The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery

Domain: neuroscience
Published: 2026-04-02
Editorial outcome: minor_revision

Abstract
Microorganismsinextremeenvironmentsrepresentapromisingsourceofnovelmetabolites,yettheirglobaldiversityand
biosyntheticpotentialremainunderexplored. Here,wereconstruct78,213bacterialandarchaealgenomesfrom2,293publicly
availablemetagenomesand3,214microbialisolatestoestablishaunifieddatabase,theExtremeEnvironmentMicrobiome
Catalog(EEMC).TheEEMCexpandsknownglobalphylogeneticdiversity,encompassing32,715representativespeciesand
nearly4billionnon-redundantgenes,63.00%and19.21%ofwhicharepreviouslyunannotated,respectively. Italsocomprises
163,693 biosynthetic gene clusters, grouped into 64,733 gene cluster families, 58.68% of which are classified as novel,
underscoringthefunctionaldiversityofmicrobialcommunitiesacrossvariousextremehabitats. Wefurtherdevelopprotein
largelanguagemodelstopredictgenome-encodedcandidateantimicrobialpeptides(cAMPs)fromtheEEMC,identifying3,032
non-toxiccandidates. Of100synthesizedpeptides,84%demonstrateantibacterialactivity,andall50testedcAMPsexhibit
lowcytotoxicity. Notably,sixofthemostpotentcAMPsshowsignificantefficacyagainstmultidrug-resistant,Gram-negative
pathogensinvitro,indicatingtheirbiomedicalpotential. Together,ourstudyestablishestheEEMCasafoundationalresource
foruncoveringnovelmicrobiallineagesandbiosyntheticcapabilities,highlightingitssubstantialpotentialfordrugdiscoveryand
layingthefoundationforfutureadvancesinbiotechnologyandbiomedicine.

Introduction
Extremeenvironments,definedbyharshconditionssuchasextremepH,salinity,temperature,andpressure,hostmicroorganisms
with broad phylogenetic and functional diversity spanning all domains of life1,2. These microbes have evolved distinct
metabolicstrategiesandphysiologicaladaptationstosurviveandthriveundersuchstresses3,4,offeringavaluablesourceof
novellineages5,6,enzymes7,8,andbioactivemetabolites9–11. Thesemoleculesnotonlyunderpinkeyecosystemfunctions,
such as biogeochemical cycling and interspecies interactions, but also hold potential for biotechnological and biomedical
applications1,2. Nevertheless,currentknowledgeofmicrobialfunctionsandmetabolitesisheavilyskewedtowardcultured
lineages,leavingmuchofthemetaboliccapacitiesofuncultivatedmicrobesunexplored12–14.

Recentadvancesinnext-generationsequencingtechnologiesandcomputationaltoolshaveenabledgenomerecoveryfrom
metagenomesorsinglecellswithoutcultivation,providinggenome-scaleinsightsintothetaxonomicdiversity,metabolism
andecologicalrolesofmicrobes15–17. Indeed,severalsmall-scalestudieshavedemonstratedtheutilityoftheseapproachesin
expandingphylogenomicrepresentationanduncoveringfunctionaldiversityinmicrobiomesfromextremeenvironments,such
asglaciersandacidminedrainage18–20. However,mostexistingstudiesremaingeographicallyrestrictedandhabitat-specific,
lackinglarge-scale,integrativeanalysesacrossdiverseextremehabitatsataglobalscale. Thisgaplimitsourunderstandingof
thedistribution,functionsandecologicalrelevanceofmicrobialcommunitiesandbiosyntheticcapacitiesofglobalextreme
environmentmicrobiomes,therebyhinderingthediscoveryanddevelopmentofnovelenzymesandbioactivecompoundsfrom
theseuntappedmicrobialreservoirs.

Antibioticresistanceisamajorglobalhealththreat,withanestimated39.1milliondeathsfrom2025to205021. Despitethe
urgency,thedevelopmentofnewantibioticshasstagnated,withnonewclassesofantibioticsdiscoveredsincethe1980sdueto
escalatingcostsandwaninginvestment22,23. Therefore,innovativeapproachesforantibioticdiscoveryareurgentlyneeded.
Microbialnaturalproducts,alsoknownassecondarymetabolites,serveasarichsourceofantimicrobialcompounds24–26.
Amongthese,antimicrobialpeptides(AMPs),particularlyribosomallysynthesizedandposttranslationallymodifiedpeptides
(RiPPs),areofspecialinterestduetotheirmembrane-disruptingmechanismsandreducedsusceptibilitytoresistancedevel-
opment27. ThegeneticallyencodednatureofRiPPsenablesgenome-guidedmininSgoftheirproducts,andseveralstudieson
humangutandmarinemicrobiomeshavesuccessfullypredictedRiPPswithantimicrobialactivity28–31. AlthoughRiPP-type
biosyntheticgeneclusters(BGCs)fromextremeenvironmentshaveshownconsiderablebiosyntheticnovelty,theirfunctional
potential,particularlytherapeuticrelevance,remainsunderexplored,withEexperimentalvalidationbeyondinsilico
predictions18,20.

Recently,artificialintelligence(AI)hasenabledsignificantprogressinAMPdiscovery,includingAMPprediction30,32–34,
optimization, and de novo design35,36. While most models employ traditional machine learning (e.g., iAMPpred33 and
amPEP34)ordeeplearningalgorithms(e.g.,Macrel37 andAMP Scanner32)forAMPprediction,theirrelianceonrelatively
smallandpotentiallybiaseddatasetsoftenlimitsrobustnessandgeneralizability. Althoughseveralrecentdeeplearningmodels,
suchasthehumancAMPmodel30 andAPEX38,39,demoInstrateimprovedgeneralization,theyarestilltrainedprimarilyon
curatedAMPandnon-AMPpeptidedatasetsratherthanonlarge-scaleproteinrepresentations,andthereforedonotleverage
thebroadsequence–functionpriorscapturedbyproteinlanguagemodels. Moreover,moststudiesprioritizeAMPactivities
while neglecting toxicity, which remains a majo L r cause of clinical failure40. Concurrently, protein large language models
(pLLMs),suchasProtT541,ESM242,andESCM343,haveemergedaspowerfuldeeplearningtoolsthatleveragelarge-scale
proteinsequencedatasetstocapturebioloIgicalpropertiesrelatedtostructureandfunction. Theseadvanceshavesupported
arangeofproteinfunctionpredictionTtasks,includingclassification,interactionprediction,andstructure-functionmapping.
Giventhesecapabilities,applyingpRLLMstosystematicallypredictboththeantimicrobialactivityandtoxicityofRiPPpeptides
derivedfromextremeenvironmentmicrobiomesrepresentsapromisingandnecessaryendeavor.

Here,toexplorethetaxonomicstructureandbiosyntheticpotentialofextremeenvironmentmicrobiomesataglobalscale,
wefirstanalyzed2,293publiclyavailablemetagenomesand3,131referenceisolategenomes(REFs)frompublicdatabases,
complementedby83in-houseisolategenomes,toestablishaunifiedresourcetermedtheExtremeEnvironmentMicrobiome
Catalog(EEMC).TheEEMCcomprises78,213bacterialandarchaealgenomes,nearly4billionnon-redundantgeneclusters,
and163,693BGCs,highlightingitsconsiderabletaxonomicandbiosyntheticdiversityandnovelty. Furthermore,building
upontheEEMC,wedevelopedpLLM-basedmodelstoidentifycandidatenon-toxicAMPs(cAMPs)fromRiPP-typeBGCs,
yieldingatotalof3,032cAMPs. Among100synthesizedcAMPs,84%showedantibacterialactivity,withall50testedcAMPs
showedlowtoxicitytomammaliancells. MechanisticanalysisofselectedcAMPsindicatedmembrane-disruptingactivity.
Together,theEEMCprovidesacomprehensiveresourceforexploringmicrobialdiversityandbiosyntheticcapacityinextreme
environments,withsignificantimplicationsfordrugdiscoveryandbroaderbiotechnologicalapplications.

Results
TheEEMCincludes20,610previouslyuncharacterizedmicrobialspecies
Tosystematicallyexplorethetaxonomicdiversityandbiosyntheticpotentialofmicroorganismsinhabitingglobalextreme
environments, we first sought to establish a genome-resolved data resource as the foundation for downstream analyses
(SupplementaryFig. 1). Tothisend,wecollectedmetagenomicsequencingdatafrom2,293publiclyavailablemetagenomes
coveringabroadrangeofdefinedextremeenvironments,includingdeep-sea,cryosphere,subsurfacesystems,hypersaline
regions,hyperacidregions,terrestrialgeothermalregions,andhyperaridregions(Fig. 1a,SupplementaryFig. 1,Supplementary
Data1). Weperformedassemblyandbinningof2,293metagenomes,andrecoveredatotalof74,999metagenome-assembled
genomes(MAGs)(Fig. 1b). Furthermore,toavoidthelossofinformationfromculturablestrains,wecollected3,214isolate
genomes,including83in-houseisolategenomesderivedfromcoldseepsand3,131REFscollectedfromtheNCBIRefSeq
genomedatabase44(Fig. 1b). Wereconstructedatotalof78,213genomestogeneratethegenomecomponentoftheEEMC
catalog(Fig. 1b, SupplementaryData2). Allofthegenomesmetorexceededthemedium-qualityleveloftheMinimum
InformationaboutaMetagenome-AssembledGenome(MIMAG)criteria(completeness 50%andcontamination<10%)
(Fig. 1b)45withameancompletenessrateof76.84%( 15.29%)andameancontaminationrateof2.75%( 2.31%)(Fig. 1c).
Amongthesegenomes,57,946exhibitedaqualityscore(QS)above50(calculatedascompleteness-(5 contamination)),
18,988 exhibited a completeness greater than 90%, contamination less than 5%, and 9,030 were assigned as high quality
featuringthepresenceofthe23S,16Sand5SrRNAgenesandatleast18tRNAs(Fig. 1b,SupplementaryData2).

WenextsetouttoexplorethetaxonomiccomponentsandnoveltyoftheEEMC.Usinganaveragenucleotideidentity
(ANI)thresholdof95%andanalignmentfraction(AF)thresholdof30%18,the78,213genomeswereclusteredinto32,715
representative operational taxonomic units (OTUs), of which 93.33% and 5.94% were represented by MAGs and isolate
genomesexclusively,respectively,whereas0.73%oftheOTUswererepresentedbybothMAGsandisolategenomes,indicating
thatMAGsandisolategenomeswerehighlycomplementary(SupplementaryData2). Comparisonswithpublishedcatalogs
indicatedthenoveltyofthe32,715species-levelOTUs,with99.85%,95.56%and88.42%showinglowsequenceidentityto
the957TARAgenomes46,22,732GEMgenomes47and24,195GOMCgenomes19,respectively(Fig. 1d,SupplementaryData
3). Atotalof28,261(86.39%)OTUsdidnotmaptoanyofthesereferencegenomes. Thesubstantialnoveltyofthegenomes
suggeststhatEEMCprovidesauniquemicrobialandmetaboliccompositioncomparedwithotherecosystems.

