Publicphosphorylationdatabases such as PhosphoSitePlus (PSP)and PeptideAtlas (PA) compile results from published papers or openlyavailable mass spectrometry (MS) data. However, there is no database-levelcontrol for false discovery of sites, likely leading to the overestimationof true phosphosites. By profiling the human phosphoproteome, we estimatethe false discovery rate (FDR) of phosphosites and predict a morerealistic count of true identifications. We rank sites into phosphorylationlikelihood sets and analyze them in terms of conservation across 100species, sequence properties, and functional annotations. We demonstratesignificant differences between the sets and develop a method forindependent phosphosite FDR estimation. Remarkably, we report estimatedFDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine(pThr), and phosphotyrosine (pTyr) sites, respectively, that are supportedby only a single piece of identification evidence—the majorityof sites in PSP. We estimate that around 62 000 Ser, 8000 Thr,and 12 000 Tyr phosphosites in the human proteome are likelyto be true, which is lower than most published estimates. Furthermore,our analysis estimates that 86 000 Ser, 50 000 Thr,and 26 000 Tyr phosphosites are likely false-positive identifications,highlighting the significant potential of false-positive data to bepresent in phosphorylation databases. The dominance of MS approaches has led tothe development of multiple strategies to both understand and helpmitigate the high levels of phosphopeptide false discovery rate (FDR),particularly in sets of mapped peptide spectral matches (PSMs) thatresult from LC-MS/MS and sequence database analysis.15 The goal of such approaches is to separate true identificationsfrom false ones. Even without considering noncanonical phosphorylation, many confidently identified phosphopeptidespossess multiple Ser, Thr, or Tyr residues that could be differentiallymodified in a given proteolytically generated peptide.9 Phosphosite occupancy is variable on any given proteinunder different biological conditions such that analysis of a peptidecontaining, for example, two Ser residues that have the potentialto be phosphorylated with different dynamics could present evidencefor either only one or both being modified, depending on the samplestudied.17 Many phosphorylation events are also substoichiometric,possibly falling below the limit of detection of certain analyses.16 As such, careful data handling and statistical processes shouldbe applied, either within the search engine used for peptide mappingor in a downstream software application to calculate additional statistics,such as a local false localization rate (FLR) or conversely the probabilitythat a given site within a peptide is correct or incorrect. Software/algorithmsinclude phosphoRS,18 Ascore,19 Andromeda’s PTM Score,20 and recently released PTMProphet.21 We have previously benchmarked the performance of some instrumentalparameters and software pipelines for phosphoproteomics,22 demonstrating that there is considerable variabilityin how such scores map to robust statistics, such as local or globalFLR, depending on the instrument fragmentation mode and resolution.Historically, the focused analysis of phosphorylation sites inproteins tended to rely on biochemical analysis including, for example,chromatography and solid-state Edman sequencing.23 There aresome caveats to this logic though, as it is possible for the samePSM to be wrongly assigned to a phosphopeptide multiple times. Thiscan occur if the correct interpretation for the spectrum had a verysimilar peptide sequence and identical mass to the wrongly assignedphosphopeptide.24 Although LC-MS/MS and computationalanalysis is generally recognized as very effective and reliable forphosphosite detection, from each study, it is likely that there issome element of remaining false discovery of peptides and sites wronglylocalized, depending on the applied statistical thresholds. This isparticularly problematic for studies that set relatively weak thresholdsfor phosphosite localization to maximize sensitivity—more true positives may be identified,but at the expense of very large numbers of false positives passingthe threshold. A multicenter benchmarking study highlighted some ofthe challenges in practice, showing considerable variability in thenumber of true-positive, false-positive, and false-negative sitesreported across different laboratories, with particular issues arisingwhen a peptide carried multiple phosphate groups.25 Methods and guidelines for FLR are still evolving and notconsistently applied in phosphoproteome studies, and so it is likelythat most published studies contain considerable numbers of falselylocalized phosphosites.29 This can lead to overestimation of the total number of known truehuman phosphosites if database providers do not control for FDR acrossmultiple datasets.30Following confident identification of phosphopeptides and localizationof given sites, data tend to be compiled from within a single studyor across multiple studies  to determine the extentof evidence for a given site from multiple PSMs. In general, wherethere are independent observations of PSMs supporting a phosphosite,it can be reasonably assumed that the evidence for a site to be realincreases, although to our knowledge, there are no current statisticalmodels to calculate this phenomenon accurately. Multiple PSMs canbe observed per identified phosphosite as a result of either differentpeptide sequences containing that site, or the same peptide sequencebeing detected several times.29 As of March 2020, PSP encompassed phosphosite evidenceacross 17 830 human protein sequences, which are defined ascanonical in UniProt . The evidencefor phosphorylation comes from manually curated reviews of literatureprimarily describing tandem MS studies and also low-throughput experiments,or from in-house MS studies.29 Interestingly,the majority of phosphosites in PSP only have a single piece of evidenceassociated with their identification . As mentioned in the PSP documentationitself, researchers should be cautious when accepting such sites astrue positives.29 It is possible that manyusers of PSP are not aware of the need for caution when reviewingor reusing data, and we are not familiar with any previous effortto assess phosphosite FDR within PSP. A second curated proteomicsresource is PeptideAtlas (PA)26 which isa repository of tandem MS datasets that have been processed throughTrans-Proteomic Pipeline to ensure high and consistent quality ofphosphopeptide identifications.32 The latestPA builds incorporate the use of the PTMProphet algorithm for phosphositelocalization where each potential phosphosite within an observed PSMis assigned a probability score between 0 and 1 of being phosphorylated.21 As with PSP, researchers should also be carefulwhen accepting sites in PA with only a single piece of identificationevidence  as positively identifiedphosphosites. Instead, phosphosites that not only have multiple PSMobservations in PA but also have high phosphorylation probabilityscores assigned within the majority of those PSMs are most likelyto be true-positive identifications. In addition to PSP and PA, otherdatabases containing data on human phosphosites include UniProt, whichcollates mostly manually curated phosphosites from the literaturebut is planned to start incorporating high-throughput derived datain later releases;31 dbPTM, a server importingdata from other resources, but currently unavailable as of July 2021;33 and PhosphoDB containing results from a setof studies on phosphopeptides derived from multiple proteases.34 Even with easy access to these accumulated phosphorylationsite resources, to our knowledge, no estimates have been made to predictthe scale of phosphosite FDR across large datasets.One such databaseis PhosphoSitePlus (PSP) which represents a comprehensive,manually curated, and well-cited resource containing experimentallydefined PTMs primarily focusing on phosphorylation.In thiswork, by profiling the reported human phosphoproteome,we aimed to estimate the false discovery rate of phosphosites withevidence in PSP and/or PA and use these estimates to predict the countof true phosphosites within the currently explored human phosphoproteome.We categorized the sites into sets of various predicted phosphorylationlikelihood based on the amount of positive identification evidencereported in PSP and PA, properties not readily available in otherdatabases. Using orthogonal features of phosphosites assigned to thesesets, such as evolutionary conservation, sequence properties, andfunctional annotations, we aimed to demonstrate significant differencesbetween the sets and develop an improved method for independent FDRestimation, which can be used to indicate the extent of true phosphositeswithin the human phosphoproteome.26 was filtered to only include human Ser/Thr/Tyrsites from canonical UniProt protein sequences with at least one PSMobservation (1 069 709 sites across 63 616 sequences)(Table S1). The sites were categorizedaccording to the number of PSM observations with a certain phosphorylationprobability score assigned by PTMProphet.21 The counts of observations with a probability of >0.95 were usedas “positive” evidence for site phosphorylation. Thecounts at a probability threshold of ≤0.19 were used as “negative”evidence in favor of a site being a nonphosphosite. The total numberof PSM observations per site was considered to distinguish sites forwhich ≥10% of all associated PSMs had a PTM probability >0.95,from sites where a small minority (<10%) of associated PSMs hadthis probability. Based on this, selected confidence categories wereapplied to predict site phosphorylation likelihood in PA . PhosphoSitePlus(PSP) data 29 was filtered to only include human Ser/Thr/Tyrsites from canonical protein sequences labeled by UniProt identifiers(231 607 sites across 17 830 sequences) (Table S2). The sites were ranked based on thenumber of times they have been characterized in low/high-throughputstudies. The sum of observations across all studies was used to predictsite phosphorylation likelihood in PSP .Phosphorylation data in PeptideAtlas (PA) (2020 build)31 which have phosphorylationevidence in PSP and PA, human reference proteome  and the proteomes of 100 eukaryotic species were downloaded from UniProt (UniProt release 2019_10). Each sequencein the human proteome was used as a query in a BLASTp search (BLAST+2.10.0 version)35 against all 100 eukaryoticproteomes. The BLAST output was processed to extract a top matchingsignificant orthologue from each species for each human target. Human targets were then alignedwith their matched orthologues using the MUSCLE algorithm (version3.8.31)36 with default settings if allsequences to be