Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer.We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences.Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases. Cancer is a collection of diseases characterized by uncontrolled growth and spread of abnormal cells. It constitutes a major health burden in our society. For example, in 2012, approximately 14.1 million new cancer cases were diagnosed globally, and 8.2 million deaths or 14.6% of human deaths were caused . Hence, Some signaling pathways are already known to be cancer-related , 5. NeveThere are mainly three types of approaches to identify signaling pathways, which are the experimental approach , the sysProtein-protein interaction networks are often very large. Therefore, it may be preferable to use the data-driven approach to identify cancer-related signaling pathways at the level of proteins. The Steiner tree approach is an efficient data-driven approach that has been applied to process biomedical data –14. It cThere are different types of Steiner tree approaches. Researchers have already applied the classical Steiner tree approach  and the G= be a connected, undirected network, where V is the set of vertices, E is the set of edges, w is a function which maps each vertex in V to a real number called the node weight, and c is a function which maps each edge in E to a positive number called the edge cost. Let T⊆V be a subset of V called compulsory terminals. The purpose of this problem is to find a connected subnetwork G′=,T⊆V′⊆V,E′⊆E which minimizes the objective function The definition of node-weighted Steiner tree problem is given as follows: Let Nevertheless, it is still challenging to solve the node-weighted Steiner tree problem at present. Most existing techniques can only solve special cases of this problem, such as the classical Steiner tree problem in graphs  and the The subnetwork identified by our node-weighted Steiner tree techniques contains important elements of cancer-related signaling pathways. It is necessary to select these important elements from the subnetwork for a further, more detailed analysis. There are many metrics to evaluate the importance of network elements , 22, amoIn summary, our main contributions are as follows: we propose a method to generate protein-protein interaction networks with both positive and negative node weights; we modify two preprocessing techniques and a state-of-the-art heuristic algorithm to identify subnetworks in them; we propose two new metrics to select important elements of cancer-related signaling pathways from the identified subnetworks; we apply our node-weighted Steiner tree approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK; we conduct an experimental-evidence-based analysis on the identified important elements, and a deeper understanding towards these two signaling pathways is gained in this process.Protein-protein interaction networks are often very large, and critical information on cancer is hidden in them. In this section, we propose a method to generate node-weighted protein-protein interaction networks for the identification of important elements of cancer-related signaling pathways. We define a node-weighted protein-protein interaction network as a connected network with the following five types of elements:∙ vertex: each vertex represents a protein.∙ edge: each edge represents a protein-protein interaction.∙ compulsory terminal: each compulsory terminal represents a protein that must be contained in the identified subnetwork. Since the purpose is to identify important elements of cancer-related signaling pathways, proteins that are well known to be important to cancer signal transduction are selected to be compulsory terminals.∙ edge cost: edge cost is a positive value attached to each edge. Since the node-weighted Steiner tree technique tends to minimize the total edge cost in the identified subnetwork, we use edge costs to represent in-confidence scores of the existence of protein-protein interactions. As a result, the identified subnetwork tends to contain the most credible protein-protein interactions for cancer signal transduction. The quantified edge cost is calculated using the equation below, i and j are indexes of two different proteins, c is the cost of edge , α,β are positive constant values, and con is a score reflects the confidence of the existence of this protein-protein interaction.where ∙ node weight: node weight is a real value attached to each vertex. The identified subnetwork tends to contain proteins with big positive node weights while avoid proteins with big negative node weights. Hence, we use node weights to represent confidence scores of the existence of proteins in cancer-related signaling pathways. The quantified node weight is calculated using the equation below, w(i) is the node weight of vertex i, γ is a positive constant value, degree(i) is the degree of vertex i in the protein-protein interaction network, and T is the compulsory terminal set. Note that, the degree centrality has been widely used to quantify the importance of vertices in networks [∞ ensures all the important proteins represented by compulsory terminals are contained in the identified subnetwork.where networks , and proNode-weighted protein-protein interaction networks with these five types of elements can be generated using existing information on protein-protein interactions and cancer-related signaling pathways. An example is our generated node-weighted protein-protein interaction network for Homo sapiens see Fig. . After tThe node-weighted Steiner tree problem was separately proposed by Segev  and DuinMany preprocessing techniques have been proposed for various Steiner tree problems , 28. How∙ Terminal degree 1 test:if |T|≥2, the edge adjacent to a compulsory terminal with degree 1 is in the optimal solution.T|≥2 does not exist since it is implicitly met in all classical Steiner tree instances. However, in node-weighted Steiner tree instances, |T| may be 0 or 1. When |T|=1, the edge adjacent to a compulsory terminal with degree 1 may not be in the optimal solution. An example is a node-weighted Steiner tree instance where the optimal solution is the only compulsory terminal with degree 1. Therefore, by adding this condition, we modify this test to node-weighted Steiner tree instances.The initial version of this test was proposed by Koch et al. in 1998  to reduc∙ Non-terminal degree 1 test:for any vertex i∉T with degree 1, if |T|≥1 and w(i)≤c (vertex j is its adjacent vertex), then vertex i and edge  can be eliminated.T|≥1 and w(i)≤c. We modify this test to node-weighted Steiner tree instances by adding these two conditions.The initial version of this test was proposed by Beasley in 1984  to reducO(|V|). Therefore, they can be conducted in large protein-protein interaction networks in a short time. Note that, more sophisticated preprocessing techniques can also be modified to node-weighted Steiner tree instances, and they may reduce instance sizes more significantly than these two techniques. However, sophisticated preprocessing techniques may be too slow in large protein-protein interaction networks. Hence, in this paper, we only modify these two simple techniques for our application. We leave the modification of more sophisticated preprocessing techniques to the future work.The time complexity of these two modified techniques is Many Steiner tree algorithms have been proposed in the last decades. However, most of them cannot be applied to networks with negative node weights, while the ones that can may be too slow to process large protein-protein interaction networks. In this subsection, we modify a fast implementation of the unrooted Goemans-Williamson algorithm proposed by Hegde et al.  in the 2i,j) into two edge parts ep and ep. Let us define the edge splitting ratio s (s≥1) as follows. There are two phases in our modified algorithm: the growing phase and the pruning phase. In the growing phase, we use the “dynamic edge splitting” idea proposed by Cole et al. in 2001  to find slack{ep} is the slack of edge part ep, s is a constant value and s≥1.where ep and ep share the slack (or cost) of edge  at the ratio of 1:(s−1), and they associate respectively with vertex i and j. The total number of edge parts is 2|E|, and the number of edge parts associated with each vertex equals to the degree of this vertex. An edge part is active when the vertex it associates with is in an active cluster, otherwise the edge part is inactive. Initially, we set each vertex as a cluster, and the slack of each cluster equals to its node weight. All the clusters with positive slacks are active. Note that, the slack of an inactive cluster may be negative.The two edge parts tg. As tg increases, the slacks of active edge parts and clusters decrease. At any time, the remaining slack of an active cluster is the gap between its event time and tg; the remaining slack of an inactive cluster is the gap between its event time and its deactivation time; the remaining slack of an active edge part is the gap between its event time and tg; the remaining slack of an inactive edge part is the gap between its event time and the deactivation time of its cluster.All the active clusters and edge parts have their event time, which initially equals to their slacks. We maintain a global time value ep is the corresponding edge part for an edge event, and let r be the slack of edge part ep.There are two types of events in the growing phase, which are the edge event and the cluster event, and they are triggered in the order of their event time. In the cluster event, we simply deactivate the corresponding cluster. In the edge event, the slack of the corresponding edge part becomes 0. Assume edge part r=0, then we merge the two clusters connected by edge  and their edge parts. The slack of new cluster equals to the sum of slacks of the two merged clusters. Suppose the slack of new cluster is sl, we set the event time of new cluster to be tg+sl. Note that, an inactive cluster may be merged into an active cluster in an edge event. In that case, we need to increase the event time of edge parts in the inactive cluster by the gap between tg and its deactivation time. Furthermore, the most significant difference between our modified algorithm and its initial version is that the newly merged cluster may be inactive in our modified algorithm since the slack of the inactive cluster being merged may be negative.If