The present review proposesan overview of the approaches most commonly employed in proteomicsapplications for the identification of nucleic acid-binding proteins,such as affinity purification (AP) protocols, EMSA, chromatin purificationmethods, and CRISPR-based chromatin affinity purification, which aregenerally associated with mass spectrometry methodologies for theunbiased protein identification.The fields of applicationof functional proteomics are not limitedto the study of protein–protein interactions; they also extendto those involving protein complexes that bind DNA or RNA. These interactionsaffect fundamental processes such as replication, transcription, andrepair in the case of DNA, as well as transport, translation, splicing,and silencing in the case of RNA. Analytical or preparative experimentalapproaches, both  Therefore,we are still very far from understanding and elucidating the complexnetwork of interactions taking place in living organisms.3 In the cell, biological functions are not exertedby single individual proteins, but by transient complexes they form,interacting each other or with other molecules such as nucleic acids7 and metabolites.9 Thus, the study of proteins andtheir interactions is essential to understand their roles within thecell and to elucidate the organization of functional networks.“Life is a relationshipbetween molecules, not a propertyof any one molecule. So is, therefore, disease, which endangers life,”wrote Zuckerkandl and Pauling10 Furthermore, a detailed description of cell signaling pathwayscould greatly benefit from the elucidation of interactions in vivo.11A complete description of cellular processes is then strictly dependenton a clear definition of the complexes that take part in the molecularmechanisms and the individual protein components involved in thesefunctional entities. The association of an unknown protein with partnersbelonging to a specific complex involved in a particular mechanismmight strongly suggest its biological function.12 It is well-known that cell processes involvingDNA  as well as RNAs  are the result of the constant interactionbetween functional nucleic acids and specific proteins.13Modern functionalproteomic studies are not solely addressed tothe study of protein–protein interactions but also to the investigationof the interactions between multiprotein complexes and nucleic acids,thus to define both the biological functions of specific proteinsand their influence on nucleic acids dependent events.14 The same modalities belong also to RNA–proteininteractions, as amply illustrated by Guenther et al. in their work,where the meaning of “specific” and “non-specific”of RNA–protein interactions and their complexity is well illustrated.The authors highlight the presence of specific regions of RNA (RNABinding Domain) that exclusively bind classes of “specific”proteins as well as proteins that perform as many important functionsalthough in absence of specific binding capabilities.16 Both binding modes at the level of DNA affect gene expression: theformer through epigenetic modifications, the latter through the recognitionand binding of protein factors on specific nucleotide stretch .20The evolution selected two different waysof protein–DNAbinding: a nonspecific manner, as occurring in histones–DNAinteraction, and a very selective and specific mode, in which theprotein recognition site is strictly nucleotide sequence-dependent.23 Already in 1940, the biologist Conrad Hal Waddington,introducing the term epigenetics, emphasized the importance of theinteractions of genes with the surrounding environment for the understandingof a specific phenotype. Hence the need to elucidate the mechanismsunderlying these complex phenomena and the development of techniquesto be applied for their investigation.25 Therefore,in these past years, we have assisted in the implementation of a largenumber of analytical methods for studying both the profiling of epigeneticsmodification in different conditions 29 and the isolation and identifications of specific DNA/RNA bindingprotein complexes.33The impairment of either process, the modulation of histonesmodifications,or the recruitment time- and space-specific of protein complexes ontheir nucleotide-binding site, is often involved in the onset of particularlyserious pathologies.in vivo and in vitro, were developed to isolate and identify DNA/RNA binding proteincomplexes by tacking advantage from their affinity toward a specificoligonucleotide sequence. In proteomics experimental workflows, theseaffinity-based isolation methods are usually coupled to advanced massspectrometry (MS) methodologies, for protein identification.35The highest number of experimental approaches, either analyticalor preparative, both in vitro investigations, suchas affinity purification and electrophoresis mobility shift assay(EMSA). Conversely, chromatin immunoprecipitation (ChIP) based protocolsaim for the isolation and identification of both protein partnersand nucleic acid targets of a specific protein “bait”in ex vivo approaches. Finally, newborn techniques based on CRISPR-Cas9technology have been set up for the isolation and identification ofproteins interacting with a specific genomic locus in vivo. For each mentioned strategy, several variants have been finelytuned to respond to specific biological problems and to be successfullyapplied for RNA binding protein investigation fields, too. This reviewprovides an excursus on the main strategies developed in the fieldof protein–nucleic acid interactions: starting from classicalbiochemical and/or molecular biological approaches, we focused mainlyon those, which coupled with mass spectrometry methodologies, havefound the largest applications in the field of -omics sciences. Foreach approach, the points of strength and drawbacks will be criticallytreated.Nowadays, many strategies have been borrowed from molecularbiologyprotocols and then improved for high-throughput -omics approaches.Many of them rely on 238 The AP-MS procedureexploits the intrinsic affinity of DNA/RNA binding proteins for aspecific oligonucleotide sequence which is used as bait to fish theprotein partners out from the cellular extract. In a generic protocol,the protein extract is incubated with the oligonucleotide bait immobilizedonto insoluble support. Following several washes proteins, specificallyretained by the bait, are eluted and identified by mass spectrometry(The unbiasedprocedure defined as affinity purification–massspectrometry (AP-MS) is based on the combination of affinity isolationstrategies with MS procedures and constitutes an important branchof the functional proteomics approach.trometry1.40 On the contrary, too-short probesmight affect the stability of protein binding. In order to improvethe interaction of the bait with transcriptional factors, which generallyare under-expressed and only transiently bind their nucleic acid targets,the oligonucleotide consensus sequence can be multimerized to providemultiple copies of the specific sequence thus increasing the numberof potential interacting regions onto the same bait.41 In addition, for both DNA and more frequently for RNA probes,the establishment of secondary stable structures have to be considered.Finally, in the design of the oligonucleotide target, the presenceof a spacer sequence to properly outdistance the solid support andthe binding site of interest, reducing the steric hindrance, has alsoto be taken into account.42 Several procedureshave been explored to bind the oligonucleotide bait to the polymericsupport, with two methods being the most successful. The nucleotidesequence can be covalently bound by chemical cross-links on a suitablyderivatized resin;43 alternatively, theoligonucleotide bait can be labeled with biotin and immobilized onstreptavidin-coated beads.46 Binding buffer composition alsoexerts a critical role in favoring specific DNA–protein interactions.Physicochemical characteristics including pH value, the concentrationof mono- and bivalent ions, the addition of specific DNA competitorsto reduce nonspecific binding , and finally theamount of glycerol have to be strictly controlled.47The crucial point in the entire procedure relieson the designof the nucleotide fragment. The length of the probe is chosen accordingto the number of nucleotides composing the sequence of interest. Anoptimal bait length is difficult to fix. It would strictly dependon the aim of the study in terms of how narrow the fishing has tobe carried out. Generally, longer probes that also include sequencesoutside the binding region might increase the retention of unspecificproteins lacking the affinity for the consensus sequence. Nevertheless,a longer probe including different regulatory regions can contributeto a cooperative and more stable interaction between DNA/RNA and largemultiprotein complexes.49 and (iii) a precleaningphase. The latter relies on a preincubation of protein extract witha not specific oligonucleotide showing the same nucleotide compositionof the bait, but modified in specific sites or simply with a randomsequence.38 However, the discriminationbetween the true and false interactors is a longstanding central themein all applications of functional proteomics. More recently, quantitativemass spectrometry-based approaches relied on label-free,35 stable isotope labeling by amino acids in cellculture (SILAC),50 or tandem mass tag (TMT)51 quantification methodologies have been employedto distinguish random interactions from effective ones. In particular,the false positives are expected to be equally abundant in both conditions,while the true binders will be quantitatively prevalent in the presenceof the bait.52 Finally, software such asCRAPome can provide information on proteins more frequently identifiedas background in each specific affinity purification experimentalsystem as well as in different cell lines, allowing the recognitionand, therefore, the elimination of proteins considered false positives.53The occurrence of nonspecific interactions constitutesa not negligibledrawback, which must be faced using the following precautions: (i)stringent washes using at high salt concentrations, (ii) oligonucleotidecompetitors, like salmon sperm, lacking the target sequence and ableto capture background proteins,57 In one of the first application, indeed, Medugno and collaboratorsidentified the interaction between the negative cis-element (AldA-NRE) and ZNF224, a Kruppel-like zincfinger transcription repressor factor as a key step in modulatingtranscription of the human and mouse aldolase A (AldA) gene during the cell cycle and differentiation.58 Since then, affinity purification strategies have beenfurther developed and largely applied to the investigation of severalbiological processes involving key DNA–protein interactionsleading to the discovery of previously unknown crucial DNA bindingproteins. The nature of the regulatory complex bound to the proximalpromoter region to regulate EPHX1 expression wasexplored using a biotinylated oligonucleotide encompassing this regionin conjunction with mass spectrometric analysis. A 4-component regulatorycomplex including the inhibitory factor PSF, CAR, RXR and HNF-4α was identified.59The “fishing” strategy has found great applicationin studies addressed to the isolation and identification of nucleicacid-binding proteins involved in fundamental biological processessuch as transcription, translation, and/or splicing regulation.TRIM25) as a Caspase-2 mRNA-binding protein incolon carcinoma