aligned were <2000 amino acids long. If any sequencesto be aligned (either the human sequence or any of the orthologuesequences) were ≥2000 amino acids long, two iterations of thealgorithm were run using settings for large alignments (-maxiters 2 option).36 Fromthe alignments, percentage conservation scores were calculated forevery Ser, Thr, and Tyr site within each human target out of 100  and out of the number of aligned orthologues.Conservation percentages were calculated considering any Ser/Thr substitutionsin orthologues, whereby an orthologue was included in the count if,for example, a Thr in its sequence was aligned with a Ser in the targethuman sequence and vice versa. Conservation data was then cross-referencedwith PSP/PA datasets to identify sites in the human proteome withphosphorylation evidence in PSP/PA and determine their conservation.To ensure consistency in terms of proteins and sites used, any humanprotein target for which it was not possible to calculate site conservationeither due to the protein having no matches in BLAST (14 proteins),no significant matches in BLAST (236 proteins), no Ser/Thr/Tyr sitesin its sequence (1 protein) or due to failed alignments (10 proteins),was excluded from any further analysis (Table S4). Any human targets labeled with the same UniProt identifierin the reference human proteome, PSP and PA, but which correspondedto different protein sequences across the datasets  were also excluded. Conservation wasassessed for the remaining targets (Table S5) by linear regression models with nonassumed intercept for a simplerinterpretation of slope between phosphosites and nonphosphosites.The average conservation of likely phosphosites (sites ranked “High” or “Medium”in PSP and/or PA) was plotted against the average conservation oflikely nonphosphosites (sites in “Not phosphorylated” and “Other” sets) withineach target protein that had at least three likely phosphosites andthree likely nonphosphosites. Conservation scores (%) were also comparedacross all sites within phosphorylation likelihood sets using boxplots.To determine thecross-species conservation of all Ser, Thr, and Tyr sites in the referencehuman proteome,Table S6) were processed to identify amino acids at the −1and +1 proximal positions adjacent to every Ser, Thr, and Tyr site.If a target sequence ended with a Ser, Thr, or Tyr site then its +1amino acid was marked as “Not found”.For each amino acid, its frequency at each proximal position was firstnormalized to 1000 and then to its frequency in the prefiltered humanreference proteome (expected distribution). Proximal amino acid frequenciesaround target Ser, Thr, and Tyr in “High in PSP andPA” set were compared to those in the “Not phosphorylated” set and to the expected aminoacid distribution. The comparisons were assessed by Fisher’sexact statistical test37 performed usingscipy module in Python38 with Bonferronicorrections to generate adjusted p values. For eachamino acid, any significant difference  between the compared sets was used toestimate phosphosite false discovery rate across all phosphorylationlikelihood sets. FDR estimates assumed that all sites in the highestphosphorylation likelihood set “High in PSP and PA” set were true-positive phosphosite identifications, whereasall sites with the weakest phosphorylation confidence (either the“Not phosphorylated” or the “Other” set) were nonphosphosites. Therefore, theobserved count of a certain proximal amino acid in the “High in PSP and PA” (nPos) corresponded to its expectedcount at 0% FDR, whereas its observed count in the “Not phosphorylated” or “Other” set (nNeg) corresponded to its expectedcount at 100% FDR. To estimate % FDR in any other phosphorylationlikelihood set based on the observed count of the compared proximalamino acid in that set (nObs), we used the followingequationTargetprotein sequences  is explainedby assumed false positives (nNeg) and what proportionby true positives (nPos). For example, if amino acidX was found at +1 position next to 500 Ser sites in the highest phosphorylationconfidence set  comparedto 10 Ser sites in the “Not phosphorylated” set , and nextto 350 sites in the set of interest (nObs = 350),then pSer FDR within the set of interest would be 31%. This wouldsuggest that 31% of sites in that set behave like false-positive pSerin terms of X amino acid frequency at +1 position, whereas 69% ofthose sites behave like sites in the highest phosphorylation likelihoodset (true pSer).Theequation has the effect of estimatingwhat proportion of the observed count  was calculatedper each likelihood set using all significantly enriched amino acidsaround a particular target phosphosite and which had an enrichmentof >1.5 relative to the expected distribution. Final FDR estimateswere used to derive the total number of true-positive (TP) phosphositeidentifications across phosphorylation likelihood sets.High” sets ineach database were presented as a weighted average between FDR estimatesin sites ranked “High” in that databaseonly and sites ranked “High” in bothPSP and PA. For example, the FDR in “High in PA” set was a weighted average of FDR estimates in “High in both” set and “High in PAonly” set.To compareFDR/TP estimates between individual PSP and PA sets,the method was replicated using alternative phosphorylation likelihoodsets, where sites were categorized according to the highest amountof positive phosphorylation evidence from one database , without taking into account any evidence in the other. PhosphositeFDR estimates within “Table S4) were also removed from thisanalysis. The resulting protein sequences (n = 9481)and sets of Ser, Thr, and Tyr phosphosites were analyzed in termsof adjacent amino acids and conservation using the above method. PhosphositeFDR was calculated for the large-scale study set (ECO:0007744) using“High in PSP and PA” as 0% FDR setand “Not phosphorylated” as the 100%set.To analyze phosphosites in UniProt,phosphorylation data for thereference human proteome was downloaded directly from UniProt  and processed to split phosphositesaccording to associated evidence codes from the Evidence and ConclusionOntology . Any target proteins removed earlier(Table S6) were categorized into sets according to what their highest-rankedSer, Thr, and Tyr site was in terms of phosphorylation evidence . Each protein set within Ser, Thr, andTyr datasets was analyzed with DAVID (version 6.8)39 using all proteins in filtered proteome with any Ser, Thr,or Tyr evidence in PSP or PA  as control background. Protein sets containingno reported evidence in PSP or PA were searched against a backgroundof all proteins in the filtered reference proteome to determine anydifferences in their functional enrichment compared to proteins withPSP/PA evidence. Per each set searched, the top 10 (where possible)significant  functional terms with the highest percentage ofproteins mapped were identified, replacing any near-synonymous termswith additional terms from outside the initial top 10. All targetprotein sets were also searched in UniProt (release 2020_04) to determinethe percentage of proteins mapped to UniProt keywords “Phosphoprotein”, “Alternative splicing”, “Nucleus”, “Transcription”, “Acetylation”, “Membrane”, “Glycoprotein”, “Signal”,and “Disulfide bond”.All protein sequencesin the filtered reference human proteome (Tables S5 and S7).Any target proteins searched in UniProt which were marked as obsolete(15 proteins) or represented different sequences despite being labeledwith the same identifier (25 proteins) were removed further and markedas “NA” (Table S5). Normalized (to 1000) counts of target amino acids withinprotein structures were assessed with Fisher’s exact statisticaltest37 using the scipy module in Python38 to generate p values and indicateany significant enrichment (p < 0.05) between“High in PSP and PA” set and the “Not phosphorylated” set. The method was also appliedseparately for Ser sites, which had phosphorylation evidence in UniProt,and which were mapped to the described phosphorylation likelihoodsets based on PSP/PA evidence.Categorized Ser, Thr,and Tyr sites in filtered reference human proteome were mapped toprotein structures described for those proteins in UniProt (release 2020_04) (S5). The majority of Ser, Thr, and Tyr sites  with phosphorylation evidence in PSP were placed intothe “Low” phosphorylation likelihoodset, meaning that there was only a single piece of evidence supportingtheir positive identification  than Thr sites . Those sets were then cross-referencedwith data in PSP to determine whether PSP contained any sites rankedas nonphosphosites in PA. Interestingly, we found that 2489 Ser, 1341Thr, and 891 Tyr sites assigned to the “Not phosphorylated” set in PA were found to have evidence in PSP (Table S9). In fact, out of those potential PAnonphosphosites, 146 Ser, 97 Thr, and 293 Tyr sites were placed into“High” phosphorylation likelihood setaccording to PSP evidence (Table S9). Thisstrongly indicated the presence of potential false positives in PSPand/or false negatives in PA. For example, Ser42 in protein P17066(HSPA6) and Ser59 in Q8N488 (RYBP) had 8 and 6 phosphosite identificationreferences in PSP, respectively (mostly from in-house MS studies),but had no positive identification evidence in PA or any mention inUniProt31 (Table S5). On the other hand, Ser4 in P15927 (RPA2) had 33 phosphosite identificationreferences in PSP and was also mentioned in UniProt’s annotations,but has never been positively localized in any of its 127 associatedPSMs in PA (Table S5). To eliminate potentialfalse assignments when considering evidence in both PSP and PA, asite was only categorized as a nonphosphosite if it had no evidencein PSP in addition to having “negative” phosphorylationevidence in PA (Other” PA set (sitesin that set must have no evidence in PSP) which contained the majorityof analyzed PA sites , we established a “gold standard” set of phosphosites,all of which had “High” phosphorylationlikelihood according to both PSP and PA evidence (Table S10). This set contained 16 978 Ser, 2747 Thr,and 986 Tyr highly likely true phosphosites (Table S10). As for the general agreement between PSP and PA in termsof phosphorylation evidence, we found that 37.7% Ser, 20.5% Thr, and9.10% Tyr sites with PSP evidence also had at least one observationat PTM probability >0.95 in PA (Table S9). This variation in phosphosites observed between the two databasescan be explained by the likely use of different methods for phosphositedetection and localization between PA and the sources referenced inPSP, as well as due to a considerable presence of random false positivesin both datasets before thresholding has been applied , weighed toward vertebrates, but also including examples of insects,plants, and unicellular eukaryotes (Table S3). In our first analysis, we explored the mean conservation of phosphositesand nonphosphosites per protein (at least three of each present perprotein) and performed a correlation analysis across all proteins(High” or “Medium” in PSP and/orPA) were around 4.6, 5.4, and 2.0%, respectively, more conserved acrossall 100 eukaryotes than corresponding likely nonphosphosites (sitesin “Not phosphorylated” and “Other” sets) within analyzed proteins when allowingSer/Thr substitutions toward the conservation score . The results(S1) provide additional evidence that phosphositesare generally more conserved than nonphosphosites.45 The difference in conservation is thus subtleand variable, but statistically robust. Furthermore, in our analyzedsets of proteins which had at least three likely phosphosites andthree likely nonphosphosites, we found 104, 88, and 19 proteins whereconservation of Ser, Thr, and Tyr likely phosphosites, respectively,was at least 20% higher than conservation of likely nonphosphosites(Table S11).Phosphoproteomesfrom all species are constantly evolving, although many ancient phosphositesare conserved across species and taxa, increasing the likelihood ofthem being functionally relevant.proteins2. We fiton score 2. Simila results2 and S1 High in both PSP and PA”) hadthe highest average conservation across all 100 eukaryotic proteomesconsidering Ser/Thr substitutions (Table S12). In comparison, the sites in “Low in PSP and/or PA” set had slightly lower averageconservation scores of 54.3, 55.4, and 64.0% in 35 126 Ser,13 253 Thr, and 7471 Tyr sites, respectively (Table S12). Assuming that high conservation is a property of truephosphosites, the results . It is possible that the predictedphosphosites within those proteins were either false positives orwere nonfunctional true phosphosites, explaining the comparative weakerselective pressure. In fact, previous reports estimated that as manyas 65% of known phosphosites may be nonfunctional as individual sites due to limitedkinase specificity and therefore have similar evolution rates comparedto nonphosphosites which would explain the observed trends.47 It is also possible that some proteins were formed by recent genefusion events leading to regions containing phosphorylation sitesonly found in a few closer related orthologues (low conservation),with other protein domains being more highly conserved. In addition,higher evolutionary rates in closely related species  could lead to new protein functions unique to that groupof species, further explaining low conservation of some phosphositesin our analysis.There were numerous cases in our analysis of likelynonphosphositesand sites with “High in PSP and PA”) had a highermean conservation (69.4%) compared to Ser/Thr sites in that set  (Table S12). Thereare several possible explanations for this result, including the ideathat pTyr is under stronger conservation pressure  in animals that make up the vast majority(84/100) of the species analyzed (Table S3). It is also possible that there is a degree of experimental biasdue to the comparison of the much larger set of pSer/pThr to pTyr.The typically higher data quality for pTyr, enhanced by the availabilityof epitope-specific monoclonal antibodies may also contribute to thisphenomenon.We further note that Tyr sites in the highestphosphorylation likelihoodset  in the mechanism of phosphorylationfor families of kinases such as the cyclin-dependent kinases, mitogen-activatedprotein kinases, and, more recently, the centrosomal kinase PLK4.56 Consequently, there is a high prevalence of Pro in numerous phosphorylationmotif sequences as part of Ser/Thr-Pro combinations.58Aminoacids directly adjacent to known phosphorylation sites are often involvedin optimizing substrate capture for subsequent phospho-transfer bythe kinase enzymatic machinery.High in PSP and PA”) (Table S13). In fact, Pro was observed at the +1 position next to 44.3 and74.9% of all Ser and Thr sites, respectively, in that set (Table S13). The enrichment of Pro at +1 positionaround those sites was significant  in relation to the normalized distribution of Pro in thehuman proteome, where it is, in fact, only the sixth most observedamino acid (Table S13). The normalizednumber of observations of Pro at +1 relative to Ser and Thr sitesin the highest phosphorylation likelihood set was also significantly higher than around Ser/Thrsites in the “Not phosphorylated” set(Table S13). Therefore, the enrichment of Proaround highly likely Ser and Thr phosphosites suggests that this feature,among others, can be used as a differentiating characteristic forphosphosites compared to nonphosphosites.In our analysis, we identified the frequency of −1and +1 amino acids relative to a possible phosphosite and comparedit across different sets of sites ranked by the relative strengthof phosphorylation evidence in ted” set4A,B, whe59 At the −1positions around target Ser, we found significant enrichment  of Asp and Gly in the highest phosphorylationlikelihood set compared to “Not phosphorylated” set  across the sites within different phosphorylation likelihoodsets and used the comparison to estimate phosphosite false discoveryrate across those sets. Using the counts of all four enriched aminoacids  around Ser sitesof various phosphorylation likelihood  inset 2 “High in PSP or PA”; FDR = 54%(CI ± 25%) in set 3 “Medium in PSP and/or PA”; FDR = 84% (CI ± 11%) in set 4 “Lowin PSP and/or PA” and FDR = 91% (CI ± 4%) inthe “Other” set. Similarly, using theenrichment of Pro at +1 and Gly at −1 around target Thr sites,we estimated Thr phosphosite FDR = 59% (CI ± 7%) in set 2; FDR= 86% in set 3 (CI ± 8%); FDR = 98% (CI ± 5%) in set 4 andFDR = 99% (CI ± 1%) in the “Other”set (S14). Our FDR estimates clearly highlight thatthe majority of Ser and Thr sites with just one piece of phosphositeidentification evidence are likely false-positive identifications,and users of these databases can reasonably assume that if a sitedoes not have multiple levels of evidence, then it is unlikely torepresent a true phosphorylation site.We compared the frequency of significantlyenriched amino acids ofAla, Glu, and Asp at +1 positions, in addition to enriched Ile, Val,and Asp at −1 in “High in PSP and PA” set compared to “Not phosphorylated” set  in set 3 “Medium in PSP and/or PA”, FDR = 82% (CI ± 9%) in set 4 “Low inPSP and/or PA”, and FDR = 98% (CI ± 4%) in the“Other” set (S14).In our analysis of proximal sites around target Tyr, we found asignificant enrichment , and thus the FDR estimates obtainedwith our method should be seen as approximate indicators of the extentof false positives in a set of sites with quantifiable phosphorylationevidence.Our FDR estimates varied depending on the selected enriched proximalamino acid in the highest phosphorylation likelihood set  were falsepositives , 2747 (pThr), and 986(pTyr), following more closely previously reported estimates of phosphorylationsite frequency. It thus remains to be seen if the pTyr sites reportedin PSP, but without independent evidence are true or false.Based on our Ser, Thr, and Tyr phosphosite FDR estimates,we predictedthat there were around 62 000 Ser, 8000 Thr, and 12 000Tyr true-positive (TP) phosphosite identifications in the human proteomethat were supported by evidence in PSP and/or PA 2A. Furthositives 2A.Intery in PSP 2A, whereHigh”, “Medium”, or“Low” sets) in one database withouttaking into account any evidence in the other . Theanalysis revealed a generally lower FDR per each set in PA comparedto the respective set in PSP, overall suggesting that a higher proportionof analyzed sites in PA are true phosphosites compared to the analyzedsites in PSP (S15).Using the same method, we compared phosphosite FDR between PSPand PA sets by considering positive phosphorylation evidence . Due to potential phosphosite differencesand biases associated with different discovery methods , we suggest that our method should only be used to analyzesites from high-throughput studies because it was built primarilyusing sites of similar evidence type. This is further evident fromthe conservation analysis of UniProt sites , which revealed different conservation patterns betweenthe set of sites identified by large-scale studies and the other UniProtsets. As a result, we were able to estimate FDR for a set of UniProtsites with evidence from large-scale proteomics studies . In that set, we estimated averagepSer FDR = 7% (CI ± 8%); pThr FDR = 22% (CI ± 14%) and pTyrFDR = 6% (CI ± 7%) , suggestingthat there is a much higher proportion of true-positive phosphositesin UniProt compared to PSP or PA datasets. The FDR difference betweenpSer and pThr follows the statistical expectation from analyses oflarge datasets with unbalanced counts of true positives for differentresidues. For example, if a study reported 1200 phosphosites at 5%FDR, of which 1000 are pSer and 200 are pThr, the false positives(∼60) would assort approximately equally across pSer  and pThr , meaning that in the vast majority of studies (whichdo not correct for this issue) the general pThr FDR will be significantlyhigher than for pSer. For pTyr, the majority of sites comes from separatestudies that specifically enrich for pTyr via antibodies, which likelyaccounts for the pTyr FDR being similar to the pSer FDR.As noted in the Table S5) according to what theirhighest-ranked Ser, Thr, and Tyr site was based on phosphorylationlikelihood sets in Table S16) were analyzed in DAVID39 to comparefunctional enrichment patterns between phosphorylation likelihoodsets. First, we found that across all datasets the protein sets containing sites ranked “High in bothPSP and PA” were associated with the most significantfunctional groups  suggestingtheir functional coherence i.e., sharing mappings to keywords, ontologyterms or pathways. Interestingly, proteins with sites from “Low in PSP and/or PA” set as their highest-rankedsite and proteins which did not have any evidence phosphorylationevidence (“No evidence in PSP or PA”set) were also enriched for numerous functional categories suggestingthat they too share some functional properties . Proteins containing sites from the “Not phosphorylated” set as their highest-ranked Ser/Thr/Tyrsite were enriched for one significant functional group in the caseof Tyr dataset and no functional groups in the case of Ser/Thr datasets,which was likely due to small protein sample size in those sets.In our analysis, wecategorized all 20 271 proteins in the filtered human referenceproteome S5 accordHigh in PSP and PA” set) were significantly enrichedfor categories and terms associated with phosphorylation such as “Phosphoprotein”, “Transcription”, “Nucleus”, and “Alternative splicing”  are likely to have betterquality data associated with isoform identification and