r>0, then we distinguish two cases to update the event time of these two edge parts:If ep is active. Since we expect the slacks of these two edge parts to become 0 at the same time to trigger a merge event, we split the slack r evenly, and update the event time of both of these two edge parts to be tg+r/2.Case 1: the cluster containing edge part ep is inactive. We assume the cluster containing edge part ep stays inactive until a merge event is triggered by edge . Then, we update the event time of ep to be tg+r, and the event time of ep to be the deactivation time of its cluster.Case 2: the cluster containing edge part i,j), assuming both clusters maintain their current activity status. If one of the two clusters changes its activity status, this will not hold. An extreme situation is that both clusters were active and the cluster containing edge part ep becomes inactive since then. As a result, the next event on edge  will still have r>0, and we need to split the slack r again. In the worst case, the slack splitting case may keep happening endlessly. In this paper, we use a small value μ to deal with this case. If r<μ, we trigger the merge event. The optimization process of the growing phase terminates until there is no more than one active cluster left, and the subtree in the last active cluster is the raw solution tree we obtained in the growing phase. Note that, there may be no active cluster in the end of our growing phase, while there is always one active cluster left in the initial version of this algorithm.Note that, we update the event time of these two edge parts in the above way so that the two corresponding clusters would be merged in the next event on edge >nw(i), then we remove edge  and the subtree rooted at vertex i, or we update the nw value of vertex j using the following equation, In the pruning phase, we prune the raw solution tree above using the strong pruning algorithm proposed by Johnson et al. in 2000 . In thisO(|E|log|V|). Thus, it is fast in large networks.We keep processing all the non-root vertices until all of them have been processed. The remaining subtree is the identified protein-protein interaction subnetwork. The steps of our modified algorithm are shown in Table We use node-weighted Steiner tree techniques to identify a protein-protein interaction subnetwork. After the identification, we evaluate the importance of proteins and protein-protein interactions in it. The important ones are selected as the identified important elements of cancer-related signaling pathways.There are many metrics to evaluate the importance of network elements, among which betweenness centrality is the most popular one . The oriS and T′ be respectively the sets of source and terminal proteins of cancer-related signaling pathways, then we define the betweenness degree of protein m as Let SPij is the shortest path between source protein i and terminal protein j in the identified subnetwork , and SPij(m)=1 if protein m is in this path, or SPij(m)=0. A protein with a high betweenness degree is considered important.where emn as Similar to betweenness degree of proteins, we define the betweenness degree of protein-protein interaction emn is the interaction between protein m and protein n, SPij(emn)=1 if emn is in SPij, or SPij(emn)=0. A protein-protein interaction with a high betweenness degree is considered important. The following inequality is always met. where Therefore, proteins connected by interactions with high betweenness degrees will also have high betweenness degrees, which is reasonable since proteins connected by important interactions are important too.O(|V|2) [Calculating betweenness degrees needs to find the shortest path multiple times. Since the time complexity of finding the shortest path is O(|V|2) , it is tThe PI3K/Akt and MAPK signaling pathways are widely known to account for the causes of various cancers –38. NeveFirst, we generate a node-weighted protein-protein interaction network using existing information on protein-protein interactions and PI3K/Akt and MAPK signaling pathways. There are many databases on protein-protein interactions, such as BIND , BioGRIDString data can be directly used in the generation process. On the contrary, KEGG data cannot be directly used since it is recorded at the level of genes and genomes, not at the level of proteins. We need to transform the genes and genomes in the PI3K/Akt and MAPK signaling pathways in KEGG to the corresponding proteins. After the transformation, we obtain the PI3K/Akt and MAPK signaling pathways at the level of proteins, which are shown in Fig. α=2×106, β=2, γ=5. Note that, con is the experimental score in String that reflects the confidence of the existence of protein-protein interactions. Since protein-protein interactions in the PI3K/Akt and MAPK signaling pathways in KEGG are more likely to exist and be important, we increase their confidence scores by 50% while calculating edge costs. Moreover, in the PI3K/Akt and MAPK signaling pathways, signals transduce from source proteins to terminal proteins. Since these source and terminal proteins ) in the identified subnetwork are not in these KEGG pathways. These newly identified protein-protein interactions may also be important to cancer signal transduction .The identified subnetwork, which is shown in Fig. To select important elements of PI3K/Akt and MAPK signaling pathways from the identified subnetwork, we calculate betweenness degrees of all the proteins and protein-protein interactions in it using Eqs.  and 6)..6). The There are 9 proteins and 8 protein-protein interactions  is β induces a small variance between edge costs, while a large β induces a large variance between edge costs. Since the experimental scores in String reflect, but not accurately reflect the confidence of the existence of protein-protein interactions, it is not recommended to set β too small or too big. In this paper, we set β=2 to show our “moderate” confidence in these scores. After β, we set the value of α, which does not affect the ratios of costs of different edges. From the aesthetic perspective, we set α=2×106 to make the edge costs distributed around 100 to 300. After α,β, we set the value of γ. A small γ gives small negative node weights to non-compulsory proteins, while a large γ gives large negative node weights to them. Hence, a small γ may result in the identification of unrelated proteins, while a large γ may result in the missed identification of proteins in possible new cancer-related signaling pathways. In this paper, we set γ=5 to make a balance between removing unrelated proteins and keeping interested ones. Furthermore, we apply our node-weighted Steiner tree approach multiple times with different values of β,γ (α is fixed at 2×106), and the resulting percentages of identified proteins that are in the PI3K/Akt and MAPK signaling pathways in KEGG are shown in Table β has a bigger impact on the identification result than γ, and with the parameter settings above, the identification result overlaps the most with KEGG data.There are three parameters in the generation of node-weighted protein-protein interaction network, which are β in Eq.  and γ inγ in Eq. . α,β detWe aim to identify important elements of cancer-related signaling pathways at the level of proteins. There are mainly three types of approaches to identify signaling pathways for human diseases, which are the experimental approach, the systematic approach, and the data-driven approach. Protein-protein interaction networks are often very large, and only the data-driven approach is fast enough to process them. The Steiner tree approach is an efficient data-driven approach. Two types of Steiner tree approaches have already been applied to biomedical networks, which are the classical Steiner tree approach and the prize-collecting Steiner tree approach. However, as to protein-protein interaction networks, the classical Steiner tree approach fails to consider the properties of different proteins, while the prize-collecting Steiner tree approach may identify irrelevant proteins. Therefore, neither of them is suitable for processing protein-protein interaction networks. On the contrary, our node-weighted Steiner tree approach advantages these two approaches since it considers the properties of different proteins by attaching them with node weights and it can avoid irrelevant proteins by attaching them with negative node weights.Furthermore, our node-weighted Steiner tree approach is fast in processing protein-protein interaction networks. To our knowledge, our generated node-weighted protein-protein interaction network is the largest single protein-protein interaction network that has ever been generated and analyzed as a whole. Even so, on a commonly used personal computer, our approach only takes less than 2 seconds to identify important elements of PI3K/Akt and MAPK signaling pathways . Especially, our modified node-weighted Steiner tree algorithm only takes around 0.05 s to identify a subnetwork. As a matter of fact, this algorithm is still fast in much larger networks. The running time of this algorithm in three networks of different sizes is shown in Table Cancer is a major health problem in our society. A complete understanding of cancer-related signaling pathways will greatly benefit its prevention, diagnosis, and treatment. In this work, we propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. In this new approach, we first generate a node-weighted protein-protein interaction network using existing information on protein-protein interactions and cancer-related signaling pathways. Then, we modify two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in it. After that, we propose two new metrics to select important elements of cancer-related signaling pathways from this subnetwork. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: the PI3K/Akt and MAPK signaling pathways. On a commonly used personal computer, this new approach takes less than 2 seconds to identify their important elements in the full-scale node-weighted protein-protein interaction network for Homo sapiens. We analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. A deeper understanding towards the PI3K/Akt and MAPK signaling pathways is gained in this process. In conclusion, our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Hence, it can be applied to areas where fast processing of large protein-protein interaction data is required.


