Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.The increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published  Experimental techniques to characterize proteins at a biochemical, structural and physiological level have improved considerably in the last 25 years providing researchers with the tools necessary to understand protein function at a cellular and an organism level. Combined with detailed functional data, large-scale genome sequencing efforts have also greatly increased the scale of proteomic data available from model and non-model species. However, a major challenge facing researchers today is simply keeping pace with the sheer volume of low-throughput and high-throughput data being generated. Although scientific publications are used to disseminate research findings to the wider community, manually identifying, curating and collating individual experiments is a time and labour intense process. In addition, the large number of papers published, and the unstructured and versatile format of the content make the data difficult to integrate and analyse. The development of biological databases to curate protein experimental data from published literature have, as a result, become indispensable research tools for researchers. However, manual curation makes it difficult for databases to keep up with the ever-increasing amounts of data being generated.2 for extracting and organising unstructured biological information into a structured form that is accessible to biologists. Central to these automated systems, is the process of unambiguously extracting semantic relationships between two or more biological entities in the literature3. When referring to proteins or genes, entity relationships can describe protein–protein interaction, drug interactions, physicochemical properties or functional motifs, disease/trait interaction, and are very useful for biological network construction7. In addition, ML techniques have the potential to assist in protein annotation efforts such as the manually curated UniProt database by automatically extracting protein knowledge from research articles2.To address these issues, automated and augmented curation systems for extracting protein functional data from scientific literature are becoming increasingly desired. In particular, Machine Learning and Natural Language Processing techniques are beginning to be employed for biocuration efforts8. This type of system worked well for entity detection if the dictionary was big but still struggled to extract relations especially if there was a large distance between related words. Further improvements to biological entity RE were described by Fundel et al. using a multi-step process9. The approach relies on first preprocessing data and then creating a parse tree of the sentence that can be filtered against extraction rules. Other methods rely on Support Vector Machines (SVM) and kernel-based solutions or by combining the two approaches10. In the combined approach, kernels were utilized by converting the input text to a vector format and then finding the similarity between two entities and the sum of their sub structures similar to a “bag of words” approach. A SVM was then used for entity classification. This gave both the benefits of kernels such as being able to search a large feature space and regularization methods such as boundary detection between classes from SVM’s. These types of methods improved the task of relation classification but ultimately were suboptimal in the field of relation detection.Initial methods for entity relationship extractions (RE) relied on dictionary-based and rule-based methods and parsers11. The tree was then passed into a bi-directional Long Short-Term Memory (bi-LSTM) model with the word embeddings and eventually to a dense layer. The end result was then put through a softmax layer and returned a binary classification corresponding to if the sentence contains an entity relation or not. It is shown that this method gets good results, but a major disadvantage is that due to the dependency tree, inter-sentence relations are not able to be extracted, limiting the scope of the biological entity integration.With the advent of machine learning, many methods started to treat words as sequence data and applied deep learning models to find solutions. In the study by Li et al. the authors suggest creating a dependency tree which then feeds into a Convolutional Neural Network (CNN) to pretrain character level embeddings from words in the tree12. Often, the experimental methodology that was used to verify the result, although mentioned in the sentence, is not processed as an entity type and hence not linked to the protein. The experimental methodology is an important factor for interpreting the results. For example, when analysing protein subcellular localisation, two commonly used approaches are fluorophore tagging of the protein (e.g. Green Fluorescent Protein) or by a proteomic approach of identifying all the proteins within a specific cellular compartment (e.g. Mass Spectrometry). Each approach has their advantages as the fluorophore approach provides better spatial resolution of protein location whereas MS methods allow for better quantitative analysis of cellular proteomes. Knowing what technique was used together with protein subcellular information can guide researchers in designing future experiments.Furthermore, text analytic methods described for biological research are largely optimised for extracting binary entity relationships such as interaction between two proteins or association between proteins and diseases13 and another dataset that includes Crop Proteins with Annotated Locations (CropPAL)14. We demonstrate, through this method, triplet entity relationships can be predicted with high accuracy. Thus, the pipeline provides a framework for high-throughput extraction and linking of biological entities from unstructured text giving researchers access to the latest scientific information from diverse datasets.In this study, we describe the development and implementation of a pipeline to extract protein subcellular information and the experimental methodology from published studies using deep learning techniques. Protein subcellular location was chosen as a feature to extract as knowing where a protein resides within a cell provides important clues to its cellular function and represents a fundamental unit of how proteins function in nature. We tested the deep learning system across two datasets, the SUBcellular location database for Arabidopsis proteins (SUBA)13. Three biological entity types were considered for this classification: protein name, experimental methodology and subcellular location. Following retrieval of full-text articles, subsections and sentences that contain relevant biological entities were identified by text parsing, followed by annotation of entities and relationship types. By using full-text for pre-training vocabulary (continuous bag of words) and a set of annotated sentences for training the classifier , the deep learning model was subsequently assessed for its ability to classify ‘True’ or ‘False’ relationships between these three entity types using two independent and non-overlapping testing datasets . A ‘True’ relationship between the entities indicates the given protein was experimentally verified to be located within a specific cellular organelle and a ‘False’ relationship indicates the co-occurrence of these entity types without being related. Implementation details and source codes of the pipeline are available at GitHub: https://github.com/RhysMenezes/find-a-protein.We describe a semi-automated pipeline for extraction of complex protein features from unstructured published literature Fig. . We analhttp://suba.live/) includes subcellular information relating to 11,740 proteins in Arabidopsis that was manually extracted from 1768 published studies by expert curators in the plant biology field13. The database contains experimentally verified and predicted subcellular information relating to Arabidopsis proteins. For the purposes of this study however, we only selected proteins with experimentally verified information as this is considered as a high confidence dataset for Arabidopsis protein subcellular locations. Experimental evidence is based on fluorescence protein visualisation and large-scale subcellular proteomics extracted from published studies and organised in a MySQL database format. Access to full-text was a requirement for the NLP analysis as often the protein subcellular information, and particularly the supporting experimental evidence are not described in the abstracts accessible in PubMed. As SUBA only stores citation details for proteins with experimentally verified subcellular information and not the associated full-text, we used a combination of strategies including an in-house developed NLP publication retrieval and processing tool (outlined in the “The SUBA collection (version 4.0) (https://github.com/RhysMenezes/find-a-protein/tree/master/Data). Given the time-consuming process of manual annotation, we chose a random subset of 200 studies from the SUBA corpus to annotate entities and relationships. To identify relevant text, article subsections were parsed using regular expressions terms to find co-occurrences of three biological entities; protein names, subcellular locations and experimental methodology in a forty-word sliding window. Entity names were matched against a list of all Arabidopsis protein names (including synonyms) from The Arabidopsis Information Resource (TAIR), subcellular compartment names derived from the Gene Ontology cellular components annotations15 and fluorescence tagging methodologies  and ‘Materials and Methods’ subsections were extracted as these are likely to include protein subcellular information and experimental methods , were the most commonly used protein descriptors in the SUBA corpus. In such cases, the concatenated word gets labelled as one entity type called ‘tagged protein’ and sub labels are used to differentiate between protein and methodology 16 Fig. . ProteinThe RNN model’s task is to classify an input sentence as either ‘True’ relationship or ‘False’ relationship, the output is discrete either a 1 (true) or a 0 . For all input sentences annotated, a ‘True’ relationship is defined as one that satisfies both semantic and syntactic relationship between a triplet entity group. The entities are considered to have a semantic relationship if they are structured such that the protein’s subcellular location and the methodology used can be logically discerned from the sentence Fig. .21, four separate true states, one for each triplet group (underlined) can be presented to the classifier:“Both _PROTEIN_ and _BLANKP_ have been localized to the _LOCATION_ and _BLANKL_ using mass _METHOD_.”