E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the  E. coli cytochrome biogenesis system I yields confident computational models for three sought-after assemblies.Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Here the authors present AF2Complex and show that application to the  In CASP14, a blind test for protein structure prediction, AF2 achieved remarkable performance when assessed on the individual domains of target protein sequences1. Because deep learning is a data-driven approach, two key factors contributing to the success of AF2 are the completeness of the structural space of single-domain proteins and an abundance of sequences in sequence databases3. Together, these factors have made it possible to train sophisticated neural network models for accurate protein structure prediction.AlphaFold2 (AF2), a deep learning approach developed by DeepMind for predicting protein structure given a sequence, has greatly advanced protein structure prediction1 and has been applied to such proteins in several model organisms2. Considering that intra-protein domain-domain interactions are not physically different from interprotein interactions, these AF2 results are very intriguing. They hint that, in principle, AF2 could be repurposed to predict whether a pair of proteins interact and if so, to predict the quaternary structure of the resulting protein complex. After all, many proteins that form complexes in prokaryotes are fused into long, single-chain, multidomain proteins in eukaryotes4. The same physical forces that drive protein folding are also responsible for protein-protein associations5. Moreover, it is known that the structural space of protein-protein interfaces, the regions where direct physical contacts are found between protein partners forming a complex, is quite small6. Taken together, it is very likely that the neural network models trained for single-chain structure prediction have already learned the representations necessary to model protein complexes made of multiple single-chain proteins3. Indeed, some examples of using such a neural network model to predict complex structures were demonstrated with RoseTTAFold7, whose design was inspired by AF2, even though its examples were likely present in its own model training set.In addition to single-domain targets, AF2 also delivered excellent results on multidomain proteins11, which is limited by force-field accuracy and the completeness of conformational space sampling. A second class of approaches is template-based methods, which utilize experimentally resolved multimeric structures16. More recent methods incorporate inter-protein residue-residue contact predictions obtained via co-evolutionary analysis17 and a deep learning algorithm18.Until now, conventional approaches for predicting the structure of protein complexes include a docking component19. A much better solution is to modify the “residue_index” feature used by AF2, which eliminates the need for a covalent linker that likely creates artifacts21. Meanwhile, studies have been carried out whereby models of single proteins generated with AF2 are used with docking methods. They are based on the idea that AF2 generates high-quality monomeric models that could improve the chance of native-like poses in docking22. One issue with these studies, as some authors pointed out, is that the benchmark set tested includes protein structures used to train the AF2 deep learning models. Although the AF2 models were not trained on protein complex structures, the use of the holo monomers in training compromises rigor because AF2 likely provides an “observed” holo-structure for docking.Can AF2 be adapted to predict the structure of a protein complex? After the release of AF2, efforts immediately began to seek an answer. One early such study involved simply connecting two protein sequences with a poly-glycine linker, thus converting it into a single-chain structure prediction problem26, but their results are far from complete and are often mutually inconsistent. Computationally, template-based approaches have been used27, but they are limited to the detection of homologs. Combining standard protein-protein docking methods with co-evolutionary signals28 or even with deep learning models29 has also been carried out on full proteomes. These are powerful approaches, but they rely on paired multiple sequence alignments (MSAs) as inputs. Generating paired MSAs requires the identification of orthologous sequences across species, which is impractical in many cases because it is confounded by the presence of paralogs in eukaryotes, protein cross-talk in disease pathways, and pathogen-host interactions. After all, one main consequence of evolution is the diversification of protein functions by producing paralogs30. These paralogs may interact with different partners without using a conserved interaction mode. Therefore, it is highly desirable to develop an approach that is not dependent on paired multiple sequence alignments.Going beyond the prediction of the structure of the protein complex given that the proteins interact, are more fundamental but more challenging questions: Can AF2 be adapted to predict protein-protein interactions given an arbitrary pair of protein sequences, and more generally, can it identify higher-order protein complexes? Several high-throughput experimental techniques have been designed to identify interacting protein partners31, we introduce metrics for assessing the likelihood of protein-protein interactions. When AF2Complex was applied to a previously defined “gold standard” interaction set in E. coli32, it found that many protein pairs are likely due to associations in large assemblies that are not necessarily in direct physical contact. Finally, we apply AF2Complex to make predictions on sought-after assemblies of a cytochrome c biogenesis system34.In this study, using multiple test sets and without using paired sequence alignments, we demonstrate that AF2 can be adapted to predict both the presence of protein-protein interactions and the corresponding quaternary structures. Although our tests are primarily conducted on dimers, the method, AF2Complex, can be applied to higher-order oligomers, and we show examples of such. Critically, it is necessary to devise proper metrics to estimate the confidence of a predicted protein complex model. By adapting metrics previously introduced for comparing the similarity of protein-protein interfacespredicted interface TM-score (piTM), both of which evaluate the confidence of the predicted protein-protein interface if found in an assessed model. Each of these two scores ranges from 0 to 1, where a higher score indicates higher confidence.The design of AF2Complex is illustrated in Fig. 35, additionally AF2Complex was adapted to take five multimer DL models for predictions using unpaired MSAs. Throughout this work, without note, we present structural models obtained with AF2’s monomer DL models, rather than with AF-Multimer models.We note that AF2Complex was originally based on the five monomer DL models of AF2. After the release of AF-Multimer37. To simulate a CASP14 prediction scenario, the input feature predictions described below were obtained by using databases available prior to May 15, 2020, CASP14’s starting date. Because the structures of many assembly targets have not been released to the public, it is impossible to conduct a reliable statistical analysis of our predictions. However, they do showcase the potential of AF2Complex.We first applied AF2Complex to the multimeric targets of CASP142B2) consisting of two copies of two coiled-coil protein sequences38. Despite the simple topologies and the availability of an experimental structure for one monomer, H1072 is a difficult target. No group participating in the CASP14 competition provided a correct model of the complex36. In contrast, the top model by AF2Complex achieved a remarkable TM-score39 of 0.90 when superimposed onto the experimental structure  cases, and 13 (76%) models are of medium or high quality according to the DockQ score42  if the overall top-ranked model of both monomer and multimer DL runs are assessed for the heterodimers. It must be pointed that AF-Multimer (version 2.1.1) may yield unphysical models with severe clashes for a complex, especially for large oligomeric targets, including homo-oligomers. In contrast, AF2Complex mitigates this issue with unpaired MSAs using the same multimer DL models and deals much more effectively with unpaired MSAs and the original monomer DL models of AF2  in the receiver operating characteristic (ROC) curve. The normalized area under the curve (AUC) of this plot, AUC0.1, is 0.72 and 0.69 for interface-score and piTM, versus 0.49 and 0.10 for pTM and pLDDT, respectively. For reference, random guessing yields an AUC0.1 of 0.05. As expected, pLDDT is not ideal for evaluating protein complex models because it was designed for single domain evaluation. Although the pTM metric is much more discriminating than pLDDT, it is still much worse than interface-score or piTM in this regard. The same trend is also displayed in the precision-recall plot, whereby we achieved ~45% recall  at ~90% precision, and the recall increases to ~70% at ~45% precision. Correspondingly, the interface-score and piTM values are 0.55/0.59 and 0.45/0.50, respectively. Overall, the results encouragingly conclude that AF2Complex can be used to predict protein-protein interactions.Having been rather successful, an obviously tantalizing question is: Can this approach be applied to predict direct protein-protein interactions for an arbitrary pair of proteins? To answer this question, we devised a test using the 34 unique protein sequences from the CP17 set. The goal was to find the 17 true interacting pairs given in CP17 from the 561 all-against-all pairwise combinations. Here, we naively assumed that all protein pairs other than the CP17 pairs are non-interacting, and any hit above a cutoff value of a metric adopted for evaluation is a false positive. Figure E. coli proteome consists of about 4,400 protein sequences. An all-against-all run with AF2Complex would require about 0.5 to 2.4 million computing node hours on the Summit supercomputer, which is beyond our allocation. Instead, we focused on a “gold standard” set of 701 PPIs previously curated largely from high-throughput experiments, and a set of 6,849 randomly selected, putatively