Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder). LinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI developments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology. Nearly all aspects of cell behaviour are controlled by phosphorylation events and intricate networks of kinases-substrate relationships mediating these phosphorylations . DependiIn order to improve the design of kinase inhibitors, understand their mode of action and potential side effects, a better understanding of kinase-substrate relationships and the networks they form is necessary. With the advent of modern high-throughput mass-spectrometry based phosphoproteomics, many thousands of phosphorylation sites in substrate proteins can be identified . HoweverReliable automated prediction of phosphorylation candidates is therefore much desired, because it can substantially reduce the number of possibilities that have to be tested experimentally. During the last decade, several tools for predicting phosphorylations have become available. The most widely used and recently described include: Scansite , GPS 8]8], NetPhHere, we show that predicting kinase-substrate relationships can be formulated as finding missing links in a knowledge graph . Knowledge graphs are a powerful way to organise descriptions of properties of objects and their connections . HoweverTo test this concept, we have built a predictive model based the known phosphorylation network in PhosphoSitePlus  interpreThe concept of our approach in comparison with related existing techniques is illustrated in The workflow of our methodology is illustrated in In the following, we present the performance of the LinkPhinder model. First, we benchmark LinkPhinder against six commonly used existing tools. Then, we present results of biological validation experiments focused on selected kinases of clinical relevance and their substrates. Finally, we introduce a web interface that allows the scientific community convenient access to LinkPhinder.While the LinkPhinder model learns its parameters from the input data automatically, the optimal model configurations  cannot be inferred that way and need to be determined empirically. In order to find these hyperparameters that optimise the performance of our model, we used the knowledge graph generated from PhosphoSitePlus  and evalThe trained model can predict the likelihood of phosphorylation reactions that exist in the training dataset but have not been observed yet. In principle, any phosphorylation dataset can be used, but we chose PhosphoSitePlus because it is widely considered the most comprehensive and accurate dataset on known phosphorylations in many different organisms including human .kinase-phosphorylation site-substrate triples). A pool of negative statements was generated by random associations between all human kinases and  pairs available in PhosphoSitePlus. This pool was used for sampling as many negatives as there were positives in each train/test split. For each of the 100 splits, we trained our model on the 90% of the data and validated it on the unseen 10%. For the existing techniques, we generated all their predictions relevant to the proteins in the PhosphoSitePlus dataset and assessed them using the test splits.The computational validation experiments compared our approach to a selection of six existing and commonly used phoshorylation prediction techniques: Scansite , GPS 8]8], NetPhNote that to make sure the presented relative differences between the methods are not merely due to the specific way we prepared the benchmarking data, we have also experimented with different train-test split and positive-negative ratios. The relative performances of the compared methods have not, however, changed from what is presented here. More information on the benchmarking methodology and results corresponding to the different ratios can be found in the Materials and Methods section.The results are summarised in To gain additional insights into the presented results, we analysed to what extent each tool covered the phosphorylations in the test splits. The coverage is an important factor influencing the results since we assign zero scores to phosphorylations which the systems are not able to process . Therefore, a tool that does not produce scores for negative examples will have these annotated with zeros automatically and thus they will be at the bottom of their ranking lists. This is a possible advantage over tools that do produce scores for such negative phosphorylations, as any positive scores can only move the negatives up in the ranking, resulting in more false positive assignments.The coverage of the different tools, i.e., their ability to make predictions for proteins represented in PhosphoSitePlus, is given in Thus, LinkPhinder outperforms existing popular tools in terms of sensitivity and specificity (by means of the area-under-the-curve and precision metrics used), but also in terms of the number of predictions it can make. Importantly, LinkPhinder also covers a larger fraction of the human kinome than the other tools. Comprehensive visualisation of this fact is given in Figs The results diplayed in To provide a complementary computational validation using a dataset independent of the one we trained our model on, we have used a very recent data on site-specific interactions of 103 human kinases with their substrates in cancer cells . Table 3While the performance of all tools is substantially weaker than when using the PhosphoSitePlus benchmark (i.e. only slightly above the random baseline for both area-under-the-curve metrics), LinkPhinder is still the best in three out of four metrics, and close second in the remaining one. The overall poor performance can be attributed to a relatively small coverage of the  gold staThe rich dataset of over 11 million candidate predictions was assessed regarding its potential for discovering new phosphorylation sites for kinases that have biomedical relevance, such as AKT and LATS1. Both kinases regulate cell survival, growth, proliferation, and are frequently altered in cancer –22. AKT To this end, we have extracted high-stringency predictions for the LATS1 and AKT1 kinases. By visual inspection of this list, we were able immediately pinpoint several known and promising substrates of these kinases. YAP1 for example is the best characterized substrate of LATS1, and our tool predicted that LATS1 would phosphorylate YAP at serine 127, which is the best studied phosphorylation site that contributes to YAP inactivation . AdditioTherefore, we decided to validate some of the new substrates experimentally. In order to select the most promising candidates we selected three proteins that are part of the wider LATS1 signaling network and for which antibodies were commercially available. To experimentally validate predicted LATS1 substrates we used the following strategy. HEK293 cells were transfected with two specific siRNAs against LATS1 in order to downregulate LATS1 protein levels (knockdown). Following this LATS1 knockdown, we would expect to see a decrease in the phosphorylation of LATS1 substrates. For confirmation, we used a positive control where we measured the phosphorylation of the known LATS1 substrate YAP1-S127, and indeed observed a decrease in YAP1-S127 phosphorylation in total cell lysates c.f. . This coOne of the proteins that we selected for validation was CREB which is transcription factor that is regulated by phosphorylation . This trIn the case of AKT we decided to monitor putative substrates by manipulating the level of AKT activation using two strategies. Firstly, we inhibited endogenous AKT activity by using the specific chemical inhibitor AKTi IV. Secondly, we increased AKT activity by transfecting a kinase hyperactive form of AKT with gag-AKT . One of After confirming the new site-specific kinase-substrate relationships involving the LATS1 and AKT1 kinases as reported above, we found out that none of the six existing systems used in our comparative validation could predict these phosphorylations on high stringency settings. This further demonstrates the unique power of LinkPhinder in the context of computational phosphorylation prediction.To extend the targeted validation experiments we cross-referenced our predictions with high-throughput phospho-proteomic data on the LATS1 interactome . This stUsing this approach, we obtained phospho-proteomic data on the LATS1 interactome from cells treated with two proapoptotic signals: FAS and etoposide, which both activate LATS kinase activity . To idenAfter confirming the new site-specific kinase-substrate relationships involving the LATS1 kinase as reported above, we searched for these in the prediction data provided by the existing tools. However, on high stringency settings, only GPS could predict one of the seven predictions made by us  of these predictions in the context of each specific kinase assay. The sensititivy was computed asSUBSpredicted is the number of substrates for which LinkPhinder provided at least one site-specific phosphorylation prediction with a score above the high confidence threshold, and SUBStotal is the number of substrates that were identified in the kinase assay and that are also present in the PhosphoSitePlus knowledge graph. Identified substrate proteins that were not in the knowledge graph were excluded for this analysis, because no predictions can be generated for those proteins.The experimentally validated PKA, MST2 and LATS1 substrate predictions made by LinkPhinder are listed in The sensitivity of the PKA predictions was 0.57, which we consider a good result given that they were validated in an unbiased approach that has inherent technical limitations. For the poorly characterised MST2 and LATS1 kinases the sensitivities were lower, 0.13 and 0.17 respectively. It must be noted that generating predictions for MST2 and LATS1 is challenging because only a few substrates have been described experimentally, and most of the existing predictions tools could not generate predictions for