Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes.Based on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.The online version of this article (doi:10.1186/s12920-016-0228-z) contains supplementary material, which is available to authorized users. Mass spectrometry (MS)-based proteomics is becoming increasingly important in contemporary biological and clinical research . Yet, deProteomics allows the simultaneous expressional profiling of thousands of proteins . The first order of business is usually to identify proteins which are strongly and consistently differential, with the expectation that these are phenotypically relevant. This process is known as “feature selection”, and helps to concentrate analysis on a smaller feature (protein) set which is easier to study, understand and validate experimentally . Unlike Contextualization at the level of subnets, or more specifically, protein complexes, can resolve proteomic coverage and consistency issues –18. Use t-test to generate a differential protein list for subsequent enrichment analysis based on the hypergeometric test is a known contributing factor towards its high instability [HE is actually a two-part test see . But itstability , 28, andtability . We may QPSP, and the rank-based network approaches (RBNAs), SNET (SubNET) , FSNET .t-test differential protein pre-selection, a new spin on the original HE technique, Fuzzy-FishNET, is introduced here. Its name comes from the incorporation of the fuzzy weighting system in QPSP/FSNET/PFSNET with the one-sided Fisher’s exact test . Fuzzy-FishNET is evaluated based on precision and recall on three simulated datasets, and also its stability/reproducibility on real data.By incorporating the fuzzy weighting system into HE, and doing away with upstream Two simulated proteomics datasets, D1.2 and D2.2, from the study of Langley and Mayr are used . D.1.2 iFor both D1.2 and D2.2, 100 simulated datasets each with 20% randomly generated differential features are generated. The 20% threshold is arbitrary, for D1.2 and D2.2, this corresponds to 177 and 710 differential proteins respectively. For a given feature measured amongst samples derived from two different sample classes A and B, the effect size is the magnitude of the inter-class difference e.g. the differences of the means amongst samples derived from classes A and B. Here, the effect sizes of these 20% differential features are randomly selected from one out of five possibilities or p , increased in one class and not in the other, and expressed as:i,j and SCi,j’ are respectively the original and simulated spectral count from the jth sample of protein i.where SCRC1 comes from the 12 controls from the renal cancer (RC) dataset (see below). As with D1.2 and D2.2, 20% random proteins are randomly selected as differential, an effect size sampled from one of 5 possibilities, and inserted in only half of the controls, thus creating 6 control and 6 artificial test samples. This is also repeated 100 times to generate 100 simulated datasets.The renal cancer (RC) study of Guo et al.  is derivAll SWATH spectra maps are analyzed using OpenSWATH  against Although subnets or clusters are predictable from large biological networks, real biological complexes are enriched for biological signal, far outperforming predicted complexes/subnets from reference networks , 33, 34.To avoid high fluctuation in the test statistics used by some of the methods considered here (e.g. QPSP), complexes with at least 3 proteins that were identified and measured in the proteomics screen are retained (1363 complexes)t-test [N proteins (with B of these belonging to a complex) and n test-set proteins , the exact probability P that b or more proteins from the test set are associated by chance with the complex is given by [HE is a frequently used form of protein complex/subnetwork evaluation and consists of two steps : First, t-test . This isgiven by :\documenP(X ≥ b) is the p-value of the hypergeometric test.The sum t-test-based protein pre-selection step. In GSEA, a complex is tested by comparing the distribution of constituent protein expression between phenotype classes against that of proteins outside the complex using a Kolmogorov-Smirnov (KS) statistic [The direct-group (DG) analysis approach, Gene-Set Enrichment Analysis, or GSEA is developed as a more powerful alternative to HE, as it obviates the tatistic .D,D‘is expressed as:Denoting proteins in the complex as the set D and proteins outside the complex as the set D’, the KS-statistic KSFD1,(x) and FD *2,(x) are respectively the number of proteins in D and D’ that whose rank is higher than the rank x. The null hypothesis is rejected at an alpha of 0.05 ifwhere c is the critical value at a given alpha level. Here, at an alpha of 0.05, c = 1.36.where In QPSP, each sample is sorted based on abundance. The most abundant proteins above a certain percentile  are first selected. A second percentile value  is then used to extend the protein list . To penaAlpha1 and alpha 2 are typically set as the top 10%, and top 20% ranks respectively. Rank-based weighting is achieved via discretization of the ranks from top 10-20% into four bins: 10–12.5% (weight 0.8), 12.5% to 15% (0.6), 15–17.5% (0.4), 17.5 to 20% (0.2). All other proteins beyond alpha2 (viz. remaining proteins) have a weight of 0 (and are thus ignored). Proteins above alpha1 are assigned a full weight of 1.A) and a vector of complexes of length n, for each complex Ci in the complex vector, the hit-rate is the intersection of proteins in SA and Ci, modulated by the weight, over the total number of proteins in Ci.Given each sample, a vector of hit-rates is generated by considering the overlaps of the proteins (given their weights) against a vector of complexes. Given a sample in class A . Therefore, the vector of hit-rates for sample SA is HSA = 〈H…, H〉. This vector of hit-rates signifies the sample’s signature profile based on complexes.Let the hit-rate for St-test. For each complex C in the complex vector, two lists are compared against each other, HA = 〈H,…, H〉 and HB = 〈H,…, H〉, where A and B are phenotype classes of lengths m and n respectively. The t-statistic, t_score, between HA and HB is computed by:QPSP works with simple two-sample wherei is significant , then Ci is considered differential.If t_score for Cgi and a tissue pk, let fs = 1, if the protein gi is among the top alpha percent (default = 10%) most-abundant proteins in the tissue pk; and = 0 otherwise.In SNET, given a protein gi and a class of tissues Cj, letGiven a protein β is the proportion of tissues in Cj that have gi among their top alpha percent most-abundant proteins.That is, score be the score of a protein complex S and a tissue pk weighted based on the class Cj. It is defined as:Let fSNET for some complex S is a t-statistic defined as:The function mean and var  are respectively the mean and variance of the list of scores {score | pk is a tissue in #}.where S is considered differential (weighted based on Cj) in X but not in Y if fSNET is at the largest 5% extreme of the Student t-distribution, with degrees of freedom determined by the Welch-Satterwaite equation.The complex C1 and C2, the set of significant protein complexes returned by SNET is the union of {S | fSNET is significant} and {S | fSNET is significant}; the former being complexes that are significantly consistently highly abundant in C1 but not C2, the latter being complexes that are significantly consistently highly abundant in C2 but not C1.Given two classes FSNET is identical to SNET, except in one regard:fs is replaced such that fs is assigned a value between 1 and 0 as follows: fs is assigned the value 1 if gi is among the top alpha1% (default = 10%) of the most-abundant proteins in pk. It is assigned the value 0 if gi is not among the top alpha2% (default = 20%) most-abundant proteins in pk. The range between alpha1% and alpha2% is divided into n equal-sized bins (default n = 4), and fs is assigned the value 0.8, 0.6, 0.4, or 0.2 depending on which bin gi falls into in pk. This tiered weighting system is termed fuzzification.For FSNET, the definition of the function fFSNET is defined analogously to fSNET. Given two classes C1 and C2, the set of significant complexes returned by FSNET is the union of {S | fFSNET is significant} and {S | fFSNET is significant}.A test statistic fs function as in FSNet is used. But it defines a score delta for a complex S and tissue pk wrt classes X and Y as the difference of the score of S and tissue pk weighted based on X from the score of S and tissue pk weighted based on Y. More precisely: delta = score – score.For PFSNet, the same S is irrelevant to the difference between classes X and Y, the value of delta is expected to be around 0. So PFSNet defines the following one-sample t-statistic:If a complex mean and se are respectively the mean and standard error of the list {delta | pk is a tissue in Z}. The complex S is considered significantly consistently highly abundant in X but not in Y if fPFSNet is at the largest 5% extreme of the Student t-distribution.where C1 and C2, the set