    theme +    theme(panel.border = element_blank)pThe UniProt API provides information on protein regions, protein motifs and protein phosphorylation sites. By using the functions shown in the script below, it is possible to show the features of the protein desired to create. Altering colors and adding customisation is possible.For the human protein RelA, also known as the p65 subunit of NFkappaB, a transcription factor with diverse functions including a role in leukaemia, inflammation and cancer, here is a good workflow that generates a nice schematic of the protein showing domains and phosphorylation sites .draw_canvas(rel_data) -> pp <- draw_chainsp <- draw_domainsp <- draw_regions # add regionsp <- draw_motif   # add motifsp <- draw_phospho # add phosphorylation sitesp <- p + theme_bw(base_size = 10) + # white backgnd & change text size    theme +    theme+     theme(panel.border = element_blank)# add titlesrel_subtitle <- paste0p <- p + labspWith internet access, the script below shows the workflow and generates a visualization of the five human proteins of the NF-kappaB transcription factor family .# accession numbers of five NF-kappaB proteinsprot_data <- drawProteins::get_features("Q04206 Q01201 Q04864 P19838 Q00653")## [1] "Download has worked"prot_data <- drawProteins::feature_to_dataframe(prot_data)p <- draw_canvas(prot_data)p <- draw_chainsp <- draw_domainsp <- draw_repeatp <- draw_motifp <- draw_phospho# background and y-axisp <- p + theme_bw(base_size = 10) + # white backgnd & change text size    theme +    theme +    theme(panel.border = element_blank)# add titlesrel_subtitle <- paste0p <- p + labs# move legend to topp <- p + theme(legend.position="top") + labs(fill="")pThe proteins, domains and phosphorylation sites (yellow circles) are drawn and positioned according to amino acid number.12 to pull out the UniProt accession numbers for a Gene Ontology (GO) term. For example, the GO term for “MAP kinase activity”. This has a GO number ofGO:0004707. This example script borrows heavily on the biomaRt users guide written by Steffen Durinck, Wolfgang Huber and Mike Smith13. The script below generates a visualization containing 14 protein schematics # biocLite# biocLite("biomaRt")library(biomaRt)# chosing a database = MART and a dataset - gets more focussed each step...ensembl = useMart# Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have# a “MAP kinase activity” GO term associated with it.# this is the GO:0004707# this gives 14 proteins....# create output in a dataframe and add uniprotswissprot# which is the UniProt IDoutput <- getBM,                  filters = ’go’,                  values = ’GO:0004707’,                  mart = ensembl)# returns a dataframe... pull out uniprotIDs for drawing...uniprotIDs <- output$uniprotswissprot# get rid of blank entries - turn into NAuniprotIDs[uniprotIDs==""] <- NA# remove NAuniprotIDs <- na.omit(uniprotIDs)# make the IDs charactersuniprotIDs <- as.character(uniprotIDs)# just the unique onesuniprotIDs <- unique(uniprotIDs)# combine into one elementuniprotIDs <- paste# this can now be used in drawProteins# now get features from Uniprotlibrary(magrittr)uniprotIDs %>%  drawProteins::get_features %>%  drawProteins::feature_to_dataframe ->  prot_data## [1] "Download has worked"# data frame with 722 observationslibrary(ggplot2)# basic drawingp <- draw_canvas(prot_data)p <- draw_chainsp <- draw_domainsp <- draw_repeatp <- draw_motifp <- draw_phospho# background and y-axisp <- p + theme_bw(base_size = 10) + # white background and change text size  theme +  theme +  theme(panel.border = element_blank)  # add titlesp <- p + labs  # move legend to topp <- p + theme(legend.position="top") + labs(fill="")pVarious customizations are possible:1. Alter chain color and outline.2. Change the labels to a custom list (but remember that the plots are drawn from the bottom up)3. Change the size and color of the phosphorylation symbols.These are illustrated with the script below which generatesdata("five_rel_data")p <- draw_canvas(five_rel_data)p <- draw_chains,             label_size = 5)# change size and color of phosphorylation sitesp <- draw_phosphop + theme_bw17, the need for a more sustainable and programmatic method seemed important and worthwhile. It seemed wise to develop an approach that would integrate protein visualizations with other bioinformatic tools available in Bioconductor. This package represents an approach to enabling the reproducible and programmatic generation of protein schematics.After 20 years of manual drawing of protein schematics and experience in proteomic studiesGitHub. Bug reports, feedback on desired features or code contributions can be made throughGitHub.The plan is to develop this package further in terms of generating use cases and adding features. A list of issues for future development has already been added by the author onThe challenge with protein visualization is that specialist domain knowledge sometimes trumps databases. Thus, while integrating knowledge from UniProt is an excellent starting point, it is also essential to allow customisation of plots. This can be done by adding or removing information about the proteins, protein features and post-translational modification to the dataframe object made with R.All data underlying the results are available as part of the article and no additional source data are required.The drawProteins package is available at:http://bioconductor.org/packages/drawProteins/Source code is available at:https://github.com/brennanpincardiff/drawProteinsArchived source code as at time of publication:https://github.com/brennanpincardiff/drawProteins/tree/v1.0.2 andhttps://doi.org/10.5281/zenodo.130661918License: MIT+ The author describes the creation of a Bioconductor/R package for generic visualization of 2D protein diagrams.  The tool does not address 3D visualizations or visualization of protein sequences or alignments. Rather it allows the user to view the position of protein annotations within an arbitrary number of proteins. By default, UniProt sequence identifiers and associated annotations are supported. The demonstrated examples depict protein domains/motifs and phosphorylation sites.  Identifying a set of proteins to view from the Gene Ontology via bioMart is also demonstrated. There are likely to be many additional useful queries that could be achieved by similar bioMart queries. The package leans on ggplot2 functionality for the actual drawing of each protein schematic. The drawings consist of simple elements: colored rectangles and circles that can be nested, overlaid, and labeled.  A few simple use cases are illustrated.  The writing is brief and straightforward. The code is available under an open access document.  I was able to install the package and run through the examples in the paper in a few minutes with only minor difficulties. In particular, I had trouble getting this line to work in the example: ``` output <- getBM, filters = 'go', values = 'GO:0004707', mart = ensembl) ``` Perhaps some problem with the way the single quotes are being encoded in that section? Overall the tool is quite basic but also appears to be customizable. I anticipate it could be useful to many who wish to automate visualization of protein drawings and integrate these with visualization of various types omics data.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. The paper describes a protein annotation visualization tool for R/Bioconductor that has already been in use for more than a year. The software is mature, I was able to install it easily, all the examples of code in the manuscript are functional. I only noticed a small deviation in the biomaRt example, most likely caused by slightly different data returned by biomaRt. The manuscripts describes the purpose and functioning of the package in sufficient detail. However, I found the introduction a bit too brief, especially in terms of mentioning other similar software and advantages of drawProteins. For example, I found a Bioconductor package called Pviz that may be able to generate protein annotation visualizations. As a minor remark, the last sentence in this paragraph on page 3 seems unclear to me: "The feature_to_dataframe function will also add an “order” value to allow plotting. The order goes from the bottom in the manner of a graph." Finally, a suggestion. The software would be even more useful if it could handle reading and writing of standard protein annotation files in GFF or similar format. UniProt wikipedia page describes how it is applied to proteins in this example: https://www.uniprot.org/uniprot/P0A7B8.gff Although GFF is much more popular with genomic data, it seems to be ocassionally used for annotating protein sequences.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.