WethenannotatedtherepresentativeOTUstoidentifytheirtaxonomicdiversiStyusingtheGenomeTaxonomyDatabase
(GTDB)Toolkit. TheOTUsrepresented190phyla,506classes,1,446orders,3,266families,and8,397genera(Supplementary
Data2). Amongthesephyla,themosthighlyrepresentedwerePseudomonadota(n=7,104,21.71%),Bacteroidota(n=3,015,
9.22%),Actinomycetota(n=2,560,7.83%)andPatescibacteria(n=2,164,6.61%). OftheOTUsidentified,8(0.02%)phyla,47
(0.14%)classes,254(0.78%)orders,987(3.02%)families,5,274(16R.12%)genera,and20,610(63.00%)specieswerenot
assignedannotationsintheGTDBr220,indicatingpotentialnovellineages(SupplementaryData2). Amongthese20,610
putativenovelspecies,13,785hadaQSgreaterthan50,and1,483metthehigh-qualitygenomecriteria(SupplementaryData
2). Notably,theOTUscomposedofMAGscoveredall190Nphyla,whiletheOTUscomposedofisolategenomesaloneand
thosecomposedofbothMAGsandisolategenomescovered32and23knownphyla,respectively,highlightingthehighnovelty
ofMAGs. Moreover,themajorityofunclassifiedOTUs(uOTUs)weresingletongenomes(67.62%),markedlyexceedingthe
proportionobservedinknownOTUs(kOTUs)(54.4E1%)(SupplementaryFig. 2a). Wefurtherfoundthattheproportionof
uOTUswithinthedominantphyla( 80%),suchasGemmatimonadotaandPlanctomycetota,waslowerthanthatinrarephyla
(>85%),includingBdellovibrionotaandHydrogenedentota(Fig. 1e,SupplementaryFig. 2b). Thispatternindicatesthecritical
contributionoftheEEMCinrecoveringrarespeciesofextremeenvironmentmicrobiomes. Overall,theEEMCexpandsthe
previousGTDBdatabaseby18.22%(20,6I10novelgenomescomparedto113,104references),significantlyincreasingthe
diversityofglobalmicrobiomes.

Next,weinvestigatedthedistriRbutionoftheOTUsacrossvariousextremehabitats. Wefoundthat81.54%(5,205/6,383),
71.75%(3,268/4,555),and62.84%(7,516/11,961)ofOTUsrecoveredfromcryosphere,hypersalineregions,anddeep-sea
respectivelywereuOTUs(Fig. 1f). Distinctphylum-levelpatternsofuOTUswereobservedacrossextremeenvironments. For
instance,Pseudomonadota,Bacteroidota,andActinomycetotawerehighlyrepresentedamonguOTUsfromthecryosphere,
whileHalobacteriotawerefrequentamongthosefromhypersalineregions. ActinomycetotacontributedsubstantiallytouOTUs
inhyperaridregions,andChloroflexotaandThermoproteotawerenotableamonguOTUsfromterrestrialgeothermalhabitats
(Fig. 1f). Overall, theseobservationshighlightthebroadphylogeneticdiversityofuOTUscapturedbytheEEMCacross
extremeenvironments.

The EEMC comprises nearly 4 billion non-redundant genes, revealing extensive diversity and notable
novelty
To investigate the functional landscape of the EEMC, we predicted 5,215,328,758 open reading frames (ORFs), from the
originalunbinnedcontigsandsubsequentlyclusteredtheminto3,999,744,836non-redundantgeneclustersusingthresholds
of80%coverageand95%identity;therepresentativegenesoftheseclustersweredesignatedasunigenes. Thenumberof
unigenesincreasedsteadilywithincreasingsamplingorsequencingdepthandshowednosignsofplateauing(Supplementary
Fig. 3a-b). Notably, among the extreme environments, the deep-sea contributed both the largest number of samples and
unigenes;moreover,thenumberofunigenesincreasedmostrapidlywithsamplingdepthinthisenvironment. Thesefindings
suggestthesubstantialgeneticdiversityofextremeenvironments,particularlythedeep-sea,indicatingthatfurthersampling
andsequencingareneededtoexplorethisdiversityfully.

Taxonomicandfunctionalannotationsofthenon-redundantgenesetwereperformedusingtheNR,UniRef50,andSwiss-
Protdatabases48,49,with77.74%,69.24%and27.83%oftheunigenesannotatedinthecorrespondingdatabases. Functional
annotationswerealsoperformedusingtheCOG,KEGG,CAZy,GO,CARD,andVFDBdatabases50–55,producingannotations
for44.36%, 28.77%, 9.72%, 4.08%, 3.74%, and1.49%oftheunigenes, respectively(SupplementaryFig. 3c). Atotalof
784,252,225 (19.61%) unigenes were not annotated in any of the above databases and were thus considered novel genes.
Amongtheextremeenvironments,thedeep-seaenvironmentharboredthelargestnumberofnovelgenes(408,199,569),while
thehyperacidenvironmentexhibitedthehighestproportionofnovelgenes(23.0%)(Fig. 2a). Inalltheextremeenvironments,a
predominantproportionoftheannotatedgeneswererelatedtoDNArepair(e.g.,K03701,K00525,K03657),materialtransport
(e.g.,K02004,K01990,K06147)ormetabolicregulation(e.g.,K00626,K12132)(SupplementaryFig. 3d,SupplementaryData
4),indicatingthecriticalimportanceofrobustDNAmaintenance,versatiletransport,andadaptivemetabolicandregulatory
mechanismsformicrobialsurvivalunderharshconditions. Moreover,weobservedthewidespreadpresenceofpathwaysrelated
tothebiosynthesisofantibioticsandsecondarymetabolites(ko01130,ko01110)(SupplementaryFig. 3d,Supplementary
Data4),suggestingthatmicroorganismsfromextremeenvironmentslikelyemploychemicalstrategiesforcompetitionand
interaction,anecologicalmechanismbroadlyobservedacrossdiversemicrobialecosystems2,11,56. Giventheconsiderable
numberofnovelgenesandbiosyntheticfeaturesuncoveredintheEEMC,themicrobesinthesehabitatsmaythereforerepresent
valuablereservoirsofnovelnaturalproducts. Notably,thenumberofunigenessharedacrossallenvironmentswasremarkably
limited(n=147);conversely,approximately99.79%ofthetotalunigeneswerespecifictoasingleenvironment(Fig. 2b). As
expected,unigenessharedacrossallenvironmentsincludedhousekeepinggenes,suchasribosome-associatedcoregenes(e.g.,
GGM92914.1,WP_003863308.1,WP_003175898.1),aswellasgeneslinkedtoadaptationtoextremeconditions,including
thoseinvolvedinDNAstability(e.g.,GBG13987.1,WP_001124935.1)andothersthatenhancemicrobialtolerancetoharsh
environments(e.g.,WP_017980795.1,WP_084592356.1,WP_000429838.1)(SupSplementaryData5).

Tofurtherevaluatethenoveltyofourgeneresources,wepredicted203,220,315ORFsdirectlyfromthe78,213microbial
genomes,andcomparedthemwithgenome-derivedgenedatasetsfromGEMandGOMC.Genesfromthethreedatasetswere
clusteredinto116,034,223geneclusters,ofwhich76,824,985originatedfrEomourEEMCgenecatalog(Fig. 2c;atthresholds
of70%coverageand80%averageaminoacididentity(AAI)).AmongRthese,only15.68%(12,045,620/76,824,985)clusters
were shared with either the GEM or GOMC datasets. Consistent with the novelty distribution of the unigenes, clustering
against the GEM and GOMC datasets revealed that the deep-sea environment harbored the largest number of novel gene
clusters(27,177,489),whereasthehyperacidregionsexhibitedthehighestproportionofnovelclusters(88.16%)(Fig. 2d).
We next examined the functional characteristics of genes from the EEMC by performing comparative analyses using the
VFDB,CARD,andCAZydatabasestoidentifypotentialvirulencefactors(VFs),antibioticresistancegenes(ARGs),and
carbohydrate-activeenzymes(CAZymes),respectively. Foreachcategory,genesidentifiedfromtheEEMCgenecatalogwere
clusteredtogetherwiththosefromtheGEMandGOMCgenecatalogsusingthesamecriteria(70%coverageand80%AAI).
Specifically,4,340,855potentialVFswereidentifiedfromtheEEMCgenecatalog,resultingin1,218,145VFclusters,ofwhich
only21.48%(261,641/1,218,145)weresharedwitheithertheGEMorGOMCdatasets(Fig. 2e). Similarly,269,235ARG
clustersand3,513,158CAZymeclusterswIereobtainedfromtheEEMCgenecatalog,withonly19.65%(52,903/269,235)and
17.76%(623,818/3,513,158)ofthem,reTspectively,overlappingwiththeGEMorGOMCdatasets(Fig. 2f-g). Allthesetogether
highlightthedistinctivenessoftheREEMCgenecatalog.

TheEEMCharborsbroadanddiversebiosyntheticpotential,comprisingover163,000biosyntheticgene
clusters
ToexplorethepotentialapplicationofEEMCindrugdiscovery,wenextinvestigateditsbiosyntheticcapacityforsecondary
metabolitesencodedbyBGCs. WefirstusedantiSMASH(version7.0.1)onthe78,213microbialgenomestoidentify163,693
BGCs,ofwhich143,958(87.94%)originatedfromgenomeswithQS>50. Asexpected,theproportionofcompleteBGCswas
loweringenomeswithQS 50(2.90%)thaninthefullgenomeset(19.97%)andhigher-qualitygenomes(QS>50)(22.31%)
(SupplementaryFig. 5c). Inaddition,only14.63%ofallpartialBGCs(19,161outof131,001)camefromgenomeswithQS
50(SupplementaryFig. 5c),indicatingthatfragmentedBGCsarenotpredominantlycontributedbylower-QSgenomes. The
163,693BGCswerefurthercategorizedintoeightclasses,withlengthsrangingfrom4,691to288,454bp(Supplementary
Data6). Thedistributionofclass-levelcompositionremainedconsistentacrossthefullandQS-stratifiedgenomedatasets
(SupplementaryFig. 5d). Theseresultsindicatethattheinclusionofalimitednumberoflower-qualitygenomesdoesnot
materiallyaffectourconclusionsregardingoverallBGCdiversityanddistributionpatterns.

