In the interaction network of baker's yeast, we find that statistically proteins that receive higher energetic benefits from the interaction network are more likely to misfold. A simplified fitness landscape wherein the fitness of an organism is inversely proportional to the total concentration of unfolded proteins provides an evolutionary justification for the proposed trends. We conclude by outlining clear biophysical experiments to test our predictions.In addition to their biological function, protein complexes reduce the exposure of the constituent proteins to the risk of undesired oligomerization by reducing the concentration of the free monomeric state. We interpret this reduced risk as a stabilization of the functional state of the protein. We estimate that protein-protein interactions can account for  in vivo and constantly faces the risk of aggregation, unfolding/misfolding, and other aberrant interactions. For most proteins, the folded form is also the functionally relevant one and forces of natural selection strongly modulate its stability. In vivo, proteins interact with each other on a genome-wide scale. Usually, the interaction of a protein and its binding partners requires both the proteins to be in the folded form and as a result, the interactions tend to shift the population of a protein towards the folded form. Consequently, protein-protein interactions interfere with the evolution of protein stability. Here, we present empirical evidence and theoretical justification for proteins' ability to stabilize the folded form of their interaction partners and allow them to explore the region of the sequence space that corresponds to proteins with less stable structure. We argue that the ‘evolutionary capacitance’ – previously thought to be a property of the chaperone HSP90, a special class of proteins – is a property of all proteins, albeit to a different degree.The folded form of proteins is only marginally stable  The toxicity due to protein misfolding and aggregation has a considerable effect on the viability of living organisms Recent high throughput experiments have established that proteins interact with each other on a genome-wide scale effectively present a lower monomer concentration inside the cell. Since free monomers are susceptible to misfolding/unfolding and toxic oligomerization, interacting proteins may face a reduced risk towards the same. This reduced risk can be interpreted as interaction-induced stabilization evolutionary capacitorHere, we hypothesize an additional biophysical advantage for protein-protein interactions. Proteins bound to their interaction partners Below we outline the empirical evidence for our hypothesis and suggest clear biophysical and evolutionary experiments to test it further.We present our estimates of the interaction-induced stability  see  and explners see  below.The estimated To explore the evolutionary consequences of the interaction-induced stability, we investigate a simplified fitness model of a toy proteome consisting of 15 proteins see . Brieflyevolutionary temperature quantifies the importance of genetic drift. The effective population size modulates the competition between destabilizing random mutations and stabilizing protein-protein interactions.In the model, the concentration of unfolded proteins and thus the fitness of the proteome depends on the total stability effective stability of proteins. We find that at higher effective populations, proteins are inherently stable and only the least stable proteins  See . This refree monomeric state in vivo  equilibrium equationsthermodynamic equilibriums. The equilibrium constant In cellular homeostasis, the total concentration ing  See . In our in,  See , as two By combining mass conservation  with Eq.Note that in the absence of interactions, We downloaded the latest set of interacting proteins in baker's yeast from the BIOGRID database in vivo stability of a protein is a combination of its thermodynamic stability, resistance to aggregation or oligomerization, and resistance to degradation in vivo protein stability on its sequence is unclear and there exist no reliable data or sequence dependent computational estimates for the thermodynamic stability of proteins. Moreover, The nd thus  , can be aterials  and S5, The dissociation constants We solve for free concentrations As noted above, the toxic effects of misfolding and aggregation may be the chief determinant of protein sequence evolution eome See ,(11) isin vivo stability of proteins. We construct the cytoplasm of a hypothetical organism with 15 proteins. The number of proteins is low due to computational restrictions. The proteome is evolved by sampling the dissociation constants from the lognormal distribution while introducing random mutations in proteins that change their stability. At each generation, the fitness is evaluated and the progeny is accepted at a certain evolutionary temperature Click here for additional data file.Figure S2The histogram of interaction-induced stabilities (TIF)Click here for additional data file.Figure S3The histogram of interaction-induced stabilities (TIF)Click here for additional data file.Figure S4The histogram of estimated disorder in the proteins of the yeast proteome.(TIF)Click here for additional data file.Table S1A table for the parameters and topology of the toy proteome.(PDF)Click here for additional data file.Table S2A table reporting correlations between stability and interaction using TANGO (PDF)Click here for additional data file.Table S3A table reporting correlations between stability and interaction using AGGRESCAN (PDF)Click here for additional data file.Table S4A table reporting correlations between stability and interaction when protein stabilities depend on their chain length.(PDF)Click here for additional data file.Table S5A table reporting correlations between stability and interaction when protein stabilities are set to their minumum.(PDF)Click here for additional data file.Table S6A table reporting correlations between stability and interaction when all dissociation constants are set at 5 nM.(PDF)Click here for additional data file.Text S1An inventory of population genetics terms, additional information about the toy model, and misc. information about the analysis.(PDF)Click here for additional data file.


Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins.The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.Protein pair-wise functional relationship scores for  In recent years we have experienced an exponential growth of biological data, including primary data such as genomic sequences resulting from worldwide DNA sequencing efforts and as well as functional data from high-throughput experiments, respectively. This abundance of primary sequence data and the large availability of public gene and protein sequence databases have the capability to provide many new insights into the biology of organisms. Several studies have shown that very often functional properties of a protein are not necessarily determined by the whole sequence but only by some of its sub-sequences Discovering sequence homology and modelling functional interactions between homologues from sequence and experimental data constitutes an important problem in molecular biology, as these can help to describe their behaviour in cellular processes and reveal the interplay between particular genes and proteins. In order to determine functional similarity between proteins, many approaches try to identify the sub-sequences of the proteins that may contribute to their function. Several Bioinformatics tools have been designed for deriving and storing these functional features. These include standard sequence comparison tools such as BLAST Using homologous datasets obtained from pair-wise sequence similarities, and protein domains and families in public databases, the inference of functional connections can be carried out based on the fact that two proteins sharing common domains or belonging to the same family are more likely to be functionally linked There are several problems with generating functional interaction networks using diverse data types such as sequence and functional genomics data. Considering that we are dealing with inaccurate data obtained from different experiments Several scoring schemes have been proposed for sequence data and are, so far, limited to only finding the similarity scores of proteins that are referred to as scoring functions. In the case of protein domain and family data, the scoring function is deduced from the number of common signatures shared by two proteins Mycobacterium tuberculosis (MTB) strain CDC1551 to produce a functional network from sequence data for this organism. This approach is compared to the STRING  In order to overcome these shortcomings, we propose an information-theoretic based measure to score protein-protein relationships in functional interaction networks predicted from homology data. This approach is shown to be effective for scoring functional pair-wise relationships from homology data, and translating the amount of biological content shared between proteins into the score of their functional relationships. We apply our method to score functional relationships between proteins in http://www.ebi.ac.uk/integr8. Scoring functional relationships for data from protein families and domains has been widely addressed by the Bionformatics community. However, the approaches described so far in the literature are limited to finding the similarity scores between proteins by the number of common signatures shared by proteins. Two examples of such a scheme are given below.This section describes novel scoring schemes for protein family and domain data extracted from protein family databases, as well as for protein sequence similarity obtained by running sequence comparison tools such as Basic Local Alignment Search Tool (BLAST). Sequences in Fasta format and InterPro data for the organism were downloaded from the Integr8 project of the European Bioinformatics Institute (EBI) at Scheme 1: Scoring Function of Pfam Domain Sharing The scoring function Scheme 2: Scoring Function based on Protein Signature Profiling The similarity score between a pair of proteins Note that the scheme These two schemes just count the number of shared signatures without taking into account the nature of the data and experiments used to derive them. In addition, the limitation of the second scheme can be seen in this small illustration: Let's consider three proteins In the case of sequence similarity, the existing scoring schemes rely on the use of the negative logarithm of Thus, these schemes are not equipped to capture all the parameters related to the data under consideration and technology used to derive them. In order to overcome these shortcomings, we introduce novel scoring schemes