of significant complexes returned by PFSNet is the union of {S | fPFSNet is significant} and {S | fPFSNet is significant}; the former being complexes that are significantly consistently highly abundant in C1 but not C2, and vice versa.Given two classes In Fuzzy-FishNET, a gene, gi, in sample pk, is assigned a weight ff between 5 and 0 as follows: ff is assigned the weight value 5 if gi is among the top alpha1% (default = 10%) of the most-abundant proteins in pk .In simulated data, differential proteins are are defined Proteins in the same complex are expected to be expressionally correlated. To incorporate this principle in pseudo-complex generation, a Euclidean distance is calculated for all differential protein pairs across all samples. These are then clustered via Ward’s linkage. The differential proteins are reordered such that those with similar expression pattern are adjacent to each other. This reordered list is then split at regular intervals to generate 20, 101 and 62 differential pseudo-complexes for D1.2,D2.2 and RC1 respectively. An equal number of non-differential proteins are randomly selected, reordered based on expressional correlation, and then split to generate an equal number of non-differential pseudo-complexes.The purity of the pseudo-complexes is lowered by decreasing the proportion of differential proteins . This maThe differential and non-differential pseudo-complexes are combined into a single complex vector, which can be used for precision and recall-based evaluation of complex-based feature-selection methods:FS) which is the harmonic mean between precision and recall:where TP, FP and FN are the True Positives, False Positives and False Negatives respectively. Since precision and recall are both important performance measures, they can be combined to generate an average. A common way of doing this is the F-score :Reproducibility can be gauged based on the overlaps between technical replicates. To compare the technical replicates in RC, let TThis comparison may not be sufficiently robust and doesn’t allow evaluation at small sample sizes. So, one may perform resampling at different levels  and generate a binary matrix for each method, where a value of 1 indicates significance at a significance level of 0.05 and 0 otherwise per complex. The binary matrix may be analyzed in 2 ways: Row summation to evaluate the numbers of predicted differential complexes and column summation to evaluate the stability of predicted differential complexes .To demonstrate that Fuzzy-FishNET selects class-relevant differential complexes and only works when sample classes are real, cross-validation is performed 1000 times in two scenarios: where real classes exist(A) and where classes are shuffled/randomized (B) in RC. In each instance, half the data is used for feature-selection using Fuzzy-FishNET. A quarter of the remaining data is used for training, and the final quarter, validation. The classifier used is the deterministic Naïve Bayes method . Cross-vA good feature-selection approach will select features that can build highly accurate prediction models when true class labels are present. But if the class labels are shuffled, then this is expected to lead to strong decrease in predictive performance.The F-score distributions suggest that under noisier conditions (purity below 75%), Fuzzy-FishNET is an improvement over conventional HE and GSEA methods but overall appears to be a weaker method than earlier complex-based feature selection methods such as PFSNET Fig. .Fig. 2F-t-test), it is only powerful when purity is at 100%. If purity drops to 75% and below, GSEA’s F-score distributions quickly plummets. At purity of 50%, GSEA becomes the second worst method, beating only HE. Since noise and uncertainty in biological data is certainly expected, be it expressional or complexes, GSEA is unlikely a superior alternative to HE.HE typically has the worst F-scores over all methods surveyed but in actuality, does very well in precision , Fuzzy-FishNET falls behind SNET even . But as sample size increases to n = 6 (per class) in RC1, then Fuzzy-FishNET beats most methods, is comparable to FSNET but weaker than PFSNET.It is fascinating that swapping the differential protein selection step in favour of a fuzzy weighting system based on expressional ranks can greatly improve the precision-recall performance in HE. This is consistently observed over three sets of simulated data. However, there is no gold-standard for generating pseudo-complexes, nor is it known if the lack of biological coherence in the pseudo-complexes unfairly penalizes certain complex-based feature-selection approaches. Therefore it is also essential to consider results based on real data and real complexes for a comprehensive evaluation.Since technical replicates are present in RC. Each complex-based feature-selection method can be applied independently on each replicate. Inter-replicate overlaps is used as an indicator of complex-based feature-selection reproducibility.Reproducibility is a strength of Fuzzy-FishNET Table . MoreoveInter-replicate overlap is a good and simple way of evaluating reproducibility given that technical noise should be the only source of variability . But resampling at various levels to evaluate feature-selection stability is also possible. This is useful, as it also allows explicit evaluation of feature-selection stability in the small sample-size scenario .Given the full RC dataset, random resamplings of sizes 4, 6 and 8  followed by feature-selection are performed 1000 times. Two aspects are considered: the number of selected features at each resampling level, and the stability over all selected features.It is observed that HE, GSEA, Fuzzy-FishNET are particularly stable even as resampling size increases Fig.  Top. WhiOn the other hand, Fuzzy-FishNET’s feature-selection stability is second only to that of PFSNET’s. However, PFSNET is more affected by sampling size increments, and it also selects considerably more features than Fuzzy-FishNET. There is a possibility that PFSNET may suffer from higher hyper-sensitivity and therefore feature-inflation issues than Fuzzy-FishNET.Both PFSNET and Fuzzy-FishNET do very well on reproducibility. In the previous section, as PFSNET selects more features and appears to be more affected by sampling size increments, it is possible it is relatively hyper-sensitive, thus leading to feature-selection inflation.p-values distributions for intersecting and non-intersecting complexes are compared, revealing that Fuzzy-FishNET selects higher quality complexes  coupled to bootstrap resampling , the conMany Fuzzy-FishNET differential complexes are associated with ribosomal complexes , althougFuzzy-FishNET selected complexes that are also class-relevant, i.e., it doesn’t select features that are weakly associated with sample classes. The distribution of cross-validation (CV) accuracies when class labels are real, and when class labels are shuffled, reveals strong differences where the prediction model is far more accurate in the former than in the latter. If the feature-selection method selects a large number of irrelevant or weakly associated complexes (hyper-sensitive), then it is expected there will be little to no differences in CV accuracy between real and shuffled class labels.The positive impact of incorporating fuzzification in Fuzzy-FishNET is not known. It is possible the method may work just as well with a uniform weight of 1 across the top alpha%. So, an unweighted version of Fuzzy-FishNET, FishNET is tested. Note that Fuzzy-FishNET is analogous to SNET’s uniform weight of 1 for the top alpha proteins, and 0 for all others.Figure As with the RBNAs, a valid concern is that alpha adjustments may lead to highly different differential complexes being selected. Parameterization of alphas however is not fixed: Increasing alpha from top 10% onwards can increase sensitivity, but comes at the cost of introducing more false positives as signal from lower ranked proteins are introduced into the complex scores . However, we would expect the top scoring complexes to be stable.To examine this concern, the top ranked complexes generated from top alphas of 10, 20 and 30% is compared against the default Fuzzy-FishNET setting of alpha1 (top 10%) and alpha2 (top 10–20%) based on overlaps .The results are generally stable, with overlaps of 62% at alpha = 10, 64% at alpha = 20, and 69% at alpha = 30 respectively. However, alpha should not be set too low at the onset, as this will likely introduce many poorer quality complexes into the significant complex-based feature set too early into preliminary analysis.Fuzzy-FishNET is a powerful improvement over its predecessor, the hypergeometric enrichment (HE) approach. It differs only in the differential protein pre-selection step yet exhibits high precision-recall in simulated data while being the most reproducible over evaluations on real data. Based on cross-validations, it selects relevant features. Given these properties, Fuzzy-FishNET is a potentially powerful new entrant amongst complex-based feature-selection methods.


