Salmonella Typhimurium and the plant host Arabidopsis thaliana which is a host-pathogen pair for which no known PPIs are available. To achieve this, we present approaches, which use homology and statistical learning methods called “transfer learning.” In the transfer learning setting, the task of predicting PPIs between Arabidopsis and its pathogen S. Typhimurium is called the “target task.” The presented approaches utilize labeled data i.e., known PPIs of other host-pathogen pairs . The homology based approaches use heuristics based on biological intuition to predict PPIs. The transfer learning methods use the similarity of the PPIs from the source tasks to the target task to build a model. For a quantitative evaluation we consider Salmonella-mouse PPI prediction and some other host-pathogen tasks where known PPIs exist. We use metrics such as precision and recall and our results show that our methods perform well on the target task in various transfer settings. We present a brief qualitative analysis of the Arabidopsis-Salmonella predicted interactions. We filter the predictions from all approaches using Gene Ontology term enrichment and only those interactions involving Salmonella effectors. Thereby we observe that Arabidopsis proteins involved e.g., in transcriptional regulation, hormone mediated signaling and defense response may be affected by Salmonella.We consider the problem of building a model to predict protein-protein interactions (PPIs) between the bacterial species  Salmonella is one of these bacterial species with extremely broad host range that infects not only animals, but also plants  Salmonella actively invades plant cells, proliferates there and can cause disease symptoms  are known to date. Because there exists no plant-Salmonella interactions data, we need to rely on computational methods to predict them [reviewed in the accompanying paper  to the plant-Salmonella protein pairs. This similarity is computed using the features of the protein-pairs. Since it is distributional similarity, it involves a comparison over all protein pairs from both organisms. Only the most relevant human-Salmonella protein pairs are used to build a model.Distributional similarity between the protein pairs: here, we identify which of the human-The main contributions of this paper are:We present methods that combine known PPIs from various sources to build a model for a new taskWe evaluate our methods quantitatively and our results show the benefits in performance that are possible if we incorporate the similarity information discussed in the previous paragraphsSalmonella PPIs.We present the first machine learning based predictions for plant-In the rest of the paper, we start by describing the host-pathogen PPI datasets we use in Section 2, followed by a detailed description of our methods in Section 3 and a quantitative and qualitative analysis of the results in Section 5.Salmonella we use the manually literature-curated interactions reported in Schleker et al. . Both techniques use two sources of interactions: true PPIs from Schleker et al. , here we use them to generate “bootstrap” interactions. The known 62 PPIs are used to build a classifier using the method published in Kshirsagar et al.   and sh is the host protein. Let the target task example be the protein pair < ts, ta > where ta is the Arabidopsis protein; and the corresponding feature vector be ktx. The kernel function that computes the similarity between the given two pairs of proteins  is defined as shown below.Figure sim is computed using the bit-score from BLAST sequence alignment, normalized using the sequence length of the larger protein. We used the SVMlight package  was done using cross validation on the PPIs where we have the true labels. We found C = 0.1 was the best setting. This best model is subsequently used to generate predictions on all Arabidopsis-Salmonella protein-pairs. The model outputs a score indicating the distance from the classifier hyperplane. A positive score indicates that the protein-pair is on the positive side of the hyperplane and hence closer to the known interacting protein-pairs. All such protein-pairs will be considered as potential interactions predicted by this model.The first equation is used in the case where the two protein pairs come from different tasks. We use homology-distance between the pathogen proteins and the host proteins to compute the kernel. The homology distance itself is simply the BLAST protein sequence alignment score. The next two equations show the computation when the examples both come from the same task. Here we simply take the dot product of the two feature vectors. This kernel is symmetric. The similarity between two sequences Our transfer learning scenario here consist of the following setting: multiple “source” tasks with small amounts of labeled data, a single “target” task with no labeled data. The first challenge is to pick the best instances from the source tasks, such that the resultant model when applied on the target task generates high confidence predictions. Toward this, we use the instance reweighting technique Kernel Mean Matching (KMM). The reweighted source task instances are used to build a kernelized support vector machine (SVM) model, which is applied on the target task data to get the predicted PPIs. This brings forth the second challenge—selecting appropriate hyperparameters while building a model for a task with no labeled data. For simplicity we also use the same set of features across all tasks (protein sequence features). However the data distribution will be different across tasks due to the different organisms involved.This approach is based on instance-transfer where the goal is to pick from each of the source tasks, the most relevant instances w.r.t the target task. We use a two-step process: (1) the first step does the instance weighting on the source tasks. (2) the second step uses the reweighted instance to build several SVM classifier models—one model for each hyper-parameter setting. To deal with the second challenge, we present two heuristic methods to select the best set of hyperparameters.PS and Pt. Here PS represents the joint distribution of all source tasks. Since we do not have access to the labels y on the target, we make a simplifying assumption that there is only a covariate shift between the source and target tasks—i.e., the conditional distribution P(y|x) is the same for both tasks. Mathematically, r. Sugiyama et al. . In the form written below, it minimizes the difference between the empirical means of the joint source and target distributions.We use the nonparametric Kernel Mean Matching (KMM)  is a quadratic program and can be efficiently solved using sequential minimal optimization (SMO), projected gradient based methods. We use the KMM implementation from the Shogun  is infeasible for two reasons. The optimization involves the computation of the gram matrix K of O(n2) where n is the number of instances. Typically the total number of protein-protein pairs between a host-pathogen are of the order of 100 million. Secondly, the total number of labeled source instances is quite small (≈ 1500). This set is likely to get underweighted  if there are too many unlabeled source instances. To represent the source's empirical mean, in addition to the labeled instances we randomly sample four times as many unlabeled instances. For the target, we randomly sampled Sn instances.i were obtained in Step-1.Once we have the optimal set of source instances, we can train a Kernel-SVM model using these. Along with the first step, we thus call this two step process KMM-SVM. We pick a kernel-based learning algorithm since we plan to extend our work to deal with different feature spaces across the tasks. In such a scenario, the only mechanism to operate on the target data is via similarities, i.e., the kernel. The dual formulation for the weighted version of SVM solves the following problem, where the weights βParameter tuning and selecting the best model in the absence of labeled data is a very hard problem. The model built on the source data cannot be tuned using cross validation on the source data because doing so will optimize it for the source distribution. Hence we developed two heuristic approaches to select the best hyperparameters. The first one uses the expected class-skew on the target task while the second uses reweighted cross-validation.Class-skew based parameter selection: We first built several models by doing a grid-search on the classifier hyper-parameters. There are 3 parameters to tune for the Kernel-SVM: the kernel width γ, the cost parameter C, the weight parameter for the positive class w+. The total number of parameter combinations in our grid-search were 50. We thus had 50 models trained on the reweighted source data obtained after KMM in Step-1 (Section 3.3.1). We applied each model on the target data and computed the predicted class-skew predr using the predicted class labels. The expected class skew based on our understanding of the PPI experimental literature is roughly 1:100 (= truer). We ranked all 50 models on the statistic |predr − truer|. The top k models were selected based on this criteria and a weighted voting ensemble was built using them. This ensemble was used to get the final class label on the target data. We used k = 5.Aggregating the models and assigning interaction scores: In our experiments, we used k = 5 to pick the best models w.r.t the ranking statistic described above. Note that each model gives us a classifier score for every protein-pair in the test data, which can be considered to be the probability of interaction. For k = 5, we have five scores for each test protein-pair. These scores were aggregated using two criteria:The majority vote over the five models where each model votes “yes” if the output probability score is greater than or equal to 0.5.The averaged of all five probability scores.n-mers of a given input sequence and is defined as: x, x′ are two sequences over an alphabet Σ. Instead of using the 20 amino acids as the alphabet Σ, we used a classification of the amino-acids. There are seven classes based on the electrostatic and hydrophobic properties of proteins, i.e., |Σ| = 7. Here ϕnsp transforms a sequence s into a |Σ|n-dimensional feature-space. One dimension of ϕnsp corresponds to the normalized frequency of one of the 7n possible strings in s. We use n = 2, 3, 4, 5.We used a variant of the spectrum kernel, based on the features used by Dyer et al.  for HIV-Salmonella, we construct the negative class using random pairs of proteins sampled from the set of all possible host-pathogen protein pairs. The number of random pairs chosen as the negative class is decided by what we expect the interaction ratio to be. It is a parameter that can be changed as our knowledge of the size and nature of the host-pathogen interactome improves.Classification