non-interacting pairs. Here, we have two goals: one is to test AF2Complex on a large scale; the other is to build complex models for some known interacting protein pairs whose structures are difficult to determine experimentally. We speculated that the “gold standard” set contains pairs from a large assembly that are not necessarily in direct contact, e.g., as in a ribosome. To test this hypothesis, we divided the positive set into subsets, whereby each monomer in the subset does not appear more than C times in the putative interacting set (the full set is covered when C < ∞). Figure C values correspond to lower true positive rate or recall. This analysis suggests that some of the pairs in the positive set do not interact directly, yielding low or even zero scores. When we considered proteins that appear only once , we obtained a result that largely recapitulates the benchmark performed above, with a slightly lower AUC0.1 of 0.60. The AUC0.1 drops to 0.50 at C = 3, 0.40 at C = 5, and 0.22 for the full set, likely due to the inclusion of more non-direct interacting pairs as C increases. Further analysis also found a barrier to accurate modeling is the lack of the exact context of the protein-protein interaction. For example, the chaperonin protein GroL is the most frequent monomer in this set, appears in 79 pairs. It is part of a large assembly that requires seven GroL copies forming a ring stacked with another heptameric GroE ring. However, when GroL was modeled alone with another putative partner, we only found models with low confidence scores. Despite this difficulty, the result suggests that AF2Complex performs as expected in a large-scale test. Of the positive set, among the predicted models with confident scores (interface-score > 0.45), about 40% of these predictions have not been experimentally characterized (defined if both monomers share >70% sequence identity with sequences found in the same PDB entry). Therefore, interesting discoveries are expected from these models.The E. coli targets with high-confidence models, CcmE/CcmF and CcmF/CcmH, caught our attention. These proteins belong to the cytochrome c maturation (Ccm) system I, which is composed of eight constituents (CcmABCDEFGH)34. We note that E. coli CcmH has a fused C-terminal domain that appears as the standalone protein CcmI in other species with a similar Ccm system. As illustrated in Fig. 43. CcmE then shuttles the heme from module 1 to module 2, composed of CcmFGH, where the heme is delivered to CcmF44. Subsequently, CcmFGH covalently attaches the heme to nascent cytochrome c-type proteins46.Two 47. The main reason for this knowledge gap is that the assemblies involve transient but essential interactions among membrane proteins (except for CcmA) that are difficult to capture experimentally. We sought to address this knowledge gap with AF2Complex by modeling about two dozen combinations of Ccm components. From these computational experiments, we present the confident models of three assemblies, A2B2CDE, EFH, and FGH, the last one with and without apocyt peptides, all with high interface-score . These models are likely biologically relevant, and we were able to model the heme in the expected sites in all these models.Many mechanistic details of the Ccm system are still unclear, in part because there are no structures of the assembled modules. To date, the best effort is a partial model of the CcmCDE complex generated using co-evolutionary analysis2B2CDE  in CcmC that includes His60CcmC and His184CcmC, and another conserved residue, Tyr17 of CcmD. The heme-binding loop of CcmE is oriented away from the HBS of CcmCD in the other model  of the CcmC backbone is ~1 Å. Corresponding to this movement, the number of residue-residue contacts between CcmB and CcmC drops by 45%, and the CcmBC protein-protein interfaces display larger changes between the two models than the other interfaces . These large conformational changes could be the result of ATP hydrolysis within CcmA. As previously proposed43, the energy of the hydrolysis could be harnessed to release the cargo-loaded CcmE.First, we identified two different conformational states in the top two highest-ranking models of ACDE Fig. . Extensi48), sitting at one edge of the binding pocket of the heme , which is the proposed site of covalent attachment to His130CcmE to complete the heme delivery to CcmF.Extensive contacts are present in the model with CcmC tightly bound with CcmE. The interactions involve Trp114 and Trp119 of a tryptophan-rich loop  in a recent published X-ray structure49. Without using this structure as a template, our predicted CcmF model has a TM-score of 0.92 compared to the experimental structure. However, a critical loop containing the equivalent histidine of E. coli His303CcmF is missing in the TtCcmF structure, suggesting that the P-heme binding pocket might be flexible. Indeed, only one of the two expected tryptophans (W229 and W236) from the WWD domain is in contact with the P-heme in the heme-bound model. Moreover, CcmF in our model does not expose a pore as observed in the TtCcmF structure, in which the pore was speculated to enable P-heme access49. The structural difference may be attributed to a lipid molecule occupying the pore in the crystal structure but absent in our computational model.Consistently, the heme-bound model displays the P-heme coordination role of His303cmF Fig. . TM-hemec (apocyt c). This step involves a complicated mechanism that is not fully understood46. However, our model of the CcmFGH complex provides structural insights into the mechanism . In the absence of CcmE, the N-terminus of CcmH occupies the site otherwise occupied by CcmE, essentially moving closer to the HBS of CcmF. This configuration leaves an opening for CcmG, another thiol-disulfide oxidoreductase (like CcmH), now sitting at the site previously occupied by the CcmH N-terminal domain. Remarkably, the CcmFGH complex is arranged such that a reaction groove is formed in which an apocyt c can be sequentially passed among the CXXC motifs of CcmG (Cys80 and Cys83) and CcmH (Cys43 and Cys46) to reach the HBS of CcmE . For the purpose of large-scale applications, the original data pipeline was separated from the neural network inference. The split allows us to derive input features for individual protein sequences and then reuse them to assemble input features for subsequent complex predictions. We used different sets of sequence libraries55 and Protein Data Bank (PDB)56 releases to generate appropriate input features for different test sets, as described below.AF2Complex was initially built upon the official release of the source code and monomer neural network models of AlphaFold2 (version 2.0.1)N distinct protein sequences, each with a length Li and a stoichiometry number Si (i = 1…N), we apply Algorithm 1 to the MSAs of individual proteins. The application creates a new set of complex MSAs, whose length is the sum of all individual sequences including multiple copies in the case of homo-oligomers, and whose depth is the sum of the depths of all individual MSAs. The complex MSAs are primarily composed of gaps, except for the regions in which each individual target sequence has its own window of MSAs col_start ← 0row_start ← 0fori ← 1 to Ndoforj ← 1 to Sido Li  col_end ← col_start + Di  row_end ← row_start + msa ← msai ▷ msai is the set of MSAs of protein i    col_start ← col_end  row_start ← row_endend for end forreturnmsadef make_complex_msa (protein_msa_list):b(p-1), where b is an arbitrarily chosen number of 200, which satisfies the condition larger than the coverage of the relative positional encoding at 32 , and p denotes the index of each monomer starting from 1. Likewise, the template structures for individual proteins were also collected for the complex prediction. However, we did not specifically search for and supply a complex template for the target. Without a note, the neural network models used for subsequent structure prediction were the five monomer AF2 models, each with a fine-tuned head for predicting paired alignment errors, which allows the prediction of the TM-score (pTM)1. We took advantage of this head for deriving metrics for evaluating complex predictions (see below). In the tests described in this study, we increased the number of recycles up to 20, depending on sequence lengths; the maximum number of recycles for target sequences longer than 500 residues was progressively decreased to reduce computational costs. The recycle steps were stopped early if the backbone Cα distogram converged20. In practice, these options may be adjusted by end users.When supplied to a monomer AF DL model, the “residue_index” input feature for the target complex was modified by increasing the residue indices of individual protein sequences by 31. Both scores were introduced to deal with issues associated with the TM-score, which is not ideal for comparing structure similarity of protein complexes57. In this study, we used a similar concept but modified it accordingly for estimating the confidence of a predicted complex model. We first introduce the predicted interface TM-score, piTM,I ≡ |i, the predicted alignment error head of AF2 gives an estimated distance 〈eij〉 for interface residue j from its position in the experimental structure1. The piTM score is the optimal rotation/translation that gives the best-estimated score, and d0(I) is a normalization factor given by,Previously, we introduced the interface TM-score (iTM-score) and interface-similarity score (IS-score) for measuring the structural similarity between protein-protein interfacesd0 to better deal with the cases where a low number of contacts are observed. Furthermore, we define the interface-score S as the follows,p of the complex separately and then sum the scores. Each chain p has an observed number of interface residues p, and p. The optimal local reference for calculating the score for chain p can only be selected by interface residues not belonging to chain p. An important difference between our metrics and the ipTM score introduced in ref. 35 is that we focus on interface residues (versus full chains35), which is the most relevant for our interaction predictions.Note that we adjust the original formula of 58, and the reduced BFD, MGnify, and Uniclust30 libraries55 provided with the AF2 release. All these libraries are composed of sequences available prior to