cells. TRIM25, known to be an E3ubiquitin ligase, seems to exert important tumorigenic functions,controlling metastatic gene signatures both at the transcriptionaland post-transcriptional levels. In particular, although TRIM25 lacks any typical RNA binding domains (RBDs), it can bind and affectthe processing and stability of the specific mRNA, acting as a negativeregulator of caspase-2 translation. By unveiling this TRIM25 unexpected function, a novel mechanism of drug resistance in humancolorectal carcinoma cells associated with the activity of TRIM25 as an inhibitor of chemotherapeutic drug-inducedapoptosis was described.60Nasrullah etal. identified tripartite motif-containing protein25  was a novel factor involved in themodulation of ENPP1 expression as well as insulinsignaling and action in human liver HepG2 cells.62 The same adipic acid dihydrazide-agarose beads were usedto bind in vitro transcribed RNA probes containingeither the wild type or mutated sequence to explore the molecularmechanisms underlying splicing defects in the DMD gene. Incubation with a cellular extract followed by mass spectrometryanalyses identified proteins that display differential binding affinitiesfor the wild type and mutant RNA probes.63RNA pull-down experiments in which theRNA bait was covalentlyimmobilized using adipic acid dehydrazide derivatized beads were appliedto the identification of specific GFP) to identify the interactors of a GFP-tagged protein. This approachled to the identification of molecular chaperones and translationelongation factors that interact with human heat shock factor 1 (HSF1). This technique provides a significant enrichmentin terms of sensitivity, in evolutionarily different organisms, andallows identification of PTMs without the need for specific enrichments.64Slightly different variants of the AP-MS approach were designedto address specific biological problems involving functional RNAs.Ray and collaborators introduced AptA–MS (aptamer affinity–massspectrometry), a robust strategy involving a specific, high-affinityRNA aptamer against green fluorescent protein -neamine hybrid targeted to HCV RNA was developed for the insitu capture of cellular and viral factors associated with HCV leadingto the identification of both cellular factors including transcriptionalregulators, RNA helicases, DEAD-box proteins, and translational regulatorsand three viral proteins  associatedwith the viral genome.67An RNA/DNA hybrid formed by the In-1 transcript anda 5′-biotinylatedDNA oligonucleotide complementary to the upstream exon sequence wasused to probe HeLa nuclear extracts for spliceosome investigation.The hybrid probe bound with the nuclear proteins was coupled to streptavidinmagnetic beads and the retained proteins were identified by mass spectrometryhighlighting the occurrence of canonical spliceosome core componentsbelonging to the spliceosomal B-complex.DDX3, CNBP, DDX21, DDX17) and heterogeneous nuclear ribonucleoproteins.68Recently,G-quadruplex has also been used in pull-down experiments.Guanine quadruple helices, or G-quadruplex, are guanine tetramersstacked together forming a helical structure within the DNA/RNA molecule.They spontaneously form in guanine-rich regions of DNA/RNA givingrise to a variety of conformationally different quadruple helicesdepending on various factors. G-quadruplex motifs are known to beinvolved in several biological processes including the mechanismsof initiation of DNA replication and the maintenance of genomic stabilityby interacting with proteins like chaperones and DNA helicases. SantiMestre-Fos et al. used G-quadruplex bait in pull-down experimentsto identify proteins that specifically recognize these structuresin rRNA and applied the SILAC approach for protein quantification.They identified several G-quadruplex linked proteins including helicases of in vitro one-by-one interactionsare usually measured by classic biochemical analyses . Makowski et al. proposedan innovative assay in which DNA affinity purification was coupledwith tandem mass tag (TMT) labeling to measure the apparent KdApp values for the identified interactorsin pull-down experiments carried out at different bait concentration.51 They calculated the protein DNA-bounded fractionby comparing the ion signal of each protein in a single pulldown withthat recorded in saturation condition of oligonucleotide bait (micromolarconcentration). The KdApp valueswere calculated by plotting DNA concentration and the bound fractionsin a Hill-like curve. The curve also allowed to filter out nonspecificinteractions, since background proteins would display randomly distributedsignal ratios near 1:1 for all titration points in comparison to controlpulldown.Although the identification and quantification ofDNA/RNA interactingproteins are necessary to understand the biological role of theseassociations, also the strength of the interactions plays a centralrole in protein complex characterization. Dissociation constants  oneither polyacrylamide or agarose gel has constituted the most largelyemployed biochemical procedure to detect 69 In the beginning, the electrophoresis shift was usuallydetected either by using P32 radiolabeled oligonucleotide probes72 or staining the gel with ethidium bromide.74 However, nowadays, these methods have completely been replaced byfluorophores 75 or biotin taggednucleotides76 or by the employment of fluorescentdyes.77The binding of a specificprotein to a stretch of DNA (probe) caneasily be verified by monitoring the delay (shift) of the oligonucleotideprobe following its binding to proteins in comparison with the freeprobe on a native electrophoretic gel. Moreover, these experimentscan also monitor the formation of multiple component complexes simplyby observing the supershift originated in the gel electrophoresisby the addition of protein components one at a time.78 More recently, an unbiased approach was developed by coupling theclassic EMSA assay with advanced mass spectrometry methodology forthe identification of DNA-binding proteins (EMSA/MS). The EMSA/MSprocedure combined the simplicity and effectiveness of the EMSA experimentswith the ability of high sensitivity, high-resolution mass spectrometryto identify all the proteins interacting with the probe in an unbiasedoperative mode. Following the EMSA experiment, the shifted band containingthe probe-protein complex is excised from the gel and the proteincomponents are identified by tryptic digest and nanoLC-MS/MS analysisof the resulting peptide mixture ,a potential biomarker for response to selective estrogen receptormodulators (SERMs) therapy for breast cancer prevention. After EMSA,the shifted gel bands indicating specific DNA–protein interactionswere isolated and submitted to mass spectrometry analysis identification.Calmodulin-like protein 3 (CALML3) was identifiedas a key sensor of this SNP and a coregulator of ERα, whichcontributes to differential gene transcription regulation in an estrogenand SERM-dependent fashion.83Analogously, EMSA/MS was performed to identify proteins bindingto the F55, atranscription repressor belonging to S. solfataricus (S.so) spindle-shaped virus 1 (SSV1) when the transcription factor is bound to its specific DNA promotersequence. When the probe was incubated with the S.so protein extract containing F55, two different delayedbands were clearly detected by fluorescence. Western Blot assay revealedthe presence of F55 in only one of the two bandsthat were in situ digested with trypsin and the proteins identifiedby a nanoLC-MS/MS-based strategy. Among the putative F55 interactors, RadA, a homologue of E. coliRecA, was identified,suggesting that the archaeal molecular components F55 and RadA are functional homologues of bacteriophage λ (factor CI) and Escherichia coli (RecA) system.84A slightlydifferent approach was used by Fusco et al. in theirinvestigation on proteins associated with 4in vitro experimental systems,detailed information aboutthe interactions between transcription factors and their targets in vivo has been obtained following the introduction ofchromatin immunoprecipitation (ChIP) technique .85 According to ChIP protocol, the in vivo interactions between transcriptional factors or other DNA bindingproteins are stabilized by chemical cross-links. The protein–DNAcomplexes are then immunoprecipitated using a specific antibody forthe protein of interest following sonication. In a classical approach,proteins are completely digested and the immunoprecipitated DNA regionsare identified by PCR amplification and/or DNA sequencing (ChIP-seq) leading to the identification of new targets  has been proposed, with the aim to provide simultaneous identificationof the specific DNA target sequences and the protein components ofthe functional complex bound to those regions.101Since the introduction of the ChIP technique,new upgrades havebeen developed like the Re-ChIP method, in which two antibodies directedagainst two different antigens are used, one after the other, allowingthe identification of DNA fragments where two protein factors aresimultaneously bound; targets 3H. Initiin vivo and thencovalently cross-linked to the DNA stretch. The stabilized complexis immunoprecipitated following the classical ChIP procedure and theneither enzymatically digested with trypsin directly on the beads (RIME)or eluted, de-cross-linked, and digested with trypsin (ChIP-MS)  3E. In boTaf1, Jup, Tdrd3, Cdh1, Cenpj, and several histones were involved in transcriptionalregulation.103Hwang and co-workers used the chromatinimmunoprecipitation procedurecoupled to mass spectrometry (ChIP-MS) to identify β-catenin-interactingproteins within a multifunctional protein that might be involved intranscriptional regulation in rat inner medullary collecting duct(IMCD). Several β-catenin-binding proteins were identified,including several known β-catenin-binding partners as well asnovel interacting proteins among which 107 The ChIP-MS method provideda detailed read-out of the transcriptional landscape representativeof the investigated cell type, leading to the identification of severalprotein factors, most of which drive reprogramming to pluripotentstem cells100 such as Oct4, Esrrb, Klf5, Mycn, and Dppa2.A large systematicinvestigation of proteins that bind transcriptionalenhancers and promoters in embryonic stem cells was carried out byexploiting chromatin immunoprecipitations (ChIP) by using antibodiesfor characteristic histone modifications and identification of associatedproteins using mass spectrometry.CBX7 in cancer progression. CBX7, a component of the polycomb repressive complex (PRC1), can positively or negatively regulate the expression of genesinvolved in cell proliferation and cancer progression, including E-cadherin.Using the ChIP-MS approach, Federico et al. demonstrated that CBX7 effectively interacts with histone deacetylase 2 (HDAC2)92 and with protein arginine methyltransferase1 (PRMT1)91 on the E-cadherinpromoter. These findings demonstrated that CBX7 activityon E-cadherin promoter is strictly related to several enzymes involvedin epigenetic modifications and belonging to both so-called writers and erasers  categories.A functional proteomic experimentbased on ChIP-MS was designedto understand the molecular mechanisms that underlie the involvementof 108The main drawbacks in the ChIP-MS procedure layin a large numberof contaminants due to the possible capture and identification ofnonchromatin-associated complexes like those involving proteins ortranscription factors that form different