be consequentlylinked to “Alternative splicing”.To investigate this further, we compared the top 10 enriched functionalgroups between the protein sets and found that proteins containingSer, Thr, and Tyr sites with the most phosphorylation evidence  were not enriched for clear phosphorylation-associatedterms and were instead enriched for categories such as “Glycoprotein”, “Signal”and “Disulfide bond” and “Membrane” .In comparison, proteins that only had sites from“embrane” 5, suggeslgroups 5.In facPhosphoprotein” and other terms likely to be associated with phosphorylation generally decreasedacross the sets of reduced confidence, which suggested higher FDRin sets with less phosphorylation evidence , suggesting that mostproteins in those sets were not phosphoproteins and further highlightingthat the associated sites with only a single piece of evidence arelikely false-positive identifications.Our investigationof UniProt terms linked to protein sets revealedthat the enrichment for the term “evidence 6. For ex66 We found that significantly more Ser, Thr,and Tyr sites with the strongest phosphorylation evidence (“High in PSP and PA” set) were localized within coiledcoils compared to sites in the “Not phosphorylated” set , there was no significantenrichment of sites from the highest phosphorylation confidence setwithin those structures compared to the “Not phosphorylated” set (70We also investigated whetherSer, Thr, and Tyr sites with strong phosphorylation evidence werelocated more frequently within specific protein secondary structures,compared to sites with less evidence. For example, previous analysisof thousands of phosphosites from multiple species identified hotspotswithin domain families of proteins, particularly near domain interfacesand adjacent to catalytic residues, where they presumably regulateenzymatic output.ted” set 7. This mted” set 7. In fac72 particularly for Ser/Thr sites, presumably due to the previous inclusionof false positives and subsequent overestimation of the number oftrue phosphosites. We conclude that researchers must be aware of thepotential for false-positive sites in both public and self-generateddatabases and should always evaluate the evidence behind the phosphositesused in their research. As a general rule, phosphorylation sites with<5 independent observations should be treated with caution, andthose with only one observation in a database are likely to be falsepositives. In a recent phosphoproteomics study from our group, wedemonstrated the utility of the classification presented here, bymatching the sites identified by LC-MS/MS to their evidence categoriesfrom PSP and PA.73 For new phosphoproteomicsstudies, it will be common for some ambiguity to remain regardingphosphosite localization, and many sites will be observed that wouldnot pass a 1 or 5% false localization rate cutoff from a single dataset,but for which there may be some supporting evidence. By evaluatingnew datasets in combination with all of the evidence collated froma large number of previous studies, greater confidence can be assignedto “borderline” significant phosphosites that may indeedbe correct, or conversely, sites with weak evidence that have neverbeen reported can be rejected.In our analysis, we ranked all potential Ser,Thr, and Tyr phosphositesin UniProt reference human proteome according to how much quantitativeand qualitative phosphorylation evidence they were assigned in PSPand PA databases. Having analyzed the sites and the proteins thatcontain them in terms of conservation, proximal site patterns, functionalenrichment, and structural properties, we established that Ser, Thr,and Tyr sites with weak phosphosite identification evidence, particularlysites with a single piece of supporting evidence, were likely to befalse-positive identifications. This finding was further confirmedby FDR estimations across the established phosphorylation likelihoodsets which revealed phosphosite FDR of 84, 98, and 82% in sets ofSer, Thr, and Tyr sites, respectively, where only one piece of identificationevidence was present. Since there is a considerable presence of suchsites in PSP and PA datasets, our results implied high FDR in boththose datasets, although PSP was predicted to have a generally higherproportion of false-positive phosphosites compared to PA. This ispotentially a cause for concern since many potential false positivesare presented to scientists as true phosphosites, without a clearexplanation of the likelihood of such claims. Nevertheless, usingour FDR estimates we predicted that there are around 62 000Ser, 8000 Thr, and 12 000 Tyr true-positive phosphosites inthe human proteome that are supported by evidence in PSP and/or PA.These estimated counts are lower than other published estimatesHere, we have provided a methodologicalframework for estimatingglobal FDR in large-scale phosphorylation datasets, which does notrely on native scores from search engines or site localization software.Methods for estimating global FDR in meta-analyses of phosphositesare not yet robust, and thus we would recommend that other groupsprofile orthogonal properties of ranked sets, as we have done here,to estimate the real distribution of true and false phosphosites intheir data.


The aim of this selective review paper is to clarify potential confusion when referring to the term proactive inhibitory control. Illustrated by a concise overview of the literature, we propose defining reactive inhibition as the mechanism underlying stopping an action. On a stop trial, the stop signal initiates the stopping process that races against the ongoing action-related process that is triggered by the go signal. Whichever processes finishes first determines the behavioral outcome of the race. That is, stopping is either successful or unsuccessful in that trial. Conversely, we propose using the term proactive inhibition to explicitly indicate preparatory processes engaged to bias the outcome of the race between stopping and going. More specifically, these proactive processes include either pre-amping the reactive inhibition system (biasing the efficiency of the stopping process) or presetting the action system (biasing the efficiency of the go process). We believe that this distinction helps meaningful comparisons between various outcome measures of proactive inhibitory control that are reported in the literature and extends to experimental research paradigms other than the stop task. This paper presents a brief and selective review of the literature on proactive inhibitory control over behavior. It mainly seeks conceptual clarification and is by no means meant to be exhaustive. A PubMed literature search in July of 2022 for the keywords “proactive inhibit*” in the publication title or the abstract yielded an outcome of 570 results, about 25% of which have been published in the last five years, underscoring a rising interest in the topic. One potential source of confusion with this topic is that sometimes the term proactive inhibitory control refers to various mechanisms and other times to a performance outcome . Often, these various distinctions are used interchangeably, which led to our consideration of a rather simple framework that might offer conceptual clarity and consistency in studies of proactive inhibitory control. The framework is introduced within the context of the response-stopping paradigm , although we later discuss later how the framework may be useful in response conflict paradigms  as well.Let us first define proactive inhibitory control as adaptive preparatory processes that modulate or control the chances of inhibition success in the near future. In this sense, the proactive nature refers to processes mobilized in advance of upcoming situations in which the need for inhibition may be called upon. Moreover, this definition adopts the position that proactive inhibitory control refers to the outcome or success of inhibition, irrespective of the specific mechanism or process that may be mobilized to achieve the outcome. Most usages of proactive inhibitory control in the literature, including our prior work, seem to conflate mechanism and outcome. However, as will become clear from a brief inventory of definitions that have been used in the literature, these adaptive inhibitory control processes are multifaceted and may refer to different underlying mechanisms, all of which impact the success or failure outcome of inhibitory control. Defining proactive inhibitory control in terms of inhibition success, irrespective of the mechanism at play, does not imply that we are not interested in the underlying mechanisms. Quite to the contrary, we hope to clarify the different guises that proactive inhibition can take by focusing on these mechanisms and processes. Focusing on the outcome allows us to consider proactive inhibition under one unified umbrella definition, while focusing on underlying processes allows us to clarify its different uses in the literature, a clarification that we deem useful if not urgent.Before we elaborate on the framework of proactive inhibitory control, we first focus on the concept of reactive inhibitory control, which provides the mechanism by which an action is ultimately cancelled or stopped. Thus, this paper will start with presenting a brief overview of behavioral correlates of reactive inhibitory control, as measured by the renowned stop-signal paradigm and its variants. The second part integrates the concept of proactive inhibitory control as a set of processes that bias the likelihood that reactive inhibitory control will be successful in any situation in the immediate future. For the sake of conceptual clarification, we distinguish between the following two general classes of proactive control processes that shape the success of inhibiting an action: pre-amping reactive inhibition versus pre-setting action. Pre-amping inhibition refers to the adaptive process of amplifying  the reactive inhibition mechanism to increase or decrease, respectively, the proficiency of stopping an action should the need to do so present itself in the immediate future. The second manifestation, pre-setting action, refers to the adaptive tuning of action-related processes other than reactive inhibition per se to decrease or increase the difficulty of stopping an action should the need to do so present itself in the imminent future. Note that both mechanisms modulate the outcome of the reactive inhibition process by biasing its chances for success or failure. We will argue that for assessing the manifestations of proactive inhibitory control in previous and future studies, it will be useful to consider this taxonomy because of its consequences for interpretation and implications.In the following, we present a brief inventory of annotations in the