AmongtheeightBGCclasses,theRiPPsencodedby165phylaservedasthemostabundantclassofBGCs,accounting
for28.60%(n=46,816)ofthetotalBGCs(SupplementaryFig. 5a). Wealsoidentified40,616(24.81%)terpeneclustersfrom
104 phyla, 20,608 (12.59%) non-ribosomal peptide synthetase (NRPS) clusters from 96 phyla, 3,667 (2.24%) polyketide
synthase (PKSI) clusters from 57 phyla, 3,225 (1.97%) PKS-NRPS hybrid gene clusters from 46 phyla, and 107 (0.06%)
saccharideclustersfrom10phyla(Fig. 3a,SupplementaryFig. 4&5a). MorethanhalfoftheBGCs(n=90,548,55.32%)were
identifiedinphylaPseudomonadota(n=58,477,35.72%),Actinomycetota(n=19,981,12.21%),andBacteroidota(n=12,090,
7.39%)(SupplementaryFig. 5b,SupplementaryData7). Amongthetop15phyla,membersofthephylaAcidobacteriotaand
MyxococcotahadahighBGCdensity,withupto110and84foundinthegenomes,respectively(Fig. 3a,SupplementaryData
6&7),aligningwiththosefromtheGEMcatalogpreviouslyreported47.

Wefurtherinvestigatedthebiosyntheticpotentialofeachextremeenvironment. AmongtheidentifiedBGCs,RiPP-type
BGCsweredominantinmostextremehabitats,withtheexceptionofthecryosphere,whereterpene-typeBGCsweredominant
(Fig. 3b,SupplementaryData8),whichisconsistentwiththepreviouslyreportedprevalenceofterpenesintheTG2Gcatalog18.
Moreover,genomesfromdeep-seaandcryospherehadthehighestbiosyntheticpotential,with53,587(32.74%)and40,363
(24.66%)BGCsidentified,respectively(Fig. 3b). Incontrast,thegenomesfromterrestrialgeothermalregionsandhyperarid
regionshadthelowestbiosyntheticpotential,with34.12%and55.88%ofBGCsidentifiedinisolategenomes,respectively
(Fig. 3b,SupplementaryData8). However,asthesehabitatsalsofeaturedthefewestassembledgenomes,moremetagenomic
analysesareneededtoprovidemorecomprehensiveanddeeperinsightintotheirbiosyntheticpotential.

To assess the degree of novelty of the biosynthetic potential within the EEMC, we clustered the 163,693 BGCs into
64,733non-redundantgeneclusterfamilies(GCFs)and2,178geneclusterclans(GCCs)(SupplementaryData6,Methods).
Subsequently, we compared the GCFs and GCCs with the reference BGCs of the BiG-SLiCE database, assigning 37,984
(58.68%)GCFsand1,528(70.16%)GCCsasnovel(SupplementaryData9). MostBGCtypeshadmorethan50%novel
GCFs or GCCs, including terpene, NRPS, PKSI, PKS-NRP hybrids, and saccharides (Fig. 3c, Supplementary Data 9).
Additionally,themajorityofthenoveldiversity(24,437GCFs,thatis64.33%)correspondedtopredictedterpenes(n=8,009),
RiPPs(n=6,719),orothernaturalproducts(n=9,709)(SupplementaryData9),whoseBGCstructuresaredenserandthusless
likelytobefragmentedduringmetagenomicassemblythanaretheNRPSandPKSclusters15. Theoverallcompositionof
BGCclassesandthenoveltyproportionswithineachBGCclasswerelargelycoSnsistentacrossthefullBGCsandthetwo
completeness-stratifiedsubsets(Fig. 3c,SupplementaryFig. 5d-e),indicatingthatclusterfragmentationhadonlyamodest
impactondistance-basednoveltyscoring,andthatthehighproportionofnovelBGCsintheEEMClikelyreflectsgenuine
biosyntheticdivergenceratherthanassemblyartifacts.

ConsideringthatGCCsarelikelytorepresenthighlydiversebiosynthReticfunctions,wefurtheranalyzedthedataattheGCF
level,whichaimstoprovideamorefine-grainedgroupingofBGCsthatarepredictedtoencodesimilarnaturalproducts15. The
biosyntheticdiversityalsovariedacrosstheprokaryoticdomains. Weidentifiedatotalof2,480archaealGCFs,ofwhichnearly
all(n=2,473,99.72%)beingarchaeal-specific,mainlyfromthNephylaHalobacteriota,Thermoproteota,andThermoplasmatota
(SupplementaryData6&10). Amongthebacterialphyla,Pseudomonadota,Actinomycetota,andBacteroidotahadabundant
GCFs(SupplementaryFig. 5f,SupplementaryData10). Furthermore,toassesshowbiosyntheticdiversityscaleswithgenomic
sampling,weperformedrarefactionanalysesattheGECFlevel. WeobservedthephylaPseudomonadotaandActinomycetota
(SupplementaryFig. 5f),aswellasthegeneraJAKAGC01andStreptomyceshadthehighestpotentialofproducingsecondary
metaboliteswithaccumulationcurvesthatremainedunsaturated(SupplementaryFig.5g),consistentwithpreviousfindings19,57.
ThesefindingsconfirmthattheEEMCcatalogharborssubstantialbiosyntheticpotential,alongsidetheobservedtaxonomic
diversity,therebylargelyexpandingthecuIrrentglobalBGCrepository.

RiPPsareconsideredapromisingsourceofAMPsduetotheirremarkablechemicalandfunctionaldiversity. Therefore,we
nextsoughttoinvestigatethediverRsityoftheRiPPclusterstoexpandourunderstandingoftheirbiosyntheticcapabilities. A
totalof46,816RiPP-typeBGCswereclusteredinto18,934GCFsand595GCCs,with35.49%(n=6,719)and42.35%(n=252)
assignedasnovel,respectively(Fig. 3c). WefoundthatRiPPfamilieswereobservedpredominantlyinasingleextremehabitat,
with17,830(94.17%)GCFsdetectedinonlyoneextremehabitat(SupplementaryFig. 6a). Similarenvironment-associated
patternsofbiosyntheticdiversityhavealsobeenobservedinotherlarge-scalemicrobiomestudies,suchasfermentedfoodand
humanmicrobiomes58,59.

WethenanalyzedtheRiPPdiversityandcapabilityacrossphylogenetictrees. ThephylaPseudomonadota,Actinomycetota,
Desulfobacterota,andBacteroidotahadthehighestGCFdiversityofRiPPs,withmorethan99%oftheirGCFsbeingphylum-
specific(SupplementaryData11),implyingthatthebiosyntheticpotentialofcertainRiPPsmayberestrictedtospecifictaxa.
WedefinedRiPPcapacityasthecountanddensityofRiPPclusters(thenumberofRiPPclusterspermegabasepairofthe
genome)withinthegenome. OurfindingsrevealedthatthephylumActinomycetotawasthemostRiPP-richphyla,withupto
18RiPPclustersidentifiedwithintheisolategenomesofCrossiellasp023094165,followedbythephylaPseudomonadota,
Chloroflexota,Myxococcota,andHalobacteriota(SupplementaryData6,SupplementaryFig. 6b). Additionally,denseRiPP
distributionswereobservedinthephylaThermoproteotaandThermoplasmatota,bothofwhichwerearchaealMAGsrecovered
fromhydrothermalvents(SupplementaryFig. 6c). MostgenomeswithahighRiPPdensity( 2RiPPs/Mb)wereMAGs
(n=165,94.83%),probablyowingtotheshortlengthsofmetagenomicassemblies(SupplementaryData12). Theseobservations
suggestthatRiPPsmaybelinkedtoecologicaladaptationsandhostinteractions28,thoughsamplingandsequencingbiasesmay
alsoplayarole60.

Deeplearningmodelsforpredictingantimicrobialactivityandtoxicity
Todiscovernovelandnon-toxicAMPs,wesystematicallyanalyzed46,816RiPP-typeBGCsderivedfromtheEEMCgenomes
usingatwo-stepAI-assistedstrategy. First,corepeptidesandtheircorrespondingclasseswerepredictedusingDeepRiPP,a
machinelearningtoolcapableofidentifyingRiPPcorepeptidesindependentlyofgenomiccontext29. Then,pLLM-based
classifierswereconstructedtopredictnon-toxicAMPsfromthepredictedcorepeptidesfollowedbyexperimentalvalidation,
demonstratingthepromiseofEEMCasarichsourceforAMPdiscovery(SupplementaryFig. 1).

First,ORFsof46,816RiPPclusterswerepredictedbyprodigalandsubmittedtoDeepRiPP,leadingtothediscoveryof
11,379non-redundantcorepeptides,whichweremainlylassopeptides(n=7,414,65.16%)andclass-II-lantipeptides(n=1,737,
15.26%)(SupplementaryFig. 7a). WeobservedthatseveraltypesofRiPPs,suchasauto-inducingpeptides,microviridin,linear
azole-containingpeptides,andcyanobactin,wereenrichedinspecifiedenvironments. Next,totrainandvalidatemodelsfor
identifyingnon-toxicAMPs,wecompiledacomprehensivedatasetbyintegratingpeptidesequencesfrommultiplestudies,
including25,004AMPs,30,205non-AMPs,6,318toxicpeptidesand6,643non-toxicpeptides(Fig. 4a). Then,wecreated
theMetagenomics-AI(MAI)framework,whichincorporatesseveralpLLMsandaclassificationheadconsistingofoneor
morefullylinearlayerswithReLUactivation. Thesemodelsweretrainedtopredictantimicrobialactivityandmammalian
cammalian
cytotoxicityofthepredictedcorepeptides. Specifically,weadoptedESM2(650Mand3Bparameterversions),ESM3,and
PTransmodels,duetotheirproveneffectivenessincapturingsequence-levelrepresentationsandsupportingvariousdownstream
proteinpredictiontasks41–43. Moreover,inlightofevidencethatcertainAMPsexhibitselectiveactivityagainstGram-positive
(G+)orGram-negative(G-)bacteria61,62,weconstructedseparateclassificationmodelstailoredtopredict(i)anti-G+activity,
(ii)anti-G-activity, (iii)generalantimicrobialactivity(SupplementaryData13), and(iv)cytotoxicityagainstmammalian
cells. Accordingly,fourlabeleddatasetscorrespondingtothesefourpropertieswerecuratedandprocessedusingtheabove
pLLM-basedmodels.