As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at  RNA-protein interactions (RPIs) have a critical influence on post-transcriptional gene regulation ,2,3, virproteins ; severalproteins ; lncRNAsproteins ; lncRNA proteins . Hence, The experimental determination of RPIs remains expensive and time-consuming ,19,20, bIn this paper, we classified RNA-protein pairs as interacting or non-interacting by integrating derived kernel with regularized least squares (RLS) . The motk continuous amino acids, while the template set l continuous nucleic acids. In order to extract discriminant subsequence features from amino acid sequences and nucleotide sequences, we explored the effect on RPI prediction over a range of choices for the template sizes of protein and RNA. The training set RPI2662 was used to determine these parameters. In our case, we used different template sizes of protein and RNA chosen from set k and l, respectively. The experiment results showed that when the protein template size We considered the derived kernel with two-layer architecture, hence there were two template sets, denoted as eriments , therefoIn order to evaluate the reliability and robustness of RPiRLS and RPiRLS-7G, we compared them with other two state-of-the-art methods RPI-Pred and IPMiner. The RPI2241 and RPI369 data sets after removing overlapping RPIs with the training data were evaluated. Both the RPiRLS and RPiRLS-7G classifiers were trained on the RPI2662 data set, and tested on the RPI2241 and RPI369 data sets, respectively.As shown in Similar results were observed on the RPI2241 data set in Furthermore, we evaluated the RPiRLS classifier on large-scale RNA-protein pairs in the currently available RPIntDB data base. The RPiRLS method correctly predicted 35980 out of 43010 RPIs, reaching the predictive accuracy of 84%.Homo sapiens and Saccharomyces cerevisiae, RPI-Pred performed best for Drosophila melanogaster, Escherichia coli and Mus musculus, and both methods obtained the same predictive accuracy for the Caenorhabditis elegans. RPiRLS outperformed RPiRLS-7G over all six organisms. For 13153 ncRNA-protein pairs, the RPiRLS method achieved an accuracy of 91% compared to 76% for RPiRLS-7G and 88% for the RPI-Pred method. We further tested RPiRLS and RPiRLS-7G on the LNRPI12114 data set which was a subset of the NRPI13153 data set and consisted of only lncRNA-protein interactions (lncRPIs). Our model achieved an overall accuracy of 92% compared to 77% for RPiRLS-7G and 89% for the RPI-Pred classifier as shown in To explore the effectiveness of the proposed method on predicting ncRNA-protein interactions, a large-scale ncRNA-protein interaction data set  was retrieved from the NPInter data base . We traiCaenorhabditis elegans, Drosophila melanogaster, Escherichia coli and Saccharomyces cerevisiae, consisting of 4, 61, 78 and 437 lncRPIs, respectively.Predicting lncRNA-protein interaction networks is useful to explore the molecular mechanisms that are regulated by lncRNAs ,39. In tCaenorhabditis elegans, the RPiRLS method correctly identified all 4 lncRPIs (blue edges) while the RPI-Pred method correctly identified 3 out of 4. As shown in Drosophila melanogaster. These 6 out of 7 incorrect predictions which were observed between two proteins P49963 and Q9VSS2 (yellow rectangle) and three signal recognition particle (SRP) RNAs n5330, n5333 and n389 (green ellipse), formed the SRP RNA-protein interactions. For Escherichia coli, the RPiRLS classifier made much more errors than the RPI-Pred method, with predictive accuracies of 45% vs. 86%. The performance of RPiRLS for Escherichia coli was much poorer than that for the other five organisms. In order to analyze why RPiRLS had relative poor performance on Escherichia coli, we estimated the amino acid composition of Escherichia coli compared with that of the other five organisms. As shown in Escherichia coli had relative higher observing frequencies of Alanine and Valine as well as much lower content of Serine compared with that of the other five organisms. The amino acid composition bias in Escherichia coli probably leaded to poor results. As shown in Saccharomyces cerevisiae, as showed in For Mammalian cells contain more than 1000 different proteins interacting with RNA . NormallThe work presented here provided a computational method, called RPiRLS, to classify RNA-protein pairs as interacting or non-interacting by integrating a sequence-based derived kernel with regularized least squares. The derived kernel exploited the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. Our results demonstrated that only the sequence structures of RNAs and proteins provide sufficient information to accurately predict RNA-protein interactions, especially long non-coding RNA-protein interactions. Specifically, the RPiRLS classifier considered each protein sequence comprising up to 20 diverse amino acids, while the RPiRLS-7G classifier encoded each protein sequence by using the 7-letter reduced alphabets according to amino acid physiochemical properties. The computational results showed that the RPiRLS classifier was superior to the RPiRLS-7G classifier in reliability and effectiveness, indicating that the diversity of amino acids at a sequence has critical impact on the function of RNA-binding proteins. On two non-redundant benchmark data sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Compared with RPI-Pred and IPMiner, the RPiRLS method obtained a reasonable sensitivity at a lower false positive rate. Further, RPiRLS achieved an accuracy of 92% compared to 89% for RPI-Pred on the prediction of lncRNA-protein interactions. The RPiRLS method can be extended to construct RNA-protein interaction networks and therefore helps us to gain insights into post-transcriptional gene regulation.k-mer frequencies alone captured rich statistical information on the repetitive conserved motif of RNA-protein pairs and the diversity of amino acids at a sequence has also contributed to an observed improvement contrast to RPI-Pred which just applied 1-letter frequency for both protein and RNA. Finally, a kernel works as a measure of similarity and supports the application of powerful machine learning algorithms such as regularized least squares which we used in this paper. The RLS mehod enables us to efficiently search for an optimized parameter The reason for the good performance of the proposed method may be due to several factors. Firstly, the use of similarity scores is a significant conceptual change in protein/RNA evaluation, quantifying the overall similarity between proteins, RNAs and their interactions. Combining kernels by tensor product for the set of RNA-protein pairs allowing to share information across the RNA-binding proteins considerably improved the prediction, especially in the case of RNAs with few known binding proteins. Secondly, we have found that contiguous nal cost . FurtherThe increase of the number of protein-RNA complexes in Protein Data Bank  has openSeveral data sets were employed to evaluate the performance of the proposed methods.Our RPiRLS method was first evaluated using two popular non-redundant data sets of RPIs studied in  . The RPhttp://pridb.gdcb.iastate.edu/RPISeq/download.php). This data set consisted of 43,010 experimentally validated RPIs from several sources, including the RPIDB, NPInter data base and high-throughput experiments published in literature.Next, we tested the performance of the RPiRLS method on a large scale data set extracted from the RNA-Protein Interaction Data Base (RPIntDB)  if Step 1.k. Set an initial kernel Step 2.f, so here f at t :k making up the template set f, denoted as Let Step 3.Compute the second layer derived kernel by normalizing the inner product of two neural responses:With correlation normalization:Suppose This process can continue if appropriate higher level templates are defined. At each layer  derived kernels are built by recursively pooling over previously defined local kernels. Here, for the 2-layer derived kernel, pooling is accomplished by taking an average over a set of transformations which calculating the frequency of a template that occurs in a sequence.K which satisfies the Mercer Condition, are known to be an instance of reproducing kernels. Next with correlation normalization, In this paper we deal with inner product kernels The kernel function is symmetric  is further employed to measure the predictive performance, which is 1 for perfect prediction and 0.5 for random prediction.


Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction. We compare region-level predictive performance of our method against that of a whole-protein baseline method using proteins with structurally verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into region-specific and whole-protein terms and select prediction methods for different classes of GO terms.https://github.com/ek1203/rsfp.The code and features are freely available at: Bioinformatics online. Proteins are involved in nearly every cellular process and function, including cell organization, biochemical catalysis, signaling and transport. The exponentially large number of possible sequence combinations enables proteins to exhibit the necessary diverse sequential, structural and functional properties required by the cell. Protein function is often defined or modified by specific interactions with other molecules, therefore knowing what, where and how proteins interact is important in elucidating the cellular machinery of life ], by binWith the advent of the Gene Ontology (GO) project  experiments  branch of the ontology. Currently, a few such annotated domain-centric resources exist through a combination of manual and automated curation  was useWe show the ability of our method to localize binding labels to specific regions within proteins by comparing the predictions directly to binding sites extracted from protein-ligand structures. We also show that the added region-specific framework can lead to improvements in protein-level function predictions for many of the MF-GO terms that we tested in the section after.Here, we detail tests of our method over a subset of GO terms that can be tied directly to protein sequence via structure, focusing on cases where protein structure analysis provides unambiguous localizations to proteins with residue-level resolution. Results from the region-level evaluation for four binding GO terms and four feature sets are shown in We include both the BLAST and whole-protein baseline performances for comparison, with the bottom panels showing the differences in performances between the median AUPRs of our region-specific method and whole-protein baseline. This head-to-head comparison allows us to eliminate the differences in raw performances based on lack of informative features or annotations, allowing us to focus entirely on the effectiveness of using this region-specific framework for this particular set of feature types and GO terms. The main difference between the models (aside from the region-specific constraints) is that the whole-protein baseline model uses a combination of all the features found within the regions plus additional protein-level features that span multiple regions, like protein families, which would not exist when the regions are looked at in isolation.InterPro IDs and Signature IDs do not benefit much from this approach and perform worse than baseline in most binding terms at the region-level, while Keywords and K-mers show significant improvements across binding terms. This is not surprising given that both features can be found in regions without domain family assignments and are more discriminating within regions compared to InterPro IDs and Signature IDs, allowing for greater propagation of labels between regions.When we look at regions with InterPro assignments, InterPro IDs—the box plots show the performance when all scores are 0), Keywords and Signature IDs both show significant improvements across binding terms, while K-mers show decreases in performances. This suggests that there are informative features that exist in other databases that have yet to be incorporated into InterPro and that these regions likely have less sequence homology to the currently annotated proteins and regions.On the other hand, when we look at regions without InterPro assignments , and in the majority of cases, two regions are either assigned the same InterPro ID, and thus have a similarity score of 1, or are assigned different InterPro IDs, and thus have a similarity score of 0. As shown in Supplementary InterPro are very sparse relative to the other feature types and that sparsity has the effect of reducing the effectiveness of term 2 in the cost function  is at its maximum for the given GO term. Different shades are used simply to delineate separate regions. None of these regions have been annotated at time of training or testing.Here we leverage the binding proteins with known structures to investigate the performance and resolution of our method. More details on the structures and binding residues can be found in Supplementary Section 5.2.After the initial proof of concept, which shows that our region-specific method can successfully localize binding GO term labels to specific regions without prior domain-level annotations, we extend the analysis to include GO terms from a larger number of functional categories by evaluating the predictions at the protein level.relative to the whole-protein baseline model using the same training, validation and test sets. Protein-level predictions over all four feature types are evaluated and all GO terms are grouped into the shared parental terms (shown in blue on the top left) for clarity, with isolated terms grouped together as ‘Others’. The differences are shown as colored bars stacked either in the positive (right) or negative (left) direction. Only differences that are significant  are shown on the plot. The numbers next to each GO term represent the number of positive training and testing proteins for that particular GO term (i.e. the particular class distributions during training and testing). Majority of the binding terms that do not show much difference from baseline are not predictable using any of the methods and this can be observed from the raw performances found in Supplementary Section 6.K-mer features that allow for better label propagation between regions. The results also suggest that the performance relative to the whole-protein baseline model can give us a way to characterize region-specificity of different GO terms and thus inform us on how to treat them during the function prediction process.Overall, our results here show that this framework can improve protein-level function predictions for many of the MF-GO terms evaluated, especially for Keywords and Binding terms show improved performance regardless of feature type, especially for terms with well-annotated binding site annotations from InterPro and UniProtKB like metal ion and DNA bindings. About two-thirds of the Catalytic activity GO terms evaluated also show better performance across features when the region-specific method is used, suggesting that it is possible and helpful to localize some GO terms to single regions. For those that perform worse, it is likely that specific features from multiple regions are needed for the association or if the protein family is composed of domains found in diverse proteins, such that a strong association can only be made at the level of the protein family itself.The majority of Transporter activity GO terms show a decrease in performance with InterPro and Signature IDs, but improved performance with Keywords, highlighting the advantage of using text descriptions directly for better label propagation between regions. For InterPro IDs in particular, if the association to the region features are weak in the training set (i.e. the fraction of proteins with those features that are positively annotated with that GO term is low), then term 2 of the cost function  under Binding and ‘RNA polymerase II transcription factor activity, sequence-specific DNA binding’ (GO: 0000981) under Others.Cases where In this work, we have described a function prediction pipeline to localize protein-level annotations to specific regions within the protein. It is based on the compact biologically reasonable assumption that functional homology is mediated by regions with similar (if unknown) features and is built around a cost function that takes into account regions with unassigned domain families but contain existing feature annotations.The results from our region-level evaluation using ligand binding datasets show that our method can successfully localize functions known to be site-specific to their respective functional regions and performs significantly better than the whole-protein variant. We also evaluate the performance of our region-specific prediction method at the whole-protein level to determine the protein functions that benefit from our explicit region localization and find that, while localization improves performance for some functions, it also decreases performance for others.This difference in the effect of mapping function to specific regions supports the notion that different GO terms have different levels of operational units and that they should be treated differently in protein function prediction pipelines to take that into account . Our results serve as a starting point to begin categorizing GO terms into region-specific and protein-wide sub groups to maximize the predictive performance of protein function prediction for each GO term and to provide a framework for selecting correct function prediction methods for different functions.Future work would include introducing a hierarchy of region boundaries within a single protein to allow for different levels of label propagation for different GO terms, and also the use of different feature types simultaneously to consolidate different sources of information. One could, in principle, use autoencoders or NNMF to integrate the different feature types into a single, low-dimensional feature space that would be well-suited to our region-level model (bty834_Supplementary_DataClick here for additional data file.