based on the information-theoretic approach, taking into account the nature of the data and technology used and where the user can tune parameters based on their confidence in the data source.Consider two proteins denoted Broadly speaking, the reliability or confidence score increases with the confidence-level of data, which depends on the data source and is torn down by the uncertainty-level of data linked to the dispersion measure Given the confidence-level In fact, the uncertainty measure function To illustrate the dependency of this new measure on the data under consideration and the technology used to produce them, we plot the variation of confidence level These results show that the confidence level For a given set of pair-wise homologous sequences, Bastian http://www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-1.html) derived from the scoring matrix and amino acid composition of the sequence Let Equation (11) shows that the mutual information Thus, we define the reliability or confidence score Using equation (11), the mutual information It is obvious that this scoring scheme relies only on the two protein sequences for which the confidence score is being computed. Two protein sequences whose mutual information of their evolutionary history embedded in their similarity score is Mycobacterium tuberculosis strain CDC1551 proteome to produce functional links between proteins from homology data, including pair-wise links from sequence similarity and protein family data derived from the InterPro database. Sequence similarity searches were carried out using BLASTP under a BLOSUM62 matrix based on the premise that if the The computation of relationship scores (as described in the methods section) was performed on the whole We investigated the general behaviour of the link confidence scores induced from homology datasets. Results are depicted in We compared our approach for scoring functional interactions inferred from sequence similarity to the STRING homology scoring scheme. STRING is a database of known and predicted protein-protein associations for a large number of organisms derived from high-throughput experimental data, the mining of databases and literature, and from predictions based on genomic analysis. For this assessment we used only their links derived from homology data, which uses a scoring scheme based on E-values obtained from the Smith-Waterman algorithm with a reasonably strict cut-off score to ensure high quality matches The STRING scheme classifies its functional link confidence scores into three different categories, low, medium and high confidence, with corresponding scores less than 0.4, between 0.4 and 0.7, and greater than 0.7, respectively The SFSP as defined by equation (2) may produce several link scores for the same number of shared domains, we have considered the maximum score when over-estimating, their minimum when underestimating and their average score, referred to as SFSP-Max, SFSP-Under and SFSP-Mean, respectively. We plot the scores obtained using our approach and these from SFSP, and results are shown in We evaluated the statistical significance and biological relevance of the functional interactions inferred using our scoring approach in terms of functional classification coherence. To measure this, an interaction between two proteins is said to be significant or correct if these proteins belong to the same functional class.http://genolist.pasteur.fr/Tuberculist), and the repartition of interacting proteins in the functional network per functional class or category for different configurations is shown in The functional classes were extracted from Tuberculist  curve analysis that measures the number of true positive (TP) predictions  against the number of false positive (FP)  For a given number of P-subgraphs ranging from We performed a general analysis of the homology-based functional network produced by integrating into a single network all functional interactions inferred from sequence similarity and protein family and domain data using our scheme. The number of functional links in the combined network, which contains a total of Several approaches have been proposed for predicting protein functions from functional networks and are mainly classified into two categories, namely global network topology and local neighborhood based approaches. Global network topology based approaches use global optimization In the case of local neighborhood based approaches, known as ‘Guilt-by-Association’ or ‘Majority Voting’ or ‘Neighbor Counting’ The functional network produced from sequence data was used to predict, where possible, the functional class of proteins in the Tuberculist unknown functional class using a local neighborhood based approach. Through this, a new functional class is assigned to an unknown protein based on the functional class frequently occurring among its direct interacting neighbors. In this case, the score of a given functional class Since the objective is to assign to an unknown protein only one functional class, we make use of global network information, and the prediction of a given protein functional class is based on an over represented functional class found amongst its direct neighbors. The functional class with the largest chi-squared score is assigned to the protein. The chi-square score of functional class As an illustration, protein ‘fadA6’ (MT3660 or Rv3557c), named Acetyltransferase FADA6 (UniProt accession P96834), which is involved in lipid metabolism , is funcOnce again, the classification performance of these approaches can be evaluated with modified ROC curve analyses. We used leave-one-out cross-validation to evaluate the efficiency of these prediction approaches at computing the number of proteins correctly classified and those incorrectly classified. Note that when using the level-We have developed novel information-theoretic based schemes for calculating the link confidence scores or link reliability for homology data, Mycobacterium tuberculosis strain CDC1551 to produce a protein-protein functional network. Results showed that the novel scheme is efficient and effective compared to the existing schemes and can be used to improve functional networks inferred from sequence data in terms of precision and coverage.We applied these schemes to the genome of We analyzed the global behaviour of the network obtained from the new scoring schemes. Furthermore, the functional network produced was used to classify proteins in the unknown class using a local neighborhood based approach extended to level-2 protein neighbors in order to improve genomic coverage.Currently, we are integrating into a single protein-protein functional network, all pair-wise functional interactions obtained from different data sources, including genetic interactions, and functional genomics data, in order to predict functions, where possible, of uncharacterized proteins in the genome and to study the biology of the organism.Table S1# scores of functional interactions derived from sequence data.(XLS)Click here for additional data file.


One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods. A cell is deemed to be the most basic construction unit of every living creature on this planet. Every living cell is composed of even more basic components, which are known as the subcellular compartments or subcellular organelles Changes in protein location are associated with a host of genetic disorders There are several experimental methods that can determine the protein subcellular locations. For example, in yeast, the subcellular location of proteins can be visualized systematically by fusion of each ORF (Open Reading Frame) to the gene encoding GFP (Green Fluorescent Protein), either through transposon mutagenesis or PCR (Polymerase Chain Reaction) tagging Unfortunately, all these experiments are costly and time consuming According to the cell biology, the proteins are usually synthesized in the cytosol and are transported to other subcellular compartments either during or after the translation However, due to the limitation of protein sequencing technology, the accuracy of the N-terminus of a protein sequence is not ideal, which restricted the application of signaling peptides based methods Rather than the above two sequence-based categories of methods, a number of sequence-based meta-predictors have been developed in the last few years. These works focused on developing voting schemes to combine the results of existing sequence-based predictors. Liu et al. proposed a weighted and adaptive voting scheme to integrate the prediction results of twelve independent predictors From the system biology point of view, the proteins within a cell do not work independently. They interact with different proteins under different conditions. Because the physical interactions between a couple of proteins actually implied that the physical distance between interacting proteins is very close, the interacting proteins tend to localize within the same subcellular compartments Several methods have been developed based on the protein-protein interactions in predicting protein subcellular locations. Scott et al. integrated protein-protein interaction as a module in their PSLT2 method to analyze the subcellular location in proteome-wide in yeast For almost all the existing methods, the protein-protein interaction network was used either as an independent predictor In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate other sequence based predictors. The results of sequence-based predictors were combined on the protein-protein interaction network. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. These interacting partners were not treated equally, as a weight parameter was given to every interaction between the given protein and each partner. Unlike existing methods, our method does not require the comprehensive knowledge of the protein-protein interaction network. Moreover, given a protein, our method does not require the experimentally annotated subcellular locations of all its neighbors to predict its subcellular locations.These characteristics of our method makes it possible to work on an incomplete and inaccurate protein-protein interaction network with only limited number of proteins annotated with subcellular locations. Besides, our method can be used as a framework to integrate multiple predictors. We demonstrate that, in human proteome, even with only one sequence based predictor, our method can improve its prediction performance with the help of protein-protein interaction data. Therefore, we can expect that our method can improve the prediction performance of most existing sequence-based predictors.The protein-protein interaction data were retrieved from BioGRID database version 3.2.96. The following filtering steps were carried out. (1) Only interactions between two human proteins were kept. (2) The interactions between two identical proteins were removed. (3) If some interactions appeared more than once in the dataset, only one interaction was kept. (4) The non-physical interactions were removed. After these filtering steps, there were 96967 interactions covering 13942 proteins remaining in the dataset. The dataset of this study can be obtained from the authors by email.The 13942 proteins were mapped to the UniProt database version 2013_07. These proteins in the protein-protein interaction network can be mapped to 18036 proteins in the UniProt database. The subcellular location annotations of these 18036 proteins were collected. If an annotation was marked as “Probable”, “By Similarity” or “Potential”, this annotation was discarded. The remaining subcellular location annotations were mapped to 11 different terms, including “Cell membrane”, “Cytoplasm”, “ER”, “Extracell”, “Golgi”, “Mitochondrion”, “Nucleus”, “Peroxisome”, “Lysosome”, “Endosome” and “Microsome”. The mapping was carried out following the keyword searching strategy as the state-of-the-art studies To avoid ambiguous descriptions, we termed the 13942 proteins as the “BioGRID proteins” and the 18036 proteins as the “UniProt proteins”. If a BioGRID protein can be mapped to one or more than one UniProt proteins, the subcellular location of this BioGRID protein is the collection of all subcellular locations of all mapped UniProt proteins. Otherwise, the subcellular location of this BioGRID protein is “Unknown”. There were 6951 BioGRID proteins that can be annotated from the above procedure. Among 6951 proteins, there were 4879 proteins with only one subcellular location, 1709 proteins with two locations, 286 proteins with three locations, 50 proteins with four locations, 24 proteins with five locations and 3 proteins with six locations. According to the locative protein concept There are a number of existing methods that can predict protein subcellular locations based on primary sequences. We find that the Hum-mPLoc 2.0 As indicated by existing studies, the reason why physical protein-protein interactions can be used to predict protein subcellular locations is that the physical locations of two interacting proteins are very close, which make them tend to localize within the same subcellular organelle ECC, which was originally proposed in the analysis of social networks ip and jp, the ECC of this interaction can be defined as follows:For an interaction between two proteins i,jz is the number of triangles that actually involve the edge connecting ip and jp in the network, id and jd the degrees of protein ip and jp in the network, respectively. The denominator means the number of triangles in which the edge connecting ip and jp may possibly participate at most.where G =   be a PPI network, where V is the set of n vertices and E is the set of edges. Each vertex represents a protein  and an edge i,jE indicates that protein ip and protein jp has a physical interaction.Before we describe our network based method, we define some notations as follows. Let ip∈V , there are m possible subcellular locations . In the current study, m = 11. Every protein can have one or more subcellular locations. The set of experimentally determined subcellular location of ip was denoted as SCL(ip). The set of Hum-mPLoc 2.0 predicted subcellular locations of ip was denoted as MP(ip). The set of Y-Loc predicted subcellular locations of ip was denoted as YC(ip). They are all subsets of L (i.e. SCL(ip) ⊆ L, MP(ip) ⊆ L and YC(ip) ⊆ L). If the experimental subcellular location does not exists, SCL(ip) = ∅. If the Hum-mPLoc 2.0 or Y-Loc cannot provide subcellular locations for protein ip, MP(ip)  = ∅ or YC(ip)  = ∅.For every protein kl  in experimental annotations (kS(SCL)), Hum-mPLoc 2.0 predictions (kS(MP)) or Y-Loc predictions kS(YC) as:We defined the set of proteins with subcellular location src ∈ {SCL, MP, YC}.where With all the above definitions, we now describe the network based method for predicting protein subcellular locations. Given a protein, its subcellular locations will be predicted based on the experimental and predicted subcellular locations of its interacting partners as well as the interactions between them. The sequence-based predictors can also provide predictions directly to the given protein. Our method considered all the above information to make the final predictions.up∈V, which has no experimental subcellular locations, we use the following steps to predict its subcellular locations.For every protein NE(up). We calculate the probability that observing any member of kS(src) in NE(up) as follows:Step 1: we find its neighbors in the PPI network. The set of the neighbors was denoted as where src ∈ {SCL, MP, YC}, and |.| the cardinal of a set.up to a subcellular locations kl can be computed as Step 2: the membership degree of a protein whereexp(.) the exponential function, and src ∈ {SCL, MP, YC}. Here NECC ≠ NECC, as we found that NECC can achieve better performance than ECC.src ∈ {SCL, MP, YC}, a set of subcellular locations can be predicted for protein up, as follows:Step 3: for every wheresrcNET(up) the set of predicted subcellular locations and C a parameter between 0 and 1.srcNET(up)  = ∅.In addition, if the following condition cannot be satisfied, we forced that srcθ is an integral parameter for every src∈{SCL, MP, YC}.where SCLNET(up), MPNET(up) and YCNET(up). The sequence based predictors can also provide direct predictions on up, as MP(up) and YC(up). We defined the following three sets SEQ(up), SEQNET(up) and NET(up):Step 4: the previous step provided three predictions: SEQ(up) is the prediction results from sequence information directly. The SEQNET(up) is the prediction results from network information with only the predicted subcellular location of the neighborhood proteins. The NET(up) is also the prediction results from network but with both the predicted subcellular locations and the experimental subcellular locations of the neighborhood proteins.The meaning of these definitions can be explained as follows. The kl∈L, kl belongs to the final predictions if and only if it satisfy either of the following two conditions: (1) kl∈NET(up)∩SEQ(up); (2) srcm  =  srcupper for all src∈ {SCL, MP, YC}. This can be represented as follows:For every PRED(up) is the final prediction results. When PRED(up)  = ∅, we used MP(up) as PRED(up), as Hum-mPLoc 2.0 performs better than Y-Loc.where C in SCLθ was searched from 1 to 20 with step 1. The parameter MPθ and YCθ was searched from 10 to 100 with step 10. Altogether 2000 trials were carried out. We finally achieved an optimized combination when C = 0.75, SCLθ = 1 and MPθ = YCθ = 60.In AIM), coverage (CVR), accuracy (ACC), absolute-true rate (ATR) and absolute-false rate (AFR). They can be formulated as follows:Jackknife test has been widely used by many investigators to examine the quality of various predictors, as summarized in a recent review n and m are the total number of proteins and subcellular locations respectively, andwhere AIMCVRACC reflects the average ratio of correctly predicted subcellular locations over the total locations including the predicted and the real ones ATRAFRAFR value indicates a better prediction performance. A more comprehensive discussion of these measures can be found in several reviews These measures can be interpreted briefly here. The ATR measure AIM, CVR and ATR, as the performance measures in our study.The performance measures, which were utilized by Hum-mPLoc 2.0, were based on the locative protein concept ip and jp, which are connected by an edge ijE in the protein-protein interaction network, we defined the co-localization score (ijQ) as follows: Given two protein SCL, ip and jp have the same meanings as in the Method section. The numerator of ip and jp both localized to. The denominator of ip and jp localized to. When ip and jp have identical subcellular locations, ijQ = 1. When ip and jp have no common subcellular location, ijQ = 0. When ip and jp have some common subcellular locations, but not identical subcellular locations, the value of ijQ indicates the fraction of the number of common subcellular locations over the total number of subcellular locations of ip and jp (i.e. 0 < ijQ < 1). Therefore, ijQ can indicate whether ip and jp tend to have the same subcellular locations. When ip or jp have no subcellular location annotations, ijQ cannot be computed.where ijE that connects two proteins with subcellular location annotations, a ijQ, as well as an ECC, can be computed. We plotted the average ijQ as a function of the average ECC in different ranges, such as [0,0.1), [0.1,0.2), …, [0.9,1.0). As shown in ECC<0.1, the average ijQ is about 0.41. When 0.1≤ECC<0.2, the average ijQ is about 0.48. The average ijQ continues to increase along with the ECC. When 0.9≤ECC<1, the average ijQ reaches 0.77. When ECC varies in different ranges, the linear correlation coefficient between the average ijQ and the average ECC is 0.96.For every edge With the above observations, it is reasonable to use ECC as an indicator to whether two interacting proteins tend to have common subcellular locations.The prediction performance of our method was estimated using jackknife test on our dataset. In order to compare prediction performances, the performance of Hum-mPLoc 2.0 and Y-Loc was also estimated on the same dataset.ATR of our method achieves 56.0%, while the ATR of Hum-mPLoc 2.0 is 51.4% and Y-Loc 47.4%. The ACC of our method achieves 70.0%, while the ACC of Hum-mPLoc 2.0 is 67.1% and Y-Loc 59.8%. The only measure that our method is slightly lower than Hum-mPLoc 2.0 is the CVR. Our method is 74.9% in CVR and Hum-mPLoc 2.0 is 75.4%. Since the ATR is the most strict and harsh measure of a predictor that deals with multi-label data As shown in Here, we provide some examples that our method gives better predictions than Hum-mPLoc 2.0. Because when our method cannot make predictions based on network information, we used the Hum-mPLoc 2.0 predictions as the final results, it is important to look into the details that how our method use network information to improve the prediction results of Hum-mPLoc 2.0.The first example is the BioGRID protein 107454. It can be mapped to UniProt protein P11802, which has two experimentally annotated subcellular locations, “Cytoplasm” and “Nucleus”, in the UniProt database. Hum-mPLoc 2.0 provided only one result “Nucleus” based on sequence information. Our method, based on the network information, supplied the “Cytoplasm” location.The second example is the BioGRID protein 107479. It can be mapped to UniProt protein P49715, which has only one experimentally annotated subcellular location, “Nucleus”, in the UniProt database. Hum-mPLoc 2.0 provided two subcellular location predictions, including “Cytoplasm” and “Nucleus”. Our method, based on the network information, removed the “Cytoplasm” location.The third example is the BioGRID protein 107693. It can be mapped to UniProt protein P25067, which has only one experimentally annotated subcellular location, “Extracell”, in the UniProt database. However, Hum-mPLoc 2.0 provided “Cytoplasm” as its prediction result. Our method, based on the network information, corrected this result to “Extracell”.There are a number of examples like the above three that our method actually provided better predictions than Hum-mPLoc 2.0. These better predictions can be achieved by supplying the extra predictions, removing the redundant predictions or correcting the wrong predictions.ATR is 48.6%, which is higher than the 47.4% of Y-Loc predictor independently. Again, without optimizing parameters, we directly applied our method with only Hum-mPLoc 2.0. Our method can achieve 54.9% in ATR, which is also higher than the 51.4% of Hum-mPLoc 2.0 predictor. A comprehensive performance can be found in In the above results, we integrated two sequence based predictors. Actually, our method can work with only one sequence based predictor. Without optimizing the parameters, we directly applied our method with only Y-Loc predictor. The Given that our method can be applied on single sequence based predictor and can improve its performance. It is interesting to investigate a tricky case, whether our method can be iteratively applied to further improve the prediction performance, as once a sequence based predictor was integrated into our method, the whole predictor can be integrated again as if it is another sequence based predictor.The iteration process was carried out as follows. In the first round of iteration, the method was identical to all the above, integrating two sequence based predictors. From the second round of iteration, the output from the last round would be used as the only integrated predictor in the method. Now, let us have a global view of the framework. We have illustrated an information flow chart in Although we only treat these sequence-based predictors as black-boxes, which require only protein sequences as input and give subcellular locations as output, we need to remind the readers that some characters of these sequence-based predictors should not be ignored. Some existing sequence-based predictors may use the input protein sequences to generate or to derive other types of features by querying the public databases. For example, the GO annotations, which were generated covertly in some predictors, may cause some potential bias in the results.In the second phase, there is a problem regarding the protein-protein interaction network. It is well known that the protein subcellular localizations were used as an approach to detect the protein-protein interactions. Therefore, from a view of machine learning, directly using these interactions may cause over-fitting problem. However, this problem does not exist in the current study. Among 96967 protein-protein interactions, there are only 255 interactions that are supported solely by co-localization experiments. These interactions make less than 0.3% of the whole dataset. After manually removing these interactions, there is no observable difference in most of the performance measures. The only observable difference is that the coverage (CVR) increased from 74.9% to 75.0%. Therefore, the risk of over-fitting in the current study can be eliminated.The final thought regarding this framework is how to characterize the protein-protein interactions. Theoretically, there should be infinite number of measures that could characterize a protein-protein interaction with a number. However, in the current framework, a feasible measure must satisfy the following conditions: (1) it can be calculated solely from the network, as the other types of knowledge may be inconsistent or unavailable to the protein-protein interaction network; (2) it must be highly correlated with the probability that the interacting proteins share subcellular locations; (3) the first two conditions must be satisfied even only incomprehensive and inaccurate protein-protein interaction networks are available, as the knowledge of protein-protein interaction network is still not comprehensive or accurate. As far as we know, the ECC is the only choice we have for this task. Therefore, we believe that, to some extent, ECC is an optimal choice in characterizing the protein-protein interactions in this study.In this paper, we proposed a method that can predict protein subcellular locations using protein-protein interaction network as well as the results of existing sequence based predictors. Unlike existing method using a voting scheme to integrate other existing predictors or the other PPI network based methods, the protein-protein interaction network does not provide predictions results all by its own, it works as an infrastructure to coordinate the prediction results of interacting proteins from sequence based predictors.We applied our method in the human proteome and protein-protein interaction networks. The results shows that our method can improve the sequence based predictions. Since our method can integrate any number of sequence based predictors, this method could serve as a common approach to combine the results of existing methods and improve the prediction performance of almost every existing sequence based methods.


Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.The FluShuffle and FluResort algorithms were tested with both experimental and simulated mass spectra of whole virus digests. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm employing a mixed protein Markov chain Monte Carlo (MCMC) method. FluResort utilizes those identities to calculate the weighted distance of each across two or more different phylogenetic trees constructed through viral protein sequence alignments. Each weighted mean distance value is normalized by conversion to a Z-score to establish a reassorted strain.The new FluShuffle and FluResort algorithms can correctly identify the origins of influenza viral proteins and the number of reassortment events required to produce the strains from the high resolution mass spectral data of whole virus proteolytic digestions. This has been demonstrated in the case of constructed vaccine strains as well as common human seasonal strains of the virus. The algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. Influenza is one of the oldest and deadliest infectious diseases known to man. Seasonal human influenza epidemics are responsible for over 250,000 deaths worldwide and over 3 million cases of severe illness each year -3. When Of the four influenza pandemics of the past century , at leasThe most conventional method to identify reassorted influenza viruses involves the construction of phylogenetic trees based on the alignment of gene sequences for each viral protein . Genes aGiven that full gene sequencing  of a larThere remain advantages to studying protein over gene sequences for monitoring influenza strains and establishing reassortment  due to tWe have recently developed a new rapid and direct proteotyping approach with which to characterize the influenza virus -30. BrieHere two new algorithms, known as FluShuffle and FluResort, are described which have been specifically written to identify reassortant influenza viruses from such data sets. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm. FluResort maps those identities onto phylogenetic trees, constructed through viral protein sequence alignments, to calculate the weighted distance of each across two or more different trees. Each weighted mean distance value is normalized by conversion to a Z-score that is used to establish the probability of a reassorted strain.The overall computational approach is shown in Figure in silico proteolytic digest of all non-redundant complete sequences was performed based on the specificity of trypsin or Glu-C endoproteinases (D and E cleavage). Autolysis products for these enzymes were also included. Peptides resulting from N-terminal post-translational cleavage were included in this dataset while all peptides with unknown residues were discarded. The theoretical monoisotopic mass was calculated for each protonated peptide ion [M + H]+ with and without methionine oxidation, N-terminal pyroglutamate formation and cysteine carbamidomethylation.Viral protein sequence data derived from the NCBI Influenza Virus Resource , and thoThe assignment of peaks in a mass spectrum consisting of a mixture of viral proteins is not trivial. A simple naive approach, where the distribution for each protein is estimated separately, will fail to account for the possibility that the peaks may originate from other proteins. This leads to incorrect assignments . FluShufθ, is the probability that the combination is present in the sample given the mass spectral data D. The expression for the posterior probability in equation 1 is a modified version of the posterior function presented in the ProFound algorithm [The posterior probability for any given combination of proteins, lgorithm .r is the number of peaks in the spectrum that were matched to the set of theoretical peptides from all accessions in θ. w is the number of unmatched peaks. N Is the total number of theoretical peptides produced from digestion of all proteins in θ. αi is the number of theoretical peptides matching to peak i. σi is the maximum mass error for peak i in Daltons. ΔM is the mass acquisition range. The posterior probability P(θ)) and likelihood functions according to Bayes’ theorem.