Therefore, determining the structure of protein–protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein–protein interactions. Recently, deep learning has led to a breakthrough in intra-protein contact prediction, achieving an unusual high accuracy in recent Critical Assessment of protein Structure Prediction (CASP) structure prediction challenges. However, due to the limited number of known homologous protein–protein interactions and the challenge to generate joint multiple sequence alignments of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue–residue contacts across homo-oligomeric protein interfaces, named as DeepHomo. Unlike previous deep learning approaches, we integrated intra-protein distance map and inter-protein docking pattern, in addition to evolutionary coupling, sequence conservation, and physico-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-Critical Assessment of Predicted Interaction (CAPRI) targets. It was shown that DeepHomo achieved a high precision of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis and machine learning-based approaches. Integrating predicted inter-chain contacts into protein–protein docking significantly improved the docking accuracy on the benchmark dataset of realistic homo-dimeric targets from CASP-CAPRI experiments. DeepHomo is available at  As one of the most important biological macromolecules in living organisms, proteins have evolved to conduct diverse cellular functions, from serving as reaction catalysts to regulating DNA translation and transcription. Proteins rarely work in monomeric form but are rather assembled in homo-oligomers or hetero-oligomers to perform their biological functions , 2. The Subsequently, such DCA methods have been extended from the contact prediction within the monomer protein structure to residue–residue contact prediction at the hetero protein–protein interface , 27. Deset al. have focused on the interactions between homo-oligomers rather than hetero-oligomers, for which the produced individual MSA was directly used to perform the statistical analysis on the basis of the assumption that the protein in each line of the MSA also forms homo-oligomeric interface like the queried protein [et al. assumed that the predicted residue pair contacts with long intra-monomer distances are inter-protein contacts. Recently, a similar method has been proposed by the Cheng’s group to predict the inter-protein contacts of homo-oligomers [To avoid the joint MSA challenge, Uguzzoni  protein . This av protein . Uguzzonligomers . The truligomers , and theRecently, deep learning has achieved great successes in intra-protein residue–residue contact prediction and demonstrated high accuracy in the 12th and 13th Critical Assessment of protein Structure Prediction (CASP12 and CASP13) challenges . HoweverFigure Like other deep learning methods proposed for monomer structure contact prediction, the central part of our convolutional neural network (CNN) is a stack of residual network (ResNet) blocks , 56. Resmentclass2pt{minimThe 1D CNN contains six 1D ResNet v2 blocks with an increased number of filters. A convolutional layer with two Protein homo-oligomers have different symmetry types and stoichiometry, which may form different residue contacts at the interfaces. To avoid the effect of multiple interfaces and reduce potential noise, we focused on the homo-dimeric proteins with C2 symmetry type, which is the largest class of the homo-oligomers  to make In addition, another independent data set, called CASP-CAPRI test set, was also constructed to evaluate the performance of our deep-learning model in realistic applications. The joint CASP-CAPRI challenge has been established since 2014, which is a double blind experiment and aims to assess the computational methods of modelling protein assemblies in the community . We collSimilar to the inter-protein contact definition in the previous studies , 27, 39,Give a monomer of  HHblits  with a m HHblits  by threeBesides the sequence for each target, the 3D monomer structure was also used to produce the input features. It is expected that the features derived from monomer structures are helpful to distinguish inter-protein from intra-protein contacts. The Dictionary of Protein Secondary Structure (DSSP) program  was firs program  was used program  to calcu program  where th program . To bettHSYMDOCK , 71. TheThe Keras with the Tensorflow as backend was used to implement our deep learning model. The hyper-parameters were set as follows: mini-batch size: 1, optimizer: Adam , learnincal Loss  as the lAs homo-dimeric protein often has a C2 symmetry type, the ideal contact map for a homo-dimeric interface is diagonally symmetric. Hence, the labels and the input features used to train our model have to be operated to ensure the symmetry. For the input pairwise features, the transposed and untransposed matrixes were averaged to get the final input symmetric matrix. For the labels of the contact map, if the residue pair  contacts among the top  docking , so the dictions , 45–47. and Gong  to evaluFor docking applications, the quality of a predicted binding mode is measured by the TM-score , 62, 75 et al’s method [We first evaluated the performance of our DeepHomo model in inter-protein residue–residue contact prediction on a large test set of 300 experimental homo-dimeric complexes from the PDB. Figure entclass1pt{minimas method . First, s method . It was s method  and has It can be seen from Tables entclass1pt{minimaAlthough our deep learning model used the same MSA and monomer structure with DCA-based methods, the precision of DeepHomo almost doubled the precision of DCA_APC when the top predicted contact was considered. For top 100 predicted contacts, the improvement was much more significant and the precision has been improved almost over four times, compared with DCA_APC method. For DCA-based methods, DCA_APC method performed better than DCA_DI method for all the top 100 predicted contacts. This is consistent with the previous study in monomer protein contact prediction that APC helps improve the prediction quality , 78.As the monomer structure for a sequence is normally unknown in real applications, we have further tested our DeepHomo model on the CASP-CAPRI set of 28 realistic homo-oligomeric targets. To be consistent with the blind prediction challenge in CASP, we have used the top Zhang-Server model, which has been blindly predicted by the I-TASSRER server from the Zhang group , 54, as It can be seen from Figure ns Table . Similarentclass1pt{minimaTo investigate the effects of the MSA and the monomer structure on the performance of DeepHomo, we have conducted the ablation studies and tested the retrained models on the PDB test set of 300 homo-dimers and the CASP-CAPRI test set of 28 realistic targets. In the ablations studies, all the hyper-parameters were fixed, only the input features were changed during each experiment. The structural features include distance map, docking map, secondary structure and two hydrophobic features, and the MSA features consist of DCA scores and PSSM matrix. As shown in Figure As the MSA is crucial to our DeepHomo model and other DCA-based approaches, we have examined the effect of the MSA depth on the performance of our method. Here, the effective number of sequence homologs in an MSA, Figures To investigate the impact of the quality of monomer structures, we have examined the performance of our DeepHomo model on the monomer structures with different accuracies in the CASP-CAPRI test set. Here, TM-score  was usedFrom Figures In addition to assessing our DeepHomo using the monomer models predicted by the Zhang-Server on the CASP-CAPRI test set, We have also evaluated our DeepHomo model using the native monomer structures, whose results are shown in Supplementary Figure ab initio HSYMDOCK symmetric docking program by applying the predicted contacts in a post-docking filter, named as DeepHSYMDOCK, and evaluated its performance on the CASP-CAPRI test set of 28 realistic targets. For each target, the same monomer structure, i.e. the first Zhang-Server model, was used as the input structure for the DeepHomo and HSYMDOCK processes of DeepHSYMDOCK. All the default parameters were used during the HSYMDOCK docking calculations. Given that the accuracy of contact prediction is critical to the docking process, only the top predicted contact by DeepHomo was used to filter the final binding modes predicted by HSYMDOCK.Accurate prediction of inter-protein contacts will greatly help the structure determination of corresponding protein–protein complexes. To investigate the practical role of DeepHomo in complex structure prediction, we have integrated the predicted contacts by DeepHomo into our ab initio HSYMDOCK docking program. Similar trends can also be observed in the TM-scores of docked complex structures, where the TM-score was calculated using MMalign [Figure  MMalign . Namely,We have presented a deep learning model for inter-protein residue–residue contact prediction across homo-oligomeric protein interfaces by integrating both structural and MSA features of monomers, named as DeepHomo. Our DeepHomo model was extensively evaluated on two independent data sets, the PDB test set of 300 experimental structures and the CASP-CAPRI test set of 28 realistic homo-dimeric targets, and compared with state-of-the-art DCA-based methods (DCA_APC and DCA_DI) and ML-based approaches (BIPSPI_seq and BIPSPI_struc). It was shown that DeepHomo achieved a high accuracy in inter-protein contact prediction and outperformed existing DCA and ML-based methods. DeepHomo was robust to the depth of MSAs and the quality of monomer structures, and can achieve good accuracies for the MSA of Protein–protein contacts in homo-oligomeric complexes were accurately predicted using a deep learning model.Unlike traditional approaches, our model includes distance and docking maps based on monomer structures.Our model much outperformed existing direct-coupling analysis and machine learning-based approaches on realistic complexes.Our model was robust to monomer structures and still performed well with predicted protein structures like CASP models.Integrating predicted contacts into protein–protein docking significantly improved the docking accuracy.http://huanglab.phys.hust.edu.cn/DeepHomo/. The package consists of the processed files of the datasets, the trained model, source code and a step-by-step instruction for users.DeepHomo is available at Suppl_bbab038Click here for additional data file.


Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at  Protein-protein interactions are key components of many biological processes, and the three-dimensional (3D) structures of the protein-protein complexes thus formed are a crucial resource for reasoning about their molecular functions. Ideally, structures of these complexes would be determined experimentally, through techniques such as X-ray crystallography or cryo-electron microscopy. However, it is possible to instead use computational methods to construct atomic structure models of protein complexes . This clBiologists seeking to model a complex computationally may already have structures of individual subunits determined by experiment. However, where no such structures are available, a structure model can be constructed. Single-chain protein structure prediction methods have recently matured, and can often generate models in the absence of clear global template structures . Such mede novo prediction of subunit structures by AttentiveDist and applying symmetry constraints for homodimer modeling. We provide step-by-step instruction with examples of modeling in three different scenarios.The underlying methods implemented in the LZerD webserver, LZerD , Multi-LTo perform protein docking, it is best if users have experimentally determined 3D structures. In case the structure is not available, structures can be modelled from the amino acid sequences of the proteins in question. In practice, it is recommended that as much information about the protein structures and their interactions as possible be gathered in advance. For example, there should be evidence that the proteins in question do in fact form a complex, e.g., from a biochemical assay or a biophysical experiment. It is even more desirable that information be known about specific residue interactions or non-interactions. As discussed in later sections, such information can even be provided directly to the server.For structure modeling of individual proteins, the LZerD server uses AttentiveDist . If userIn the first stage of AttentiveDist, four multiple sequence alignments (MSAs) with E-value cutoffs of 0.001, 0.1, 1, and 10 are generated using the DeepMSA  method, Once generated, the predicted distance distribution is converted into full-atom structure models by L-BFGS minimization of predicted short-, medium-, and long-range distance restraints in sequence using PyRosetta . This miHeuristically, a model output by AttentiveDist with a ranksum score Although AttentiveDist was shown to have competitive performance at the time of the development , there ahttps://lzerd.kiharalab.org/upload/upload_sequence) allows users to submit up to six separate protein sequences for structure prediction at a time. Jobs are limited to six proteins because six is the maximum number of proteins the LZerD server can dock using Multi-LZerD. To submit a sequence, users can simply paste their sequence in FASTA format into the large text box, as shown in The AttentiveDist web interface  as input and samples of all possible interaction interface regions and interaction angles exhaustively. If a putative complex structure contains excessive steric violations at the interface, has too small an interaction area, or has low shape complementarity at the interface region, that model is rejected. In LZerD, a protein structure is represented by a molecular surface, which is segmented into overlapping local surface regions. Each local surface region is represented both by a mathematical moment-based shape descriptor called a 3D Zernike descriptor (3DZD) . 3DZDs aLZerD generates tens of thousands of docking models. After clustering (by default at an RMSD cutoff of 4.0 Å), which generally culls the model pool to a few thousand to a few tens of thousands, the complex models are then scored by a ranksum method  which agMulti-LZerD takes three to six protein structures and builds them into a complex. It first runs pairwise LZerD for each pair of subunits to generate a pool of pairwise docking models. Then, it subsequently selects pairwise models and assembles into full subunit models. Combinations of pairwise models are iteratively optimized by a genetic algorithm. In Multi-LZerD, models are selected with a molecular mechanics force field with terms reweighted specifically for protein docking.2 symmetry, i.e., homodimers, in the current version of the server because homodimers are quite common. A measure of the C2 symmetricalness of a model can be constructed by transforming the atomic coordinates of the subunit by applying the rigid body transformation from docking twice and calculating the root-mean-square deviation (RMSD) to the original coordinates. If the model is perfectly symmetrical, this RMSD will be 0 Å. For the Symmetrical protein complexes are often observed in nature . Cyclic via the upload widget. It is also possible to model one of the subunit structures by AttentiveDist and dock it with a structure from PDB. Clicking the Submit button will start the docking computation.This will run LZerD with the recommended default settings and without constraints. If users prefer, they can directly change parameters: the clustering RMSD cutoff, which controls the redundancy of the output model pool, as well as the surface reduction cutoff, which in part controls how finely the conformational space is sampled. If users are modeling a symmetrical homodimer, they can select the checkbox to model with a Below, in the advanced options section, users can specify distance constraints for specific residue-residue interactions or residue-subunit interactions. Distances used for these constraints should ideally come from experiment, but can also come from computational predictions . All disThe submission process is essentially the same for Multi-LZerD. Only the difference is, naturally, to specify three or more (up to six) subunit structures to assemble. To switch to Multi-LZerD, click the large gray button of “Switch to Multi-LZerD docking for more than 2 proteins”.After the docking job has been submitted and has finished running, the user is presented with a results summary page . This paFor more information about the job submission steps and interpretation of results page, users are encouraged to refer to the instructions on the LZerD web server. From the top bar, the information is available from the “About” pull-down menu.We discuss three case studies of docking modeling in different scenarios. The first case is the regular pairwise protein docking. The second case is homodimer docking, while the last case is docking with structure models built through the AttentiveDist pipeline.fnat) of 0.28 relative to the native structure PDB 5MZV shown in The first case study provides an example of basic pairwise protein docking modeling. During CAPRI Round 39, a complex of a human cytokine heterodimer IL23 with a human IL23R monomer was given for prediction as target T122. CAPRI is a blind experiment where the structure of the complex to be modeled is not known to the predictors until well after all predictions have been submitted. In this example we show a similar result to the LZerD server group’s modeling performance on this target . As the M. tuberculosis, which appears to directly relate to the drug resistance of the organism (This is an example of modeling a homodimer complex with the newly implemented homodimer constraint. The complex used is aminoglycoside 2′-N-acetyltransferase (AAC (2′)) from organism . This exorganism . To creaorganism  and clicorganism , a dockeorganism , M-ZDOCKE. coli colicin-E2:DNase-Im2 complex was presented as the target T133. The structure of this complex has since been released, and is available as an entry in PDB, 6ERE. The designed sequences of the first assembly, i.e. chains B and C, can be taken from the PDB entry page (https://www.rcsb.org/structure/6ERE). We modeled this complex from the sequence information without using any template structures. We clicked Upload Protein Sequences to bring up the AttentiveDist submission form and pasted the colEdes3 sequence. Then, we clicked the green plus button to add another sequence input field and pasted the Imdes3 sequence there. The filled submission form is shown in In this last example, we started from the sequence of individual proteins to model their tertiary structures by AttentiveDist, which were then docked to obtain complex models. In CAPRI round 43, a redesigned version of the From the site-directed mutagenesis experiments, we knew that Tyr55 of DNase-Im2 is a hotspot residue . FurtherOn the results page, shown in This example demonstrates several qualities of the pipeline components and qualities of the pipeline as a whole. As shown quantitatively by the metrics and visually in de novo predictions of subunit structures and dock them with the click of a button. Future development of the LZerD web platform is expected to include modeling complexes with intrinsically disordered proteins using IDP-LZerD (With the upgraded LZerD web interface, biologists can conveniently construct protein complex models that they can use to reason about the interactions in their system. Information about the system can be integrated in the form of geometric distance and symmetry constraints. Now, even without known or template-modeled subunit structures, users can generate DP-LZerD , as wellDP-LZerD .