Homo sapiens, Saccharomyces cerevisiae, Mus musculus, and Drosophila show that DRBM achieves better performance than other related methods across different evaluation metrics, and it also runs faster than these comparing methods.Accurately annotating biological functions of proteins is one of the key tasks in the postgenome era. Many machine learning based methods have been applied to predict functional annotations of proteins, but this task is rarely solved by deep learning techniques. Deep learning techniques recently have been successfully applied to a wide range of problems, such as video, images, and nature language processing. Inspired by these successful applications, we investigate deep restricted Boltzmann machines (DRBM), a representative deep learning technique, to predict the missing functional annotations of partially annotated proteins. Experimental results on Proteins are the major components of living cells, they are the main material basis that form and maintain life activities. Proteins engage with various biological activities, such as catalysis of biochemical reactions and transport to signal transduction , 2. HighVarious computational methods have been proposed to predict functional annotations of proteins. These methods are often driven by data-intensive computational models. Data may come from amino acids sequences , proteinkNN classifier to predict novel annotations of a protein. Yu et al. [Functional associations between proteins and GO structure have been directly employed to predict protein functions –18. Funcu et al.  on the GA protein is often engaged with several biological activities and thus is annotated with several GO terms. Each term can be regarded as a functional label, and protein function prediction can be modeled as a multilabel learning problem , 22. FroHomo sapiens, Saccharomyces cerevisiae, Mus musculus, and Drosophila) to explore the hidden associations between proteins and GO terms and the structural relationship between GO terms. At the same time, it optimizes the parameters of DRBM. After that, we validate the performance of DRBM by comparing its predictions with recently archived GO annotations of these four species. The empirical and comparative study shows DRBM achieves better results than other related methods. DRBM also runs faster than some of these comparing methods.In this paper, we investigate the recently widely applied technique, deep learning , to captThe structure of this paper is organized as follows. Some pioneers have already applied deep learning for some bioinformatics problems , but fewDeep AE takes much more time in fine-tuning network; if the network is very deep, it will lead to vanishing gradient problem. In this work, we suggest to use deep restricted Boltzmann machines (DRBM), instead of AE, to predict functional annotations of proteins. DRBM has rapid convergence speed and good stability. DRBM has been used to construct the deep belief networks , for spe Homo sapiens, Saccharomyces cerevisiae, Mus musculus, and Drosophila and compare it with a number of related methods. The experimental results show that DRBM achieves better results than these comparing methods on various evaluation metrics.To the best of our knowledge, few teams investigate DRBM for large-scale missing functions prediction. For this purpose, we study it for predicting functions of proteins ofIn this section, we will describe the deep restricted Boltzmann machines to predict missing GO annotations of proteins.v ∈ {0,1}, and a set of hidden units h ∈ {0,1}. Input units and hidden units are fully connected; there is no connection between nodes in the same layer. In this paper, the number of visible units is equal to the number of GO terms, and these units take the protein function association matrix as inputs.A restricted Boltzmann machine (RBM) is a network of undirected graphical model with stochastic binary units . As showc (the number of GO terms) visible units and m hidden units in an RBM. vi   indicates the state of the ith visible unit, where vi = 1 means the ith term is annotated to the protein and vi = 0 means the ith term is not associated with the protein. Binary variable hj   indicates the state of hidden unit, and hj = 1 denotes the jth hidden unit which is active. Let Wij be the weight associated with the connection between vi and hj.  is a joint configuration of an RBM.RBM is an unsupervised method; it learns one layer of hidden features. When the number of hidden units is smaller than that of visual units, the hidden layer can deal with nonlinear complex dependency and structure of data, capture deep relationship from input data , and repθ = {Wij, ai, bj} are parameters of RBM, while ai and bj are biases for the visible and hidden variables, respectively. W ∈ ℝc×m encodes the weights of connection between c visual variables and m hidden variables. Then, a joint probability configuration of v and h can be defined asThe energy function capturing the interaction patterns between visual layer and hidden layer can be modeled as follows:Z is a normalization constant or partition function, Z = ∑v,heE−. The marginal distribution over visible data is where σ(x) = 1/(1 + exp(−x)) is a logistics sigmoid function. where It is difficult to train an RBM with a large number of parameters. To efficiently train an RBM and to optimize the parameters, we maximize the likelihood of visible data with respect to the parameters. To achieve this goal, the derivative of log probability of the training data derived from  can be aϵ controls the learning rate. Since there are no direct connections in the hidden layer of an RBM, so we can get an unbiased sample of 〈vihj〉data easily. Unfortunately, it is difficult to compute an unbiased sample of 〈vihj〉model, since it requires exponential time. To avoid this problem, a fast learning algorithm, called Contrastive Divergence (CD) [vi to 1 with a probability given by  , is propnce (CD) . CD setsgiven by . In addiIn this paper, we will use a fully connected restricted Boltzmann machine and consider learning a multilayer RBMs  (archived date: 2015-10-22), which describes hierarchical relationships between GO terms using a DAG. These GO terms are divided into three branches, describing molecular functions (MF), cellular component (CC), and biological process (BP) functions of proteins. We also downloaded the Gene Ontology Annotation (GOA) (archived date: 2014-10-27) files (http://geneontology.org/page/download-annotations) of Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Drosophila. We preprocessed the GO file to exclude the GO terms tagged “obsolete.” To avoid circular prediction, we processed the GOA file to exclude the annotations with evidence code “IEA” (inferred from Electronic Annotation). The missing annotations of a protein often correspond to the descendants of the terms currently annotated to the protein. So the terms corresponding to these missing annotations are located at deeper level than their ancestor terms, and these terms characterize more specific biological functions of proteins than their ancestors. These specific terms are usually annotated to no more than 30 proteins; they are regarded as sparse functions. On the other hand, root terms, GO:0008150 for BP, GO:0003674 for MF, and GO:0005575 for CC, are annotated to majority of proteins; the prediction on these terms is not interesting, so we removed these three root terms. We kept the terms annotated at least one protein in the GOA file for experiments. The statistics of preprocessed GO annotations of proteins in these four model species are listed in To study the performance of DRBM on predicting missing GO annotations of incompletely annotated proteins. We downloaded the GO file  of these four species to validate the performance of DRBM and processed these GOA files in a similar way. We use the data archived in 2014 to train DRBM and then use the data archived in 2015 for validation.L2 regularization is used on all weights, which are initialized randomly from the uniform distribution between 0 and 1. We set the hidden unit function as sigmoid and the number of hidden units as half of visible units and the number of the second hidden layer as half of the first hidden layer and so on. The number of hidden layers is 5. In the following experiments, to prevent overfitting, we used weight-decay and dropout. Weight-decay adds an extra term to the normal gradient. This extra term is the derivative of a function that penalizes large weights. We used the simplest L2 penalty function. As well as that, dropout is a regularization technique for reducing overfitting in neural networks by preventing complex coadaptations on training data [In order to comparatively evaluate the performance of DRBM, we compare it with SVD , NtN 1616, dRW , and AE ing data .MacroAvgF1, AvgROC, RankingLoss, and Fmax. These evaluation metrics measure the performance of protein function prediction from different aspects. The first three metrics have been applied to evaluate the results of multilabel learning [AvgROC and Fmax are recommended metrics for evaluating protein function prediction [MacroAvgF1 gets the F1-Score of each term and then takes the average of F1-score across all the terms. AvgAUC firstly calculates the area under receiver operating curve of each term and then takes the average value of these areas as whole to measure the performance. Fmax [RankingLoss computes the average fraction of wrongly predicted annotations ranking ahead of ground-truth annotations of proteins. To be consistent with other evaluation metrics, we use 1 − RankLoss instead of RankingLoss. Namely, the