MST2 and LATS1. Together these results indicate that LinkPhinder can be used to predict kinase-substrates interactions for poorly characterised kinases.Finally, we wanted to benchmark LinkPhinder performance against the exsisting tools. However, we found this was not an easy task. Comparing LinkPhinder with existing tools using the results of these experiments is not as straightforward as in the cases reported before. The main reasons are conceptually different methods for determining the decision threshold employed by each of the tools. This does not allow for direct comparisons in terms of sensitivity as defined above. However, one high-level observation can be made: Only GPS matches the coverage of LinkPhinder as it can produce predictions for all the three kinases we assayed. NetworKin and NetPhorest cannot compute any predictions for LATS1, NetPhos and Phosphopredict only cover PKA, and Scansite covers none of the assayed kinases.https://LinkPhinder.insight-centre.org/.In order to facilitate usage of LinkPhinder by the community we have developed an online interface available at About tab of the tool’s web page. Briefly, the protein of interest can be entered into a search box with auto-completion (box A). Gene names and UniProt accession numbers are supported. The search is performed for high-stringency statements by default. However, all predicted statements can be searched as well (cf. the radio buttons in A). The query protein is evaluated by the system in two different ways, as a kinase and as a substrate and each type of predictions can be browsed independently (box B). The results can be filtered, and the predicted kinase-substrate pairs can be expanded to see the list of corresponding phosphorylation sites and prediction scores. Export of the predictions into a CSV file is also possible. Further, users can easily access contextual information from a comprehensive protein database (UniProt) by clicking on the proteins in the results (box C).A typical interaction with LinkPhinder is depicted in In this work, we have overcome several limitations of the current phosphorylation prediction tools by representing phosphorylation networks as knowledge graphs. Knowledge graphs are a relatively new approach to representing relational knowledge in the Machine Learning, Artificial Intelligence and Semantic Web communities. They have quickly gained popularity for two main reasons. First, they can represent diverse types of knowledge in a simple format. Secondly, they are amenable to robust techniques of statistical relational machine learning, that can for example be used to discover new facts. The discovery naturally makes use of the entire structure of the knowledge graph . This makes the representation very useful in domains where complex network dependencies are critical. Kinase-substrate relationships are a good example of such a domain. Our results show that knowledge graphs enabled phosphorylation predictions that were not possible with existing tools that are primarily based on local features.In particular, we have shown that phosphorylation networks can be meaningfully captured by knowledge graphs with kinases and substrate entities linked by relationships based on phosphorylation site motifs. Therewith, modern link prediction methods can be used to predict novel phosphorylation reactions and estimate their probability based on the entire network context. The resulting predictive model allows for making predictions about any protein present in the input data. This is a substantial advantage when compared with the existing tools. These tools typically focus on substrates as initial queries and include only a limited number of kinases. LinkPhinder not only covers a much broader range of possible kinase-substrate relationships than existing tools, but also shows very high generalization power and desirable ranking properties not exhibited by other, currently gold standard approaches. This aspect has been validated in experiments showing that our tool can generate numerous biologically valid predictions. Crucially, these predictions were not possible with a representative range of state-of-the-art tools . On medium stringency, the GPS tool could identify one prediction; the CREB1 phosphorylation by LATS1. On low stringency, the NetPhosK tool could also identify one prediction; the MST2 phosphorylation by AKT1. No existing tool could identify both predictions. The LATS1 predictions validated by the mass spectrometry experiments were not be predicted by any of the existing tools but one. Specifically, the GPS tool could predict one out of the seven predictions we made (LATS1 auto-phosphorylation at S464) on high stringency (and no further ones on lower stringencies). The other five tools could not identify any of our validated predictions. When cross-referencing the list of LATS1 predictions from other tools with our predictions, no additional predictions were made, demonstrating that our tool has the best coverage. Together, these results clearly illustrate the advantages of our tool.Experimental validation using the global PKA, MST2 and LATS1 kinase assays showed promising results in terms of LinkPhinder’s sensitivity for identifying new substrates. Direct comparison with the existing tools was not possible due to disparate methods employed by each tool in determining their decision/high-stringency threshold. However, the results reconfirmed one significant benefit of LinkPhinder. We were able to produce substrate predictions for all three kinases studied, which was not possible with five of the six existing tools, with the exception of GPS, again demonstrating that LinkPhinder’s increased kinase covarage is an important contribution.To build on the work presented here, we intend to incorporate more contextual data  to see whether they can bring new and/or more accurate predictions pertinent to clinically relevant pathways. We also want to develop predictive models that would utilize the biology of phosphorylation directly in the training process and not only in the knowledge graph conversion and negative example generation. As demonstrated, incorporating more network context and biological knowledge into the prediction process has great potential to further increase the coverage, predictive power, and usefulness of the resulting tools.Another research direction to explore in future is the applicability of our predictive model to improving the accuracy and scope of methods for predicting downstream effects of kinase signalling or the kinase activity profiles. An example of such method that could benefit from our results is described in . We belihttps://www.phosphosite.org/staticDownloads.action). There were 10,173 phosphorylation statements on 362, 7,302 and 2,377 distinct kinases, substrate-site combinations and substrates in the compiled phosphorylation network, respectively. Note that in the construction of all datasets, we have focused only on the Homo Sapiens species, unless specified otherwise.To compile the phosphorylation network that is the primary input for building the LinkPhinder model, we used the PhosphoSitePlus dataset in a version available on 26th of June 2017 (c.f. http://meme-suite.org/doc/download.html?man_type=web).In order to convert the phosphorylation statements extracted from PhosphoSitePlus into a knowledge graph, we had to compute motifs characteristic to the context sequences of phosphorylation sites. For that task, we used the MEME tool, version 4.11.2  was also used for generating a mapping between substrates and their possible phosphorylation sites. This mapping was used in the conversion of the internal, motif-based knowledge graph statements to phosphorylation statements when computing scores of possible phosphorylations that have not been known before. We focused only on substrates present in our knowledge graph, which resulted in 74,142 distinct substrate-site pairs that can be used for generating candidate phosphorylations .The PhosphoSitePlus dataset, together with UniProt (c.f. http://scansite3.mit.edu), KinomeExplorer , Netphos (c.f. http://www.cbs.dtu.dk/services/NetPhos/), GPS (c.f. http://gps.biocuckoo.org/index.php) and PhosphoPredict (c.f. http://phosphopredict.erc.monash.edu/). The numbers of predictions that can be made with the corresponding tools are as follows: 6,130,542 (GPS), 5,192,235 (KinomeExplorer), 3,614,271 (Netphos), 2,006,185 (PhosphoPredict), 311,196 (Scansite 3). The numbers of high-stringency predictions are not straightforward to determine using the set of all predictions available, since some tools allow for stringency settings just at the level of manual, single-protein queries. Thus we were only able to establish the number of high-stringency predictions for Scansite, NetPhos and PhosphoPredict: 12,346, 212,107 and 132, respectively.To assess LinkPhinder in comparison with related state of the art systems, we downloaded and/or generated full sets of phosphorylation predictions that can be made with the following tools: Scansite 3 (c.f. KIN_ORGANISM == SUB_ORGANISM == ‘human’). Although the dataset includes phosphorylation site’s amino acids context sequence of size 7, we did not use that information as we wanted to experiment with different and potentially larger context sequence sizes. Instead we extract the context sequence from UniProt  and more specifically from the reviewed (Swiss-Prot) main protein sequence (uniprot_sprot.fasta) and from isoform sequences (uniprot_sprot_varsplic.fasta). We discard any relation in the kinase-substrate dataset for which the phosphorylation site does not match the UniProt sequence. The construction of the phosphorylation network requires data sources containing relation information of kinase, substrate and substrate’s amino acid phosphorylation site. In our experiment, we used PhosphoSitePlus kinase-substrate dataset, an experimentally determined substrates, sequences, cognate kinases, and metadata curated from the literature . Only reThe knowledge graph conversion makes use of kinase family consensus