σi is very small. This results in a large ‘Note from equation 1, that as r increases (N-r)! will decrease. However, this is offset by the probability of a random peak. For two possible accessions, A and B, if A has a one extra peptide which matches to an observed peak, r increases by 1 and the value of (N-r)! decreases. However, an increase in r results in one more product to multiply within the ‘Larger values of the posterior probability represent an improved fit to the observed data. Increasing the number of matched peaks and decreasing the number of unmatched peaks will increase the posterior probability. The prior probability enables information about the strain to be included in the identification process. For example, the expected similarity between influenza strains of consecutive seasons can be used to define priors such that viral protein accessions from the previous season have a higher prior probability. The default priors are the historical frequencies of each accession in the database.FluShuffle uses a Gibbs sampling algorithm to estimate the marginal posterior probability for each known accession. The marginal posterior probability simply represents the probability of that accession being present in the sample given the data. A higher probability indicates that there is more evidence for the presence of that particular accession. The Gibbs sampler is chosen as it can handle many parameters (i.e. proteins) simultaneously.1, s2, … sn} where si is the current accession for protein i. At a new iteration, each accession in the database for protein 1 is combined with {s2, … sn} to generate a combination that is supplied to equation 1. This produces a conditional probability  for each accession of protein 1. A new accession is then randomly chosen with probability of selection equal to the conditional posterior probability for each accession. This new accession is used to replace S1 in the combination. This process is repeated for protein 2 except that each accession for protein 2 is combined with the updated accession for protein 1 as well as before calculation of the conditional posterior. More generally, the conditional posterior is calculated for each accession of protein i based on the updated values for proteins 1,2,…,i-1 and the existing values for proteins i + 1, i + 2, …n. The iteration is complete when the accession for each protein is updated.The Gibbs sampler algorithm generates a new combination of accessions at each iteration step. Let the combination of accessions be described by {sFluShuffle repeats the process of step generation for a user-defined number of iterations (default is 5000) to produce a “chain” of steps. The Gibbs sampler is designed such that steps to high probability solutions are more likely to be generated than steps to low probability solutions. To avoid a biased posterior estimate, the first 10% of steps were discarded as burn-in. The MCMC algorithm nominates a starting combination of viral proteins (randomly or otherwise) and traverses the solution landscape by switching proteins to identify higher posterior probability solutions. In theory, if the algorithm were to run indefinitely, it doesn't matter if the starting combination is a low probability solution. In practice, due to run time limitations and computing power, there is some bias afforded by the starting combination that may never be visited during the rest of the run. To remove this bias, the first 10% of the steps of the run were discarded.The remaining steps were used to estimate the posterior probability for each accession based on the proportion of steps that contained that accession. Accessions that match more peaks or match uniquely to a peak will be selected more often at each step in the Gibbs sampler as they have a higher conditional posterior probability. This means that they will be present in more steps and will have a higher posterior probability estimate.Once the identity of the proteins arising from the virus digest has been established by FluShuffle, these identities are used to establish whether the virus is a reassorted strain. To facilitate this, the FluResort algorithm was developed. To establish a statistical model for the likelihood that a virus has been reassorted, the phylogenetic relationship between the strain of each identified protein and all other strains must be determined. To this end, the patristic distance derived from the phylogenetic tree of each viral protein was used.i, FluResort calculates the patristic distance d to an observed protein accession from FluShuffle j using the sum of branch lengths from i to j in the phylogenetic tree. The distance is then weighted by the posterior probability of pj in order to account for uncertainty from FluShuffle identification. Since FluShuffle will typically identify multiple candidate accessions with varying posterior probability, the weighted mean distance (i.e. the disparity between the proposed strain identity and the observed accessions), xi is expressed as,For each accession K is the set of all accessions identified by FluShuffle.such that , is estimated using equation 3. This represents the uncertainty of disparity between each accession and the observed accessions. Uncertainty results in a large spread of distances as the observed accessions are spread throughout the tree. This results in a large variance.The variance of the weighted distances, Zi, for the accession i corresponding to each strain based on its weighted mean distance, Each weighted mean distance value calculated by the FluResort algorithm was converted to a Z-score (equation 5). The accession with the lowest weighted mean distance, The Z-score represents the fit of the proposed strain to the observed accessions. A higher Z-score corresponds to a poorer fit i.e. more evidence against the proposed strain. The difference between the protein accession and the lowest weighted mean distance, c, for that strain. The “best-fitting” strain is that with the lowest composite Z-score. This can be repeated for combinations of strains where each strain contributes a complementary subset of proteins. A combination of two strains represents a reassorted virus from one reassortment event, a combination of three strains represents a reassorted virus from two reassortment events and so on. Minimum composite Z-scores were compared across differing numbers of reassortment events to determine whether or not the virus was reassorted. The decrease in composite Z-scores with increasing reassortment number must be large enough to justify an increase in the number of parameters. This was assessed using the standard deviation of the composite Z-scores (equation 5).The Z-scores for all accessions corresponding to a strain are then summed to provide the composite Z-score, N, being examined assuming that the identity of each viral protein is independent of one another. The standard deviation of the composite, σc, can then be calculated as a function of N. A large difference in composite Z-scores was considered to be equal to or greater than The variance of the composite Z-score is equal to the sum of variances for each Z-score contributing to the composite. As each variance is normalized to 1, the sum of variances is equal to the number of proteins, The FluShuffle and FluResort algorithms were first tested with mass spectral data obtained from the digestion of a type A H1N1 strain produced from the reassortment of a 2009 H1N1 pandemic strain  and a lab-modified H1N1 strain (A/Puerto Rico/08/1934). It was produced for a vaccine (PanVax 2009) against the 2009 H1N1 pandemic swine-originating influenza virus (SOIV) strains and retains the surface viral proteins, hemagglutinin and neuraminidase, of the pandemic strain to elicit an immune response against the native strain.The FluShuffle algorithm was first used to perform a combined analysis on the high resolution MALDI mass spectra obtained from the respective whole virus digests of PanVax using trypsin and Glu-C endoproteinases Figure . MonoisoMonoisotopic mass values for 77 ion peaks were identified in the mass spectrum resulting from whole virus digestion with endoproteinase Glu-C Figure b. Most oOther viral proteins matched to fewer than 2 mass values in each spectrum. This resulted in low confidences for each predicted identity. The low number of matches is associated with the low copy numbers of the polymerase subunits, non-structural (NS) proteins and M2 protein within each virion. Smaller proteins like M2 and NS2 are also less likely to be detected, as they contain fewer tryptic peptides for matching.The predicted protein identities obtained from the mass spectrum by FluShuffle were visualised by edge colouration in the mid-point rooted phylogenetic tree for each protein. The HA protein was clearly sub typed as H1 Figure  with theThe nucleoprotein was clearly identified as originating from a type A strain Figure . Its preThe analysis was repeated using differing combinations of the data and the probabilities of the expected identities were determined as shown in Table The FluResort algorithm was used to determine the possibility of reassortment based on predicted protein identities. For simplicity, the analysis was limited to those that were confidently identified . All vaccine strains were banned to avoid a trivial match to the PanVax strain itself. The threshold value for reassortment of 4 proteins is 2√4 = 4 composite Z-score units (see equation 5). The composite Z-score without reassortment was 39 units greater than that of the fully reassorted combination . Segments of the hemagglutinin protein were matched to a combined 7 peptide ion mass values in the combined spectral data whereas the nucleoprotein was matched to only 2 peptide ion mass values in the tryptic digest spectrum only. The matrix M1 protein was matched to 2 peptide ion mass values in this spectrum   or singlThe PanVax H1N1 vaccine was donated by CSL Biotherapies, CSL Limited  and used without further purification. The vaccine contains the NYMC X-181 strain at a dose of 30 ng/mL of HA. The genomic segments encoding the HA, NA and PB1 proteins in the vaccine strain are derived from the pandemic H1N1 2009 strain A/California/07/2009. The other segments are derived from NYMC X-157, a lab-modified A/Puerto Rico/08/1934 strain. Accession numbers used for the expected viral protein identities were GenBank: ACP44189 (HA), ACT36688 (NA), ACF41835 (M1) and ABD77679 (NP).Strains B/Florida/07/2004 and A/Solomon Islands/03/2006 were obtained from Advanced ImmunoChemicals Inc.  as inactivated virus preparations from egg allantoic fluid. Strain B/Florida/07/2004 is a type B virus from the Yamagata 88-like lineage. The accessions used for the expected protein identities were GenBank: ACF54213 (HA), ACF54217 (NA), ACF54214 (M1) and ACF54218 (NP). Strain A/Solomon Islands/03/2006 is a seasonal virus with a H1N1 subtype. Accessions used for the expected viral protein identities were GenBank: ABU99109 (HA), ABU99068 (NA), ACD37437 (M1) and ACX46205 (NP).4HCO3, 10% acetonitrile and 2 mM dithiothreitol, pH 7.8). Sequencing grade porcine trypsin  or Glu-C from Staphylococcus aureus V8  was added at 6–14 ng/μL and the solution was incubated overnight at 37°C and 25°C respectively.A suspension corresponding to 3 μg of whole virus was concentrated to near dryness and resolubilized in 50 μL digestion buffer , spotted onto a MALDI sample plate  and air-dried at room temperature. MALDI FT-ICR mass spectra were recorded on a 7T Bruker APEX-Qe mass spectrometer  in the positive ion mode using a 35% laser power as previously described . Raw peaPeptide library files and the annotation library file were produced by PepGen. FluShuffle was used to identify viral proteins from the experimental peak lists of monoisotopic mass values. Analysis of peak lists was performed with consideration for variable methionine oxidation, variable pyroglutamate formation, a maximum of 1 missed cleavage and a mass tolerance of 5 ppm. Matches for both trypsin and endoproteinase Glu-C digests were simultaneously incorporated into the calculation of the posterior probability during Gibbs sampling. The information from multiple digests was integrated into one prediction for the viral protein identity.Predicted identities were plotted onto phylogenetic trees that were visualised using the Archeopteryx software. Phylogenetic trees were produced with FastTree 2.0  using frin silico from viral proteins and contaminant protein sequences. 