complexes on and off chromatinin response to different stimuli. Rafiee et al. proposed a modifiedversion of the ChIP-MS protocol to specifically identify proteinsin their DNA-bound state. The method combines ChIP with selectiveisolation of chromatin-associated proteins (SICAP) 3C followOct4, Sox2, and Nanog in mouse ESCs, the so-calledOSN system, leading to the discovery of Trim24 asa component of the pluripotency network. ChIP-SICAP uniquely benefitsfrom the double purification of protein–DNA complexes, accomplishedby subsequent ChIP of the protein of interest and pull-down of biotinylatedDNA allowing the exclusive capture of protein complexes bound to DNA.109The effectiveness of ChIP-SICAP was demonstrated by characterizingthe chromatin-bound network around 20 strategy, the formaldehyde-cross-linked protein/chromatincomplexes are isolated through nucleic acid hybridization and identifiedby mass spectrometry methodologies. A specific capture probe calledlocked nucleic acid (LNA), consisting of an RNA nucleotide sequencein which the ribose moiety is modified with an extra bridge connectingthe 2′ oxygen and 4′ carbon with increased 3′-exonucleolyticstability and improved hybridization affinity,110 is employed for soluble chromatin hybridization and thenfor the isolation of the specific genomic locus of interest. The probeis also tagged with a desthiobiotin that allows its affinity purification.In general, ChIP based methods consistof protein-centric approachessince the purification strategies described are tailored accordingto the identity of the protein of interest that interacts with DNA/RNA.Conversely, alternative DNA/RNA-centric approaches have been developedin order to isolate protein complexes by fishing them from the specificgenomic locus. In the proteomics of isolated chromatin segments (PICh)HeLa S3, HeLa 1.2.11, and WI38-VA13ALT. By designing a specific probe for the PICh experimentand a scrambled probe as control, they were able to isolate the telomericregions of interest and identified 85% of the proteins known to beassociated with them.20Déjardin et al. developed and used the PICh strategy topurify and analyze protein complexes bound to two distinct types ofthe telomere, as a proof-of-principle, in three human cell lines: 112 or RNA antisense purification(RAP-MS)113 were set up to isolate RNAor ncRNA binding proteins, by employing DNA  ortranscribing DNA into RNA (for RAP)114 hybridizationcapture-based approach. The main difference among these techniquesconsists in probe design: in ChIRP and RAP techniques, a pool of oligonucleotidesthat cover the full length of the RNA target is used, while in CHART-MSonly a few shorter probes are required.115In vivo, RNA–protein interactions are chemicallycross-linked and purified using biotinylated oligonucleotides complementaryto the RNA stretch of interest. Coprecipitated proteins are elutedand identified by mass spectrometry. This approach resulted effectivefor both abundant housekeeping and relatively low expressed RNAs.117 ChIRP-MS analysis allowed the identification of multiple splicingfactors in nuclear stress bodies (nSBs) containing long noncodingRNAs, including serine and arginine-rich pre-mRNA splicing factors(SRSFs) which affect splicing patterns.118 The same strategy was employed to explore carcinogenic mechanismsinvolving long noncoding RNA SNHG6 in colorectal carcinoma onset,leading to the identification of several proteins involved in spliceosomesand mRNA processing.119Additionalanalogous strategies for comprehensive identificationof RNA-binding proteins (ChIRP), in capture hybridization analysis(CHART-MS)NEAT1 (nuclearenriched abundant transcript 1) and MALAT1 (metastasis-associatedlung adenocarcinoma transcript 1). In this study, they showed that NEAT1 and MALAT1 bind multiple active genes.The two lncRNA, when colocalized on the same DNA region, displayeddifferent protein partners indicating different but synergistic functionalroles.111CHART-MSstrategy was used by West et al. to elucidate genomicbinding partners of two human lncRNAs, 120 thatenables mapping of RNA interactions with chromatin. As for other severalexamples reported in this review, in its original version, this approachaimed for the identification of DNA loci interacting with the targetRNA by using high-throughput DNA sequencing. Three years later, thesame research group proposed an upgrade of the method called RAP-MS,in which the RNA antisense purification was coupled with mass spectrometryfor the identification of proteins directly interacting with a specificRNA molecule.113RAP (RNA antisense purification)is a biochemical method describedin 2015 by Engreitz and co-workers121 Wanowska et al. investigatethe effect of ENST00000501665.2, OIP5-AS1 (OIP5 Antisense RNA 1) splicing variant, on Opa interacting protein5 (OIP5) expression with RAP-MS strategy. In HEK293cells, they demonstrate that ENST00000501665.2 is a positive regulatorof OIP5 expression by binding SMARCA4, a component of the SWI/SNF complex and facilitating the interactionbetween SWI/SNF chromatin remodeling complex and OIP5 promoter.122RAP-MS strategyuses ultraviolet light to cross-link and stabilizeonly direct protein interactions and is coupled with SILAC proteinquantification.5123 In the latter, the optimal experimental control is provided by thesame cell line transfected with dCas9 in the absence of gRNA.Recently, another locus-specificstrategy exploits the CRISPR /dCas9 system has been developed.A specific locus is targeted using nuclease-deficient Cas9 (dCas9) in combination with a specific guide RNA (gRNA). Normally,the gRNA is designed to bring the dCas9 upstream to the locus of interest.Then, an experiment resembling chromatin immunoprecipitation is carriedout: following the cross-linking and sonication procedure, dCas9 isimmunoprecipitate bound to chromatin through the gRNA anchor. De-cross-linkedproteins are then identified by mass spectrometry methodologies ina combined approach known as CRISPR-ChAP-MS.GAL1 promoter in yeast under transcriptionally active conditions. Cellswere treated with formaldehyde to stabilize protein–DNA interactions,chromatin was sheared to fragments, and the target chromatin regionharboring the Gal1 promoter was specifically locatedusing the CRISPR-Cas9 system. The complex was affinity purified bya Protein A tagged version of Cas9 together withthe proteins gathered at the promoter that was then eluted and identifiedby mass spectrometry123  isa protein that induces metastasis in colon cancer. The mechanismsby which its expression is transcriptionally regulated is still notfully known. Huang and collaborators exploited the CRISPR-ChAP-MStechnique to identify proteins interacting with MACC1 promoter. They use the catalytically inactive 3xFLAG tagged dCas9along with a gRNA to target and isolate the promoter region of the MACC1 gene.125 The c-JUN transcriptionfactor was found physically bound to the MACC1 promoterand able to upregulate its expression.126 was introduced by Schmidtmann et al. and was based on BirA* anddCas9 to label by biotinylation protein components present at specificDNA sequence within a 10 nm range, thus to map also transient interactions.By using the construct BirA*-dCas9-eGFP, Schmidtmann and collaboratorsidentified, for example, TERF2, TINF2, ACD that are known to directlybind telomeric DNA but also new chromatin factors, demonstrating thatCasID is a robust method to investigate native protein environmentat specific genomic loci. This approach provides a more detailed viewof complexes that involve specific chromatin loci under dynamic andfunctional aspects if compared to ChIP. On the basis of the integrationof CRISP technology and the proximity labeling enzyme APEX2 (dCas9-APEX2),Gao and collaborators proposed the new method C-BERST127 as an alternative to CasID.In addition to the basicapproach, many following versions havebeen enriched with further experimental steps and then have been proposed.For instance, a new hybrid approach (CasID)128 (CRISPR-assisted RNA–protein interaction detection).It has been proposed to overcome the limitations of many other techniquesused to elucidate the interactions between lncRNAs (noncoding RNAconsisting of more than 200 nucleotides in length) and proteins inliving cells. They designed a gRNA array composed of two gRNA sequencesspaced by a 30-nucleotide direct repeat to target 2 adjacent locion the same lncRNA transcript which could offer both greater specificityof targeting and a reduction in background noise. Yi et al. used anX-inactive specific transcript (XIST), a well-studiedmammalian lncRNA, to validate the efficiency of the method. Besidesalready known protein partners, several novel XIST-interacting proteins have been identified; in particular, TAF15 (transcription activators) and SNF2L (repressive factors) that confirmed previous models of XIST-mediated X chromosome inactivation (XCI). This technique allowedthem to confirm that it is an excellent method for identifying RNA–proteininteractions but still has limitations and should be considered complementaryand associated with other types of investigations.128Another validstrategy inspired by CRISPR-dead Cas9 was introducedby Yi and collaborators and was called CARPIDCas13, has been introduced to purify RNA-specific targetsand identifyproteins associated with an endogenous RNA within the RNA proximityproteomic methods (CBRPP),129 a new RNA-centricmethod.More recently, another Cas enzyme, i.e., 6Over the years, the advancementof technologies and the epochalchanges in the way of looking at the biological world typical of -omicapproaches have allowed the comprehension of many cell processes aswell as the definition of new scenarios hitherto unknown. The combinationof functional proteomics experiments coupled with mass spectrometryfor the isolation and identification of protein complexes on specificDNA/RNA regions or investigation of epigenetic modifications bothas profiling and at the level of gene promoters have had the largestimpact in the elucidation of processes concerning gene regulation,splicing, translation, and so on. The findings deriving from thesehigh throughput studies have allowed the understanding of the complexseries of events, most of them also involved in the onset of severaldiseases.132 Among these methods,those in vitro, such as AP and EMSA, are more approachableand cheaper, although they suffer of all limitations associated with in vitro approaches. Others, i.e., DNA/RNA-centered ChIPand CRISPR-based, provide more detailed clues relative to a singlegenomic locus analysis in vivo but require more elaboratecell systems to be developed. Despite that the latter are time-consumingand expensive procedures to be set up, they are the only that allowthe investigation of different DNA/RNA interactomes, modulated in vivo by different stimuli.133 All the described procedures might be enriched by the integrationwith structural data, such as the analyses of contact interfaces atthe amino acid level,134 or the identificationof post-translational modifications (PTMs) and how they tune the bindingto DNA/RNA,135 and finally with innovativestrategies for the global analysis of all putative DNA/RNA bindingproteins.136 Overall, mass spectrometryis and could further be a master technology for all the present andfuture applications in the investigation of the nucleic acid interactome.From a methodological point of view, affinity purification(AP),ChIP, EMSA, CRISPR, and their variations provide complementary information.