“Both _PROTEIN_ and _BLANKP_ have been localized to the _BLANKL_ and _LOCATION_ using _METHOD_.”“Both _BLANKP_ and _PROTEIN_ have been localized to the _LOCATION_ and _BLANKL_ using _METHOD_.”“Both _BLANKP_ and _PROTEIN_ have been localized to the _BLANKL_ and _LOCATION_ using _METHOD_.”For the input data used in the RNN classifier, the actual protein names, methodologies and subcellular locations were not needed as we investigated the possibility of classifying relationships using the entities neighbouring context words. The names in the input sentences were replaced by tokens that represent the entity types. All protein names were replaced with a “_PROTEIN_” taken, methodologies were replaced with a “_METHOD_” token and subcellular locations were replaced with a “_LOCATION_” token. As the model classifies if a given relationship is true or false, the input sentences will have to be presented such that only one relationship is present in a sentence. If a sentence contains multiple tokens then a valid triplet will be tokenized, and the rest blanked out using _BLANK_ tokens. For example, the sentence from Tanz et al., “Both Deg1 and Deg9 have been localized to the plastid and nucleus using mass spectrometry”Lastly to ensure all input data was consistent and the dimensions remained the same in the classifier model, sentences were padded using the PAD term to the maximum sequence length. From 1400 tokenized text input sentences, we created 700 positive examples that satisfied both semantic and syntactic criteria for entity relationships. To generate negative cases, we randomly permutated the entities within the sentences to create 700 negative examples.22. This allows the CBOW model to predict the next word in the sentence given the target word’s neighbouring context words. The trained word representations/embeddings were extracted and integrated into the classifier model.In order to utilize the SUBA data more effectively and due to the relatively small number of annotated sentences used for training the classifier, we pretrained the network’s vocabulary on the SUBA dataset comprising of 1361 full-text articles. Pretraining the model with plant biology specific domain knowledge has the advantage of improving the performance of the classifier in entity relationship prediction. Pretraining was achieved using CBOW NLP model that represents the meaning of each word in a sentence as a single weighted vector representations23. However, relation extraction in the biomedical domain has been mainly restricted to binary relations between entities, such as protein–protein interaction24, drug–drug interaction25. Here, we evaluate the accuracy of the model to extract relations from three entities incorporating both biological information and experimental methodology into the prediction model. The model takes annotated sentences as inputs and reads it word by word to determine a binary classification of a “yes” or a “no” (1 or 0) if a sentence contains a valid entity relationship between the protein, methodology and subcellular location. The input data was split into a ratio of 1:4, 20% for testing and 80% for training the classifier, making sure the data was separated so that papers did not overlap between the testing and training sets. This prevents the classifier model from analysing very similar input sentences both within the training and testing data sets.The classifier model’s architecture is that of a bidirectional LSTM network, which is a type of Recurrent Neural Network (RNN) and has the advantage of capturing long-term contextual information and is suitable for NLP tasks involving long sequence of words. Similar models have been used for biomedical named entity recognition (BNER) to recognize proteins and gene names and for Relation Extraction tasks from unstructured text26. Although our model improvedaccuracy by 8.6% compared to this study, the different dataset, triplet entity group and annotation used in this study make any direct comparison difficult. A comparable study using a distant supervised learning approach for predicting relation between a protein and a subcellular location, achieved an accuracy of 82%27, a drop in 7.3% compared with our model and crucially focussed only on binary relations without including experimental methodology as we did in our study.We achieved an average accuracy score of 89.3%  for all test input sentences in the SUBA testing data set Table –S4  database stores published protein subcellular information for the crop species rice, wheat, barley and maize using a similar manual curation process to the SUBA database. Although both databases address similar problems in curating protein subcellular information, there was no overlap in the published articles or input sentences used in training and testing from the SUBA corpus and the subsequent independent testing from the CropPAL corpus. Literature retrieval and pre-processing of the full-text article and input statements for CropPAL were carried out as described for the SUBA dataset . Using a smaller subset of randomly selected papers from the CropPAL dataset, 65 input sentences were extracted, annotated and tagged using the same process described above. Using the Bi-LSTM classifier, we achieved a similar accuracy score of 89.7%  as the SUBA dataset demonstrating transfer learning capacity of the model   on both SUBA and CropPAL datasets Table . We foun30. While these approaches are important for uncharacterised proteins and those predicted from large-scale sequencing datasets, there is a wealth of published protein subcellular information that can be integrated and exploited for practical use by researchers. A Natural Language Processing system that relies on deep learning Technique such as the one described here will help bridge the gap between the data generation process and integrating the vast amount of published molecular and physiological information available about proteins. In particular, a future opportunity exists in improving the sustainability of biological databases by augmenting manual curation efforts with semi or fully automated neural network approaches. Despite their importance in primary research, biological databases face a survivability challenge due to funding cycles typically lasting 3–5 years and essential services such as data curation and maintenance becoming increasingly difficult to sustain31. This is best illustrated with the number of biological databases that are listed as obsolete in the annual database issues published by Nucleic Acid Research (NAR) journal. In 2020, NAR reported 125 discontinued databases, with the number of obsolete databases increasing five times over the last five years. In contrast, 65 new database resources were published in the same database issue, with the number remaining steady over the same five year period32. While the study here focuses on protein subcellular information, the pipeline developed provides a framework for capturing key functional properties of proteins from published studies and assisting in biocuration efforts. In addition to improving the overall prediction accuracy, we envisage the Bi-LSTM RNN-based pipeline to be applied for extracting relationships involving two or more entities that describe complex protein features such as protein interaction with other biomolecules such as proteins, DNA and RNA as well as for protein-phenotype associations. The transition from manual to semi-automated to potentially, fully automated biocuration systems that rely on deep-learning processes in the future can be exploited by researchers in expanding proteome knowledgebases and for predicting novel functions for individual proteins.We demonstrate the Bi-LSTM RNN classifier model uses contextual information in the input sentence to predict triplet entity relationships with a high degree of accuracy and can distinguish from input sentences in which the three entities co-occur by chance. This approach can be effective for extracting biological information from unstructured text in a meaningful way that can be made accessible to the research community. Earlier reports of Machine Learning and neural network models have primarily focussed on amino acid sequence information for predicting protein subcellular localisation with varying degrees of successhttp://suba.live/stats.html) to retrieve full-text articles from various sources from both datasets. For the SUBA database, 178 articles were retrieved from the Europe PMC repository open access full-text RESTful API service (https://europepmc.org/RestfulWebService). In addition, full-text articles from the SUBA dataset were also retrieved from the Elsevier text mining service and the CrossRef association of scholarly publishers bringing the total number of articles to 227. The remaining full-text journal articles were retrieved and processed using an in-house developed tool called ‘NLP-ready’ bringing the total number included in the analysis to 1700 published studies. For the CropPAL dataset, a smaller subset of 10 randomly selected full-text articles from the CropPAL dataset were retrieved from which 65 input sentences were extracted for testing the model performance. All pre-processed articles from the SUBA and CropPAL corpora and extracted input sentences used for training and testing purposes are available in the data folder of GitHub repository: https://github.com/RhysMenezes/find-a-protein/tree/master/Data. Details for the automated retrieval and pre-processing tool can be accessed from: https://github.com/arabidopsis/NLP-ready.As SUBA and CropPAL does not store full-text articles, we used PubMed ID   artificial neural network model from Word2Vec16, using standoff format in which annotation information is kept separate from the text , false positives (FP), true negatives (TN), and false negatives (FN). To test our result, we use an F1 score which is the balance between recall and precision.https://github.com/RhysMenezes/find-a-protein. The output for the Bi-LSTM and Uni-LSTM prediction was independently verified through manual inspection by an expert in the field using the unannotated input sentences extracted from articles.The scores of Recall, Precision, Loss and F1 for 30 experimental runs for both SUBA and CropPAL testing dataset is provided in the GitHub repository: Supplementary Information.