literature to reactive inhibitory control, as measured by the stop-signal paradigm. Next, serving conceptual clarification, we present a selective overview to distinguish between two manifestations of proactive inhibitory control, namely pre-amping reactive inhibition versus pre-setting the action system. A concluding section reiterates the advantages of this conceptual interpretation when comparing results across studies on response inhibition, including experimental paradigms other than the stop-signal task.Definition 1 (Reactive Inhibition).Reactive inhibition is the adaptive mechanism that stops ongoing motor actions abruptly or on the fly. Reactive inhibition can be triggered by external events, such as a stop signal, or by changed internal goals, such as the voluntary decision to stop.Imagine that you are going to get a coffee from the cafeteria in the psychology department where you have been working for the past 24 years. During these 24 years, you have walked that same route from your desk to the coffee corner countless times so that you could even find your way blindfolded. However, upon turning a corner you suddenly halt and come to a full stop while stumbling upon a red sign hanging on the hallway door, indicating “Passage Temporarily Closed Due to Construction”. This example illustrates the need for the ability to inhibit or stop ongoing behavior if the situation calls for it ,2. ThrouThe foremost and most well-established research paradigm to study reactive inhibitory control over actions is the stop-signal paradigm ,2,4,5; fPerformance on stop-signal trials has been conceptualized as a horse race between two processes, the go and stop processes, which are triggered by the onsets of the go signal and the stop signal, respectively . If the A major advantage of the stop-signal task over other experimental paradigms that tap into inhibitory control, such as the Go/Nogo and Stroop tasks, as well as more complex neuropsychological tests, such as the Wisconsin Card Sorting Task, is that the latency of the covert response-inhibition process, the stop-signal reaction time (SSRT), can be estimated within the conceptual and computational framework of the race model see  and thatIn sum, SSRT derived from the stop-signal paradigm has proven to be very useful for quantifying individual differences in the efficiency of reactive stopping control. Next, we turn to the conceptual framework related to proactive inhibition as the preparatory processes modulating the success or failure of reactive inhibition.Definition 2 (Proactive inhibition as pre-amping reactive inhibition).Proactive inhibition can be expressed by “amping up” the reactive inhibition system to increase the chances of successful stopping in the near future.Suppose you are in a hurry to be on time for an important appointment. Traffic is growing increasingly busy and your anxiety is elevating. Just ahead, the traffic light turns orange, and you instinctively punch the gas to make it through the intersection before it turns red. Confidence in your quick wits gives way to panic and doubt as the flash of a traffic-camera in your rear-view mirror catches your attention—did the camera catch you? With no time to stop and think, the next traffic-light is already fast approaching and might turn orange at any moment. Harboring concerns about tickets and fines, one strategic option is to maintain your speed , but heighten your readiness to stop pressing the gas pedal so you can quickly switch to the brake pedal if the light suddenly turns orange.This scenario illustrates the need for proactive inhibition, where amping up your readiness to inhibit without altering any go- or action-related processes increases the speed and likelihood of successful inhibition. The purpose of this proactive adaptation strategy is to facilitate the efficiency of reactive inhibitory control in case of an upcoming, anticipated stop signal . Referring to the race model underlying cognitive processing in the stop-signal paradigm, this strategy of preparatory proactive inhibition in essence biases the race in favor of stopping efficiency. The intended net effect will be an increase in the probability and speed of successful reactive inhibition when the situation calls for it. Studies that report ‘proactive inhibition’ typically refer specifically to the preparatory amping up of reactive inhibition processes e.g., ,27)..27]).Pre-amping reactive inhibition is typically studied using variations in the standard stop-signal task as well. In such cases, it is measured as the degree to which SSRT is affected by the experienced likelihood of an upcoming stop signal. A straightforward test of expectation and preparatory effects on the efficiency of reactive stopping is provided by comparing the latency of reactive inhibition (SSRT) derived from a condition with relatively low stop-signal probability  versus a condition in which stop-signal probability is relatively high  ,29. For Another example is provided by Zandbelt and colleagues  who usedChikazoe and colleagues however provided a clear case of the proficient amping up of reactive stopping . They emAlternatively, in several other paradigmatic examples of the pre-amping of inhibition, the level of reactive inhibition may be said to be premeditated, in that the resolve to not act in a certain way under certain conditions was made ahead of time. Such examples include the cold pressor test  to stop signals increased from about 365 ms for the group of 18–24-year-olds to about 411 ms for the 60+ group [As mentioned above, neurodegenerative conditions, such as Parkinson’s disease, impair inhibitory control . HoweverWhen discussing proactive control, we typically consider increases in control, such as amping up reactive inhibition, as described in the previous section. However, the dimension of proactive control may also include ‘letting go’, ‘letting one’s guard down’, or in general adopting more lenient levels of control. Whether this leniency takes the form of active amping down reactive inhibition or a more passive lack of investment remains to be studied, but it is evident that what goes up must come down. As an illustration, consider the previous traffic example in which you hurry to make it to an important appointment in time. Alas, due to road works blocking the way, you have to take an alternative route that is unfamiliar to you. You are being redirected by a series of arrow signs that are planted along the roadside, but these are hardly visible because of the twilight hour. The passenger next to you, commenting incisively on the obvious fact that you risk being late, taxes your limits even more. You find yourself slamming the brakes just in time for a red traffic light, the presence of which has been brought to your attention by the squabbling passenger blurting out “STOP!”. Conceivably, under these demanding concurrent circumstances, your reactive inhibition control system might be amped down to the extent that you were slow to stop to the red traffic light itself and needed an additional auditory cue to trigger the reactive inhibition process.This down-scaling of reactive inhibition has been illustrated by modifications of the classical stop-signal task in order to capture more subtle manifestations of reactive inhibition control, as opposed to the all-or-none form of stopping. Whereas the standard version requires a full stop of ongoing actions, the stop-change variant captures the ability to stop an ongoing action and initiate an alternative action instead upon the presentation of an external stimulus ,50. The In addition, task variants drawing upon selective stopping capabilities may involve the selective inhibition of certain responses or selective inhibition to certain stimuli. An example of selective response stopping is the instruction to stop when hearing the stop tone but only if you are about to issue a button press response with the left hand. Conversely, a right-hand button press response should not be inhibited upon a stop signal and should always be completed ,56,57. SImportantly, amping reactive inhibition up or down is often reported in relation to changes in the reactions to the go signal. For example, amping up reactive inhibition is often reported together with a strategic slowing of reactions to the go signal ,61,62,63Definition 3 (Proactive inhibition as pre-setting the action system).Proactive inhibition can be expressed by an advanced presetting of the action system to increase the chances of successful reactive inhibition if needed in the future.Recall the real-life example from the previous section. You were pressed to be on time for your appointment and just accelerated to run an orange traffic light. You cut it close this time but do not want to risk a traffic penalty. Instead of maintaining speed and being extra prepared and vigilant to stop , this time you decide to slow down to ensure that you will be able to stop should the light turn orange; slowing down will also prevent the need to suddenly override your rushed action in the first place. Better safe than sorry.We refer to this proactive control mechanism as pre-setting action-related processes  to prepare to suppress your action, should the need arise in the immediate future, in order to increase reactive inhibition success. The purpose of this proactive strategy is (again) to facilitate the efficiency of reactive stopping control in case of an upcoming stop signal . Like pre-amping reactive inhibition, pre-setting action processes also biases the outcome of the race between going and stopping processes in favor of the stopping process. The intended net effect will be an increase in the probability of successful reactive inhibition if the situation calls for it. Note that the mechanism underlying this variant of proactive inhibitory control is not reactive inhibition per se. Instead, (p)re-setting action-related processes benefits the efficiency of reactive inhibition as an indirect or secondary effect. Importantly, action may also be pre-set in opposite directions. For instance, a riskier action strategy may make one less prepared for suppressing one’s action, should that need arise. In this case, pre-setting action-related processes biases the race between going and stopping processes in favor of the go process. The intended net effect will be a decrease in the probability of successful reactive inhibition should the situation call for it.The research paradigm of choice for measuring reactive inhibition and the proactive amping up of reactive inhibition, the stop-signal paradigm, also allows the quantification of the proactive pre-setting of actions. In the stop task, the latter type of proactive control is often expressed as the slowing down of responses to go signals, despite