CASP14. For template retrieval from the PDB, we restricted ourselves to structures released before the starting date of CASP14 as well. From the final predictions, we selected some difficult targets with promising results to present.We modeled all assembly targets if the total size of an assembly is less than 3000 residues, which is a limit imposed by our available computing resources. The sequence libraries employed for our predictions are UniRef90 created in 2020-0122 and consists of 17 heterodimer targets released after 2018-04-30, on which the experimental structures were collected from the PDB for training the AF2 neural network models. We used the same sequence library as above but restricted the structural templates from the PDB to those dated before the same cutoff date for the AF2 training structures. In the benchmark test of the 17 targets, three independent runs were carried out, and each generated five top models. We evaluate the overall top-ranked model of the 15 models according to the interface score. This protocol was used to evaluate both monomer and multimer AF neural network models. In the second benchmark test of identifying the 17 heterodimers out of all 561 pairs of these 34 individual protein sequences of the dimers, one single run was conducted for all pairs. Up to 20 recycles were allowed in each of these runs.This set was taken from ref. 40 and DockQ42. Inaccurate, acceptable, medium, and high-quality models are defined by DockQ score regimes , respectively.The top-ranked (by the interface-score) models were compared with their corresponding experimental structures with the programs IS-score35. We assume all assemblies in the Earlier Set were used for DL training and removed all the “easy” homologs in the Later Set by using the 30% sequence identity clusters of all protein sequences provided by the PDB. For a complex in the Later Set to be considered further, at least one protein chain of this complex must not be found within the same 30% sequence identity cluster as any chain of any structure in the Earlier Set. This selection procedure left 8544 assembly structures. To further remove the redundancy among them, we applied the same sequence identity criterion and arbitrarily selected 3353 structures that have at least one chain not found in the same 30% cluster as any chain in any other complex structure within these 8544 structures. A maximum length of 1480 residues was also applied during the selection to the total size of the oligomer  to prevent GPU memory overflow, a limitation imposed by the memory capability of the GPUs available for our tests.To create large benchmark sets for objective evaluations of the AF2 models, we curated two sets from the experimental structures released recently in the PDB. First, we retrieved the information of all 120,703 protein assemblies on Jan 7, 2022, from the PDB. They were split into two subsets, 92,047 complex structures (Earlier Set) released prior to Apr 30, 2018, and 24,853 assemblies (Later Set) released after that date, which is the cutoff date for the structure collection used to train the AlphaFold DL models including both monomer and multimer DL modelsThe sequences and coordinates of the 3353 assemblies were subsequently examined. We further require that, in a good target, each protein chain shares a protein-protein interface of at least 20 residues with another chain of the complex. This requirement removed many complexes with short peptides and left 1,884 assembly structures, of which 47 entries with nonstandard amino acids in their PDB SEQRES records were removed, as we used the sequences given by SEQRES as the input sequences to model prediction. The remaining 1838 assemblies consist of 1275 dimers and 563 higher-order oligomers. One entry in the oligomers failed to generate the input during our evaluation of AF-Multimer, and we removed it from further evaluation. These two final sets are called Dimer1275 and Oligomer562, respectively. Because the mapping of chains between model and experimental structure is an open issue for automated assessment of high-order oligomers, primarily due to the combinatorial growth of identical or homologous copies, we mainly focused on the dimer set in this study. However, we provide an evaluation of the physicality of the predicted Oligomer562 models.In addition, AF-Multimer uses different template identification protocols between the multimer and monomer predictions. The multimer data pipeline uses a protocol that searches essentially all PDB structures up to an optional cutoff date , but the monomer protocol searches only a subset of PDB structures . All targets of Dimer1275 have identified four templates in the multimer protocol, but 82 targets have none or fewer than four templates identified in the monomer protocol that AF2Complex relies on for input generation. To alleviate this unfairness, we removed these 82 dimers, leading to the final Dimer1193 set for the benchmark tests. The union of Dimer1193 and Oligomer562 are named as Oligomer1755.E. coli strain K12 was downloaded from UniProt58 (Proteome ID UP000000625). We used a positive and negative protein-protein interaction set curated previously32. We first filtered out pairs whose total size is longer than 1,480 residues, which is a limit imposed by the 16 GB GPU memory per node on the Summit supercomputer. Filtering led to 701 pairs from the positive set. Because the original negative set is too large to run all, we randomly selected 6,849 pairs from them, which yielded roughly a 1:10 ratio between the positive and negative set.The sequence of 2B2CDE, CcmEFH, and CcmFGH. All models were generated with the AF2 monomer DL models by applying up to 20 recycles. Application of the AF-Multimer DL models with AF2Complex did not yield better models with higher confident scores and are thus not presented.Three rounds of modeling were performed for this system composed of eight proteins, CcmA, B, C, D, E, F, G, and H. In the first round, based on the literature, we tested 22 combinations of these proteins. Among them, top models with high confidence interface scores and literature corroborations were presented for three assemblies, CcmAc peptides and the CcmFGH complex. A cytochrome c protein, NrfA of E. coli, was arbitrarily chosen for modeling. NrfA contains five CXXC motifs for covalent heme attachment59. We cropped peptides spanning the heme-binding motifs, including 8 peptides with one CXXC motif and three with two motifs. The lengths of these apocyt c peptides range from 8 to 52 residues, mostly around 18 AAs. During modeling, an apocyt c substrate and CcmFGH were folded simultaneously by the DL models, in contrast to typical docking, whereby a substrate is placed into a putative binding site of a folded protein or complex structure. By simultaneously modeling both the receptor and the acceptor, one might obtain a better complex structure that requires large conformational changes due to interactions. Note that we did not re-generate the MSAs for each peptide substrate. Rather, we cropped out the input features for each peptide from the input features, including the MSAs, of the full NrfA sequence. A domain cropping option implemented in AF2Complex enables this practice, which is convenient and likely more accurate in comparison to regenerating MSAs using partial sequences, especially for short peptides.In the second round, we tested the interactions between various apocyt b molecules in their putative binding sites of CcmC or CcmF of top models. Because AF2 does not currently support the incorporation of cofactors and other prosthetic groups into structural models, we ran the Rosetta relax application60 using the ‘-in:auto_setup_metals’ option to model heme-bound systems starting from the AF2C models. The ‘molfile_to_params.py’ script was used to generate the required parameters for heme b. In some cases, the positions of histidine residues were adjusted to place them in proper position for axial coordination prior to Rosetta refinement.In the last round, we modeled heme E. coli sets, both consisting of a true positive and negative set. The predictions were labeled using the pre-defined classification and the numbers of true positives, false positives, true negatives, and false negatives were then designated as TP, FP, TN, and FN, respectively. Performance measures are defined as follows,Standard metrics were applied to the benchmark tests on the CP17 and 0.1, which is the area under the ROC curve up to an FPR of 0.1, divided by 0.1. ROC curves were plotted using ROCR61.We also employed the normalized AUCχ ≡ NiRes/NiCnt, where NiRes is the number of interface residues, and NiCnt is the number of interface residue-residue contacts. An interface residue-residue contact is defined if any heavy-atom of a residue of one chain is within 4.5 Å of any heavy-atom of a residue from another chain. The value of χ ranges from 0 to 2 with values close to zero indicating a significant number of clashes. For experimental structures, χ is typically between 0.6 and 1.2. The metric can be directly applied to un-relaxed models to detect severe clashes. In practice, this evaluation can help to save computing time on unphysical models that are unlikely to be fixed by the relaxation protocol of AF2.To characterize the clashes observed in the predicted computational models, we defined an interface clash indicator To test the hypothesis that the model quality of one model is better than another one in terms of a scoring metric, e.g., the DockQ score, we used the Wilcoxon signed-rank test, a non-parametric statistical test, because the score distributions do not follow normal distributions. The tests were paired, as every method makes a prediction on the same set of targets. All tests are one-tailed.E. coli sets were performed on the Summit supercomputer at Oak Ridge National Laboratory. A Singularity container was built to run AF2 on Summit63. An AF2Complex run of ~7000 pairs of proteins using 923 nodes required about 2 hours in wall clock time. Each node has 6 Nvidia 16 GB V100 GPUs. For an individual target of fewer than 1000 residues, models may be obtained within 20 min for each deep learning model using “super” mode, which is a preset of configurations used with AF2Complex for this study.The development tests and predictions on CASP14 assembly targets were carried out locally using about 10 workstations each with four