Brucea javanica, Holarrhena antidysenterica, Dichroa febrifuga, Erythrophleum guineense, etc. which contained active compounds might be a novel medicine formula toward Huntington's disease.Several pathways are crucial in Huntington's disease (HD). Based on the concept of multitargets, network pharmacology-based analysis was employed to find out related proteins in disease network. The network target method aims to find out related mechanism of efficacy substances in rational design way. Traditional Chinese medicine prescriptions would be used for research and development against HD. Virtual screening was performed to obtain drug molecules with high binding capacity from traditional Chinese medicine (TCM) database@Taiwan. Quantitative structure-activity relationship (QSAR) models were conducted by MLR, SVM, CoMFA, and CoMSIA, constructed to predict the bioactivities of candidates. The compounds with high-dock score were further analyzed compared with control. Traditional Chinese medicine reported in the literature could be the training set provided for constructing novel formula by SVM model. We tried to find a novel formula that can bind well with these targets at the same time, which indicates our design could be highly related to the HD. Additionally, the candidates would validate by a long-term molecular dynamics (MD) simulation, 5 microseconds. Thus, we suggested the herbs Huntington disease (HD) is a kind of involuntary movement, mental disorders, and progressive dementia as the main clinical features of dominant hereditary neurological degeneration disease. The pathology is identified as the protein named Huntingtin , which iNeurodegenerative diseases were studied together frequently  because A novel method of merging traditional Chinese medicine (TCM) with network pharmacology-based could be a reliable way to overcome disease. In the past decades, the single-target drugs sometimes did not achieve favorable therapeutic effects. It would prefer to use the network pharmacology-based approach to develop a treatment in a multitargeted, multidrug manner . RecentlMost diseases are associated with multiple target proteins, and it is difficult to achieve proper therapeutic results with a single target. Similar to evidence-based medicine, protein-protein interaction (PPI) network is in line with both western medicine and traditional Chinese medicine. The theory of traditional Chinese medicine holds that the body is an organic whole, and its components are inseparable in structure. This association includes the PPI in modern medicine, which is interdependent in physiological conditions and influences each other in pathology .TCM is the quintessence of China. After thousands years of accumulation, there are enough clinical examples to confirm its rationality . TherefoThe energy metabolism of medium-sized thorny neurons in the striatum of HD patients is affected by the abnormal energy metabolism that rapidly dissipates neurons and is susceptible to excite-toxicity and ultimately damage the cortical neurons, so abnormal energy metabolism may also be related to the pathogenesis of HD . KnockdoAnother idea to cure HD is to protect neurons. The neuronal stress-protective transcription factor HSF1 will be abnormal degradation , which rThe biological significance of YAP binding to TEAD has been more and more clear, but the three-dimensional structure of both and the molecular mechanism of their action remain unclear. In recent year, this pathway is regarded as a potential target toward HD . HuntingBased on the preliminary work, Kyoto Encyclopedia of Genes and Genomes (KEGG) database was utilized to generate the related proteins to construct a protein-protein interaction network. The hub protein should be identified in the pathway network. It was clear that the inhibition of Mitochondrial 70 kDa heat shock protein  and CK2A2 benefitted from HD resistance. Based on the multitarget idea, the related targets would be identified after network pharmacology-based analysis about YAP1 pathway. The YAP level would decrease in HD brain model , which iThe protein LATS1 has no enough structure information for structure-based drug design. The origin structure of LATS1 in PDB (5BRK:B) was further modeled to obtain a complete structure . The binThe sequence of human CK2alpha' protein (residues 1-350) was acquired from the Uniprot Knowledgebase (P19784). The 3D crystal structure of human CK2alpha' protein, 5M56 (residues 1-350) was obtained from Protein Database Bank (PDB) website . 5M56 waThe same way was descripted above; another two targets screened small molecules in the TCM database as well. The information of HSPA9 (mtHSP70) protein was gained from UniprotKB (P38646). 3D structure of HSPA9 was acquired in PDB . The binding pocket referred to the nucleotide-binding domain (NBD), which is an active binding region . MST2  which is calculated in the regression. Several molecules with known activity were used for external verification as well. The predictive models were chosen from the models which have high value of square correlation coefficient. And then the predictive models were performed to forecast each candidate drugs after docking.The bioactivity was predicted through QSAR model . The GenThe docking scores of candidates with CK2A2 were provided, as well as the docking score with other related proteins, mtHSP70, STK3, and LATS1. And the predicted activities or scores through several known compounds or traditional Chinese medicines about HD could also take a reference to the final drugs' selection. Some proteins (CK2A2 and STK3) also provided a control for comparing.Thirty-one known ligands  for CK2 2), and q2 for cross-validation were referred as evaluation indexes of 3D-QSAR models. CoMFA analyzes the effects of stereoscopic fields and electric fields, while CoMSIA evaluates stereoscopic fields, electric fields, hydrophobic fields, and hydrogen bonds for acceptors and donors. Various field combinations had been studied separately and the best model would be further studied.3D-QSAR was constructed by known ligands  for CK2 π-conjugated effect. The models were verified through leave-one-out cross validation (LOOCV) [Pharmacophore models with 38 compounds , 37, 38  (LOOCV) . Fisher'In order to verify the results of docking screening, molecular dynamics simulations were performed on all candidate compounds screened above using gromacs5.0.4 program software with world's top computing resources, Tianhe No. 2 supercomputer for long enough (5000 ns) simulation. The 5000 ns is basically the longest simulation in the current stage (2018). The energy minimization was produced by the steepest descent algorithm with the maximum number of 5000 steps minimization. The NVT equilibration was set in 20 ns with each step of 2fs and constrained with Lincs algorithm. The NPT equilibration was running after NVT in total of 20 ns. All bonds (even heavy atom-H bonds) were constrained with Lincs algorithm at 300K. The long time molecular dynasty was set in 5000 ns (for LATS1 protein nearly 3500 ns because of the computing resources). With the analysis of root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), mean square displacement (MSD), secondary structure of protein, solvent accessible surface area (SASA), radius of Gyration (gyrate), the residue contact map (mdmat), Hydrogen bond distance (H-bond), energy analysis, and torsion angle, even the cluster research, the candidates will be validated to acquire a reasonable conclusion. After all, please be aware of short time MD simulation. Several candidates were “flying away” from protein during 5000 ns, even though they interacted well with targets at the first period.From the interaction information about the known targets, some high-related targets could be further confirmed in the research. The decreased expression level of YAP in human HD brains neurons was known . Up-adjuThe hub node in the pathway was important and probably got more interactions with other related proteins. The proteins in the pathway were gathered; it showed the related proteins which could adjust . It coulThe modeling structure of LATS1 was further verified by 3D-profile program and Ramachandran plot validation . The 3D-Appropriate descriptors were selected using the Genetic Function Approximation (GFA) algorithm . And theProtein CK2A2:Protein mtHSP70:Protein STK3:Protein LATS1:2 were reasonable.The model was regressed by the compounds from treatment drugs. Blue spheres represented the training set, and the red inverted triangle indicated the test set . Externa2), standard error of estimate (SEE), and F test (F ratio) for evaluating, as well as the cross validation (q2cv) for assessing  and where no substituent groups were demand (yellow area)  . The CoMow area) . Externaow area) The activity predicts model would be verified with cross-validation. The leave one out cross-validation (LOOCV) was used to illustrate the reliability of the model. The information of 10 hypoGen models was shared . Cat-ScrTop 50 compounds in each target were created in a network to suggest the multitarget effect . The scr2D structure of candidate compounds was displayed in MD results were analyzed in the Gromacs5.0.4 program.Trajectory files obtained after MD were made as videos (supported video set ) for obsMSD=2+log10 (MSD). In fact, several proteins remain changing at the end of simulation, even if we simulate very long time. SASA analysis could help us understand the hydrophobic nature and protein surface state. SASA of CK2A2 was little difference when combined with different ligands as well as LATS1 protein , solvent accessible surface area (SASA), and Radius of Gyration (gyrate) analysis provided the information about both protein and ligand during MD Figures  and 13.  