the explicit task instruction not to prolong go reaction time, in an effort to increase the chances of successful inhibition should the go signal be followed by a stop signal.The proactive slowing of responses on go trials might be the direct result of increasing the frequency of a stop signal, compared to a task condition in which stop signals were presented less often ,61,62,64Another typical quantification of presetting action control as a proactive strategy to increase stopping success involves the comparison of performance in two type of tasks. Participants complete a standard version of the stop-signal task in which choice trials are typically randomly mixed with stop trials. In addition to SSRT as an index of the efficiency of reactive stopping control, the stop task also provides mean reaction latencies to the go signal , reflecting the efficiency of action-related processes. Importantly, participants also complete a pure choice RT task that is similar in design, compared to the stop-signal task, with the exception that stop signals never occur in the pure choice task and, therefore, never require reactive inhibition. Proactive control is typically indexed by the relative lengthening of go RT in a stop-signal context, compared to go RT in a pure choice task, a context without stop trials ,64. A diWe have described reactive inhibition as the mechanism by which action is stopped and differentiated proactive mechanisms that bias the likelihood of reactive inhibition success. However, it is worth considering that proactive mechanisms are always at play in situations where a reaction might need to be inhibited. That is, there is a context to every situation that affords preparatory processes, amped up or down, primed or slackened, perhaps even shut off at times, that place reactive inhibition in a better or worse place for success. Considering the standard stop-signal task that generally recommends a context where 25% of trials deliver a stop signal, changing the frequency of stop signals alters reaction and stopping speeds. Thus, proactive inhibitory control reflects the state of preparatory processes that influence the ultimate success or failure of inhibiting a reaction. If the context is held constant, one is better able to quantify reactive inhibition efficiency with minimal preparatory adjustments . If the context is varied, one has to consider the potential engagement of pre-amping and/or presetting processes. Even within a consistent context , there is room for proactive adjustments on a trial-by-trial basis, even though the macro proactive strategy might be fairly constant.Using the basic framework presented here can help clarify the language, design, and inferences of future studies interested in studying the role of proactive processes in modulating the success of response inhibition. At a minimum, studies of reactive inhibition must carefully record and acknowledge the state and biases of preparatory processes given the context. When studies attempt to modulate proactive control mechanisms, it is helpful to specify whether variables are pre-amping the reactive control mechanism or pre-setting action-related processes.The distinction between pre-amping reactive inhibition and pre-setting action may perhaps seem almost trivial. However, given the state of the literature in which proactive inhibition is treated as an undifferentiated unitary construct while actually pooling together different processes under the same head, we think such a conceptual distinction helps to disentangle these processes, disambiguate what we mean when we talk about proactive inhibition, and clarify our view on this consequential topic. This approach could help determine whether experimental conditions , interventions , or individual/group differences  affect the ability to pre-amp reactive inhibition, pre-set action, or both. Interestingly, the selective impairment of reactive versus proactive inhibitory control can mark the phenotype of distinct clinical conditions or even different phases of disease . For exaAt this point, it is worth mentioning the term pre-emptive inhibition . Pre-empProactive inhibition, whether through pre-amping reactive inhibition or pre-setting action, typically affects the probability of successfully suppressing an action impulse. Conceptually, however, proactive inhibition might also apply to the probability of successfully suppressing ongoing processes other than action. For instance, proactive inhibition might serve to suppress one’s fears in bungee-jumping. Proactive inhibition might even serve to suppress action inhibition, for instance in cliff-diving, where one may proactively suppress the tendency to stop running at the last second before reaching the edge; or in baseball hitting, where one may proactively decide to go after a pitch and suppress the tendency to inhibit a swing, even when the pitch is wide. These alternative targets of proactive inhibition may again be accomplished through either pre-amping reactive inhibition or pre-setting action.In sum, different researchers mean different things when talking about proactive inhibition. When approaching a traffic light that might turn orange, one strategy is to maintain your speed while preparing to stop pressing the gas pedal (and slam the brakes instead) if the light suddenly turns orange; another is to slow down to ensure that you will be able to stop should the light turn orange. Although both strategies are considered as expressions of proactive inhibition and both aim to bias the outcome towards increased probability of stopping if the light turns orange, their underlying mechanisms are quite different. One involves pre-amping reactive inhibition; the other involves pre-setting the parameters of our actions and reactions. Both can save lives, but via different approaches. By exploring these roads, we hope to have clarified what it is we talk about when studying proactive inhibition.


Staphylococcus aureus (Sau), MKs are essential for aerobic and anaerobic respiration. As MKs are redox-active, their biosynthesis likely requires tight regulation to prevent disruption of cellular redox balance. We recently found that the Mycobacterium tuberculosis MenD, the first committed enzyme of the MK biosynthesis pathway, is allosterically inhibited by the downstream metabolite 1,4-dihydroxy-2-naphthoic acid (DHNA). To understand if this is a conserved mechanism in phylogenetically distant genera that also use MK, we investigated whether the Sau-MenD is allosterically inhibited by DHNA. Our results show that DHNA binds to and inhibits the SEPHCHC synthase activity of Sau-MenD enzymes. We identified residues in the DHNA binding pocket that are important for catalysis  and inhibition . Furthermore, we showed that exogenous DHNA inhibits the growth of Sau, an effect that can be rescued by supplementing the growth medium with MK-4. Our results demonstrate that, despite a lack of strict conservation of the DHNA binding pocket between Mtb-MenD and Sau-MenD, feedback inhibition by DHNA is a conserved mechanism in Sau-MenD and hence the Sau MK biosynthesis pathway. These findings may have implications for the development of anti-staphylococcal agents targeting MK biosynthesis.Menaquinones (MKs) are electron carriers in bacterial respiratory chains. In This article is part of the theme issue ‘Reactivity and mechanism in chemical and synthetic biology’. The  by DHNA . AlthougSau-MenD is allosterically inhibited by DHNA. Thus, our results demonstrate that inhibition by DHNA of the MK biosynthesis pathway is conserved between Mtb and Sau, which hints at an ancient origin of this allosteric feedback mechanism in Gram-positive bacteria and mycobacteria. Furthermore, we show that exogenous DHNA inhibits the growth of Sau bacteria, underscoring the importance of the MK pathway for growth. These findings suggest that small molecules based on the DHNA scaffold may be effective for the development of new antibacterial drugs.In this work, we report that the . 2 (a)Sau IS1  and Sau ATCC 25923 (gene locus tag KQ76_RS04840 in NCBI reference sequence NZ_CP009361.1) were amplified by PCR using MenD_Sau_F1 and MenD_Sau_R1 primers specified in electronic supplementary material, table S1. The amplified MenD ORFs were cloned into a modified pET30a vector using the restriction enzymes NcoI and HindIII to generate rTEV (recombinant tobacco etch virus protease)-cleavable N-terminally His6-tagged proteins.The MenD open reading frames (ORFs) from Sau IS1 MenD ORF was performed by site-directed mutagenesis and In-Fusion cloning using primers listed in electronic supplementary material, table S1.Construction of alanine mutants  of the 6-tagged Sau-MenD was expressed in E. coli BL21 (DE3) cells. Seed cultures were grown for 18 ±2 h in Luria–Bertani Miller (LB) medium (Thermo Scientific) with 50 µg ml−1 kanamycin at 37°C. Seed cultures were diluted 1/200 in Terrific Broth autoinduction medium  glycerol, 0.0162 M KH2PO4, 0.0528 M K2HPO4, 2 mM MgSO4, 0.375% (w/v) aspartic acid, 0.015% (w/v) glucose and 0.5% w/v lactose, made up concentration pH 7.3) containing 50 µg ml−1 kanamycin and cultured at 37°C under agitation (180 r.p.m.) for 3 h, followed by a further 20 h incubation at 18°C and 180 r.p.m. Cells were harvested and lysed in lysis buffer  glycerol, 50 mM imidazole and 1 mM tris(2-carboxyethyl)phosphine (TCEP)) using a Microfluidics cell disrupter . Recombinant Sau-MenD was purified at ambient temperature by immobilized metal affinity chromatography (IMAC) with 5 ml HisTrap HP columns  and an imidazole gradient of 20–500 mM over 50 ml. Peak fractions were immediately diluted 1 : 2 in 1× lysis buffer to avoid protein precipitation. To remove the N-terminal His6-tag from the recombinant protein, TEV-cleavage ,32c) [31ric site d) [31]. Mtb-MenD DHNA binding allosteric site across MenD enzymes appears limited. Sequence comparisons with Sau-MenD indicated low conservation of the allosteric region, with the potential for conservation of one of the arginine cage residues and substitution of another for a Lys (Sau-MenD Arg98 and Lys283). Attempts to co-crystallize DHNA with Sau-MenD resulted in precipitation of the enzyme, while soaking greatly impaired diffraction. Therefore, structural comparisons of the Sau-MenD structure and DHNA-bound Mtb-MenD were used to gain insight into the structural conservation at the putative allosteric site. These overlays reveal that the likely structural equivalents for the arginine cage are Arg98, Lys283 and Lys309 (d). None of these residues matches the side chain conformers that would be needed for DHNA binding, suggesting that for DHNA to bind, conformational rearrangements would be needed along with backbone movements around the 305–312 region. In the Sau-MenD