higher the value of these metrics is, the better the performance is. The formal definition of these metrics can be found in [The accuracy of protein function prediction can be evaluated by different evaluation metrics, and the performance of different prediction models is affected by the adopted evaluation metrics. To do a fair and comprehensive comparison, we used four evaluation metrics, learning . AvgROC ediction , 41. Macce. Fmax  is the ofound in , 22, 40. Homo sapiens annotated with BP, CC, and MF terms, respectively. The results on Mus musculus, Saccharomyces cerevisiae, and Drosophila are provided in Tables 3 boldface.Based on the experimental protocols introduced above, we conduct experiments to investigate the performance of DRBM on protein function prediction. In p values are all smaller than 0.004), and it also gets better performance than deep AE across these four metrics . dRW often obtains larger Fmax than DRBM; the possible reason is that dRW utilizes threshold to filter out some predictions and thus increases the true positive rate.From these tables, we can see that DRBM achieves better results than NtN, dRW, SVD, and AE in most cases. We further analyze the differences between DRBM and these comparing methods by Wilcoxon signed rank test , 43, we dRW applies downward random walks with restart on the GO directed acyclic graph to predict protein function; dRW takes into count the hierarchical structure relationship between GO terms and achieves better results than NtN and SVD. This observation confirms that the hierarchical relationship between terms plays important roles in protein function prediction. Although dRW utilizes the hierarchical structure relationship between terms, it is still a shallow machine learning method and it does not capture the deep associations between proteins and GO terms as DRBM does, so it is often outperformed by DRBM.The results of NtN and SVD are always lower than those of AE and DRBM. The possible reason is that singular value decomposition on sparse matrix is not suitable for this kind of protein function prediction problem, in which there are complex hierarchical relationships between GO terms. NtN uses the ontology hierarchy to adjust the weights of protein function associations, but it does not get better results than SVD. The reason is that NtN gives large weights to specific annotations but small weights to shallow annotations. From the true path rule, ancestor terms are generally annotated to more proteins than their descendant terms. For this reason, NtN is often outperformed by SVD and say nothing of AE and DRBM. Both AE and DRBM are deep machine learning techniques, but DRBM frequently performs better than AE. That is because the generalization ability of AE is not as well as that of DRBM, and AE is easy to fall into local optimal. In summary, these results and comparisons demonstrate that DRBM can capture deep associations between proteins and GO terms, and thus it achieves better performance than other related methods across different evaluation measures. DRBM is an effective alternative approach for protein function prediction. Homo sapiens and Mus musculus in GO BP subontology, since this subontology includes much more annotations and GO terms. The experimental platform is Windows Server 2008, Intel Xeon E7-4820, 64 GB RAM. The recorded runtime for these comparing methods is reported in Here, we study runtime (include training phase and test phase) cost of these comparing methods onFrom this table, we can see that DRBM is faster than these comparing methods, except SVD. NtN and dRW spend a lot of time to compute semantic similarity between GO terms, so they take more time than others. In contrast, SVD directly applies matrix decomposition on the protein function association matrix and the matrix is sparse, so SVD takes fewer time than DRBM. AE employs back propagation neural networks to tune parameters; it costs a large amount of time. DRBM utilizes Contrastive Divergence, which is a fast learning algorithm, to optimize the parameters, so its runtime is fewer than AE. This comparison further confirms DRBM is an efficient and effective alternative solution for protein function prediction. Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Drosophila shows that DRBM outperforms several competitive related methods, especially shallow machine learning models. This paper will drive more research on using deep machine learning techniques for protein function prediction. As part of our future work, we will integrate other types of proteomic data with DRBM to further boost the prediction performance.In this paper, we study how to predict additional functional annotations of annotated proteins. We investigate deep restricted Boltzmann machines (DRBM) for this purpose. Our empirical study on the proteins of


It is the first database to include theoretical models of transmembrane protein structures, making it a large repository with more than 11 000 entries. The TMPL database also contains experimentally solved protein structures, which are available as either atomistic or coarse-grained models. A unique feature of TMPL is the possibility for users to update the database by uploading, through an intuitive web interface, the membrane assignments they can obtain with our recent OREMPRO web server.Knowing the position of protein structures within the membrane is crucial for fundamental and applied research in the field of molecular biology. Only few web resources propose coordinate files of oriented transmembrane proteins, and these exclude predicted structures, although they represent the largest part of the available models. In this article, we present TMPL ( Transmembrane proteins are quantitatively important biological molecules which play various and central roles in numerous physiological processes, essentially as transporters, receptors and enzymes. The study of their function is critical for many industrial and medical applications, such as the development of novel drugs. However, understanding the function of these fat-soluble proteins is hampered by the technical difficulty of determining their three-dimensional structure and, to a greater extent, by the high difficulty of experimentally observing the orientation of the protein structure in the lipid bilayer. Indeed, proteins for which both the tilt angle and the hydrophobic thickness have been determined can literally be counted on the fingers of one hand . FindingBased on such algorithms, the PDBTM  and OPM One may regret that none of the existing databases includes predicted structures of transmembrane proteins, especially when considering (i) the few native structures available for these proteins, (ii) the steady progress in protein structure modeling and (iii) the growing number of theoretical models available in specialized databases, such as ModBase  and SWIStransmembrane protein models positioned in the lipid bilayer. The database includes atomistic and coarse-grained protein models—such as those used with the MARTINI force field , while theoretical models generated by comparative modeling were downloaded from ModBase (modbase.compbio.ucsf.edu) and SWISS-MODEL (swissmodel.expasy.org) databases. Additionally, de novo predicted structures were taken from the top-ranked models of the EVfold_membrane dataset  but can be modified on the OREMPRO web server. For all the membrane assignments in TMPL that have been automatically generated, the default membrane propensity scale has been used. When a user-deposited structure is oriented using a different scale, the modified propensity values is recorded in the TMPL database and displayed on the protein entry page , and ends by ′D′ (default) or ′G′ (geometric), this terminal letter corresponding to the methods used by the orientation algorithm to determine the membrane boundaries. Thus, the pattern of the accession codes for native structures can be written as: TM[PDB ID][AT|CG][D|G]. For predicted structures, the entry code includes the UniProtKB AC of the protein chain, the source of the model, i.e. ′S′ (SWISS-MODEL), ′M′ (ModBase), ′O′ (ORION), ′E′ (EVfold), or ′U′ (user-deposited), the number of the model , and the positioning method employed by ANVIL ('D' or ′G′). Thus, the pattern for predicted structures is: TM[UniProtKB AC][S|M|O|E|U][1-99][D|G]. Finally, when several valid orientations are available, a number is appended to their accession codes to distinguish them (see discussion).Thanks to the automated procedure for generating TMPL, our database will be regularly updated, with a frequency that depends on the source of protein models. Thus, every 6 months the PDB, ModBase and SWISS-MODEL databases will be searched for new structures of transmembrane proteins, which will constitute a major update. Smaller and more frequent updates of our database will be performed for every predicted structure that we will produce with ORION, and for every user-deposited model. Finally, the entries in TMPL may be subject to change, if any correction appears necessary. In such a case, the old entry page and its content will be superseded and unreferenced, while being still accessible through the new entry page. Of course, the creation date of each entry in TMPL is recorded and displayed on its page.The protein structures in TMPL can be accessed by filling a search form with any of several data types: the protein name or any keywords from its description, the organism (genus and/or species), the type of model , the structural representation (atomistic or coarse-grained), or the type of transmembrane protein structure . Users can also search TMPL by