Nvidia RTX6000 GPUs, where each GPU has 24 GB memory. The benchmark tests on CP17 and Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins. Proteomics essentially refers to the study of the characteristics of proteins on a large scale, including the expression level of proteins, the functions of proteins, protein–protein interactions, and so forth. The study of proteome not only provides the material basis for the law of life activities but can also provide the theoretical basis and solutions for elucidating and solving the mechanism of many diseases . HoweverGene Ontology (GO)  describeGO has been widely studied in the field of biology . GO termThis study merged the three categories of ontologies and GO annotations into a large graph called the GO Annotation (GOA) graph. We used three categories of ontologies transformed into a GO graph. Effective graph analysis on GOA and GO graphs can improve our understanding of the structure and node information of GO and proteins. Using the GOA information of the proteins, the similarity among proteins can be calculated, and the relationship between proteins can be predicted. In recent years, graph learning-based analytical methods have made remarkable progress in bioinformatics and other fields . At presTo explore the performance of graph embedding methods in measuring protein similarity based on GO and GOA, we used four typical graph embedding methods to learn the features of GO terms and proteins. These methods can be divided into two categories. The first category is the random walk method, such as the DeepWalk and Node2Vec methods. The DeepWalk method uses the truncated random walk strategy to obtain the sequence of nodes and point embedding obtained from learning with Word2Vec . Node2Ve1. The GO protein annotations were obtained from the UniProt GOA website2. The Yeast dataset contained 2,887 proteins, and the Human dataset contained 9,677 proteins. The GO data were then preprocessed based on the following processes. First, since several GO terms annotate a protein, term–term relations of GO terms and term–protein annotations between GO terms and proteins were combined into a GOA graph. Second, the GO terms were then transformed into an undirected, unweighted GO graph, regardless of the type and direction of the relationship. We summarize the numbers of GO terms and edges in We downloaded GO data in Open Biomedical Ontologies (OBO) format from the GO Consortium WebsiteBased on different graph embedding methods, the feature of GO terms and proteins was learned into vector representations by fusing GO and GOA graph topologies, respectively. Thus, we could capture the global information based on the graph embedding method, and its learned vectors could calculate the similarity between proteins by the DTW distance and cosine similarity.L). The more neighborhood nodes (higher-order neighborhood nodes) two nodes have, the more similar they are. p and q to control the random walk and adopts a flexible biased random walk procedure that smoothly combines BFS and DFS to generate node sequences. ci are generated based on the following distribution:In this paper, we used the methods of graph embedding based on random walk and deep learning to learn the features of GO terms and proteins through fusing the topology of GO and GOA graphs, respectively. Random walk-based methods include DeepWalk  and Nodetx is the transition probability between nodes t and x, and Z is the normalization constant. According to the node context information, node sequences are generated by setting the sizes of the hyper-parameters p and q to control the random walk strategy. The Skip-gram model is used to obtain the vector representation of the nodes. The random walk graph embedding of nodes reflects the local and global topology information of nodes in the graph.where πSi in the model, where Si is compressed by the auto-encoder, the feature is then reconstructed. Finally, its loss function is defined as follows:The second kind of embedding method is SDNE, which proposed a new semi-supervised learning model. Combining the advantages of first-order and second-order estimation, SDNE can capture the global and local structural properties of the graph. The unsupervised part uses a deep auto-encoder to learn the second-order similarity, and the supervised part uses a Laplace feature map to capture the first-order similarity. LINE is another method based on deep learning, which optimizes the first-order and second-order similarities . The firg is defined by the negative log-likelihood and is given byIn this paper, we chose two typical IC-based methods to measure the semantic similarity of GO terms, based on p(g) is the frequency of term g and its offspring in a specific GO annotated corpus. N represents the total number of annotated proteins in the corpus. If there are 50 annotated proteins in a corpus and 10 of them are annotated by term g, the annotation frequency of term g is p(g) = 0.2.where Jiang and Conrath and Rel’s methods rely on comparing the attributes of terms in GO. Jiang and Conrath’s method considered the fact that the semantic similarity between two terms is closely related to the nearest common ancestor corresponding to the two terms. The semantic similarity between two terms is estimated by calculating the amount of IC in the nearest common ancestor. Jiang and Conrath’s and Rel’s similarities are expressed as follows:gc is the most informative common ancestor of g1 and g2 in the ontology. Given two proteins Pm and Pn annotated with GO terms Gm = {g1,⋯,gi} and where gm and term g′n, which could have been calculated using IC-based similarity methods.where gi, we use SDNE . For constructing the matrix Dm×n, the element D represents the distance between points vm and v′n and can be expressed as follows:We let W through several lattice points in the matrix. The shortest path is the distance between the set of vectors Vm = {v1,v2,…vm} and We used the DTW distance method to find a path Pi, the low-dimensional feature ωi is directly learned from the GOA graph, which contains the information of term–term and term–protein relations. We use the cosine distance of the proteins’ vector ω to measure the similarity of the proteins. Cosine distance can be expressed as follows:For any protein G, we let E be the complete set of Er in the network. The remaining 80% of the edges Es are then divided into Ep and Et, where Es = Ep ∪ Et, EP ∩ Et = ∅, and E = Er ∪ Es. Given a link prediction method, each pair of unconnected node pairs vx and vy is given a link probability of two nodes. Sorting all the node pairs according to the score value in descending order, we have the top node pair with the highest link probability. The calculation process of the AUC value is presented in L prediction edges. If m prediction edges exist, sort the link probability score value in descending order. If m of the top L edges are in the Et, the precision is defined as follows:When it is difficult to use a unified standard to measure the advantages and disadvantages of a network model, link prediction can be used as a unified comparison method for the similarity nodes in the network. It provides a standard to measure the reliability of the structure of the network. In the comprehensive evaluation, we use two commonly used evaluation indicators, AUC  and AUCPM = E−Er, where m is the number of edges predicted by the prediction algorithm. The recall index is defined as follows:The number of existing edges in the network The similarity between nodes is an essential precondition for link prediction, and the more similar the two nodes are, the more likely that a link exists between them. The similarity of network-based structural information definition is called structural similarity. Link prediction accuracy based on structure similarity depends on whether the structure similarity can grasp target structure characteristics. In the link prediction task, there are many methods to calculate the structural similarity between nodes, such as the following:vx and vy in the protein similarity network, their neighbors are defined as Γ(x) and Γ(y), and the similarity of the two nodes is defined as the number of their CN. The index of CN is defined as follows:Common Neighbors (CN)  similariS represents the similarity matrix and A represents the adjacency matrix of the graph. CN index is based on local information similarity index.where Based on the common neighbors and considering the influence of the node degree at both ends, the Jaccard (JC) similarity index  is propoResource Allocation (RA)  index coKz is the degree of the common neighbors of nodes vx and vy. The calculation process of the RA similarity index is shown in vx and vy.where We downloaded the human yeast protein interaction network from the String database. We then mapped the proteins to the UniProt database, filtered out those proteins that could not be found in the UniProt database, and removed duplicate edges. After filtering, the Yeast dataset consisted of 2,877 proteins with 228,468 interactions, and the Human dataset consisted of 6,882 proteins with 892,054 interactions. Finally, to verify the validity of our calculated protein similarity network, we compared protein similarity and the actual PPI network coincidence degree.This paper only shows the Human dataset experiment results in We selected the protein similarity networks (τ > 0.4) and compared them with the PPI dataset downloaded from the String database to analyze the coincidence degree of the Human and Yeast protein networks. Furthermore, we compared the edge coincidence of the protein similarity network based on different graph embedding methods (as shown in By comparing the GO(DTW) and GOA(cosine) methods, it can be seen that the Node2vec graph embedding method performed best in the GO graph. SDNE and LINE methods performed better in the GOA graph, and there was little difference between them in the GOA graph and GO graph. However, Node2vec and DeepWalk performed better in the GO graph. In general, the performance of protein similarity calculation based on different graph embedding methods in the GO graph was better than in the GOA graph. As shown, using graph embedding methods can be effective in calculating protein similarity in GO and GOA graphs. We also proved that using the DTW method to calculate different dimensional protein vector similarities is feasible.The features of