structures, Arg98 is extensively hydrogen bonded to the backbone of Asn308, Lys309 and the Asn308 side chain. Interaction and stacking with DHNA would interfere with these interactions and could facilitate the movement of the region to accommodate DHNA.By contrast to the high conservation at the active site, conservation of the d Lys309 d. None o (d)Sau-MenD  are important for activity and DHNA inhibition, we performed alanine mutagenesis experiments of the WT Sau-MenDIS1. Interestingly, all three mutants showed enhanced activity compared with the WT Sau-MenDIS1 when measured under the same conditions and in the absence of DHNA . As such, we were unable to determine IC50 values for these mutants. The truncated inhibition curves of the Arg98Ala and Lys283Ala mutants are an indication that these residues play an important role in DHNA inhibition. By contrast, the Lys309Ala mutant remained sensitive to DHNA inhibition with an IC50 of 4.6 µM, similar to that of the WT.IC mutants b. As sucTo illustrate the differences in DHNA sensitivity between the mutant and WT enzymes, the percentage inhibition in activity at 12.5 µM DHNA was also determined . This wa (e)Sau strains, including methicillin-resistant and methicillin-sensitive clinical isolates, were grown in media supplemented with various concentrations of DHNA (0–150 µM). Pseudomonas aeruginosa was used as a control as this bacterium does not produce MK, only ubiquinone, and therefore does not contain classical MK biosynthesis genes [Sau strains (a–d). The growth impact was more severe at 100 µM DHNA, and at 150 µM DHNA the growth of all four Sau was completely inhibited for the duration of the assay (24 h). By contrast, the growth of P. aeruginosa was unaffected at DHNA concentrations up to 150 µM (e).As the first metabolite in the MK biosynthesis pathway with a complete naphthoquinol ring, DHNA levels in the cells are likely to require tight regulation, as excessive amounts could result in cell toxicity if the redox balance is disrupted. To test this premise, four different is genes . We foun strains a–d. The o 150 µM e.Figure (f)−1 MK-4) to culture medium with (100 µM) and without DHNA present, and monitoring growth of the Sau bacteria over 24 h. In the absence of DHNA, the addition of MK-4 slightly impacted the growth of the bacteria (f–i). This may be due to oxidative stress, as MK is a redox-active metabolite. Despite this, MK-4 addition to culture medium containing 100 µM DHNA resulted in a significant growth improvement of the strains compared with growth in the presence of 100 µM DHNA only. Notably, growth was not completely restored to levels observed in the absence of DHNA, which is possibly because MK-4 is not the preferred MK for Sau (predominant MK in Sau is MK-8).To ascertain whether growth inhibition caused by DHNA is due to inhibition of the MK biosynthetic pathway, rescue experiments were performed by adding 337 µM MK-4  conserved in Sau-MenD (Arg98), with the other two (Mtb-MenD Arg277 and Arg303) being conservatively replaced by Lys283 and Lys309, respectively.To better understand the basis for the conservation of allosteric inhibition of Sau-MenD allosteric site cage were reflected in notably higher IC50 values for DHNA, in the low micromolar range, compared with the low nanomolar range for the Mtb-MenD [Sau-MenD at the equivalent positions to two key arginine residues in Mtb-MenD is likely to impact the strength of the stacking interactions with the aryl ring of the DHNA. Mutation of the three Sau-MenD allosteric cage candidates validated a significant role for two (Arg98 and Lys283) in the inhibition of SEPHCHC synthase activity by DHNA. The inhibitory potency of DHNA remained relatively unaffected by the mutation of Lys309, suggesting this residue plays a less significant role in DHNA binding and inhibition.These differences in the Mtb-MenD . The preMtb-MenD, Arg97 (the residue with hydrogen bond and electrostatic interactions with the DHNA carboxylate) is the arginine cage residue whose mutation results in the largest change in DHNA response. It is also the most conserved residue in sequence alignments [Sau-MenD) for allosteric regulation. Furthermore, they show that regulation, albeit at different potencies, can be conserved across bacterial species despite significant sequence divergence, likely reflecting their metabolic needs. However, it is not yet known how concentrations of DHNA vary across different bacterial species, or are modulated over time within a bacterial cell, or how DHNA is partitioned between the cytosol (where it is produced by MenI) and the membrane (where it is a substrate for MenA). By contrast to Mtb, which relies on oxidative phosphorylation for growth [Sau to grow slowly. In fact, respiration-deficient Sau, known as SCVs, have a slow-growth phenotype as a result of resorting to energy generation via fermentation. Therefore, Mtb and Sau may have different needs for the regulation of this pathway, requiring adaptations of the allosteric site.In ignments . Our resr growth ,58, subsSau-MenD, our overlays with the Mtb-MenD structure suggest that several residues in the Sau-MenD allosteric site  would need to undergo movement to accommodate DHNA. It is plausible that the initiation of DHNA binding facilitates a series of structural changes that help to achieve this. In the Sau-MenD structure, the Arg98 side chain forms hydrogen bonds to residues 308–309. Movement of the Arg98 side chain to interact with DHNA would free the region around residues 305–312 and enable the adoption of a more open conformation for these residues. There is precedence for the movement of this region in Mtb-MenD; the equivalent region (residues 300–308) has conformational differences between the DHNA-bound and DHNA-free forms. Rearrangement of this part of the allosteric site upon ligand binding is not unprecedented across other ThDP-dependent decarboxylase superfamily enzymes either. In fact, it can be important for the allosteric mechanism; pyruvate decarboxylase binds the positive allosteric regulator pyruvate in a similar structural location to the DHNA binding site in MenD, causing significant rearrangement of the allosteric site region and propagation of allosteric signal to the active site [Assuming DHNA adopts a similar binding pose in ive site .in vivo. One advantage of allosteric sites is that they tend to bind relatively hydrophobic compounds, which are associated with cell permeability [Sau bacteria with exogenous DHNA inhibits their growth. Furthermore, the addition of MK-4 to the culture medium of DHNA-treated cells results in growth rescue, suggesting that growth inhibition of the bacteria is caused by a lack of MK. While we cannot rule out that DHNA may also target other enzymes within the pathway, these data suggest that inhibition of cell growth may be attributable to the inhibition of the Sau-MenD enzyme. This is supported by recent findings that the addition of DHNA to culture medium increased the susceptibility of Sau to the antibiotic adjuvant cannabidiol, while MK-4 addition had the opposite effect and prevented cannabidiol-mediated growth impairment [The exploitation of allosteric sites is an emerging strategy for inhibitor development, but it was not known whether compounds targeting the MenD allosteric site would inhibit the growth of whole cells eability . This ispairment .Sau and Mtb, feedback inhibition by DHNA is observed in Sau-MenD and the Sau MK biosynthesis pathway. Our results suggest MenD may be a suitable target for the development of anti-staphylococcal agents.This work demonstrates that despite a lack of strict sequence conservation of the DHNA binding pocket between


While alot of recently developed polarizable many-body potential functionsgive a normal VIE contrary to experimental results, we find that oneof them, MB-pol, yields the anomalous VIE in good agreement with themost recent high-resolution neutron diffraction measurements—betterthan DFT calculations. The short-range three-body terms in the MB-polfunction, which are fitted to CCSD(T) calculations, are found to havea surprisingly large influence. A vibrational mode group decompositionof the zero-point pressure together with a hitherto unconsidered benchmarkvalue for the intramolecular stretching modes of H Most materials show a normal VIE,which means substitution with heavier isotopes results in a smallermolar volume at temperatures approaching the absolute zero. A hand-wavingrationalization in a classical picture is that the larger vibrationalamplitude and concomitant volume ascribed to a lighter isotope comparedto a heavier isotope, which both experience the same chemical interactionpotential at the same temperature. In ice Ih, the most common formof solid water on earth, the VIE is anomalous, resulting in a smallerunit cell and thus molar volume of the H2O compared tothe D2O isotopologue (about 0.1% up to 200 K4). Despite its small magnitude, the effect has been very well quantifiedexperimentally. Only recently, high-resolution neutron powder diffractionmeasurements4 have reduced the uncertaintiesfor the unit cell volumes of ice Ih compared to earlier work3 over a wide temperature range and thus providean excellent benchmark for atomistic interaction models that can beemployed in computational studies.Nuclear quantum effects canmanifest themselves quite prominently in macroscopic thermodynamicproperties, like for example phase transition enthalpies,7 Density functional theory, on the other hand, is able to model theVIE of different ice phases.9 However, even a qualitativelycorrect description depends very strongly on the computational settings,in particular the choice of the exchange-correlation functional.7 Quantification of the VIE using embedded-fragmentab initio second-order many-body perturbation (MP2) theory has faredsomewhat better.10 But also here, the resultsare very sensitive to computational details like the basis set sizeand the embedding field. This also holds for the individual contributionsof the different groups of phonon modes to the zero-point pressure,which are ultimately responsible for the VIE.10 Thesecontributions are commonly expressed in the form of mode-specificGrüneisen parameters and have not been benchmarked againstexperimental data. Consequently, a detailed understanding of the VIE’sorigin in terms of the competition of different contributions to thechemical interaction potential has been elusive so far.Computational modeling ofthe VIE is very challenging. So far,water force fields ranging from simple fixed point charge up to sophisticatedpolarizable models have all predicted a normal VIE for ice Ih.12 we employ the quasi-harmonicapproximation to account for nuclear quantum effects in the Helmholtzfree energy by means of extensive and