Figures . SASA of Figures  displayeMean square displacement (MSD), solvent accessible surface area (SASA), and Radius of Gyration (gyrate) analysis of each ligand showed in Nonbond relationship provided the binding information between the ligands and receptors; it was an important reference for drug design . For exaHydrogen bond distance analysis was vital to explain the interaction between targets and proteins. The variation of main H-bond was observed to interpret the stability of receptor-ligand complexes . And theRoot mean square fluctuation (RMSF) can be used to test fluctuation of each residue, which can notify the key residues change during MD. The key residues contributed nonbond interaction which would mainly be focused on. CK2A2 complexes had similar residue fluctuations , which cThe average protein structures would be superimposed to observe whether the binding structures are alike, which can predict these structures related to their inhibition. CK2A2 and mtHSP70 were nearly superimposed on one structure, respectively. STK3 can be superimposed except the outside loop. The two LATS1 protein structures cannot merge into one because one ligand of structure “flies away”. It manifested the structure which acting with ligand was completely different from the origin protein .The protein residues contact map provided 3D information about the residues distance of proteins in 2D matrix. Average amino acid distance was shown in different color, which indicates the looseness of protein. The distance between key residues was focused on. It demonstrated a tight conformation for SK2A2 protein. The key amino acids R48 and N119 of CK2A2 are all close to other residue that can construct a “hotspot” cavity. Similarly, key residues D59, G247, etc. of mtHSP70 constitute an active cavity site. The amino acids S37, K56, E70, and D164 of STK3 are also in a reasonable range. Different ligands for CK2A2 or mtHSP70 protein would get similar 3D structure shown in residues distance matrix, respectively, which further validate our inference above.β-sheet during MD period can keep structural stability. Basically secondary structure was stable, including the key residues. A change was observed like residue 176-181 of mtHSP70-Febrifugine since 3000 ns, where A-helix changed to β-turn. It is possible that complex structure changes to a more stable configuration since this moment influenced by the ligand . Considering the docking scores comprehensively, SVM prediction activity and MLR prediction activity could explain the effect well. The verification of the simulation was provided in the experiment, such as the validation of the homology modeling and the activity prediction module.Traditional Chinese medicine combined with network pharmacological analysis could get more effective information. It could consider multiple targets synthetically, which is quite beneficial for treatment, especially for stubborn diseases. It is impossible to employ only one target or one drug against disease nowadays. Considering the comprehensive consideration of the disease, it is exactly the same as the concept of traditional Chinese medicine. Small molecules in traditional Chinese medicine formula can be linked to multiple targets through a network, just as western medicine has been studied. Traditional Chinese medicine modernization, or integration of TCM and western medicine (WM). Network analysis can find the intercommunication between WM and TCM, bridge the current gap, and promote integrated treatment . NetworkLong-term MD simulation would tell us more reliable information. In addition, the short-term MD is actually debatable; however, it was often performed at past several years. From this experiment, we want to tell the scholars who use MD to be careful about molecular dynamics simulations, especially the short-term MD. The MD simulation used in our experiment ran for 5000 ns , which is quite long for the current computing resource (2018). It could be found that some complexes that can be stably combined in tens or hundreds of nanoseconds may “fly away” in subsequent simulations. This shows that short-term MD is likely to have false positive results. Of course, our MD is not long enough but using the computational resources that can now be achieved. We want to tell the researcher there are some problems in MD, so be aware of MD.Dock screening result is validated by QSAR model and molecular dynasty simulation; we got the more reliable candidate of small molecule compounds from TCM database.Several molecular dynamics were connected to the targets through TCM formula candidates. The novel formula can achieve anti-HD effects through the multicomponent and multitarget strategy. Prescriptions and details of their small molecule effects were provided; they could form a drug-multitargets and multidrug synergistic effect against HD . It was


The methylotrophic yeast Komagataella phaffii is an industrial workhorse yeast species that has been widely used in biotechnologyindustries for recombinant protein production. Genome sequencing of this yeast in 2009 have enabled scientists to assign and characterizefunctions to most of its proteins while few hypothetical proteins remain uncharacterized. Therefore, it is of interest to characterize thehypothetical protein coding gene PAS_chr2-2_0152 as SET containing the ZNF-MYND (SMYD) domain. They share a homology with othermethylotrophic and non-methylotrophic yeast species together with known SMYD proteins of Homo sapiens, with conserved distinctiveSMYD domain patterns. A homology model is developed using the crystal structure of human histone-lysine methyl transferase smyd3 astemplate. These data points to that the hypothetical protein is a potential histones and non-histone lysine methyl transferase regulating cellcycle, chromatin remodeling, DNA damage response, homologous recombination and transcription in Komagataella phaffii. Data alsosuggests the evolutionary syntenic conservation of DNA damage regulator (RFX) and lysine methyl transferase (SMYD) genes in someyeast lineages, pointing to a conserved role requiring further confirmation.  Accessibility of simpleand vigorous high cell density cultivation methods with easygenetic manipulations, extraordinary strong promoters with tightregulation, secretion capabilities, and excellent post-translationalmodification has further enhanced the utilization of this yeast astep forward [evidence . Charactevidence . ProteinMethyltransferases are the major class of proteins responsible forpost-translational protein methylation which ranges fromapproximately 1-2% of genes in a variety of eukaryotic andprokaryotic organisms. Methyltransferases are categorized intothree major structural protein families as seven-beta-strand, SETdomain,and SPOUT. In eukaryotic cells, mostly protein methylation takes place at arginine and lysine residues . ThemodeThe complete genome sequence of K. phaffii GS115 was retrievedfrom the NCBI database  .Using HIn order to study the sequence conservation of the protein, BLASTPhits from UniProtKb/Swiss-Prot and NR databases weredownloaded from the NCBI protein database. The proteinsequences were aligned with the help of MUSCLE software and a neThe physicochemical properties like isoelectric point (pI), molecularweight (M. Wt), amino acid composition, atomic composition ,charge (positive or negative), extinction coefficient(EC), aliphaticindex (AI), instability index (II), grand average of hydropathicity(GRAVY) and estimated half-life were determined by using theExPASy ProtParam tool (http://web.expasy.org/protparam) .SubcellThe ab initio secondary structure of the hypothetical protein waspredicted with the aid of PSIPRED . Phyre2 The SET domain hmm profile search in K. phaffii GS115 genomeidentified eight SET domain-containing methyl transferaseproteins. Domains analysis using InterProScan and ConservedDomain Database (CDD) tools further confirmed the presence ofSET domains in the HMM search hits. BLASTP analysis usingNCBI Non-redundant UniProtKB/SwissProt sequences databasesidentified known proteins in other organisms including S. cerevisiae.The protein with Gene Id: PAS_chr2-2_0152  was lackingany clear ortholog in the model S. cerevisiae genome. But theorthologous protein in other methylotrophic and nonmethylotrophicyeasts could be detected. BLASTP of XP_002492054.1 in UniProtKb/Swiss-Prot database identifiesseveral SET and Smyd proteins in the Schizosaccharomyces pombeand Homo sapiens genomes. We named this hypothetical protein asKpSMYD as it harbors a unique SET  and ZNF-MYND (Zinc Finger-Myeloid-Nervy-DEAF1) domain (SMYD) as revealed by BLASTPand InterproScan. Based on its annotation as a hypothetical protein,we have chosen KpSMYD in the present study for In silicocharacterization. KpSMYD protein showed 16 to 22 % identitieswith other Smyd proteins using global blast alignment tool . SequenGenome sequencing of an organism involves gene annotation andcuration of genomic sequences from gene to protein functions. Wedescribe the annotation of hypothetical protein PAS_chr2-2_0152from K. phaffii GS115 genome and linked it to a SMYD domaincontaining protein. The human SMYD domain containing proteinsare known to be lysine methyl transferase enzyme family in theregulation of cell cycle and differentiation. SMYD proteins are alsoinvolved in other cellular functions like transcriptional activationand repression, protein-protein interactions and DNA damageresponse. The hypothetical protein is orthologous with the knownyeast model S. pombe and also with other methylotrophic yeastssharing similar synteny. A homology model of the hypotheticalprotein was developed using the human SMYD3 protein as thetemplate while SMYD3 promotes homologous recombination viaregulation of H3K4-mediated gene expression. Interestingly, thesynteny analysis suggests the DNA damage regulator RFX andKpSMYD proteins are conserved in various yeast lineages,suggesting similar function. Thus, the potential function of thehypothetical protein is homologous recombination and/orassociated with DNA damage response. These observations needfurther functional validation.