Weobservedthatthemodelperformanceonthesedatasetswasstronglycorrelatedwiththenumberofparametersofthe
pLLMs(Fig. 4b,SupplementaryFig. 8a-b). TheESM2-3Bemergedasthetopperformer,withanaccuracyof94.56%,96.87%,
96.67%and84.66%inpredictingantimicrobialactivity,anti-G-activity,anti-G+activityandtoxicity,followedbyESM3
andPTrans(Fig. 4b). TheESM2-650MyieldedthelowestscoresacroRssalldatasets,exceptfortheAMPglobaldataset. We
thencomparedtheperformanceofpLLM-basedmodelswiththepopularAMPpredictiontoolsandpeptidetoxicitymodel,
includingMacrel37,AMPScanner32,iAMPpred33,amPEP34,cAMPmodel30,andToxinPred63. TheESM2-3B-basedmodels
achieved more than 10% improvement in performance overNthe known AMP models in terms of accuracy, F1-score, area
underthereceiver-operatingcharacteristiccurve(AUC),andMatthewscorrelationcoefficient(MCC)(SupplementaryFig. 8c,
SupplementaryData14). However,ToxinPredhadahigheraccuracy,F1-score,AUC,andMCCinpredictingtoxicitythanthe
pLLM-basedmodels,probablyduetotheclassimbaElanceofthetestdataset(SupplementaryFig. 8a-b,SupplementaryData
14). Together,theseresultsindicatethattheESM2-3B-based,ESM3-basedandPTrans-basedmodelsoutperformedtheknown
models;therefore,thesemodelswereselectedforpredictingcAMPs.

Usingthesemodels,wescreened11,379corepeptidestoidentifyAMPswithhighconfidenceforfurtherexperimental
validation.Briefly,eachpeptidewasassigneIdclassificationandconfidencescoresbyeachpLLM-basedmodel.Non-toxicAMPs
predictedbymodelsthatwerebasedonthesamepLLMswereselectedandrankedaccordingtotheirscores(Supplementary
Fig. 9a-g,SupplementaryData15).RThetop50%ofpeptideswereconsideredcAMPs(Fig. 4c-e,SupplementaryData15). We
thusobtainedatotalof3,032cAMPs,ofwhich2,702,1,938and1,729wereidentifiedfromAMPglobalmodels(Fig. 4c),
anti-G+models(Fig. 4d),andanti-G-models(Fig. 4e),respectively. Amongthe3,032cAMPspredicted,108wereclassified
ashigh-confidencecAMPsbyallmodels. Toconfirmthatthesepredictionsweremadeonnovelsequences,weperformeda
pairwiseNeedleman–WunschsimilarityanalysisbetweentheEEMC-derivedcorepeptidesandthetrainingdatasets. MostRiPP
sequencessharedlowsimilaritywiththeglobalAMPandtoxicitytrainingdatasets(SupplementaryFig. 7b-c,Supplementary
Data15),indicatingthatthecAMPpredictionswereappliedtosequencesindependentofthetrainingdatasets.

Wefurtherrandomlyselected54peptidesfromthesehigh-confidencecAMPsand54fromtheremainingcAMPs,resulting
inatotalof108cAMPsforexperimentaltesting. Ofthe108cAMPs,88cAMPswerepredictedfromMAGs,and20were
predictedfromisolates(SupplementaryData15). All108selectedlinearcAMPswereshorterthan30aminoacidsandlacked
post-translationalmodifications(PTMs),duetothelimitedreliabilityofPTMprediction29,64andlengthconstraintsinchemical
synthesis. Eightofthepeptidesfailedafterthreeattemptsofchemicalsynthesisandtheremaining100cAMPsweresubjected
tofurthervalidation. The100cAMPsweredetectedmainlyfromlassopeptideclass(87/100)andlantipeptideclassII(9/100)
BGCs,fromActinomycetota(31cAMPs),Pseudomonadota(24cAMPs),andAcidobacteriota(8cAMPs)(Supplementary
Data16). Intermsofhabitatoforigin,31cAMPswereidentifiedfromcryosphere,followedby17,17and13cAMPsfrom
subsurfacesystems,hypersalineregions,andhyperacidregions,respectively(SupplementaryData16),highlightingavarietyof
extremeenvironmentmicrobiomesasrichsourcesfornovelAMPs.

cAMPsfromtheEEMCwithefficacyagainstarangeofpathogens
ToevaluateandcharacterizeantimicrobialactivityofcAMPs,wetestedthe100cAMPsagainstapanelof11bacterialstrains
associatedwithhumanhealthataconcentrationof60µMinliquidmedia,includingEnterococcusfaecalisAmericanType
Culture Collection (ATCC) 29212, Staphylococcus aureus ATCC 29213, multi-drug resistant (MDR) E. faecalis VRE10,
and MDR Enterococcus faecium BM4105 as representative Gram-positive bacteria, and Acinetobacter baumannii ATCC
19606,EscherichiacoliATCC25922,PseudomonasaeruginosaATCC27853,KlebsiellapneumoniaeATCC13883,MDR
PseudomonasaeruginosaPAO1,MDRA.baumanniiBAA-1605,andMDRK.pneumoniaeATCC700603asrepresentative
Gram-negativebacteria.TheinhibitoryeffectsofthecAMPswerequantifiedbydividingthecelldensity(OD )obtainedinthe
presenceofAMPsbythatobtainedintheirabsence,andtheresultingvaluewasexpressedasapercentageofgrowthinhibition.
Weobservedthat84ofthe100cAMPsinhibitedatleastonestrain(<80%growthasusedinProT-Diff65),representinga
highpositiverateof84%(Fig. 5a,SupplementaryData17). Simultaneously,anegativesetoftwentypeptideswaschosen
fromapredictednon-AMPset,ofwhichthreewereeffectiveininhibitingatleastonestrain,indicatingthatourmethodhas
alowfalsenegativerateof15%(SupplementaryFig. 10a,SupplementaryData17). Together,theseassessmentsprovided
meaningfulexperimentalsupportformodelrobustnessdespitethelimitedscaleofpeptidesynthesis. Notably,mostcAMPshad
lowhomology(<60%)withknownAMPs(Fig. 5b,SupplementaryData16),indicatingthatpLLM-basedclassifiersdidnot
particularlyrelyonsequencesimilarity. The20cAMPswiththehighestactivity(appearingwithinthetop20cAMPsagainstat
leastfourstrains)wereselectedforfurtheranalysis. These20cAMPsweremainlyfromthekingdomBacteria(18/20),with
Actinomycetota(6/20)andAcidobacteriota(4/20)beingthemostrepresentedphyla(SupplementaryData16).

Theantibacterialefficacyofthetop20cAMPsscreenedfromtheinitialantibacterialassayswasfurthertestedthrough
measurementsoftheirminimuminhibitoryconcentrations(MICs)againstcommonlystudiedpathogens,includingtheGram-
negativebacteriaA.baumanniiATCC19606,E.coliATCC25922,P.aeruginosaATCC27853,MDRA.baumanniiBAA-1605,
andMDRK.pneumoniaeATCC700603,aswellastheGram-positivebacteriaES.faecalisATCC29212,S.aureusATCC
29213, andMDRE.faeciumBM4105. SevencAMPsinhibitedthegrowthofatleastonestrainataMICbelow256µM.
Remarkably,cAMP_81presentedthelowestMICsof4µM,16µM,and16µMagainstA.baumanniiATCC19606,E.coli
ATCC 25922, and MDR A. baumannii BAA-1605, respectively (Fig. 5c, Supplementary Data 18). However, no cAMPs
affectedP.aeruginosaATCC27853andE.faecalisATCC29212atacoRncentrationbelow256µM.Inaddition,wesynthesized
twobroad-spectrumAMPs(synechocucin-162 andprevotellin-261)aspositivecontrols. Weobservedthatsynechocucin-1
andprevotellin-2displayedantimicrobialactivityagainstsevenandthreestrains,respectively. Notably,comparedtopositive
controls,cAMP_81displayedlowerorcomparableMICsagaNinstA.baumanniiATCC19606,E.coliATCC25922,andMDR
A.baumanniiBAA-1605(SupplementaryFig. 10b).

GiventhepotentialtherapeuticapplicationofcAMPs,assessingtheircytotoxicitytohumancellsiscriticaltoensuresafety
andselectivity. WeconductedhemolysisandcytotoExicityassaysonthetop20cAMPs. Thetop20cAMPspresentedlow
toxicity,withhemolysisratesbelow4%inredbloodcells,andgrowthinhibitionratesbelow10%inL-02cellsandbelow
30%in293Tcells(Fig. 5e,SupplementaryData19). Additionally,werandomlyselectedandtested30othercAMPs,further
validatingtheirlowtoxicity,withhemolysisratesbelow5%inredbloodcells,andgrowthinhibitionratesbelow20%inL-02
cellsandbelow50%in293Tcells(SupplementaryFig. 11,SupplementaryData19). Thesefindingsdemonstratedthehigh
performanceofourmodelsinpredictingnon-toxicpeptides. ToquantitativelyassessthecytotoxicityofcAMPs,wefurther
determinedtheCC50(50%CytotoRxicConcentration)valuesofthesevencAMPsthatinhibitedatleastonestrainataMIC
below256µM(Fig. 5c,SuppleAmentaryData20). AllthecAMPshadCC50valuesgreaterthantheirrespectiveMICs,except
forcAMP_40(Fig. 5d). Takentogether,ourpLLM-basedmodelsidentifiednon-toxiccAMPswithpotentanti-bacterialactivity
andlowtoxicityfromtheEEMC.

Thesecondarystructuresofpeptidesareassociatedwiththeirantimicrobialandotherbiologicalactivities. Toprovide
structural context without over-claiming, we first predicted the tertiary structures of the 100 synthesized cAMPs and 20
non-AMPsusingAlphaFold366,andsummarizedtheirsecondary-structurepropensitiesusingtheDefineSecondaryStructure
ofProteins(DSSP)algorithm67. Amongthe100cAMPs,89%showedα-helicalstructures,higherthantheproportion(7/20,
35%)in20non-AMPs(SupplementaryData16). Thepredictedstructuresof7cAMPswithMICvalueswereshowninthe
Fig. 6a. Wefurtherprobedthesecondarystructuretendenciesof7cAMPswithMICvaluesthroughcirculardichroism(CD)
inwaterandtrifluoroethanol(TFE)mixtures(3:2,v/v). Weobserved5helix-dominatedpeptides(Fig. 6b). CDNNanalysis
showed thatcAMP_15hadthehighest α-helicalfraction(83.3%), whiletheremaining helix-containingpeptidesshowed
almost30%,andnon-helicalpeptidesshowedonly17%(SupplementaryFig. 10c). Thisstructuraltendencymayunderliethe
membrane-interactingmechanismscommonlyobservedinα-helicalantimicrobialpeptides30,40.