A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem.We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgoWeb server: Bioinformatics online. Our final dataset contains 60 710 proteins annotated with 27 760 classes . The dataset covers more than 90% of all proteins with experimental annotations in SwissProt. We use SwissProt’s  and testing set (20%). Due to computational limitations and the small number of annotations for very specific GO classes, we ranked GO classes by their number of annotations and selected the top 932 terms for BP, 589 terms for MF and 436 terms for the CC ontology. These cutoff values correspond to selecting only classes with the minimum number of annotations 250, 50 and 50, for BP, MF and CC, respectively.We create three binary label vectors for each protein sequence, one for each of the GO hierarchies. If a protein sequence is annotated with a GO class from our lists of selected classes, then we assign 1 to the term’s position in the binary label vector and use it as positive sample for this term. Otherwise, we assign 0 and use it as negative sample. For training and testing, we use proteins which have been annotated with at least one GO term from the set of the GO terms for the model.The input of our model is the amino acid (AA) sequence of a protein. Each protein is a character sequence composed of 20 unique AA codes. We generate trigrams of AA from the protein sequence. The trigrams can be represented as one-hot encoding vectors of length 8000; however, the sparse nature of one-hot encodings only provides a limited generalization performance. To address this limitation, we use the notion of dense embeddings . An embeWe built a vocabulary of unique AA trigrams where each trigram is represented by its 1-based index. Using this vocabulary, we encoded a sequence of length 1002 as a vector of 1000 indices. If the length of the sequence is less than 1002, we pad the vector with zeros. We ignore all the proteins with sequence length more than 1002. The first layer in the deep learning model is intended to learn embeddings where each index is mapped to a dense vector by referring to a lookup table, using an embedding size of 128 and therefore representing a protein sequence of length of 1002 as a matrix of 1000 × 128.h(y) between f(x) and g(x) with stride d is defined as:jh(y) is obtained by a sum over i of the convolutions between ig(x) and ijf(x). The output vector h represents the feature map learned through convolution.Convolutional Neural Networks (CNNs) are biologically inspired NN which try to mimic the receptive field of biological neuron. In CNNs, convolution operations are applied over the input layer to compute the output . They exw. The features after convolution and the temporal pooling layer are intended to be higher level representation of protein sequences which can then be used as input to fully connected layers for classification.The resulting feature map will contain redundant information and is of significant size. Therefore, to reduce the feature space, redundant information is discarded through temporal max-pooling  networks for multiple species from the STRING database  is a set of predicted classes for a protein i using a threshold t, and iT is a set of annotated classes for a protein i. Precision is averaged over the proteins where we at least predict one term and m(t) is the total number of such proteins. n is a number of all proteins in a test set.We evaluate our model performance with two measures  that aref we compute AUC of a ROC Curve of a sensitivity  for a given false positive rate (1 - specificity). We compute sensitivity and specificity using the following formulas:iP(t) is a set of predicted terms for a protein i using a threshold t and iT is a set of annotated terms for a protein i. Additionally, we report a term-centric maxF measure where for each term f we compute the F-measure using threshold t and all proteins in our test set. Then, we take the maximum for all the thresholds.t:The second measure is a term-centric where for each term C in the GO. Each of these layers has exactly one connection to an output neuron, Out(C), and, for each direct subclass D of C, a connection to another layer representing D. This architecture resembles the hierarchical structure of GO and the dependencies between its classes, ensures that discriminating features of each class can be learned hierarchically while taking into account the symbolic relations in GO. More generally, each dense layer of this ontology-structured neural network layout is intended to learn features that can discriminate between its subclasses. We build a machine learning model that aims to address three challenges in computational function prediction: learning features to represent a protein, predicting functions in a hierarchical output space with strong dependencies, and combining information from protein sequences with protein–protein interaction networks. The first part of our model learns a vector representation for a protein sequence which can be used as features to predict protein functions. The second part of the model aims to encode for the functional dependencies between classes in GO and optimizes classification accuracy over the hierarchical structure of GO at once instead of optimizing one model locally for each class. The intention is that this model can identify both explicit dependencies between classes in GO, as expressed by relations between classes encoded in the ontology, as well as implicit dependencies such as frequently co-occurring classes. While a single model over the entire GO would likely yield best results, due to the size of the GO, we independently train three models for each of GO’s three sub-ontologies, Molecular Function (MF), Biological Process (BP) and Cellular Component (CC), and focus exclusively on subclass relations between GO classes. We generate a series of fully connected layers, one for each class We train three model in a supervised way (one model for each of the GO ontologies). For this purpose, we first split all proteins with manually curated GO annotations in SwissProt in a training set (80%) and an evaluation set (20%). We use the manually assigned GO functions of the proteins in the training set to train our models. The performance of each model is globally optimized over all the GO functions  through back-propagation. We then evaluate the performance of our model on the 20% of proteins not used for training, using the evaluation metrics developed and employed in the CAFA challenge  was needed to perform the BLAST search. Actual prediction time for the neural network ranges between 2.2 ms per protein (for the CC model) to 3.5 ms per protein (for the BP model). Our results are similar to the reported results of GoFDR , will not be predictable from a single protein’s sequence or structure alone but require complex pathways and interacting proteins, all of which contribute to the function. For this purpose, several methods use protein–protein interaction networks to identify significant links between proteins that can be used to transfer functions, or significant network patterns that may be predictive of a function  to parts of these data types. For example, feature learning approaches have significantly improved the prediction of transcription factor binding sites and functional impact of genomic variants , and DeeIn addition to the multi-modal nature of features used in DeepGO, another contribution of our work is the deep hierarchical classification model that optimizes predictive performance on whole hierarchies, accounts for class dependencies (i.e. the semantics of annotations in GO) during training time, learns features in a hierarchical manner, and is optimized jointly together with the feature learning component of our model in an end-to-end manner. Our method can be applied to other applications with a similarly structured output space and which rely on learning feature representations. In particular, we plan to apply our model for predicting disease associations of genes which are encoded using the Disease Ontology , and information extracted from text.Supplementary DataClick here for additional data file.


Receptor Activity Modifying Proteins (RAMPs) serve as accessory proteins that modulate the signaling activities of G-Protein Coupled Receptors (GPCRs). RAMPs function by interacting with the N-termini and transmembrane domains of GPCRs, and the receptor phenotypes of the resulting complexes are determined by the specific isoform of the interacting RAMPs. RAMPs were discovered in 1998, and since that time the number of known RAMP-GPCR interactions has steadily increased; RAMPs are now known to interact with nearly every member of the class ‘B’, Secretin receptor family of peptide-binding GPCRs as well as some members of the class ‘A’ and ‘C’ peptide-binding GPCRs. Given the steadily increasing number of known RAMP–GPCR interactions, phenotypes and functions, there is a pressing need for a central resource dedicated to their storage, prediction and dissemination. We have developed a web application and database—RampDB—with the goal of addressing this need. RampDB consists of a custom RAMP–GPCR–ligand database integrated with a search utility, which together facilitate the exploration and analysis of RAMP interactions. The RampDB search utility allows users to explore known RAMP interactions, or to predict novel interactions, via either protein sequence (bioinformatic) or ligand (chemoinformatic) queries. The underlying architecture of RampDB was designed using best database practices in order to enable rapid retrieval of search results, automated updates and the seamless incorporation of additional features.Database URL:http://rampdb.biology.gatech.edu Receptor Activity Modifying Proteins (RAMPs) are single transmembrane domain accessory proteins that interact with G-Protein Coupled Receptors (GPCRs) to induce specific biological effects via the resulting ‘receptor phenotypes’ . Biologi1–3 receptors with RAMP1–3 which have high affinity for amylin, a peptide involved in the regulation of food intake (1 receptor) has high affinity for both amylin and neuropeptide calcitonin gene-related peptide (CGRP) and lower affinities for related peptides such as adrenomedullin2/intermedin and adrenomedullin. The ligand affinity order for CTR:RAMP2 complex (AMY2 receptor) is poorly defined for related peptides; however, these are high-affinity amylin receptors. The CTR:RAMP3 complex (AMY3 receptor) has higher affinity for amylin and lower affinities for CGRP, adrenomedullin2/intermedin and adrenomedullin , each containing an extracellular N-terminal domain, a single transmembrane domain and a short intracellular C-terminal domain . To dated intake . The CTRmedullin . These lmedullin ,19. Moremedullin .Subsequent studies have shown that RAMPs interact with other members of the ‘B’ class of peptide binding GPCRs, consisting of secretin receptors, such as glucagon receptor, vasoactive intestinal polypeptide receptor and parathyroid hormone receptors ,21,22. IRampDB is created to serve as a unified, web-enabled tool for the exploration and analysis of RAMP interactions. Each of these interactions consists of a specific combination of a RAMP, a GPCR and a ligand. RampDB is distinguished by its dual search utility, which allows users to explore known RAMP interactions via either protein sequence or ligand queries. Users can provide RAMP or GPCR protein (amino acid) sequences that are used in sequence similarity searches against a custom database of known RAMP–GPCR interactions, or they can provide ligand names or identification keys to search for similar ligands using state-of-the-art chemoinformatic similarity search methods. Matches to sequences or ligands yield a list of known interactions along with relevant functional information and references to the supporting literature. RampDB is set up in such a way as to continually update its catalog of known RAMP interactions.https://www.djangoproject.com/). The core of RampDB consists of a MySQL database (https://www.mysql.com/) coupled to several APIs that allow for rapid retrieval and updating of the underlying data. The database currently contains over 2500 proteins, from >300 species, all of which are known to function in RAMP–GPCR interactions. The database also contains all currently known RAMP interactions, each of which consists of a specific combination of a RAMP family, a GPCR family and their cognate ligands.RampDB was