techniques need a negative class (set of non-interactions) in order to identify the special characteristics of the positives . Since there is no published experimental evidence about “non-interacting” host-pathogen proteins for any plant with The interaction ratio/ negative examples are used in different ways as described below. The homology-based transfer method does not directly use any negative examples/ interaction ratios. In the case of T-SVM, while training the transductive model, we use negative examples from the source task. In the case of KMM-SVM, the data used to build the model comes from the source tasks, where negative examples from each source task are used. Next, during the model selection phase we pick the best models based on the interaction ratio of the model over the predictions on the target task (See Section 3.3.3 for details). No explicit negative examples are used in this part; the interaction ratio is simply used to pick the best model.Salmonella genes, but if we restrict the binding partners to only the so-called Salmonella effector proteins, the ratio we use is reasonable. . Also note that, while the exact examples that we choose as negative data may not be true negatives, we expect the false negative rate to be low enough (≈ 1%) to justify our choice of this heuristic.We initially chose a positive:negative class ratio of 1:100 meaning that we expect 1 in every 100 random bacteria-human protein pairs to interact with each other. This has been a common practice in host-pathogen PPI prediction in the past  possible amino acid three-mers. We use two-, three-, four-, and five-mers. For each host-pathogen protein pair, we concatenated the feature vectors of the individual proteins. Therefore, each host-pathogen protein pair had a feature vector of length at most 98, 646, 4802, and 33614, in the cases of two-, three-, four-, and five-mers, respectively.n et al.  introducn et al.  partition et al.  for detaGene expression features: These features depend only on the human protein (gene) involved in a human-Salmonella protein pair. We selected 3 transcriptomic datasets from GEO  give us a total of 7 features representing differential gene expression of human genes in 7 different control conditions. The intuition behind this feature is that genes that are significantly differentially regulated are more likely to be involved in the infection process, and thereby in interactions with bacterial proteins. Note: these were used in only the human-Salmonella task.GO similarity features: These features model the similarity between the functional properties of two proteins. These were used in only the human-Salmonella task. Gene Ontology  computes the similarity between the GO term sets of the Salmonella protein and the human protein's binding partners in the human interactome. We used HPRD to get the human interactome.Code: The executable files from the packages used to build our methods, and the scripts that we used to run these can be downloaded here: http://www.cs.cmu.edu/~mkshirsa/data/frontiers2014/code.zip.Salmonella is currently not feasible as there is no known PPI data. Hence for the purpose of evaluation, we used some of the PPI datasets as “sources” for building a model and one as the “target.” We evaluate the machine-learning based methods in two settings of transfer: pathogen-level transfer, where the host is fixed to be human and the pathogen is one of various bacterial species. The second setting host-level transfer, is more relevant and refers to the case where the pathogen is fixed to be Salmonella and we modify the host species. Since there are few known PPIs involving Salmonella, we are only able to experiment with mouse as an alternate host. There are 14 known mouse-Salmonella PPIs. Interestingly they involve mouse proteins whose human homologs also interact with the same Salmonella proteins—i.e., these 14 PPIs have interologs in the human-Salmonella dataset.A quantitative evaluation on the target task i.e., plant-Our evaluation criteria does not use accuracy (which measures performance on both the positives and negatives). Our PPI datasets are highly imbalanced with a large number of negative samples, and a trivial classifier that calls all protein pairs as “negative” will achieve a very good performance. So we instead use precision (P), recall (R) and F-score(F1) computed on the interacting pairs (positive class).The source tasks  and target task  are shown in the Table Inductive Kernel-SVM (Baseline): This model assumes that the source and target distributions are identical. All source data is pooled together and used to build a single model. For the kernel we used the RBF-spectrum kernel.Transductive SVM (T-SVM): This is the method described in Section 3.2.KMM-SVM: This method is discussed in Section 3.3.Salmonella-human to Salmonella-mouse. The recall is very high at 93.7 since the mouse-pathogen PPIs are interologs of the human-pathogen PPIs. The precision is not as high as some additional positives are predicted and we found that they had a high classifier score. These “false positives” are likely to be true interactions. For the reverse setting, T-SVM does slightly better than the KMM-SVM and 2 points higher than the baseline. Note that here, the source data is very small in size with only 14 PPIs. In the pathogen-level transfer, on the Salmonella-human target task, the F1 of the KMM-SVM method is the highest at 19.9 and is 5 points better than the other two methods. On the E.coli-human task, the performance is 18.4 which is 5.7 points better than the other methods.The host-level transfer performance is shown in the first two rows of Table Salmonella-human in the two settings. In the host-level transfer, the F1 is 52 whereas in the pathogen-level transfer it is much lower at 19.9. The hosts human and mouse are much more similar than the group of bacterial species namely: Salmonella, E. coli and F. tularensis. The source tasks are indeed very critical in determining the performance on the target.A very interesting observation to make from the table is, the performance on the target task: Arabidopsis-Salmonella protein-pairs to get predictions for potential interactions. The homology based approach does not assign any confidence scores to the predictions while both T-SVM and KMM-SVM allow us to obtain a score for every predicted interaction. All predictions from T-SVM with a positive score (>0) are considered to be interacting. For the KMM-SVM method, we filter the predictions using a threshold of 0.7 on the averaged probability-score. (See Section 3.3.3 for details on the probability score computation for the KMM-SVM method). We chose this threshold of 0.7 since all positives in our training data are assigned a score ≥0.7 by the classifier model. The full lists of predicted interactions from all three approaches are available at the following link: http://www.cs.cmu.edu/~mkshirsa/data/frontiers2014/predictions.zip.We apply the models trained using the procedures from previous sections on Arabidopsis proteins and 5 Salmonella proteins. Choosing by thresholding the prediction score is one way to select potential interactions for further scrutiny. Another approach is to analyze the predictions based on the biological functions one is interested in. To demonstrate the type of biological functions that are represented in the predictions, we performed GO term enrichment analysis of the Arabidopsis proteins involved in the predictions. We can then look at Arabidopsis genes with the most enriched GO terms and what their predicted Salmonella partners are.The total number of PPI predictions based on the score thresholds described above are: 106,807 for homology-based, 1088 for T-SVM and 163,644 from KMM-SVM. Hundreds of thousands of interacting pairs may not be likely and we therefore expect that many of the predictions are likely to be false positives (FPs). We would like to emphasize that, by ranking the predictions on the classifier scores and picking only the top few we are likely to filter out most of the false positives, since the machine learning models are expected to score FPs lower than the true positives. The threshold of 0.7 for KMM-SVM was chosen just to ensure consistency with the threshold that we observed in the training data . If one considers say the top 10% of the predictions from the KMM-SVM method, we have 1636 PPIs over ≈1300 unique A Venn diagram depicting the overlap between the predicted pairs of proteins interacting according to the three approaches is shown in Figure http://www.cs.cmu.edu/~mkshirsa/data/frontiers2014/predictions_class_skew_500.txt.Because the ratio of 1 positive to 100 negative pairs likely overestimates the number of interactions, we next changed this ratio to 1:500 and generated a new model. As expected, a much smaller number of pairs are predicted namely, 6035. This is a more manageable list and the predictions of the new model are provided at As with any predictions, experimental validation is ultimately needed to verify them. The choice depends on the interest of the experimentalist. Here we have chosen for discussion a few predictions that are interesting to us, but we encourage the reader to look at the list of predictions for others of potential biological interest.Arabidopsis proteins predicted to be targeted by the Salmonella proteins. We are interested in analyzing the characteristics of the plant proteins predicted to be the most popular targets for pathogenesis. We defined the “popular targets” using the following criteria: (a) the Arabidopsis protein is predicted to be targeted by at least 3 Salmonella effectors with a probability greater than 0.9 and (b) the GO term annotations of the Arabidopsis protein are significantly enriched [with a p-value of <0.001 as obtained by GO enrichment analysis using FuncAssociate  enrichment in the Arabidopsis proteins, nearly one third are transcription factors. These function e.g., in hormone-mediated signaling pathways. It has been reported that jasmonic acid and ethylene signaling pathways are involved in plant defense response against Salmonella  in tomato to inhibit ethylene induced transcription . Because there are currently no known The interactome predicted by each method is not the true interactome, but is a set of predictions. There will be false positive and false negative interactions. Thus, each individual prediction has to be considered a hypothesis not a fact.In line with point 1 above, the size of the predicted interactomes does not necessarily relate to the true interactome. We dont know how many interactions to expect. Our different predictions vary greatly in size, with one method predicting only one thousand interactions, while others predict more than 100,000 interactions. While it is more likely that smaller numbers of interactions are more likely, it does not mean that this method is inherently better than