In silico protein–ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to create an accurate model of the protein–ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous studies in PCM modeling rely on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings that outperform complex, human-engineered representations. Several different embedding methods for proteins and molecules have been developed based on various language-modeling methods. Here, we demonstrate the utility of these unsupervised representations and compare three protein embeddings and two compound embeddings in a fair manner. We evaluate performance on various splits of a benchmark dataset, as well as on an internal dataset of protein–ligand binding activities and find that unsupervised-learned representations significantly outperform handcrafted representations. A main goal of cheminformatics in the area of drug discovery is to model the interaction of small molecules with proteins in silico. The ability to accurately predict the binding affinity of a ligand towards a biological target without the need to conduct costly and time-consuming in vitro experiments has the potential to accelerate drug development processes by enabling early prioritization of promising drug candidates . A commoQSAR models can be broadly classified into two types: single-task QSAR models and multi-task QSAR models . In singWhile these methods have been employed on various protein targets and are particularly useful when total data are limited to only a few targets, there are two major methodological limitations to their use ,10. BothAn attractive solution to this problem is to also include protein information to the input in a so-called proteochemometric (PCM) model . The addRecent advances in the field of deep learning have also resulted in its use in QSAR single and multi-task modeling as well as PCM modeling ,16,17,1832 substructures, the resulting sparse set of bits is usually hashed and folded to a much smaller size (∼103) at the expense of hash and bit collisions [All of the aforementioned studies utilize features for both small molecules and proteins based on hand-crafted feature extraction protocols. For small molecules, the most widely used method of representation is chemical fingerprints based on substructure presence or substructure counts ,19,20. Allisions ,22.Meanwhile, the most commonly used handcrafted protein descriptors are computed by amino-acid based or sequence-based methods. For amino-acid based descriptors, physicochemical structure ,23,24 anIn many application domains of deep learning, recent research has shown that these methods generally work better when the input data representation is of a lower level and unabstracted, allowing the model to learn hierarchical features directly rather than relying on features that are hand-crafted by humans ,18. For In this study, we follow this reasoning and utilize unsupervised and self-supervised-learned embeddings to represent both ligand and protein spaces for proteochemometric modeling. We hypothesize that proteochemometric modeling might benefit from such embeddings, since the number of labeled data points, i.e., measured ligand-target binding affinities, are scarce, but the number of unlabeled data points, i.e., molecular structures and protein sequences, are vast.We compare the performances of two compound embeddings and three protein embeddings, described further in the following section, on a benchmark dataset. We also compare performance to a No-Interaction-Terms model, which does not allow information flow between protein and compound input representation, in order to analyze the properties of these embeddings in a PCM model. Finally, we train and test models using these unsupervised descriptors on a large internal protein–ligand activity dataset.In the following section, we will discuss the compound and protein embeddings tested in our experiments, describe the datasets and types of train-valid-test splits utilized, provide details on the deep learning models trained and explain the metrics used to evaluate performance.For compound embeddings, we use the Continuous and Data Driven Descriptors (CDDD), developed by Winter et al. , and MolFor protein embeddings, we use embeddings generated by UniRep , SeqVec All the descriptors discussed have been shown to be useful for many different protein-related prediction tasks, including secondary structure and contact prediction, as well as remote homology detection. However, to the best of our knowledge, this is the first study that utilizes these unsupervised-learned protein and compound embeddings for the purpose of training a protein–ligand binding prediction model.We evaluate the performance of different descriptors on a large-scale benchmark PCM dataset created in Lenselink et al. , which cIn order to evaluate performance of the representations, we use three different types of hold-out-sets. On the ChEMBL dataset, we use random leave-compound-cluster-out and leave-protein-out splits, while on Internal, we used only leave-compound-cluster-out splits. Below, we will describe the difference between these splits generally. Details on how these splits were implemented can be found in Random splits randomly divide bioactivity measurements into train, valid and test sets. A drawback of the random split is that it can assign bioactivity measurements from the same experimental assay into the training and test sets. Since a single assay can involve similar, congeneric compounds, it is likely that there will be compounds in the training set that are highly similar to compounds in the test set and measured on the same protein. This split can, thus, report over-optimistic results .Leave-compound-cluster-out (LCCO) splits directly address the issue of compound similarity between training and test sets. For these splits, compounds are divided into several clusters based on a pairwise distance metric. Then, one cluster is held out as a test set, while the remaining clusters are used for training and validation; this process can be repeated for each cluster to perform multi-fold cross-validation over the entire dataset. Since the test set explicitly contains chemical matter that is, according to the distance metric, distinct from the training set, a model’s performance on this split is a better proxy for its ability to generalize to unseen regions of the compound space compared to random splits.Leave-protein-out (LPO) splits instead test the ability of the model to generalize to new proteins. This is very challenging, as while the model can show hundreds of thousands of unique compounds, it can only be shown thousands of unique proteins with currently available datasets. Nevertheless, this is a problem mode that single-task and multi-task models cannot operate on by construction; therefore, it is interesting to test if the PCM models can learn to generalize to new proteins in this manner.In order to make a fair comparison between the different descriptor combinations, we aimed to use a “neutral” model and hyperparameter settings. Based on previous work ,41, we kFor all of our experiments, we also used a standard hyperparameter setup that is similar to the setup used in . We use We did not tune the model architecture or hyperparameters on any dataset because we are more interested in the relative performance of these descriptors rather than absolute maximal performance.In addition, we use a No-Interaction-Terms model as a baseline to compare against the “full” PCM model. The No-Interaction-Terms model keeps compound and protein channels separate throughout the network and makes the final model prediction by taking the average of the separate predictions made from compound and protein information—otherwise, it has an identical architecture as the “full” PCM model. We can investigate the difference between this model, which can at best only recover compound and protein bias in the dataset, and the full model, which is potentially able to model the specific bio-chemical interaction between the protein and the ligand. A diagram is available in The descriptors are compared by using the Matthews Correlation Coefficient (MCC) and Boltzmann-Enhanced ROC (BEDROC) . The MCCOn random and leave-compound-cluster-out splits on ChEMBL, the models that use unsupervised-learned descriptors perform well, particularly attaining MCCs of around 0.5 and BedROCs of around 0.94 on the leave-compound-cluster-out split. In concrete terms, by examining the MolBert and UniRep-based model, for example, on the leave-compound-cluster-out split, we observed on average a 25% overlap between the top 10% highest activity compounds and top 10% highest predicted activity compounds for each protein .Our results are consistent with our hypothesis that the descriptors generated via unsupervised representation learning are more powerful than handcrafted protein and compound descriptors. Across all splits and models, unsupervised descriptors outperform handcrafted ones to a statistically significant degree. Additionally, on the random and leave-compound-cluster-out splits on ChEMBL, we observed that models that use MolBert to represent molecules significantly outperformed models that use the CDDD, highlighting the power of Transformer models for extracting meaningful representations of compounds. Similarly, on those splits, models using UniRep significantly outperformed models using ESM or SeqVec protein descriptors . For fulPerformance on Internal, on which the leave-compound-cluster-out split was applied, improves relative to the performance on ChEMBL, which is expected as the model now has more data to learn from. Additionally, the finding that MolBert + UniRep is a good descriptor combination to use for the protein–ligand binding prediction task is replicated, as we find that this combination significantly outperforms all other combinations, except for MolBert + ESM .The small but significant improvement of using UniRep relative to SeqVec and ESM is surprising, especially because SeqVec and UniRep are both trained on the same next-character-prediction task and on the same protein corpus . We hypoOn the leave-protein-out split, although the unsupervised-learned descriptors perform significantly better than the handcrafted descriptors, the overall performance is much worse compared to the random or leave-compound-cluster-out splits. There are many reasons why this may be the case—our dataset may simply contain insufficient proteins for protein-space generalization to be possible, or the protein descriptors might struggle in capturing some of the relevant information for binding, since they are trained on sequences and not on 3D-structure or binding site information. Additionally, the leave-protein-out split is more difficult because it requires the model to generalize both unseen proteins and new compound clusters. Compounds tested on a given protein are often specific to that protein, and so splitting by holding out all compound–protein activities for a given protein inadvertently can also hold out compound clusters. We can detect this by observing the average distance of each test-set compound to the nearest compound in the training set, as measured by cosine distance between MolBert representations. On the random split, the average distance from each test set compound to its nearest compound in the training set is 0.026; on the leave-compound-cluster-out split, it is 0.14; and for the leave-protein-out split, it is 0.085. Thus, the leave-protein-out split is made even more difficult because it is also indirectly testing compound-cluster generalization.Furthermore, we compare the performance of the model on all splits with the performance of the No-Interaction-Terms model . For fulIn summary, the results show that unsupervised-learned descriptors offer significant improvements over handcrafted descriptors in Proteochemometric modeling (PCM). Additionally, we observed that the Transformer-based MolBert descriptors performed the best with respect to representing compounds, while for protein descriptors, the choice of which of the three unsupervised-learned descriptor generators to use appears to be less significant. By comparing performance of the full model to a No-Interaction-Terms model, we were able to confirm that PCM formulation allows the model to learn relevant interaction terms between ligand and target and is not merely reflecting the bias in the dataset. We believe that comparing to such a baseline should be standard practice when evaluating the performance of PCM models. While the model is effective at generalizing unseen compound clusters, generalizing unseen proteins remains a challenge—this may be improved by generating more data with more unique proteins and by developing more powerful protein descriptors that contain binding site information, which we believe are the most relevant directions for future research in this area.


Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required.In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.The online version contains supplementary material available at 10.1186/s13321-021-00547-7. In living organisms, all biological processes involve proteins that are dynamic molecules with functions almost invariably dependent on the interactions with other molecules, which are affected in physiologically important ways through subtle, or striking changes in the protein conformation . Such inIn recent years, numerous methods have been proposed to identify the potential druggable binding sites. Fpocket  is a geo3 sized voxel. DeepSite uses 16 × 16 × 16 voxels, whereas kalasanty uses 36 × 36 × 36 voxels to represent a protein structure. DeepSurf is a surface based learning approach where a new representation of the 3D protein surface is introduced, based on local voxel grids centered at sample points of the surface and uses 16 × 16 × 16 voxels. DeepPocket is a multi-step approach to get the final pocket location where first Fpocket is used to get the pockets and later classified whether they are binding site or not. All these methods shows promising results and uses scPDB [P2Rank  is a maces scPDB  dataset.Our work is focused on improving the training data, so that our deep learning model can generalize more and provide better predictions. Therefore, we developed an independent training dataset, which is a subset of scPDB , a publitructure ). Finalltructure  and 5020In this study, ResNet  architecProtein structure is treated as a 3D image of the shape (36 × 36 × 36 × 18) which is input to PUResNet, and the output is the same as the input shape with a single channel , where each voxel (point in 3D space) in the output has a probability that whether or not the voxel belongs to the cavity. Later, these predictions can be saved as mol2 files, which can be later visualized using the molecular modeling software (PYMOL).In this study, new training dataset is developed, which is a subset of scPDB. scPDB dataset consists of protein structures belonging to 2050 different protein families . The famhttps://www.uniprot.org/uploadlists/). A total of 5462 clusters of UniPort ID were obtained, of which 2964 contained a single protein structure and 2498 contained multiple protein structures. The cluster of UniPort ID (P00388), which had 19 protein structures, was the largest of all. Second, each protein structure in the cluster fingerprint was determined, where we used a substructure-based fingerprint calculation molecular access system (MACCS) [304 protein structures that were erroneous while loading using openbabel , 25 were (MACCS) , and theFor calculating Tanimoto index as shown in Fig. On an average, for each cluster having multiple protein structure, the Tanimoto index was found to be 80%, and therefore, we decided to select the longest sequenced protein structure from each cluster because of high similarity between the protein structure in the cluster . The totWe split our data into four folds by addressing the problem of data leakage during validation, based on the protein family, all the structures belonging to one family were kept in the same set of each fold . In each fold, the training set consisted of 3765 protein structures, whereas the validation set had 1255. We used k-fold , 28 traiHere, the protein structure was treated as a 3D image of size 36 × 36 × 36 × 18, where a 3D cube of size 36 × 36 × 36 is placed at the center of a protein with 70 Å distance in each direction, and was described based on nine atomic features , such asTo treat it as a binary segmentation problem where input size is 36 × 36 × 36 × 18 and output size is 36 × 36 × 36 × 1, each binding site was represented using same sized 3D voxels (36 × 36 × 36 × 1) placed at the protein center, and for each voxel, if the binding site was present, then the assigned value was 1 or else 0.PUResNet is derived from the concept of U-Net  and ResN−5, kernel regularizer as L2 with value of 10−3, batch size of 5) without skip connections is almost constant which implies as the model is deep, gradients are either exploding or vanishing . As expected, dice loss performs better in the case of highly a imbalanced dataset [Our approach to optimize the hyperparameters was conducted through K-fold training, and we implemented the hit and trial approach using a heuristic method for optimizing the model. To select the value of K during the K-fold training, we assessed the validation and training curves for different values of K and found that K = 4 exhibits a smoother validation and training curve for our dataset. Hyperparameter optimization was conducted through selecting two sets of hyperparameters in such a way that the difference in values was high. K-fold training was conducted using the two sets of hyperparameters and determined which set had good performance, and then, the average value of the two sets was computed. After that, K-fold training was performed using individual values keeping others the same, and the results were obtained. If the performance was better than the previous result, then those values were selected and otherwise discarded. Further, we selected the top two results from K-fold training, which was conducted recursively until optimal parameters were obtained. Here, while selecting the optimal parameter, we considered every data point as the validation data using cross-validation so that our parameters were not biased towards a certain protein structure. Finally, after obtaining a set of optimal hyperparameters, we conducted K-fold cross-validation using K = 4, and the results were obtained. Final training was performed on the entire dataset with the obtained optimal parameters (learning rate = 10in keras ). Dice l dataset . TherefoDistance center center (DCC)It is distance between center of predicted binding site to the center of actual binding site or ligand. If the distance is Discretized volume overlap (DVO)pbs) and actual binding site(Vabs) to their union. For predicted binding sites having DCC DCC metric does not consider the volume and shape of the predicted and actual binding sites or ligands. Therefore, DVO, which provides insight into the volume and shape, is the ratio between the volumetric intersection between the predicted(VProportion of Ligand Inside (PLI)L) resides inside binding site(Vpbs). For predicted binding sites with a DCC less than or equal to 4 Å, PLI was calculate as follows: In case of ligands using DVO metrics to find overlap does not provide a comprehensive idea of the overlap, the binding sites are usually larger than the ligand. The DVO metric is similar to a shape analysis between the two binding sites, but in case of ligands and binding sites, it is not appropriate. Therefore, we developed a new matrix to determine the proportion of ligand  in a 3D grid of size 36x36x36. To calculate the F1 score, we considered a predicted binding site with a DCC less than or equal to 4 Å as true positive (TP), greater than 4 Å as false positive (FP) and no prediction as false negative (FN). In this problem, there is no true negative since every protein structure has a binding site.Distance center center (DCC) and discretized volume overlap (DVO) are the matrices used to evaluate model in different studies , 11, 12.−3, kernel regularizer as L2 with value of 10−4, batch size of 5 and others as default values as in keras [We conducted our experiment in 4 folds, where the entire dataset was divided into four parts, leaving one part as the validation set and the other as the training set; and thus, we obtained four different models. Each model was compared with the kalasanty, which we trained on each fold-keeping with obtained optimal (using our optimization technique) parameters (learning rate = 10in keras ). CombinThe evaluation was conducted using the Coach420 and BU48 datasets individually to determine the performance of PUResNet and kalasanty. In the Coach420 dataset, kalasanty did not provide any output for 26 protein structures , whereas PUResNet did not provide any output for 19 protein structures , as shown in Table  In case of BU48 dataset, PUResNet did not provide any output for 3 protein structures , whereas kalasanty did not provide any output for 7 protein structures , as shown in Table  To better understand the performance of PUResNet, we further investigated each individual prediction made using PUResNet and kalasanty in the Coach420 and BU48 datasets. Figures Out of 298 protein structures in the Coach420 dataset, both the models correctly predicted 137 protein structures, incorrectly predicted 100 protein structures, and for 11 protein structures, no site was predicted, as shown in Fig. Similarly, for BU48 dataset containing 62 protein structures (31 pairs of bound and unbound structures), 33 structures were correctly predicted, 14 were incorrectly predicted, and for one structure, no site was predicted, which was common among both the models, as shown in Fig. In the Coach420 dataset, protein structures 2zhz, 3h39, and 3gpl  and bound structures (5cna and 1gca); however, PUResNet predicted all unbounded structure and did not predict bound structures . For pairs , and  as shown in Fig. −5, kernel regularizer as L2 with value of 10−4, batch size of 10) trained on the original dataset.To validate, whether our data cleaning process improves the performance of PUResNet, we performed an experiment in which PUResNet is trained on the original scPDB dataset. As shown in Additional file https://github.com/jivankandel/PUResNet, along with the trained model and all datasets used in this work. Predicted sites are provided in a mol2 file and can be visualized using different software, such as PYMOL. This work can be further improved by using a sequence alignment tool before calculating similarity using our method which will remove the step of taking maximum over shifted sequences, representing the protein structures along with water molecules, as well as differentiating the surface residue and incorporating the depth of the residues.We introduced a new deep learning model, PUResNet, to predict the ligand-binding sites on protein structures trained on a newly formed dataset, which is a subset of scPDB. We compared our results with those of kalasanty, which was previously mentioned to exhibit better performance than DeepSite, Fpocket, and Concavity. Our results suggest that PUResNet provides a better prediction than kalasanty. In K-fold experiment, PUResNet has a success rate of 61% whereas kalasanty has a success rate of 51%. The results from independent test sets (Coach420 and BU48) revealed that PUResNet exhibits better accuracy than kalasanty . It is important to note that although PUResNet is trained with approximately 1/3 of the dataset that was used to train kalasanty, we were able to exceed kalasanty in terms of performance while evaluating K-fold as well as in independent tests. The model was developed in Python using the Keras library. All the information regarding the use of the trained model is publicly available at Additional file 1. Feature visualization. Includes 3D plot of different features used in the studyAdditional file 2.Model description. Includes description of different model blocks with figures.Additional file 3. KFold training and validation results. Includes validation, training graph, success rate graph and histogram of DVO of different foldsAdditional file 4. Miscellaneous results.