GO terms are learned from the GO graph based on different graph embedding methods, and the similarity among proteins is calculated. By selecting the top 5%, middle 5%, and the last 5% of the protein similarity network data, the link prediction is computed for the filtered protein similarity network, and the AUC and AUCPR values are calculated  method is also better than other graph embedding methods. For the Yeast protein similarity network, we also performed the same experiment and obtained the same experimental conclusions as described above. We found that SDNE graph embedding methods also showed excellent performance in the Yeast dataset  method performed better than GOA(cosine), and most of the protein similarity networks calculated by the GO(DTW) method are denser than those calculated by the GOA(cosine) method.We performed three different link prediction similarity index experiments on the top 5% of the protein similarity network and found that based on different similarity indexes, the difference in the AUC value is small, which indicates that the calculated protein similarity network structure has improved  structure of system definition, and describe the functions of proteins. Therefore, it is important to understand protein function based on GO terms to describe protein similarity.In this paper, by fusing the GO terms’ topology information, we learned the features of GO terms and proteins into vector representations in GO and GOA graph based on different graph embedding methods. Then, the similarity of proteins was calculated based on these vectors using DTW and cosine similarity. Finally, protein similarity networks were screened by selecting different percentages, and a link prediction experiment was used to evaluate the prediction accuracy of different networks. The experimental results indicate that the graph embedding method is better than the IC-based method in protein similarity calculation. Among the two graph embedding methods, the performance of the GO(DTW) method is better than that of the GOA(cosine) method. This is because the GO terms and proteins are treated equally in the GOA graph, and some information may be ignored when learning protein low-dimensional embedding. Therefore, the coincidence degree between the protein similarity network calculated by the GOA(cosine) method and the actual PPI data is not as high as that calculated by the GO(DTW) method. There are potential limitations to our method. First, we transformed directed graphs into undirected graphs, which might result in a loss of structural information. We also treated the GO terms and the proteins equally in the GOA graph, which may ignore some information. Therefore, in our future study, we plan to learn the protein representations in the graph by combining the information in the directed graph and by considering representation learning of heterogeneous graphs that contain GO terms and proteins.The original contributions presented in the study are included in the article/YZ conceived the idea and prepared the experimental data. ZW and YZ debugged the code, conducted the experiments, interpreted the results, and wrote and edited the manuscript. SW and JS advised the study and reviewed the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations.We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement.https://github.com/yourh/DeepGraphGO.Bioinformatics online. Proteins are building blocks of life, playing many crucial roles within organisms, such as catalyzing chemical reactions, coordinating signal pathway and providing structural support to cells . In ordeno-knowledge and limited-knowledge proteins  of International Society for Computational Biology (ISCB) has organized a community challenge, the Critical Assessment of protein Function Annotation algorithms (CAFA)  and instance (protein). For the label side, there are more than 44 000 GO terms, where GO is a directed acyclic graph (DAG), meaning that for one protein annotated by one GO term, all ancestor GO terms in GO can be also assigned. In fact, one human protein is currently associated with 47 GO terms on average, according to Gene Ontology Annotation (GOA) Database (Dec 2020) , trained by GNN, is generated from InterPro  layers: GNN has been developed for various tasks, such as node embedding, link prediction, node classification and graph classification  . DeepGraWe thoroughly validated the performance of DeepGraphGO through comprehensive experiments on large-scale datasets under the CAFA settings. We compared DeepGraphGO with a number of methods, including DeepGOPlus , a stateThere are a large number of studies for AFP  with N nodes (proteins) or weighted adjacency matrix N binary feature vectors, generated by InterProScan  Input (fully connected) layer: the binary feature vector of each protein is transformed into a non-binary vector, to be used as the initial representation vector. (ii) Graph convolutional (GCN) layer: updates the representation vector of each node (protein) to capture high-order information through graph edges, by renewing the vector using those of neighboring nodes. (iii) Output (fully connected) layer: predicts scores of GO terms for each protein.ip, we use InterProScan to generate a binary feature vector m is the number of signatures (domains and families in InterPro) related to at least one of N proteins in G, and the jth element of jth signature belongs to ip. We use a fully connected layer to obtain a low-dimensional representation vector f is a non-linear activation function. We generate initial representation matrix N proteins.For protein lth GCN layer -order] information of each node.Following , at the ith protein and the jth GO term, prediction score jth GO term, respective and σ is the sigmoid function. We use the binary cross-entropy as loss function J:K denotes the number of GO terms and G), which means semisupervised learning.For the k largest weights for each node, instead of sampling neighbors (which is done by GraphSAGE). Note that training uses data of all species, i.e. multispecies strategy.We use mini-batch training, with which GraphSAGE shows a better generalized performance . PracticWe collected data following the standard CAFA protocol (https://www.uniprot.org/downloads) .1. Protein sequences: We downloaded protein sequences from UniProt (https://string-db.org/) . This database covers around 24.6 million proteins from 5090 organisms with more than two billion interactions in total, which was generated before T0 (Jan. 2019).Protein networks: We used version 11.0 of STRING , GOA (http://www.ebi.ac.uk/GOA)  and GO (http://geneontology.org/page/download-annotations)  in January 2020. We extracted all experimental annotations in: ‘IDA’, ‘IPI’, ‘EXP’, ‘IGI’, ‘IMP’, ‘IEP’, ‘IC’ or ‘TA’. All are combined to generate an annotation dataset.GO terms: We downloaded from SwissProt1  and GO term-centric. For protein-centric, DeepGOCNN, DeepGOPlus and three most important components of NetGO: BLAST-KNN, Net-KNN and LR-InterPro. For GO term-centric, three most representative network-based methods: DeepNF, clusDCA and GeneMANIA. These three methods have one model for each species, and each model is trained independently. We explain BLAST-KNN, Net-KNN and LR-InterPro below, while all other competing methods were introduced in Section 2.4.2.1iZ of proteins which are homologous to protein ip . Then score ip and GO term jGO can be computed as follows:ip and kp by BLAST and jGO belongs to protein kp; otherwise zero.The idea is that similar proteins may have similar protein functions. We run BLAST over all proteins in the training set to obtain set 4.2.2ip and GO term jGO can be computed as follows:V is all nodes (proteins) in graph G and ip and kp. In testing, if a given protein ip (in STRING) which is most homologous to Similarly score 4.2.3For each GO term, logistic regression (LR) is trained using the binary feature vector (obtained by InsterProScan) which is the same as the input of DeepGraphGO. The trained LR is used for prediction.M = 2  to We trained DeepGraphGO for MFO, BPO and CCO separately. We used two GCN layers [In preliminary experiments, we examined deeper GCN layers, while the performance was not highly improved regardless of the dramatically increase of computational cost. Then we set ptimizer  with theFor the competing methods, we downloaded their implementations from the original authors’ websites. We trained these methods using our training data and tuned the parameters using the validation data. As GeneMANIA, clusDCA and deepNF utilized multiple networks as input, all 7 types of networks in STRING were used, which include neighborhood, fusion, co-occurrence, co-expression, experiment, database and text mining. For DeepGraphGO, an integrated network from above 7 types provided by STRING was used.τ) and rc(τ) are so-called precision and recall, respectively, obtained at some cut-off value, τ, defined as follows:τ for at least one GO term, and We used three evaluation metrics: Fn metric . M-AUPR In tables of experimental results, the best and second best performance values are highlighted in bold face and underlined, respectively.4.5.1To check the robustness of improvement by DeepGraphGO, we conducted bootstrap with replacement 100 times, to generate 100 testing sets. We then ran paired t-test over 100 trials to examine the statistical significance on performance improvement between DeepGraphGO and competing methods. The results show that the performance improvements by DeepGraphGO over competing methods were all statistically significant  STRI: proteins in STRING, (ii) HOMO: proteins being not in STRING but homologous to proteins in STRING and (iii) NONE: all other proteins. 4.5.3We explored the performance of each species listed in 4.5.4difficult proteins, where the definition of the difficult proteins is: the sequence identity of the protein (in the training set) most similar (homologous) to a difficult protein is less than 60%. The number of difficult proteins in testing set is 303 in MFO, 649 in BPO and 437 in CCO, respectively. Note that obviously it is hard to make accurate function prediction of these difficult proteins by homology-based methods. difficult proteins. On the other hand, by taking advantage of both protein domain/family and network information through GCN layers, DeepGraphGO could outperform LR-InterPro in all six settings. All these results suggest that DeepGraphGO is the most reliable and effective model among all compared methods for the AFP of difficult proteins.Inspired by the result analysis of CAFA2 (4.6.1According to the number of annotations per GO term: we grouped annotations (GO terms) in the testing set into four groups: 10–30, 31–100, 101–300 and >300. 