500 random peak lists resulting from a simulated tryptic digest of viral protein sequences were generated using FluSim utilizing 5-20% sequence coverage with the addition of 20% spurious noise peaks, 0.1% contaminant coverage, variable methionine oxidation, variable pyroglutamate formation, a maximum of 1 missed cleavage and a mass tolerance of 5 ppm.Simulated peak lists were constructed for analysis by FluShuffle to evaluate its performance in predicting protein of known identities. FluSim generates random peak lists from the monoisotopic mass value for proteolytic peptide ions generated m/z value within a specified mass tolerance.One accession was randomly selected for each protein. Each accession was also associated with a set of peptide masses resulting from proteolytic digestion. A subset of the peptide ion masses was then randomly picked for inclusion into the peak list such that the specified sequence coverage was achieved. Noise peaks were then added at random across the mass acquisition range to the peak list. The peptide set for contaminants was also collated and contaminant masses were added according to the specified contaminant coverage. A random mass error was added to each FluShuffle was used to establish the expected protein identities as described above. A correct identification is defined as a predicted identity that is less than 0.05 substitutions per site from the expected identity.The FluShuffle and FluResort algorithms correctly identified the reassorted nature of the PanVax strain and the identity of the viral proteins that comprise it. Both of the seasonal strains studied were found not to be reassorted and the clade containing the strain under investigation was identified with 100% confidence in terms of the hemagglutinin protein in both cases, and with less confidence for other proteins where fewer peptides were detected and high sequence conservation exists among strains. In the case of highly related strains, with similar protein sequences, peptide segments that span regions of sequence difference must be detected in the mass spectrum in order for strains to be differentiated from one another. As peptides spanning the entire sequence are usually not detected, particularly in the mass spectra of multiple viral proteins from whole virus digests, the identification of a single strain or a set of strains within a clade may not be possible.Nonetheless, the algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. The FluShuffle algorithm extends the capabilities of the proteotyping approach, beyond the determination of viral type, subtype or lineage, by allowing the identification of the strain origin of each protein. FluShuffle can also perform a combined analysis of data from multiple proteolytic digests that outperforms the single protein approach common to protein mass mapping or fingerprinting algorithms . FluResoProject name: FluShuffle and FluResortProject home page:http://sydney.edu.au/science/molecular_bioscience/downard/flushuffle.htmlOperating system: web-based platform independentProgramming language: C++License: Free access for non-commercial use.The authors have no competing interests.KMD conceived the project, oversaw its design, coordination and progression and drafted the manuscript. ATLL and JWHW contributed to the writing of sections of the manuscript. ATLL designed, developed and tested the algorithms with the advice and participation of JWHW. ATLL prepared the virus digests while KMD carried out the mass spectrometry experiments. All authors read and approved the final manuscript.Figure S1. Phylogenetic tree for the neuraminidase protein.Click here for fileFigure S2. Phylogenetic tree for the matrix M1 protein.Click here for fileFigure S3. Phylogenetic tree for the hemagglutinin protein.Click here for file


GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM  and MK-TLM  for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar.Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology ( Hum-mPLocHum-mPLoc 2.0Recent years have witnessed much progress in computational modelling for protein subcellular localization signal peptidesequence domainPSSMk-mer GO) project has developed three structured controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner, and the GOA database GO terms to UniProt Knowledgebase (UniProtKB) entries GO terms of cellular component contains direct indicative information about protein subcellular location, GO has become a generally effective feature for the prediction of protein subcellular localization Hum-PLoc for human protein subcellular localization. The model consists of two parts: GO-based kNN and PseAAC-based kNN, and the latter part was designed to compensate for the model performance in the case of GO unavailability. To cover multiplex human proteins that reside in or transport across multiple subcellular locations, Shen HB et al Hum-mPLoc, which extended the number of subcellular locations from 12 to 14 and formally formulated the concept of locative protein and the success rate for multiplex protein subcellular localization. Hum-PLoc, Hum-mPLoc and the work GO information to train model, thus inapplicable to novel protein prediction. Many recent GO-based methods generally exploit the homolog GO information for novel protein subcellular localization Hum-mPLoc, Shen HB et al Hum-mPLoc2.0 for multiplex and novel human protein subcellular localization, where a more stringent human dataset with 25% sequence similarity threshold is constructed to train a kNN ensemble classifier. Hum-mPLoc2.0 incorporated those homologs with sequence similarity significant homolog  resides in subcellular locations: Cytoplasm and Cytoskeleton. High threshold of sequence identity, e.g. 60%, can not guarantee that no noise would be introduced to the target protein; (2) remote homolog ,O02766, Q63688 ,P22680,Q16850,O88962 , Q64505  , also reside in the subcellular locations: Endoplasmic reticulum, Membrane and Microsome. High threshold of sequence identity (60%) would filter out all the convergent remote homologs that are informative to protein subcellular localization, and thus no homolog knowledge would be transferred to the target protein P21291. We can see that both significant homolog and remote homolog can be convergent homolog, or divergent homolog in terms of protein subcellular localization, thus we should conduct homolog knowledge transfer in a proper way, so that the noise from divergent homolog can be effectively depressed. Mei S et al GO-TLM) to measure the individual contribution of GO three aspects to the model performance, where the kernel weights are evaluated by simplenonparametric cross validation. Mei S MK-TLM), which conducted improvements on GO-TLM from the two major concerns: (1) more rational noise control over divergent homolog knowledge transfer; (2) comprehensive survey of model performance, especially for novel protein prediction. However, many human proteins reside in or transport across multiple cellular compartments, and the proteins with multiple locations may help reveal special biological implications to basic research and drug discovery GO-TLM nor MK-TLM is applicable to multiple protein subcellular localization prediction.Although many protein sequence feature extraction methods have been successfully developed for protein subcellular localization, such as MLMK-TLM). Based on the work MLMK-TLM proposes a multi-label confusion matrix and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM gains more practical applicability. To validate MLMK-TLM's effectiveness, we conduct a comprehensive model evaluation on the latest human protein dataset Hum-mPLoc 2.0 In this paper, we propose a multi-label multi-kernel transfer learning model for large-scale human protein subcellular localization (iid), which doesn't hold true under many practical circumstances, especially in the field of biological data analysis. For example, the microarray gene expression data from different experimental platforms would be subjected to different level of experimental noise As a research field of machine learning community, transfer learning has attracted more and more attentions in recent years GO term. Such a way of transfer learning modelling is computationally simple and biologically interpretable. In order to reduce the risk of negative transfer, GO-TLMMK-TLMGO three aspects, target GO information and homolog GO information to the model performance. In this paper, we redefine confusion matrix, so that the GO kernel weights can be derived by cross validation for multi-label learning scenario.Transfer learning modelling is generally conducted around three central dogmas: (1) how to define the relatedness between domains; (2) what to transfer; (3) how to transfer. In our work, we explicitly define the relatedness between protein sub-families and super-families by protein sequence evolution, i.e. protein homolog. Evolutionally closely-related proteins share similar subcellular localization patterns with high probability. Correspondingly, what to transfer is naturally the homolog GO terms and the homolog GO terms, which are extracted from GOA database SwissProt 57.3 database PSI-Blastu GO terms ix , then protein GO term ix is assigned to the protein x in GOA database, then ix = 1; Otherwise, ix = 0. To expressly estimate the individual contribution of the three GO aspects, GO-TLMGO-TLM aggregated the target GO information and the homolog GO information into one single feature vector, such that the two kinds of GO information are treated equally. We know that such a way of feature construction is not rational because divergent homolog GO information carries much noise. P61221 thru. Q9Y2Q3) and its homolog protein. The homologs are queried against SwissProt 57.3 database Blast options . E-value is relaxed to 10 to obtain remote homologs for those proteins that have no significant homologs. For a target protein, we may encounter three cases for the selected homologs: (1) all homologs are significant homologs; (2) one part of homologs is significant homolog and the other part of homologs is remote homolog; (3) all homologs are remote homologs. Some remote homologs are convergent to the target protein in terms of protein subcellular localization , thus we should exploit the useful information from remote homologs; meanwhile, some remote homologs are divergent to the target protein, thus we