motifs to transform phosphorylation network statements to knowledge graph relations. The kinase families classification is extracted from UniProt’s human and mouse protein kinases: classification and index. Only information about human kinases which are part of the phosphorylation network are kept.The conversion of phosphorylation network data into knowledge made use of the MEME tool in a pipeline graphically described in meme command line utility for sequence motif discovery, version 4.11.2. MEME was applied in parallel on batches of site context sequences drawn from substrates targeted by kinases of the same family. The size of the batches was a configurable hyper-parameter of the conversion and model training process. We used values ranging over the set {50, 100}. The static parameters used for every invocation of the MEME tool were: -text, -protein, -mod zoops, -x_branch, -minw 2.To realise the step 3 of the above pipeline we used specifically the ’-maxw MW, -maxsize MS, -nmotifs NM, -bfile BF where MW was the maximum width of a sequence in the batch, MS was the maximum width multiplied by the number of sequences in the batch, NM was the maximum number of motifs to be generated (set conservatively to 10 in the reported experiments as no batch generated more motifs than that number under any tested settings) and BF was a background Markov model of order 5 generated from the sequence batch.The MEME parameters that were dependent on the specific properties of the sequence batch and/or hyper-parameters of the whole model were: Generating a phosphorylation knowledge graph. Before we could train a statistical relational learning model, we had to construct a knowledge graph representing the known phosphorylation information. As the primary input into the knowledge graph, we chose a phosphorylation network compiled from the PhosphoSitePlus ). A 10 cm long, 75 μm inner diameter, HLPC c18-reversed pahes column was used. Samples were loaded at 600nl/min and peptides were eluted at a constant flow rate of 250nl/ min for 40 min. A multisegment linear gradient of 2-135% buffer (98% Acetonitrile and 0.1% formic acid) in positive ion mode was used. Data were acquired with the mass spectrometer operating in automatic data dependent switching mode selecting the 12 most intense ions prior to MS/MS analysis. Mass spectra were analysed by MaxQuant. Label-free quantitation was performed using MaxQuant.HeLa cells were transiently transfected with a GFP-tagged LATS1 construct or a GFP construct as control. After 2 days they were serum starved over-night and left untreated (control) or were treated with FasL (50nM) or Etoposide  Nonidet P-40, protease inhibitors and phosphatase inhibitors). Lysates were treated at 1 mg/ml with 10 mM 5’-4-fluorosulphonylbenzoyladenosine (FSBA) solubilised in DMSO and then incubated at 31°C for 2 hour. Samples were spun down at 200 x g to remove any precipitate. Sample were diluted down with 2 ml of PKA kinase buffer  and desalted using a Millipore Amicon ultrafiltration columns with a 3 kDa molecular weight cutoff. Following concentration, the samples were incubated with PKA kinase buffer , 500 uM ATP-biotin and 1250 units of recombinant PKA (New England Biolabs) in a total volume of 60 μl. Control samples without recombinant PKA and ATP-biotin were also made up. The controls and kinase-added samples were incubated at 31°C for 2 hours. 300 μl of phosphate buffer was added to the samples. Streptavidin resin (100 μl of a 50% slurry) was incubated with the samples overnight at 4°C. Samples were spun down samples at 2000 x g for 1 minute and the supernatant was removed. Samples were washed 5 times with 1 ml of phosphate buffer. Samples were analysed by mass spectrometry.The full results of the assay are given in the kinase assays supplement .MST2 Kinase assay. Serum straved HEK293T cells were treated with 3 μM of the MST2 kinase specific inhibitor, XMU-MP-1 or DMSO for 3 hours. Cells were lysed in a Nonidet P-40 buffer  Nonidet P-40, protease inhibitors and phosphatase inhibitors). Lysates were treated at 1 mg/ml with 10 mM 5’-4-fluorosulphonylbenzoyladenosine (FSBA) solubilised in DMSO and then incubated at 31°C for 2 hour. Samples were spun down at 200 x g to remove any precipitate. Sample were diluted down with 2 ml of MST2 kinase buffer  and desalted using a Millipore Amicon ultrafiltration columns with a 3 kDa molecular weight cutoff. Following concentration, the samples were incubated with MST2 kinase assay buffer , 500 uM ATP-biotin and 32 ng of recombinant MST2 (made in house) in a total volume of 60 μl. Control samples without recombinant MST2 and ATP-biotin were also made up. The controls and kinase-added samples were incubated at room temperature for 3 hours. 300 μl of phosphate buffer was added to the samples. Streptavidin resin (100 μl of a 50% slurry) was incubated with the samples for 1 hour at room temperature. Samples were spun down samples at 2000 x g for 1 minute and the supernatant was removed. Samples were washed 5 times with 1 ml of phosphate buffer. Samples were analysed by mass spectrometry.The full results of the assay are given in the kinase assays supplement .LATS1 Kinase assay. Serum straved HEK293T cells were lysed in a Nonidet P-40 buffer  Nonidet P-40, protease inhibitors and phosphatase inhibitors). Lysates were treated at 1 mg/ml with 10 mM 5’-4-fluorosulphonylbenzoyladenosine (FSBA) solubilised in DMSO and then incubated at 31°C for 2 hour. Samples were spun down at 200 x g to remove any precipitate. Sample were diluted down with 2 ml of LATS1 kinase buffer  and desalted using a Millipore Amicon ultrafiltration columns with a 3 kDa molecular weight cutoff. Following concentration, the samples were incubated with LATS1 kinase assay buffer , 500 uM ATP-biotin and 100 ng of recombinant LATS1 (Abcam) in a total volume of 60 μl. Control samples without recombinant LATS1 and ATP-biotin were also made up. The controls and kinase-added samples were incubated at 30°C for 30 minutes. 300 μl of phosphate buffer was added to the samples. Streptavidin resin (100 μl of a 50% slurry) was incubated with the samples for 1 hour at room temperature. Samples were spun down samples at 2000 x g for 1 minute and the supernatant was removed. Samples were washed 5 times with 1 ml of phosphate buffer. Samples were analysed by mass spectrometry.The full results of the assay are given in the kinase assays supplement .Mass spectrometry sample preparation. The streptavidin resin containing the bound proteins were incubated with 400 μl of elution buffer I  at 37°C for 30 minutes. The samples were spun at 2000 x g and the superntant was retained. To the streptavidin resin 330 μl of elution buffer II  at 37°C for 1 hour. The samples were spun at 2000 x g and the superntant was retained. The two supernatant of elution buffers I and II were combined and incubated overnight at 37°C. After the incubation 130 μl of 5 mg/ml Iodocetamide was added to each and the samples were incubated for 30 minutes at room temperature in the dark. C18 stage tips that were previously prepared were mounted into a 1.5 ml eppendorf were activated by adding 50 μl of 50% acetonitrile (AcN) and 0.1% Trifluoroacetic acid (TFA). The samples were spun at 5000 rpm for 1 minute. 50 μl of 1% TFA was added to the C18 stage tips and the samples were spun at 5000 rpm. After the Iodocetamide incubation the reaction was stopped by adding 1 μl of 100% TFA. The samples were loaded onto the C18 stage tips and they were spun at 5000 rpm. The C18 stage tips were then washed by adding 50 μl of 1% TFA and then the samples were spun at 5000 rpm, this was done twice. Before elution of the samples, the C18 stage tips were mounted into fresh 1.5 ml eppendorfs. The peptides were eluted of the C18 stage tips by adding 25 μl of 50% AcN and 0.1% TFA and spinning the samples at 5000 rpm, this was repeated twice. Samples were evaporated for 10-15 in a CentriVap concentrator until 5 μl was left. The sample was then respuspended in 20 μl of TFA. The samples were then analysed by mass spectrometry.Mass spectrometry. Mass spectrometry was performed using a Ultimate 3000 RSLC system that was coupled to an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific). Following tryptic digest, the peptides were loaded onto a nano-trap column  running at a flow rate of 30 μl/min in 0.1% trifluoroacetic acid made up in HPLC water. The peptides were eluted and separated on the analytical column  after 3 minutes by a linear gradient from 2% to 30%of buffer B (80% acetonitrile and 0.08% formic acid in HPLC water) in buffer A (2% acentonitrile and 0.1% formic acid in HPLC water) using a flow rate if 300 nl/min over 150 minutes. The remaining peptides were eluted using a short gradient from 30% to 95% in buffer B for 10 minutes. The mass spectrometry parameters were as follows: for full mass spectrometry spectra, the scan range was 335-1500 with a resolution of 120,000 at m/z = 200. MS/MS acquisition was performed using top speed mode with 3 seconds cycle time. Maximum injection time was 50 ms. The AGC target was set to 400,000, and the isolation window was 1 m/z. Positive Ions with charge states 2-7 were sequentially fragmented by higher energy collisional dissociation. The dynamic exclusion duration was set at 60 seconds and the lock mass option was activated and set to a background signal with a mass of 445.12002.Analysis of mass spectrometry data. Analysis was performed using MaxQuant (version 1.5.3.30). Trypsin was set to be the digesting enzyme with maximal 2 missed cleavages. Cysteine carbmidomethylation was set for fixed modifications and oxidation of methionine and N-thermal acetylation were specified as variable modifications. The data was then analysed with the minimum ratio count of 2. The first search peptide was set to 20, the main search peptide tolerance to 5 ppm and the “re-quantify” option was selected. For protein and peptide identification the Human subset of the SwissProt database (Release 2015_12) was used and the contaminants were detected using the MaxQuant contaminant search. A minimum peptide number of 1 and a minimum of 6 amino acids was tolerated. Unique and razor peptides were used for quantification. The match between run option was enabled with a match time window of 0.7 minutes and an alignment window of 20 minutes.MaxQuant (version 1.3.0.5.) was used to analyse raw mass spectrometric data files from LC-MS/MS for protein quantification. Default settings were used unless stated otherwise, including the following parameters: Trypsin/P digest allowing for 2 misscleavages; variable modifications included oxidation and acetylation; fixed modification included carbamidomethylation (at Cysteine); to detect phosphopeptides we included phospho (STY) as a modification; first search at 20 ppm: main search at 6 ppm mass accuracy (MS) and 20ppm mass deviation for the fragment ions. The MS data were searched against a human database (Uniprot HUMAN) with a minimum peptide length of 6, unfiltered for labelled amino acids, at a false discovery rate (FDR) of 0.01 for peptides and proteins. The results were refined through the re-quantify option; also “match between runs” was selected with a 1 min time window, and label free quantification was selected with the minimum ratio count set at 1.