using entry codes from UniProt, PDB and Pfam databases. A quick search can be performed from anywhere in the website, by using the text area of the TMPL navigation bar, and will match protein keywords, organism, or entry codes. Alternatively to the search, our database can be browsed by either protein families or organisms. The protein families correspond to the Pfam families to which belong the transmembrane domains of the protein structures in TMPL. The other browsing option consists in an interactive phylogenetic tree displaying all the taxonomic ranks, with each terminal taxon being a hyperlink to the corresponding TMPL entries.Every entry page displays the following information about the protein structure : the namAllowing users to deposit models in TMPL—which is made easy by the connection with our OREMPRO web server—has a 2-fold purpose: (i) completing the database with native or predicted protein structures that have not yet been included and (ii) uploading structures of transmembrane proteins that are already part of TMPL, but with an alternative membrane assignment. Indeed, the inclination of a protein in a biological membrane is not a constant value, due to the thermodynamic entropy of the lipid bilayer environment , hence tThe protein structures included in TMPL have been selected for having the term ′transmembrane′ in their name or description. The use of this keyword approach is made necessary by the design of our ANVIL algorithm. Indeed, unlike the other automated methods that may fail at orienting predicted transmembrane structures of low quality , ANVIL has been aimed at systematically positioning the lipid bilayer planes, without trying to discriminate actual transmembrane proteins from others. This means that the current version of ANVIL cannot detect transmembrane proteins and that we have to rely on external annotations for selecting the structures to include in TMPL. As a consequence, TMPL may contain models that do not correspond to actual transmembrane proteins. These are essentially either structural predictions mistakenly annotated as ′putative transmembrane′, or truncated theoretical models of actual transmembrane proteins, e.g. those for which only the non-membrane (globular) domain has been modeled. These ′false positives′  will be discarded intermittently—whenever encountered or reported—or systematically, as soon as a new version of ANVIL can detect transmembrane proteins with a high statistical sensitivity, to avoid discarding true transmembrane proteins.By including theoretical models for transmembrane proteins of unsolved structures, in addition to atomistic and coarse-grained representations of experimentally solved structures, TMPL fills some of the gaps left by the insufficiently numerous databases of its kind. As a unique feature, TMPL enables users to enrich the database with their own membrane assignments of native or predicted structures, through its easy-to-use web interface and its connection with our OREMPRO web server. Thus, TMPL will centralize our current and future tools for studying transmembrane proteins, as well as our research on high-throughput structural modeling with ORION. This work places itself within the global efforts in structural genomics, focusing on the medically and industrially important transmembrane proteins.http://www.dsimb.inserm.fr/TMPL/. Its web interface is compatible with different browsers , on different operating systems  and platforms . The visualization of protein 3 D structures with the PV molecular viewer requires the browser to have WebGL enabled.The TMPL database is freely available at Database Online.Supplementary DataClick here for additional data file.


Cells execute their functions through protein interactions. The pathways they link are neither discrete nor spatially separated, as typically depicted in cellular diagrams. Cellular diagrams are useful; however, they neglect the physical structure of cell signaling. In reality, functions are shaped by molecular transitions between small—and large—supramolecular assemblies. Even though they are preorganized, they consist of clusters that are loose and dynamic. Importantly too, they are often anchored in the membrane and interact with scaffolding proteins and the cytoskeleton. Their continuum may physically span the cell. Indeed, efficient, productive, and reliable cell signaling can only take place through transient and cooperative protein–protein interactions, not through stochastic, diffusion-controlled processes. Despite this, current computational approaches to the modeling of the structural proteome still do not fully account for the in vivo, real physical cell organization. The enigmas of the assembly sizes and dynamic conformational distributions—and the diverse cellular environments that influence them—present daunting challenges, which we have only begun to address.How will we then compute the realistic structural proteome in the next decade? How will we overcome the challenges that we confront, and which methods should we develop to meet them? Clearly, our views of protein structure and function have undergone a revolution. We no longer believe that a protein exists in only two distinct (active and inactive) states. We now recognize that even though a specific function is executed by a distinct active state, proteins (and other bio-macromolecules) exist in ensembles of states. The structure–function paradigm that now dominates molecular biology was inspired by physics and chemistry, which stipulate that even living things must abide by the laws of quantum mechanics and structural chemistry. This paradigm argues that biomolecules should be viewed—and described—statistically, not statically. Though challenging, eventually, to realistically capture the functional versatility and model the working proteome, we must consider conformational ensembles and their allosteric shifts, which result in changes to the populations of the conformations. Moreover, within this framework, the heterogeneous cellular environments, as well as allosteric covalent post-translational modifications, cannot be overlooked. Have we indeed treated the structural proteome as such in our computations?Determining the structures of protein assemblies has long been a vastly important aim of structural biology. The problem is challenging: a pair of protein structures can interact by complementary patches of surfaces. The patch size and identity are unknown, and it is difficult to assess which patches on one protein interact with which patches on the other. In principle, each patch of surface of one protein needs to be matched with each of the other, while at the same time making sure that no other part of each of the proteins penetrates the volume occupied by the other. The problem is compounded by protein flexibility. Proteins are not static sculptures; instead, they are dynamical objects that are always interconverting between a variety of structures with varying energies. Association of two proteins (or biomolecules) involves matching of chemical and geometrical signatures out of the enormous number of possible ones for all conformers.The scientific community took up this challenge. Over 45 years, scientists have developed algorithms to predict, refine, and score molecular interactions, starting with Harold Scheraga’s landmark paper in 1972  in 1978.Despite these advances, at the dawn of 2017, the challenge is still there. Which directions to pursue in the next decade? Development of algorithms to increasingly, efficiently, and reliably exploit the emergence of data from electron microscopy (EM) maps to obtain small and large complexes; accounting for ensembles of states, which can also be helped by EM data as well as efficient sampling methodologies; developing of next-generation, multiscale simulation protocols for large assemblies with the ability to account for the different cellular environment; and more are all possible avenues of exploration. At the same time, there needs to be a sufficient level of detail to permit us to account for the effects of mutations. So far, structural modeling of protein complexes has largely assumed that the proteins interact in dilute solution. However, adequate representation of the crowded in vivo environment has to deal with much more complex systems, requiring coarse-graining and simplifications. Closing the gap between coarse-grain and atomic resolution methodologies will require development of multiscale approaches to introduce higher resolution structural information into large-scale modeling protocols. Put together, such approaches would potentially provide an integrated, self-consistent model of the interactome in vivo.PLOS Computational Biology, the premier computational biology journal, aims to help spearhead and map initiatives toward the development of transformative tools and methodologies (and their applications) to foster scientific advancements in the next decade. It aims to empower biological computations as a leading force in the life sciences. PLOS Computational Biology can drive research to fill fundamental knowledge gaps; PLOS Computational Biology aspires to serve as a forum that impels, formulates, defines and articulates, and, finally—conjointly with the experimental sciences—advances and guides to achieve these aims.Modeling the proteome forms the basis for diverse computational and experimental endeavors. Among these are prediction of function and elucidating functional mechanisms; figuring out signaling pathways in the cell and communication routes; drug discovery; and elucidation of disease, catalysis, viral packaging, and more.


Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing. Protein-protein interaction networks have been extensively used in systems biology to study the role of proteins in cell function and disease. However, current network biology studies typically assume that one gene encodes one protein isoform, ignoring the effect of alternative splicing. Alternative splicing allows a gene to produce multiple protein isoforms, by alternatively selecting distinct regions in the gene to be translated to protein products. Here, we present a computational method to predict and analyze the large-scale effect of alternative splicing on protein-protein interaction networks. Starting with a reference protein-protein interaction network determined by experiments, our method annotates protein-protein interactions with domain-domain interactions, and predicts that a protein isoform loses an interaction if it loses the domain mediating the interaction as a result of alternative splicing. Our predictions reveal the central role of alternative splicing in extensively remodeling the human protein-protein interaction network, and in increasing the functional complexity of the human cell. Our prediction method complements ongoing experimental efforts by predicting isoform-specific interactions for genes not tested yet by experiments and providing insights into the functional impact of alternative splicing. Protein-protein interaction (PPI) networks  have been extensively studied in systems biology to understand genotype-phenotype relationships and several have been constructed for different model organisms such as human, yeast and bacteria –10. The It is estimated that more than 100,000 AS events occur in pre-mRNA transcripts of human multi-exon genes , 23 and In this paper we present a computational method, named DIIP: Domain-based Isoform Interactome Prediction, that predicts the isoform interactome from an experimentally determined reference interactome, with application to human . StartinWe used the 11,557 DDIs combined from the 3did database of three-dimensional interacting domains  and the From each domain-resolved reference interactome, we predicted an isoform interactome that includes experimentally determined interactions between reference proteins, as well as predicted interactions for the alternative isoforms of these proteins, where the isoform data are obtained from UniProt . We predNext, we focused on protein pairs interacting with the same target protein in the reference interactome, and classified these protein pairs into those interacting with the same subset of isoforms of the same target gene, and those interacting with different subsets of isoforms of the same target gene . The vasWe retrieved Gene Ontology (GO) associations from the UniProt-GOA database  and cons−5 each), but the similarity is not significantly different in cellular component (p = 0.25) , but less similar in molecular function (p < 10−3). However, the difference is not statistically significant in biological process (p = 0.054) and all three GO categories combined (p = 0.71)  tend to share a smaller fraction of disease subnetworks than protein pairs interacting with the same subset of isoforms of the same gene   . Our res−3, two-sided bootstrap test with 100,000 resamplings; −5, two-sided bootstrap test with 100,000 resamplings; −3, two-sided bootstrap test with 1,000 resamplings; Since tissue expression strongly correlates with biological function and disease phenotype , 42, we Here, we present case studies of two types of protein pairs predicted by our method to interact with different subsets of isoforms of the same gene. We first looked at the epidermal growth factor EGF and the growth factor receptor-bound protein GRB2, which were predicted by our method to interact with different subsets of isoforms of the epidermal growth factor receptor EGFR (UniProt ID: P00533) . Our metWe also looked at the protein pair XIAP and APAF1 that were predicted by our method to interact with different subsets of isoforms of CASP9 (UniProt ID: P55211) . Our metOverall, these two case studies highlight specific mechanisms for how AS-mediated remodeling of interactions of the isoforms of the same gene leads to divergence in their biological function and disease phenotype, and illustrate that our computational method is capable of identifying biologically relevant isoform-specific interactions.−5, two-sided Fisher’s exact test), indicating that our prediction method is of high-quality, performing significantly better than random predictions. Since the benchmark experimental dataset may contain errors, the actual TPR of our method is expected to be even higher than the observed TPR.Here, we empirically assessed the quality of our isoform-interaction prediction method by validating it against the experimental dataset of Yang et al. (2016) , which iOur computational method for predicting isoform-specific interactions is reliable because it does not aim to predict new PPIs from scratch. Rather, it starts with the annotation of experimentally determined PPIs with known DDIs, and predicts the retention or loss of PPIs based on the retention or loss of the annotated DDIs in different isoforms. All these individual steps are expected to generate high-quality predictions; thus our overall isoform-interaction predictions are expected to be of high quality as well. Indeed, the high quality of our predictions is confirmed by experimental validations. At the same time, our method is limited in that it can only predict isoform interactions from known reference PPIs. If a reference protein does not have any interactions in the reference interactome, it is not possible to predict interactions for any of its alternative isoforms. Therefore, the size of the isoform interactome predicted by our method is ultimately constrained by the size of the reference interactome.Since our method predicts isoform interactions based on experimentally determined interactions between reference proteins, it is important to ensure the high quality of these experimentally determined interactions. Interactions in the HI-II-14 reference binary interactome are of high-quality since they were subjected to multiple screening and several other quality-control measures. On the other hand, interactions in the IntAct reference interactome were curated from different sources with varying quality. Furthermore, IntAct may contain indirect interactions between proteins in the same complex. To ensure the high quality of the IntAct-derived interactions, we only included physical interactions in IntAct reported by at least two independent experimental studies. In addition, our DDI-annotated interactome significantly enriches for direct binary interactions and filters out indirect interactions, as indirect interactions are much less likely to be annotated with DDIs than direct binary interactions.For reference PPIs with multiple DDI annotations, our method imposes a strict requirement that for an interaction to be lost, all DDI annotations of that interaction must be lost, otherwise the interaction is retained. Assuming that the interactivity of different domains is independent of each other within the same protein, this strict requirement maximizes the accuracy of predicted lost interactions, at the cost of possibly reducing the accuracy of predicted retained interactions. It should be noted that this is not a significant problem in our study, as about half of the PPIs in the HI-II-14 and IntAct domain-resolved interactomes have only one DDI annotation .While our predicted isoform interactomes reveal extensive network remodeling by AS, the ratio of remodeled protein pairs  is different in the two predicted isoform interactomes . This difference is due to systematic differences in the types of interactions reported in the two reference interactomes and in the fraction of protein hubs between the two domain-resolved interactomes. Since proteins with significant medical and scientific interests are intensely studied in the literature, protein hubs are much better annotated with domains in the literature-curated IntAct interactome than in the systematic HI-II-14 interactome. Indeed, protein hubs with vertex degree >23 (~5% of proteins in both binary interactomes) are 3.4 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome, whereas average proteins are only 1.6 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome. As a result, the fraction of protein hubs is significantly larger in the IntAct domain-resolved interactome than in the HI-II-14 domain-resolved interactome . A larger fraction of protein hubs in the IntAct domain-resolved interactome is the major cause for the observed larger ratio of remodeled protein pairs in the IntAct isoform interactome, since protein pairs interacting with different subsets of isoforms of the same gene can only be created by isoforms that lose some but not all interactions (which is more likely to occur for isoforms of a protein hub with many interactions), and cannot be created by isoforms that lose all interactions at once (which is more likely to occur for isoforms of a protein non-hub with few interactions). Indeed, the observed difference  in the ratio of remodeled protein pairs between the two isoform interactomes is almost completely eliminated  upon the removal of all protein hubs with vertex degree >5. This observed difference does not significantly bias our isoform-specific interaction predictions for the following reasons: we predict isoform-specific interaction retention/loss one isoform at a time based on sequence information only; no significant differences are observed between the two isoform interactomes (IntAct and HI-II-14) in terms of the fraction of isoforms per gene losing at least one interaction (8.2% and 8.6%) and the fraction of isoform pairs per gene with different interaction profiles (14.2% and 15.3%); we only draw conclusions from comparing protein pairs in terms of their biological properties, and