The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.The importance of protein subcellular localization problem is due to the importance of protein’s functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du  Moreover, accurate prediction of subcellular localization helps to identify the potential molecular targets for drugs2. Furthermore, protein SCL has an important role in many other fields such as genome annotation, cytobiology and proteomics1. Today, protein data banks are growing rapidly, demanding fast and accurate tools for identifying the SCLs of new proteins.Sub-Cellular Location (SCL) prediction of a protein is a substantial problem in Bioinformatics, because there is a close relationship between the SCL of a protein and its function3, microscopic detection4 and subcellular proteomics5 have been already introduced to identify subcellular locations of a protein. Unfortunately, experimental methods are time consuming and costly. That is why a large information gap exists between protein sequences and their location, and the gap grows by the day. Consequently, various computational methods have been developed to fill this gap11.Generally, there are two approaches for the protein subcellular localization problem: experimental methods and computational methods. Several experimental approaches such as green fluorescent protein11. On the other hand, most of these methods are developed for particular proteins or species14. Hence, it seems that a more comprehensive method is desired to predict multiple locations for various proteins while remaining applicable to different species.Computational methods have their advantages and disadvantages. These methods outperform experimental methods, both in terms of time and cost, but they may not be as accurate as experimental methods. Moreover, most of these computational methods focus on the single site SCL of a protein whereas the experimental researches show that many proteins are located in several subcellular locations16. GO is a bioinformatics tools to unify gene and gene products across all species. In fact, GO provides an ontology of predefined terms covering three domains that includes cellular component, molecular function and biological process17. UniProtKB/Swiss-Prot is also a database which is used in many computational algorithms. The Universal Protein Resource, UniProt, is a comprehensive, knowledgebase database of protein information which includes protein sequences and functional annotations. One of the main parts of UniProt is UniProtKB repository. UniProtKB is subtended by two sections: UniProtKB/Swiss-Prot which contains the manually annotated protein location and reviewed entries, and UniProtKB/TrEMBL which consists of automatically annotated protein location and non-reviewed information19.Subcellular location prediction methods need a reliable protein-location dataset to learn their system and to evaluate their algorithm. Some computational algorithms provide improved SCL prediction by using GO information21. The fact that interacting proteins may share at least one location has been validated by Jiang et al.22. Therefore, protein-protein interaction information could be useful in predicting protein subcellular locations and several methods have been developed based on protein-protein interactions to predict protein subcellular locations25. One recent prediction methods which is based on protein-protein interactions is introduced by Du et al.25. In this method, protein-protein interactions are used to improve the results of another prediction method named Hum-mPLoc 2.026.On the other hand, proteins within a cell do not work independently and interact with different proteins. The physical interactions between a pair of proteins imply that the physical distance between interacting proteins is very close, and so the interacting proteins tend to localize within the same subcellular compartments27. Development of recommender systems is a multi-disciplinary effort, which involves experts from various fields such as artificial intelligence, data mining, statistics, decision support systems and physics29. In case of a new user, most of the recommender systems are weak to predict proper items. This is called the cold start problem. There are several ways to overcome this problem, for instance content-based methods use tags and categories to make it easier to recommend to new users or users with considerably low information30.Here, we present a method based on recommendation systems to predict the locations of a protein. Recommender systems are introduced to recommend products available in e-shops like entertainment items  that are likely to be of interest to the userIn this paper, we present PMLPR (Protein Multiple Location Prediction based on Recommendation systems) which is a recommendation method based on the bipartite network to predict the SCL of proteins. In our problem, being able to predict the SCL of a new protein is important. Thus, we use the interaction score between proteins in order to overcome the cold start problem.32. Therefore, we use the interaction score between two proteins, which is derived from STRING database33. STRING database is a web resource of experimentally known and predicted protein-protein interactions.The PMLPR algorithm, for a given protein, produces a recommendation list of potential locations which are sorted in a descending order with respect to their score, i. e. the location with the higher scores are expected to have a higher chance to be a SCL of that protein. In this algorithm, to construct the bipartite network, the information of SWISS-PROT and the cellular component ontology of GO has been used. The studies show that proteins who interact with each other are more likely be found in the same subcellular localization34, WOLF-PSORT9, the prediction channel35, MDLoc36, Du et al.25 and MultiLoc2-HighRes37. Unfortunately, the method introduced by Du et al. does not have an online software. Hence, in order to compare with their method, we did the same evaluation test on the same dataset as they mentioned in their publication. The datasets which we used for the evaluation are the set of RAT, FLY and HUMAN proteins and predefined datasets Du et al., DBMLoc and Höglund.To evaluate PMLPR method, we compared it with six other state-of-the-art methods, Yloc28. In the first part of PMLPR algorithm, the NBI method is used. Then, by applying interaction scores between proteins, PMLPR predicts a list of locations for a protein. In this section, we introduce the NBI method followed by a detailed explanation of our approach.In this section, we present PMLPR algorithm for protein localization problem. PMLPR is based on one of the existing methods for recommender systems, NBIet al.28. NBI is a network-based method which constructs a bipartite network of users and objects. Then, the algorithm performs a resource-allocation process in two steps; First, from objects to users, second from users to objects. The amount of resources after two steps is used to predict new objects for users. Up to now, NBI and its variations are utilized in different research areas. For example, recommending new movies, music and Internet bookmarks to users28, predicting new drug targets38, and so on.Recommender systems consist of two sets, users and objects. Each user collects a number of objects. The purpose of such systems is to analyze this information and offer new objects to each user. One of the famous recommender systems is NBI algorithm introduced by Zhou p is a new protein that there is no information about its locations. Our algorithm predicts locations for p using the information of all proteins in Suppose E if pi has already collected lj. In other words, protein pi belongs to the location lj.A bipartite graph rij] with n rows and m columns is calculated similar to NBI method. To obtain R, let A = [aij]n×m be the adjacency matrix of G where aij = 1 if pi and lj are neighbors and aij = 0 otherwise. Define W = [wij]m×m as follows:d(lj) and d(pt) are the degree of vertices lj and pt in G respectively. To obtain the kth row of R, vector WT is the transpose of matrix W.In this step, the personal recommender matrix R =  derived from Swiss-Prot are considered as well. For all RAT, FLY and HUMAN datasets, 9 subcellular locations, including Cytoplasm, Cytoskeleton, ER (Endoplasmic reticulum), ExR (Extracellular region), Membrane, Mit (Mitochondrion), Nucleus, GA (Golgi apparatus) and Peroxisome are considered. Most of the Intermembrane/Transmembrane proteins are identical among Plasma Membrane, ER membrane, etc. In this study, we consider all as Membrane.et al., eleven subcellular locations have been considered, including Cell