While informative, protein amounts and physical protein associations do not provide a full picture of protein function. This Commentary highlights the potential of structural and stability proteomic technologies to derive new insights in biology and medicine. The ultimate goal of proteomics is to provide a holistic view of the biological processes that explain phenotypes. To this end, proteomics approaches have been frequently used to map protein expression in multiple experimental conditions and to identify protein–protein interactions. While informative to a certain extent, protein amounts and physical protein associations do not provide a full picture of protein function. Here, we highlight the novel insights that are made possible in biology and medicine by structural and stability proteomic technologies.Understanding the relationship between genotypes and phenotypes is one of the open challenges in life sciences. Since proteins orchestrate all cellular processes, their systematic study in the cellular context is a powerful strategy for dissecting cellular phenotypes. Many research groups have studied the fundamentals of protein cellular networks by cataloguing proteins differentially expressed in tissues, cells and organelles. Mass spectrometry (MS)‐based methods can detect close to 90% of annotated protein‐coding genes, achieving almost complete coverage of the human cell proteome. This comprehensive protein identification combined with precise quantification of protein abundance makes it possible to map which genetic functional units are required in different cellular contexts. While the expression proteomics strategy has been successful in capturing a subset of functional changes in biological systems, a large part of biological regulation does not manifest itself in changes in protein level.functional proteomics” was introduced to describe the early attempts to characterize the complexity of protein‐related molecular events and their functional consequences. Until recently, this concerted effort has mostly focused on the identification of protein–protein interactions, as physical associations among proteins suggest their involvement in a common biological function (guilt by association). However, protein activity is not only affected by protein–protein interactions, but it is also dependent on interactions with small molecules, cofactors, metals, and nucleic acids, as well as on the folding state . All these factors combined with the local cellular environment  result in alterations of the biophysical properties of proteins that ultimately influence biological processes.More than a decade ago, the term “In vitro assays provide clear mechanistic insights about structure–activity associations but they are limited in throughput and do not necessarily represent the behaviour of proteins in cells. Recently developed proteomic‐based methods allow the global characterization of protein structure and interaction states under near‐physiological conditions, unifying reductionist and systematic biophysical approaches. These include thermal proteome profiling (TPP)  and protein thermal stability parameters in vivo. In analogy with the principle that functionally related genes are co‐expressed, proteins with coordinated changes of thermal stability are involved in the same biological processes  or alter their protease sensitivity (LiP fingerprint). Thermal stability workflows have been developed for target identification of different drug classes, including antibiotics, and can be applied to intact eukaryotic and prokaryotic cells, mammalian tissues and can map transmembrane targets. Notably, TPP experiments performed on whole cells determine which protein targets are engaged in situ, while experiments performed in lysates can help deconvolute direct targets from downstream effects. In addition, using natural compounds instead of drugs, it is possible to discover new metabolite binding proteins. With LiP‐MS, the position of proteolytic cleavages provides hints for identifying small molecule binding sites, bringing the resolution of structural proteomics methods to single compound binding sites  or super‐resolution microscopy. The present challenge is thus to come up with ways to monitor the dynamics of native protein structures at high resolution over time, including protein structural changes occurring due to interactions with small molecules, nucleic acids and the formation of condensates. We can envision a new form of protein structural analysis that integrates static images obtained from electron tomograms with biophysical parameters of proteins measured on a cell‐wide scale with mass spectrometry Fig . For insmilieu. Likewise, in situ structural models from cryo‐ET provide a scaffold for annotating different kinds of interactions from native structures. We discussed the application of LiP‐MS and TPP technologies for the unbiased capturing of protein state changes . However, in‐cell crosslinking mass spectrometry can also add valuable modelling information relating covalently linked sites to their proximity in protein complexes. Even more excitingly, these new integrative microscopy‐proteomics workflows could become a routine way of including high‐resolution protein 3D structures into network models of metabolism, gene expression and proteostasis.Stability proteomics and cryo‐ET are complementary. They can elegantly link endogenous interactions to molecular machines by visualizing variations of localization and structure of protein assemblies upon changes of the cellular The authors declare that they have no conflict of interest.HuangJXet al (2019) High throughput discovery of functional protein modifications by Hotspot Thermal Profiling. Nat Methods16: 894–90131384043OchoaD, JarnuczakAF, ViéitezC, GehreM, SoucherayM, MateusA, KleefeldtAA, HillA, Garcia‐AlonsoL, SteinFet al (2019) The functional landscape of the human phosphoproteome. Nat Biotechnol38: 365–37331819260PotelCM, KurzawaN, BecherI, TypasA, MateusA, SavitskiMM (2021) Impact of phosphorylation on thermal stability of proteins. Nat Methods18: 1–333408396SmithIR, HessKN, BakhtinaAA, ValenteAS, Rodríguez‐MiasRA, VillénJ (2021) Identification of phosphosites that alter protein thermal stability. Nat Methods18: 760–76234140699ChildsD, BachK, FrankenH, AndersS, KurzawaN, BantscheffM, SavitskiMM, HuberW (2019) Nonparametric analysis of thermal proteome profiles reveals novel drug‐binding proteins. Mol Cell Proteomics18: 2506–251531582558KurzawaN, BecherI, SridharanS, FrankenH, MateusA, AndersS, BantscheffM, HuberW, SavitskiMM (2020) A computational method for detection of ligand‐binding proteins from dose range thermal proteome profiles. Nat Commun11: 578333188197KurzawaN, MateusA, SavitskiMM (2020b) Rtpca: an R package for differential thermal proximity coaggregation analysis. Bioinformatics37: 431–433PiazzaI, BeatonN, BrudererR, KnoblochT, BarbisanC, ChandatL, SudauA, SiepeI, RinnerO, de SouzaNet al (2020) A machine learning‐based chemoproteomic approach to identify drug targets and binding sites in complex proteomes. Nat Commun11: 4200–421332826910SchopperS, KahramanA, LeuenbergerP, FengY, PiazzaI, MüllerO, BoersemaPJ, PicottiP (2017) Measuring protein structural changes on a proteome‐wide scale using limited proteolysis‐coupled mass spectrometry. Nat Protoc12: 2391–241029072706BecherI, Andrés‐PonsA, RomanovN, SteinF, SchrammM, BaudinF, HelmD, KurzawaN, MateusA, MackmullM‐Tet al (2018) Pervasive protein thermal stability variation during the cell cycle. Cell173: 1495–150729706546DaiL, ZhaoT, BisteauX, SunW, PrabhuN, LimYT, SobotaRM, KaldisP, NordlundP (2018) Modulation of protein‐interaction states through the cell cycle. Cell173: 1481–149429706543KalxdorfM, GünthnerI, BecherI, KurzawaN, KnechtS, SavitskiMM, EberlHC, BantscheffM (2021) Cell surface thermal proteome profiling tracks perturbations and drug targets on the plasma membrane. Nat Methods18: 84–9133398190MateusA, BobonisJ, KurzawaN, SteinF, HelmD, HevlerJ, TypasA, SavitskiMM (2018) Thermal proteome profiling in bacteria: probing protein state in vivo. Mol Syst Biol14: e8242–1529980614PerrinJ, WernerT, KurzawaN, RutkowskaA, ChildsDD, KalxdorfM, PoeckelD, StonehouseE, StrohmerK, HellerBet al (2020) Identifying drug targets in tissues and whole blood with thermal‐shift profiling. Nat Biotechnol38: 303–308.31959954


Interoperability between polymer sequences and structural data is essential for providing a complete picture of protein and gene features and helping to understand biomolecular function.Herein, we present two resources designed to improve interoperability between the RCSB Protein Data Bank, the NCBI and the UniProtKB data resources and visualize integrated data therefrom. The underlying tools provide a flexible means of mapping between the different coordinate spaces and an interactive tool allows convenient visualization of the 1-dimensional data over the web.https://1d-coordinates.rcsb.org and https://rcsb.github.io/rcsb-saguaro.Bioinformatics online. With rapid improvements in genome sequencing technologies and protein structure determination tools, bioinformatic resources compiling gene and protein structure information are growing faster than ever and new biochemical and molecular content is released weekly if not daily. Currently, the NCBI RefSeq database contains >178 million non-redundant protein sequences and >32 million transcripts . To dateThe problem of providing protein-to-genome sequence alignments is not fully solved at present. For example, the Structure Integration with Function, Taxonomy and Sequence (SIFTS) uses a semiautomatic pipeline to compute residue level mappings between sequences in PDB 3D structures and proteins in UniProtKB . The Unihttps://github.com/ebi-webcomponents/nightingale) were designed as general tools to display annotations over sequences; however, these tools and other extant libraries were principally designed to display biological features for a single sequence. As such, they are not well suited for visualization of positional annotations integrated from multiple sources, a key requirement of the current structure–protein–gene integration framework where one-to-many relationships commonly occur among databases.In addition to structure–protein–gene sequence alignment integration, facile data visualization is critical for understanding gene and protein biological features. Graphical summaries of sequence annotations enable identification of relationships and patterns among positional features. Most existing graphical libraries that display positional features were originally developed around individual data resources  and NighHerein, we present two new RCSB PDB resources: the 1D Coordinate Server and the 1D Protein Feature Viewer. Both tools were developed to simplify data integration and visualization of gene and protein features at both sequence and 3D structural levels. Moreover, these tools have significantly broadened data integration of RCSB PDB structural data with gene and protein feature annotations available from NCBI and UniProtKB. The 1D Coordinate Server is a web service that maintains a weekly updated mapping among NCBI, UniProtKB and PDB structure data. This resource provides both relationships between database entries and alignments of protein sequences and alignment of protein sequences with genome sequences. The second resource, the 1D Protein Feature Viewer, is a new library developed to display sequence positional features using a web browser client. The motivation for this development was to provide a collection of modern performant tools capable of displaying structural and biological features integrated from multiple sequence references in a context of one-to-many relationships and alignments containing gaps.1D Coordinate Server: The 1D Coordinate Server is a web service that integrates information at protein and genome level from NCBI, UniProtKB and RCSB PDB. Integrated data includes both residue level mappings between protein and genome sequences drawn the different databases and positional annotations collected from UniProtKB and PDB structures. Alignments are encoded into JSON format documents that contain aligned regions as arrays of ranges  written in TypeScript designed for visualization of biological features of proteins on the web .Conflict of Interest: none declared.btaa1012_Supplementary_DataClick here for additional data file.