from observations that are consistent between the two isoform interactomes.Detailed sequence information is available for some reference proteins, but not others. When the precise sequence information is not available, we chose the reference isoform designated by UniProt to represent each reference protein in the reference interactomes. These UniProt-designated reference isoforms are typically either the longest isoform (~88%) or the most prevalent isoform; hence they are most likely the ones used in different experimental studies to map the reference interactions. In the unlikely event that some reference proteins in the reference interactomes are represented by a different isoform than the one designated by UniProt, our method only requires the knowledge that a reference PPI exists, and can still work well without knowing the exact sequence of the reference protein. Given the experimental knowledge that a reference PPI exists, our method predicts that an alternative isoform loses this interaction if and only if the isoform loses all possible interacting domains present in the UniProt-designated reference isoform with typically the longest sequence, which in half of the cases is just one interacting domain. Hence, our predictions of interaction loss and retention remain very reasonable even in the presence of minor discordances in reference isoform annotations.In summary, we developed a domain-based computational method for predicting an isoform interactome from a reference interactome by integrating structural domain information with experimentally determined interactions. Our predictions reveal extensive remodeling of the human interactome network by AS: ~22% of genes with two or more isoforms in the predicted isoform interactome have at least one isoform losing an interaction, and ~18% of isoform pairs encoded by the same gene in the isoform interactome network have different interaction profiles. Our isoform-interaction prediction framework is of high quality as it performs significantly better than random predictions when assessed by experimental data. In addition, our predicted isoform interactome is larger and probes a different part of the isoform space than the experimental isoform interactome of Yang et al. (2016) . In term−5. After annotating each protein in the reference interactome with its structural domains, each PPI in the reference interactome was annotated with a DDI if one of the interacting proteins was annotated with an interacting domain of the DDI and the other interacting protein was annotated with the other interacting domain of the same DDI. Only PPIs annotated with at least one DDI were included in the domain-resolved interactome.Domain-domain interactions (DDIs) were retrieved from the 3did Database of Three-Dimensional Interacting Domains   associations from the UniProt-GOA database  (retrievhttp://www.orpha.net), CTD [We retrieved gene-disease associations from the DisGeNET database , 40 (retet), CTD , and theet), CTD . DiseaseWe used the RNA-Seq dataset of Illumina Body Map 2.0   , which crom 3did  and DOMIrom 3did  were thehttp://bioinfo.lab.mcgill.ca/resources/diip. The code used for predictions and analysis is available at the following URL: http://github.com/MohamedGhadie/isoform_interactome_prediction.We have created a web tool called “DIIP: Domain-based Isoform Interactome Prediction” that allows users to query our predicted isoform interactome for isoform-specific interactions of a protein of interest. In addition, our web tool gives users a second advanced option to predict isoform-specific interactions using our isoform interactome prediction method (DIIP) from interactions provided by the user. Interactions provided by the user do not need to be part of our predicted isoform interactome. The web tool can be accessed at the following URL: S1 TableAll interactions in the HI-II-14 domain-resolved reference interactome with their DDI annotations.(XLSX)Click here for additional data file.S2 TableAll interactions in the IntAct domain-resolved reference interactome with their DDI annotations.(XLSX)Click here for additional data file.S3 TableAll known and predicted interactions in the predicted HI-II-14 isoform interactome. Known interactions are the experimentally observed interactions also belonging to the HI-II-14 domain-resolved interactome. Predicted interactions are isoform interactions predicted to be either retained or lost.(XLSX)Click here for additional data file.S4 TableAll known and predicted interactions in the predicted IntAct isoform interactome. Known interactions are the experimentally observed interactions also belonging to the IntAct domain-resolved interactome. Predicted interactions are isoform interactions predicted to be either retained or lost.(XLSX)Click here for additional data file.


We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability  in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at  The accurate identification of proteins in a proteomics sample, called the protein inference problem, is a fundamental challenge in biomedical sciences. Current approaches are based on applications of traditional neural networks, linear optimization and Bayesian techniques. We here present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a standard proteomics mixture, given all protein sequences and a peptide profile. Comparison to leading methods shows that DeepPep has most robust performance with various instruments and datasets. Our results provide evidence that using sequence-level location information of a peptide in the context of proteome sequence can result in more accurate and robust protein inference. We conclude that Deep Learning on protein sequence leads to superior platforms for protein inference that can be further refined with additional features and extended for far reaching applications. PLOS Computational Biology Methods paper.This is a The accurate identification of proteins in a proteomics sample is a key challenge in life sciences. Proteins, the final gene product and the fundamental blocks of all cellular processes, are elusive to detect. The standard technology that provides fast, high-throughput characterization of complex protein mixtures is mass spectrometry-based shotgun proteomics. Initially, proteins are fragmented in small amino acid chains that are called peptides that then pass through a mass spectrometer. This results in a specific mass spectrum signature for each peptide, which correlates current intensity with a peptide’s weight and charge. Next, this signature is matched to a peptide database to identify which peptides are present in the sample (peptide identification step). Finally, the peptide profile is used to predict which proteins were more likely to produce the observed peptide set (protein inference step) . More prThe protein inference problem has been particularly challenging due to existence of degenerate peptides and ‘one-hit wonders’ . A degenSection 2.5, and sparsity-aware, redundancy-aware computations Section 2.7. In the following sections we present the DeepPep’s architecture, the inference algorithm and its application in seven benchmark datasets.To address these challenging aspects of protein inference, we have developed a deep learning technique, DeepPep, that uses the sequence information of proteins and peptides as inputs. Artificial neural networks have been applied in the past in proteomics, with applications such as the prediction of the retention time in LC-MS data , 11, preSection 2.6. The framework is composed of four sequential steps as shown in DeepPep is a deep learning-based framework that predicts the list of scored proteins (output) that are more likely to generate a given peptide profile (input). Each candidate protein is scored based on its effect on peptide probability predictions of the deep learning model when it is present/absent Section 2.2), the population of protein pairs having similar peptide matches (PCC > 0.7) represent on average below 0.05% among all possible pairwise comparisons of candidate proteins (S1 Table in There are two key ideas here that are translated to tasks for the CNN to (indirectly) learn: first, that a protein that matches multiple peptides in the sample is more likely to be part of the protein set that gave rise to that particular sample; second, a protein with non-overlapping peptide hits is more likely to exist in the sample than a protein with the same amount but overlapping peptide hits. “Non-overlapping peptide hits” are the peptide matches to a specific protein that do not coincide, that is, there is no position in the protein sequence where two or more peptides match. The more frequent occurrence of non-overlapping hits stems from the chemical treatment of the proteins; proteins are typically degraded to peptides by trypsin, which deterministically cleaves at the carboxyl side of the amino acids lysine or arginine (except when either is followed by proline ) and theWe used seven separate MS/MS datasets for evaluation purpose as follows:Sigma49: synthetic mixture of 49 proteins from Sigma Aldrich, made available by .UPS2: sample mixture consisting of 48 human proteins with various concentrations made available by .18Mix: synthetic highly purified extract of 18 proteins .Yeast: yeast cellular extract with reference generated by intersection of various MS and non-MS based techniques .DME: protein extract of Drosophila melanogaster S2 cells .HumanMD: protein extract of human medulloblastoma Daoy cells .HumanEKC: protein extract of human embryonic kidney cells T293 .S2 Table  first preparing the amino-acid sequences of all proteins that have at least one match for any of the observed peptides in the profile, (b) then replacing their amino-acid sequences of each protein with ones in any place where there is a full match of peptide ppi and zeros otherwise. In other words, xi is a set of vectors that contains the location where the peptide ppi matches to the organism’s proteome. The