DNA–protein interactions are essential for several molecular and cellular mechanisms, such as transcription, transcriptional regulation, DNA modifications, among others. For many decades scientists tried to unravel how DNA links to proteins, forming complex and vital interactions. However, the high number of techniques developed for the study of these interactions made the choice of the appropriate technique a difficult task. This review intends to provide a historical context and compile the methods that describe DNA–protein interactions according to the purpose of each approach, summarise the respective advantages and disadvantages and give some examples of recent uses for each technique. The final aim of this work is to help in deciding which technique to perform according to the objectives and capacities of each research team. Considering the DNA–binding proteins characterisation, filter binding assay and EMSA are easy in vitro methods that rapidly identify nucleic acid-protein binding interactions. To find DNA-binding sites, DNA-footprinting is indeed an easier, faster and reliable approach, however, techniques involving base analogues and base-site selection are more precise. Concerning binding kinetics and affinities, filter binding assay and EMSA are useful and easy methods, although SPR and spectroscopy techniques are more sensitive. Finally, relatively to genome-wide studies, ChIP–seq is the desired method, given the coverage and resolution of the technique. In conclusion, although some experiments are easier and faster than others, when designing a DNA–protein interaction study several concerns should be taken and different techniques may need to be considered, since different methods confer different precisions and accuracies. It has been known since the second half of the last century that the binding of a protein to a DNA molecule has a very important role in the function of a living cell and in life’s sustainability itself. For many decades and representing a big segment of the molecular biology research conducted, scientists tried to unravel how DNA links to proteins, forming complex and vital interactions. In the early beginning of these studies, even before the publication of the DNA molecular structure, Stedman and Stedman  already Escherichiacoli catabolite gene activator protein (CAP) does bind to the DNA major groove. Later, Pabo and Sauer [From a molecular point of view, lifting the veil from the way a DNA molecule binds to a protein and starting to distinguish some patterns and possible favoured interactions between amino acids and DNA base sequences, in the sixties Leng and Felsenfeld  discovernd Sauer  and Mattnd Sauer  continuend Sauer . The majnd Sauer .lac operon [As for the study of transcription factors (TFs) and the regulation of gene expression, it was also in the sixties that François Jacob and Jacques Monod discovered the genetic regulatory mechanism in prokaryotes moderated by the c operon . Many fic operon .This review intends to compile and briefly describe the majority of the existing techniques, and the respective variants, that enable to access information related to DNA–protein interactions, trying to combine them according to their purpose, knowing that there is an overlap between certain methods. The positive and negative aspects, as well as some improvements and slight modifications performed to each procedure referred in recent studies will also be noted. The final aim of this article is to aid researchers in the moment of deciding which technique to perform according to the objectives and capacities of each research team, hoping to contribute to the increase of the current knowledge related to DNA–protein interactions and gene regulatory networks.The interaction between nucleic acids and proteins was not yet totally described when Yarus and Berg  developelac repressor-operator interaction. This new approach replaced the tedious and limited techniques that were being used until then, such as glycerol or sucrose density gradients and DNA columns.Membrane filters were already being used for some time to detect binding between molecules , when JoHowever, this procedure presents several drawbacks that have led to its disuse. Firstly, not all the proteins bind to nitrocellulose membranes and some even denature when clinging to these filters. Moreover, if the interaction between the molecules of the complex is not strong enough, it may not withstand the filtration process . FurtherFinally, a technique was developed that uses gel electrophoresis and that surpasses these inconveniences. Fried and Crothers  and GarnUsually, the technique starts by mixing the protein, present in a crude cell extract or purified, with the labelled nucleic acid and an appropriate buffer, under the right specific conditions and concentrations for the binding reaction to occur, and the final products are separated in a non-denaturing gel electrophoresis. To obtain purified protein, one can insert the coding sequence of the gene that codifies the protein of interest in an expression vector for bacteria or yeast transformation, induce the transcription and translation of this gene and extract and purify the protein using purification columns. As for the DNA fragments, these are usually labelled with radioisotopes, covalent or noncovalent fluorophores or biotin. The results are then observed by autoradiography, fluorescence imaging, chemiluminescent imaging and/or chromophore deposition Fig. . It mustSuch as filter binding assays , 16, theOverall, EMSA is easy to execute, but powerful and sensitive, comprising a broad spectrum of binding conditions and enabling the use of small concentrations of protein and nucleic acid and small sample volumes, especially when the nucleic acid is radio-labelled. However, fluorescence, chemiluminescence and immunohistochemical detection can also be used in case a high sensitivity is not necessary. Concerning the advantages related to the nucleic acid used, this assay is able to test oligonucleotides with a large variety of sizes, which can go from short nucleic acids to molecules with thousands of nucleotides or base pairs, and structures, which can be single, double-stranded, triplex or quadruplex nucleic acids, or even small circular DNAs. As for the protein under study, proteins used in EMSA can have very distinct sizes and still efficiently provide mobility shifts. Using ESMA, one can even distinguish how proteins are spread across different interactions with various nucleic acid molecules in one solution, as well as the existence of complexes with different protein stoichiometry and/or binding site distribution .Nevertheless, EMSA also presents some disadvantages. One of them is that, during the electrophoresis run, partial or total dissociation of the nucleic acid–protein complex can happen, or, on the contrary, many complexes acquire more stability in the gel than in a free solution. Either way, the results can be misleading, not reflecting the state of the interaction before the loading of the sample in the electrophoresis gel. To solve this issue, shorter electrophoresis times are suggested. Another drawback is that, in case a shift is observed in the mobility of a nucleic acid during the electrophoresis step, it is not necessarily due to the size of the proteins that are bound to it. Many other factors may be in the origin of the mobility shift, such as the conformation and structure of the complex. Moreover, this assay does not give information about the location of the sequences of the nucleic acid that are linked to the protein. These last two problems can be addressed using alternative techniques that complement or replace EMSA and that are referred in the next sections .Overall, filter binding assay and EMSA are two important techniques that can help in identifying nucleic acid–protein interactions quite easily and rapidly. Advantages, disadvantages and applications of these two methods are resumed in Table An EMSA technique drawback is the dependence of its results on several factors besides the protein size. This assay does not allow to identify nor reveal the molecular weights of the proteins present in the complex being studied. Obtaining this information is crucial especially when dealing with impurified or partially purified cell extracts, since they contain many nucleic acid–binding proteins, being that different proteins can bind to the nucleic acid of interest. Thus, other techniques need to be performed complementary to EMSA to clarify which protein is causing the electrophoretic mobility shift .In the seventies, quaternary structures related to interactions between proteins were analysed using cross-linking techniques . CovalenThere are different cross-linker agents and strategies, which can be grouped into chemical and photo-cross-linking. Chemical cross-linking techniques often use formaldehyde and glutaraldehyde as the cross-linker agents. These two agents are usually employed in the study of nucleoproteins, such as histones . BrutlagE. coli RNA polymerase and T7 DNA, inducing the cross-link between these two molecules. Apart from being a simple and fast method, this upgrade to the UV cross-linking technique decreases even more the chance of damaging the structure of the molecules being analysed, since it uses short and powerful pulses. Besides, this approach can also be applied in in vivo studies, without affecting the metabolism of a cell, as it happens with conventional UV cross-linking. It also enables the immediate cross-link of a complex involving several proteins. However, this procedure may cause more chain scission in DNA than the standard UV cross-linking method referred before [Photo-cross-linking was firstly performed by Smith , that ird before .Overall, cross-linking techniques have the advantage of identifying the molecules that participate in a DNA–protein complex, even though some of them may not be directly in contact with the DNA. Nevertheless, when analysing the molecular weight of the proteins present in the complex through a sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) Fig. a, DNase Western Blotting was initially developed by Towbin et al.  enablingKristie and Roizman  created Singh et al.  also creDemczuk et al.  performecis elements, upstream to the GAL4 promoter and a reporter gene, usually the lacZ gene. These two elements are inserted into a yeast cell