developed using Django, an open-source, industry standard Python web development framework (The MySQL database schema  was desihttp://getbootstrap.com/), with the Javascript framework AngularJS (https://angularjs.org/) handling the database API calls and data visualization. The multiple sequence alignments that result from protein query matches are displayed using the JavaScript package MSAViewer (The web application front end for RampDB was implemented using the Bootstrap framework (SAViewer .http://http://guidetopharmacology.org/) (RampDB is populated by RAMP and GPCR protein (amino acid) sequences taken from NCBI’s RefSeq protein database . RefSeq gy.org/) . LiteratThe entire collection of RAMP sequences can be organized into three discrete families , and the GPCR can similarly be organized into six discrete families . The family membership of all RAMP and GPCR proteins were characterized and are stored to inform pairwise sequence similarity search results as described in the next section. In addition, family-specific hidden Markov model (HMM) profiles were created for interacting domains of all three RAMP families and all six GPCR families. The HMM profiles were used for the domain-based sequence similarity search utility as described in the next section.RampDB chemical ligands were obtained from the NCBI PubChem database  based onThe main feature of RampDB is the dual search utility tool, which allows the user to input either a protein sequence or a ligand query in order to predict any potential RAMP interactions . ProteinThe ligand similarity search utility also entails a sequential combination of  text basHMM domain profile-based sequence similarity searching was chosen based on its greater sensitivity, compared with pairwise sequence similarity searches, as well as the fact that it focuses the searches on the relevant interaction domains that define the functional activity of RAMP-GPCR interactions. The generation and searching of HMM profiles was done using the program HMMER, which provides a series of command line tools for aligning and analyzing HMM profiles. Family-specific HMM profiles were generated for the interacting domains of all 3 RAMP families and all six GPCR families. The generation of family-specific profiles was initiated with pairwise blastp searches with representative protein sequence queries against the Genbank non-redundant protein sequence (nr) database. For each family-specific search, the top 5000 results from the nr database were retrieved in an effort to be exhaustive. This exhaustive list of hits was then filtered using the program CD-HIT a program that clusters similar sequences based on an identity threshold and keeps the representative sequence from each cluster . This fi. For example, the RAMP2 profile has one less cysteine, compared with the highly conserved cysteine residue found near the 55 residue position in the other two profiles, as well as a less conserved cysteine at position 27. For the RAMP3 profile, the histidine at position 70 is far less conserved compared to the other two profiles. These are just some examples that underscore the differences among the profiles that are used to characterize each family.The HMM logos for each of the main three RAMP profiles are shown in Upon accessing the main page of RampDB, the user is presented with several options . Users cOnce a protein or ligand query is submitted, the search utility attempts to identify a result as illustrated in For ligand queries, the results page displays a summary consisting of the query ligand name and the matching ligand name along with the PubChem Id, the molecular formula, the molecular weight and the InChiKey of the matching ligand . A 2D imThe protein and ligand search results can all be exported in a tab-delimited format by pressing the Export Results button at the top right of the page.RampDB was developed to meet the need for a central repository for the storage, dissemination and prediction of RAMP–GPCR–ligand interactions. RampDB was created and deployed as a web-enabled tool that allows users to visually explore and analyze RAMP interactions. The web-portal is distinguished by its dual search utility, consisting of a traditional protein sequence similarity searches coupled with a ligand similarity search tool. This dual search utility provides users with a comprehensive approach for the prediction potential RAMP interactions.The database that underlies RampDB contains thousands of currently known RAMPs and RAMP-interacting GPCR proteins. Furthermore, it provides the infrastructure and utility to quickly integrate new proteins or ligands as they are found to have RAMP interactions. Finally, there are database APIs in place that allow for periodic scanning against primary databases, such as NCBI’s Genbank or RefSeq, for adding new proteins or updating current protein entries.


Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins  either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable. The revolutions in biotechnology of the past two decades opened an unprecedented ability to analyze and organize biological information. The advent of the next generation sequencing technologies and accompanying software tools enables the sequencing and analysis of complete genomes, not only of whole species but of individual specimens also, often at a single cell level ,2,3. MorTo fully comprehend the biological process on the molecular level we first have to understand the physical laws that govern the interactions of biological polymers. Protein-DNA and protein-lipid interactions had been successfully addressed ,11,12,13The application of coarse-grained force fields in the analysis of protein-protein associations also attracted the attention of the research community ,30. BasdThe elucidation of physical interactions of proteins is appealing to the pharmaceutical industry as well , with anThe aim of this paper is to address the physical interactions between individual protein chains that form protein dimers. The approach described here uses the structural information only and the theory of phantom networks through its Gaussian network model (GNM) implementation ,57,58,59The connection between kinetically hot residues and interface residues has been established already . The met(n)either the change in total side-chain solvent-accessible surface area on complex formation (ΔASA) nor the sidechain ΔASA of hydrophobic atoms is well correlated to the change in free energy” [conservation does not have significant effect in hot spot prediction as a single feature”. However, their results indicate that the “residue occlusions from solvent and pairwise potentials are found to be the main discriminative features in hot spot prediction”. Lise et al. [ΔΔG upon alanine substitution but achieved only a limited success. Den et al. [possess a general tendency to bind organic compounds with a variety of structures, including key side chains of the partner protein”. This sentence is emphasized, because the results depicted in this paper also show that the binding interface of large host proteins is often determined by their own structure only. That may imply that host proteins are receptive for other proteins and/or small molecules besides their usually encountered binding partners. The method depicted here is also able to distinguish dimer decoys that were created with structures of unbound chains . Similarly, Tuncbag et al. [globally different protein structures can interact via similar architectural motifs”. They employed that fact through the PRISM algorithm that “utilizes rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function.” The term “kinetically hot residues” is similar to the term “hot spots” that is often used in protein science, but these terms have much more in common than linguistics. Residues that often appear in structurally preserved interfaces (in more than 50% of cases) are termed hot spots. The hot spots are important, because they are general contributors to the binding free energy. They are screened using the alanine-scanning mutagenesis and are therefore defined as spots where alanine mutation increases the binding free energy at least 2.0 kcal/mol ,67,68,69 energy” . They co energy” . Tuncbage et al. ,67 combin et al.  also useTo develop a really useful prediction method for a biological system as demonstrated in a series of recent publications  calculates the weighted sum of modes [The software for the Adjustable Gaussian Network Model code is composed of several different programs. The first program calculates contact maps and the corresponding eigenvectors and eigenvalues  for both contact ,55,56. Tof modes  as(1)〈(k1 to k2). The equation produces an estimate of a kinetic contribution of each residue for a given set of modes. The above equation is very similar to the singular value decomposition method [This equation, normalized by dividing the sum by , produces mean square fluctuations of each residue by a given set of modes (n method  used in The aim of the methods presented here is to recognize contact patches on protein surfaces and the corresponding scaffolds in the protein interiors. The first aim is to recognize contact residues. These are amino acid residues in which at least one atom is at the maximum distance of 4.5 Å from one or more atoms from the surface of the other chain. The distance of 4.5 Å corresponds to the size of one water molecule. The second aim is to recognize the first-layer residues (FLR), i.e., residues in contact with contact patches, but which are not contacts themselves. Therefore, those are neighboring residues from the same protein (at the maximum atom-atom distance of 4.5 Å from contact residues). They form scaffolds that surround contact residues ). Only dimer sets (41 of the 61 decoy sets) were used in this research. The first attempted method is based on the approach of Demirel et al. , which uTo test the assumption that heterodimers behave differently from homodimers, the above-described method was applied on heterodimers only (278 chains). The previous analysis and the corresponding distribution of good and bad predictions over the protein sequence lengths indicate that the weighted sum based on five modes only is not able to decipher the contact patterns on the surface and in the interior of proteins. With shorter protein chains (up to 100 residues), the weighted sum of five fastest modes is able to give a satisfying prediction of binding and first-layer residues, but with longer chains, the prediction efficiency fails . TherefoWhen this approach is applied to heterodimers, the amount of true positives becomes increased. That can be observed with the four protein chains analyzed previously . HoweverHeterodimers are protein complexes composed of two protein chains with no apparent sequential and structural similarity. What forms such entities? What kind of attraction forces two or more different protein chains to from a stable structure? In protein-DNA or protein-lipid interactions, electrostatic forces are often the key binding factors, but with protein interactions such forces usually have a small or negligible influence. When a protein dimer is analyzed, one may wonder whether its two constituents evolved separately, or whether they created by a mutation that broke a single protein chain into two separate parts. If we expand this premise, we can assume that such mutation can more easily survive if a point of separation is close to the terminal ends of the initial, single chain . In that case, a longer sub-chain has a higher probability of preserving its fold and function, because it will be highly homologous to the initial chain , reveals a different picture . There iThe previous attempts to recognize contact and first-layer residues via the Gaussian Network Model were based on a static approach in which protein dimer structures were analyzed using either 5 fastest normal modes or modes that corresponded to the top 10% of the eigenvalues range. Those approaches showed that kinetically hot residues may play a role in protein-protein interactions, but they did not offer enough proof for that assertion. With some protein chains they produced excellent results, but with some they failed. More importantly, the percentage of good predictions  was comparatively small . Many of the proteins had a very high percentage of both true positives and false positives. In addition, a significant number of proteins had a very small percentage of both true and false predictions. All of this implied that prediction algorithm had to be improved.The analysis of the average percentage of targets per sequence length reveals that the amount of targets and the chain length are inversely proportional. Larger proteins with longer sequences have a smaller percentage of contact and first-layer residues than shorter chains. –If the overall percentage of predictions is too large for that protein’s sequence length class , the number of fast modes should be reduced by one, and the whole prediction procedure should be repeated (Equation (1)).