input yi is the probabilistic score of a specific peptide-spectrum match for peptide ppi, calculated from PeptideProphet  is the output string from position m to n for protein pj.Input:Output:xforj = 1 to |p| doxj to zeros where  Initialize form = 1 todo ifthen       end if   end for  end forThe deep convolutional network (CNN) is organized by a series of layers . Unlike X is the input, p is the index of protein, l is the index of the convolution layer, i is the index of the output position and f is the index of filters. Each convolution filter Wf,l is an w × n weight matrix of filter f at layer l with w being the window size and n being the number of input channels.The CNN uses three different types of layers: the convolution layer, the pooling layer and the fully connected layer. The convolution layer computes output by one-dimensional convolution operation with a specified number of filters (weight matrices) and all convolution operation outputs are then transformed by the rectified linear activation function (ReLU):pool (max-pooling) in a window with the specified length (w) for each filters computed from preceding convolutional layers. The step size is equal to the size of the pooling window. This reduces the size of the output to that of the window size and thus allows sequence learning patterns of higher abstraction in the next convolution layer:Xp,l is the input of protein p from preceding convolution layer l − 1, i is index of output position, f is index of filter, w is pooling window size.A pooling layer computes the function WlXl, where Xl is the combined input of all proteins from convolution layer l and Wl is the weight matrix for the fully connected layer. There is one weight matrix Wl corresponding to the output node of the output layer that calculates the predicted peptide probability. The optimal configuration with respect to the pooling function pool, number of filters, window sizes in convolution layer and pooling layer and number of nodes in fully connected layer was empirically determined (Section) and the final architecture is depicted in The number of filters for each of the four convolution layers increase at each level , so that higher-level convolution layers can produce more complex patterns. We added a fully connected layer over the fourth pooling layer, which is the high-level representation of information computed from the binary proteome sequence that encodes the input peptide profile. This fully connected layer computes the product i is the peptide index of all observed peptides and yi indicates the measured probability of peptide i, i. Derivatives of the objective function with respect to the model parameters are calculated and used in standard backpropagation [The objective function is the minimization of the sum of squared errors between predicted and measured peptide probability:pagation . For updpagation . This alpagation . We didnppj due to protein pi as:xj, pi) represents the predicted probability of peptide ppj in the absence of protein pi. The normalization quantity pi that were reset to zero to declare the absence of the protein. In other words, these are the number of amino acids in the protein that have a perfect match with the peptide ppj and it is always a multiple of its length. This normalization is necessary as the probability difference will increase with the number of zeros that we impose. Finally, the score of protein pi is assigned to be the average of ci,j for all j. The scores of all proteins p are the outputs of DeepPep.The key idea behind DeepPep is that if a protein is the origin of the input peptide, the peptide probability will depend on the presence or absence of the protein from the input. To quantify this, we define the normalized change in the probability of peptide Section 3.4). Next we explain how to take advantage of this sparsity in the context of Deep Neural Networks.Given the way DeepPep represents peptide-protein matches, the memory requirement for loading inputs can be massive. For example, the input for the Yeast dataset takes 26GB. Even worse, the memory that a deep network needs is several times more than the actual input size (for storing gradients in each layer). Furthermore, in terms of CPU, computation of convolution, linear transformations and gradients in the network given such data, can pose a limit to scalability of DeepPep. However, given that any protein only matches with a handful of peptides, the input to DeepPep is largely sparse (between 95% to 99% depending on the dataset) and therefore taking advantage of this property can greatly reduce the memory and computational overhead of DeepPep  term in all layers. Although this has the potential of limiting the modeling capacity of the network (e.g. negative correlations), our experiments did not show a practical difference for the given datasets. This can be due to i) input dataset being non-negative, ii) peptide abundance is expected to increase monotonically with added proteins. Hence we don’t expect to have negative trends.Avoiding gradient calculation: In DeepPep, only the Convolution and Linear layers have parameters (i.e. weights) for which gradient need to be calculated. When the module input is zero, there is no contribution of the back-propagated error to the local gradient, hence those calculations are omitted.N-1) fold improvement in terms of runtime where N is the number of candidate proteins.The final phase of DeepPep consists of calculating the share of each protein in predicted peptide probabilities. In a naïve implementation, for each protein the CNN is applied on all the inputs while peptide matches for the given protein is set to zero. To make this efficient, the CNN’s output of the layer prior to the final layer is first saved without setting any match to zero. Then for each protein, only the outputs corresponding to that protein are multiplied by their related weights in the final layer to calculate the share of that protein in the final prediction. This implementation provides  was all measured and compared on Two Intel E5-2630 v3 2.4GHz CPUs with eight cores with 64GB of RDIMM RAM.α, β, and γ using FidoChooseParameters with decoy dataset  and executed Fido with the optimal hyper-parameters for each dataset . Finally, we run MSBayesPro to estimate the protein priors and run again with priors to obtain the final protein probabilities.MSBayesPro  utilizesϵ = 0.001 and K = 100 in our experiment. From the peptide identification and peptide detectability files, we can calculate the λmax value. Then we measure λmin by assigning ϵλmax. K intervals are chosen from λmax to λmin on the log scale. Based on each λ value, ProteinLasso finally outputs a list of protein probabilities.ProteinLasso  formulatϵ = 0 in the experiment for ProteinLP as instructed from [ProteinLP  minimizeted from .Section 2.8. The “Data Preparation” step is done in Python while the training and protein inference using CNN architecture are implemented with the torch7 framework. For efficient implementation of convolutional layers, sliding windows between neighboring proteins are omitted to avoid any biases coming from concatenation of strings amongst heterogeneous proteins. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/.Hyper-parameter optimization of DeepPep was performed on parallel under the CPU environment on the NCSA Blue Waters supercomputer . The computing environment used for comparison of running time of protein inference methods was described in pool, number of filters, window sizes in convolution layer and pooling layer and number of nodes in fully connected layer. The performance of each configuration was evaluated using the target decoy approach on 18Mix dataset (Section 2.2). In this approach, the performance is measured based on how well the method differentiates target proteins from decoy proteins and therefore, this evaluation does not use the information of the true protein set. As shown in S4 Table  was investigated with respect to the pooling function Table in , DeepPepTable in .Section 2.2 as done for Fido Section 2.8.1. DeepPep ranks first by a small margin or ties with others in first place for four  out of seven datasets.S5 Table in S5 Table . ANN-Pep uses fully connected layers to connect inputs to outputs. The results  enables it to run between 2.5 minutes and 90 minutes depending on the size of dataset  in inferring proteins without need of peptide detectability on which recent methods mostly rely. This has significant implications in proteomics pipelines, where peptide detectability quantification is a major step. We also demonstrated the predictive value of the convolutional layers, by comparing DeepPep to various other ANNs, highlighting the importance of spatial dependencies in peptide/protein sequences.In performance comparison, while DeepPep was trained on the same datasets that required protein inference, the detectability-based methods (ProteinLasso and MSBayesPro) were executed with peptide detectabilities predicted using models trained on totally different datasets. As shown in , detectaThe architecture of DeepPep can be extended to predict quantities of proteins beyond identification of proteins. For example, one can use the concentration or count of each peptide as an informative feature for predicting the concentration of each protein in the original sample. Furthermore, although we have addressed scalability issues in DeepPep by employing the sparsity of proteome datasets, other advances to tackle computational complexity in deep learning, for example, distributed training  and optiWe would like to emphasize that application of a similar architecture to the one present in DeepPep can be introduced to solve biological problems beyond protein inference. For example, it can be applied in metagenome sequencing where genetic profiles are derived directly from environmental samples and there the task will be to identify the microbial consortia . SimilarS1 Text(DOCX)Click here for additional data file.