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S1 Data(BZ2)Click here for additional data file.S2 Data(TBZ)Click here for additional data file.S1 Fighttps://doi.org/10.6084/m9.figshare.13118561).Supporting details on the experimental validation of the LATS1/YAP1 phosphorylation: (A-B) HEK293 were transfected with the indicated siRNAs. 48 hours after transfection the cells were lysed and blotted with the indicated antibodies. (C) HEK293 were transfected with empty vector (EV) or GAG-AKT or treated with AKTi IV (10M) for 1 hour. Phosphorylated proteins were immunoprecipitated using an anti-AKT antibody and the immunoprecipitates were blotted with the indicated antibodies Click here for additional data file. 27 Feb 2020Dear Dr Novacek,Thank you very much for submitting your manuscript "Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs" for consideration at PLOS Computational Biology.As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Anand R. AsthagiriAssociate EditorPLOS Computational BiologyDouglas LauffenburgerDeputy EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors present a novel approach based on knowledge graphs to predict kinase-substrate interactions that benchmarks favourably against well established methods in the field. Moreover, they experimentally confirm several of the top predicted interactions. This work tackles an important challenge and utilises a methodological distinct method that expands substrate predictions to many kinases that are not covered by existing approaches.While this approach represents a significant development the method needs to be benchmarked using independent data-sets to better understand the precision of its predictions. Moreover, in several sections conclusions are not well supported. Thus, it would be important to expand the current set of figure panels and supplementary materials to provide clear evidence supporting the conclusions made.1. It would be important to exclude that the precisions reported are not inflated due to overfitting to the training data-set. Besides the 90-10% train-test split, can the authors use, for example, 60-40% and 80-20%?2. Taking advantage of a recent systematic study of kinase-substrate interactions (PMID:31959955), the authors could use this as an independent test set, while making sure the version of PhosphositePlus used for training does not contain this study to avoid circular training-testing. Can the authors show how well LinkPhinder captures these interactions and how it compares to the other approaches?3. LinkPhinder identifies 2,009,171 and 7,232,636 high and medium stringency interactions, respectively. It would be important to see overall statistics of the predicted kinase-substrate networks of each method, for example, the number of kinases covered, distribution and average number of predicted substrates per kinase, distribution and average number of kinases targeting each phosphorylation-site.4. Some sections of the manuscript, for example section 2.3 and 2.4, have statements that are not supported with links to either figures, tables or supplementary material. I would recommend the authors to expand the number of figure panels accordingly to provide better support to the text. Also, data not shown statements are problematic to assess and reproduce. I would suggest either to provide the data as supplementary information or remove this statement.5. Improved kinase-substrate networks are fundamental for methods that aim to estimate kinase activity profiles based on their substrates phosphorylation status. It would be interesting to see if LinkPhinder predicted network improves kinase activity predictions. An existing benchmark data-set (PMID:28200105) of quantitative phosphoproteomics measurements upon kinase inhibition could be used to test this.Minor revisions1. Is the predictive power of LinkPhinder networks significantly different between each run? How would LinkPhinder benchmark against other methods if instead of 100 runs of the experiment only one was performed?2. Table 1 could be complemented with plots of the AU-PR and AU-ROC as supplementary figures.3. Typo in “equal” in Table 1 legend.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see  29 Apr 2020Attachmentreview_response_WK-updated.docxSubmitted filename: Click here for additional data file. 27 Jun 2020Dear Dr Novacek,Thank you very much for submitting your manuscript "Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Anand R. AsthagiriAssociate EditorPLOS Computational BiologyDouglas LauffenburgerDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have substantially addressed my comments and I believe this method is of overall interest for the community, although I still have some significant concerns. I found it hard to navigate through the changes because point-by-point replies are mostly pointers to the manuscript and the generated PDF seems to have the manuscript included three times, with revised and unrevised versions, hence the total pages of 258.Despite the fact that LinkPhinder outperforms all other methods, the poor results with the independent data-set, with AUPR and AUROC < 0.54 for all methods (Table 3), raises some substantial concerns regarding the performance of LinkPhinder in the internal validation (Table 1). It is important to understand if this is (i) an inherent problem of the validation data-set; (ii) potential overfitting of LinkPhinder to the test data-set; or (iii) methodological problem of the validation, e.g. data leakage from train to test.Can the authors comment on the fact that PhosphoSitePlus is both used to train LinkPhinder and to generate the true positive sets for the AUPR and AUROC curves. This could lead to data leakage between train and test, i.e. training data is also used to test. Could the authors generate random sets of positive/negative interactions that are kept from LinkPhinder training and use them only for validation and comparison with other methods? In addition, please report the total number of positive and negative statements , add details to the methods on how these are generated, and plot the AUPR and AUROC curves with the mean curve obtained for each model.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. 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A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Anand R. AsthagiriAssociate EditorPLOS Computational BiologyDouglas LauffenburgerDeputy EditorPLOS Computational Biology*********************************************************** 23 Nov 2020PCOMPBIOL-D-19-02065R2 Accurate Prediction of Kinase-Substrate Networks Using Knowledge GraphsDear Dr Novacek,I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. 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Translation of mitochondrial messenger RNA (mt-mRNA) is performed by distinct mitoribosomes comprising at least 36 mitochondria-specific proteins. How these mitoribosomal proteins assist in the binding of mt-mRNA and to what extent they are involved in the translocation of transfer RNA (mt-tRNA) is unclear. To visualize the process of translation in human mitochondria, we report ~3.0 Å resolution structure of the human mitoribosome, including the L7/L12 stalk, and eight structures of its functional complexes with mt-mRNA, mt-tRNAs, recycling factor and additional trans factors. The study reveals a transacting protein module LRPPRC-SLIRP that delivers mt-mRNA to the mitoribosomal small subunit through a dedicated platform formed by the mitochondria-specific protein mS39. Mitoribosomal proteins of the large subunit mL40, mL48, and mL64 coordinate translocation of mt-tRNA. The comparison between those structures shows dynamic interactions between the mitoribosome and its ligands, suggesting a sequential mechanism of conformational changes. Translation in humans takes place in the cytosol and mitochondria. Mitochondrial translation is responsible for the maintenance of the cellular energetic balance through synthesis of proteins involved in oxidative phosphorylation. This is required for adenosine triphosphate (ATP) production and the folding of the cristae. Therefore, impaired mitochondrial translation results in severe combined respiratory chain dysfunction leading to diminished ATP production and consequent cellular energy deficit. This condition is pathogenic in humans, causing myopathies and neurodegenerative diseases . The defTranslation in mitochondria is carried out by specialized mitoribosomes that are structurally distinct with 2-fold reduced mt-rRNA and ~1 MDa increased protein mass. Specific autosomal recessive mutations associated with these extra proteins cause severe, infantile onset disease with growth retardation, neurological phenotypes and cardiac involvement, and are associated with Perrault syndrome . AnalysiOn the mechanistic level, upon transcription and maturation the flow of genetic information in mitochondria requires mt-mRNAs coding for 13 proteins to be delivered to the mitoribosome. Human mt-mRNAs have unusual features, and they lack conventional 5’ and 3’ untranslated regions, Shine-Delgarno sequences and 5’ 7-methylguanosine caps. Consequently, specific RNA-binding proteins were identified that complement the unusual features of mt-mRNAs . HoweverDuring translation on the mitoribosome, mt-tRNAs use anticodons to decode the mt-mRNA, and acceptor terminus (CCA 3') that is esterified to a cognate amino acid. Conventionally, the structural recognition between a tRNA and a ribosome is based on the canonical L-shape of tRNAs, where D- and T-loops interact with each other, giving rise to the elbow-like structure. This is followed by the transit of tRNA through the three distinct sites on the mitoribosome: aminoacyl (A-site), peptide (P-site), and exit (E-site). However, in human mitochondria, the genetic arrangement and the structures of human mt-tRNAs are remarkably diverse, including non-canonical and truncated species with reduced D- and/or T-loops . How theProgress has been made in obtaining structural data on the mitoribosomes from porcine tissues , HEK293STo provide insight into the cooperation between the mitochondria-specific features, newly identified components and mitoribosomal ligands, as well as to reveal how the protein synthesis function in human mitochondria is modulated by mitoribosomal proteins, we determined eight cryo-EM structures of human mitoribosomes in complex with mt-mRNA, mt-tRNAs and additional factors in different states. These structures report on sequential steps for mt-mRNA binding, mt-tRNA movement, and identify key components for translation in human mitochondria.We reasoned that a suitable source for structural studies would be mitochondria from dividing cells grown in nutrient-rich expression medium requiring active translation. Mitoribosomal complexes were stalled with antibiotics quinupristin and dalfopristin that bind to the exit tunnel and peptidyl-transferase center, respectively . Cryo-EMSix copies of bL12m N-terminal domain, including loops, are resolved in this reconstruction, as well as the improved model for mL54 was assigned on the basis of the new densities . In addiSince weak but distinct densities in the tRNA binding sites were observed in the consensus map, we performed 3D classification employing signal subtraction  on thoseIn a subset of particles, a density was found in the A-site with a recognizable three-helix bundle corresponding to domain I of the native mitochondrial ribosome recycling factor (mt-RRF) . The mitVal (CP-tRNA), as well as shared with bL31m and interactions with mitochondria-specific protein mL46  approaches the P-site tRNA anticodon stem-loop . This reIn the E-site, the mt-tRNA elbow is stabilized by the C-terminal helix of mL64, extending from the central protuberance, which was not previously resolved. E-tRNA is further stabilized by uS7m with its C-terminal helix aligned with the major groove of E-tRNA anticodon stem. The residues Lys221 and Lys228 form potential interactions with E-tRNA anti-codon stem backbone phosphates , Video 1The mitoribosomes bound to A- and P-tRNA also contain additional density associated with the anticodon stem of A-tRNA, stretching from residue 40 down to the backbone phosphate of the last residue of mRNA codon in the A-site. The shape of the elongated 15 Å long density matches structures of polyamines, which are known to reside in mitochondria . PolyamiWe next examined which conformational changes of the mitoribosome occur by comparing individual structures. We monitor those changes from the perspective of the tRNA moving in a step-wise manner through the mitoribosome from A- to E-site over a distance of ~50 Å .The binding of mt-tRNA to the A-site is held via interactions of the mt-tRNA elbow with the mitochondria-specific protein mL40. Upon the displacement of the acceptor arm from the A-site to A/P transition state, the mitoribosomal small subunit ratchets 6.5° and the head swivels. The following conformational changes, which are related to the formation of the hybrid state, allow accommodation of a rotated mt-tRNA. They involve primarily the N-terminal helix of mL40 that shifts 5 Å towards the P-site. Therefore, the protein element maintains the contact with mt-tRNA from A-site to A/P state, contributing to the stability of the transition state . Upon thThe movement of P-tRNA to the P/E state upon ribosome ratcheting is facilitated by the exchange of protein contacts from mL40 to mL64 . TherefoNext, P/E to E-site movement is facilitated by the rotation-associated 6 Å conformational change of the mL64 C-terminal helix , Video 1In regard to the L1 stalk, in the density maps we observe its binding to the elbow of the deacetylated tRNA, as the mitoribosome adopts the non-ratcheted state (from P/E to E-site) . This mo2, representing by far the largest protein-protein interface on the mitoribosome. The upper globular domains of the new density and mS39 are separated from each other forming an overall heterodimer-like arrangement with two parted volute architectures pointing away from each other. While the mS39 volute is docked to the head of the small subunit, the new density counterpart extends away from the mitoribosome. Apart from mS39, no other direct interaction between the newly identified density and the mitoribosome is observed in our map. An in situ electron cryo-tomography study suggested presence of a moiety in a similar position  protein mS39 to the decoding center. Comparison with the structures of the mitoribosome without P-site tRNA suggests that this density represents mt-mRNA . Furtherposition , confirmposition .To identify the additional factor from the cryo-EM map, we performed focused 3D-refinement in attempt to improve the local resolution in this peripheral region. However, due to the intrinsically dynamic arrangement of the outer bound protein moiety, it was not possible to resolve the secondary structures by cryo-EM. Therefore, we analyzed the highly purified native sample by mass spectrometry. Consistently with the size of the density, the analysis suggested leucine-rich PPR motif-containing protein (LRPPRC) . LRPPRC Additional studies also showed that LRPPRC forms a stable complex with an 11 kDa protein named Stem-Loop Interacting RNA binding Protein (SLIRP) . In vivotrans-acting factors. The structural analysis of translating complexes provided an improved model for the human mitoribosome, including the L7/L12 stalk, and revealed new insights into the functionally important regions involved in mt-tRNA translocation and mt-mRNA binding. Although less stable and short-lived intermediates of partially rotated states that involve minor conformational changes are likely to have escaped detection in our study, we identified populations corresponding to stable classical and hybrid states covering the entire span of mt-tRNA movement on the mitoribosome. The comparison between the states allowed describing a sequence of concerted steps in the mitochondrial translation that have not been previously reported.In this study, the preparation of mitoribosomes from HEK cells led to the isolation of functional complexes with associated The structural analysis revealed how the diverged mt-tRNAs fit in the protein-rich interface of the two mitoribosomal subunits, and how its trajectory is correlated with mitochondria-specific protein elements. Upon binding to the A-site and the recognition of the anticodon stem-loop, the tRNA elbow is stabilized by the mL40 N-terminal helix, while the mitochondria-specific extension of uL11m restricts a potential backward movement of the elbow. N-terminal helix of mL40 through electrostatic interactions further regulates the subsequent transition to the P-site. In the P-site, the tRNA acceptor arm is primarily in contact with the uL27m N-terminal loop. From the P-site upon peptidyl transfer, the tRNA elbow is handed over to the mL64 C-terminal helix and the deacylated tRNA is moved to the E-site, involving mL64 conformational change. The ratcheting and L1 stalk then facilitate the ejection of tRNA from the E-site.Through the different stages of mt-tRNA movement, mL40, mL48 and mL64 undergo specific conformational changes, which support the progress of mt-tRNA from A- to E-site. In the absence of mt-tRNAs, these protein elements involved in translocation are disordered or structurally heterogenous. Therefore, at all three sites protein elements unique to the mitoribosome play roles in the tRNA recognition, particularly the elbow region. The multiple recognition events relying on the tRNA elbow and its specific coordination are likely co-evolved with the structural properties of human mt-tRNA, and they are different from fungi  and alvetrans-acting LRPPRC-SLIRP module  were grown in Freestyle 293 Expression Medium containing 5% tetracycline-free FBS in vented shaking flasks at 37°C, 5% CO2 and 120 rpm. The identity has been authenticated using STR profiling. The cell lines tested negative for mycoplasma contamination. Culture was scaled up sequentially, by inoculating at 1.5 × 106 cells/mL and subsequently splitting at a cell density of 3.0 × 106 cells/mL up to a final volume of 2 L of cell culture. The cells were harvested from the 2 L culture when the cell density was 4.2 × 106 cells/mL by centrifugation at 1000 g for 7 min, 4°C  and allowed to swell in the buffer for 15 min in the cold room by gentle stirring. About 45 mL of SM4 buffer  was added to the cells in being stirred in MIB buffer and poured into a N2 cavitation device kept on ice. The cells were subjected to a pressure of 500 psi for 20 min before releasing the nitrogen from the chamber and collecting the lysate. The lysate was clarified by centrifugation at 800 g and 4°C, for 15 min, to separate the cell debris and nuclei. The supernatant was passed through a cheesecloth into a beaker kept on ice. The