Recently substantial progress has been obtained in the case of single proteins using a combination of unsupervised coevolutionary sequence analysis with structurally supervised deep learning. While reaching impressive accuracies in predicting residue-residue contacts, deep learning has a number of disadvantages. The need for large structural training sets limits the applicability to multi-protein complexes; and their deep architecture makes the interpretability of the convolutional neural networks intrinsically hard. Here we introduce FilterDCA, a simpler supervised predictor for inter-domain and inter-protein contacts. It is based on the fact that contact maps of proteins show typical contact patterns, which results from secondary structure and are reflected by patterns in coevolutionary analysis. We explicitly integrate averaged contacts patterns with coevolutionary scores derived by Direct Coupling Analysis, improving performance over standard coevolutionary analysis, while remaining fully transparent and interpretable. The FilterDCA code is available at  de novo prediction of tertiary and quaternary protein structures has recently seen important advances, by combining unsupervised, purely sequence-based coevolutionary analyses with structure-based supervision using deep learning for contact-map prediction. While showing impressive performance, deep-learning methods require large training sets and pose severe obstacles for their interpretability. Here we construct a simple, transparent and therefore fully interpretable inter-domain contact predictor, which uses the results of coevolutionary Direct Coupling Analysis in combination with explicitly constructed filters reflecting typical contact patterns in a training set of known protein structures, and which improves the accuracy of predicted contacts significantly. Our approach thereby sheds light on the question how contact information is encoded in coevolutionary signals.The  The prediction of protein structure using amino-acid sequence information has seen important progress in the last decade. This progress is substantially based on two subsequent methodological advances: unsupervised sequence-based coevolutionary models, and subsequent structure-based supervision using deep learning.First, global coevolutionary methods like Direct Coupling Analysis (DCA) , 2, PSICIn the case of tertiary structure, this problem has been solved more recently. Benefitting from the large numbers of proteins with experimentally determined structures available in the PDB, and from the large amount of residue-residue contacts (and non-contacts) in each structure, supervised machine-learning techniques have allowed to substantially increase the accuracy in predicting residue-residue contacts and even distances. In particular convolutional neural networks (CNN) based on deep learning have shown success, cf. the performance of methods like RaptorX , DeepMetDespite their impressive predictive performance, CNN and other deep neural architectures have some important disadvantages. First, they need to be trained on large datasets. While these are available for tertiary protein structures, the applicability to inter-protein contact prediction and protein-complex assembly has remained limited, even if promising results have been found when training was performed on intra-protein contacts, but testing on inter-protein contacts, resulting in a RaptorX variant called RaptorX-ComplexContact . Second,Our works aims at assessing the following question: Can we understand how contact information is encoded in the patterns of coevolution unveiled by DCA-type unsupervised methods? Can we use this to improve on DCA in a simple and interpretable way? It is very unlikely that performances similar to the best CNN-based predictors can be achieved, but a better understanding of coevolutionary patterns may be exploited in future by a next generation of deep neural networks.The architecture of CNN may actually give a first hint. Instead of looking individually to residue pairs, their first layer applies convolutional filters to a neighbourhood of each residue pair. Protein contact maps are not random graphs, they are locally structured, and this structure can be exploited for contact prediction. As was pointed out in , the denBased on this observation, our questions may be made more precise: can we use local contact patterns to construct simple and interpretable supervised approaches to contact prediction? Can we use such approaches in the case of inter-protein and inter-domain contacts, which are less represented in the protein data bank PDB  than intTo this end, we introduce FilterDCA: we first analyse contact patterns related to different secondary structure elements, and show that these are faithfully reflected by the scores derived using plmDCA (i.e. DCA based on pseudo-likelihood maximisation) . We therA next logical step (going beyond the scopes of this work) would be to understand if this kind of information is also used by CNN-based contact predictors, or if their architecture can be refined to better take this information into account. Surprisingly, FilterDCA reaches perfomances comparable to the CNN-based contact predictor PconsC4 , a furthThe plan of the paper follows this reasoning: We first find characteric patterns, which are shared by protein contact maps and coevolutionary couplings. Using domain-domain interactions as a proxy for protein-protein interactions, we develop FilterDCA as a simple supervised contact predictor combining these contact patterns with the outcome of standard DCA, leading to a cleaning of predicted contact maps and thereby to higher prediction accuracy. Subsequently, we show that results can be extended to protein-protein interactions, and provide a comparison with the performance of deep-learning based contact predictors.5 inter-domain contacts, cf. Materials and Methods. For each of these domain pairs, a multiple-sequence alignment (MSA) is created, with each line containing the aligned sequences of both interacting domains. According to the effective number Meff of sequences , we distinguish three cases: large (Meff > 200), medium (50 < Meff < 200), and small (Meff < 50) MSAs. These data will be used throughout the paper, if not explicitly stated differently.To improve DCA-based contact prediction between interacting domains, we have collected an extensive dataset of more than 2500 intra-protein domain-domain interactions of experimentally known protein structures, containing more than 6 ⋅ 10Jij of dimension 21 × 21 between two residue positions i and j (MSA columns) are compressed into a single ad-hoc score, either using direct information DIij  is the maximum [minimum] filter score in the training set. While the classifier itself is invariant under this transformation, the ℓ2-regularisation used by sklearn is not, thereby influencing the final parameter values.Pairs of residues forming a contact are only a small fraction of all possible pairs, cf. S1 FileMeff.The file contains a list of all domain-domain interactions used in the manuscript, with Pfam IDs, PDBs and effective sequence numbers (CSV)Click here for additional data file.


Identifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments.“Here, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features  to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model, and to two popular protein function prediction methods (Mashup and deepNF), we demonstrated that our model can achieve state-of-the-art performance. We show the robustness of our model by testing on multiple species. We also provide a web server supporting function query and downstream analysis on-the-fly.Graph2GO is the first model that has utilized attributed network representation learning methods to model both interaction networks and protein features for predicting protein functions, and achieved promising performance. Our model can be easily extended to include more protein features to further improve the performance. Besides, Graph2GO is also applicable to other application scenarios involving biological networks, and the learned latent representations can be used as feature inputs for machine learning tasks in various downstream analyses. Knowledge of protein functions is of great importance to understanding life at a molecular level, studying disease mechanisms, and helping explore novel therapeutic targets. However, the experimental identification of protein functions is time-consuming and expensive, which is not suitable for large-scale applications. Therefore, high-throughput computational methods are required for discovering protein functions with reasonable quality and accuracy , and proMost existing computational algorithms exploit homology inference to infer protein functions , which aGiven the limited prediction capacity of a single source of information, many methods have been proposed to combine several kinds of information and take advantage of the power of machine learning techniques. For instance, INGA  performsGraphs, such as those representing social networks, molecular graph structures , and bioA crucial challenge in machine learning on graphs is finding a way to incorporate multiple types of information about the structure and attributes of the graph into the machine learning model. Traditional approaches usually use summary graph statistics , kernel In this paper, we propose Graph2GO, a multi-modal graph-based architecture, that can make use of several kinds of data sources in a unified way to predict protein function. Unlike other consensus methods that we mentioned earlier, we first use PPI and sequence similarities to build two graphs separately and use protein sequence information, protein subcellular location, protein domains, or any other possible information as node attributes in both graphs. Given the attributed graph, we use the attributed network representation learning algorithm to obtain informative embeddings for each node in each graph. In the graph, a node represents a protein. In this way, we manage to learn representations by modeling both node attributes and network structures comprehensively and simultaneously. Then, we combine these two embeddings and use them to predict the protein functions with a feedforward neural network model.As far as we know, we are the first to use attributed network representation learning methods to model both an interaction network and a sequence similarity network (SSN) with node attributes and predict protein functions, and we have successfully achieved state-of-the-art performance on the benchmark data set. Besides, our model is extensible to other function-related information to further improve the performance. Graph2GO does not rely on any manually crafted features and is entirely data driven. Graph2GO is also applicable to other similar scenarios, such as predicting new therapeutic applications of existing drugs, since the learned embeddings can be used as feature inputs for various downstream machine learning tasks.Graph2GO consists of two parts: the first part is an unsupervised graph-based representation model that utilizes both network information  and node attributes  to generate unique embedding vectors for each protein; the second part is a fully-connected deep neural network (DNN) classifier, which use embeddings as features and gene ontology (GO)  as functWe use reviewed and manually annotated proteins from SwissProt (release 2018_11) . The datFrom the network data in STRING, we use the “combined score” provided by STRING as the confidence score. As shown in In order to build a SSN, we use the BLAST program to search similar sequences for each protein in our data set . We onlyWe encode amino acid sequences following the conjoint triad (CT) method , which hWe obtained subcellular location information from the SwissProt data set. In total, there are 359 different subcellular locations. We use bag-of-words encoding to represent this information; that’s to say, the location is encoded as a binary vector of length 359 with each element indicating whether the protein is annotated with this location. Therefore, for a protein without any subcellular location annotation, it is represented as a vector of all 0s.In the data set we obtained from SwissProt, there are 5,817 unique protein domains. In order to avoid the curse of dimensionality and to decrease the complexity, we only use protein domain terms that appear more than 5 times in our data set, and we are left with 655 terms. The protein domain knowledge is also encoded by way of bag-of-words encoding.In the first part of Graph2GO, the core is a variational graph auto-encoder (VGAE) , which cN is the number of proteins, and each vertex of N × R matrix R is the sum of feature dimensions of matrix We are given an undirected graph, iv in the graph to a low-dimensional latent variable ijA corresponding to the pair of nodes iv and jv. By jointly optimizing the encoder and decoder, the model learns to compress graph structure information and original node attributes into the low-dimensional latent space. In principle, the model learns to propagate features across the proteins based on the network structure, and the encoder-decoder architecture ensures that the learned representations in the latent space are meaningful and informative.The model depicted in Fig. g to model this probability:q is a function that encodes proteins into latent variables g, and g from data directly. Then The inference module is a graph convolutional network (GCN) encoder , which ion trick , \documeg is a two-layer network as defined:Our GCN y matrix .The intuition of GCN is as follows. The multiplication of feature matrix  similar . In ordeAs our latent embedding already contains both node attributes and structure information, the generative module we define here is a simple inner product decoder that aims to reconstruct adjacency matrix KL [q( · )‖p( · )] is the Kullback-Leibler divergence between q( · ) and p( · ). The first term is to minimize the reconstruction error of the adjacency matrix Similar to variational auto-encoder (VAE) , the cos encoder .In the second part of Graph2GO, as shown in Fig. learning . It consWe train three classifiers: one each for MF, BP, and CC respectively. For each classifier, it is a multi-class, multi-label model, and the dimension of the output space is the number of GO terms within each ontology. Each protein may be predicted with multiple GO terms simultaneously. The classifier predicts the probabilities of the protein having each GO term annotation. The performance of the classifier can be excellent without many complex neural network structures, since the embeddings already contain enough information and are highly representative in the learned low-dimensional vector space.As shown in Fig. https://github.com/yanzhanglab/Graph2GO.Graph2GO was implemented using Tensorflow in Python and took advantage of the powerful computing capacity of a graphics processing unit (GPU). All the simulations were carried out on the Owens cluster provided by the Ohio Supercomputer Center  with 27 processors and 127 GB memory. The GPU we used was a NVIDIA Tesla P100 with 16 GB memory. Our source code is available at For our experiments, we use SwissProt’s reviewed and manually annotated human proteins (release 2018_11). We only use proteins that also exist in the STRING database (v10.5), where corresponding interaction information is available. In order to use the CT method to encode amino acid sequences, we delete proteins whose sequence contains ambiguous amino acids, including B, O, J, U, X, and Z. After filtering out 5,279 proteins, our final data set contains 15,133 proteins, along with 1,713,652 PPIs and 843,212 edges in the SSN. For GO annotations, we only consider the experimental evidence code among EXP, IDA, IPI, IMP, IGI, IEP, TAS, and IC [In the first part of our architecture, the encoder is a two-layer neural network structure with one layer having 400 neurons and the other having 800 neurons. Using two layers of graph convolutional operations is recommended in Kipf and Welling  and Li epr means precision, rc means recall, I is the indicator function, f is a GO term, iP(t) is a set of predicted GO terms for protein i using the threshold t, and iT is a set of annotated GO terms for protein i.We use metrics that are similar to those adopted by the Critical Assessment of protein Function Annotation algorithms (CAFA) challenge to evaluate our model and compare it with others . The firfpr and frc are precision and recall for a single GO term f, AUPRf is the area under the precision-recall curve (AUPR) for f, and fN is the number of GO terms used for evaluation. The macro-averaged AUPR (M-AUPR) is defined as the unweighted mean of the AUPR for all labels, while the micro-averaged AUPR (m-AUPR) is calculated globally by considering each element of the label indicator matrix as a label.We also use similar term-centric metrics to evaluate the model, as suggested by CAFA. However, instead of