4.6.2Existing network-based methods focus more on the term-centric metric over a specific species, and so we compared DeepGraphGO with GeneMANIA, clusDCA and deepNF (state-of-the-art network-based methods) over HUMAN and MOUSE in term-centric and pair-centric manners. As the number of testing proteins in each species is limited, we collected all GO terms (appearing more than twice in the testing set) together to compute M-AUPR . 4.6.34.6.4The main feature of DeepGraphGO is the GCN layers for the input protein network. Instead of the GCN layer, we train representation vectors by using a fully connected layer for the input InterPro feature vectors. We call this alternative as DNN-InterPro. 4.6.5no-knowledge protein in the testing set. no-knowledge protein, Q9BQD7, which were predicted by competing methods and DeepGraphGO. In difficult protein, which has no homologous proteins  in the training set, by which BLAST-KNN could not predict any GO terms. LR-InterPro predicted 12 true GO terms out of the predicted 19 GO terms. Net-KNN predicted the largest number (45) of GO terms, out of which 18 was true. DeepGOCNN predicted 20 GO terms, with eight true GO terms, while DeepGOPlus predicted only five GO terms, with four true GO terms. This may be due to that one homology-based component, DiamondScore, did not work well on the difficult protein. Finally DeepGraphGO achieved 18 true GO terms out of 25 predicted. Thus 18 was the highest number of correctly predicted GO terms (by DeepGraphGO and NetKNN), while the number of wrongly predicted GO terms was only 7 by DeepGraphGO and 23 by Net-KNN. This difference was clearly shown by the difference in the F1 score in the last column. That is, DeepGraphGo achieved 0.766 of F1 while Net-KNN was 0.537, which was even lower than 0.585 of LR-Interpro. Finally we show a typical example obtained by DeepGraphGO and competing methods, to illustrate the real performance difference on annotating GO to a We have designed an end-to-end, graph neural network-based model, DeepGraphGO, for the challenging AFP problem, to make the most of both protein sequence and protein network information. DeepGraphGO uses ‘multispecies strategy’, which allows only one single model to be trained by using proteins of all species. Extensive experiments under diverse settings revealed that DeepGraphGO outperformed a number of compared methods, such as DeepGOCNN, DeepGOPlus and three representative network-based methods, GeneMANIA, deepNF and clusDCA. Furthermore DeepGraphGO can be integrated into an ensemble method as a component. Then DeepGraphGO-LTR, a method obtained by plugging DeepGraphGO into NetGO, the state-of-the-art ensemble method of AFP, outperformed both GOLabeler and NetGO. Possible future work would be to build a single model for AFP, which can incorporate all kind of protein information including sequence, structure and network.Financial Support: S.Z. was supported by National Natural Science Foundation of China (No. 61872094), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), ZJ Lab, and Shanghai Center for BrainScience and Brain-Inspired Technology. S.Y. and R.Y. have been supported by the 111 Project (No. B18015), Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01) and Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences.H. M. has been supported partially by Academy of Finland (315896), JST ACCEL (JPMJAC1503), NEXT KAKENHI (19H04169).Conflict of Interest: none declared.btab270_Supplementary_DataClick here for additional data file.


The analysis of protein-protein interactions (PPIs) is essential for the understanding of cellular signaling. Besides probing PPIs with immunoprecipitation-based techniques, peptide pull-downs are an alternative tool specifically useful to study interactome changes induced by post-translational modifications. Peptides for pull-downs can be chemically synthesized and thus offer the possibility to include amino acid exchanges and post-translational modifications (PTMs) in the pull-down reaction. The combination of peptide pull-down and analysis of the binding partners with mass spectrometry offers the direct measurement of interactome changes induced by PTMs or by amino acid exchanges in the interaction site. The possibility of large-scale peptide synthesis on a membrane surface opened the possibility to systematically analyze interactome changes for mutations of many proteins at the same time. Short linear motifs (SLiMs) are amino acid patterns that can mediate protein binding. A significant number of SLiMs are located in regions of proteins, which are lacking a secondary structure, making the interaction motifs readily available for binding reactions. Peptides are particularly well suited to study protein interactions, which are based on SLiM-mediated binding. New technologies using arrayed peptides for interaction studies are able to identify SLIM-based interaction and identify the interaction motifs. Cellular signaling is in large parts based on a complex network of protein interactions with other proteins or other biological molecules. Studying protein-protein interaction (PPI) networks is pivotal for the understanding of cellular signaling –3. ProteSince its development in the early 1980s, immunoprecipitations have increasingly been used to study PPIs . Using sIn parallel, advances in the chemical synthesis of peptides allowed the production of larger quantities and longer peptides. This enabled the use of peptides in pull-down assays as an alternative to the genetic generation of tagged protein fragments . The che. Differently from protein-based interaction studies, peptide-protein interactions are usually of low affinity, thus preferring high-affinity interactions with more abundant proteins [The full potential of a peptide pull-down is only utilized in combination with a powerful detection methodology, which allows the identification of new interaction partners. With the rise of mass spectrometry–based proteomics, interacting proteins can be identified in a peptide pull-down (reviewed in ). Differproteins . This prproteins , 32.A significant advantage of peptides over protein pull-downs is the possibility of including post-translational modifications (PTMs) in the peptide. As peptides are chemically synthesized, any PTM can be included as long as it is compatible with the synthesis technique. The binding surface is thus fully modified, which is usually not the case with complete proteins –36.Fast regulation of biological networks relies on the rapid addition and removal of PTMs during signaling, leading in many cases to the formation or loss of protein interactions. Capturing these transient interactions is challenging , 37. DisMany proteins adapt a defined fold, which is determined by their amino acid sequence. These folded domains are contrasted by regions lacking a specific fold, which are called intrinsically disordered regions (IDRs) . DespiteSLiMs promote interactions via specific amino acid patterns, which are recognized by their interaction partners Fig. . BecauseSynthesized peptides covering short parts of a protein can contain complete SLiMs, making them the perfect carrier for SLiM-based interaction studies. This has been used in studies, where a known sequence with and without the PTM was used to identify the interaction partners, as demonstrated in a study using two different peptides derived from EGFR and Sos1 and 2. The peptides were synthesized in phosphorylated and unmodified form and used in a pull-down experiment utilizing SILAC-labeled (stable isotope labeling in cell culture) cell extracts. The interaction partners were identified using mass spectrometry .The advancements of peptide synthesis by SPOT synthesis allow the creation of arrays holding many different peptides on a single membrane surface , 64, 65.While peptide matrices have been used to find the optimal binding sequence –69, the A screening technique targeting transient SLiM-based interactions along the primary structure was recently developed. The technique, PrISMa (Protein Interaction Screen on a Peptide Matrix), is based on a membrane-bound array of overlapping peptides, spanning the entire sequence of the protein of interest, creating a sliding window for the detection of SLiM-mediated interactions  Fig. 1d. The intAdditionally, the setup of the peptide array allows the inclusion of PTMs in the matrix to probe simultaneously for PTM-mediated interactions Fig. . For theA limitation of the method is the restriction of the screen: it can be only applied to intrinsically disordered regions, in which the structure depends only on the amino acid sequence. For interactions that depend on three-dimensional structures, this cannot be applied. To address this limitation, Ramberger and coworkers combined the PrISMa method with a BioID interaction screen to explore the C/EBPα interactome. They observed a significant overlap of the interaction partners between both technical approaches, and interestingly, common protein binders with C/EBPβ interactome .Over time, peptide-based interaction studies have significantly helped to reveal interaction sites or confirm specific PTM-regulated interactions. The new matrix-based interaction screens open a new era of interaction studies allowing to test many SLiM-based interactions at the same time. Besides the costs for the peptide matrix, using a mass spectrometric readout, the time of the measurement for all the interaction screens is a major restriction. Although the measurement of certain interactions can be parallelized by the use of isotopic labeling techniques, like SILAC and maybe TMT/iTRAQ in the future, the measurement of a peptide matrix can consume weeks on data acquisition in the mass spectrometer. Here, the technical developments of fast liquid chromatography systems might reduce the time constraints and open the use of peptide matrices for more laboratories. This will broaden the use of these techniques to identify more, potential druggable, PPI-driven diseases and will promote the deeper understanding of PPI networks, which depend on low affinity interactions.