membrane, Cytoplasm, ER, Extracellular region, Golgi Apparatus, Mitochondrion, Nucleus, Peroxisome, Lysosome, Endosome and Microsome. For a protein, if a subcellular location has been marked as “Probable”, “By Similarity” or “Potential”, the subcellular location has been discarded.In order to compare our results with Du To assess the performance of PMLPR against other algorithms, four different measurements are employed.l(p) = {xp1, … xkp} and p localized according to the dataset and the order set of locations that a prediction algorithm predicts for protein p, respectively. In this evaluation, we do not consider the order of locations predicted for each protein. Using this approach, we globally evaluate the performance of an algorithm regardless of the order of locations introduced for a protein. For example, if the order set  is introduced for protein p, Precision does not consider the order of locations and there is no significant difference between  and . However, with more reliability the algorithm suggest that the protein p is located in nucleus in the first prediction and cytoplasm in the second prediction. In order to consider this difference, we introduce an extra measurement. Let the intersection of l(p) and Measurements commonly used in many evaluation methods are Precision, Recall and F-measure. The Precision calculates the fraction of retrieved instances that are relevant and Recall calculates the fraction of relevant instances retrieved.Define:FMP-measure is defined as follows:Finally, et al.36. For each location c, Prec and Recc are defined as follow:The second measurement is introduced by Simha c and Recc obtain the Precision and Recall of an algorithm for each location c. Moreover, Simha et al. considered F1-scorec, the harmonic mean of Precision and Recall for each location c. Furthermore, the average F1-score for all locations are calculated as follow:In this part, precet al.25. They introduced 5 statistical measures, Recall (AIM), Precision (CVR), ACC′, ATR and AFR. The first two statistical measures, Recall and Precision are introduced in Measure 1. ACC′, ATR and AFR are accuracy, absolute true-rate and absolute false-rate, respectively. They can be formulated as followed:C| is the number of subcellular locations. andThe third measurement is introduced by Du The forth measurement is ACC(accuracy), which is slightly different from ACC′ ACC can be formulated as followed:et al. mentioned in their publication41, in order to compare the results of various prediction algorithms, there are three methods, Independent dataset, k-fold cross-validation and jackknife test . Since the proteins of the independent test should be apart from the training set, there is a major problem to choose the independent dataset. How to select this independent dataset can completely change the final results. It is axiomatic that this method is not efficient for our comparison.As Chuo et al. mentioned in their publication41, the number of possible selections to divide a benchmark into k classes is an immense number. Hence, selecting one of the divisions cannot be a fair demonstration of the performance of the algorithm.On the other hand, in the k-fold cross-validation test, the benchmark should be divided into k class of data. As Chuo Jackknife method considers each protein as a test case. In fact, in this method each protein moves between the train and test datasets. Moreover, this method is more efficient in memory usage. For these testimonies, jackknife method does not have the mentioned problems and it truly fits our problem. Thus in this paper, jackknife method is mainly used due to representing the performance of the algorithms impartially. Plus, we applied k-fold cross-validation method for more affirmation. In order to evaluate the accuracy of the algorithm, per each test protein, a list of locations is predicted according to the training dataset.In PMLPR algorithm, for each prediction, we introduce a reliability threshold. According to this threshold, a set of sorted locations can be assigned for each protein. This threshold is used to exclude predictions with low reliability score. It is possible for the users to change this reliability threshold in the online version of PMLPR algorithm. For example, if the reliability threshold of 80% is considered for sample protein P35213, PMLPR’s sorted result will be Table ordered-measure and FMP-measure) is used to compare the performances of algorithms in Table ordered-measure and F-measure on RAT proteins by 0.1 and 0.18 with respect to the results of WP, which has the best result between the other methods. As can be seen from Table ordered-measure results for 0.31. Albeit, Table ordered-measure, PC shows the highest F-measure and FMP-measure. To sum up, in most cases, Table ordered-measure and FMP-measure values have been increased significantly by PMLPR algorithm with respect to other algorithms, which implies the efficiency of our method.The predefined Measure 1  between different algorithms, per each 9 locations on RAT, FLY and HUMAN proteins.The other comparison used to evaluate the performance of PMLPR is the one introduced by Simha As it can be distinguished from Table 1-score, the average F1-scorec over all 9 locations. This table shows that, PMLPR has the best overall performance on RAT and FLY, competing results on HUMAN.Table Overall, all these tests depicted the efficiency of PMLPR method. PMLPR has a significant improvement on RAT and FLY datasets. Furthermore, on HUMAN dataset, PMLPR has almost the same performance as other reported state-of-the-art methods.et al. did not provide their software, we were unable to obtain their result for any protein to perform Measure 1 and Measure 2. In order to compare our method with them, we applied the same evaluation test as they performed. Hence, we would be able to use their result in our comparison. The results are shown in Table et al. just worked on HUMAN proteins, so we could not test their algorithm on RAT and FLY proteins. However, we had competing results on HUMAN proteins.Whereas Du et al. in their publication has been used. This 5-fold cross-validation test has been repeated thirty times, and the average outcome is represented in Table 42 sequence alignment between proteins has been applied, to obtain the protein-protein similarity for these two datasets.In order to further evaluate PMLPR on other species based on the existing datasets, two of the well stablished datasets, DBMLoc and Höglund has been used. A similar 5-fold cross-validation test as the one performed by Zhou As can be seen from Table We performed a 10-fold cross-validation test on PMLPR results. Since the implementation of the other existing methods are not available, we were unable to make change to the training data to compare the methods by 10-fold cross validation test. Besides, as the authors do not provide all the details of their implementations in their papers, re-implementing these methods may cause in unreliable results. Hence, we performed a 10-fold cross validation on PMLPR results for thirty times. The average outcome of this test, demonstrates that there is a negligible difference between the results of jackknife and cross-validation test. For instance, Table 49. We have selected these proteins in order to have a transpicuous comparison between PMLPR and the 4 other methods. Table Table We presented an efficient protein localization method using personal recommender systems and protein-protein interactions. Using such approach for protein localization problem is the main contribution of this paper. The results demonstrate the utility of using recommender systems and protein-protein interactions in the prediction process. PMLPR not only improves the results, but also has a fast algorithm. The related algorithm is implemented using C++/R languages.To the best of our knowledge, there are no available subcellular prediction software using protein-protein interactions, especially on HUMAN proteins. PMLPR software is available online and it is useable by biologist and other scientist.30, collaborative filtering50, matrix factorization51 and etc. These methods can be applied in this problem, and they may improve the prediction results.NBI is one of the basic recommender systems, there are more complex recommender systems, such as content-based methodsIn recent methods such as MDLoc, the interdependency of the locations has been taken into the account, because some of the locations have high interaction with each other and many proteins travel between these locations constantly. These interdependencies can be used in the future studies of this problem. Moreover, a fusion between our method and the other best existing methods will improve the results.


Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes. The biological activity of proteins involves adopting a specific conformation, their local fluctuations and, in many cases, structural transitions between different conformations. Structural flexibility can range from small side-chain fluctuations to large rearrangements of the entire protein backbone or its fragments, for example, disordered regions ,2,3. MosIn this review, we briefly discuss two representative CG simulation models of protein dynamics  and variMolecular modeling of protein dynamics plays important roles in many branches of biophysics, molecular biology, and related sciences. The most popular strategies for modeling proteins and other biomolecular systems are classical MD simulation methods. MD, however, has several limitations . ImportaMany CG approaches have been proposed, using different levels of reduced representations of biomacromolecules, different force-field schemes, and different strategies for sampling the conformational space ,21,22,23Below, we discuss in more detail two representative models of various resolutions based on the above-outlined strategy of CG modeling: a relatively high-resolution Cα, Cβ, side-chain (CABS)  CG modelCABS  is a coaThe force field of CABS is knowledge-based, derived from the statistical analysis of a representative set of known protein structures, although it also works surprisingly well for partially unfolded proteins ,24. The The sampling scheme of the CABS model  is based on the MC dynamics method. The dynamics of the entire protein structure is modeled as a very long random sequence of small local moves, including single amino acid moves to small-fragment  local moves. MC dynamics provides very reasonable dynamics trajectories for long-time processes, although the time unit of such dynamics is not a priori defined. MC dynamics can be properly scaled by fitting  fragments of the MC trajectories to corresponding MD trajectories and/or to appropriate experimental data. CABS MC dynamics simulations are very fast, not only due to the reduced representation of protein structures, but also thanks to the high-co-ordination lattice representation of the model structures. The underlying lattice enables use of precalculated data tables of local geometry and interactions. This makes computing new randomly selected structures and interactions extremely fast. On the other hand, the very high co-ordination number of interacting pseudoatoms (due to the high resolution of the underlying lattice) does not impose any noticeable directional biases that is so painful for lower-resolution lattice models of protein structures .http://biocomp.chem.uw.edu.pl and http://lcbio.pl). The CABS-based tools are targeted onto specific simulation tasks, including simulations of near-native (or in the vicinity of another provided structure) protein flexibility [The CABS model has been successfully used in numerous studies, including the folding of globular proteins ,31,32,33xibility ,38,39,40xibility ,50, and xibility ,46,47,49By implementing specific structural regularities characteristic to protein globules, it is possible to design deeply coarse-grained representations of protein chains that are much simpler than CABS (and other models of similar resolution), and which, regardless of their simplicity, maintain some of the most important features of real systems. SURPASS is an example of such approach. SURPASS ,51 is baIn the past decades, a large number of studies have shown the usefulness of NMA and Principal Component Analysis (PCA) for the prediction and analysis of protein co-operative motions ,60,61,62In the early 1980s, the NMA method was adapted by Go , Karplusi and j that provides information about correlations of equilibrium fluctuations of residues in protein structure can be simply computed with elastic network models. Pairs of residues that have large values of such correlations may communicate with each other because of functional reasons, for example, by participating in common allosteric communication paths. In the GNM, it is assumed that fluctuations of residues around their mean positions are spherically symmetric. ENMs have become extremely successful in biology after their reformulation as the Gaussian Network Model (GNM) of proteins ,68. GNM The Anisotropic Network Model (ANM) assumes that fluctuations of residue positions around their starting points are, in contrast to the spherical approximation of GNM, anisotropic and are represented by ellipsoids. The model can be used to calculate normal modes from a single structure ,74. The m = 1, 2, 10, 40) have shown that the slowest modes of motion are preserved regardless of the level of coarse-graining. Mixed coarse-graining has also been introduced for EN models, where a protein’s native conformation is represented by different regions having high and low resolution [Most co-operative (lowest-frequency) modes of motion are almost insensitive to the details of the model and parameters, and can be satisfactorily captured by adopting low-resolution ENMs. The idea was applied by Doruker and Jernigan  to low-rsolution . The aimExcept as mentioned above, ENMs have been successfully used in many combinations with different input information and methods, such as, for example, data from electron microscopy , atomistCABS and SURPASS obviously have several limitations, characteristic for all CG models of low and moderate resolution. First, in multiscale simulations the shift from atom level to CG representations is easy (unique mapping), while the reverse projection from CG to the atomistic representation is always nonunique and less accurate, and needs to be carefully performed . KnowledWhen the range of protein structural dynamics studies can be limited to a roughly defined vicinity of a specific folded structure (determined by experiment), ENM and CG models or their modifications can be very effective in predicting protein flexibility ,81,82,83In the past decades, ENM-based modeling has definitely been the most common approach to modeling fluctuations near the input folded structures. There are many available methods based on ENM, some of them as easy-to-use web servers ,93,94,95NMR ensemble: data calculated using 10 models deposited in the PDB code: 1hpw.MD all-atom simulation: data obtained using a 10 nanosecond trajectory with an AMBER8.0 force field taken from the MoDEL database of MD trajectories ; RMSF waCG simulation using the CABS model: data obtained using the CABS-flex 2.0 web server ; resultsT = 0.2), 10,000 MC steps, 1 and 3-bead motions; RMSF was calculated for the entire trajectory of 100 models.CG simulation using the SURPASS model: data obtained with the following SURPASS  settingsENM modeling: data obtained using the DynOmics web server ,95 that Below, in The ENM-based exploration of protein conformational space near the given folded structure can also be practically used in the refinement of models derived from protein-structure prediction methods ,103,104.ENMs have been shown to be useful to study large-scale protein conformational transitions in cases when starting and ending conformational states of the transition are available ,117,118.CG protein simulation models, such as CABS or SURPASS, offer an alternative approach to predicting large-scale transitions that do not require starting and ending conformational states. The CABS model has been used for simulations of folding mechanisms from the fully denatured to the folded state ,31,32,43Computational exploration of protein conformational transitions has already been quite successful in many tasks of structural biology, including rational drug design ,127,128,ENM- (or NMA)-based methods are well suited only for certain problems . They caIn the above context, the future will see combinations of ENM and other CG methods of various resolution with atomistic MD ,132,133.