However, the analysis of protein structures often requires researchers to use several software packages or web servers, which complicates matters. To provide long-established structural analyses in a modern, easy-to-use interface, we implemented ProteinTools, a web server toolkit for protein structure analysis. ProteinTools gathers four applications so far, namely the identification of hydrophobic clusters, hydrogen bond networks, salt bridges, and contact maps. In all cases, the input data is a PDB identifier or an uploaded structure, whereas the output is an interactive dynamic web interface. Thanks to the modular nature of ProteinTools, the addition of new applications will become an easy task. Given the current need to have these tools in a single, fast, and interpretable interface, we believe that ProteinTools will become an essential toolkit for the wider protein research community. The web server is available at   The number of deposited structures in the protein databank is growing at an exponential rate, with 90% of today's available structures deposited in the last 20 years. Not only provide experimental methods a wealth of structural data faster than ever before, but also computational efforts to predict structures have significantly advanced in the last decade. Particularly promising have been recent deep-learning-based methods on structural prediction, such as DMPfold  or AlphaThe 3D structure of a protein is important for its biological function, and therefore, its characterization or accurate prediction is of vital importance. Proteins fold into their native structures in an interplay driven by various non-covalent interactions such as hydrogen bonds, Van der Waal forces, hydrophobic, and ionic interactions. Thus, to understand a protein's features and functions at the molecular level, it is essential to characterize these interactions. While most computational efforts in structural biology have focused on implementing tools that predict protein structures, a few remarkable tools have also been released for structural analysis.Many of these tools originated from the necessity to understand interactions in the context of protein dynamics  and thusRegarding the function of a protein, another property that is interesting to characterize is the detection and analysis of cavities and channels. Tools such as PASS  and PockDespite these significant advances, most of these tools offer an analysis of an individual structural property and are often available in software packages written in different programming languages. Therefore, users have to download and install several tools, and to consult various documentations. It is thus vital to improve such tools to make them usable in an intuitive manner. Particularly valuable are toolkits that gather many tools of interest in a single website, reducing users’ analysis times and learning curves. To our knowledge, not many web servers in the protein field have been published that collect several tools in a single site, although we expect this trend to change. We would like to highlight the Bioinformatics Toolkit  for the https://proteintools.uni-bayreuth.de), a toolkit for analyzing protein structures. At this stage we added four applications: The identification of hydrophobic clusters, hydrogen bond networks, salt bridges, and contact maps. Hydrophobic clusters prevent water molecules' intrusion into the protein core and serve as bodies of stability in high-energy partially folded states. Previous software and servers to compute hydrophobic clusters, such as the Contacts of Structural Units (CSU) algorithm (https://molstar.org/), we can bring back the computation of hydrophobic clusters to the community. Hydrogen bond networks enable the communication between residues far apart in the protein structure , leucine (LEU) and valine (VAL) residues often form hydrophobic or so-called (ILV)-clusters that prevent the intrusion of water molecules and serve as cores of stability in high-energy partially folded states . VariousIn practical terms, ProteinTools takes each ILE, VAL, and LEU heavy atoms into account and then retrieves the coordinates of their neighboring atoms. In case of alternate conformations only the first state is considered. These are atoms that are closer than the sum of the two Van der Waals radii, each enlarged by the water molecule radius (1.4 Å). Hence, for two carbon atoms to be considered candidates for atomic contacts, they must be within 6.56 Å. ProteinTools discretizes each atom sphere into 610 uniform sections using the Fibonacci grid ,33. The 2. The adjacent matrix is converted to a graph, where every component corresponds to a (hydrophobic) cluster. The cluster's total area is computed by the sum of the individual residue areas that comprise it. ProteinTools shows each of the computed hydrophobic clusters in a different color in an interactive panel. Properties of each cluster are summarized in a table. The results are available for download in the form of a PyMOL session  and Fraction of Charged Residues (FCR) parameters, primarily studied by the Pappu Lab . κ is a Protein contact maps represent distances between all amino acid residue pairs in the form of a matrix. ProteinTools calculates contact maps by computing an all-against-all distance matrix of residues and takes the minimum distance between any two atoms in the two evaluated residues. The raw data is plotted in an interactive panel, which can be exported as a CSV table.https://proteintools.uni-bayreuth.de.ProteinTools is developed using the Django Python framework (version 3.1.2). The backend is entirely implemented in Python. The website interface is designed with JavaScript using the Bootstrap framework (version 4.2). The proteins are visualized with the PDBe Molstar web package . Specifiet al. (We demonstrate ProteinTools' four applications by using the protein Di-III_14 (PDB code 2LN3) as an example. In 2012, in an exceptional work by Koga et al. , rules wet al. . Di-III_et al. performed hydrogen exchange (HDX) NMR analyses of Di-III_14 that showed an exchange process between conformational states in slow timescales, and that stands in striking disparity with the fast process of unfolding revealed in guanidinium chloride denaturation , but well within one standard deviation (±1078.9 Å2). The average residue number is 14.1, in line with the results for Di-III_14. A representative IF3-like protein with three clusters and an area of 1978 Å2 for the largest cluster is shown in Figure Basak turation . While b3 Figure . The anaom SCOPe , a databet al. observed two electrostatic networks, one spanning α1 and α2’s surfaces and the other containing a quartet of salt bridges that link the two internal β-strands, β2 and β4. To recapitulate these findings, we computed Di-III_14’s hydrogen bond networks (https://proteintools.uni-bayreuth.de/bonds/structure/2ln3). ProteinTools computes hydrogen bond networks among sidechains by looking at nitrogen and oxygen donors and acceptors within 2.5 Å and an angle over 120° . Di-III_14 contains eight hydrogen bond networks  comprises residues Glu 45, Glu49 and Lys61 and brings together α2 and β3. Similarly, network 0 (red) links strands and helices by networking Ser9 in β1 with Asn11 and Glu14 in α1. The other two networks correspond to network 2 (yellow), entirely contained in α2 (residues Asp50 and Lys 54) and network 1 (dark green), linking α1 and α2 via Lys12 and Glu43.https://proteintools.uni-bayreuth.de/salt/structure/2ln3  versus κ for almost the entire proteome of the thermophile Sulfolobus solfataricus and observed that Di-III_14 appears at a different region than the rest of the proteins. ProteinTools is also capable of computing these parameters, giving an FCR of 0.35 and a κ of 0.25, in agreement with Basak et al.’s results. We wondered whether this differences between Di-III_14 and natural proteins also extends to the other designs in the work by Koga et al. , Fold-II: Rossmann 2 × 2 (c.2), Fold-III: IF3-like fold (d.68), Fold IV: P-loop 2 × 2 fold (c.37), Fold V: Rossmann 3 × 1 (c.23). The natural proteins belonging to these folds tend to present FCR values around 0.25 and κ values around 0.2 clustering in a similar region in space  do not require installation, (ii) are accessible from any internet-connected computer, and (iii) liberate the user from learning specific programs. Among web servers, toolkits are particularly valuable as they gather several applications that would otherwise require various packages or web servers. These toolkits not only ease the use, but also help to guide the analysis and to view protein structures in a more complete manner and reveal common patterns . Motivathttps://proteintools.uni-bayreuth.de.The web server is available at gkab375_Supplemental_FileClick here for additional data file.


Protein-protein interaction (PPI) prediction is meaningful work for deciphering cellular behaviors. Although many kinds of data and machine learning algorithms have been used in PPI prediction, the performance still needs to be improved. In this paper, we propose InferSentPPI, a sentence embedding based text mining method with gene ontology (GO) information for PPI prediction. First, we design a novel weighting GO term-based protein sentence representation method to generate protein sentences including multi-semantic information in the preprocessing. Gene ontology annotation (GOA) provides the reliability of relationships between proteins and GO terms for PPI prediction. Thus, GO term-based protein sentence can help to improve the prediction performance. Then we also propose an InferSent_PN algorithm based on the protein sentences and InferSent algorithm to extract relations between proteins. In the experiments, we evaluate the effectiveness of InferSentPPI with several benchmarking datasets. The result shows our proposed method has performed better than the state-of-the-art methods for a large PPI dataset. Protein-protein interaction (PPI) plays a vital role in cellular systems of organisms . Most biDue to the importance of PPI in the field of biology, a variety of computational methods based on various sources of biological information have been proposed for PPI prediction. Researchers have been predicting PPIs using a protein sequence  and PPI Gene ontology (GO) information is applied to PPI prediction . GO Con is a staText mining techniques have been applied to extract protein information and construct PPI networks . A text In this paper, we propose InferSentPPI, an efficient supervised sentence embedding based PPI prediction method by capturing information of GO structure and GO annotation. Comparing with the normal corpus-based approach, InferSentPPI considers three kinds of relationships together, which are protein-GO relations, GO-GO relations, and protein-protein interactions. To utilize protein-GO relations, InferSentPPI regards a protein as a sentence, and it represents protein with GO terms. Its related GO term’s vectors are the words that make up the sentence. To utilize semantic relations between GO terms, the GO term vectors are created by Word2vec from the GO graph structure. InferSentPPI uses the modified supervised sentence embedding model InferSent , which c(1) A new protein sentence embedding based PPI prediction method with GO information was designed and implemented.(2) Three kinds of biological relationships are applied to PPI prediction together, which are protein-GO relations, GO-GO relations, and protein-protein interactions. It fully mines the information of GO graph, GO annotation, and PPI information to obtain high quality protein vector representations for improving the performance of PPI prediction.(3) An efficient GOA preprocessing method, generation of weighted protein-GO annotation axioms for protein sentence representations based on the reliability, is proposed for improving the performance of PPI prediction.The main contributions of this study are as follows:InfersentPPI includes three main stages: preprocessing, protein sentence representation, and InferSent_PN as shown in Preprocessing consists of three parts: annotation axiom generation, GO term vector generation, and protein screening process. Annotation axiom generation is a task to extract the relationship between protein and GO terms and represent protein with its related GO terms. GO term vector generation is a word embedding based task to mine semantic information between GO terms from GO structure. The protein screening process is a task to find the available PPIs from the PPI databases.GOA includes the term-protein annotations between GO terms and proteins. Therefore, we extract the reliable annotation relationship between GO terms and proteins from GOA.To obtain reliable protein-GO annotation axioms, we filter GOA records according to reliable evidence. The record of protein-GO annotation axioms from GOA is defined as the following: Protein_GO_record  = {GO ID, protein ID, Evidence Code}.The specific generation steps are as shown in 1, v2, v3, … ,vn are the components of GOV.GO graph includes the semantic relationships between GO terms. Thus, we apply the Word2vec  algorithTo obtain available PPI datasets for constructing the Infersent_PN model, first we select the PPIs whose protein can map UniProt ID because proteins without UniProt ID cannot find their related GO terms. Then we select the PPIs whose proteins have their related GO terms and can be represented by GO terms.1, GOV2, GOV3, … ,GOVn are the GO term vectors.Protein is annotated by several GO terms. Therefore, protein can be represented by a set of vectors of a GO term. An n-dimensional protein vector P can be specified in the following form:i, Pj, L) where Pi and Pj are any two proteins, and L is the relationship label.In this work, a protein is regarded as a sentence; a GO term is regarded as a word; a sentence corpus PC is composed of protein sentences and relationship label between protein pairs. PC can be specified