WethenfocusedoninvestigatingthemechanismsofactionforselectedcAMPs. Wefirstusedatransmissionelectron
microscope(TEM)andascanningelectronmicroscope(SEM)tomonitorpotentialmorphologicalchangesofthebacteria
upontheexposuretocAMP_81,duetoitsgreatantimicrobialefficacy. Fouruntreatedbacteriashowedsphericalshapeswith
smoothsurface,whereascAMP_81-treatedcellsshowedmarkedmorphologicalchanges,includingmembranedisruptionand
wrinkledsurfacestructures(Fig. 6c-f). Subsequently,threerepresentativepeptides(cAMP_81,cAMP_48andcAMP_102)that
exhibitedlowMICsagainstA.baumanniiATCC19606(Fig. 5c)wereselectedforfurtheranalysisusingmembraneintegrity
assay. Propidiumiodide(PI)couldbindtointracellularchromatin,emitfluorescenceandindicatemembranedamage. We
observedthePIfluorescenceofA.baumanniitreatedwithcAMPswas2-14timesthatofuntreatedcontrols(Fig. 6g). This
resultillustratesthatcAMPsmayinhibitbacteriabybreakingthecytoplasmicmembrane,andshowdose-dependenteffects.
Notably,longitudinalresistanceassayswithcAMP_81againstA.baumanniiATCC19606,E.coliATCC25922,MDRE.
faeciumBM4105,andMDRA.baumanniiBAA-1605,usingpolymyxinBasthepositivecontrol,demonstratedthat0.5 MIC
cAMP_81treatmentfor30daysinducedlessresistancecomparedwiththecontrol(SupplementaryFig. 10d,Supplementary
Data21). Insummary,theseresultssuggestthatthecAMPsidentifiedfromtheEEMCexerttheirantimicrobialeffectsprimarily
throughdisruptionofthebacterialcytoplasmicmembrane,andthelowresistanceinductionobservedinthelongitudinalassays
furthersupportstheirpotentialasmembrane-targetingantimicrobialagents.

Methods
Metagenomesequencedatacollection,assemblyandbinning
Wecollectedandreanalyzedmetagenomicsequencingdatafromsamplescollectedfromextremeenvironmentswithavailable
metadatapublishedfromJanuary2010toFebruary2024(SupplementaryData1). Wefirstscreenedpublishedstudiesinthe
NCBIPubMeddatabase,bysearchingkeywordsrelatedtovariousextremeenvironmentsandecosystems. Wecross-referenced
theidentifieddatasetsandremovedduplicates. WefurthercheckedandremovedsamplesofrRNAgeneampliconsequences,
metatranscriptomicsequencesandhost-associatedsequences. Atotalof2,293metagenomicsampleswithmorethan23Tb
sequencingdataweredownloadedfromtheNCBIandNGDCdatabases.

Rawmetagenomicsequencingreadswereobtainedusingsratoolkit(version3.0.0)andtransformedtofastqformatusing
fastq-dump(version2.9.6). Readswithlowsequencingqualityandadaptersequenceswereremovedusingfastpwithdefault
parameters(version0.12.0)77. CleandataofeachmetagenomicsamplewereassembledbyMEGAHIT(version1.2.9)withthe
parameters"–presetsmeta-sensitive"78. TheassembledcontigswerebinnedandrefinedusingMetaWRAP(version1.3.2)with
defaultparameters79,whichintegratedthebinningresultsofmetaBAT2(version2.12.1)80,MaxBin2(version2.2.7)81,and
CONCOCT(version1.1.0)82. Atotalof77,146refinedMAGswerefurtherrefinedusingMAGpurify(version2.1.2)with
defaultmodulesandtheparameters"phylo-markers","clade-markers","tetra-freq","gc-content",and"known-contam"to
removecontaminatedcontigsoriginatingfromadifferentspeciesthanthedominantorganismintheMAG83.Thispurification
processresultedintheremovalofatotalof236,681contigs,accountingfor0.74%oftheinitial32,034,758contigswithinthe
MAGs.

Sampling,sequencing,assemblyandcollectionofisolates
Tobroadenthegenomecatalog,isolategenomeswereincludedindownstreamanSalyses. Atotalof3,131publiclyavailable
bacterialandarchaealisolategenomesderivedfromextremeenvironmentsweredownloadedfromtheNCBIRefSeqgenome
database44. Specifically,wedownloadedtheavailablemetadataof350,267genomesfromthedatabaseusingtheNCBIDatasets
(uptoAugust,2024),whichisusefultodownloadbiologicalsequencedataacrossalldomainsoflifefromNCBI.Thehigh
qualitygenomes(completeness 95%andcontamination<5%)wereRselectediftheir"isolation_source"or"geo_loc_name"
matched the regular expression of any extreme environment (Supplementary Data 22). We further curated the dataset by
manuallyremovinggenomesderivedfromhumansorotherspecies.

Inaddition,wederivedbacteriafromcoldseepsedimentaNndgeneratedin-houseisolategenomes. Briefly,sedimentsamples
wereretrievedfromHaimacoldseepfield,northernSouthChinaSeainAugust2023. Thesedimentsampleswereresuspended
insterileseawaterandmixedthoroughly. Thesamplesweresubsequentlydilutedusinggradientdilutionmethod,andthe
dilutionswerespreadon2216Emedium(ParkHopEe,Shandong,China)andGauze’sSyntheticMediumNo.1(ParkHope,
Shandong,China). Theplateswerethenincubatedinaninvertedpositionat28 C.Afterseveraldaysofculture,singlecolonies
wereselectedandtransferredtonew2216Eplatesforpurification. Theisolatesweretheninoculatedintoglyceroltubesfor
storageat-80 C.Thecultivatedisolateswereculturedin2216Ebrothat28 C,andcellswereharvestedbycentrifugationat
4,000 gfor10minutesatthemid-logphaIse. GenomicDNAwasextractedfromtheculturesusingaTIANampBacteriaDNA
Kit(Tiangen,Beijing,China)andthensequencedbyBGIusingtheDNBSEQ-T7platformwith150-bppaired-endreads. Fastp
(version0.12.0)wasusedforqualitRycontrolofthepaired-endreads;specifically,adaptersequencesandlow-qualityreadswere
removed. ThegenomesofthecultivatedisolateswereassembledusingUnicycler(version0.5.0)withdefaultparameters,and
onlycontigs 500bpwereretained84. UnicyclerfunctionsasaSPAdes-optimiserwhenappliedtoshort-readonlysets.

QualityevaluationofMAGsandisolategenomes
ThecompletenessandcontaminationofeachpurifiedMAGandisolategenomeswereevaluatedusingthemodule"lineage_wf"
inCheckM(version1.2.1)85. ThepresenceofribosomalRNAs(rRNAs)andtransferRNAs(tRNAs)wasidentifiedusing
Infernal(version1.1.15)withtheparameters"–cut_ga,–rfam"86andmodelsfromtheRfamdatabase(version14.10)87. For
MAGs, we retained only assemblies meeting the MIMAG medium- and high- quality standards45, which were defined as
follows: 1)medium-qualityMAGs: completeness 50%andcontamination<10%;and2)high-qualityMAGs: completeness
>90%andcontamination<5%withthepresenceofthe23S,16Sand5SrRNAgenesandatleast18tRNAs. Forisolate
genomes, assemblies with completeness 95% and contamination < 5% were retained. Retained genomes were further
assignedaQS,calculatedascompleteness-5 contamination,followingtheimplementationintheTibetanPlateauMicrobial
Catalog(TPMC)16.

ClusteringMAGsandisolategenomesintospecies-levelOTUs
The78,213genomeswereclusteredinto32,715representativespecies-levelOTUsusingthemodule"dereplicate"indRep
(version3.4.3)88,onthebasisofanAFthresholdof30%andagenome-wideANIthresholdof95%withtheparameters"-nc
0.3,-sa0.95,-comp0,-con1000"asusedintheTPMC16.

ComparingrepresentativeOTUstothegenomeswithinpubliccatalogs
The32,715representativeOTUswerecomparedto22,732representativeMAGsofenvironmentaloriginfromGEM47,24,195
representativegenomesfromGOMC19and957representativeMAGsfromTARAOcean46,usingthemodule"compare"in
dRep. TheOTUsexhibitinganANI<95%withthecomparedgenomesweredesignatedasnovel.

Taxonomicannotationandphylogenetictreeconstruction
Taxonomicannotationof32,715representativeOTUswasperformedusingthemodule"classify_wf"intheGenomeDatabase
TaxonomyToolkit(GTDB-Tk,version2.4.0)withdefaultparametersagainsttheGTDBreleaseR22089. TheGTDBrelease
R220comprises113,104representativegenomes, 107,235ofwhicharebacterialspeciesand5,869ofwhicharearchaeal
species. Thephylogenetictreesbasedon122archaealor120bacterialmarkergenesetsweregeneratedusingGTDB-Tkand
visualizedusingtheRpackage"ggtree".

ORFpredictionandnon-redundantgenecatalogconstruction
GeneORFswerepredictedfrommetagenomiccontigsusingProdigal(version2.6.3)90withtheparameters"-pmeta".AllORFs
wereclusteredintonon-redundantgeneclustersatathresholdof95%identityand80%coverage,usingMMseqs2(version
113e321)91withtheparameters"easy-linclust-e0.001–min-seq-id0.95-c0.80",asusedintheTPMC16.Thegenerarefaction
analysisforeachextremeenvironmentwasperformedbasedonthebasisoftheMMseqs2geneclusteringresultsusingan
in-housePythonscript,withrandomsamplingat10%intervals100times.