pellet was resuspended in half the previous volume of MIBSM buffer (three volumes MIB buffer + 1 vol SM4 buffer) and homogenized with a Teflon/glass Dounce homogenizer. After clarification as described before, the resulting lysate was pooled with the previous batch of the lysate and subjected to centrifugation at 1,000 g, 4°C for 15 min to ensure complete removal of cell debris. The clarified and filtered supernatant was centrifuged at 10,000 g and 4°C for 15 min to pellet crude mitochondria. Crude mitochondria were resuspended in 10 mL MIBSM buffer and treated with 200 Units of RNase free DNase for 20 min in the cold room to remove contaminating genomic DNA. Crude mitochondria were again recovered by centrifugation at 10,000 g, 4°C for 15 min and gently resuspended in 2 mL SEM buffer . Resuspended mitochondria were subjected to a sucrose density step-gradient  centrifugation in a Beckmann Coulter SW40 rotor at 28,000 rpm for 60 min. Mitochondria seen as a brown band at the interface of 32% and 60% sucrose layers was collected and snap-frozen using liquid nitrogen and transferred to −80°C.HEK293S-derived cells  was added to mitochondria and the tube was inverted several times to ensure mixing. A small Teflon/glass Dounce homogenizer was used to homogenize mitochondria for efficient lysis After incubation on ice for 5–10 min, the lysate was clarified by centrifugation at 30,000 g for 20 min, 4°C. The clarified lysate was carefully collected. Centrifugation was repeated to ensure complete clarification. A volume of 1 mL of the mitochondrial lysate was applied on top of 1 M sucrose in a ratio of 2.5:1. Centrifugation was carried out at 73,000 rpm for 45 min in a TLA120.2 rotor at 4°C. The pellets thus obtained were washed and sequentially resuspended in a total volume of 100 µl resuspension buffer . The sample was clarified twice by centrifugation at 18,000 g for 10 min at 4°C. The sample was applied on to a linear 15–30% sucrose  gradient and centrifuged in a TLS55 rotor at 50,000 rpm for 120 min at 4°C. The gradient was fractionated into 50 μL volume aliquots. The absorption for each aliquot at 260 nm was measured and fractions corresponding to the monosome peak were collected. The pooled fractions were subjected to buffer exchange with the resuspension buffer to dilute away sucrose, and incubated with a bacterial antibiotic quinupristin/dalfopristin  at 2.2 mM for 30 min.The samples for mass spectrometry analysis were prepared to identify the non-ribosomal proteins associated with the translating mitoribosome. The complex was purified exactly as for the cryo-EM analysis. The gradient was fractionated, and the monosome peak was collected. The pooled fractions were subjected to buffer exchange with the resuspension buffer to dilute away sucrose. The purified solution was submitted for mass spectrometry analysis. The data were visualized and analyzed using Scaffold_4.7.5. The proteins identified by mass spectrometry are given in 2 fractionated over 20 frames. A defocus range of 0.8 to 2.8 μm was used. Detailed parameters are listed in For cryo-EM analysis, 3 μL of ~120 nM mitoribosomes was applied onto a glow-discharged (20 mA for 30 s) holey-carbon grid  coated with continuous carbon (of ~3 nm thickness) and incubated for 30 s in a controlled environment of 100% humidity and 4°C temperature. The grids were blotted for 3 s, followed by plunge-freezing in liquid ethane, using a Vitrobot MKIV (FEI/Thermofischer). The data were collected on FEI Titan Krios (FEI/Thermofischer) transmission electron microscope operated at 300 keV, using C2 aperture of 70 μm; slit width of 15 eV on a GIF quantum energy filter (Gatan). A K2 Summit detector (Gatan) was used at a pixel size of 1.05 Å  with a dose of ~30 electrons/ÅBeam-induced motion correction and per-frame B-factor weighting were performed for all movies using MotionCorr2 . Motion-Using the consensus 3.1 Å refined map as reference, the corresponding aligned particles were subjected to 3D classification with fine angle searches which gave distinct unratcheted  and ratcheted  states. Partial signal subtraction was carried out using a mask covering the tRNA binding sites. Signal subtracted particles were 3D classified using a mask around A+P-sites. These sites were chosen as they displayed nebulous density, consistent with mixed occupancy. This yielded one class with E-site only occupied  and three additional classes, class one showing a weak RRF-like density; class two with A-, P-sites occupied and a partial density for E-site tRNA; and class3 with partial densities in P- and E-sites but lacking any density in the A-site. The masked 3D classification was extended to the RRF-like density for class one particles. This gave two sub-classes, based on presence  or absence of RRF-like density. The sub-class lacking RRF-like density was further classified with a mask covering A- and P-sites to yield two final classes containing: A/P- and P/E state (4906 particles); and P/E state alone . Class 2 and class three particles were subjected to 3D-classification using a mask around A- and P-sites, yielding four final classes: A-, P- and E-sites occupied ; A- and P-sites occupied ; P- and E-sites occupied  and P-site only occupied (6692 particles) respectively . ClassesHuman mitoribosome structure with PDBID 3J9M  was usedVisualization and analysis of the models and maps was carried out using UCSF Chimera  or PyMOL In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Aibara et al. illuminate how gene expression within the mitochondrial matrix is governed by the human mitochondrial ribosome. Through analysis of eight new structures of different compositional and conformation mito-ribosome states, their manuscript teaches us how mito-specific ribosomal proteins guide tRNA movements through the mito-ribosome. Their structures also suggest how a novel complex, a heterodimer of the proteins LRPPRC-SLIRP, engages the mRNA entry site in the small ribosomal subunit to facilitate translation, providing a potential explanation for how mutations in LRPPRC causes Leigh syndrome.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Cynthia Wolberger as the Senior Editor. Three of the reviewers have opted to remain anonymous.Thank you for submitting your article "Structural basis of mitochondrial translation" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:Aibara et al. report improved resolution updates of the human mitoribosome structure with matching insights into aspects of translation within the mitochondrial matrix. Their cryo-EM reconstructions of mito-ribosomes purified in the presence of antibiotics revealed eight different compositional or conformational states. Analysis of these states suggests new roles for a subset of the 36 mito-specific ribosome proteins in the mechanics of translation-including guiding mt-tRNA movements through the ribosome and the docking of leaderless mt-mRNA to the small subunit. Of particular note, the authors discovered a new density bound to the mitochondria-specific protein mS39 that correlated with well-resolved mRNA density. Mass spectrometry of the purified sample used for cryoEM suggests this density is attributable to a heterodimer of LRPPRC-SLIRP. While the cryo-EM density for this complex is too blurry to interpret, the notion that these proteins "hand-off" messages to the small mito-ribosomal subunit is attractive and consistent with prior data showing that these two proteins play roles in transcription-translation coordination and that mutations in LRPPRC are associated with Leigh syndrome. Aibara also present analyses of proteins mL40, mL48, and mL64-plus a density that may correspond with polyamines like spermine-during the translocation of mt-tRNAs. Finally, the authors provide improved models of other functional regions, such as the L7/L12 stalk. Overall, this manuscript is an exciting advance in understanding the structural basis of translation by the highly divergent mitoribosome and tRNAs-but specific outstanding issues should be addressed before acceptance.Essential revisions:1) The authors write, "the structures of human mt-tRNAs are remarkably diverse, including non-canonical and truncated species with reduced D- and/or T-loops." From the structural data presented here, though, which must be an average of many different tRNA species, it appears the authors modeled tRNA using generic structures that are indistinguishable, at this resolution, from elbow-shaped cytosolic tRNAs. Please described whether and how the interactions seen between tRNA and mL40 or mL48 depend on structural properties of mito-tRNAs. If so, please support this claim by showing the cryo-EM maps used to support the modeling of the tRNA "elbow" regions and contacts with mL40 and mL64.2) The interpretation of the density between the A-site and P-site tRNAs as a polyamine is reasonable but speculative-it could be protein. The authors acknowledge this in the Results, yet reach too far by highlighting polyamines in the Abstract and Discussion. Was the putative spermine density found bound in a position similar to the first crystal structure of tRNA-Phe? Is the alleged polyamine density present in all the maps or only those containing tRNAs in the P and A sites? Please discuss and mention which other polyamines are present in mitochondria that could, in principle, correspond with the density attributed to spermine here.3) Given the poor resolution of the mS39-adjacent density , the authors should soften their claim to have unambiguously "identified" this as LRPPRC-SLIRP. Based on the mass spectrometry and EM data, this is certainly a reasonable hypothesis worthy of discussion. Still, the authors are encouraged to be more cautious in their interpretation of the density, and the figures and text modified accordingly. Essential revisions:1) The authors write, "the structures of human mt-tRNAs are remarkably diverse, including non-canonical and truncated species with reduced D- and/or T-loops." From the structural data presented here, though, which must be an average