in the following form: PC=. Training data of InferSent_PN is composed of pairs of protein sentences and relationship label between protein pairs. The workflow of InferSent_PN model is shown in The formula for predicting PPI is as following The input of InferSent_PN is  as . In Section 2.2.1, we introduced two versions of a method to generate annotation axioms of InferSentPPI. Based on the two methods, we have implemented two versions of the InferSentPPI method. Using “PGAA_noweight” in the InferSentPPI method is named as the “InferSentPPI_ noweight_ PGAA” method, and using “PGAA_Weight” in the InferSentPPI method is named as “InferSentPPI _ weight_ PGAA".S. cerevisiae core) dataset, an E. coli dataset, a Homo sapiens dataset, and a mice dataset  dataset and a human dataset from the STRING database , a yeast dataset  from a dS. cerevisiae dataset contains 6,392 proteins and 2,007,135 interactions, and the DIPS. cerevisiae core contains 5,594 positive protein pairs and 5,594 negative protein pairs. The STRING human dataset contains 19,577 proteins and 11,353,057 interactions, and the HPRD human dataset is made up of 3,899 positive protein pairs and 4,262 negative protein pairs. Interaction pairs with reliable GO annotation records were left through the preprocessing step. Then, the dataset used for the experiment is shown inThe STRING To evaluate the performance of PPI prediction, we used six measures, including Accuracy, Precision, Recall, F1, Area Under the ROC curve (AUC_ ROC), and area under PR curve (AUC_ PR). Accuracy is the ratio of the number of samples correctly classified by the classifier to the total number of samples. Precision calculates the proportion of the number of positive samples for correct prediction to the number of samples whose prediction is positive. Recall calculates the proportion of the number of samples whose prediction is positive and correct to the number of samples that are actually positive. ROC curve and PR curve are widely used to evaluate the performance of classification and prediction tasks . ROC curWe randomly selected 90% of yeast dataset and human dataset to train the InferSentPPI model. The selection of batch size has some influence on the training of the InferSentPPI model. By setting batch size = 2 in model training, InferSentPPI has the best performance on the yeast test set. In addition, by setting batch size = 1 in model training, InferSentPPI has the best performance on the human test set. So, we set the batch size to one for the yeast dataset and set the batch size to two for the human dataset.The similarity between GO terms is calculated by three exiting methods, Resnik , Lin Li, and PekAccording to the similarities between two terms in GO, the semantic similarity between two proteins is calculated by AVG , Max Pe, and besTo evaluate the effectiveness of proposed methods, we compare InferSentPPI with representative GO structure-based PPI prediction methods . In the On the DIP yeast and HPRD human datasets, five leading evaluation indicators of InferSentPPI_weight_GOA are better than InferSentPPI_unique_GOA. It means the model’s performance generated on a corpus with weighted GO annotations is better than the model generated on a corpus with weightless GO annotations. The result indicates that the quantitative index of GO annotation reliability successfully provides valuable information for PPI prediction.To evaluate the effectiveness of proposed methods, we compare InferSentPPI with state-of-the-art GO information-based PPI prediction methods, Onto2Vec  and GO2VThe performance comparison results of the methods are shown in To deeply evaluate the effectiveness of proposed methods, we compare InferSentPPI with a state-of-the-art sequence-based method DeepFE-PPI . The resThe number of PPIs in the DIP yeast dataset used in the experiment is 10 times larger than the HPRD human dataset. On the DIP yeast dataset, the four evaluating indicators of the two methods of the InferSentPPI are better than the sequence-based PPI prediction method DeepFE-PPI. However, neither of the two methods of the InferSentPPI outperforms DeepFE-PPI on the HPRD human dataset, which is much smaller than the DIP yeast dataset. The experiment result shows that the InferSentPPI performs better than DeepFE_PPI when there is sufficient training data.E. coli, H. sapiens, mice)  .E. coli positive dataset, including 1,112 records, the accuracy of our model also reaches 0.90. The availability of our model in predicting multiple species is proved. It means that the InferSentPPI method can obtain a better generalization model from a single species data set with sufficient data.On three species-specific PPI datasets, Accurate prediction of PPI can help us understand the underlying molecular mechanisms and significantly promote drug discovery. The method based on GO information can be used to make reliable PPI predictions. In this paper, we apply the modified supervised sentence embedding model InferSent to mine GO information and PPI data, used to predict PPIs. We used seven different datasets to evaluate our method to thoroughly test the InferSentPPI model. Compared with representative GO information-based methods and a sequence-based PPI prediction method, the experimental results show the effectiveness and generalization of the InferSentPPI method. The result also indicates that the quantitative index of GO annotation reliability successfully provides valuable information for PPI prediction. “PGAA_ Weight” can improve the performance of PPI prediction.


AlphaFold is the most ground‐breaking application of AI in science so far; it will revolutionize structural biology, but caution is warranted. Last December, the artificial intelligence (AI) AlphaFold achieved what had been considered impossible: near‐perfect protein fold predictions. The shockwaves reverberated well beyond the scientific community: one of the grand challenges in biology, how to fold a protein, appeared to have been solved finally—something that most of us did not expect to happen in our lifetimes. Notwithstanding all the justified excitement about AlphaFold, this achievement does not mean though that AI will make experimental structural biology or its practitioners and tools redundant. Structural biology will remain essential for understanding how proteins work and how they dynamically interact with each other.et al, et al, The publications of the AlphaFold method  that gives structural biologists a “treasure trove” of new information. Here, we try to address the scope and implications of this watershed moment in structural biology.The DeepMind team, collaborating with the European Bioinformatics Institute (EBI), then applied AlphaFold to whole genomes, including human, mouse, To better understand the enormous advance that AlphaFold and RoseTTAfold have achieved, it is worthwhile to look at the problem. Protein folding involves rearranging a linear sequence of amino acids in space to a physiologically preferred low‐energy state. Predicting the correct structure from the amino acid sequence alone was deemed an intractable problem, as the degrees of freedom in the peptide bonds create an astronomically high number of possible conformations: Sequential sampling would take longer than the age of the universe, even for a small protein domain , and cryogenic electron microscopy (cryo‐EM) that are publicly available from the Protein Data Bank (PDB) , but also give indications of dynamic movement between them. A nice example is USP7, a complex enzyme that consists of multiple domains that undergo dynamic conformational changes. Figure et al, et al, AlphaFold models have several limitations though. The most obvious one is that interactions with partner proteins or multimers are currently not in the database. However, RoseTTAfold  that are used for instance in phase separation? As more structures and sequences become available, and as the methods further improve, it seems likely that the fraction of poorly predicted protein will decrease. A major step forward will come from inclusion of multimers and complexes in the predictions, but there may be other regions that adopt defined states only temporarily under very specific conditions.https://ccd.rhpc.nki.nl/); to solve experimental structures faster, especially for X‐ray crystallography (Millán et al, Yet, AlphaFold models are already useful for many practical aspects of structural biology: to design better protein expression experiments (Above all, the analysis of the models themselves can generate new and testable hypotheses about protein function. This actually is the great joy of structural biology: to derive mechanistic insight from a protein structure. Here, we have a whole candy store of new structures to play with. There is a lot to do, and many of us are already busy designing mutants and experiments to test new ideas and hypotheses based on the AlphaFold models themselves. In many cases the biochemical, biophysical or cellular validation, will be enough and further structural validation may not be required.In this sense, AI provides structural biologists with a new technique, bringing the fun of structure‐gazing without the effort of experimental work. However, as models now become readily available, some of the joy in the discovery of a new structure will be gone, and so might be the drive and enthusiasm to interpret it. Moreover, there is a danger that people will be less inclined to prepare high‐quality protein, which is actually useful for many more experiments than determining structures. It will thus be crucial to educate the next generation of biologists to learn how to critically analyze the predicted fold, notice new interactions, and to get to know each protein of interest in sufficient detail, so as to differentiate between “bugs” and “features”.High‐resolution protein structures are extremely useful for drug discovery. Current drug development uses structures at every step of the process: from initiating discovery by fragment screening in crystals or in solution, to improving ligand interactions, to engineering high‐affinity ligands with favorable drug‐like properties. The heavy investment of the pharmaceutical industry into X‐ray crystallography and NMR spectroscopy in the past, and in cryo‐EM nowadays, is testament to the importance of structures. Nonetheless, it would be obviously attractive if the experimental process could be entirely replaced by a computational approach. The current AlphaFold implementation does not yet have the accuracy that is necessary for drug discovery. More importantly, the ability to accurately predict novel ligand interactions may be limited, as publicly available data for binding small molecules are scarce. This reduces the opportunities for deep learning. More data are potentially available in private databanks, and the expected acceleration in fragment screening by X‐ray crystallography and cryo‐EM could change this situation.For now, drug discovery will require experimental analysis, and here, the AI revolution may actually be counterproductive. The easy availability of computational models might result in fewer target proteins for which good purification protocols are developed, and fewer well‐diffracting crystals or well‐developed sample preparation processes for cryo‐EM. All these are necessary for drug discovery as X‐ray crystallography remains the method of choice to study structures in complex with small molecules, while cryo‐EM becomes the method of choice for studying complexes with biological therapeutics, such as antibodies or nanobodies.The AI solution to the protein folding problem is not only one of the most important advances that happened to structural biology, and it is arguably the most ground‐breaking application of AI in science to date. As such, we can expect that many of its methodological ideas will find their ways into AI applications beyond structural biology. However, with all revolutions peril lurks along the way. It is important to keep in mind, that it does not matter how correct AI thinks a model is, “if it doesn't agree with experiments, it's wrong”, to paraphrase Richard Feynman. For this reason alone, experiments remain essential. Similarly, pathologists and oncologists should remain critical of AI analyses of biopsies or scans, evaluate the results, and communicate them to patients. It is likely that the next generation of structural biologists will no longer be mainly experts in experimental methods, but will primarily interpret, design, and perform experiments based on structures, to prove or disprove mechanisms in biology or to design new protein functions or therapeutics.Experimental structure determination will not become obsolete for the foreseeable decades. The more intricate questions about conformational states, dynamics, and transient interactions will still need attention and experiments to answer. It is thus important that funders—and peer‐reviewers—do not come to believe that “the folding problem has been solved”. They should continue to invest both in the critical infrastructure needed for experimental structural biology and the research that uses experimental structure determination, where needed.The protein folding example will provide valuable lessons in how we can benefit from the application of AI models without creating an irrevocable dependence on themAs AI applications become more mainstream in modern society, from AI‐based chat‐bots, virtual assistants, self‐driving cars or “intelligent” robotic vacuum cleaners and lawn mowers, the problem of transforming the roles of the people who had done these tasks before will be ever present. The protein folding example will provide valuable lessons in how we can benefit from the application of AI models without creating an irrevocable dependence on them. At the end of the day, the development of deep learning algorithms is no different from human learning: We all learn from experiments, every day.


In silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks.We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.https://github.com/THinnerichs/DTI-VOODOO.DTI-Voodoo source code and data necessary to reproduce results are freely available at Bioinformatics online. Determining DTIs computationally can therefore help to mitigate the costs and risks of drug development.Identifying drug–target interactions (DTIs) is a crucial step in drug discovery; finding novel DTIs for approved drugs can be used for drug repurposing, either by finding new drugs for a known target or finding a drug for a novel target involved in a disease process. Inferring the interactions between drugs and their targets can help to analyze and identify potential desired or adverse drug effects as well as desirable therapeutic effects. While Computational methods are widely applied to predict DTIs and many computational methods have been developed. These methods can be broadly classified into ‘top-down’ and ‘bottom-up’ approaches. Top-down approaches start from observable characteristics resulting from a DTI, such as side-effects or the diseases treated by a drug, and infer likely molecular mechanisms (i.e. the interaction) using these observations. Bottom-up approaches start from molecular features such as molecular structure or fingerprints associated with drug and protein, and predict interactions from this information.Both bottom-up and top-down approaches to DTI prediction bear some advantages and limitations. Generally, bottom-up methods face the challenge to predict whether a chemical structure binds to a protein given their molecular properties; whether two entities interact depends not only on the molecular structure of the entities (where binding sites and molecular forces need to be determined for accurate prediction) but also properties such as in which celltypes and anatomical structures a protein is expressed. Top-down methods use information about physiological effects of drugs for DTI prediction, such as side-effect similarity  have significantly more known interactions with drugs than others. In the STITCH database, 5% of the proteins have 40% of the interactions, and similar distributions are present in other datasets  the molecular organization of each drug in an unsupervised manner. DeepGOPlus provides features derived from protein amino acid sequences which are useful to predict protein function.Our model combines ‘top-down’ and ‘bottom-up’ information for drug–target identification. We consider an approach to be ‘top-down’ when observable characteristics of either a drug (such as a drug effect) or protein  are used to provide information about a molecular mechanisms; we consider an approach ‘bottom-up’ when structural or other molecular information is used to determine a mechanism. In order to build a method that incorporates both top-down and bottom-up features, we first create a model for each type of feature separately. As features for the bottom-up model, we use features derived from molecular structures of drugs from the epGOPlus . SmilesTAs phenotypes and functions are encoded through ontologies, we use DL2Vec  . The gray D is described by A graph convolutional layer  consistsy ije of the graph. With simple, single-valued edge weights such as id, i.e. the degree of each vertex i. We denote this type of graph convolutional neural layers with GCNConv.The update rule for each node is given by a message passing scheme formalized bσ, aggregation AGG and linear neural layer MLP with this formulation as proposed by GENConv following the notation of PyTorch Geometric  are separated and all their associations included in the split. Only when splitting by protein or drug are unseen entities guaranteed to be shown to the model in the validation and testing phase. The models resulting from the different splitting schemes may have different expressiveness and exploit different information in DTI prediction, as different information is known in the training and testing phase.2.4.2To assess each model, we compute a variety of common metrics for binary classification. As the datasets are highly imbalanced, we use the area under the receiver operating characteristic curve (AUROC) on training, validation and testing split.We calculate the AUROC by computing true positive rate at various false positive rate thresholds and use trapezoidal approximations to estimate the area under the curve. We refer to this measure as MacroAUC.D and P of drugs and proteins, respectively, and a set of known interactions MicroAUC is calculated as the average per entity AUROC score. For example, the protein-centric score can be formalized as: given labels e We also calculate the MicroAUC score. For given lists MicroAUC score may not be defined as in some datasets some proteins or drugs have no interactions, leading to an infeasible TPR = 0 for all thresholds and an undefined AUROC score for that entity. As this is quite common for DTI datasets, we do not omit but impute the MicroAUC interpolating linearly for those entities using the accuracy for this subset:In some cases, the pMicroAUC) and a drug-centric microAUC (dMicroAUC). We further compare MicroAUCp with imputation and MicroAUCMicroAUC values (but omitting them) in Drugs and proteins can be interchanged in this formulation. We refer to the different measures as protein-centric microAUC  as primary metric to compare different methods; AUPRC is sensitive to data imbalances . As structural features, DTI-Voodoo uses structural representations of drugs from the SMILES transformer . DTI-VooWe construct a graph with proteins as nodes and protein-protein interactions as edges, mapping the protein features to each target as node features. DTI-Voodoo then propagates information among the PPI network utilizing graph convolutional steps, calculates the similarity of drug and protein representations, and predicts whether there is an interaction. The full workflow scheme is depicted in We evaluate our model’s ability to identify DTIs using different approaches and datasets. First, we perform a cross-validation over proteins and validate our results. A cross-validation over proteins aims to evaluate how the model performs when tasked to identify drugs that may target a ‘novel’ protein, i.e. one not seen during training, or a protein for which a drug that targets it should be predicted.pMicroAUC, see Section 2.4.2), and retrain them from scratch in a 5-fold protein-split cross-validation on the Yamanishi benchmark dataset to avoid validation overfitting and yield more realistic testing results. To evaluate the influence of the different features separately, and to determine whether they ‘localize’ on the PPI graph , we train and evaluate models with different types of features, and with and without inclusion of the PPI graph, separately. We comparing the molecular (MolPred) and phenotype-based (OntoPred) prediction model, and a combination of both where we concatenate both types of features. We trained, validated and finally tested all considered models on the STITCH dataset using a 5-fold cross-validation over a protein split; we then selected the best-performing models (with respect to OntoPred) is showing better performance on STITCH compared to using only molecular features. We also observe that only the model using ontology-based features results in increased performance when incorporating the PPI graph. This increase can be observed with different graph neural network architectures and configurations  over OntoPred for STITCH  and significantly with respect to MicroAUCp (P<10-2) for Yamanishi dataset We find that the model using ontology-based features ; to determine MicroAUC, we average the performance  per protein instead of across all drug–protein pairs; the resulting measure can therefore better estimate how DTI-Voodoo performs when tasked with finding a drug that targets a specific protein.The goal of DTI-Voodoo is to find candidate drugs that target a specific protein; however, so far, we do not evaluate this application but rather how DTI-Voodoo would perform in finding plausible DTIs among all possible interactions . This evaluation does not correspond to the application of DTI-Voodoo in finding drugs that target a specific protein. To provide a better estimate on how DTI-Voodoo performs for individual protein targets, we use micro-averages between proteins and compute the D and P of drugs and proteins and a set of known interactions h ijM and ranking these sums:Furthermore, we hypothesize that it may be possible for a machine learning model to exploit biases in DTI data to achieve relatively high prediction performance without obtaining a biologically meaningful signal. For example, hub proteins may have a large number of interactions, or certain drugs interact with many proteins, and preferentially predicting these interactions may increase predictive performance even in the absence of any biological features. To test this hypothesis, we design a ‘naïve’ baseline model that predicts the same list of proteins for each drug based only on the number of known DTIs for a protein. Specifically, given lists kP predicts all drugs to interact with the top k targets with respect to the introduced ranking:k.Our ‘naïve’ predictor kP is not dependent on the drug id and will predict the same ranked list of drugs for all proteins; consequently, this naïve predictor does not rely on any biological features and will not predict any novel information about interactions between drugs and proteins; the naïve predictor only exploits imbalances in the evaluation set to make predictions that may perform well. The way in which we formulated the naïve predictor, it is not applicable for a protein split cross-validation as the number of interactions for each protein in the validation set is unknown.The prediction k to determine the best performance for each fold. Using the full dataset, drug–target split and drug split, we obtain the following MacroAUC results: for the STITCH database, we obtain a performance of 0.76 on the whole dataset, 0.70 for the drug–target pairs and 0.73 in case of the drug splitting scheme; on the Yamanishi dataset, we obtain MacroAUC scores of 0.88, 0.84 and 0.85, for the total dataset, drug–target pair and drug split, respectively. The naïve predictor shows higher performance on the Yamanishi dataset than on STITCH, and a substantial gain in comparison to an expected random predictor on both datasets. In the following, we utilize this naivïe predictor as baseline to compare its performance to state of the art models and DTI-Voodoo.We apply this naive predictor on both the STITCH and Yamanishi datasets, using the full datasets as well as a 5-fold cross-validation over drugs and over drug–protein pairs to compare the prediction results directly to DTI-Voodoo. For each fold, we gradually increase For comparison with the state of the art methods, we chose the best performing methods for DTI prediction that were previously evaluated on the Yamanishi benchmark dataset. These methods include DTIGEMS+  over MolTrans (t = 36.6), DeepDTI (t = 40.5), DeepDTA (t = 36.6), and DeepConv-DTI (t = 40.5) on the BioSnap dataset, and significantly (t-test) over DTINet (t = 20.8) and DTIGEMS+ (t = 18.0) on the Yamanishi dataset; DTI-CDF performs better than DTI-Voodoo (t-test). We further perform another one-tailed t-test to compare pMicroAUC performance. We find that DTI-Voodoo improves