using receiver operating characteristic (ROC) curves, we choose to consider precision-recall (PR) curves and calculate the area under the curve. As pointed out by Davis and Goadrich , when deIn this section, we compare the results of using different network information and node attributes. The purpose is to show which type of feature is most informative and how we can further improve the performance by combining multiple types of features. In Table Looking at the performance of using different network types, the combined network is always better than the other two single network types across all three ontologies, which indicates that the PPI network and the SSN can provide complementary information for function prediction. Comparing the effect of the PPI network and the SSN, the PPI network shows better performance than the SSN, except on MF ontology. We can conclude that the PPI network provides more function-related information than the SSN, and that MF ontology has a great correlation with sequence information.From the analysis of the effects of using different node attributes, we can conclude that the integration of all node attributes is better than using any single attribute across all three ontologies, no matter what kind of network type is used. We can see that using a sequence attribute can achieve reasonable performance for predicting functions in all three ontologies when combined with the PPI network, proving the importance of sequence information. It also shows that the protein domain attribute is the most informative feature for predicting MF functions, while the subcellular location attribute is important for CC function predictions. Overall, each type of node attribute contributes to the function prediction, and the combination of all features improves the performance and provides more robust predictions. Based on the results shown in Table We first compare our method with a baseline model, BLAST, which is a widely used homology-based method for annotating protein functions . We randAs shown in Table To evaluate the performance of our method, we compare our method with two popular protein function prediction methods: Mashup and deepNF, which are also based on the concept of embedding. In their initial implementations, Mashup and deepNF use support vector machine (SVM) as the classification model. In order to make the performance more comparable, we also use DNN that is used by Graph2GO as their classification models. Each method is evaluated using 5-fold cross-validation, repeated 10 times. We first compare the performance using all GO terms, as shown in Fig. For both CC and MF ontology, our model outperforms deepNF and Mashup in terms of m-AUPR and F-max, and achieves comparable results with deepNF and Mashup in terms of M-AUPR. Our model is comparable to Mashup and outperforms deepNF in BP ontology in terms of m-AUPR and F-max and is slightly worse than Mashup in M-AUPR. Looking at the detailed comparison in three sparsity levels shown in It can be observed that Graph2GO works best for GO terms with enough annotated proteins, and is not as good as Mashup for very specific GO terms with few annotations for CC and BP ontology. In summary, our model can achieve state-of-the-art performance in both CC and MF ontology, especially as the number of annotated proteins increases, and can obtain comparable results in BP ontology. Compared with Mashup and deepNF, we can conclude that both the inclusion of discriminating node attributes and the power of attribute propagation across two types of networks enable Graph2GO to obtain competing results.Saccharomyces cerevisiae, and Bacillus subtilis), and tested the performance of Graph2GO on these species. For each species, we followed the same procedure as when we tested on a human data set, and the performance was evaluated in each species individually. Table Saccharomyces cerevisiae and Bacillus subtilis is a lot better than on other species.In order to demonstrate the power and robustness of Graph2GO, we downloaded data from SwissProt and the STRING database for another 5 species  and node attributes (subcellular location and protein domains) are displayed in our web server. The function prediction results are ranked by the probability score for each ontology separately, and users can specify the threshold to control the confidence of the results.We also developed a web server based on Shiny app in R that supports protein function query on-the-fly: A promising application of our model is to utilize the latent representations to perform downstream analyses, since the generated representations already summarize various kinds of informative features. We support downstream clustering analysis for the query protein, where similarities are measured based on latent representations. Most similar proteins can be detected for the query protein to constitute a network module. A GO enrichment analysis can be performed on all proteins in the network module to analyze functions of the module. In the future, we are going to extend the server to include more proteins from other species.A challenge in protein function prediction is the lack of annotated features for proteins, which are crucial for machine learning models. This is due to the bias in the experimental annotation of proteins and has impeded the development of biomedical research to some extent , 43. In The encoding of original features into the model is of high importance to Graph2GO, not only for preserving information as much as possible, but also for feature transfer. We represent protein domains and subcellular location using the bag-of-words encoding method, which best preserves the original information and supports the addition of features that the feature transfer relies on. As for protein sequence, the CT encoding method loses sequential information and is not well suited for the addition operation. To solve this, we also utilize the similarity network to represent the sequence information, and we discover that the inclusion of both types of sequence representations is better than either of them alone, which might indicate that they can provide some complementary information.Compared to other methods that also use multiple protein features , 14, 17,The architecture we proposed in this paper can not only be used to predict protein functions, but can also be applied to other tasks that involve biological networks, since the learned embeddings are informative and can be used as feature inputs for various downstream machine learning tasks. For example, one can predict the interactions between any two proteins, predict new therapeutic applications of an existing drug, or run a clustering algorithm to cluster genes into modules based on learned embeddings.In principle, predicting GO terms is a hierarchical classification problem, since GO terms are organized in a hierarchy and are interacting with each other . CurrentIn this work, we present Graph2GO, a graph-based deep learning model that can make use of heterogeneous information in a unified way to predict protein functions. Our network representation learning method can take into account both network structure and protein attributes, to make predictions by organizing proteins into an attributed network architecture. Graph2GO achieves state-of-the-art performance on the benchmark data set and can easily be adapted to solve other tasks involving biological networks, such as link prediction, node classification, and sub-network discovery.Project name: Graph2GOhttps://github.com/yanzhanglab/Graph2GOProject home page: Operating system(s): Platform independentProgramming language: Python, ROther requirements: not applicableLicense: MITRRID: SCR_018726biotoolsID: biotools:graph2goGigaScience database, GigaDB [All data are publicly available in our Github project home page. An archival copy of the code and supporting data are available via the , GigaDB .AUPR: area under the precision-recall curve; BLAST: Basic Local Alignment and Search Tool; BP: biological process; CAFA: Critical Assessment of protein Function Annotation algorithms; CC: cellular component; CNN: convolutional neural network; CT: conjoint triad; DNN: deep neural network; GCN: graph convolutional network; GO: gene ontology; GPU: graphics processing unit; MF: molecular function; M-AUPR: macro-averaged area under the precision-recall curve; m-AUPR: micro-averaged area under the precision-recall curve; PPI: protein-protein interaction; ROC: receiver operating characteristic; SSN: sequence similarity network; SVM: support vector machine; VAE: variational auto-encoder; VGAE: variational graph auto-encoder.Y.G. serves as a consultant at Eli Lilly and Company and a consultant at Merck Group.This work was partially supported by The Ohio State University Startup Funds to Y.Z.Y.Z. and Y.G. conceived the project and supervised the study. K.F. performed the analysis and developed the tools. K.F. and Y.Z. drafted the manuscript. All authors read and approved the final manuscript.giaa081_GIGA-D-19-00430_Original_SubmissionClick here for additional data file.giaa081_GIGA-D-19-00430_Revision_1Click here for additional data file.giaa081_GIGA-D-19-00430_Revision_2Click here for additional data file.giaa081_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa081_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa081_Reviewer_1_Report_Original_SubmissionIddo Friedberg -- 1/21/2020 ReviewedClick here for additional data file.giaa081_Reviewer_1_Report_Revision_1Iddo Friedberg -- 5/29/2020 ReviewedClick here for additional data file.giaa081_Reviewer_2_Report_Original_SubmissionKarin Verspoor -- 1/24/2020 ReviewedClick here for additional data file.giaa081_Reviewer_2_Report_Revision_1Karin Verspoor -- 5/13/2020 ReviewedClick here for additional data file.giaa081_Supplemental_FileClick here for additional data file.