the other methods.The size of the predicted interactions list also depends on a critical parameter, the positive to negative class ratio. This parameter is important but it is tuneable, so the methods validity is not dependent on its choice. However, it is important to appreciate that the predictions will differ greatly when this parameter is changed. Thus, biological insight in choosing predictions to validate still needs to be applied, regardless of the prior choice of ratio in generating the model.Arabidopsis genes participate in PPIs with 31 distinct Salmonella genes  enters into productive interaction with only 31 Salmonella proteins. It also implies that, on average, each interacting Salmonella protein is capable of productive interaction with over 5000 Arabidopsis proteins. It is unlikely that this is the case, again suggesting that a large number of false positives have to be expected.These general limitations in the context of the specific results of the models presented here translate to the following issues, pointed out by a reviewer of this paper: The data presented for the KMM-SVM model indicate that 163,644 PPIs are predicted Figure . This iss Figure . This imThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Homopolymeric amino acids repeats (AARs), which are widespread in proteomes, have often been viewed simply as spacers between protein domains, or even as “junk” sequences with no obvious function but with a potential to cause harm upon expansion as in genetic diseases associated with polyglutamine or polyalanine expansions, including Huntington disease and cleidocranial dysplasia. A growing body of evidence indicates however that at least some AARs can form organized, functional protein structures, and can regulate protein function. In particular, certain AARs can mediate protein-protein interactions, either through homotypic AAR-AAR contacts or through heterotypic contacts with other protein domains. It is still unclear however, whether AARs may have a generalized, proteome-wide role in shaping protein-protein interaction networks. Therefore, we have undertaken here a bioinformatics screening of the human proteome and interactome in search of quantitative evidence of such a role. We first identified the sets of proteins that contain repeats of any one of the 20 amino acids, as well as control sets of proteins chosen at random in the proteome. We then analyzed the connectivity between the proteins of the AAR-containing protein sets and we compared it with that observed in the corresponding control networks. We find evidence for different degrees of connectivity in the different AAR-containing protein networks. Indeed, networks of proteins containing polyglutamine, polyglutamate, polyproline, and other AARs show significantly increased levels of connectivity, whereas networks containing polyleucine and other hydrophobic repeats show lower degrees of connectivity. Furthermore, we observed that numerous protein-protein, -nucleic acid, and -lipid interaction domains are significantly enriched in specific AAR protein groups. These findings support the notion of a generalized, combinatorial role of AARs, together with conventional protein interaction domains, in shaping the interaction networks of the human proteome, and define proteome-wide knowledge that may guide the informed biological exploration of the role of AARs in protein interactions. Homopolymeric amino acid repeats (AARs) are found in a large number of eukaryotic proteins Faux, . These rOne of the possible mechanisms by which AARs could regulate the function of proteins that contain them is by mediating the interactions of these proteins with other proteins or with other cellular components such as nucleic acids and lipids in membranes. In support of this hypothesis, we have shown for example that polyQ or polyA repeats can mediate interactions between proteins that contain them . These interactomes thus contain two types of interactions, which are schematized in the lower part of Figure a” interactions, are between two proteins that both contain the AAR or that are both part of the list of randomly selected proteins. The other type of interactions, i.e., “type b,” is formed by an AAR protein with a protein that does not contain the AAR, or by a protein of the random group with another protein that is not part of the group.To determine the potential involvement of AARs in establishing protein-protein interaction networks, we first analyzed the interactomes formed by the proteins of each of the 20 groups of proteins of the human proteome containing repeats of at least four units of any one of the 20 amino acids. This AAR length threshold allows one to identify proteins that contain not only long, pure homopolymeric AARs but also more fragmented repeats at a more advanced stage of their “life cycle”  rather than with proteins devoid of it. The second index shows the density of type a interactions in each AAR network.To determine whether proteins containing a certain AAR have an increased, decreased, or similar propensity to establish interactions among themselves in comparison with randomly selected proteins, we calculated two quantitative indexes by analyzing the AAR and the random protein networks Figures . The fira interactions as a proportion of the total number of interactions, i.e., type a/(type a + type b). The same proportion was also calculated for each of the five random control networks. The value of the proportion in the AAR protein network was then normalized to the value of the proportion calculated for each of the five corresponding random protein networks. The resulting five normalized values were then averaged and are plotted in Figure a interactions in the interactome of polyQ proteins is on average 1.81 ± 0.06 (n = 5) times greater than in the corresponding control networks. This difference in the distribution of type a and type b interactions between the polyQ network and the average of the control random networks was statistically significant , indicating that polyQ proteins tend to establish significantly more interactions with other polyQ proteins than expected by chance. This observation was not unique to polyQ proteins, and in fact similar results were found also for other networks of proteins containing repeats formed by other polar (polyS), charged  or small/cyclic  amino acids. Conversely, proteins containing polyL or other hydrophobic repeats  tend to have fewer interactions with each other than expected, although this trend is statistically significant only for the polyL group. Other networks of proteins with hydrophobic AARs (poly-I and -V) display non-significant trends toward a slight increase in the proportion of type a interactions. The case of polyF networks is difficult to interpret due to the small number of proteins (n = 61) containing this repeat and to the consequently higher statistical variability that was observed in the five corresponding control networks. Finally, poly-N, -H, and -T networks did not deviate from what expected by chance in terms of type a connectivity.Thus, we first calculated for each AAR protein network the number of type a interactions per AAR-containing protein and we normalized this value to the corresponding value calculated for each of the five control random networks. The average of the resulting five normalized values for each AAR group is shown in Figure F = 53.69, p < 0.001]. Furthermore, the Dunnett post-hoc test, using as a control group the polyM group which has the mean value closest to 1, revealed significant differences  from the polyM group for all the AAR protein groups that were also significant in Figure Second, we calculated for each polyX network the average number of type n = 5) times more interactions with other polyQ proteins than expected by chance. Also other networks of proteins containing repeats of polar (polyS), charged , or small/cyclic  amino acids display 1.25–1.7 times more per-node interactions than expected by chance, as also illustrated in the sample networks shown in Figure a connections than the corresponding control networks. Again, networks of proteins containing certain hydrophobic AARs  displayed a significantly lesser number of per node connections than expected by chance, as one can also appreciate visually in Figure a connections in poly-T, -N, -I, -H, -F, -M, and -V networks did not significantly differ from that of the corresponding control networks. Taken together, these findings show a substantial concordance of the two indexes that we used to characterize the type a connectivity of the AAR protein networks. In fact, we observed a significant correlation between the two indexes of the 19 AAR groups . Thus, AAR groups in which the first index is high and statistically significant tend to have also higher values for the second index , and this general concordance of the two indexes strengthens the conclusion that these AARs are associated with a higher degree of interactivity among proteins that contain them. Conversely, in some particular cases like that of polyF, even though the first index is high, but not significantly, the second index is close to the value expected by chance, thus indicating overall that the presence of this repeat is not associated with a greater connectivity between the proteins that contain it.This analysis showed, for example, that polyQ proteins establish 2.39 ± 0.06 , charged , or small/cyclic  amino acids, suggesting that these repeats themselves, or protein domains they co-occur with, or they are found within, may promote protein-protein interactions and the formation of interaction networks. Conversely, the presence of certain hydrophobic repeats like polyL and polyY in proteins seems to disfavor the formation of interaction networks, possibly owing to the fact that these repeats are often found in transmembrane domains that sequester proteins in membranes (see Section Discussion).In principle, several non-exclusive structural mechanisms Figure  may undeBiological experiments will be ultimately necessary for discriminating between these possibilities for the different polyX protein groups. As a first step in this direction, however, it may be important to determine initially, through a systematic proteome-wide analysis, which protein domains are significantly overrepresented in each polyX protein group. These domains may in fact be responsible, together with AARs or by themselves, for the increased mutual interaction propensity of AAR-containing proteins. Thus, this analysis may ultimately guide the biological exploration of the role of AARs in protein interaction networks by indicating which AAR/domain associations are most likely to determine an increase in protein interactivity such as we observe in certain AAR-containing protein groups.p < 0.05 after applying the Benjamini-Hochberg adjustment). Overall, this analysis revealed