Genecatalogannotation
Forfunctionalannotation,thenon-redundantgeneswerealignedagainsttheNRdataSbases(https://ftp.ncbi.nlm.nih.gov/blast/db)
usingDIAMOND (version 2.0.15)92 with theparameters"blastp e-value 1e-S5", andagainstthe UniRef50and Swiss-Prot
databases48,49 usingMMseqs2withtheparameters"easy-search-e0.01–min-seq-id0.3–cov-mode2-c0.8–max-seqs1".
TheMMseqs2parametersfollowthoseusedintheTPMCdataset16,exceptforsettingmax-seqsto1tosavestorageresources.
Additionally,thenon-redundantgeneswerealignedagainsttheeggN R OGOrthologousGroupsdatabase(version5.0)93 for
functionalannotation,usingeggNOG-mapper(version2.1.10)94.PTheeggNOGdatabaseintegratesfunctionalannotations
collectedfromseveralsources,includingtheClusterofOrthologousGroups(COG)categoriesdatabase50,theKEGGfunctional
orthologs51, the carbohydrate-active enzymes (CAZy) dataNbase52 and the Gene Ontology (GO) resource53. Furthermore,
ARGsandVFswereannotatedbyaligningtheproteinsequencesofthenon-redundantgenesagainsttheAntibioticResistance
Database(CARD)54andVFDB(version2024)55,respectively,usingDIAMOND(version2.0.15)withtheparameters"blastp
e-value1e-5". ThegenesthatdidnotalignwithanyEoftheabovedatabaseswereconsideredtobenovelgenes.

Comparisonofgenome-derivedgeneswiththosefrompublicdatasets
WeusedProdigalwithdefaultparametersforORFpredictionontheMAGsandisolategenomesfromtheextremeenvironments,
aswellasthegenomesfromtheGEMand I GOMCdatasets19,47. TheproteinsequencesofORFsfromtheabovethreedatasets
werecombinedandclusteredintogeneclustersatathresholdof70%identityand80%coverage,usingMMseqs2withthe
parameters "easy-linclust -e 0.001 R –min-seq-id 0.7 -c 0.8", as used in the TG2G18. The VFs, ARGs, and CAZymes were
annotatedbyaligningtheproteAinsequencesofallaboveORFsagainsttheVFDB,CARD,andCAZydatabase,respectively,
using DIAMOND with the parameters "blastp e-value 1e-5 id 40 query-cover 80"95. MMseqs2 was used to evaluate the
similarityoftheVFsfromextremeenvironmentswiththoseintheGEMandGOMCdatasets. Thesameapproachwasapplied
forARGsandCAZymes.

BGCidentificationandclustering
A total of 163,693 BGCs were predicted and identified on contigs 5 kb of the 78,213 genomes to reduce the risk of
fragmentation,usingantiSMASH(version7.0.1)withtheparameters"–minlength5000"72. TheseBGCswerecategorized
intoeightgroupsusingBiG-SCAPE(version1.1.5)96: "PKSI", "PKSother", "NRPS", "RiPPs", "Saccharides", "Terpene",
"PKS-NRP_Hybrids",and"Others". TheBGCswerefurtherclusteredinto64,733geneclusterfamilies(GCFs,0.3distance
threshold)and2,178geneclusterclans(GCCs,0.7distancethreshold)usingBiG-SCAPEwithdefaultparameters. Toavoid
samplingbiasesinquantitativeanalyses(taxonomicandfunctionalcompositionsofGCCs/GCFs,aswellasGCFandGCC
distancestoreferencedatabases),the163,693BGCswerefurtherdereplicatedbyretainingonlythelongestBGCperGCFper
species,leadingtoatotalof107,829BGCs.

NoveltyofBGCs,GCFs,andGCCs
ThenoveltyoftheBGCs,GCFs,andGCCswasestimatedbyqueryingthe107,829representativeBGCsagainstapreprocessed
BiG-FAM reference database, using BiG-SLiCE (version 1.1.0) with default parameters97. This BGC reference database
contains1,225,071BGCsfrom209,206publiclyavailablemicrobialgenomesandmetagenome-assembledgenomes,which
integratesMIBiGv2.0,RefSeqcomplete/draftbacteria,GenBankfungi,GenBankarchaea,andotherMAGsfromdifferent
studies;29,955GCFreferencemodelshavebeengeneratedfromthereferencedatabase. Thedistancevaluewascalculatedfor
eachrepresentativeBGCandtheassignedGCFmodeltoindicatehowcloselytheymatched97. ThequeryBGCswithdistances
900asusedinBiG-FAMweredefinedasmatchedBGCs,whereasBGCswithdistances>900weredefinedasnovelBGCs.
TheGCFswithaproportionofmatchedBGCs<0.2wereconsiderednovelGCFs,whereastheGCCswithaproportionof
matchedBGCs<0.4wereconsiderednovelGCCs.

Peptidescollectionandpreprocessing
AMPdatawerecollectedfromsevenpublicAMPdatabases,includingAPD98,CAMP99,DBAASP100,DRAMP101,dbAMP102,
LAMP2103andBaAMP104(downloadedonAugust,2024),andmergedintoasingledataset. Thesedatabasesincludedboth
syntheticandnaturallyderivedpeptides,andtheantimicrobialactivitydataincludedbothinvitroassaysandinvivoexperiments.
Toensureadequatesamplesize,wedidnotstratifypeptidesbyoriginorexperimentalcontext.

Sequencesthatincludednonessentialaminoacidsorwerelongerthan300aminoacidswereremoved,resultinginadataset
of62,182AMPsequences. Wethenremovedtheduplicatesequencesfromthedataset,resultingin28,449non-redundant
AMPs(SupplementaryData13),whichinclude16,061anti-Gram+peptides,16,451anti-Gram-peptides,7,068antifungal
peptides,2,770antiviralpeptides,and6,771peptideslabeledasantibacterialorantimicrobial. Thenon-AMPdatasetwas
downloadedfromUniProt(https://www.uniprot.org)bysettingthe"Sequencelength"from0to300andremovinganyentry
thatmatchedoneofthefollowingkeywords: antimicrobial,antibiotic,antiviral,antifungal,effector,excreted,toxic,anticancer,
defensing,AMP,membrane,andsecretory(downloadedonAugust,2024). Wethenremovedtheduplicatesequencesinthe
datasetandpeptideswithnon-essentialaminoacids. Additionally,sequencesidenticaltoanyAMPswereremovedfromthe
non-AMPdataset,yieldingatotalof166,448non-AMPsequences.

To accommodate model training while considering the feasibility of downstream synthesis, we retained only peptide
sequenceswithalengthof100aminoacidsorless. Thisthresholdbalancestheneedforsufficientsequencecontextduring
representationlearningwiththepracticallimitationsofpeptidesynthesis(usually 30aminoacids). Thestructureofthe
remainingdatasetsisasfollows:
• GlobalAMP:Atotalof55,209sequences(25,004AMNPs,30,205non-AMPs)
• Anti-G+AMPdataset: Atotalof45,069sequences(14,864AMPs,30,205non-AMPs)
• Anti-G-AMPdataset: Atotalof45,493sequences(15,288AMPs,30,205non-AMPs)

Toxicandnon-toxicpeptideswerecollectedfromthreepublicdatabases: Hemolytik105,DBAASP100,andToxinPred3.063.
MostpeptideslackedexplicitmetadataregaIrdingtheirbiologicaloriginorexperimentalcontext;therefore,tomaintainsufficient
datadiversity,wedidnotstratifypeptidesbysourceorexperimentalcontext. Duplicatesequenceswereremovedfromthe
toxicpeptidedataset,yielding6,51R8non-redundanttoxicpeptides. Similarly,duplicatesequenceswereeliminatedfromthe
non-toxicpeptidedataset,resuAltingin6,843non-redundantnon-toxicpeptides. Furthermore,sequencessharedbetweenthe
toxicandnon-toxicdatasetswereexcluded,resultinginafinaldatasetof6,318toxicpeptidesand6,643non-toxicpeptides.
Thedistributionsofpeptideproperties,includingsequencelength,isoelectricpoint,charge,hydrophobicratio,aromaticity,
andaminoacidcomposition,areprovided(SupplementaryFig. 12and13).

Modeltraining
Insteadofwordsrepresentingtokensthatformsentencesinthehumanlanguage,inourusecase,wedealwithaminoacids
formingproteinsequences. Eachcollecteddatasetmentionedpreviouslywasrandomlysplitinto10equalparts: eightparts
wereselectedasthetrainingset,onepartwasusedforvalidationandtheremainingpartwasusedfortesting. Trainingofthe
pLLMsistypicallyperformedusingamaskedlanguagemodelingtask,wheretheinputisgeneratedbyrandomlycorrupting
5-15%oftheoriginalaminoacidsinaproteinsequence,andthemodelistrainedtoreconstructtheoriginalsequence. Existing
pLLMstrainedinthismannerincludeProtT5-XL-UniRef50(PTrans)41,ESM242,andESM343. Weadoptaprobingstrategy106
inwhichtheencoderofapretrainedpLLMisleveragedtoextractfixed-lengthembeddingsfromthelasthiddenlayerforeach
proteinsequence. Theseembeddingsarethenfedintoadownstreamclassifierheadtoinferpropertiessuchasantimicrobial
activityortoxicity. Oncetrained,themodelcanpredictthesepropertiesfornovelproteinsequencesderivedfromextreme
environmentsoranyothersources.

To maximize the utility of existing pLLMs, we developed the Metagenomics AI (MAI) framework. This framework
incorporates the pretrained pLLM backbone (referred to as the protein language model (PLM) component) and a user-
configurable classification head composed of linear and ReLU layers. When the PLM component produces sufficiently
discriminativeembeddings,asimpleclassifierisoftenadequatefordownstreamtasks. Toimproveefficiency,MAIsupports
freezingthePLMcomponentduringtrainingandcachingtheembeddingsitgenerates. Thisenablesthereuseofprecomputed
embeddingsinsubsequenttrainingsteps,significantlyreducingcomputationaltime. Theentiretrainedmodelcanbesavedand
deployedforfuturepredictiontasks.

PredictionofRiPPcorepeptidesandcAMPs
The coding sequences of 46,816 RiPP-type BGCs were extracted from the GenBank files generated by antiSMASH, and
subsequentlyanalyzedusingDeepRiPP29topredictputativecorepeptides. Theputativecorepeptideswerefurtherevaluated
forantimicrobialactivityandtoxicityusingMAIframework.

Sequencesimilarityestimation
WeappliedaglobalpairwisesequencealignmentusingtheNeedleman–WunschalgorithmwiththeBio.pairwise2modulein
Python(version3.9)toestimatethesimilaritybetween11,379RiPPcorepeptidesandpeptidesintwotrainingdatasets. The
sequenceidentityforeachalignmentwasnormalizedbythemaximumlengthofthequeryorsubjectsequence. Foreachquery,
thesubjectsequencewiththehighestidentitywasrecordedasthebesthit.

Peptidesynthesis
AllthecAMPs,non-AMPs,andtwopublishedAMPs(synechocucin-1andprevotellin-2)usedinthisstudyweresynthesized
viasolid-phasepeptidesynthesisbySangonBiotech. Themolecularmassesweredeterminedbymassspectrometry. Thepurity
ofeachpeptidewasdeterminedusinghigh-performanceliquidchromatography,withallpeptidesexhibitingapuritygreater
than95%.