significantly (t = 21.2), DeepDTI (t = 31.0), DeepDTA (t = 33.4) and DeepConv-DTI (t = 23.7) on the BioSnap dataset, and over DTINet (t = 29.6), DTIGEMS+ (t = 28.0) and DTI-CDF (t = 9.5) on the Yamanishi dataset.Considering Macro AUCs, DTI-Voodoo improves significantly . On the other hand, phenotypic consequences of changes in protein function, or drug effects, are often a result of aberrant pathway or network activity and involve more than one protein; consequently, we expect these features to benefit more from including information about protein–protein interactions. Moreover, as the protein–protein interaction relation is not transitive , we mainly transfer information to the direct neighborhood of each protein within the PPI graph. Our results  have the ability to interact, whereas information about phenotypic consequences of a drug (drug effects) or disruption of a protein function can be used to indirectly suggest candidate DTIs. Molecular features will be specific to a drug–target pair and we would not expect this information to propagate through a protein–protein interaction network; the main information about DTIs that could be obtained from interactions between proteins is information about binding sites between proteins that may also be used by a drug molecule  needs to be identified. We introduce an evaluation measure based on micro-averages per protein (Micro AUCp) to evaluate this scenario, and we often find substantial differences in predictive performance when evaluating with Macro AUC and Micro AUCp; generally, models that are trained using a split over drug–target pairs perform worse in Micro AUCp than models that use a protein-based split, further providing evidence that a drug–target split results in overfitting to dataset biases.The way in which training and evaluation data is generated is related to how the model is evaluated. An evaluation based on Macro AUC evaluated the application scenario where a set of drugs and proteins are given, and out of all possible pairs, the more likely interactions need to be identified. However, this does not correspond to most scenarios for drug repurposing where a drug that targets a Finally, a potential source of differences in model performance is how negatives are identified and treated during evaluation (and training). There are few large sets of validated negative DTIs; consequently, many models (including DTI-Voodoo) use all unknown interactions as negatives. As there are many more negative than positive interactions, negatives are then sub-sampled during training resulting in a training set that is balanced between positives and negatives (or a certain ratio is preserved). While this is a reasonable strategy to deal with imbalanced data, it may be problematic when the same sub-sampling is applied on the model’s evaluation set because it over-simplifies the evaluation process. The performance differences is not usually visible when using ROC curves but results in unrealistically high precision and therefore high area under a precision-recall curve.p and Micro AUCd) to further illustrate how prediction performance changes when evaluation data is imbalanced. We have further extended on prior results by introducing a ‘naïve’ classifier that explicitly exploits one data bias to make predictions, illustrating that this bias has a significant impact on DTI methods. The bias we identify with the ‘naïve’ classifier is similar to a previous bias found in gene networks when using methods that rely on the guilt-by-association principle  has been demonstrated before using the MacroAUC measure ; we furtrinciple  but whicP be a set of proteins P and D a set of drugs; one task can be to identify arbitrary pairs  with p and Micro AUCd.In summary, DTI prediction is not a single computational problem in bioinformatics but a set of related problems. Let As the target-based predictive power of DTI-Voodoo improves significantly over other methods, we utilized our model to predict novel drug classes for protein families. We therefore collected the 2nd level ATC  groups for each drug and all InterPro  has relatively few approved drugs in total , but DTIWe developed DTI-Voodoo as a machine learning model that combines molecular features and functional information with an interaction network using graph neural networks to predict drugs that may target specific proteins. In this task, DTI-Voodoo improves over several state of the art methods. We demonstrated that functional and phenotypic information localizes on the interaction network whereas molecular information does not. Moreover, we showed that DTI prediction datasets have some inherent biases that affect the performance of models. This led us to conclude that DTI prediction is not a single computational problem but a set of multiple problems. Experimental evaluation of DTI prediction methods must be carefully designed to reflect the problem the model aims to solve, and the interpretation of performance results should be aligned with the specific problem.btab548_Supplementary_DataClick here for additional data file.


Protein–protein interactions (PPIs) are of key importance for understanding how cells and organisms function. Thus, in recent decades, many approaches have been developed for the identification and discovery of such interactions. These approaches addressed the problem of PPI identification either by an experimental point of view or by a computational one. Here, we present an updated version of UniReD, a computational prediction tool which takes advantage of biomedical literature aiming to extract documented, already published protein associations and predict undocumented ones. The usefulness of this computational tool has been previously evaluated by experimentally validating predicted interactions and by benchmarking it against public databases of experimentally validated PPIs. In its updated form, UniReD allows the user to provide a list of proteins of known implication in, e.g., a particular disease, as well as another list of proteins that are potentially associated with the proteins of the first list. UniReD then automatically analyzes both lists and ranks the proteins of the second list by their association with the proteins of the first list, thus serving as a potential biomarker discovery/validation tool.  Protein–protein interactions (PPIs) play an important role in the proper function of living organisms. Proteins can interact directly in a binary form or as members of a complex, or can be functionally related without physical interaction . Due to the importance of PPIs in understanding how complex biological networks function, in the past 30 years, high-throughput technologies such as Y2H, protein arrays, co-immunoprecipitation, mass spectrometry and others  have beeText mining  methods have been utilized in a variety of challenging problems in biomedical research such as concept discovery in biomedical literature (BioTextQuset+) , drug diIn the last 20 years, many methods have been developed for PPI extraction from biomedical literature . In partIn addition to the aforementioned methods, there are databases/tools such as STRING that integrate information from various sources  in order to provide protein associations from a large number of species  as well Homo sapiens and Mus musculus organisms and by using the “similar articles” feature of PubMed, it creates clusters of functionally associated proteins [UniReD is a computational tool that is used to predict functional associations between proteins based on the Markov cluster algorithm (MCL). UniReD parses the references section of UniProt entries for proteins . UniReD proteins ,20,21. Iproteins ,23 for tmultiple UniReD) which automates the aforementioned process, in an effortless way. Users may provide the two lists of proteins of interest and multiple UniReD will return the validated results ranked within a few seconds. This functionality is of particular usefulness in biomarker discovery/validation pipelines, as demonstrated by a test case. UniReD is available at http://bioinformatics.med.uoc.gr/shiny/multiunired/, accessed on 16 September 2022.Herein, we present a new powerful feature of the UniReD tool . The first list (query file) represents proteins whose functional associations the user wants to explore in relation to the second list of proteins (reference file) that are selected by the user and are related to the research topic under investigation . When uploaded, an overview of these lists is displayed. Then, the organism from which these proteins derive has to be defined by the user. In its present form, UniReD (as well as multiple UniReD) can analyze human or mouse proteins but we are planning to expand the analysis to other species as well  a reference list  and (ii) a query list (list of proteins whose associations with the proteins of the reference list the user would like to identify). Proteins in each input list should be in comma- or tab-separated format using UniProt/SwissProt accession numbers.The user uploads two lists in .csv or .tsv format. These are: (Homo sapiens or Mus musculus).The user must select the relevant species to be analyzed (currently multiple UniReD searches for the existence of any association in UniReD clusters between the protein pairs of the two lists (reference and query list). We followed the same scoring scheme as in [me as in ,23. If tmultiple UniReD cannot find any association within the clusters, it will proceed to the next step in order to search for paralogues. More specifically, multiple UniReD will search for a co-occurrence of a paralogue of the query protein with the reference protein in a UniReD cluster. If such a pair is present, the association will obtain a score of 0.5.If multiple UniReD will investigate whether the query protein is part of a complex. In this case, it will search for co-occurrences of the reference protein in the complex. If such an association is confirmed, the association will obtain a score of 0.5.In the case that UniReD does not predict any association within a query paralogue and the reference protein, then Finally, if none of the above applies, UniReD will search for a relation between the orthologues of reference and query proteins. If such an association is documented, it will obtain a score of 0.5.When none of the aforementioned cases are applicable, then no score will be assigned to the specific protein pair.If, in any of the aforementioned steps, a score is assigned to a protein pair, the procedure continues to the next protein pair without searching for any further association  and 21 June 2022 (M. musculus)). Orthologues were also obtained from the same source on 25 May 2022. Complex information originates from the ComplexPortal [H. sapiens) and 21 June 2022 (M. musculus)). Furthermore, gene names, NCBI gene names and Ensembl gene IDs were retrieved from UniProt and Ensembl (data retrieved on 25 May 2022 (H. sapiens) and 21 June 2022 (M. musculus)). The result of this procedure was a dictionary (JSON format) summarizing information for each protein found to appear in clusters by UniReD. Each protein entry in this dictionary is represented by the UniProt/SwissProt accession number of proteins. For each one of them, fields for the gene name, NCBI gene name, Ensembl gene ID, paralogues, ComplexPortal complex id and mouse–human orthologues are provided. Finally, labels are used to highlight the existence of reviewed mappings between a UniProt accession number and all aforementioned identifiers. Ensembl  for bothThe pipeline was developed in a Python 3 environment  .We produced 1000 random lists of query proteins, and we calculated the sum of the overall score with the same list of reference proteins. The score differs significantly when compared to the sum of the overall score of the test case (multiple UniReD, assists in the identification/validation of putative biomarkers. It accepts two lists of genes, one with genes known to participate in a specific pathway/disease and a second one with genes suspected to be involved in this particular pathway/disease. UniReD ranks the latter in relation to the first one, thus helping in steering the experimental verification, which is required for the confirmation of the PPI prediction, in the right direction. Biomarker validation can be of great importance, because, amongst others, can shed light on disease biology by revealing new unknown interactions, unveil potential novel therapeutic targets and contribute to the understanding of the pathogenic processes.There are several experimental or in silico approaches to confront the important problem of PPI discovery/prediction. Living in the -omics era, there are different large-scale experimental approaches which produce a lot of results that can sometimes be confusing and difficult to interpret. Therefore, emerging computationally predicted PPIs can provide strong indications and hints about putative PPIs and thus complement the wet lab findings to maximum exploitation. UniReD is a tool that, among other things, predicts PPIs using biomedical literature mining and, with the updated feature of