the overrepresentation of multiple types of protein domains in most polyX protein groups, with the exception of the poly-C, -M, -N, and -Y groups. An exhaustive list of these domains is reported in the Supplementary Table To determine whether specific protein domains are overrepresented in the different groups of polyX proteins, we analyzed statistically their domain composition using the DAVID database , charged , and small/cyclic  AARs. Interestingly, in some of these cases  the AARs themselves are known to form coiled coil structures often as part of conventional coiled coil domains , helicase domains) co-occur in proteins with charged, polar, and small/cyclic AARs. The particular enrichment of DNA binding domains in groups of proteins containing charged repeats may reside in the capacity of charged AARs to bind DNA and chromatin components such as the histones , for each significant AAR-domain co-occurrence, in which proportion of proteins the AAR and the domain overlap for at least four residues. We found that in 148 out of 189 significant cases of AAR-domain co-occurrence no overlap exists between AARs and the domains that co-occur with them in the same proteins. However, for 41 cases of AAR-domain co-occurrence there is some sign of overlap between repeats and domains. In 21 cases, the overlap is observed in between 25 and 100% of the proteins containing the AAR-domain combination in the CDD database , charged , and small/cyclic  repeats show a greater tendency to interact among themselves, in comparison with proteins devoid of these AARs. This is also paralleled, in the networks formed by proteins bearing these “interaction-enhancing” AARs, by a higher number of AAR-containing interaction partners per AAR protein. The opposite phenomena were observed for the protein groups containing certain hydrophobic AARs, like polyL and polyY, whereas for other AAR protein groups there was no evidence for statistically significant changes in type Taken together these observations indicate that, given their widespread presence in proteomes and their frequent co-occurrence with protein interaction domains, polyQ -S, -D, -E, -K, -R, -A, -G, and -P AARs may play a significant, generalized role in shaping protein interaction networks. Interestingly, most of these interaction-enhancing AARs can form defined secondary and supersecondary structures like α-helical coiled coil structures in the case of polyQ and polyA repeats  in FASTA format without isoforms. The proteins containing AARs of at least four units were identified using ad hoc Perl scripts as in Pelassa et al. (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi). Necessary conversions of the different protein identifiers found in the different databases were performed using the DAVID (https://david.ncifcrf.gov/) or Biomart (www.biomart.org) databases.The complete reference proteome of a et al. . The domhttp://biit.cs.ut.ee/gprofiler/), deselecting the “significant only” option so that all interactions could be downloaded in a tab-delimited text files. Control networks formed by proteins selected at random in the human proteome and their interactors were obtained in the same way. Randomness in the selection of the proteins was achieved by using a random number generator to select protein IDs from a complete list of all the human protein IDs ordered as elements of an array. In particular, we reiteratively used the Perl “rand” function to select sets of random elements of the desired numerosity from the elements of this array. The files derived from g:Profiler for both AAR and random networks were then analyzed with ad hoc Perl scripts in order to identify and quantify “type a” and “type b” interactions (see Section Results).Protein interaction networks formed by proteins containing a given AAR and their interactors were extracted form the BioGrid database of protein-protein interactions using the g:Profiler web interface . The overlap of coiled coil domains with polyQ and polyA repeats was shown previously  selecting the “degree sorted circle layout.” Other schematic representations and figures were generated using Photoshop Elements 11 software (Adobe).Bar graphs were generated using Excel software (Microsoft). Network graphs were generated using CytoScape software  and SPSS 21 (IBM) software. Appropriate statistical tests were performed as indicated in the text and The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation). Developing methods for accurate prediction of effects of amino acid substitutions on protein-protein affinity is important for both understanding disease-causing mechanism of missense mutations and guiding protein engineering. For both purposes, there is a need for accurate methods primarily based on first principle calculations, while being fast enough to handle large number of cases. Here we report a new method, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) method. The core of the SAAMBE method is a modified molecular mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method with residue specific dielectric constant. Adopting residue specific dielectric constant allows for mimicking the effects of plausible conformational changes induced by the binding on the solvation energy without performing computationally expensive explicit modeling. This makes the SAAMBE algorithm fast, while still capable of capturing many of the explicit effects associated with the binding. The performance of the SAAMBE protocol was tested against experimentally determined binding free energy changes over 1300 mutations in 43 proteins and very good correlation coefficient was obtained. Due to its computational efficiency, the SAAMBE method will be soon implemented into webserver and made available to the computational community. PLOS Computational Biology Methods paper.This is a in vivo interactions occur in the crowded cellular environment, mutations may not only impact binding affinity but also could perturb protein interaction networks resulting in a loss or gain of interactions. Such changes in binding and interactions are frequently implicated in diseases and understanding of their molecular mechanisms is crucial for deciphering the origin of diseases. In particular, the effect of mutations on binding free energy (binding affinity) is considered to be an important component of the overall disease effect  . The motivation was that while some compounds listed in the heteroatoms section of PDB file may be biologically important, the vast majority of them are crystallographic artifacts (as ions for example). Thus, the resulting pruned database (tDB) consists of 1326 single point mutations from 43 proteins. Both datasets are available for download from .rSASA); rSASA = 1 corresponding to totally exposed residue in the protein) of the residue in the monomeric (rSASAm) and complex (rSASAc) states, as well as their mutual difference (ΔrSASA = rSASAm − rSASAc). Thus residues are considered to be at the interface if they are in COR, SUP and RIM regions; and are away from the interface if they are in SUR and INT regions. RIM and SUR locations indicate that the residue is exposed to the water solvent when the complex is formed. The parameters of each location types are provided in We assigned the location of mutated residues in the protein-protein complex based on five categories  as previously described  by compuprofix module from Jackal package to rebuild these regions Eq (9) iH) hydrophobicity scale [rSASA. For example, a residue being exposed to the water phase will have large contribution to HYRD while practically zero if buried inside the protein. Having in mind that Hj indexes have opposite signs for hydrophobic and hydrophilic amino acids, such a formulation qualitatively describes the physical basis of the hydrophobic effect. The corresponding formula is:ΔΔHYDR.The term accounting for the hydrophobicity was modeled using Wimley-White  was taken into account as well. We computed the number of HB for WT  atoms located at distance shorter than 2.4 Å. Since the nitrogen acceptor is much weaker than oxygen, for simplicity it was not considered. Similarly the geometry of the hydrogen bond was not taken into consideration. Only polar  and charge  amino acids were taken into account.The impact of the mutations on the formation of hydrogen bonds , true negative (tn), false positive (fp), or false negative (fn). The explanation of the assignment procedure is provided in the The quality of the predictions was described by six parameters: accuracy, precision, sensitivity, specificity, negative predictive value (NPV) and Matthews correlation coefficient (MCC) ,53:accuOne of the goals of SAAMBE development is to develop fast algorithm capable of large-scale calculations. Thus, the execution time is an important component of the investigation. The execution time was monitored as a function of the number of amino acids in the corresponding complex (sequence length) and as a function of the geometrical shape of the complex (monitored via the largest dimension of WT complex).To verify the agreement between experimental and predicted values of the change of binding free energy due to single point mutation we calculated the Pearson correlation coefficient. In the paper all reported correlation coefficients were significantly different from zero with p-value smaller than 0.01.We also performed five-fold cross validation test for the tDB. It was done by randomly partitioning the tDB into five subgroups of approximately equal size. Each combination of four subgroups was used for training, while the fifth—for testing the model. Then correlation coefficients were averaged over different cross-validated sets.S1 TextA—Distribution of the RMSD within charged ; polar  and other  groups of residues. RMSD was estimated based on the deviation of the last heavy atom in a side chain of the residue in the protein-protein complex and in unbound part. Both protein-protein complex and its each partner were minimized for 5000 steps in NAMD. The inserted graph illustrates the average RMSD of the residues within CRG, PLR and OTR groups for WT (dark grey) and MT (light grey) structures. The analysis was performed for all entries in tDB (see manuscript for details); B–The distribution of the change in RMSD of 1) CRG and PLR residues (orange) and 2) CRG and OTR residues (green) for WT  and MT (dash-dot line and solid circles) structures calculated for each case in tDB; Table A—The standardized weights of significant energy terms in predicting the change in binding free energy due to single amino acid substitution; Table B—Variance Inflation Factor calculated based on the Pearson’s correlation coefficient; Table C—Variance Inflation Factor calculated based on the Spearman’s correlation coefficient; Table D—Variance Inflation Factor calculated based on the Kendall’s correlation coefficient.(DOCX)Click here for additional data file.