Bacterialinhibitionexperiment
Elevenbacterialstrains,includingEnterococcusfaecalisATCC29212,StaphylococcusaureusATCC29213,MDRE.faecalis
VRE10,MDREnterococcusfaeciumBM4105asrepresentativeGram-positivebacteria,andAcinetobacterbaumanniiATCC
19606,EscherichiacoliATCC25922,PseudomonasaeruginosaATPCC27853,KlebsiellapneumoniaeATCC13883,MDRP.
aeruginosaPAO1,MDRA.baumanniiBAA-1605,andMDRK. pneumoniaeATCC700603asrepresentativeGram-negative
bacteria, were selected for bacterial inhibition assays. A.Nbaumannii ATCC 19606, A. baumannii ATCC BAA-1605, K.
pneumoniaeATCC13883,andK.pneumoniaeATCC700603werepurchasedfromBeinuoBiotechnology(Shanghai,China),
originally sourced from the ATCC. Additional bacterial strains used in this study were kindly provided by collaborating
laboratoriesaslistedintheAcknowledgements. TheEstrainswerestreakedonLuria-Bertani(LB)(ThermoFisherScientific,
Waltham,USA)agarplatesandincubatedat37 Covernight. ThesinglecoloniesweretransferredtoLBmediaandcultured
at37 Cwithshakingat120 governight. TheLBbacterialsuspensionwasadjustedtoastartingconcentrationwithan
OD of0.1,andthendiluted1,000timesfortheinhibitionassays. Freeze-driedpowdersofcAMPsandnon-AMPswere
firstdissolvedindouble-distilledwatertoafinalconcentrationof2.4mM.WesetthreegroupstotestcAMPantibacterial
activity: (1) blank group, with 200 µl of LB medium; (2) negative control group, with 100 µl of LB medium, 95 µl of
bacterialsuspension,and5µlofsRterilewater;and(3)testgroup,with100µlofLBmedium,95µlofbacterialsuspension,
and 5 µl of cAMP mother soAlution, with a final concentration of cAMP of 60 µM. Experiments were performed in 96-
wellplateswithaworkingvolumeof200 µl. TheOD value ofeachwellwasmeasuredafter12 hofcultivationat37
C.Threetechnicalreplicateswereperformedforallexperiments. The%growthofeachcAMPgroupwascalculatedas
(OD OD )/(OD OD ).

MICdetermination
The MIC of each peptide was determined using the broth microdilution method107. Briefly, E. faecalis ATCC 29212, S.
aureusATCC29213,E.coliATCC25922,P.aeruginosaATCC27853,A.baumanniiATCC19606,A.baumanniiBAA-1605,
E.faeciumBM4105,andK.pneumoniaeATCC700603wereinoculatedinMueller-HintonBroth(MHB)(ThermoFisher
Scientific,Waltham,USA)at37 Covernight. TheculturesweredilutedwithfreshMHBandadjustedto5 105CFU/ml.
Thepeptidesdilutedinwaterwereaddedto96-wellplatesat2-folddilutionsrangingfrom256to1µM.Then,100µlbacterial
cultureswereevenlyaddedtotheplates,andincubatedat37 Covernight. TheMICwasdeterminedasthetheminimum
concentrationofpeptidesatwhichnobacterialgrowthwasvisible.

Hemolysisassay
Sheep blood cells (Defibrinated Sheep Blood, BkmamLab, Changde) were centrifuged at 1,500 g to obtain red blood
cells,whichwerethenwashedandresuspendedinPBSto4%(v/v). Asuspensionofredbloodcellswasaddedto96-well
plates. Blanksand1%Triton-X100(Sigma-Aldrich,Germany)containingPBSwereusedasnegativeandpositivecontrols,
respectively. The cAMPs were added to the wells at a final concentration of 60 µM. The samples were incubated for 1
h with gentle shaking and then were centrifuged at 1,500 g for 10 min at 4 C. The supernatant from each well was
transferred to new 96-well plates. The release of hemoglobin was detected at 570 nm with a Multiskan MK3 microplate
reader(ThermoFisherScientific,Waltham,USA).Thehemolysisrateforeachpeptidewascalculatedas(OD
OD )/(OD OD ).

Cytotoxicityassaysagainstmammaliancells
CCK-8 assays were performed to assess the cytotoxicity of the cAMPs in vitro. L-02 (BNCC, Beijing, China) and 293T
(Procell,Hubei,China)cellswereseededintoeachwellof96-wellplates. After24hincubationat5%CO and37 C,the
mediumwasreplacedwithfreshmedium,andcAMPsatvariousconcentrationswereaddedtothewells,followedby48hof
incubation. Afterward,thecellswerewashed,10%CCK-8solution(Biosharp,Anhui,China)wasadded,andthemixturewas
incubatedfor3h. Theabsorbanceofthesampleswasmeasuredat450nmwithaMultiskanMK3microplatereader(Thermo
FisherScientific,Waltham,USA).Cellviabilitywascalculatedas(OD OD )/(OD OD ).
TheCC50valueofeachcAMPwascalculatedusingGraphPadPrismsoftware(version8.0.2;GraphPadSoftware,Inc.).

Circulardichroismassays
CDexperimentswereperformedusingaChirascanV100(AppliedPhotophysics,England)attheResearchCenterforDeepsea
Bioresources, Sanya. The experiments were carried out at a temperature of 25 C in triplicates. Spectra collections were
obtained using a quartz cuvette with an optical path length of 0.5 mm, covering a wavelength range from 260 to 190 nm
at a rate of 50 nm/min and a bandwidth of 1 nm. The peptides were tested at a concentration of 125 µM. Measurements
wereperformedinamixtureofwaterandTFEataratioof3:2(v/v). Baselinemeasurementswererecordedpriortoeach
measurement. Tominimizebackgroundeffects,aFouriertransformfilterwasapplied. Thehelicalfractionandotherfraction
valueswerecalculatedusingtheCDNN(version2.1)67.

TEMandSEM
BacterialcellenvelopedamageinducedbycAMP_81wasvisualizedbyRTEMandSEM.Fourpathogens(A.baumanniiATCC
19606,E.coliATCC25922,MDRA.baumanniiBAA-1605,andMDRE.faeciumBM4105)wereculturedtotheexponential
phase (OD = 0.5) at 37 C. Then, the bacterial cells were harvested by centrifugation at 4,000 g for 5 min at room
temperatureandresuspendedin5mMHEPESbuffer(pH7.0)containing5mMglucosetoanOD of1.0. Thebacterial
suspensionsweretreatedwithcAMP_81atafinalconcentrationof1 MICat37 Cfor5h. Untreatedbacterialsuspension
wasusedasthecontrol. ForTEM,the300-meshcarbon-cIoatedCugridswereglowdischargedfor30simmediatelypriorto
use. Gridswereincubatedon10µlofbacterialsuspen sionsfor1minbeforeblottingthemediaoffwithfilterpaper. Each
gridwasthendippedin5µlofdistilledwaterandblottedinbetweentransfertonewdroplets. Eachgridwassubsequently
incubatedin5µlof0.05%uranylacetatefor30Lsbeforeblottingandlefttodrybeforeimageacquisition. Thesampleswere
observedusingaJEM-F200electronmicroscCopy(JEOL,Japan). ForSEM,bacteriawerefixedat4 CwithPBScontaining
2.5%glutaraldehyde(Sigma-Aldrich,GerImany)for4h. Then,thefixedsamplesweredehydratedusingaseriesofethanol
solutions(30,50,70,90,and100%)aTnddriedwithacriticalpointdrier(LeicaEMCPD300,Germany). Thesampleswere
mountedandsputter-coatedwithplatinumandimagedusingaNOVANanoSEM450scanningelectronmicroscope.

Membraneintegrityassay
PIassayswereusedtoevaluatetheabilityofcAMPstodisruptcytoplasmicmembraneintegrity. A.baumanniiATCC19606
wasculturedtoanOD of0.5,centrifuged(10,000 g,5min,4 C),washed,andresuspendedin10mMPBS(pH7.0)toa
workingconcentration(OD =0.1). 100µlbacterialsuspensionwasmixedwith100µlofcAMPsolutionina96-wellplate
toreachafinalpeptideconcentrationof1 MICand10 MICfor1h. Thenegativecontrolwastreatedwithbufferalone.
Subsequently,PIsolution(40710ES03,Yeasen,Shanghai,China)wasaddedatafinalconcentrationof20µMandincubatedat
37 Cfor30mininthedark. Fluorescencewasmeasuredwiththeexcitationwavelengthat535nmandemissionwavelengthat
615nm.

Longitudinalresistanceassay
Toassesstheresistancedevelopmentduringserialpassage,E.coliATCC25922,A.baumanniiATCC19606,A.baumannii
BAA-1605,andE.faeciumBM4105wereculturedinMHBat37 Covernight. Subsequently,2µlofthisculturewasusedto
inoculate200µloffreshMHBcontainingcAMP_81ataconcentrationof0.5 MIC.Thisprocesswasrepeatedevery24
h: inoculating200µloffreshMHBcontainingcAMP_81ataconcentrationof0.5 MICwiththepreviousculture,ata1%
inoculumratio. Thiscyclewascontinuedfor30days,andMICassayswereperformedontheresultantbacteriaat10day
intervals.