Proteins are characterized by their structures and functions, and these two fundamental aspects of proteins are assumed to be related. To model such a relationship, a single representation to model both protein structure and function would be convenient, yet so far, the most effective models for protein structure or function classification do not rely on the same protein representation. Here we provide a computationally efficient implementation for large datasets to calculate residue cluster classes (RCCs) from protein three-dimensional structures and show that such representations enable a random forest algorithm to effectively learn the structural and functional classifications of proteins, according to the CATH and Gene Ontology criteria, respectively. RCCs are derived from residue contact maps built from different distance criteria, and we show that 7 or 8 Å with or without amino acid side-chain atoms rendered the best classification models. The potential use of a unified representation of proteins is discussed and possible future areas for improvement and exploration are presented. Proteins are molecules found in living organisms and participate in many diverse cellular and molecular functions. It is generally recognized that the three-dimensional (3D) structures of proteins are related to their functions, yet the relationship remains to be elucidated, since many attempts to predict the correct functions of proteins based on their 3D structures still result in false predictions .Different approaches have been described to account for such a relationship, including those based on physical and chemical forces ,3, proteWe have previously described a representation of 3D structure that is learnable ; such a representation allowed us to identify structural neighbors and classify the protein’s 3D structure with the best performance and reliability reported so far . The repAlgorithm 1: RCC calculation  Input: Cartesian coordinates of protein atoms Output: RCC coordinates 1: Generate graph with the contacts of all residues. Two residues are in contact if they are within a distance threshold. 2: Calculate maximal cliques using Tomita algorithm (see below) 3: Calculate RCC from maximal cliques.The pseudo code to obtain the RCC from PDB entries is depicted in Algorithm 1:d with the use of several geometric restrictions:We accelerated the calculation of all contacts given a distance threshold d that corresponded with the contact threshold distance; thus, given a residue we can identify the cube that contains it by using a hashing function; every cube in the grid has a list of all the residues that are inside of it. Given a residue r and the cube in the grid where it is found, rG, we know that any other residue inside rG must be in contact with r. Additionally, if a residue s is in contact with r, it must be that the cubes rG and sG are neighbors in the grid . This way we reduced the search space dramatically (see n (n is the number of amino acids in a protein) in the linear portions of the protein and 16*n on average (given that at 5 Å a residue is in contact with four other residues on average).(i) The hashing approach using a 3D grid over the space. We generated a grid over the 3D space of width ally see , which tr we enveloped it in a sphere of radius dr and center cr. For two residues r and s, let D be the distance between rc and sc,. They must be in contact if (ii) Rules for quick evaluation see . For a rd and center cr overlaps half the volume of the sphere enveloping s or vice versa . Any pair of residues that do not fulfill one of these rules needs the explicit computation of the distance between its pair of atoms to be performed (with an early stop if a contact is found).Finally, they must be in contact if a sphere of radius Algorithm 2: Contact map calculation  Input: Cartesian coordinates of protein atoms  Output: Contact graphr in the protein:  For each residue rG using the hash function.   Calculate its cube in the grid L inside the cubes neighboring rG.   Get the list of residues s in L:   For each residue D distance between cr and cs.    Calculate D < d and continue.    Quick inclusion if D–dr, D–ds} > d and continue.    Quick exclusion if minimum {r and s.     Perform contact check for each pair of atoms from In the scenario in which the side chains are ignored, the spheres are calculated with the remaining atoms of the residue. The geometric restrictions work in the same way as if the spheres were calculated with all the atoms in the residue, and similarly, when a pair of residues does not meet the criteria for quick evaluation, the explicit computation of the distances between their pair of atoms is performed exclusively with the atoms in the backbone. The pseudo-code for this calculation is detailed in Algorithm 2:Calculating the maximal cliques from a graph is a classical problem in computer science presented by Tomita , and theWe only considered maximal cliques containing at least three residues and at most six residues, which are grouped in 26 cluster classes. This grouping for a given protein produces different frequencies for each of these 26 clusters, wherein the first 3 frequencies correspond with maximal cliques with 3 residues ; the next 5 frequencies are maximal cliques with 4 residues ; the next 7 frequencies are maximal cliques containing 5 residues ; and the last 11 frequencies include maximal cliques with 6 residues . The number of residues that are adjacent in the protein sequence define the class of a residue cluster. For instance, an RCC with 3 residues in which all residues are not adjacent in the protein sequence is referred to as ; an RCC with 4 residues in which 2 residues are adjacent in the protein sequence, and other 2 residues are also adjacent in the protein sequence  is represented as  see . The regFor the 3D structural classification, we obtained the RCC for every protein domain reported in CATH (version 4.2.0) using 7 different distance cut-off values  and including or not the atoms of the amino acid side chain; hence, for each protein domain, 16 RCC representations were obtained. Our RCC dataset includes 354,079 different proteins . A total of 2,831,584 different RCCs corresponding to those representations with amino acid side-chains or without side see .For the functional classification, we used the Gene Ontology (GO) database  and calculated the RCCs for every protein in the PDB database . SimilarFor identifying a model to classify protein structures, the full set of RCCs was labeled according to the annotated CATH classification in each of its 2 levels: 4 Classes  and 41 Architectures. This full set was used to train models explored using the AutoWeka plugin , which pThe statistical parameters  reported by Weka and AutoWeka were used to evaluate the learning performance of the classifiers.The code implementing an efficient computation of residue cluster classes (RCCs) is freely see ; the comThis computational efficiency allowed us to compute RCC values for all protein structures in the PDB database  in lessWe built different RCC representations of protein structures to identify the best distance criterion to classify 3D protein structures. These included distances at 5, 6, 7, 8, 9, 10 and 15 Å; we originally used only a distance of 5 Å ; hence, We observed that the best models were obtained using RCC representations with a contact distance threshold of 7 or 8 Å without side chains see . Table 2We used the same protein structure representations described for structural classification for protein function classification; to include or not the side-chain atoms here represent an exploration of the relevance of side-chain contacts for protein function. For the functional annotation, we used the Gene Ontology (GO) annotations, consisting of three main classes: molecular function (F), biological process (P) and cellular localization (C). As described before, AutoWeka and cross-validation were used to identify the best models to learn GO functional annotation from RCC representations.The best models were again observed with 7 or 8 Å of distance between the atoms of residues with or without side-chain atoms see . Table 3An important aspect in functional annotation is the biased compositions of different classes; such a bias may cause predictors to classify proteins according to the most abundant functional classification. To rule out the possibility that such a bias may have affected the reliability of our predictions, we conduced a set of calculations using the ZeroR classifier in Weka; this classifier only predicts the most frequent class; hence, any machine-learning algorithm must achieve a performance better than this value to be reliable. In Including the efficient implementation of Tomita’s algorithm into the RCC calculation rendered a constant time performance independent of the protein lengths analyzed; we also noted that the time required to compute the contacting distances and get the RCCs was less than reading the input file. In terms of computing time, assuming these calculations would be done on a single central processing unit or CPU, the global time required to compute a given RCC is divided in the time required to load the protein’s 3D structure or PDB file (elapsed time) and the time to obtain the RCC (CPU time), which includes constructing the contact map of residues and identifying the maximal cliques. Our results indicate that most of the time is taken by reading the PDB file; this may be improved by implementing a non-serial reading function . This inThe latest Critical Assessment for Structure Prediction (CASP) competition for the first time showed that ML (AlphaFold) improved on previous methods using protein sequence features , yet theOur screening to build RCCs reveals that the backbone contains enough information to represent both 3D structure and function. This does not imply that side chains are not relevant for 3D protein structure or function; after all the backbone conformation depends on the side chains. The relevance of building RCCs without side chains is that on the one hand, such a representation does not need high-resolution structures to build a useful model; this would be relevant to further exploration: what is the range of protein structure resolution that renders a reliable model for protein structural and functional classification? On the other hand, having a model that concerns only on the protein backbone may facilitate the development of methods to predict 3D protein structure based on RCCs.Having the same representation of proteins to model 3D structure and function would allow one to analyze the possible co-localization of structural and functional classes in the 26-dimensional space of RCCs, for instance; this would eventually lead to a better understanding of the structure–function relationship of proteins. RCCs would allow exploring for regions in this 26-dimensional space where no examples of 3D protein structure or function are known, and potentially, designing new proteins.In summary, in this work we distribute a computationally efficient implementation with which to compute RCCs from a 3D protein structure—a representation of proteins that allows for effective modeling of both 3D protein structure and functional classification.