should prevent negative knowledge transfer from the remote homolog. As compared to remote homolog, significant homolog is more likely to be convergent in terms of protein subcellular localization, but in some case, significant homolog is also likely to be divergent. divergent homolog for each target protein. The illustrated divergent homolog has the highest sequence identity and PSI-Blast E-value among the target protein's divergent homologs. From significant homologs reside in definitely distinct subcellular locations from the target protein, which implies that we should also depress noise from the significant homologs even though we encounter the above case (1). Similar to MK-TLMGO information from its homolog GO information for the convenience of noise control. Here, we use T to denote the target protein and H to denote its homolog, thus the target GO feature vector is expressed as formula (3), and the homolog GO terms are aggregated into one homolog feature vector as formula (4):All the proteins are represented with both the target GO feature vectors {GO kernels {GO kernels are further combined in the way that MK-TLM does GO kernel weight implies more positive knowledge transfer, and lower homolog GO kernel weight can depress the potential noise by divergent homolog. Different to MK-TLM, MLMK-TLM adapts confusion matrix to multi-label learning scenario based on the concept of locative protein MK-TLMGO-TLM and MK-TLM, the final kernel is defined as the following linear combination of sub-kernels:SE denotes recall rate or sensitivity and MCC denotes Matthew's correlation coefficient. The kernel weights k-fold disjoint parts. For each fold cross validation, one part is used as validation set and the other parts are merged as training set to train the combined-kernel SVM. Thus, we can derive a confusion matrix M by evaluating the trained SVM against the test set. From the confusion matrix M, we can derive the kernel's SE and MCC measure as follows:The six binary locative protein p is located at two subcellular locations locative protein means that protein p can be viewed as two different proteins SVM labels multi-label confusion matrix can be defined as follows:locative protein correct; otherwise, the prediction would be deemed as incorrect.In single-label learning scenario, Gaussian kernel is used here:As regards with kernel MK-TLMSVM probability outputs kNN nearest neighbour protein In our work, we extend K subcellular locations, for each subcellular location k, we view the proteins that belong to k as positive examples and the proteins that belong to other subcellular locations except k as negative examples, based on which to train one binary SVM. Thus, we have K trained binary SVMs. If each binary SVM outputs {−1, +1} labels, multiple {+1} outputs can be viewed as multiple protein subcellular locations SVM yields probability output, we can choose the label with the highest probability as the protein subcellular location, which is s-called one-against-all multi-class learning SVM {−1, +1} labels to posterior class probability as defined below:A and B can be derived from data by cross validation, and f(x) is uncalibrated decision value of binary SVM.Assuming there are SVM with probability output has been implemented into the LIBSVM tool (http://www.csie.ntu.edu.tw/~cjlin/libsvm/), which can be easily used for multi-label learning. Only if we set LIBSVM prediction option “-b 1” , we can obtain the probability vector that a query protein is predicted to each subcellular location. By setting optimal probability threshold, we can determine the optimal multiple labelling for the query protein based on the predicted probability vector.Actually, the one-against-all multi-class GO-based models only reported the optimistic performance by evaluating the proposed model against information-rich  test proteins, and seldom reported the performance for novel proteins optimistic performance is not enough to be a comprehensive survey of the model's true predictive ability, especially for novel protein prediction. MK-TLMoptimistic, moderate and pessimistic cases, and demonstrated good performance for novel proteins and those proteins that belong to the protein family we know little about. In this paper, the proposed MLMK-TLM inherits all MK-TLM's advantages. The Optimistic case means the training set and the test set both abound in GO information; the Moderate case means that the test set contains no GO information at all, which can be simulated by removing the test kernels Pessimistic case means that both the training set and the test set contains no GO information at all where the target GO information is removed from both the training set and the test set, which can be simulated by removing the training kernels The existing singlex protein (only one subcellular location) and multiplex protein (multiple subcellular locations), we should conduct two performance estimation experiments: one experiment is overall performance estimation on locative dataset, where multiplex protein is viewed as multiple singlex proteins as Hum-mPLoc 2.0 Virus-mPLociLoc-EukiLoc-VirusPlant-mPLocmultiplex proteins. The first experiment is similar to traditional supervised learning estimation except that multi-label confusion matrix is adopted instead (see formula 8 & 9); in the second experiment, cross validation is conducted on multiplex proteins only and the singlex proteins are always treated as training data. Thus, the whole training set is composed of two parts: fixed part from the singlex proteins and the variable part from the multiplex proteins. In addition, the model performance estimation in the second experiment is much more complicated. To simplify the formulation, lets' first give several symbol annotations: (1) multiplex protein p; (2) multiplex protein p; (3) P whose protein p satisfies the condition F; (4) logic AND. Based on the symbols, we can formally define Label Hit Rate (LHR), Perfect Label Match Rate (PLMR) and Non-target Label Hit Rate (NT-LHR) as follows:N denotes the number of subcellular locations a protein may reside in, with maximum value 4 here; n denotes the number of correct HITs or wrong HITS, with maximum value 14). The multiplex proteins in Hum-mPLoc 2.02, 3 and 4 labels (subcellular locations), respectively. We will report LHR, PLMR and NT-LHR on each subset. Take 2-label subset as example, the prediction may hit 0, 1 or 2 true labels. Low 0-label hit rate and high 1- and/or 2-label hit rate imply good model performance. However, the prediction may also hit 1∼12 non-target labels . High NT-LHR implies high misleading tendency, which should be as low as possible. The existing multi-label learning model for protein subcellular localization NT-LHR. If the prediction hits the true labels and yields no other misleading labels, we call the case perfect label match; otherwise, we call the case non-perfect label match. High Perfect Label Match Rate (PLMR) implies good predictive ability and low misleading tendency.The performance evaluation under multi-label learning scenario seems more complicated as compared to single-label learning scenario. Because the model performance estimation involves both MLMK-TLM is a relatively complex model that requires time-consuming computation for model comparison and model selection. Apart from SVM regularization parameter C and kernel parameter MLMK-TLM introduces a hyper-parameter N proteins in the dataset and the hyper-parameter sets are MLMK-TLM has to fix one hyper-parameter to optimize the other hyper-parameters, and in each iteration has to compute kernel matrices, thus the computational complexity is Hum-mPLoc 2.0 LOOCV) (Jackknife) as GO-TLMMK-TLMmultiplex proteins are divided into 5 nearly-even parts, one part as test set, and the other parts are merged with the singlex proteins into training set, thus iterates for 5 times until all the multiplex proteins participate in the performance estimation process , Specificity (SP), Matthew's correlation coefficient (MCC), Overall MCC, and Overall Accuracy. For multi-labelling estimation, we adopt LHR, PLMR and NT-LHR.For performance estimation on locative protein in the literatures locative proteins in the dataset Hum-mPLoc 2.0 multiplex human proteins, we don't use it to evaluate the multi-labelling, because the sequence similarity reaches 80%, so high as to yield performance overestimation.Shen HB et al optimistic case assumes that both the training set and the test set abound in target GO information, that's, the training proteins and the test protein by themselves contain rich GO information before incorporating the homolog GO information. We call this case MLMK-TLM-I. As shown in MLMK-TLM-I section of MLMK-TLM achieves 87.04% accuracy and 0.8606 MCC on Hum-mPLoc 2.0 human protein data, significantly outperforming the baseline Hum-mPLoc 2.0 62.7% Hum-mPLoc 2.0 aggregated the target protein's GO information together with the homolog GO information to train classifier, thus the overall accuracy 62.7% is the model's optimistic performance. The optimal hyper-parameter setting is GO information is transferred to the target protein. The high MCC value (0.8606) implies that MLMK-TLM achieves good predictive balance among the 14 human protein subcellular locations. We can see from MLMK-TLM-I section of MLMK-TLM achieves good model performance one most subcellular locations, even the small Peroxisome  and Microsome . MLMK-TLM relatively underperforms on the small subcellular locations: Endosome  and Synapse . The poor performance may result from the sources: (1) small number of training proteins; (2) the target GO information is not as rich as the other subcellular locations; (3) the homolog GO information may be more divergent.The moderate case, referred to as MLMK-TLM-II. Novel proteins generally have no GO information at all. Most of the existing GO-based models except the work optimistic as reported. Therefore, experiments should be expressly designed for the moderate case to test MLMK-TLM's applicability to novel proteins.The most common scenario we encounter may be that we have a plenty of well-annotated training proteins and need to label some novel proteins at hand. We call the scenario as moderate case seems more complicated than that for optimistic case, because the proteins in the test set have no target GO information. Thus, the three target test kernels Test denotes test set and Train denotes training set). For the reason, we substitute the homolog GO feature vector of test protein for its target GO feature vector to calculate the test kernel as follows:The test procedure for MLMK-TLM-II section of MLMK-TLM achieves 85.22% accuracy and 0.8411 MCC on the benchmark data, still significantly outperforming the baseline Hum-mPLoc 2.0 62.7% optimistic case . Except for small performance decrease, MLMK-TLM-II demonstrates