Statistics&Reproducibility
Allexperimentswereindependentlyrepeatedatleasttwotimeswithconsistentresultstoconfirmreproducibility. Nostatistical
methodwasusedtopredeterminesamplesize. Samplesizesforeachexperimentweredeterminedbasedonpreviouspublished
studiesinthefield,ensuringsufficientstatisticalpower. Nometagenomicdatasetswereexcludedexceptthosefailingstandard
quality-controlcriteria. Noexperimentaldatawereexcludedfromtheanalyses. Theexperimentswerenotrandomized. The
studydidnotincludeanyinterventionsandthustheconventionalblindingwasnotapplicabletothisstudy.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript introduces the Extreme Environment Microbiome Catalog (EEMC), assembled from 2,293 metagenomes and 3,214 isolate genomes collected across seven extreme-habitat types. The authors reconstruct 78,213 archaeal and bacterial genomes that cluster into 32,715 species-level OTUs, an estimated 63% of which have no match to existing taxonomies. They further assemble ~4 billion non-redundant genes and 163,693 biosynthetic gene clusters (BGCs), of which 58.7% fall outside the current BiG-SLiCE reference space. Finally, they train several protein-language-model (pLLM) classifiers (ESM2-3B, ESM-3, PTrans) to predict antimicrobial activity and mammalian toxicity of RiPP core peptides, yielding 3,032 candidate non-toxic AMPs. One hundred peptides were then synthesized; 84 inhibited at least one pathogen, and six displayed sub-10 µM MICs against multidrug-resistant Gram-negative strains. The authors position EEMC as a resource for microbial ecology and natural-product discovery. Overall, the catalogue is valuable, and the integration of pLLM-guided AMP discovery is timely. However, several major issues need to be addressed (see below) prior to any further consideration. In its current form the work is too preliminary for Nature Communications standards.\nSpecific comments:\n1. Genomes as low as 50% completeness and 10% contamination (mean completeness ≈ 77 %) are included. Such relaxed criteria can inflate species counts and mis-assign BGCs or other functional elements.\n2. The 2,293 metagenomes span 2010–2024 and use varied extraction and sequencing protocols. Habitat-driven gene or BGC specificity is interpreted without demonstrating that study or batch effects are negligible.\n3. Of the 163,693 BGCs, > 50% are labeled “novel”, yet many originate from medium-quality MAGs and are fragmented. The effect of cluster fragmentation on distance-based novelty scoring should be evaluated.\n4. Reported accuracies of up to 96% for AMP and toxicity prediction are given without showing how sequence-identity overlap between public training datasets and EEMC peptides was removed, risking over-optimistic performance estimates.\n5. No assays for serum stability, resistance evolution, or in vivo efficacy are provided, which are standard in the field, further leaving translational claims unsupported by data. I really think testing the peptides in at least an animal infection model is a major issue and critical here.\n6. The authors should provide all accession numbers/DOIs for raw assemblies, gene catalogues, trained model weights, and inference scripts to enable reproducibility of the work.\n7. The discussion should benchmark the six potent AMPs against published examples (e.g., prevotellin-2) and compare the MAI framework with prior microbiome-mining pipelines for peptide discovery. Moreover, the authors do not discuss recent mining efforts of archaeal proteomes for bioactive antibiotic peptides.\nMinor comments:\n1. The ecological or clinical relevance of millions of putative virulence factors is speculative. I suggest highlighting only experimentally validated or high-confidence homologues.\n2. Please check the manuscript for typos – e.g. “enviroment” (line 71), “archael” (line 74).\n3. The authors mention the lack of generalizable models, but this is not entirely accurate (e.g., APEX).\n4. References. Check for issues. E.g., update citation 37 (Macrel) year (should be 2020, not 2010).", "reviewer_2": "The manuscript by Jiang and co-authors contributes a significant resource to the scientific community with the generation of the Extreme Environment Microbiome Catalog (EEMC) as well as making an important contribution to the field of antimicrobial peptides and genome mining for these bioactive compounds. The authors collected publicly available (meta)genomes from extreme environments, assembled MAGs from these, then analyzed them (including some pure isolate genomes from their own collection) for the presence of BGCs, with an emphasis on RiPPs. Separately they created a curated database of antimicrobial peptides (active against either Gram + or Gram – bacteria or with broad spectrum), but non-toxic, pulled from available databases. This was used as training and validation data for their custom machine learning model (Metagenome-AI) used to develop classifiers capable of detecting peptides with these desirable parameters from a pool of uncharacterized RiPPs, mined from the MAGs they reconstructed. Next, they selected 100 of the peptides predicted to be antimicrobial to be chemically synthesized. These peptides were assayed for their antimicrobial activity against a range of Gram + and Gram - bacteria. These experiments revealed that a high proportion of the peptides selected (84/100) demonstrated antimicrobial activity and seven of these were demonstrated with a CC50 higher than their MIC suggesting that they have therapeutic potential.\nGeneral Comments / Corrections to be addressed:\n1. In lines 162–164, the authors suggest that microorganisms in extreme environments may gain a competitive advantage due to the widespread presence of secondary metabolite pathways. While this is a well-established ecological principle across diverse habitats, the current analysis does not include comparative data from non-extreme environments, making it difficult to determine whether this trait is uniquely enriched or more pronounced in the sampled extremes. The statement may unintentionally imply such uniqueness, and a brief clarification would help ensure the intended nuance is preserved. Alternatively, provide references to support the idea that this is particularly enriched in extreme environments, if that is what the authors intended.\n2. The paragraph spanning lines 174–189 places considerable emphasis on the potential threat to human health posed by virulence factors. However, this framing seems at odds with several findings that highlight the novelty and niche specificity of the microbial genomes and functions: (i) the remarkably limited sharing of unigenes across environments; (ii) low percentage similarity to the GEM and UHGG databases; (iii) biosynthetic gene cluster (BGC) analysis indicating a high proportion of habitat-specific GCFs and GCCs; and (iv) the identification of highly novel virulence factors. These results support the authors’ conclusion that strong environmental selective pressures drive the evolution of microbial lineages toward peptides with specialized ecological roles. In this context, virulence appears to be highly adapted to specific environmental conditions and may not be directly translatable to human pathogenicity. A more balanced framing would better reflect the ecological significance of these findings. However, the rationale for prioritising this analysis over other metabolic traits is not clear. As presented, it does not substantially advance the study’s main focus. To strengthen the manuscript’s coherence, the authors might consider either omitting this section or, alternatively, expanding the scope to include comparable analyses of other functional traits. This would provide a more balanced and contextually grounded assessment of microbial adaptation mechanisms in extreme environments.\n3. In line 182, the authors report up to 1,191 virulence factors within a single genome. This figure appears unusually high and may warrant further context. Could the authors comment on whether such a number is typical, or whether it might reflect an overestimation due to annotation thresholds or database limitations? It would be helpful to reference comparable cases in the literature where similarly high counts of virulence factors have been reported, to support the plausibility of this finding.\n4. The authors provided access to the MAGs, as well as the software pipeline they created, but not the final curated list of peptides used as training the dataset nor the final trained model. If these are not to be patented, it would be a fantastic resource for the rest of the microbial and peptide community in particular. Could the authors comment on why these were not made available?\n5. What is the distribution of the 108 cAMPs between MAGs and isolates?\n6. While acknowledging the cost and logistical constraints, it would have strengthened the study if the authors had also synthesized and assayed a comparable number of peptides from the EEMC predicted to be non-antimicrobial. This would have provided a more balanced assessment of the predictive model’s specificity, demonstrating its ability not only to identify true positives but also to exclude false positives.\n7. Many RiPPs derive their bioactivity from post-translational modifications (PTMs) such as glycosylation and stereoselective modifications. Given that 96 of the 100 synthesized cAMPs were classified as PTM peptides (lassopeptides and class II lanthipeptides), did the synthetic synthesis incorporate these PTMs and what structural validations were performed beyond mass confirmation and purity? This could be a critical gap, as PTMs, especially those conferring conformational rigidity like disulfide bridges or macrocyclization, are essential for the biological activity of RiPPs. Given that DeepRiPP was used to predict core peptides independently of genomic context, and that most synthesized peptides were lassopeptides and lanthipeptides, it is likely that the predicted sequences represent only the core scaffold, not the full PTM landscape. Therefore, the authors should clarify whether synthetic strategies attempted to mimic native PTMs (e.g., via chemical cyclization or enzymatic modification), and whether stereochemistry was verified (e.g., via NMR or circular dichroism). Without such validation, the observed cytotoxicity and antimicrobial activity may not fully reflect the native peptide’s properties, potentially limiting translational relevance. This does not detract from the impressive success rate; however, it may help explain why the observed activities predominantly reflect similar modes of action and lack novelty in terms of distinct scaffolds or mechanistic diversity.\n8. Building on the previous point, the current AMP prediction framework relies predominantly on sequence embeddings without incorporating structural or physicochemical modeling. This approach may overlook context-dependent activity and risks misclassifying peptides with atypical conformations or noncanonical features. Could the authors expand on how the PTMs were accommodated in their ML training? Furthermore, the authors should acknowledge that the predicted peptides may differ significantly from their naturally occurring counterparts in terms of folding, stability, and bioactivity. To clarify the scope and applicability of the models, it is important to specify whether the training datasets were derived from synthetically produced or naturally sourced peptides, and whether the associated activity data reflect in vitro assays or native biological contexts. Furthermore, the authors are encouraged to discuss how structure-based modeling and molecular dynamics simulations could be integrated to complement sequence-based predictions, thereby enhancing the reliability and interpretability of AMP discovery.\nMinor Corrections\nln67: delete “s” from sequences, ie should be “sequence datasets”\nLn147: add a space between “plateauing” and (Supplementary….)\nLn160: metabolic regulation\nLn239: niche-specificity (not niche-specific)\nLine 265: Linear azole-containing peptides\nSupplementary figure6 – figure legend, two commas after e-f and are the figures in e and f duplicates (f does not refer to genera and the curves are identical)?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Thanks for the thorough evaluation and constructive comments. We appreciate the recognition of the value of the EEMC and the integration of pLLM-guided AMP discovery. We also acknowledge your concerns regarding aspects of the current analysis. The feedback is highly valuable, and we have carefully revised the manuscript to address all major points raised.\nIn brief, we performed additional analyses to assess the quality of genomes, genes, and BGCs of the EEMC, expanded the experimental evaluation of potent cAMPs, compared the MAI framework against a previously published microbiome-mining AMP predictor, and corrected minor issues throughout the text. We have also made all curated datasets and trained models publicly accessible to support transparency and reproducibility. We hope that these revisions substantially improve the rigor and clarity of the manuscript and address the reviewer’s concerns.\n[... detailed point-by-point response follows ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed most of my prior comments.", "reviewer_2": "It is evident that the authors have undertaken an immense amount of work to address the reviewers' concerns, and their efforts in expanding the experimental validation and refining the genome-quality thresholds have significantly improved the technical rigor of the study. Thank you.\nThe study approach creates a sequence-function correlation that completely ignores the natural biological context of these peptides. Moreover, it leaves one questioning the rationale for focusing on RiPPs specifically. Theoretically, the authors could have screened the entire sequence space for random peptide libraries and identified active sequence combinations through their model with similar results. By bypassing the very modifications that define RiPPs, the biological relevance of using these specific BGCs as a starting point is significantly diminished. Is this something the authors have ever considered?\nThe absence of PTMs likely narrowed the chemical space explored and resulted in a convergence of observed modes of action. While the success rate in identifying active peptides (84 out of 100) is technically impressive, the concerns pointed out detract from the study's ability to reveal truly novel or biologically \"native\" chemical diversity.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank you for the positive evaluation and for recognizing that the previous comments have been addressed.\n[... detailed point-by-point response follows ...]"}]

Decision letter