Protein–protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties.We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results.http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git.We provide an online application at Bioinformatics online. Various kinds of biological macromolecule interactions, especially protein–protein interactions (PPIs) , play a Traditionally, protein 3D structure has been regarded as an essential profile for PPI prediction. However, with the discovery of intrinsically disordered proteins whose spatial structures interconvert on a series of timescales  it can be beneficial for prediction if a model can consider PPI as a relative property rather than an intrinsic property; (ii) few efforts have been made to provide an intuitive description of the inner mechanism of pairwise-input neural networks and illustrate the effect of each amino acid residue on PPI.In this paper, we propose DeepTrio, a deep-learning framework based on a mask multiscale CNN architecture, in which multiple parallel filters provide valuable insights for PPI prediction by apprehending the multiscale contextual information of protein sequences. In comparison to existing tools, the main contributions of our work are: (i) an additional class, single-protein class, is introduced to our model, which allows DeepTrio to discriminate between the relative property and intrinsic property; (ii) due to the application of the single-protein class and masking operation, DeepTrio requires only one training set to build a model that can not only identify PPIs, but also further investigate the effect of each protein residue on PPI without any additional specific training; (iii) DeepTrio is also available as an online tool for inexperienced users in order to address the cross-platform usage and dependency related issues.Since PPI prediction is a binary classification task, most of the existing models are trained to classify the input data into two classes: interacting or noninteracting. However, we have designed DeepTrio for ternary prediction that takes as input a pair of protein sequences, and generates a three-dimensional vector output indicating the probability of interaction, noninteraction and single-protein. The overall framework of DeepTrio is illustrated in There are four datasets used for training and testing the models in this study. Two datasets are derived from the Biological General Repository for Interaction Datasets (BioGRID) (Saccharomyces cerevisiae (yeast) and Homo sapiens (human) data are widely used to evaluate the performance of PPI predictors (The BioGRID database (edictors  and restedictors  to decreThe negative samples in these two benchmarks are generated by shuffling one sequence of a positive case with 2-let counts (excluding the first residue of the protein) . It has S.cerevisiae core dataset, as a widely used benchmark, is composed of 11 188 PPI cases including 5594 positive cases proposed by S.cerevisiae positive sets from DeepFE-PPI and PIPR, so we use both of the S.cerevisiae datasets to train and test DeepTrio and other baseline approaches.The The single-protein case consists of two components: a normal protein sequence and a masked sequence whose all residues are masked by blank bits . Each un), and yields two protein representations  module to capture various protein features in multiscale windows. It takes as input a protein pair  , which form about 140 000 combinations . It is tWe randomly initialize the weights of the embedding, convolution and dense layers according to the Glorot uniform distribution , which iSuppose we have a pair of interacting proteins We report the performance of DeepTrio and other approaches on four different PPI datasets. Further, we test the performance of DeepTrio on the multiple specie dataset where proteins are filtered based on different thresholds of sequence identity. In addition to the binary prediction of PPIs, DeepTrio can generate an intuitive protein portrait for the detection of potentially important residues for interaction. Lastly, a logically concise online application has been developed to help researchers make better use of DeepTrio.The main task of DeepTrio is to estimate the interaction probability of a given protein pair based on its sequences. We compare DeepTrio with several state-of-the-art PPI prediction methods including SVM-AC  and average precision (AP) to assess the prediction performance of the models. Higher values in all these metrics indicate better performance.As shown in In addition, we report the comparison between DeepDuo and DeepTrio on the BioGRID benchmarks, which provides insights into the role of single-protein training in PPI prediction. It is observed that DeepTrio perform consistently better than DeepDuo in all of the evaluation metrics  and 2. FS.cerevisiae dataset to evaluate the performance of DeepTrio. The positive set from DeepFE-PPI is identical with that from S.cerevisiae datasets in most metrics , where proteins are filtered based on different thresholds of sequence identity . To make the data suitable for the model input, we also remove the cases containing proteins longer than 1500 amino acids. The results in Following the same strategy as PIPR . The main goal of this extension is to find out which residues take the main responsibility for the prediction results and visualize the importance score for each residue in a sequence.mutation maps’ to illustrate the effect that each possible point mutation may have on binding affinity between DNA and proteins. DeepChrome   (importance map’ to illustrate the importance score of each residue in the mutant CALR (p.L367fs*46) (importance map’ is rendered as a heat map with l squares (where l is the length of the given protein), and each line in the heat map is set to 20 squares. It can be observed in importance map of Choline kinase alpha (ChoKα). ChoKα catalyzes the phosphorylation of choline to phosphocholine, and its high expression has proven to be associated with cancer malignancy and poor patient prognosis  with the recent experimental evidence in biochemical studies. Note that all the PPIs mentioned below, along with their mutants, are not included in the training data of DeepTrio.  exon 9) . We use 67fs*46) . The ‘imrognosis . Recent ance map  that theimportance map shows a preference for finding the key residues that share similar properties in the adjacent regions and a sensitivity decrease for large protein assessment. Another noteworthy observation in both In practice, the importance maps can be easily obtained by submitting two protein sequences to the web server. Moreover, the results from multiple submissions will be recorded on the web page, and they can be conveniently filtered and downloaded from the website. This online application is available at http://bis.zju.edu.cn/deeptrio.To provide an accessible interface in a logically concise manner, we develop an online application based on the DeepTrio model. The PPI prediction results and With the development of deep-learning algorithms such as CNN , recurreIn summary, we propose a deep-learning-based model, DeepTrio, to predict PPIs using raw protein sequences. By adopting the multiple parallel convolution filter architecture that allows DeepTrio to capture the deep features from the protein profiles, our model achieves encouraging performance on the benchmark datasets in terms of various evaluation metrics. We also integrate the single-protein training strategy and masking operation to prevent weight polarization in the intermediary layers and enable DeepTrio to visualize the contribution of each protein residue to the prediction results. Furthermore, we also provide an online application for PPI prediction and important residue detection.This work was supported by the National Key Research and Development Program of China ; the National Natural Sciences Foundation of China ; the 151 Talent Project of Zhejiang Province (first level); Jiangsu Collaborative Innovation Center for Modern Crop Production and Collaborative Innovation Center for Modern Crop Production cosponsored by province and ministry.Conflict of Interest: none declared. btab737_Supplementary_DataClick here for additional data file.