The interactions of the antibiotic proteins colicins/pyocins with immunity proteins is a seminal model system for studying protein–protein interactions and specificity. Yet, a precise and quantitative determination of which structural elements and residues determine their binding affinity and specificity is still lacking. Here, we used comparative structure-based energy calculations to map residues that substantially contribute to interactions across native and engineered complexes of colicins/pyocins and immunity proteins. We show that the immunity protein α1–α2 motif is a unique structurally-dissimilar element that restricts interaction specificity towards all colicins/pyocins, in both engineered and native complexes. This motif combines with a diverse and extensive array of electrostatic/polar interactions that enable the exquisite specificity that characterizes these interactions while achieving ultra-high affinity. Surprisingly, the divergence of these contributing colicin residues is reciprocal to residue conservation in immunity proteins. The structurally-dissimilar immunity protein α1–α2 motif is recognized by divergent colicins similarly, while the conserved immunity protein α3 helix interacts with diverse colicin residues. Electrostatics thus plays a key role in setting interaction specificity across all colicins and immunity proteins. Our analysis and resulting residue-level maps illuminate the molecular basis for these protein–protein interactions, with implications for drug development and rational engineering of these interfaces. Colicins are produced by Escherichia coli strains in high-affinity complexes with their cognate immunity proteins, which inhibit the cytotoxic activity of colicins in the bacteria that produce them9. Similar to colicins, pyocins are also antibiotic proteins that are produced by Pseudomonas bacteria, including the clinically relevant pathogen Pseudomonas aeruginosa14. Colicins and pyocins have important clinical implications, as they have been shown to function as virulence factors and are considered promising candidates for protein-based antibiotics23. The binding of colicins/pyocins to their cognate immunity proteins is mediated by the cytotoxic domains of the former and is characterized by ultra-high (up to femtomolar) affinities; non-cognate pairs from different bacteria bind with weaker affinities that are 6–10 orders of magnitude lower, but are nevertheless easily measurable30. However, a complete understanding of how cognate vs. non-cognate interactions are set at the individual residue-level remains to be fully elucidated.The interactions of the antibiotic proteins colicins with immunity proteins has been a seminal model system in numerous studies of protein–protein interactions and specificity , a combined computational and biochemical analysis of Col-E9 showed that four residues in the α4 and α4–β2 loops and two residues in the α2 helix and the α3–α4 loop were important for binding Im929. Studies of the non-cognate complexes of Col-E2–Im9 and Col-E9–Im2 have suggested that the immunity protein α3 helix functions as a conserved binding motif across different immunity proteins, whereas the α2 helix determines specificity42. Several of these studies showed that alanine mutagenesis in the α3 helix of Im2 and Im9 reduced affinity in both cognate and non-cognate complexes similarly—supporting the role of the α3 helix as a common and conserved anchor for binding colicins. On the other hand, replacement of the Im2 α2 helix with its Im9 counterpart increased affinity to Col-E9 to the level of the cognate complex41, supporting a suggested role of the α2 helix as a specificity determinant. Subsequent studies mutated three to six residues in the α2 helix of Im2, Im7, and Im9, further supporting this hypothesis43. On the other side of the interface, replacement of six residues in the α4–β2 loop of Col-E9 with the corresponding residues from Col-E8 reduced interactions with Im9, suggesting this region determines the specific interactions of Col-E9 with its cognate partner44. A later study mutated additional residues in the Col-E9 α4–β2 loop and showed a stronger reduction in binding to Im9 compared to Im2, highlighting the importance of this region in determining specificity29. Nevertheless, a precise and quantitative definition of which structural elements and residues in DNase colicins/pyocins and their immunity proteins contribute to binding affinity and to partner specificity is still lacking.Previous structural studies have suggested that the majority of residues involved in interactions between colicin/pyocin DNases and their immunity proteins are located in the α4 helix and the following α4–β2 loop of colicins/pyocins, and in the region encompassing the α2 and α3 helices of immunity proteins48. Studies employing computational redesign of the Col-E7–Im7 complex have shown that replacement of 1 to 2 residues in the α2 helix and/or the α2–α3 loop of Im7 and 2 to 4 residues in the α4–β2 region of Col-E7 were sufficient to achieve Col-E7–Im7 pairs that bound each other better than either of their wild-type counterparts46. Using in vitro evolution, Levin et al. showed for the first time that changes in the α1–α2 loop of Im9 were necessary to evolve an immunity protein to bind a non-cognate partner with ultra-high affinity47. While this was the first mention of the α1–α2 loop as affecting interactions with colicins, this result was interpreted as an indirect effect mediated through conformational changes, as the residues in this loop were deemed non-contacting residues. However, in a recent study that designed novel high-affinity binders of Col-E2, the entire α1–α2 loop of Im2 needed to be replaced with artificial loops, further highlighting a role for this immunity protein loop in reaching high affinity towards colicins48. Clearly, a comprehensive framework that maps all interaction and specificity determinants across colicins/pyocins and their interactions with immunity proteins can guide future engineering and design of these systems.The colicin-immunity protein system has also been used as a model system for computational design of protein–protein interactions and in vitro evolution of interaction specificity52. Uniquely, the structure of Col-E3 with Im3 showed two separate interfaces: one with the Im3 cytotoxic domain, which is analogous to available structures of other colicins/immunity proteins complexes, and a second Im3 interface with the Col-E3 translocation domain, which is not represented in other structures49. Mutagenesis studies implicated one residue in the β3 strand of Im353, eight residues in the α2 and α3 helices of ImD51, and four residues in the α3 helix, β1 strand, and the N-terminus of Im554, as crucial residues for binding their cognate colicins. However, comprehensive information on which residues are important for binding in the Col-E3–Im3, Col-E5–Im5, and Col-D–ImD complexes is lacking.On the other hand, colicins E3, E5, and D are structurally different from the DNase colicins and from each other, as are their immunity proteinsHere, we present a comprehensive computational study that compares representative 3D structures from all available colicin/pyocin–immunity protein complexes. We used structure-based energy calculations to accurately identify the residues that substantially contribute to the interactions in all of these structures, pinpointing both general and specific determinants to inter-molecular interactions. Our structure analysis and energy-based residue-level maps provide a better understanding of the molecular basis for protein–protein interactions between colicins/pyocins and immunity proteins—as a model system for protein–protein interactions with clinical implications for drug development, and as a detailed map that