similar behaviour to MLMK-TLM-I . The results show that MLMK-TLM is convincingly applicable to novel protein prediction, and the performance decrease shows that it is necessary to expressly report the model performance in moderate case.As shown in pessimistic case, where a protein subfamily or species is not GO-annotated at all, that's, we know nothing about the protein subfamily or species but the protein sequence information. The key point is whether the homolog GO information is informative enough to train an effective prediction model for the protein subfamily or species we know little about. To validate the point, we assume that at least one GO-annotated homolog can be queried for the target protein, which is not restrictive with the rapid progress of GOA database MLMK-TLM will gain much wider application. Different from the optimistic case and the moderate case, the pessimistic test procedure contains only three homolog GO kernels with target GO kernels missing.In this section, we study an extreme case, called MLMK-TLM-III section of MLMK-TLM achieves 83.97% accuracy and 0.8277 MCC on the benchmark data, significantly outperforming the baseline Hum-mPLoc 2.0 62.7% optimistic case  and nearly 1.5% lower than the moderate case . Similarly, MLMK-TLM-II demonstrates similar behaviour to MLMK-TLM-I & MLMK-TLM-I . The results show that MLMK-TLM is applicable to novel protein prediction for the protein subfamily or species that we know little about.As shown in Homolog is a good bridge for knowledge transfer between two evolutionarily- related protein subfamilies, super-families or species. However, biological evidences demonstrate that divergent homologs are subjected to different subcellular localization patterns from the target protein , moderate case (MLMK-TLM-II), and pessimistic case (MLMK-TLM-III). The model performance slightly decreases for the optimistic case (MLMK-TLM-I) with the incorporation of more homologs, while the model performance decreases sharply for the moderate (MLMK-TLM-II) & pessimistic case (MLMK-TLM-III). When the number of homologs reaches 7, the accuracy sharply drops about 15% for moderate & pessimistic case. We can see that divergent homologs adversely contribute little to the optimistic case, partly because the target protein's own GO information can counteract the unfavourable impact of the divergent homolog GO information. For the moderate & pessimistic case, the unfavourable divergent homolog GO information greatly deteriorates the model performance. From the results, we can safely conclude that it is highly necessary to quantitatively study how much homolog GO information should be transferred to the target protein.As shown in pessimistic case contains no target GO information but slightly outperforms the moderate case beyond our expectation (except at the first & second points of the curve in GO feature vector for the target GO feature vector results in the slight performance deterioration (see Formula 13).It is worthy noting that the GO kernel weights are evaluated using 3-fold cross validation as described in Section 3 of GO-TLMH makes the model selection more time-consuming. Actually, to evaluate the model performance, we conduct two-level cross validation: the outer 5-fold cross validation uses the whole dataset to evaluate performance, and the inner 3-fold cross validation uses the training set from the outer cross validation to estimate the kernel weights. Similar to GO-TLMMK-TLM5-fold cross validation is quite similar, so we choose one typical kernel weight distribution to illustrate the GO kernels' contribution to the model performance.The x axis denotes the six GO kernels, where T denotes target, H denotes homolog, F, C and P denote the three aspects of gene ontology , respectively. We can see that both the optimistic case and the moderate case have similar kernel weight distributions on the benchmark dataset, while the pessimistic case is similar to the homolog GO kernel weight distribution of the optimistic case and the moderate case (see the latter part of curve in pessimistic case contains only three homology GO kernels in that the target protein's GO information is missing). No matter the target GO kernels or the homolog GO kernels, C (cellular component) demonstrates much higher kernel weight. For optimistic case and moderate case, both the target GO kernels and the homolog GO kernels make equivalent contribution to the model performance  & PLMR (Perfect Label Match Rate) and lower NT-LHR (Non-target Label Hit Rate) defined by Formula 12 in Section 5 of LHR & PLMR also implies higher NT-LHR. In the work, the optimal probability threshold is selected from LHR, PLMR and NT-LHR  are validated as TRUE LABEL (Correct Prediction) by the latest Swiss-Prot database   see .MLMK-TLM achieves 58.54%, 27.19% and 0 LHR  for 2, 3 and 4 multiple subcellular locations (optimistic case), respectively  for moderate case  for pessimistic case  for pessimistic case seems better than the optimistic& moderate case, because of the probability thresholds: 0.09 for optimistic case, 0.08 for moderate case and 0.07 for pessimistic case. Relax probability threshold would yields higher Label Hit Rate (LHR), but would yields higher Non-target Label Hit Rate (NT-LHR) at the same time. From pessimistic case shows higher NT-LHR than the optimistic& moderate case. The complete label hit means that all the true labels are correctly hit by the prediction, but it can not measure the model's misleading tendency, because the prediction is still likely to hit non-target labels. Perfect Label Match Rate (PLMR) is the perfect measure that demonstrates the model's multi-labelling ability with zero misleading tendency. As shown thru. PLMR measure that the optimistic case is the best , the moderate case the second  and the pessimistic case the third . We can see that even MLMK-TLM's Perfect Label Match Rate is much better than the Partial Label Match Rate that was reported in the work perfect label match in optimistic, moderate and pessimistic case, and the detailed probability outputs for the perfect label match proteins see Supporting Information .As shown in vely see ; 56.87%,case see ; and 58.case see . The resMLMK-TLM's multi-labelling ability, we list some proteins of non-perfect label match in non-perfect label match proteins see Supporting Information . Take protein O43663 in the optimistic case as an example, O43663 is labelled Cytoplasm & Nucleus in the original Hum-mPLoc 2.0 dataset GOA database version 70.0 released March 10 2008), and the prediction not only hits the two true labels but also hit a non-target label Cytoskeleton with probability 0.136. From the latest Swiss-Prot database , we can see that Cytoskeleton is truly assigned to protein O43663. The non-target labels validated as TRUE Label are underlined in TRUE Labels in optimistic, moderate and pessimistic case. For example, Cytoplasm [0.166] & Extracell [0.103] for P41222 in the moderate case; Endoplasmic reticulum [0.071], Golgi apparatus [0.074] & Plasma membrane [0.096] for P42858 in the pessimistic case, etc., where the square bracketed number denotes probability. The underlined TRUE Labels demonstrates MLMK-TLM's generalization ability rather than misleading tendency. Actually, MLMK-TLM's misleading tendency is lower than the NT-LHR measures in Swiss-Prot database. No training proteins in Hum-mPLoc 2.0 dataset Nucleus, Cytoplasm, Endoplasmic reticulum, Golgi apparatus, Plasma membrane) as P42858, whereas MLMK-TLM can correctly hit the five labels with different confidence levels, which is hard to achieve by the nearest neighbour based multi-label classifiers Hum-mPLoc 2.0 web server (http://www.csbio.sjtu.edu.cn/bioinf/hum-multi-2/) labels O43663, P41222 and P42858 as follows: (1) O43663: Nucleus, without hitting Cytoplasm & Cytoskeleton; (2) P41222: Endoplasmic reticulum, Golgi apparatus and Nucleus, without hitting Cytoplasm & Extracell; (3) P42858: Cytoplasm, Golgi apparatus and Nucleus, without hitting Endoplasmic reticulum & Plasma membrane.To further demonstrate moderate and the pessimistic case, the test proteins' own GO information is removed for the simulation of novel proteins, whereas MLMK-TLM can correctly predicts the test proteins' true labels and underlined TRUE Labels as illustrated in MLMK-TLM has a good multi-labelling ability for novel multiplex human proteins. From MLMK-TLM shows a certain misleading tendency, for example, Nucleus [0.115] & Plasma membrane [0.181] for P30533 in the optimistic case; Endoplasmic reticulum [0.089] for Q14145 in the moderate case; and Endoplasmic reticulum [0.315] for Q9UHD9 in the pessimistic case, etc. Hum-mPLoc 2.0 web server (http://www.csbio.sjtu.edu.cn/bioinf/hum-multi-2/) labels P30533, Q14145 and Q9UHD9 as follows: (1) P30533: Plasma membrane, Endoplasmic reticulum and Extracells, hitting non-target labels Plasma membrane & Extracells; (2) Q14145: Cytoplasm and Endoplasmic reticulum, hitting non-target label Endoplasmic reticulum; (3) Q9UHD9: Cytoplasm, Endoplasmic reticulum and Nucleus, hitting non-target label Endoplasmic reticulum. Misleading tendency is an important factor that should be given attention for multi-label learning scenario. The advantage of probability outputs is to inform the biologists of the confidence level of each subcellular location, and thus help biologists make a rational decision.For both the MLMK-TLM), which o further extends our published work GO-TLM and MK-TLM to multi-label learning scenario, such that MLMK-TLM has the following advantages over the existing GO-based models LHR (Label Hit Rate), PLMR (Perfect Label Match Rate) and NT-LHR (Non-target Label Hit Rate). NT-LHR is formally formulated to measure the model's misleading tendency. The experiments show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database.In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization  of the proteins; and the third line gives the predicted label(s) of the protein. Each predicted label is followed by a squared bracketed number that denotes the probability the protein is predicted to the label.(DOC)Click here for additional data file.File S2Full list of perfect label match proteins in the modest case. The file format dittos.(DOC)Click here for additional data file.File S3Full list of perfect label match proteins in the pessimistic case. The file format dittos.(DOC)Click here for additional data file.File N S1Full list of non perfect label match proteins in the optimistic case. The file format dittos.(DOC)Click here for additional data file.File N S2Full list of non perfect label match proteins in the modest case. The file format dittos.(DOC)Click here for additional data file.File N S3Full list of non perfect label match proteins in the pessimistic case. The file format dittos.(DOC)Click here for additional data file.