As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at  The accurate annotation of protein function is key to understanding life at the molecular level. With its inherent difficulty and expense, biochemical characterization of protein function cannot scale to accommodate the vast amount of sequence data already available, much less its continued growth. Thus, there is a need for reliable computational methods to predict protein function.To computationally predict protein function, various schemes have been proposed so far using different data types such as sequence information –4, proteThe traditional computational approach to predict function for an unknown protein transfers information from evolutionarily related proteins. Unfortunately, most such BLAST-motivated methods, which transfer the annotations from the most sequence-similar homologue, suffer from systematic flaws and thus have littered the databases with erroneous predictions. BLAST  is a seqSIFTER  is a statistical approach for predicting protein molecular function that uses a protein family's phylogenetic tree as the natural structure for representing protein relationships ,8. It ovThe first Critical Assessment of Function Annotation (CAFA) experiment provided an independent assessment, and SIFTER was honored as a ‘top-performing method’ . RecentlOpen source code for SIFTER has been available since its first publication and remains so. It has been used by several other groups, and adapted for their own use . HoweverUsers can access the protein function predictions by searching for one or multiple proteins (using UniProt identifiers or protein sequences), searching for all proteins in a given species, or searching for proteins in a given species that are predicted to have the given functions. For proteins not yet in our precomputed prediction set, users can submit the protein sequence and the web server will show the predictions for homologs of that gene in our prediction set (found based on top BLAST hits). The input field provides autocomplete and text suggestions to help users enter their queries. The server reports predictions based on experimental evidence alone by default for the highest precision. For more advanced predictions with broader coverage, users can combine other evidence. SIFTER then outputs predicted functions along with confidence scores for each function that reflect the reliability of the predictions.Users can also download SIFTER code and run SIFTER locally. The database of the precomputed SIFTER results and associated query scripts are available for download as well.Figure SIFTER's phylogenetics-based approach to protein function prediction avoids systematic flaws in pairwise methods like BLAST. Lineage-specific rate variation is a complex phenomenon that may lead to a situation where the most similar sequences according to BLAST  may not be the sequences most recently diverged from the query sequence. For instance in the above example, BLAST will predict the red function due to the shortest path length from the query protein B to the protein D. Thus, the approach of using the most significant hits according to BLAST is systematically flawed and may yield erroneous results even as the number of known protein sequences increases. SIFTER incorporates the evolutionary history, minimizes problems due to rate variation and suggests an evolutionarily principled means of merging functional evidence from homologous proteins.Another important factor in the SIFTER algorithm that sets it apart from some other techniques is that it systematically weighs the input evidence based on the quality of the annotations. As different experimental and computational functional annotation techniques have different error rates, uniformly treating the existing annotations in the databases as accurate propagates large numbers of erroneous annotations into predictions . For thiSIFTER has been shown to outperform other sequence-based techniques in widespread use ,8,28. FiAs an independent analysis of SIFTER performance, Figure The SIFTER web server provides easy access to SIFTER predictions of protein functions.To assist users with different types of queries, the SIFTER web server is designed to provide search results for different input types. Here, we detail the search capabilities implemented in the server.Users can access SIFTER predictions either through ‘Quick’ or ‘Advanced’ search. The Quick Search option is a single input field search mechanism that can identify different query types  and provide search results accordingly.Protein: Users can search for the predicted functions for their query protein. The server accepts both UniProt accession number and UniProt ID (for one protein).Species: Users can search for the predicted functions for all proteins in a given species. The server accepts both species name and NCBI taxonomy identifier.Function: Users can search for proteins that are predicted to have the given functions in a given species. The server accepts both function name and Gene Ontology (GO) molecular function identifier.Input to the quick search field can be any of the following:The input field in the quick search option can provide auto-complete and text suggestions for the given input to help users enter their queries.For any of the above query types, the SIFTER web server provides corresponding search results as detailed below.Protein: If the entered protein identifier is in the precomputed results, the SIFTER web server provides the list of predicted functions from the GO molecular function ontology for the queried protein, along with a confidence score (between 0 and 1) for each function. This score is determined based on the number of candidate functions, the family size, and the frequency and distribution of candidate terms in the phylogenetic tree. Figure Species: The web server provides the list of predicted functions for all of the proteins of the entered species that have predictions in the precomputed results.Function: The web server lists all the proteins in the queried species that have the entered function (or any of its descendants in the GO directed acyclic graph) as their top prediction.SIFTER web server output is the set of predictions for the submitted query. The predicted functions are selected from the Gene Ontology molecular function ontology . The SIFFor more advanced predictions, users can pick their specific querying method from the list of four available options: ‘Predict by protein ID,’ ‘Predict for all proteins of a species,’ ‘Find proteins that have given functions,’ and ‘Predict for homologs of given sequences.’ The first three options are more advanced versions of the quick search discussed above. In the advanced search, users can search for multiple proteins or functions simultaneously using the ‘Predict by protein ID’ and ‘Find proteins that have given functions’ options. The last option in the advanced search, ‘Predict for homologs of given sequences,’ allows users to search for proteins not yet in our precomputed prediction set, as discussed below.g with k domains gi, where i = 1⋅⋅⋅k, and ith domain has function f. We then compute sg(f), the probability that protein g has function f, as:SIFTER predicts function for each domain of a protein separately, using the phylogenetic tree of the family of each domain. To provide a single set of predictions for each protein, the SIFTER web server combines the individual domain predictions, assuming independence of domain functions. Consider protein sg(f) score as the default prediction for the query protein g. However, users may also access the predictions for each individual domain for more insight about domain-specific annotations. Users may also download the phylogenetic tree of each specific domain, overlaid with experimental annotations in the family.The web server reports the combined In this option, users can submit the sequences of proteins not yet in our precomputed prediction set, and the web server will show the predictions for the closest homologs in our prediction set (found based on top BLAST hits obtained from the NCBI-BLAST website ). The ouFor highest precision, the server reports predictions based on experimental evidence by default. However, in Advanced Search, users can access more predictions for special applications by combining other lower quality but more extensive evidence. Users can also balance the sensitivity/specificity trade-off by adjusting the relative weighting of the SIFTER predictions obtained based exclusively on experimental evidence versus those predictions that include non-experimental evidence. As the default, for advanced combined predictions we use the weighting scheme that performed well in the second CAFA experiment.Users can download the SIFTER source code and run it locally. Since the time complexity of the algorithm underlying SIFTER is exponential in the number of candidate molecular functions, SIFTER offers a heuristic that sets the maximum number of molecular functions present in any protein. This significantly reduces the running time with minimal impact on the prediction results . In ordeTo precompute the SIFTER web server predictions, we used the UniProt Gene Ontology Annotation (GOA) database  update  moleculaSIFTER is a state-of-the-art sequence-based protein function prediction algorithm that infers protein function using a statistical model of function evolution in the phylogenetic tree. Here, we introduced the SIFTER web server, which provides easy access to precomputed SIFTER predictions using specially optimized parameters. Users can search SIFTER predictions using different querying schemes such as querying for a protein, all proteins of a species, or proteins predicted to have a function. The web server also provides predictions for homologs of proteins not yet in our precomputed prediction set. Thus, the SIFTER web server provides a unique environment to explore the SIFTER function prediction algorithm easily and swiftly.