can guide future engineering of these interfaces.54—see “37. We therefore grouped these immunity proteins–colicins together and termed them the “Im7-like family” and the “E7-like family”, respectively. This grouping corresponds to colicin classification by mechanism of cytotoxicity, since all E7-like family members are DNases9. Furthermore, Joshi et al. compared these proteins to 12 more DNase-Im pairs, showing all 17 pairs shared sequence identities above 30% and 40%, suggesting all of these proteins belong to the same structural group14. On the other hand, the structures of colicins D, E5, and E3 and their cognate immunity proteins were structurally dissimilar—both among themselves and when compared to the structures of the E7/Im7-like families  of each residue ≤ 15 Å of the colicin–immunity protein interfaces. The non-polar energy contribution (ΔΔGnp) of each residue was calculated separately, based on residue burial in the complex relative to the unbound monomers. Note that since the electrostatic calculations output the net difference between the intermolecular interactions of a residue with a cognate protein partner, relative to the interactions with the water and ions in the solvent, they identify only residues that are predicted to contribute to protein–protein binding substantially. To reduce false positives and negatives, we applied a consensus approach that compares biological replicates across multiple dimers in an asymmetric unit or PDB structures  found in Col-E7 Fig. . Most coStrikingly, the majority of the contributions from colicins and immunity protein residues to interactions with their cognate partners involve electrostatic contributions. Some of these electrostatic interactions are long-range (> 5 Å), mediated by the following residues that are distant from their cognate partners: Im7–Asp32/Asp59 and Col-E7–Lys463/Arg496, Col-E9–Lys76, and Col-E2–Lys76. However, the ImAP41–Pyo-AP41 complex stands out in having more non-polar only interactions across the interface, which also leads to a prediction that this interaction will be less affected by changes in salt concentration. In a global view, non-polar contributions were similar across different colicin/pyocin–immunity protein complexes Figs. –S4, likeTo investigate how contributing residues interact across the interfaces, and in particular those residues that contribute via electrostatic interactions, we examined which colicin/pyocin residues interact with the following three structural regions in immunity proteins: the Im α1–α2 motif, the α2 helix, and the following extended loop region that includes the short α3 helix (the “α3 region”). For each of these structural regions, we found a different interaction scheme across the interface with the cognate colicin/pyocin.We observed a surprising conservation of electrostatic contributions among the colicin/pyocin residues that interact with the Im α1–α2 motif Fig. , despiteThe electrostatic contributions of colicins/pyocins to interactions with the Im α2 helix were more limited—two to four contributing residues Fig. . Almost Interestingly, the electrostatic contributions of colicins/pyocins to interactions with the Im α3 region were more diverse Fig. , despite30 and proteins engineered using computational redesign48 and/or in vitro evolution48—mostly modifying the immunity protein side of the interface. In the non-cognate Col-E9–Im2 complex and in the two computationally redesigned Col-E7–Im7 complexes the Im α1–α2 motif had the same conformation as the wild-type counterpart (data not shown). On the other hand, in all other non-native pairs at least part of the α1–α2 motif adopted a different conformation than in the wild-type structures  exhibited relatively high specificity to its cognate redesigned Col-E2, while the second immunity protein  exhibited high affinity for multiple designed Col-E2 proteins. We observed a dramatic reduction of electrostatic contributions in both designed immunity proteins, compared to the wild type Im2–Col-E2 complex  were listed in some previous structural studies of Im7, ImS2, and ImAP41 as potential contributors to intermolecular interactions22, our results can be directly extrapolated to a much wider dataset of colicins and pyocins and their respective immunity proteins, In a wider perspective, our results provide residue-level insights into a model system of protein–protein interactions at the family level, show how electrostatics interactions play a role in determining specificity at the family level, and detail precise residue-level information that can be used for directed therapeutic interventions.In summary, the energy-based computational analysis described here offers a quantitative framework that enables comparing, at the individual residue level, wild-type, engineered, and non-cognate complexes of colicins/pyocins with immunity proteins. Indeed, we also applied our approach to the complexes of Col-E3–Im3, Col-E5–Im5, and Col-D–ImD and observed that these pairs exhibit a divergent pattern of contributions, yet also exhibit a dominant role of electrostatics in determining intermolecular interactions. Since all of the structures we analyzed belong to larger families that can be well-aligned at the sequence level67, computational redesigned complexes of Col-E7–Im7 46, complexes of in vitro evolved Im9 with Col-E7 47, Col-E9–Im9 (1EMV)36, Col-E2–Im2 (3U43)37, complexes of rationally-redesigned Col-E2–Im2 48, the non-cognate complex of Col-E9–Im2 (2WPT)30, Pyo-S2–ImS2 (4QKO)14, Pyo-AP41–ImAP41 (4UHP)14, Col-E3–Im3 (2B5U)49, Col-E5–Im5 54, Col-D–ImD 51. Hydrogen atoms were added using CHARMM and the structures were subjected to conjugate gradient minimization with a harmonic restraint force of 50 kcal/mol/Å2 applied to the heavy atoms. Structure alignments were performed using the Combinatorial Extension (CE) method, as implemented in the RCSB protein comparison tools (https://www.rcsb.org/pdb/workbench/workbench.do). 3D structural visualizations were carried out with the PyMol molecular graphics program (https://www.pymol.org/).The following representative 3D structures were used in our analysis and visualization of colicin/pyocin–immunity protein complexes (with PDB IDs): Col-E7–Im7 68 to analyze the per-residue contributions of residues in colicins/pyocins and immunity proteins to interactions with their partners in the crystal structures mentioned above. The Finite Difference Poisson–Boltzmann (FDPB) method, as implemented in DelPhi69, was used to calculate the net electrostatic/polar contributions (ΔΔGelec) of each residue found within 15 Å of the dimer interface in each complex. For each residue, electrostatic contributions from each side chain or the entire residue were calculated separately  were deemed as substantially contributing to the interactions55. Non-polar energy contributions (ΔΔGnp) were calculated as a surface-area proportional term by multiplying the per-residue surface area buried upon complex formation, calculated using Surfv70, by a surface tension constant of 0.05 kcal/mol/Å2. Residues contributing ΔΔGnp ≥ 0.5 kcal/mol to the interactions  were defined as making substantial non-polar contributions. Note that according to these definitions, partially buried residues that make a substantial non-polar contribution to binding are often also partially exposed to the solvent. On the other hand, residues that make substantial electrostatic contributions can either also make non-polar contributions  or contribute only via long-distance electrostatic interactions. To reduce false positives and negatives, we applied a consensus approach across comparable biological replicates in multiple PDB structures (7CEI, 2JAZ, 2JB0, 2JBG, 1ZNV—see Supplementary Fig. We followed the methodology described previouslySupplementary Information.