of many different tRNA species, it appears the authors modeled tRNA using generic structures that are indistinguishable, at this resolution, from elbow-shaped cytosolic tRNAs. Please described whether and how the interactions seen between tRNA and mL40 or mL48 depend on structural properties of mito-tRNAs. If so, please support this claim by showing the cryo-EM maps used to support the modeling of the tRNA "elbow" regions and contacts with mL40 and mL64.As requested, we have added this information in Figure 3—figure supplement 3 showing the experimental cryo-EM density for the protein elements involved in tRNA interactions. We have also included a schematic indicating the residues involved in the binding of A-, P- and E-bound tRNAs. This representation clearly shows that the interactions are concentrated at the elbow regions for A- and P-site, and the elbow remains stabilized by the mitochondria-specific protein elements throughout the moving, consistent with the text. Since the density for tRNA is produced from an average of many different tRNA identities, we refrain from commenting on the exact tRNA nucleotide involved in the interaction and limit the interpretation to sections , as the reviewers comment correctly.2) The interpretation of the density between the A-site and P-site tRNAs as a polyamine is reasonable but speculative-it could be protein. The authors acknowledge this in the Results, yet reach too far by highlighting polyamines in the Abstract and Discussion. Was the putative spermine density found bound in a position similar to the first crystal structure of tRNA-Phe? Is the alleged polyamine density present in all the maps or only those containing tRNAs in the P and A sites? Please discuss and mention which other polyamines are present in mitochondria that could, in principle, correspond with the density attributed to spermine here.As requested, we have removed the too far reaching section from the Discussion. Since the density present only in the maps containing tRNAs in the P and A sites, it would be too speculative to develop further discussion, and our structural data is poor in this region and does not provide any functional insight with to respect the mechanism. Therefore, this part of the manuscript has been toned down, and the following changes were made:Abstract: deleted “polyamine”;Replaced “we modeled it as spermine and refined the structures” with “it could be modeled it as spermine”;Removed the spermine from the coordinates;Removed the model for spermine from Figure 3—figure supplement 2 and corrected the legend accordingly.3) Given the poor resolution of the mS39-adjacent density , the authors should soften their claim to have unambiguously "identified" this as LRPPRC-SLIRP. Based on the mass spectrometry and EM data, this is certainly a reasonable hypothesis worthy of discussion. Still, the authors are encouraged to be more cautious in their interpretation of the density, and the figures and text modified accordingly.As requested, we revised the relevant sections.Deleted “to identify this component of the mitoribosome:mRNA:tRNA-associated complex”.Results: replaced “revealed” with “suggested”. and deleted “a single possibility”.Figure 3—figure supplement 5: added “density”.


There are well-known simple perovskites with ferroelectric properties, while there is still no report of ferroelectricity due to symmetry breaking transition in A-site-ordered quadruple perovskites. Here we report the high pressure synthesis of an A-site-ordered perovskite PbHg3Ti4O12, the only known quadruple perovskite that transforms from high-temperature centrosymmetric paraelectric phase to low-temperature non-centrosymmetric ferroelectric phase. The coordination chemistry of Hg2+ is changed from square planar as in typical A-site-ordered quadruple perovskite to a rare stereo type with 8 ligands in PbHg3Ti4O12. Thus PbHg3Ti4O12 appears to be a combinatory link from simple ABO3 perovskites to A-site-ordered AA′3Ti4O12 perovskites, sharing both displacive ferroelectricity with former and structure coordination with latter. This is the only example so far showing ferroelectricity due to symmetry breaking phase transition in AA′3B4O12-type A-site-ordered perovskites, and opens a direction to search for ferroelectric materials.The simple ABO There are few reports of ferroelectricity due to symmetry breaking transition in A-site-ordered quadruple perovskites. Here, the authors find one with phase transition from a high-temperature centrosymmetric paraelectric phase to a low-temperature non-centrosymmetric ferroelectric phase in a high pressure synthesized compound. There is a special class of perovskite-type materials with the general chemical formula AA′3B4O12, named A-site-ordered, or quadruple perovskites, which received much attention owing to their fascinating structural and wide varieties of physical properties10, including colossal magnetoresistance under weak field11, charge disproportionation12 and giant dielectric constant over a wide temperature range14. For the simple ABO3 perovskite, the 12-fold coordinated A-site is often occupied by large size ions such as alkali metal, alkaline earth or lanthanide cations and the 6-fold coordinated B-site is often occupied by transition metals (TMs) to satisfy the so called tolerance factor t = (rA + rO)/√2(rB + rO)  with t usually in the range of 0.75 to 1.05 for stable perovskite compounds18. However in the A-site-ordered perovskite AA′3B4O12, three quarters of the A-site is substituted by a TM, A′ with much smaller ionic radius19. Generally, TM ions with strong Jahn-Teller distortions like Mn3+ and Cu2+ preferentially occupy the A′-site with square-planar coordination24. The small TM ion at the A′-site causes the distortion of BO6 octahedron in AA′3B4O12 perovskite, usually resulting in a cubic crystal structure with space group Im-325. From a structural point of view, the flexibility for tilting the BO6 octahedra in AA′3B4O12 perovskite is limited in order to maintain the square-planar coordination at the originally 12-fold coordinated A-site in the simple perovskite structure21.Perovskites and their derivatives show many interesting physical, chemical and mineral properties such as ferromagnetism, ferroelectricity, piezoelectricity, ion conductivity, photocatalysis and superconductivity3, PbTiO3, PbO3, BiFeO330. The origin of ferroelectricity is due to either the lone pair 6s2 effect of cations , or the second-order Jahn-Teller active cations with electron configuration of d0 ; these compounds are usually referred to as displacive-type ferroelectrics. However, ferroelectricity is seldom observed in AA′3B4O12 type A-site-ordered compounds, even when they contain second-order Jahn-Teller active cations or lone pair effect cations28. One reason for the absence of ferroelectricity in this series of compounds is the usual presence of a centrosymmetric structure, and the variation of temperature generally causes isostructural or centrosymmetric - to - centrosymmetric phase transition25, hence there cannot be spontaneous polarization. Currently the rare examples in this series that show ferroelectricity are AMn7O12 33 and AMn3Cr4O12 (A = La or Bi)22. Although these compounds have centrosymmetric structures, all are spin-driven multiferroic systems, due to strong magnetoelectric coupling effects32. Up to now, there is still no report of ferroelectricity from symmetry breaking transition in A-site-ordered perovskite compounds.Ferroelectricity is one of the most important properties for application in actuators, sensors, and memory storage devices, etc. Large numbers of ferroelectric materials possess simple perovskite structures, such as BaTiO3B4O12 type A-site-ordered perovskite oxide, PbHg3Ti4O12 (henceforth PHTO) was designed and synthesized at high pressure and high temperature conditions. It is found that the Hg2+ ion occupies the A′-site in AA′3B4O12 type perovskite in sharp contrast to previous reports where 3d TM ions  usually locate at the A′-site. Moreover, ferroelectricity was observed in PHTO, when its ambient centrosymmetric phase transformed to a non-centrosymmetric phase at 250 K. The origins of ferroelectricity are discussed based on the comprehensive characterizations of crystal structure using both neutron and synchrotron x-ray diffractions and density functional theory (DFT) calculation.In this work, an AA′3B4O12 type A-site-ordered perovskite. The refined structure parameters of PHTO based on NPD data collected at 295 K are listed in Table 2+Hg2+3Ti4+4O12 formula and space group Im-3 as shown in Fig. 3 or HgTiO3 was found in either NPD or SXRD patterns.The NPD refinements are shown in Fig. 2+ and Mn3+, are preferentially accommodated into the square-coordinated A′-site in the A-site-ordered perovskites, as in LaCu3Fe4O12 and BiMn3Cr4O1221. Some other TM ions have been recently introduced at the A′-site such as in CaCo3V4O1234, CaFe3Ti4O1235, CaPd3Ti4O1236. Thus PHTO is an AA′3B4O12 type perovskites with the A′-site fully occupied by Hg2+, other than 3d TM, or Pd as shown in Table 3B4O12 type perovskites (7.3–7.5 Å), while the ∠Ti–O–Ti angle (155.8°) of PHTO is less distorted from the ideal 180° than those for other AA′3B4O12 type perovskites (∠B–O–B ≈ 140°). The larger ∠Ti–O–Ti angle and correspondingly smaller ∠A′−O−Ti angle in PHTO imply the less tilting of TiO6 octahedron, which is attributed to the large ionic size of Hg2+ about 0.96 Å. Figure t versus average ionic radius of A-site ions for simple perovskites ATiO3 and A-site-ordered perovskites AA′3Ti4O12 where the t factor is calculated assuming a simple ABO3 form with the average ionic size at A site. It is found that from CaTiO3 to BaTiO3, the ionic radius of A-site increases from 1.34 Å to 1.61 Å while the tolerance factor t increases from 0.97 to 1.07. The simple perovskites ATiO3 are located at the upper right panel of Fig. 3Ti4O12 compounds, the average ionic radius of A-site is about 0.8 Å and the tolerance factor t is around 0.77. Therefore, AA′3Ti4O12 perovskites are