Nuclear magnetic resonance (NMR) spectroscopy is a pivotal method for structure-based and fragment-based lead discovery because it is one of the most robust techniques to provide information on protein structure, dynamics and interaction at an atomic level in solution. Nowadays, in most ligand screening cascades, NMR-based methods are applied to identify and structurally validate small molecule binding. These can be high-throughput and are often used synergistically with other biophysical assays. Here, we describe current state-of-the-art in the portfolio of available NMR-based experiments that are used to aid early-stage lead discovery. We then focus on multi-protein complexes as targets and how NMR spectroscopy allows studying of interactions within the high molecular weight assemblies that make up a vast fraction of the yet untargeted proteome. Finally, we give our perspective on how currently available methods could build an improved strategy for drug discovery against such challenging targets. The majDrug discovery has evolved in the last decades and within this realm, structure-based drug design (SBDD)  and fragds), its entanglement with FBLD has grown and appears to be stronger than any other exhibited by alternative FBLD-assisting techniques , relaxation times or diffusion constants are often informative-rich measures acquired from NMR experiments . These e2.1Experiments based on the resonances of the small molecule (ligand or fragment) observe only unbound ligand. Nonetheless, these are also informative of the ligand's history if their dissociation is within the timescale of these experiments (100–1000 ms). In a mixture of protein(s) and ligand(s) the effects of binding will be transferred to the unbound ligand because during the time where the ligand is bound to the protein it will not behave as a small molecule as far as NMR observables goes. During this bound period the ligand behaves like the protein – it now tumbles slowly, it portrays faster relaxation and large negative NOEs. Differences in NMR observables of ligand spectra acquired in the presence and absence of protein can therefore disclose binding .The major benefit of monitoring the ligand resonances is the absence of need to isotopically enrich the target macromolecule . AdditioLigand-based screening techniques are extensively reviewed. Some techniques are more popular and widely used than others. Transfer NOE-type experiments such as saturation transfer difference (STD)  and wate1H schemes but also heteronuclear detection methodologies (19F or 31P). In these cases, the resulting spectra are much simpler and easy to interpret and these techniques are gaining popularity in fragment screening campaigns. One such example is the binding assay known as fluorine chemical shift anisotropy and exchange for screening (FAXS)     Fig. 1ddFig. 1d 2.2Protein resonances are information-rich NMR observables that can be used to reveal structure, function and dynamics under most circumstances. With the possibility to study both solution and solid-state samples NMR offers one of the most robust methods for detecting and characterising physical interactions. In FBLD it is well documented how useful this data can be. Direct assessment of protein resonances unveils where on the protein an interaction may occur and also how this interaction is made.Protein-based NMR methods historically gave birth to fragment-based screening approaches. In order to map which specific residues in the sequence, and ultimately the structure, are involved in the interaction, a high level of backbone amide resonance assignment is necessary. This requires double isotope-labelling and additional experimentation, usually from triple-resonance approaches. Although there are restrictions imposed by the target's molecular weight (around 40 kDa) nowadays there are several relaxation-optimized NMR techniques to tackle the relaxation and linewidth problems accompanying high molecular weight targets. Additional improvements on the experimental time to undertake these types of experiments have been making NMR a high throughput approach strengthening its position in hit generation and characterization.1H–15N heteronuclear single quantum correlation (HSQC)  (1H–15N HSQC's in the presence and absence of the putative interaction partner discloses binding events. These can be monitored by following the shifting residues (when in a fast exchange regime) or decrease in intensity (and may be even disappearance and appearance again) if in a slow exchange regime. Perturbed residues are usually identified as those which exhibit changes beyond one or two standard deviations. If the protein structure is known, it is possible to immediately identify the binding site by mapping these residues onto the protein structure. In order to eliminate the possibly biased cut-off to set when analysing CSPs, one can use SAMPLEX. (The most common protein-based NMR technique is the 2D n (HSQC)  of 15N-lon (CSP)  data. A SAMPLEX.  Such an The chemical shift perturbation assay has been used extensively to identify binding sites of small molecules. This approach gained even more popularity when SAR by NMR was introduced in 1996 . SAR by The SAR by NMR method has facilitated the development of highly potent and specific compounds and it continues to be one of the most popular and successful NMR technique for FBLD .2.3Protein and protein-small molecule structure determination features on many NMR techniques, and this allows binding site identification by mapping the CSPs obtained upon small molecule titration. However its use is limited to small and medium sized protein, as it requires backbone and side chain assignment for structure calculation. Being also a time consuming duty, several amenable techniques have been contributing to structural information on protein-small molecule complexes. This information is extremely useful to guide further medicinal chemistry hit-lead optimization campaigns.13C-edited and 13C/15N-filtered HSQC-NOESY spectra to evaluate small molecule binding poses. Here only 1H assignments of the bound small molecule are critical and by comparing the experimental above-mentioned spectra with predicted ones  it is possible to identify the matches. This matching is then scored and if sufficient protein-small molecule NOEs can be measured, informative-rich models can be crafted.Of particular interest are techniques that do not require protein resonance assignments to obtain structural information. The majority of these NMR techniques use some intermolecular NOEs to generate reasonably good protein-small molecule complex models. NOE matching a Consta is one sOther popular approaches, like STD and WaterLOGSY-based ones, also can illustrate small molecule binding mode knowledge to some extent, by performing careful group epitope mapping (GEM) around the ligand resonances. However the most robust and widely used method is the so-called INPHARMA b Sanche. The INPWhen multiple hits are found for a given protein there is an opportunity to exploit the theoretical contribution from the linking coefficient  to enhanOther NMR based methods are also capable of identifying protein binding sites both using isotopically labelled or natural abundance proteins. The ability to identify a binding site using structural information obtained by Overhauser effects and selective labelling SOS; d Hajduk is a gre1H spectra to be used as docking restraints. Structural information of the protein is indispensable to obtain the three-dimensional position of the target's binding site. This promising approach can be applied to non-isotopically labelled proteins, making it very attractive for hit validation in fragment screening.A relevant approach for initial small molecule validation, both mapping its binding site and its binding pose, was recently introduced . This pa32). In hit-to-lead optimization campaigns structural data is the limiting step to guide a rationale for compound elaboration and introducing chemical modifications. Knowledge of the target protein's structure doesn't imply that the binding of a small molecule hit will be easily validated structurally. Even if the target protein can be crystallized, soaking sometimes is not possible and this would mean that co-crystallization and new crystallization conditions have to be re-iterated. These requirements may be particularly challenging for multisubunit protein complexes.Large molecular weight targets including multi-subunit protein complexes exhibit a major limitation both to X-ray and NMR structural data accomplishment. The application of NMR methods to high MW proteins is limited primarily by the fact that the linewidths increase with MW as larger molecules have longer rotational correlation times and as a result much shorter transverse relaxation times .When handling large molecular weight targets (>50 kDa) selective labelling a is a goin vitro and in cells by using a time-delayed dual-expression system to achieve effective labelling  experiment . The phaThe other face of the coin in water and NMR is water's contribution to the decreasing of spectral crowding. Experiments are available which explore the concept that, for binding studies, solvent exposed residues are most influential. Being solvent accessible these residues can act as reporters and allow the study of intermolecular interactions. Whereas buried residues will most likely not contribute directly for such binding events. This significantly decreases the number of residues in the spectra but still permits following of binding events effectively. Solvent exposed amide (SEA) techniques  have bee3.31H, 15N and 13C transverse relaxation rates, dipole–dipole (DD) coupling and chemical shift anisotropy (CSA). Because these relaxation rates present a molecular size-independent ratio, a comparable reduction of the overall transverse relaxation rates can be expected for larger proteins. It is theoreticized that cancelation of both these pathways is optimal around 1000 MHz 1H frequencies. Upon decoupling a total cancelation of transverse relaxation effects within a 15N–1H moiety can be achieved for one of the four multiplet components. The narrow component observed in this experiment will just have the residual linewidth coming from DD interactions, which can be further suppressed with perdeuteration (Using NMR spectroscopy to explore multi-protein complexes and PPIs is well known to be a laborious task  and overteration .2H in proteins has an important impact by reducing magnetization through the spin system of dipolar-coupled protons (spin diffusion). Using this incorporation to decrease the proton density in proteins also eradicates spin diffusion pathways, improving signal-to-noise in the NMR spectra. Additionally, this incorporation allows identification of PPI sites. In an elegant cross-saturation TROSY-based experiment  is usually lower compared to that observed e.g. at buried enzyme active sites . However3.4It is well recognized that solution NMR spectroscopy is a powerful method to study both structural and dynamic properties of proteins . The qua15N relaxation measurements  by gauging the reorientation of 15N–H bond vectors allowing the study of backbone dynamics and also every 15N-containing side chains. This relaxation data is of great value and it informs the rate of overall molecular tumbling  or the components of the diffusion tensor in the case of anisotropic overall motion. By comparing fluctuations in local magnetic field that lead to relaxation on the full-length molecule versus residue-specific relaxation it is possible to inform on the protein's local internal motion. A parameter describing the amplitude of internal motions (S2) (Obtaining dynamics parameters from protein backbone urements  has beenurements . Nonetheons (S2)  and effeNotwithstanding, events that cause reasonably high amplitude molecular motions, such as direct protein or ligand interaction or allosteric regulation, are most likely to fall in the microsecond to millisecond timescale. Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion (RD)  NMR expeInterrogation of biomolecular binding events endowing conformational changes has been reported and the role of pre-existing equilibria of multiple conformational intermediates can be interrogated using NMR. The role of dynamics as an entropic carrier of free energy of allostery  has been4NMR spectroscopy is now a well-established technique to elucidate the structure, interaction and dynamics of molecules in solution and to guide structure-based lead discovery approaches.Over the last decade a clear trend has been emerging in the realization that many of the most attractive drug targets are likely to be of large size and complexity. Depicting inherently more complex structures than those historically investigated , these macromolecular assemblies have proven to be demanding targets to modern structural biology. The possibility to handle large molecular weight biomolecules is however now becoming a reality and it is being diffused to more and more biological systems. Crucially, it is slowly gaining momentum into drug discovery projects too. PPIs often fall within this category of challenging and complex systems, and as a result it is not straightforward to make progress on them using conventional approaches. The combination of topological features e.g. flat, extended and often highly hydrated binding sites bearing few small pockets, and low ligandability characteristics of PPIs limit their profiteering. Nonetheless, successful campaigns and recent literature examples have established the high-risk/high-reward status of many PPIs. With the new paradigm of targeting PPIs in drug discovery, we predict that NMR approaches will continue to play a crucial role in facilitating traction against these targets. This requires studying proteins and obtaining structural information beyond what has been traditionally considered as one of the main limitations of NMR, i.e. the size of the molecular target.Understanding what governs protein dynamics in a three-dimensional manner is still a challenging task. However NMR spectroscopy has been evolving to better address both protein dynamics and structure in difficult systems. This has included the development of ingenious labelling schemes and new pulse sequences. Amongst these, we anticipate that TROSY and CPMG-RD will hold the most promise and potential at pushing the frontiers of NMR in SBDD. Furthermore, Methyl-TROSY should be considered as a probing technique to help bringing high molecular weight targets in focus for FBLD and compound screening, something that isn't done routinely.In our opinion, with the technology we have in hand and prospective developments expected in future years, we believe that the inclusion of millisecond timescale dynamics information provided by NMR will aid targeting and development of high-quality chemical probes against multi-protein complexes. This information could present structural data on allosteric intermediates and transient binding sites so that these could then be discovered and targeted more efficiently. It is well documented that these mechanisms exist, especially in multi-protein complexes however we still seem to be one small step away from their structural comprehension and further usage in lead design. It is well understood that protein conformational dynamics is encoded in their sequence and structure, and that it is a fundamental requisite for their biological function . In movi


Nanopore technology has been developed for detecting properties of proteins through monitoring of ionic current modulations as protein passes via a nanosize pore. As a real-time, sensitive, selective and stable technology, biological nanopores are of widespread concern. Here, we introduce the background of nanopore researches in the area of α-hemolysin (α-HL) nanopores in protein conformation detections and protein–ligand interactions. Moreover, several original biological nanopores are also introduced with various features and functions. In the case of provided voltage, an analyte is driven towards the nanopore producing a measurable ionic–current blockade. Analyzing the characteristic currents, durations, frequencies and even the shapes of blockades reveals the properties of analytes . Nanoporetc., can perform accurate detections for proteins, however, they are generally too slow to conduct a rapid response ..17].4.In addition to straight detecting protein, α-HL nanopore is also widely used to probe protein–ligand interactions. One of the strategies is to attach aptamer at the entrance or the lumen of α-HL. By using this engineered α-HL, the region for the recognition of individual protein molecules is shift from the cavity of the α-HL to the outside of the channel. This strategy provides a novel method to allow the single-molecule study of large protein by α-HL. Moreover, searching for the alternative membrane protein is another promising solution to broaden the applications of nanopore techniques.The cysteine residues in α-HL nanopore can link to an DNA or RNA oligonucleotide which is capable of bind various analytes, including small molecules, proteins, and even cells. Dvir Rotem and co-worker  used mod2-Ba. The protein analytes contain positively charged presequences (pb2) of varying length fused to the small ribonuclease barnase (Ba). Understanding the structure of proteins facilitates the research on protein–protein interactions. They demonstrated that many factors affected the interaction of the pb2-Ba protein with the α-HL nanopore, such as the length of pb2-Ba protein, the location of the electrostatic traps in α-HL nanopore, the analysis of hydrophilicity and the given potential. The presence of the electrostatic traps greatly enhanced the interaction of the pb2-Ba and protein of the α-HL nanopore  which is widely used in military and medical fields is an important nitroaromatic molecule. At present, nanopore technology can be applied into the detection of nitroaromatic molecules. Hagan Bayley and co-workers  engineerα-HL nanopore could be used to predict the conformational changes that result from weak interactions. Ying Yilun and co-workers  detected5.Much progress has been made in the detection of proteins by α-HL nanopores, but there remains many challenges. The narrow diameter of α-HL is 1.4 nm, and it is difficult for some large proteins to enter into the narrow opening of α-HL. The anion selectively pass through α-HL, and the positive-charge protein needs to overcome a well-defined free energy barrier for the translocation through α-HL. To overcome the inherent geometric limitations and charge distribution of the α-HL pore, birth of new nanopore materials may encourage people to solve these problems.A number of biological membrane proteins, including areolysin ,23, FhuAAeromonas hydrophila. Contrary to α-HL, the aerolysin has essentially negative global net charge. It is more suitable for studying the conformational changes of small positive-charged peptide and proteins.The aerolysin with diameter in the range of 1 to 1.7 nm is a β-pore–forming protein secreted by Since 2006, the aerolysin nanopore was first introduced in nanopore based protein detections . The aerManuela Pastoriza-Gallego and co-workers  used theFhuA is a 714-residue membrane protein which is composed of 22 antiparallel-β-strands and an N-terminal 160-residue cork domain. There are 11 loops on the extracellular side and 10 short turns on the periplasmic side to link the 22 antiparallel-β-strands. FhuA contains four loops which can fold back into the lumen of nanopore. FhuA is able to maintain its stability for long periods under different conditions and show the function as transporter and receptor. Engineered FhuAΔC/Δ4L proteins include FhuA which deletes four loops and the cork domain. Liviu Movileanu and co-workers  verify iThe ClyA nanopore, which has a crystal structure, is from Salmonella typhi. The diameter of ClyA nanopores is larger than α-HL pores. Therefore, they could distinguish larger-sized proteins. ClyA nanopores can detect the complex of analyte-specific ligands by analyzing blocking current and duration. The entrance of the ClyA nanopore can be decorated with rings of 12 protein-specific aptamers. When the protein bind to the aptamers, blockade frequencies were enhanced. Misha Soskine and co-workers  demonstrThe channel which is derived from the Gram-positive bacterium Nocardia farcinica is the hetero-oligomer whose structure is related to MspA of M. smegmatis. Two subunits, NfpA and NfpB, were reconstituted in lipid bilayers. The channel shows highly negative potential caused by negatively charged amino acid residues clustering in the channel. The channel causes asymmetric transport properties which is caused by asymmetric structure under applied potential. When peptide enter into the channel and data of blocking current and duration can reveal detailed kinetic information. The kinetic data obtained can be used to distinguish the peptide binding events from the translocation events. The channel has good prospects to detect protein in the future  28]. Th. Th28]. 6.Biological nanopores are powerful tools to detect proteins at the single-molecule level. Protein molecules and protein-ligand complexes are detected by a α-HL nanopore. Methods of analyzing the electric current data are established in order to research protein conformation and interaction of proteins and ligands, protein–pore interaction and interaction of proteins and other molecules. Although much progress has been made in the detection of proteins by biological nanopores, there are still many challenges. For example, large proteins cannot enter the narrow opening of α-HL, and non-specific bumping events cannot be analyzed with nanopores. New nanopores need to be developed for detecting proteins. There are still many challenges which need to be resolved in order to use nanopores in biotechnology, medical applications and other domains.


Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or  In the last years, recognition of the strong relationship between function and structure has driven a steady improvement in algorithms and methods to predict protein structure. These efforts are justified by the difficulties in experimentally determining the structure of several proteins and the enormous amount of biological information made available when protein structure is known. It is important to recognize that protein function is more related with protein dynamism than with any single structure ,2. FolloThe structural differences between conformers characterize the so-called conformational diversity of the protein. In spite of the time that has passed since Monod postulated the importance of the different conformations to explain protein function, this coIn this work we studied how a set of protein models obtained by different 3D structure prediction methods reproduce the conformational ensemble of selected proteins. For 91 proteins used as targets in different 3D prediction experiments, we collected the proposed computational models and evaluated their structural similarity against alternative PDB structures of the shttp://predictioncenter.org/) and two decoy sets obtained with Rosetta@home[http://depts.washington.edu/bakerpg/) and QUARK[We collected protein structure models from three public datasets: the whole set of CASP experiments from CASP3 to CASP10 >4] were considered. This cutoff is based on the statistical significance of the expected random value E for that superposition. All remaining decoy-conformer pairs were ranked according to the measures above and the similarity of the decoy rankings against each of the two available conformers were studied using Spearman’s rank correlation coefficient.For every target, each of both conformers derived from CoDNaS were used as reference to calculate the TM-score and GDT_TS using TM-score  and the The selected conformers for each target protein were studied using different structural parameters in an attempt to characterize the biological relevance of their conformational diversity. In particular, we estimated the rASA (relative Accessible Surface Area) using DSSP  which waOur studies were based on the analysis of 91 proteins for which there is experimental evidence of conformational diversity in the native state . Since tFor each of the 91 selected proteins we performed pairwise structural comparisons of all conformers derived from CoDNaS. Unsurprisingly, even subtle structural perturbations associated with the presence of a bound ligand can have serious biological implications. Following from above, and due to the importance of analyzing structural differences between conformers as a proxy to understand protein function, the next section is focused on the possibility of applying common 3D modeling tools to reproduce the conformational diversity of selected protein targets.Started 20 years ago, the CASP initiative has established itself as a landmark community-wide effort for the assessment of protein structure prediction methods,45. 3D pA single structure-based approach would be promising if the target protein can be characterized by a narrow and homogeneous native state. In such cases all 3D modeling efforts should tend towards decoys that resemble the target structure closely. Nevertheless, when considering the existence of a marked conformational diversity, then the selection of protein models by their structural proximity to a unique native conformer may lead to a biased subset of structures in which decoys resembling other true conformers are not included. Hence, we explored how structurally derived rankings would be modified when different native conformers are simultaneously considered as targets in the evaluation of several 3D models proposed by various simulation protocols. For the 91 protein targets in our dataset we performed pairwise structural comparisons between the proposed decoys and each conformer in the pair with maximum conformational diversity, as identified in the previous section using CoDNaS database. When analyzing a high-quality subset (76 proteins targets) of proteins with 3 or more statistically significant structural alignments between any decoy and target pair, based on RMSD Z-scores calculated with MAMMOTH, the highest correlations occur when comparing decoys against structurally close conformers . As the Results shown in We also split CASP targets in ‘hard’ and ‘easy’ following CASP definition, according to their level of sequence and/or structural similarity to known folds . We As previously exposed using RMSD-based comparison, for 70% of the proteins in our dataset we detected different decoys structurally closer to each of the conformers representing the target native ensemble. Left panel in The increasing availability of experimental structures from native ensembles allowed us to assess the quality of protein structure models under a different perspective. We have found that considering the native state as an ensemble of conformers improves the selection of 3D structure models, thus helping to understand protein function more comprehensively. The evaluation of protein structure models is shown to be highly influenced by the conformational diversity of the target protein. This evaluation proves to be robust to the use of different structural similarity scores, in the sense that conformational diversity can always be detected if present, although with different sensibilities. Conversely, the selection of decoys is not robust regarding the choice of similarity measures, as decoy rankings would be very different.A majority of the proteins in our dataset showing different decoys were structurally closer to alternative native ensemble conformers. This finding reveals that, although not usually taken into account, many of the current methods for predicting three-dimensional structures of proteins could be used to predict conformational diversity as well. According to our results, there is no particular tertiary structure prediction method that could stand out in the estimation of close conformers to the target. However, we did find that, when template-based methods are used, using distant evolutionary templates produces a better sampling of the conformational space than using closer homologs.The biological analysis of several selected examples of top-ranked decoys allowed us to get a better understanding of their role as members of the native ensemble. Therefore, conformational diversity estimated from 3D modeling techniques adds an additional value for a meaningful interpretation of structure-function relationships. In the same sense, we deeply encourage the consideration of conformational diversity when conducting protein structure prediction and endorse the evaluation of conformational diversity in community-wide efforts like CASP. To this end, and while current prediction methods can be used as such, a proper selection of structural decoys that may account for conformational diversity in a target protein would necessarily require new improvements and novel methodologies of model evaluation in assessment protocols.S1 Appendix(DOCX)Click here for additional data file.S1 Dataset(GZ)Click here for additional data file.S1 FigPairs were taken from the CoDNaS database of different structures (from available PDB files) for each represented protein. GDT_TS values are normalized to the range .(TIFF)Click here for additional data file.S2 Fig3.Pairs of conformers were taken from the CoDNaS database for different structures. RMSD scores are expressed in Å while ΔrASA values are in Å(TIF)Click here for additional data file.S3 FigPairs were taken from the CoDNaS database of different structures (from available PDB files) for each represented protein.(TIF)Click here for additional data file.S4 Fig(a) Correlation against average RMSD of all decoy-target pairs of each protein. (b) Same as (a) but using average GDT_TS. (C) Same as (a) but using average TM-score. (d) Correlation against the average variance of all RMSD values between decoy-target pairs of each protein. (e) Same as (d) but using average variance of GDT_TS. (f) Same as (d) but using average variance of TM-score. RMSD scores are expressed in Å while GDT_TS values are normalized to the range .(TIFF)Click here for additional data file.