We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at  Proteins are known to catalyze and control the majority of the reactions of cellular biochemistry . FrequenThe standard approach to manual literature review for identifying the putative functions of a protein complex consists of first searching for publications and database entries describing the function of individual protein subunits and subsequent consolidation attempts to retrieve the information. An additional confounding consideration is the highly biased functional annotations toward well-studied individual proteins , 29. TheTo address this dearth of direct functional annotation methods for protein complexes, in this work, we integrate text-mining and machine-learning techniques into a hybrid computational framework, termed PCfun, which can be applied to large scale, complex-centric proteome experiments for predicting the function of protein complexes. At a high level, PCfun is developed based upon a word embedding generated from the machine reading of 1 million open access PubMed Central (PMC) articles, whereby both unsupervised and supervised machine learning algorithms were used to generate two separate lists of predicted functional Gene Ontology  terms for a queried protein complex. Specifically, the unsupervised method was the nearest neighbor algorithm using cosine similarity between a protein complex query, and putative GO terms and the supervised machine-learning method was a model trained on the associations between protein complexes and their GO terms documented in the comprehensive resource of mammalian protein complexes (CORUM) database . Hence, k-d trees to easily retrieve predicted GO terms (nearest neighbors based on cosine similarity) when a protein complex was queried. To leverage a supervised learning paradigm for functional annotation, we generated datasets with binary-labeled relationships between protein complex embedding features and their GO annotations  based on the CORUM database. We generated negative labels by sampling GO annotations that were not labeled for a particular protein complex. We then trained supervised machine learning classifiers on these generated datasets and tested the models on held-out portions of the generated datasets. For a new protein complex query (presented by its word embedding), we then tested it against every possible GO term with the supervised model and reported the terms that were predicted to positively associate with the protein complex query. This resulted in two predicted GO terms lists: one from the unsupervised approach and the other from the supervised model. To consolidate the information across the two models, we check for the enrichment of the unsupervised predicted GO terms within the child nodes of the predicted GO terms from the supervised classifier. For the functionally enriched terms (where information is agreed upon between the two approaches), we optionally plotted out the cut GO tree with the supervised algorithm’s predicted GO term and the multiple GO terms from the unsupervised method that were the children of the supervised model’s GO term. We then finally output the optional plots and the raw predicted lists from each method and the statistics indicating which terms were functionally enriched within the list by the Random Forest (RF) classifier.A word embedding was generated using the ‘fastText’ algorithm trained on approximately 1 million PubMed articles to build a vector representation space where biological semantic relationships were well reflected. To use this latent semantic space to predict the function of protein complexes, we extracted out sub-embeddings corresponding to all protein complexes and GO terms. To obtain the first predicted list of unsupervised GO annotations for a protein complex query, we stored the high-dimensional GO term sub-embeddings into et al.  for the generation of its corresponding word vector and then queried the vector into the biological function, molecular function and cellular component sub-embedding k-d trees. While the k-d trees required 27,400, 2182 and 990 nearest neighbor terms in the biological function, molecular function and cellular component trees, respectively, to recover the six CORUM annotated GO terms , for this protein complex, the top returned k-d tree nearest neighbors  for more details. Despite the poor ability of the k-d tree to recover CORUM’s ground-truth annotations, the top nearest neighbor results still provided significant insight into the relevant biology.To illustrate this effect, we chose the protein complex termed ‘Mothers against decapentaplegic homolog 2 (SMAD2)-Mothers against decapentaplegic homolog 4 (SMAD4)-Forkhead box protein H1 (FAST1)-Homeobox protein TGIF1 (TGIF)-Histone deacetylase 1 (HDAC1) complex, Transforming growth factor (TGF) (beta) induced’ for a manual literature review comparison to the eighbors  still prs . In our study, the annotated association of a PC-GO term was regarded as a positive sample, whereas the synthetic pairs of a protein complex and other GO terms that were not associated in CORUM were regarded as negative ones. As the negative samples significantly outnumbered the positive samples in the resulting datasets, we generated five different training datasets with randomly selected negative samples and all positives for each protein complex to ensure an equal distribution of positive and negative samples for training the classifier. This process was conducted for both naming schemes. With the training datasets, we assessed the performance of three machine-learning classification algorithms, specifically RF, LR, and NB with a Gaussian or Bernoulli prior , through the adapted ‘protein complex’-leave-one-out cross-validation strategy using standard performance measures ‘’.canonical names’ and ‘subunit names,’ respectively. In addition, we also observed that the resulting GO term lists predicted by the RF classifiers were able to significantly reduce the number of nearest neighbors needed to recover the majority of the ground-truth GO term annotations for a protein complex when compared to the nearest-neighbor results from querying the k-d tree . The heatmaps of the pairwise similarity scores using the method reported by Wang et al  are showAlthough computational methods for gene function prediction  have sigk-d tree method with RF predictions via enrichment analysis for PCfun to deliver non-biased predicted functions for a given protein complex. Another noteworthy issue is the negative data for training the supervised RF model of PCfun. This problem has been brought to attention and discussed in our previous studies [As discussed in the ‘Results’ section, one issue during the construction of the machine-learning models is the biased functional annotations in the CORUM database, as shown in  studies , 58. In  studies , 59 onlyn-grams to build word-embedding relationships between complicated multi-character strings (e.g. protein complex names). However, protein complexes can also be viewed as functional subgraphs of a protein-protein interaction network [Further limitations to the PCfun word embedding strategy for annotating protein complex function lie in the featurization of the protein complex itself, which currently does not leverage any structured database information on the protein interactome network for achieving an informative embedding. Currently, the word embedding only leverages inferred relationships between character  network , 61, andet al [To address these limitations regarding (1) the bias of training/assessing an algorithm on just the CORUM and Complex Portal databases and (2) the current exclusion of structured protein network information in the protein complex embeddings, we propose the following future work. Firstly, a systematic analysis of the shared and diverging information between the current protein complex–functional annotation databases would be extremely informative for formally detecting biases in the databases and defining confidence metrics for certain annotations. Thus, a consolidated gold-standard database of all such protein complex–functional annotations could be constructed, thereby enabling much more accurate and generalizable models to be built for this task. Regarding the featurization limitation, we believe that tailoring embedding representations from constructed knowledge graphs such as from Himmelstein et al , where bet al , and humet al . These iet al , 66.Overall, we believe PCfun is the first-in-class word embedding-based functional annotator for protein complexes and can be applied to broad biological and personalized medicine applications. It could be possible to compare the functional differences of subunit composition by interrogating the prediction outputs of PCfun for a protein complex across different biological/medical conditions, given that the differential analysis of protein composition is possible through the development of new techniques and computational methods , 21, 67.PCfun is the first computational framework focusing on rapid and accurate annotations of functional Gene Ontology (GO) terms for protein complexes by integrating word embedding and supervised machine learning approaches.k-d tree using the word embedding was trained to rank the GO terms.The word embedding was constructed using the fastText algorithm on approximately 1 million PubMed articles, and an unsupervised We separately trained a Random Forest using the manually curated CORUM protein complex database for supervised learning of protein complex–GO relationships.We anticipate that PCfun will serve as an instrumental computational tool for annotating the functional GO terms of protein complexes.Sharma_et_al_supplementary_revised_bbac239Click here for additional data file.