–If the percentage of predictions is too small for the protein’s sequence length class , the number of fast modes should be increased by one, and the whole prediction procedure should be repeated (Equation (1)). –The procedure should be repeated until the percentage of predictions does not fit between the maximum and minimum amount of predictions for a given sequence length.The information on the targets distribution can be used to improve the prediction approach. The prediction can be adjusted to each particular protein chain through a comparison of the current prediction output, i.e., current ratio of predictions  over the total number of residues, to the expected, i.e., average, percentage of targets for that protein’s sequence length class. The improvement of the prediction algorithm can be performed as follows:A simple strategy adjusts the number of modes for each particular chain: if the number of residues in a chain is less than or equal to 300, too many predictions are taken to be 60%. In that case, i.e., if the amount of predictions is over 60% of all residues, the number of modes is reduced by one, and the prediction procedure is repeated (Equation (1)). Similarly, if the number of residues is greater than 300, too many predictions are taken to be 50%. Furthermore, if the chain length is less or equal to 500 residues, too few predictions are taken to be 40%. For cases like that, the number of modes is increased by one, and the whole procedure is repeated. For longer chains, too few predictions are 20%. To avoid infinite loops, only one increase followed by a decrease is allowed, and vice versa. The prediction procedure itself spreads the influence of kinetically hot residues linearly upstream and downstream along the sequence, as with the previously described methods. The procedure starts with a number of modes that correspond to the top 10% of eigenvalues range for the protein being analyzed. This approach ensures that longer proteins have enough predictions, and that shorter ones are not saturated with too many false positives. The analysis performed on the four chains used previously to describe the prediction procedure confirms the above results . For theWhen this approach is applied to heterodimer proteins with low sequence length ratios (with the chains longer than 80 residues), the results are less than satisfactory . The truα atoms and disregards any sequential/connectivity information. To apply this approach, the maximum cutoff Cα-Cα distance from the center of a hot residue was introduced, from which its influence can be spread. The cutoff distance of 6 Å was applied with for shorter protein chains (for sequence lengths shorter than 250), and the cutoff of 8 Å was applied for longer protein chains. All residues that are within the sphere centered at the Cα atom of the hot residue and within the assigned cutoff distance are considered to be “predictions”, i.e., they are assumed to be either contact or first-layer residues. All other residues are rejected (for that particular hot residue). The two cutoff values were estimated empirically. To extract spatial neighbors, distances between residues (Cα-Cα distances) were calculated for each particular protein and sorted in ascending order. The adjustable algorithm introduced in the previous chapter uses sequential neighbors only to spread the influence of hot residues. It produces good predictions of contact and first-layer residues but offers a room for improvement. The prediction can be improved if spatial neighbors, instead of sequential ones, are used to spread the influence of hot residues. This approach is much closer to the true nature of the GNM algorithm that uses only spatial distances between CWith proteins from low sequence length ratio dimers, the results are not as good. The true positives mean is 52.22%, and the false positives mean is 48.81%. There is 42.42% of good predictions (56 proteins) and 27.27% of very bad predictions (36 proteins). See α atoms to calculate the protein connectivity matrix). In this combined approach, the influence of a hot residue is first spread linearly to its sequential neighbors . After that, the influence is spread to the hot residue’s spatial neighbors whose Cα atoms are within a sphere of a given cutoff radius with a center in the hot residue’s Cα atom . The two methods described previously base their prediction on the adjustable number of modes. The first method spreads the influence of hot residues linearly, i.e., to sequential neighbors only, while the second method spreads the influence to spatial neighbors within a sphere of a given cutoff radius. The first method treats a protein chain as a set of amino acids that are chained together. The second method only sees the spatial-3D neighborhood of a hot residue and rejects the fact that the protein is an ordered set of amino acids that are physically connected. This chapter introduces the combination of the sequential and 3D spatial approaches in attempt to boost the overall prediction. By combining the one-dimensional linear approach with the three-dimensional one, the residue connectivity information is included into the structure based method, which thus takes into account the chain-like nature of proteins  to 29.55% in the lower right quadrant (39 proteins), see When both proteins per dimer are addressed as a pair using the combined approach , the analysis confirms that heterodimers with high sequence length ratios often behave quite differently from heterodimers with low sequence length ratios see . In majod in the e in f in In the previous chapters a number of methods for the contact and first-layer residues prediction were presented. The presentation started with a very simple approach based on the fixed number of modes (5) and ended with a protocol that adjusts the number of modes to the analyzed chain and spreads the influence of a hot residue in the adaptable fashion using the sequential and spatial influence of hot residues. The true evaluation of these protocols can be done only through a direct comparison of their prediction efficiencies. The comparison of the true positives mean vs. the false positives mean and comparison of their good vs. very bad predictions are obvious measures of the quality of the prediction, and we used them here see . As the n and the sequence length s is roughly linear  × s). However, when longer chains are included, the relationship becomes quadratic (m = 3.4794 + (0.00030756) × s + (2.8381 × 10-5) × s2). Those relationships are strongly influenced by the distribution of chains in the training set  is expressed via two scores: the best status of a near native decoy among the all decoys, and the coverage, i.e., the number of near native decoys among the best Our analysis reveals that in 19 out of 41 Vakser decoys sets , and in 4 out of 10 Sternberg decoys sets , either one or both chains are properly accessed by the adjustable GNM method. Those observations are even more significant if decoy sets badly characterized by both methods  are removed from the analysis . The chains that form these dimers probably experience significant structural rearrangements during or upon the binding. In most cases, the longer chain is better assessed through the adjustable GNM than its shorter partner, which was to be expected following the assumption of the opposite behavior of binding partners, but in some cases  the shorter partner has a higher score. The adjustable GNM protocol is fairly successful in predicting near native structures. That information should be taken in the light of fact that near native decoys are based on nonbound structures, which makes the protocol even more successful. Similar ability was reported by Kozakov et al. . The staThis paper addresses the physical interactions of proteins, which is an important issue in molecular biology and biophysics. It depicts a method based on the theory of phantom networks ,49,52 anThe adjustable GNM protocols were tested on Sternberg  and VaksThe approach described here can be an excellent guide to a more efficient drug design, especially for the design of small molecule inhibitors of protein-protein interactions ,45,46,47As pointed out in  and demoThe adjustable approach, although able to dynamically connect the kinetically hot residues and binding patches and the corresponding structural scaffolds, still has a space for improvement. For instance, the application of surface area descriptors may reduce the false positives rate. A better estimation of the number of expected target residues may also improve the prediction. The application of the latest protein-protein and protein-ligand databases  may alsoThis work was primarily focused on the behavior of heterodimers. Homodimers were not explored in detail. Their behavior should be addressed more thoroughly, for example, by using a combination of slow and fast modes . FurtherThe importance of this work is twofold. First, it gives an efficient algorithm that is able to decipher individual protein-protein interactions, and it offers a theoretical insight into the mechanism of protein binding. Second, it shows that a simple approach based only on the statistic of residue-residue interactions may lead to overfitting; thus, the shape of partnering protein chains, as well as the absolute and relative size of the interacting proteins (molecules), should be also taken into account. The fact that in heterodimers, at least one of the proteins has its binding scaffold determined by the kinetically hot residues may imply that protein-protein interactions are, at least partially, entropically driven . Highly


Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform. Common strategies in drug discovery include forward pharmacology  and ratiWe have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform ,15,16 toTo date, putative drug candidates generated by CANDO have been based on simple comparison metrics, primarily the root mean square deviation (RMSD) of the binding scores present in a pair of drug-proteome interaction signatures. Our platform is evaluated using a benchmarking method that assesses per indication accuracies based on whether or not other drugs associated with the same indication can be captured within a certain cutoff in terms of similarity to a particular drug approved for that indication. Incorporating machine learning, which is continuing to prove its utility in many aspects of biomedicine ,24,25 inTherefore, in this study, we set out to find a reduced set of proteins that can therapeutically characterize compounds, as well as using the full protein library, with the eventual goal of utilizing them in future machine learning experiments. Our strategy involves using a brute force or greedy feature selection, where the full protein library was split into smaller subsets that were subsequently benchmarked and ranked according to their performance by different metrics. The best performing subsets were recombined into supersets and benchmarked again, which produced substantial improvement with all the benchmarking metrics. Further analysis of the best performing protein subsets and supersets revealed that those that contained proteins predicted to bind to a more equitably diverse ligand structure distribution were strongly associated with increased benchmarking performance, indicating that drugs approved for human use have a specific range and distribution of protein binding site interactions. In addition, protein supersets optimized for independent compound libraries were cross-tested and were able to reproduce the performance of using the full protein library. This indicates that overtraining on a specific compound library during this iterative procedure was limited in scope, making these protein supersets broadly applicable for characterizing drug behavior. We applied one of these protein libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma, and more of the resulting predictions could be