The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods. Although most proteins are synthesized in the cytosol, about half of them are transported into or across at least one cellular membrane to reach their functional destination Several experimental methods can be used for protein localization analysis. Quantitative mass spectrometric readouts allow for the identification of proteins across fractions •A systematic introduction of features, algorithms, methods, and tools, as well as their relationships related to protein localization.•A comprehensive list of available protein localization prediction tools, many of which became available in recent years.•Extensive evaluations of localization prediction tools/methods, providing insights on why some methods have better prediction performance than others.•Significant discussion on the future direction of protein localization studies.Several papers have reviewed protein localization prediction methods. The review of 22.1Protein sequences are considered the most essential source of information for protein localization prediction, particularly terminal region sequences where targeting signals are likely to be found. Protein sequence information can be obtained from databases such as UniProt 2.1.1The simplest feature representing a protein sequence is likely amino acid (AA) composition 2.1.2The main shortcoming of using AA composition as a feature is its lack of protein sequence order information i and i + k asAs in Eq. Note that Eq. 2.1.3As subcellular localization tends to be evolutionarily conserved 2.1.4Evolutionary profiles, represented by Position-Specific Scoring Matrices (PSSMs), etc., provide informative input for protein localization prediction. PSSMs indicate the amino acid occurrence for each position in a protein multiple sequence alignment. PSSM scores are generally given as positive or negative values. A positive score means that the given amino acid substitution occurs more frequently in the alignment than expected by chance, while a negative score indicates that the substitution occurs less frequently than expected by chance. PSSMs can be created using PSI-BLAST, which finds similar protein sequences to a query sequence and then constructs a PSSM from the resulting alignment.The BLOSUM (BLOcks SUbstitution Matrix) matrix et al. proposed profile-based string kernels that use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along with protein sequences for inexact matching of k-length subsequences (“k-mers”) One particular usage of a sequence profile is as the profile kernel of an SVM. A key feature of the SVM optimization problem is that it depends only on the inner products of the feature vectors representing the input data. Several kernel functions have been proposed to avoid the explicit transformation of input data to feature vectors, explained as follows. Let 2.1.5Certain sequence patterns may correlate with a specific subcellular localization due to localization signals or functional relationships k is often set based on specific needs or prior biological knowledge. For example, TetraMito k varies in LOCALIZER to capture the target signals on protein sequences.A sequence pattern can also be extracted through a sliding window of a k-mer sequence. The motif length 2.1.6As the name suggests, this feature uses AAs' physical and chemical properties to represent protein sequences. These previously calculated properties are stored in public databases. According to Venkatarajan and Braun 2.1.7Evolutionary information significantly benefits model prediction performance; however, as the number of proteins in databases increases, retrieving such information is often time-consuming. Additionally, evolutionary information is less powerful for small protein families, e.g., for proteins from the Dark Proteome 2.2If two proteins interact, they are neighbors of each other in a protein–protein-interaction (PPI) network. The localizations of the neighbors in a PPI network carry information about the localization of un-annotated proteins. For example, if the majority of a protein’s neighbors share the same localization, the protein is likely localized to the same location. The definition of protein interaction varies and can be based on physical connections or genetic regulations. Protein interaction data can be retrieved from databases such as MINT 2.3The rationale for using gene/protein expression as a feature is that genes/proteins in the same compartment at the organelle or suborganelle level tend to be co-expressed to perform related functions. Gene/protein expression information can be used in network form like the aforementioned protein interaction feature 33.1Support vector machines (SVMs) The method proposed in Recent SVM-based methods include SubMitoPred 3.23.2.1Probabilistic models, specifically Bayesian methods such as the Bayes Optimal Classifier or Bayesian Networks, make the most probable prediction for a new example. Bayesian methods use the Bayes Theorem PSORT-B 3.2.2i. LetWhen determining the probability of a protein to be localized at a specific location given a PPI network, kernel-based logistic regression (KLR) considers the localization information of all the proteins in the network. The KLR model can be formulated as follows i to proteins targeting localization L, where i targeting location L is linear based on the summed distances of proteins targeting L or another location. Then, we haverepresent the summed distances of protein Note that the probability of being in each localization is calculated separately as a binary classification problem.NetLoc 3.2.3T(x) defined in T(x) is a random variable on the probability space Given a probability space, a random field CRFs are used for signal peptide cleavage site prediction in DeepSig 3.33.3.1The k-NN algorithm is a nonparametric method used for classification and regression WoLF PSORT 3.3.2The Mahalanobis distance S is defined as:i-th eigenvalue of covariance matrix S.The Mahalanobis distance of an observation The covariant discriminant algorithm is used in general protein localization prediction in 3.4An artificial neural network (ANN) is based on a collection of connected units or nodes called artificial neurons that loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. Each artificial neuron receives a signal and processes it, and the output of each neuron is computed by a non-linear function of the sum of its inputs. Increased GPU computing power and distributed computing allow the use of larger networks, which is known as “deep learning” Several neural network/deep learning-based methods have been proposed for protein localization prediction. SCLpred Some methods do not predict localization directly; rather, they predict the presence and location of targeting peptides from which the localization of corresponding proteins can be roughly inferred. For example, DeepSig 3.5For prediction problems involving large-scale labeled data, neural networks tend to outperform other algorithms or frameworks. However, when it comes to small- to medium-sized data, decision tree-based algorithms are often considered optimal. A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute, where each branch represents the outcome of the test, and each leaf node represents a class label. Decision tree-based methods have evolved over the years. For example, bagging (bootstrap aggregating) combines the predictions of multiple decision trees through a majority voting mechanism, random forests select only a subset of features at random to build a forest of decision trees, and boosting is achieved by sequentially minimizing the errors of previous models. Gradient boosting employs the gradient descent algorithm to minimize errors in sequential models. XGBoost Decision-tree-based methods have also been applied to protein localization problems. Pang et al. developed a CNN-XGBoost model 4Many of the aforementioned methods mention web servers or standalone tools, but some of these are inaccessible due to lack of maintenance. We summarize a list of available protein localization prediction tools regarding their coverage, algorithms, accessibility, and other characteristics. These localization prediction tools (at the subcellular or suborganellar level) are shown in −6 E-value cutoff, and the alignment was required to cover 80% of the shorter sequences, resulting in 8410 clusters for the whole dataset. The five-fold datasets generated had approximately the same number of proteins at each location. Four of the datasets were used for training and validation, and one was held out for testing. In this way, the redundancy between the training and testing datasets was reduced.To assess prediction tools, competitions can provide large-scale blind tests for objective evaluation. A well-known example is the CASP The DeepLoc, MULocDeep, SeqVec, ProtVec, and ProtTrans methods were stringently trained and tested using the training and testing samples in the DeepLoc dataset. LocTree2, MultiLoc2, CELLO, WoLF PSORT, YLoc, SherLoc2, and iLoc-Euk were run on the testing samples in the DeepLoc dataset. Thus, their performance is potentially overestimated because redundancy control was not performed. All the evaluated methods could be applied to proteins in eukaryotic cells. In the cases where a method predicted more than ten locations, the predicted locations were mapped onto the ten locations in the DeepLoc dataset. Overall accuracy is used as the evaluation criterion. The evaluation performance is directly cited from 5The computational prediction of protein localization has significantly improved prediction accuracy and localization mechanism studies over past two decades, especially with deep learning. However, the current methods still have limitations. For example, an 80% overall prediction accuracy shown in Protein localization problems have several biological characteristics. Many proteins can localize to more than one compartment. Some proteins are tissue-/cell type-specific, meaning their localization varies between different tissues or cell types. Proteins expressed at the correct location but with altered efficiency or concentration can also lead to illness. Thus, quantitively measuring or predicting protein localization in different tissues or cell types are in great demand. Additionally, proteins may be mislocalized due to mutations, which may have disease consequences Researchers could also pay more attention to biological interpretability when designing future localization analysis models. The mechanism of protein localization is complicated. In addition to targeting peptides, which are considered in some existing methods, other phenomena can affect/control protein localization. The trafficking machinery in cells controls the transport of molecules across membranes of organelles. Dysregulation of the protein trafficking machinery can have dramatic effects on general protein transport processes Yuexu Jiang: Conceptualization, Investigation, Visualization, Validation, Writing - original draft. Duolin Wang: Visualization, Writing - review & editing. Weiwei Wang: Validation. Dong Xu: Writing - review & editing, Supervision, Project administration, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.