located at the lower left panel of the Fig. 3 and AA′3Ti4O12 compounds, the average ionic radius of A-site ion for PHTO is large, about 1.1 Å and the tolerance factor t is about 0.88. Moreover as aforementioned, the coordination number for Hg ions at the A′-site in PHTO is eight, not square planar as in A′-TM quadruple perovskites AA′3Ti4O12, and it is located in the middle area of Fig. 3 (coordination number 12) and AA′3Ti4O12 quadruple perovskites (coordination number 4). Thus, PHTO appears to be a link from simple ATiO3 perovskite to AA′3Ti4O12 type A-site-ordered perovskite  is 8, which is just in between that for simple ATiO3 and AA'3Ti4O12. This result provides further evidence supporting the scenario aforementioned. Considering that the ionic size of Sr2+ (1.45 Å) is similar to that Pb2+ (1.49 Å), we also fabricated the A-site-ordered perovskite compound SrHg3Ti4O12, which is isostructural with PHTO, but without the observation of ferroelectricity. The result about SrHg3Ti4O12 will be published elsewhere. According to  the previous report37, the low energy level of d10 orbital prefers a relatively high coordination number owing to the weak d-s and d-p hybridization in perovskite compounds. As to PHTO system, the first principles calculation result indicates that the energy level of d10 for Hg2+ has a lower energy for 8 coordination than that for 4 coordination  Å in PHTO and 1.951(7) Å in SrTiO345, which suggests a weaker crystal field effect in PHTO. Figure L3 XAS spectra of PHTO together with PbTiO3 as a Pb2+ and PbNiO3 as Pb4+ reference. The weak pre-edge feature in PbNiO3 due to excitation from a 2p core electron to the 6s orbital. It has been established that this weak pre-edge feature is a sensitive finger-print of the Pb valence state in solid state materials46. The reason is that the Pb 5d10 orbitals are fully occupied, the valence state is then reflected by the 6s occupation. However, as shown in the inset, PHTO has the Pb-L3 XAS profile similar to the Pb2+ reference PbTiO3 in detail, no 2p–6s related excitation is observed in PHTO, which indicates the valence state of Pb2+ with fully occupied 6s2 state in this compound. Figure L3 XAS spectra of PHTO as well as HgO. The same energy position and very similar spectral profile of both PHTO and HgO indicate the Hg2+ valence state. The XAS results show that the electronic configuration of PHTO is Pb2+Hg2+3Ti4+4O12, which is fully consistent with the BVS results based on the crystal structure.Soft X-ray absorption spectroscopy at the χ is almost constant and negative near zero from 300 K to 30 K, while χ increases rapidly on further decrease of temperature as shown in Supplementary Fig. d0, 5d10 and 6s2 electron configuration for Ti4+, Hg2+ and Pb2+, respectively, and consistent with the BVS calculations and XAS results. The resistance of PHTO at room temperature is too large to be measured, indicating the high electrical insulating property and few vacancies in the sample. Supplementary Fig. H point shown in Supplementary Fig. The magnetic susceptibility εr of PHTO is over 220 in the measured temperature range. The temperature dependence of εr shows a clear peak around 250 K at all the frequencies of measurements. The overall temperature-dependent εr strongly indicates a low-temperature ferroelectric phase transition to a high-temperature paraelectric phase at TFE ≈ 250 K. Furthermore, the transition temperature is independent of measurement frequency, indicating that PHTO is not a relaxor-type ferroelectric material. The relative increase of the dielectric loss around 250 K is consistent with the paraelectric-ferroelectric transition. Moreover the dielectric loss keeps at a very low value (<0.03) with all of frequency in the whole measurement range, which indicate the intrinsic dielectric property of PHTO. In order to further identify the ferroelectricity of PHTO, the isothermal polarizations were measured by the PUND method20. No loops of polarization versus electric field (P–E) were observed in polycrystalline PHTO at room temperature. However, canonical P–E hysteresis loops were observed below 250 K as shown in Fig. P–E loops significantly expand with increasing electric field. For example, the P value increases from 0.028 to 0.11 µC cm−2 as the maximum electric field changes from 17 to 32 kV cm−1 at 10 K. As known, PbTiO3 is a classic and extensively studied ferroelectric material with Curie temperature about 763 K, which excludes the possibility that the ferroelectricity of PHTO originated from PbTiO3. Thus it can be concluded that the ferroelectricity is due to PHTO.The permittivity and dielectric loss measured with different frequencies from 2 K to 300 K are shown in Fig. Imm2 (No.44). The inset shows the crystal structure of the low temperature phase. The Rietveld refinement results are listed in Supplementary Table a decrease lineally from 300 K to 250 K. Below the temperature of the phase transition, the lattice parameter b and c continue to decrease while parameter a increases. Figure 6 octahedron before and after phase transition. In the high temperature phase, TiO6 forms a regular octahedron with ∠Ti–O–Ti angle of 155.8° and Ti-O bond length of 1.974 Å respectively. When this cubic phase transformed to an orthorhombic non-centrosymmetric phase below 250 K, both ∠Ti–O–Ti angles and Ti-O bond lengths divergences occurred in TiO6 octahedron. These distortions originate from the relative displacements of Ti and O around all the axis directions. The polarization in the ab-plane was canceled due to the random relative displacements of Ti and O, leaving the polarization vector only along the [001] direction. As to HgO8 polyhedron, the single type HgO8 polyhedron with only two-types of Hg-O bond length in the high temperature phase evolves into three different type of HgO8 polyhedron with different bond length in the low temperature phase, but without contribution to the polarization. Supplementary Fig. 8 during the paraelectric-ferroelectric transition. Therefore, PHTO is a displacive-type ferroelectric. The ionic spontaneous polarization can be calculated by multiplying the effective charges and the distance between the negative and positive valence weighted mean center along the c-axis then divided by the volume of the unit cell47. Based on the refined results of NPD data collected at 5 K in Table −2. Although temperature-dependent phase transition is typical in A-site-ordered perovskites, it is usually isostructural or centrosymmetric - to - centrosymmetric phase transition from Im-3 to Pn-3. Most of those phase transitions are mainly induced by charge transfer between the A′-site and B-site ions or charge disproportionation at B-site ions25. PHTO is an example of AA′3B4O12 type A-site-ordered perovskite that exhibits a centrosymmetric to non-centrosymmetric phase transition.To further understand the mechanism of ferroelectricity from the structure point of view, we performed the temperature dependent SXRD, as shown in Supplementary Fig. Tu degenerate at Γ point. By moving atoms along these soft phonon modes, the non-centrosymmetric crystal structure with space group Imm2 can be obtained. Free energy per primitive cell versus moving amplitude of atoms along one of the soft modes is plotted in Fig. H point only consist of the Ti-O phonon modes, which are consist with  the experimental conclusion. In addition, the 6s2 lone pair electrons of Pb2+ also contribute to the site instability according to the theory analysis. However, the contribution from the lone pair mechanism does not play a dominant role from the energy perspective  were mixed homogenously in a molar ratio 1:3:4 and pressed into a pellet with diameter of 6 mm in an argon gas protected glove box with oxygen and H2O level of less than 1 ppm. The pre-pressed pellet was sealed in a gold capsule. High pressure experiments were performed with the cubic anvil type high pressure apparatus52. After the pressure was gradually increased to 6 GPa, the sample was heated to 1273 K and maintained for 30 min. Then the temperature was quenched to ambient before the release of pressure. In this process, yellow polycrystalline PHTO was obtained.The polycrystalline sample of PHTO was synthesized by a solid state reaction under high pressure of 6 GPa and high temperature of 1273 K. The starting materials HgO , PbO  and TiOθ range of 10°–160°. The sample was also measured by synchrotron x-ray diffraction (SXRD) at the 11-BM-B at the Advanced Photon Source (APS) in Argonne National Laboratory. The X-ray wavelength was 0.412726 Å. Diffraction data were collected in the angle (2θ) range from 0.5° to 50° with steps of 0.002°. The obtained NPD and SXRD data were analyzed by the Rietveld method with the GSAS program53.The crystal structure was characterized by neutron powder diffraction (NPD) with the BT-1 high-resolution neutron powder diffractometer with a Ge (311) monochromator at the Center for Neutron Research (NCNR) of the National Institute of Standard & Technology (NIST). The neutron wavelength was 2.0774 Å. The intensities were measured with steps of 0.05° in the 2L2,3 edge was measured with total the electron yield mode at the Dragon beamline while the hard XAS at the Pb-L3 and Hg-L3 edge were measured with transmission geometry at the BL07A beamline at National Synchrotron Radiation Research Center (NSRRC) of Taiwan. SrTiO3, PbTiO3, PbNiO3 and HgO were also measured at the respective Ti-L, Pb-L and Hg-L edges as reference materials.The valence states of the Ti, Pb and Hg ions were determined by x-ray absorption spectroscopy (XAS). The soft XAS at the Ti-20.The permittivity was measured with different frequency by an Agilent-4980A LCR meter on a solid pellet with 4.0 mm in diameter and 230 μm in thickness. The ferroelectric hysteresis loops were measured at 20 Hz with a Radiant Precision Premier-II Ferroelectric Test System at different temperatures based on the proposed positive-up negative-down (PUND) method. Detailed experimental descriptions can be found in Ref. Supplementary Information