validated in the biomedical literature compared to those suggested by using the full protein library, indicating the utility of our approach.Sorting the supersets by size reveals that at least 80–120 proteins, or 10–15 subsets, are required to reach optimal benchmarking performance . NonreduFor the independent compound library experiment, average indication accuracy was chosen as the ranking criterion because it performed the best in all three metrics through ten iterations in the superset experiment . Box andT-test . The ligand cluster analysis was repeated for a superset and a subpar set, and both followed a similar pattern to the one previously observed, with the superset having a more equitable distribution of interactions among clusters and the subpar set being far more imbalanced.The protein subsets and supersets were analyzed based on four criteria to elucidate the feature(s) responsible for benchmarking performance: organismal source, structure source (X-ray diffraction or modeling), fold space, and interacting ligand structure distributions (see the Methods). There were no significant correlations found for the first three criteria; no organism(s) or fold(s) was consistently represented in the best performing sets, while the structure sources were evenly distributed among the best and worst performing subsets and supersets. All ligands in the COFACTOR  databaseT-test . The subBased on the subset analysis, it was hypothesized that proteins having a more diverse and equitable distribution of interactions will benchmark better. Protein libraries were created by ranking the 46,784 proteins in CANDO on the variance of the number of interactions attributed to each ligand cluster (see the Methods). Creating libraries using proteins with the lowest variance of ligand cluster interactions  results in benchmarking performance much higher than libraries composed of proteins with high variance . To elucThe lists of the putative drug candidates against malaria, tuberculosis, and large cell carcinoma generated using both the created protein library and the full protein library are available in the The splitting and ranking protocol was originally intended to find a protein subset that benchmarked as least as well as the full set. The improvement of the benchmarking performance is an encouraging sign for incorporating machine learning in the CANDO platform in the future, and discovering how more complex weighting and relating of proteins contribute to drug repurposing accuracy, which is difficult to do with simple RMSD calculations. The smaller-sized protein libraries generated as part of this study, representing a 100–1000-fold reduction in size, will be more conducive to machine learning. Feature reduction through approaches other than PCA, such as neural network-based auto-encoders, will provide an important contrast to our proposed method.The independent compound library experiment demonstrated that optimized protein sets based on a particular library were capable of therapeutically characterizing a completely different one, indicating that these supersets are generalizable. In other words, if a new drug/compound is added to the CANDO putative drug library, these reduced size supersets are likely able to describe its behavior at least as well as using every protein available. In addition to facilitating machine learning, our findings suggest a greatly reduced time required to generate new proteomic interaction vectors, which is particularly important if the program/protocol of choice for generating interactions is computationally expensive. Any repurposing candidates suggested from using the supersets are on average more clinically relevant, as they were able to recapture drug behavior more accurately than using the full protein library in a statistically-significant manner.The knowledge that ligand cluster interaction diversity is the key requisite feature in describing drug behavior may also lead to optimization strategies other than the computationally-intensive splitting and ranking protocol used in this study. The importance of ligand cluster interaction diversity may play a role in a variety of applications in systems and synthetic biology, for example in the design of protein systems that are specifically tailored to handle drug absorption, distribution, metabolism, and excretion processes. These optimizations incorporated into our repurposing platform should result in the rapid generation of more accurate putative drug repurposing candidates, thereby alleviating the problems associated with current drug discovery. We used a specific reduced size library of 100 proteins optimized for ligand cluster interaction diversity to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma to demonstrate its effectiveness. The reduced library suggested twelve drug candidates with some evidence of activity against the respective indications, whereas the full library suggested eight ; this result cumulatively corresponds to a 50% increase in predictive power and provides evidence of the effectiveness of our protein feature analysis pipeline. Performing a systematic and continuous literature analysis of the putative drug candidates generated using our pipelines for every possible indication would continue to lend further rigor in assessing its effectiveness and is one of our future goals.The ligand cluster analysis revealed that compound–protein interactions are more therapeutically relevant if the proteins used to describe the behavior of a compound are diverse in terms of the structures of the ligands that interact with their binding sites. Protein libraries with fewer predicted ligand cluster binding partners yield much worse performance than those consisting of proteins interacting with a more structurally-diverse range of ligands. Coupling this with the finding that there is a minimum number of proteins required to reach optimal benchmarking accuracies , which wIn the context of drug design, comprehending the promiscuous nature of small molecule drugs to develop strategies that optimize their interactions with macromolecular targets of interest, while minimizing interactions leading to adverse events, will address the two leading causes of drug attrition in clinical trials . To thishttps://chemaxon.com/). InChiKeyswere generated from all preprocessed compounds using Xemistry’s Cactvs Chemoinformatics Toolkit [https://www.xemistry.com/) to remove redundancies. Drug–disease associations were obtained from the Comparative Toxicogenomics Database (CTD) [The putative drug library in the CANDO platform comprises 3733 human use compounds, including all clinically-approved drugs from the U.S. FDA, Europe, Canada, and Japan, as well as some experimental compounds collected from DrugBank , the NCG Toolkit  (https:/se (CTD)  and mappse (CTD) ,16,18,20Homo sapiens and several higher order eukaryotes, bacteria, and viruses. Protein structure models were generated using HHBLITS [The protein structure library in the CANDO platform is made up of 48,278 tertiary conformations solved using X-ray diffraction taken from the Protein Data Bank (PDB)   calculated between every pair of compound interaction signatures . Two compounds with a low RMSD value are hypothesized to have similar behavior ,16,18,20The 46,784 proteins in the CANDO platform were randomly split into 5848 subsets of 8 and subsequently benchmarked using the method described above. The size of 8 was selected because it offered the widest range of benchmarking values (relative to larger sizes), reduced the computational cost of the experiments , divided into 46,784 evenly, and also provided an adequate signal for the multitargeting approach to work according to our prior studies . A totalThe CANDO putative drug library was split into two disjoint libraries comprising ≈50% of the compounds (1866 and 1867 compounds) five times. For each such library, the indication mapping was reconstructed to include only the disease associations of the drugs present. Each corresponding pipeline comprised of a disjoint compound library was subjected to ten iterations of the splitting and ranking protocol. Protein supersets, composed of the fifty best performing subsets for each metric that were progressively combined (see the previous section), were generated and benchmarked. Supersets less than a size of 80 were excluded due to the results of http://www.rdkit.org/) library based on the Tanimoto distance [http://www.daylight.com/). A total of 64,592 ligands were clustered with 7252 ligands failing due to molecular file conversion errors. Each protein in the subsets and supersets was assigned a ligand cluster signature where each value in the vector is the number of times a ligand from a given cluster was chosen while calculating the compound–protein interaction score protocol for that protein. The fraction of interactions belonging to each cluster for each protein set was calculated by first adding the ligand cluster signatures for each protein belonging to the set, then ranking the clusters from greatest to smallest, and finally dividing by the number of total interactions (size of the protein set times 3733). These signatures may provide insight into the structural diversity of ligands with which proteins are interacting, which is a measure of promiscuity. The ligand cluster signatures of the best and worst 50 subsets from the first iteration of the splitting and ranking protocol were averaged together, with the resulting distribution of cluster counts at each rank compared using Welch’s T-test for equal means. Protein subsets were chosen from only one iteration to ensure independence.The best performing protein subsets and supersets were further analyzed to elucidate the protein feature(s) responsible for increased benchmarking performance. The protein subsets and supersets were analyzed based on four criteria: organismal source, structure source (X-ray diffraction or modeling), fold space (based on the CATHclassification of proteins ), and indistance  of theirOf the features evaluated (see above), only the ligand cluster distribution could be correlated with benchmarking performance (see the Results). We then generated new protein structure libraries that captured this feature ideally and assessed benchmarking performance. Each protein was ranked based on the variance of the values in their ligand cluster signatures. A minimum cutoff of two clusters was used to prevent the trivial case of proteins with only one cluster mapped (with a variance of zero). Another cutoff of at least 1867 total mapped interactions was used to account for proteins with interactions mapped to unclustered ligands. Proteins were randomly selected from the top of the ranked list of variances at size increments of 20 and then benchmarked. The cutoff of proteins considered for random selection was two-times the size of the library . A total of 50 libraries were made for each size to generate a distribution of benchmarking values. This procedure was repeated for the bottom of the ranked list of variances for comparison.We analyzed the ability of the created protein libraries to generate putative drug candidates for the treatment of malaria and tuberculosis, indications targeted in previous publications ,17, as wWe have developed an integrated pipeline that allows for the elucidation of proteins and their features, which are important for benchmarking in the CANDO platform and therefore important for drug repurposing and design. We were able to reproduce the performance of the complete CANDO protein structure library with orders of magnitude fewer proteins, allowing for more rapid candidate generation when evaluating new putative drug libraries or any other changes to the platform. We discovered that moderately promiscuous proteins, in terms of the structures of ligands with which they are predicted to interact, are important for describing how drugs behave in biological systems, a claim validated by literature evidence supporting putative drug candidates generated by a library composed of a subset of these proteins for the treatment of malaria, tuberculosis, and large cell carcinoma. The implications for drug design are that appreciating the multitarget nature of small molecule therapies and optimizing their interactions with the range of macromolecular targets that they are exposed to in their environments during their absorption, dispersion, metabolism, and excretion may be more fruitful than traditional rational drug design using single targets.


Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts.A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein explicitly and implicitly mentioned biological molecule terms that pertain functional categories in biomedical literature. That is, PL-PPF employs a combination of statistical-based explicit term extraction techniques and logic-based implicit term extraction techniques. The statistical component of PL-PPF predicts some of the functions of a protein by extracting the explicitly mentioned functional terms that directly describe the functions of the protein from the biomedical texts associated with the protein. The logic-based component of PL-PPF predicts additional functions of the protein by inferring the functional terms that co-occur implicitly with the protein in the biomedical texts associated with it. First, the system employs its statistical-based component to extract the explicitly mentioned functional terms. Then, it employs its logic-based component to infer additional functions of the protein. Our hypothesis is that important biological molecule terms pertaining functional categories of proteins are likely to co-occur implicitly with the proteins in biomedical texts. We evaluated PL-PPF experimentally and compared it with five systems. Results revealed better prediction performance.To overcome the limitations of methods that rely solely on explicitly mentioned terms in texts to predict protein functions, we propose in this paper an Information Extraction system called PL-PPF. The proposed system employs techniques for predicting the functions of proteins based on their co-occurrences with  and the other adopting only the explicit terms co-occurrence extraction techniques . The experimental results showed that the complete version outperformed significantly the other version. This is attributed to the effectiveness of the rules of predicate logic to infer functional terms that co-occur implicitly with proteins in biomedical texts. A demo application of PL-PPF can be accessed through the following link: http://ecesrvr.kustar.ac.ae:8080/plppf/The experimental results showed that PL-PPF outperformed the other five systems. This is an indication of the effectiveness and practical viability of PL-PPF’s combination of explicit and implicit techniques. We also evaluated two versions of PL-PPF: one adopting the complete techniques The online version of this article (10.1186/s12859-019-2594-y) contains supplementary material, which is available to authorized users. Determining protein functions has been one of the central objectives for bioinformaticians, especially after the post-genomic era. This is because proteins have key roles in many biological processes. Identifying protein functions using experimental approaches is laborious and time consuming. Therefore, computational methods have been used extensively as alternatives. The underlying techniques adopted by most of these approaches revolve around computing protein functions from already annotated proteins. Most of them reference already annotated proteins using their structures , sequencimplicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts. Towards this, we propose in this paper an Information Extraction system called PL-PPF (Predicate Logic for Predicting Protein Functions) that employs techniques for predicting the functions of proteins based on their co-occurrences in texts with explicitly and implicitly mentioned biological molecule terms pertaining functional categories. PL-PPF infers the implicit terms using the rules of predicate logic. It does so by triggering protein specification rules recursively in the form of predicate logic’s premises [semantic relationships among the biological terms in sentences.We observe that some important biological molecule terms pertaining functional categories may premises . It extrinference rules for predicate logic. It does so to deduce functional relationships between proteins. Ultimately, these deduced relationships enable PL-PPF to predict the functions of unannotated proteins. Let Pu be an unannotated protein. Let Lc be a list of known protein characteristics represented in the form of predicate logic’s premises [Pu based on their co-occurrences in biomedical texts. It extracts the semantically related biological molecule terms to Pu in the sentences of the texts by employing linguistic computational techniques. It would then utilize these extracted terms as identifiers to serve as triggers for the appropriate premises from the list Lc using the standard rules of inferences [Pu in the texts.Using known protein and biological characteristics, PL-PPF composes rule-based protein specifications. These specifications are known protein characteristics in literature. PL-PPF composes these specifications in a pattern similar to predicate logic’s premises . It trigpremises . PL-PPF ferences , 16. TheSimilar to our approach, a number of studies employed logic-based approaches as complementary to statistical approaches to perform some biological-related tasks. For example,  demonstrLogic-based approaches have been successfully applied to solve complex problems in bioinformatics by viewing these problems as binary classification tasks. For example,  achievedUsing known biological characteristics, PL-PPF composes rule-based protein specifications. It composes these specifications in a pattern similar to predicate logic’s premises . “Repressemantically related to an unannotated protein pu based on their explicit co-occurrences in texts. If an extracted term denotes a functional category f, PL-PPF will assign pu the function f. PL-PPF will also use the extracted term to serve as a given premise and apply it as a trigger identifier for the appropriate protein specification rules to identify additional functions of pu. “.PL-PPF employs computational linguistic techniques to extract the biological molecule terms that are pu the functional terms that co-occur implicitly with pu in the texts by recursively triggering the appropriate premises constructed in step 1 and the given premises extracted in step 2 using the standard rules of inference for predicate logic. The conclusion will be a functional category that co-occurs implicitly with pu in the texts. “PL-PPF will assign PL-PPF infers the functions of an unannotated protein by going through the following sequential steps:sample of protein specification rules in the form of predicate logic’s premises. It includes only the rules used in the examples presented in the paper to illustrate the proposed concepts. We constructed the premises in Table 1 is constructed based on the following protein characteristics: (1) the folding of a protein takes place after a sequence of structural changes  [Premise Rprotein) , and (2)protein) .2 and R3 are constructed based on the following protein characteristic: each protein’s sequence is unique and defines the structure and function of the protein [Premises R protein .4 is constructed based on the following protein characteristics: (1) the covalent bonds of a protein contribute to its structure [Premise Rtructure , and (2)ristics:  the cova5 is constructed based on the following protein characteristic: a protein’s non-covalent interaction folding and dimensional structure can define the protein’s biological function [Premise R6 is constructed based on the following protein characteristic: protein-protein interactions form complexes by interacting with one another [Premises R another .7 and R8 are constructed based on the following protein characteristics: (1) a complex assembly can result in a new function that neither protein can provide alone  [ [Premises Runction) , and (2)unction) .9 is constructed based on the following protein characteristics: (1) proteins can be classified based on the similarities of their structural domains [p can be inferred from the functions of proteins that fall under the same structural classification as p [Premise Rristics:  proteins domains , and (3)ristics:  proteins10 is constructed based on the following protein characteristics: (1) proteins can be classified based on the similarities of their amino acid sequences [p can be inferred from the structures of the proteins that fall under the same amino acid sequence classification as p [Premise Requences , and (2)ion as p .11 is constructed based on the following protein characteristic: the sequence of a protein’s amino acids is inferred from the combination of the protein’s covalent interactions with ligands and the protein’s function [Premise R12 is constructed based on the following protein characteristic: non-covalent bonds between proteins during their transient interactions lead to Protein-Protein Interactions [Premise Rractions .13 is constructed based on the following protein characteristic: the structure of a protein can reveal an insight into its amino acid sequence [Premise Rsequence .A predicate is a statement of one or more predicate variables. It can be transformed to a proposition by assigning values to the variables. These values determine whether the statements are true or false. The propositions are constructed by connecting the statements using logical connectives. PL-PPF composes protein specifications in a similar fashion. Using known protein and biological characteristics, PL-PPF composes the protein specifications from these known characteristics. It represents the specifications in a pattern similar to predicate logic’s premises . It usesexplicitly with an unannotated protein pu in the sentences of biomedical texts. If an extracted term denotes a functional category f, PL-PPF will assign pu the function f. PL-PPF will also use the extracted term to serve as a given premise and apply it as a trigger identifier for the appropriate protein specification rules to infer the functional category that co-occurs implicitly with pu in texts. The co-occurrence of a biological molecule term and pu in a sentence does not guarantee that this term and pu are