high-precision phonon calculations.We find that the MB-pol interaction model, whose short-range partis rooted in coupled-cluster calculations, yields the anomalous VIEof ice Ih in better agreement with the experimental reference valuethan DFT calculations with the PBE functional. A decomposition ofthe zero-point pressure into contributions from different vibrationalmode groups together with a hitherto unconsidered benchmark value,which we obtain from Raman spectroscopy,13 allows us to scrutinize this further. According to the MB-pol totalenergy partitioning, the delicate competition between the librationaland intramolecular stretching modes driven by a surprisingly largeinfluence of short-range three-body effects is responsible for theanomalous VIE of ice Ih.In thiswork, we provide that understanding based on recently developedpolarizable many-body potentials as interaction models. Building onour recent studies,14 According to the QHA, the Helmholtzfree energy of the ice crystal is given byU is the internal energy that describes the interactionbetween molecules in the crystal. ωi are the vibrational modes which determine the second (zero-pointenergy EZP) and temperature-dependentthird term of F. For the sake of simplicity, theyare denoted by a collective index i that stands forboth wave vector and band indices of the corresponding phonon modes.The internal energy U and the vibrational modes ωi depend on the unit cell volume V, so that the minimum of F with respect to V at a given temperature is generally different for H2O and D2O isotopologues. The corresponding volumesare labeled V0 minimizes U(V), with thezero reference of the latter defined such that the lattice energy Elat = U(V0).For the quantification of the VIE,this work employs the quasi-harmonicapproximation (QHA), which has been used successfully for the samepurpose in the past.15 for interfacing their respectiveimplementations. This includes the fixed-point-charge-based forcefields q-TIP4P/F17 (as available in LAMMPS18), the polarizable force fields AMOEBA1420 (as implemented in TINKER21), SCME/f,22 and MB-pol (as implemented in the MBX package).25 All-electrondensity functional theory (DFT) calculations with the PBE exchange-correlationfunctional26 are performed with the FHI-aimscode,28 using the same high-accuracy settings thoroughlyverified29 and employed30 for ice Ih in previous work (see the Supporting Information for details). The DFT calculations mimic protondisorder with a simulation cell containing 12 molecules.31 A simulation box containing 96 molecules, orsome larger supercell, have been used for the force field interactionmodels to ensure the same level of convergence for V0 (±0.01 Å3 per molecule29). For all interaction models, V0 is calculated with ASE as in our earlier work by a combinedoptimization of the cell vectors and the molecular degrees of freedompreserving the space group of the lattice (as defined by the oxygenatoms)11 with a maximum force thresholdof 1.0 × 10–3eV Å–1.In this work, calculations with different interactionmodels areperformed, employing and extending the Atomic Simulation Environment(ASE)U(V) is obtained by least-squares fitting to the Rose–Vinet32 equation of state. Isotropic contraction andexpansion of V0 by ±4% yields 11structures for each interaction model, for which again all moleculardegrees of freedoms have been relaxed. Phonon calculations have beenperformed for all of these structures with the PHONOPY code,33 using a finite displacement34 of 0.02 Å in 3 × 3 × 3 supercells of theoriginal simulation cell. The Brillouin zone has been sampled by 30× 30 × 30 and 10 × 10 × 10 grids of phonon wavevectors in the 12 molecule and 96 molecule simulation cells, respectively.The implementation of the QHA in PHONOPY then yields a continuousrepresentation of the volume-dependent second and third terms in 35 and the phononmode-dependent Grüneisen parameters γi. Convergence checks for the VIE can be found in the Supporting Information.A continuous representation of V0, 4 yield an even smaller and more accurately determinedanomalous VIE (0.050(2)%) than the earlier data from the work of Röttgeret al.3 (0.090(15)%). In both studies,the lowest temperature at which measurements have been performed is10 K. For that reason, we have calculated U(V)36 and could thus significantly affect results for the VIE. This isthe most likely reason for the difference of 0.07% between earlierDFT results obtained with the same exchange-correlation functional(PBE).14 Our own PBE calculations eliminate thissource of error and perfectly reproduce the value for V0 obtained in earlier work.30 In combinationwith our meticulous convergence tests for the phonon calculationswith respect to the VIE (see the Supporting Information), we can therefore confirm without any further doubts the conclusionsfrom earlier work,14 namely that the PBE functionalreproduces the experimentally observed anomalous VIE but overestimatesit. Likewise, our computational setup also confirms that the q-TIP4P/Fforce field, which is based on fixed point charges, yields a normalVIE for ice Ih.7 The same holds for thetwo polarizable force fields AMOEBA14 and the recently establishedSCME/f. The results for 23 Indeed, omitting these terms (MB-pol w/o 3B in U(V0) in the MB-pol force field.38 Thisdoes not change when moving away from V0 as shown in At first glance, the strong influence of these termsis surprisingbecause they do not constitute a large contribution to the cohesive(lattice) energy according to the decomposition of the internal energyat the equilibrium volume 4 as well. This is illustrated in the form of therelative volume changes T ≤ 200 Kin 2O and D2O ice Ih shows a negative slope for T ≤ 60 K. This negative thermal expansion(NTE) has been modeled successfully before39 and is also reproduced by all methods considered here, except forSCME/f. This leads to a small offset in the relative volume changefor H2O between SCME/f and the experimental data at highertemperatures, which remains almost constant. Apart from that, SCME/fcaptures the shape of the experimental curve for T), resulting in the prediction of a normal volume isotopeeffect. Both SCME/f and q-TIP4P/f yield an even worse shape for T ≤ 100 K) for both H2O and D2O; error canceling ultimately results in a better descriptionof VIE(T) compared to SCME/f.To investigate the importance of the aforementionedshort-rangethree-body contributions (in MB-pol) for the VIE more closely, itis instructive to evaluate 10 their change upon compression and expansioncan be analyzed in detail by means of the mode-dependent Grüneisenparameters PZP yield expansion (contraction) of the volume due tozero-point energy effects. 2Oor D2O, respectively, as to be expected according to theresults compiled in PZP needs to be smaller (larger) for H2O than for D2O. The corresponding differences are shownin PZP, which unfortunatelycannot be measured directly, the differences between the contributionsfrom the mode groups vary much more when considering different interactionmodels. The PZP contribution from theHB and HS groups is hardly affected by H–D substitution  andthe contractive PZP of the S modes, whichpredominantly determine the sign of the zero-point pressures differencebetween both isotopologues.According to the QHA see 1 the temtion see 3c, which10 havealready pointedthat the subtle interplay of the PZP contributionsfrom different mode groups makes it additionally challenging to determinewhether a particular interaction model captures the VIE correctlyfor the right reason. We have already noted in our earlier work11 that the measurements by Minceva-Sukarova etal.13 of the pressure dependence of theRaman peak for the S mode group in H2O ice Ih at 246 Kplay a key role in this context. As further detailed in the Supporting Information, this allows us to obtaina good estimate for the contribution by the S mode group Salim et al.13 have not measured the S mode group frequencyshift for the D2O isotopologue of ice Ih. Because all interactionmodels suggesta strongly localized character of the S modes, the outcome of sucha measurement can be estimated based on the reduced masses associatedwith the O–H and O–D bonds as Supporting Information), which is consequentlyagain in excellent agreement with the MB-pol value in Unfortunately, Minceva-Sukarova et al.2O and D2O ice Ih up to 200 K. Finally, thezero-point pressure is decomposed into contributions from differentvibrational mode groups, and an estimate from experimental data forthe contribution from the intramolecular stretching modes of H2O ice Ih is extracted, which is completely independent fromall measurements related to the VIE. Among all interaction modelsconsidered here, this is in best agreement with MB-pol—whilebeing significantly overestimated by PBE and underestimated by allother force fields. This suggests that MB-pol yields the anomalousVIE of ice Ih for the right reason. It thus also enables unprecedentedquantification and atomic-scale understanding of its driving force,namely the delicate competition between the expansive librationaland contractive intramolecular stretching modes upon substitutionof hydrogen by deuterium, mitigated by short-range three-body effects.Future computational studies should investigate other ice polymorphs.However, before embarking on this endeavor, it would be highly desirableto have experimental benchmark data for contribution from other vibrationalmode groups to the zero-point pressure, ideally with smaller errorbars and for both H2O and D2O isotopologues.This bears the exciting prospect of obtaining fundamental insightsabout volume isotope effects in those other polymorphs as well andto establish a delicate benchmark that can be used for the furtherdevelopment and testing of interaction models targeting condensedforms of water. Altogether, this could provide a new angle for thefundamental understanding of hydrogen bonding in these most paradigmaticsystems.Insummary, this study provides new insights into the volume isotopeeffect of ice Ih based on calculations within the quasi-harmonic approximationby employing a variety of different interaction models. Numericallyprecise all-electron DFT calculations with the PBE exchange-correlationfunctional provide a reference value and confirm that PBE yields ananomalous VIE but largely overestimates its magnitude. Among the threestate-of-the-art polarizable force fields only MB-pol yields an anomalousVIE, the magnitude of which is in significantly better agreement withthe most recent experimental data than PBE. A detailed analysis basedon the MB-pol energy partitioning reveals a surprisingly large influenceof the cooperative short-range interaction between three water moleculesfor the VIE and the temperature dependence of the volume of H