associated. To be associated, the term and pu have to be semantically related in the sentence. We consider a term as semantically related to an unannotated protein, if their co-occurrence probability of being related is significantly larger than their co-occurrence probability of being unrelated in texts. PL-PPF computes the occurrence probabilities of terms using Z-score [PL-PPF extracts the biological molecule terms that co-occur  Z-score . For twoWe use the term “training dataset” to differentiate between the following: (1) the set of biomedical texts stored in PL-PPF’s database, and (2) the set of biomedical texts associated with an unannotated protein, whose functions need to be annotated. To differentiate between the two, we call the texts stored in PL-PPF’s database a “training dataset”. In order for two molecule terms in texts associated with an unannotated protein to be semantically related, they have to be semantically related in the texts stored in the database .Based on linguistics, two nouns are considered related within a sentence, if they are connected by a pronoun  . PL-PPF Based on linguistics, two nouns are considered unrelated within a sentence, if they are connected by a preposition modifier  , 24. PL-We present below two of the key computational linguistic techniques adopted by PL-PPF to extract the molecule terms that are semantically related to an unannotated protein based on their explicit co-occurrences in the sentences:p implicitly using the following: (1) the protein specification rules  described in “ that co-occur explicitly with p in biomedical literature and described in “inference rules for predicate logic. PL-PPF can infer the functions of p by recursively triggering the protein specification rules using the premises  and the standard inference rules for predicate logic. At each recursion, an inference rule is triggered and applied to the premises that have been proven previously. This will lead to a newly proven premise. The final conclusion will be a protein function, which will be considered as the function of p. The conclusion is valid, if it has been deducted from all previous premises [PL-PPF computes the functions of an unannotated protein premises . Table 2We now present case studies in Examples 1 to 4 to show the effectiveness of the deductive inferencing methodology presented in this section. The examples use various biological molecule terms as given premises for inferring the functions of unannotated proteins.u in biomedical texts after applying the techniques presented in “x) and ST(Px)  and ST(Px) in texts can be indicative of an implicit mentioning of the function of Px ). Therefore, the co-occurrences of FD(Px), ST(Px), and Pu can be indicative of an implicit co-occurrences of F(Px) and Pu. Accordingly, the functions of Pu is likely to be similar to F(Px). Table x) and ST(Px) are indicative of F(Px).Consider that PL-PPF extracted the following terms based on their co-occurrences with an unannotated protein Pu in biomedical texts: AAS(Px) and AAS(Py)  and AAS(Py) in texts can be indicative of implicit mentioning of the functions of Px and Py  and F(Py)). Therefore, the co-occurrences of AAS(Px), AAS(Py), and Pu can be indicatives of implicit co-occurrences of F(Px), F(Py), and Pu. Accordingly, the functions of Pu is likely to be similar to F(Px) and F(Py). Table x) and AAS(Py) are indicative of F(Px) and F(Py).Consider that PL-PPF extracted the following terms based on their explicit co-occurrences with an unannotated protein Pu in biomedical texts: ST(Px)  in texts can be indicative of implicit mentioning of the function of Px ). Therefore, the co-occurrences of ST(Px) and Pu can be indicatives of implicit co-occurrences of F(Px) and Pu. Accordingly, the functions of Pu is likely to be similar to F(Px). Table x) is indicative of F(Px).Consider that PL-PPF extracted the following term based on its explicit co-occurrences with an unannotated protein Pu in biomedical texts: NCBND and F(Px)  and F(Px) in texts can be indicative of implicit mentioning of the function of Py ). Therefore, the co-occurrences of NCBND, F(Px), and Pu can be indicative of implicit co-occurrences of F(Py), and Pu. Accordingly, the functions of Pu is likely to be similar to F(Py). Table x, Py) and F(Px) are indicative of F(Py).Consider that PL-PPF extracted the following terms based on their explicit co-occurrences with an unannotated protein Ppu the functions of the set S of already annotated proteins, if pu and S have similar characteristic terms. The classifier employed by Text-KNN is based on k-nearest neighbour and the classifier employed by Text-SVM is based on support vector machine. In the framework of GOstruct, an unannotated protein pu is annotated with the functions of a Gene Ontology (GO) term, if this term co-occurs in close proximity with pu in biomedical texts.We implemented PL-PPF in Java and used Prolog as the logic programming language. We ran it on Intel(R) Core(TM) i7 processor and a CPU that has frequency equals 2.70 GHz. The machine has 16 GB of RAM. We ran PL-PPF using Windows 10 Pro. We compared it experimentally with the following five systems: DeepGO , IFP_IFChttp://ecesrvr.kustar.ac.ae:8080/plppf/rules.pdfThe complete list of specification rules used by PL-PPF in the experiments and the abbreviations of the terms included in the list can be accessed through the following two links, respectively:http://ecesrvr.kustar.ac.ae:8080/plppf/abbreviations.pdft to be semantically related to an unannotated protein pu, if the co-occurrence probability of t and pu using Z-score [We compared the systems using GO dataset , which c Z-score  is greatWe also compared the systems using the 6086 SGD dataset . The datp using the following standard quality metrics shown in Eqs. 1, 2 and 3:Cp: The number of correctly predicted functions for protein p.Np: The actual number of correct functions of protein p.Mp: The number of functions predicted for protein p by one of the systems.We divided each of the GO and SGD datasets to five sets. The systems were assessed five times. At each time, a different set of each of the GO and SGD datasets was used for testing and the remaining four sets were used to train the systems. We considered the testing proteins as unannotated and assessed the systems for predicting their functions accurately. We evaluated two versions of PL-PPF: one adopts all the techniques described in this paper and the other adopts only the explicit terms co-occurrence extraction techniques  of the GO ontology. The size of proteins annotated with the functional category of a GO annotation term decreases as its hierarchical level increases. We aim at investigating whether the accuracy of a system for predicting the functional categories of GO annotation terms gets better as the sizes of these terms increases. We randomly divided the proteins annotated with each functional category We ran each system ten times against the GO dataset. The number of proteins, whose associate biomedical texts are used as a training dataset, keeps accumulating at each run. At each run, we randomly selected 1000 Biological Process testing proteins and 500 Molecular Function testing proteins as unannotated and assessed the systems for predicting their functions. The first run was performed using: (1) 52,386 Biological Process proteins and 11,576 Molecular Function proteins, whose associate biomedical texts are used as a training dataset, and (2) 1000 Biological Process proteins and 500 Molecular Function proteins, whose functions are considered unannotated. At each run, thereafter, the set of proteins, whose associate biomedical texts are used as a training dataset, includes also the Biological Process and Molecular Function proteins, whose functions were annotated in the prior run. Figures The protein centric maximum F-measure, which was used in evaluating the DeepGO method.The same GO dataset used in evaluating the DeepGO method . As the figures show also that the complete version of PL-PPF  outperforms significantly the version of PL-PPF, which employs only the explicit techniques. This is attributed to the effectiveness of the rules of predicate logic in inferring the functional terms that co-occur implicitly with proteins in biomedical texts.As Figs. As Fig. As Figs. explicit term extraction component and logic-based implicit term extraction component. Our hypothesis is that important biological molecule terms pertaining functional categories are likely to co-occur implicitly with proteins in biomedical texts.In general, we attribute the performance of PL-PPF over the other five systems to the fact that PL-PPF employs a combination of statistical and logic-based approaches while the other five systems employ only statistical-based approaches. That is, PL-PPF includes a combination of statistical-based Table Some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in biomedical texts. Most current information extraction approaches do not take advantage of such implicitly inferred terms and focus solely on explicitly mentioned terms in texts. In this paper, we introduced an information extraction system called PL-PPF. The system predicts protein functions based on both explicitly and implicitly mentioned functional terms in biomedical texts. PL-PPF extracts explicitly mentioned functional terms in texts using computational linguistic techniques that identify semantically related terms in differently structured forms of sentences. It extracts implicitly mentioned functional terms by recursively triggering protein specification rules using the standard inference rules for predicate logic. We compared PL-PPF experimentally with the following five systems: DeepGO , IFP_IFCAdditional file 1:The data presented in Tables S1 and S2 is the Biological Process and Molecular Function GO annotation terms used in the experiments as well as the randomly selected sets of training and testing proteins annotated with the functions of these GO terms. (DOCX 56 kb)