and, in case the TF does bind to the gene of interest, the activation domain binds to the promoter and leads to the expression of the reporter gene, being this activation independent of the TF action, either it is an activator or a repressor. Unlike in vitro techniques, this technique employs a system where a cell is involved, providing much more reliable results since it recreates an environment similar to the one where the complex under study is formed [In order to relate binding sites with the TF binding in vivo, systems like the yeast one-hybrid (Y1H) were created. This system, developed by Li and Herskowitz , implicas formed . It can s formed .inplanta was developed. The creation of a Transient Expression in Arabidopsis Mesophyll Protoplast (TEAMP) method by Yoo et al. [Wuschelrelatedhomeobox8 (WOX8) gene and the WRK2 TF. After identifying the WOX8cis elements, the group followed the TEAMP protocol and transformed Arabidopsis mesophyll protoplasts, previously transformed with another vector containing the WRKY2 gene linked to the GAL4 activation domain, with the constructs containing the cis elements linked to the GAL4 promoter and a reporter gene. As these protoplasts exhibited reporter gene expression, the group suggested that WRKY2 does bind to the WOX8 gene, which, in this experience, led to the expression of the reporter gene 5).Fig. 5“Protective” footprinting results usually present a background that corresponds to free DNA that was not linked to a protein. This problem can be overcome by adding large amounts of protein, which is not always easy to obtain, especially when dealing with purified samples, and can lead to non-specific binding. Another way to eliminate the background is to perform an EMSA, in order to separate free- and bound-DNA fractions, prior to the footprinting reaction . PapavasRelatively to “interference” footprinting methods, these techniques consist in inflicting modifications on DNA bases and identifying the alterations that inhibit or diminish protein binding. These approaches are more direct and have a higher resolution than the previous ones, since they investigate the impact of each nucleotide on the binding affinity, modifying only one nucleotide per DNA fragment .Overall, footprinting techniques present a low throughput  and needAn expensive and long procedure that can be performed to identify the contribution of each base to the DNA-binding affinity of a protein involves the integration of modified bases at particular sites in the course of the synthesis of both strands that create the DNA duplex . This teAnother radical technique consists in synthesising oligonucleotides that incorporate a random nucleotide mixture at specific positions in a certain sequence. The bases located at determined sites in sequences that bind to the protein under study interaction, usually checked by an EMSA, are established as the preferred bases . In the Apart from identifying nucleic acid–protein complexes and characterising these proteins, the filter binding assay and EMSA can also be useful to distinguish the complexes formed according to the equilibrium constants of each binding reaction, enabling to compare relative binding affinities and discriminate the interaction between a given protein with more than one nucleic acid sequence. To do so, it is essential to use a purified protein and know the respective concentration. Then, the affinity of a protein to a certain nucleic acid can be quantified performing a titration of the protein under study with a known concentration into a permanent concentration of the nucleic acid of interest .Usually, the Michaelis–Menten analysis can be applied to examine these titrations, considering the protein added as the substrate, since the nucleic acid concentration is very low. This demands a high protein concentration to obtain a significant interaction, being the free and total protein concentrations quite similar. Thus, the equilibrium constant can be estimated as the concentration of protein necessary to retain, as for the filter binding assay , or shifP) until all the DNA is bound. At this point, the ratio between the protein and DNA concentrations gives the stoichiometry. If it is higher than 1, it can mean that more than one protein link to the DNA, or that part of the protein is not active. Moreover, taking into consideration that, at the same moment, the concentration of the complex formed (PD) is equal to the DNA concentration (D) and that the equilibrium constant (K) is calculated using the formula:P by the formula:Ptotal) calculated for each dilution and the PD directly measured [Analysing DNA–protein interactions by intrinsic fluorescence, Carpenter et al.  developeument}1P . Howevermeasured .f) is linked to the protein (P). Here the equilibrium constant (K) is easily and accurately calculated by the formula:Following the same model, for intermediate DNA concentrations, when the stoichiometric point is reached, only a part of the DNA  can be calculated using the formula:Kc the equilibrium constant of the reaction where the competing nucleic acid binds to the protein. This formula also represents the equilibrium constant of the overall reaction (KT). Taylor et al. [C) of the competitor sequence needed to dissociate half of the specific complex and the formula.Finally, competition assays can also be performed and equilibrium constants can be compared. In a first approach, the relative binding constant  made it possible to decipher millions of DNA fragments quickly, simultaneously and efficiently. In 2007, Johnson et al.  combinedIn addition to the advantages of the previous two techniques, like the inexistence of hybridisation noise signals, the obtention of rigorous and quantifiable results and the genome coverage, the lower required amounts of ChIP DNA and, mainly, the higher base pair resolution are possibly the most notorious features of ChIP-seq. However, ChIP-seq also presents some disadvantages. The choice of the antibody used is crucial in ChIP-seq, as well as in all ChIP techniques. Even extremely specific antibodies may react positively with nuclear proteins different from the one under study . FurtherAnother problem associated with ChIP-seq is its precision, since the DNA molecules present in the libraries that result from standard ChIP-seq experiments have a length of approximately 200 bp. However, a protein usually binds only 6–20 bases. Furthermore, DNA contaminations are usual in these libraries, resulting from DNA that was not bound by the target protein and leading to systematic errors. In 2011, Rhee and Pugh  developeOther ChIP experiments have also been created, like Re-ChIP , accordiAlthough some of the referred techniques are not so used currently, many of them were applied recently, sometimes complementing more modern techniques, or with slight modifications and improvements, proving their significance to unravel some aspects of the nucleic acid–protein interaction.For instance, not long ago, Prabu et al.  combinedproductionofanthocyaninpigment1 (PAP1) gene in Arabidopsisthaliana seedlings, Liu et al. [PAP1. The authors integrated PAP1 promoter sequences fused to a resistant gene into Y1H Gold chromatin and identified three promoter sequences that interacted with PIF4, since the yeast strains generated presented high growth rates in media containing antibiotic. Furthermore, the authors also performed a ChIP-qPCR assay in order to test this interaction on Arabidopsis. After creating transgenic plants for the 35S:PIF4-HA construct and using anti-HA antibody in the ChIP-qPCR assay, Liu et al. [PAP1 binding site than the internal control (Actin2), suggesting that this factor could indeed bind to PAP1 in vivo. Moreover, the authors also performed an EMSA using the binding protein and a sequence containing the binding site. Liu et al. [PAP1 gene promoter via the binding motif, since the electrophoretic lane containing the promoter sequence and PIF4 presented a delayed band, analogously to the lane containing the same parts together with a mutated competitor sequence and oppositely to what happened when using a mutated target sequence or when adding a wild-type competitor fragment. Finally, the authors also performed a transient transcriptional expression analysis using Arabidopsis protoplasts, in order to understand if the interaction between the two parts would lead to the down-regulation of PAP1 expression. After introducing constructs containing the luciferase gene regulated by wild-type or mutated PAP1 promoter sequences into different protoplasts together with constructs containing the PIF4 gene, Liu et al. [PAP1 promoter function.Trying to decipher how the phytochrome-interacting factor 4 (PIF4) negatively regulates the transcription of the u et al.  performeu et al.  concludeu et al.  concludeu et al.  observedSolanumlycopersicumLeSPL-CNR (squamosapromoterbindingprotein-like-colourlessnon-ripening) gene, Wang et al. [In order to identify the proteins that link to the g et al.  conducteRecently, Manosas et al.  developeSong et al.  used a bEven though there are several and different techniques to describe, analyse and compare DNA–protein interactions, they can be organised into separate sections, according to the respective purpose. Filter binding assay and EMSA are easy in vitro methods that rapidly identify nucleic acid–protein binding interactions, being the procedures of choice in many functional studies that aim to confirm the gene targets of a given TF. Together with SDS-PAGE, EMSA can help in the identification of the proteins that bind to a certain sequence when using crude protein extracts. Cross-linking can also be used with this objective, especially when dealing with DNA–protein complexes that involve more than one protein that can be indirectly linked to the DNA. In vivo methods, like Y1H and PTA, can also be applied when analysing if a TF binds to a given sequence. These approaches provide reliable results since they recreate similar environments to the ones where the complex is formed. When trying to discover the DNA-binding sites localisation, DNA-footprinting is indeed an easier, faster and relatively reliable approach, which also allows to investigate the impact of each nucleotide on the binding affinity. Furthermore, in vivo footprinting methods are also available. Nevertheless, techniques involving base analogues and base-site selection are more expensive, but confer a higher precision and are quite helpful when trying to identify aptamers that link to a given protein, as in the example described in the previous section. Quantifying the kinetics and affinity of a given DNA–protein interaction can be useful, mainly when comparing more than one interaction. Once again, filter binding assay and EMSA are useful and easy methods in this subject. SPR and methods applied to the proteins study that can also be used in the analysis of nucleic acid–protein interactions, like circular dichroism and fluorescence spectroscopy, are quite sensitive and direct approaches. However, these procedures involve specific instruments and technology. Finally, considering genome-wide studies, ChIP-seq is the desired method, given the coverage and resolution of the technique. Nevertheless, the costs related to NGS remain a barrier to the use of this technique. In conclusion, although some experiments are easier, less expensive and faster than others, when envisioning a DNA–protein interaction study several aspects cannot be disregarded, since several useful and available methods can confer more precise and accurate results in a simpler way than one may think.


Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach  and an ab initio prediction approach . Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising.In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13  targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches.By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.The online version contains supplementary material available at 10.1186/s12859-021-04353-8. Proteins are macromolecules composed of amino acid chains and serve important roles in a wide-range of biological processes including catalysis, immunity, and information transmission. A protein performs its biological functions by folding into specific tertiary structures; thus, the knowledge of protein structure is crucially helpful for the deep understanding of its biological functions . Proteintemplates) through constructing alignments, and then build tertiary structures with reference to the structure of these homologous proteins ,  as representatives, we demonstrated the details of the prediction procedure of FALCON2, including the constructed MSA, predicted inter-residue distances, the selected templates, and the constructed structure models.The target protein T0966 has a total of 492 residues, which was classified as TBM-hard in the CASP13 competition. The protein is a MARTX toxin effector domain from Vibrio vulnificus CMCP , and itsL long-range residue contacts is only 0.443. The predicted inter-residue distances also deviate significantly from the native one , Uniref90 (as of Mar. 2018), and Metaclust (as of Jan. 2018). The constructed MSA contains a total of 126 homologous proteins, implying that its quality is relatively lower. Next, FALCON2 executed ProFOLD to predict inter-residue distances for this target. However, due to the low-quality MSA, the accuracy of the predicted inter-residue contacts is relatively lower. As shown in Table For this target, FALCON2 also executed ProALIGN to yield candidate structures. Table The target protein T0950 has a total of 353 residues, which was classified as FM in the CASP13 competition. T0950 is a membrane protein from Photorhabdus luminescens , and itsL long-range residue contacts reaches 0.675. The predicted inter-residue distance matrix is also similar to the native one , Uniref90 (as of Mar. 2018), and Metaclust (as of Jan. 2018). The constructed MSA contains a total of 462 homologous proteins, implying that its quality is relatively high. Next, FALCON2 executed ProFOLD to predict inter-residue distances for this target. Using the high-quality MSA, ProFOLD yielded accurate distance prediction. As shown in Table For this target, FALCON2 also executed ProALIGN to yield candidate structures. Table In this study, we present FALCON2, a web server for high-quality protein structure prediction. Using CASP13 and CASP14 target proteins as representatives, we demonstrate that FALCON2 can successfully predict structures for both TBM and FM target proteins when high-quality MSA can be obtained. We also observed that TBM and ab initio approaches have different emphasis, and the combination of these two types of approaches can lead to improved prediction accuracy. FALCON2 provides a user-friendly graphic interface, making it easy to use for the community. We expect FALCON2 web service to enable insights into the structure and function of proteins, especially the proteins with important roles in health and disease.Project name: FALCON2 serverhttp://protein.ict.ac.cn/FALCON2Project home page: Operating system(s): Windows, Linux, MacProgramming language: Python, PHP, C++License: GPLAny restrictions to use by non-academics: license neededAdditional file 1. Files containing additional implementation detail and additional tables of results.


Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs. membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools. In eukaryotes, there are ten main subcellular localisations which can be further subdivided into intra-organellar compartments see A. These The organelles provide suitable biological conditions for proteins and the correct transport of a protein to its final destination is crucial to its function. Failure in protein transport systems has been associated with several disorders including Alzheimer’s and cancers ,2,3.It has been observed that proteins from different organelles show signatures, in their amino acid composition, that associate with their subcellular localisation . This hak-nearest neighbors based classifier; TPpred3 ”) AND reviewed:yes’ with peroxisomal membrane sub-cellular location (SL-0203) to select reviewed, non-fragmented membrane protein sequences.We obtained 327 non-fragmented protein sequences which were then clustered using Cd-hit , with seWe restricted further the selection only to those proteins with at least one associated publication specific for the sub-cellular localization, obtaining 135 highly curated peroxisomal membrane protein sequences. Additionally, three sequences were removed from the data set, since they were not available for the UniRep embedding. The final data set contains 132 membrane proteins.Reviewed, non-fragmented matrix peroxisomal protein sequences were obtained with the query ‘fragment:no locations:(location:“Peroxisome matrix [SL-0202]”) AND reviewed: yes’.We obtained 60 entries that were reduced to 22 after clustering for similarity and further reduced to 19 after selecting only those proteins with at least one publication specific for the subcellular localisation.Due to the low number of matrix proteins in comparison to the number of membrane proteins (132), we performed another advanced search in Uniprot with query ‘fragment:no locations:(location:“Peroxisome [SL-0204]”) NOT locations:(location:“Peroxisome membrane [SL-0203]”) AND reviewed:yes’ selecting reviewed, non-fragmented protein sequences, with peroxisomal location (SL-0204), and not peroxisomal membrane location (SL-0203).We obtained 721 non membrane protein sequences, 202 after clustering, which were reduce to 22 after applying the same filtering procedure. There were 13 common entries between the two subsets; clustering using a 40% sequence similarity threshold gave 28 unique peroxisomal matrix protein sequences.Further peroxisomal protein candidates were retrieved from Uniprot (1 June 2020). We looked for peroxisomal proteins (SL-0204 and GO:5777) with a non-specific sub-peroxisomal location  and experimental evidence. We then excluded the peroxisomal proteins also found in mitochondria  and in the endoplasmic reticulum , obtaining 116 reviewed entries.To assess the applicability of our prediction tool to the prediction of other sub-organelles protein localisation we considered two well-curated data sets containing mitochondrial proteins.SM424-18 data set: this data set was used to build the DeepMito predictor [SubMitoPred data set: this data set was used to build the SubMitoPred predictor [redictor  and contredictor , with a redictor . SubMitoredictor . It contredictor , with a redictor .We considered three of the most commonly used method for the encoding of the amino acid protein sequences.Residue one-hot encoding. The one-hot encoding (1-HOT) [j is represented by a j-th position; for instance alanine (A) is represented as 100,000,000,000,000,000,000. A protein sequence constituted by L amino acid is thus represented by an  (1-HOT)  is the mResidue physical-chemical properties encoding. Akinori et al. devised a way to represent an amino-acid with ten factors [j in the protein sequence is represented by a L amino acid is thus represented by an  factors  summaris factors . Any givThe Position-specific scoring matrix (PSSM) [j in the protein sequence is represented by a L amino acid is thus represented by an x (PSSM) ,22 takesx (PSSM)  where reSI-BLAST  against SI-BLAST . Basicalhttps://github.com/churchlab/UniRep (accessed on 6 June 2021).Unified Representation. The Unified Representation (UniRep)  is basedhttps://github.com/mheinzinger/SeqVec (accessed on 6 June 2021).Sequence-to-Vector embedding. The Sequence-to-Vector embedding (SeqVec)  embeds bWe considered two recently proposed methods for the embedding of protein sequences based on deep-learning approaches:Step Forward Feature Selection was used to select the best combination of features (predictors) that is, protein encodings or embeddings to be used as input for classification algorithms .It is a wrapper method that evaluates subsets of variables, in our case, combinations of protein encodings/embeddings. It starts with the evaluation of each individual encoding, and selects that which results in the best performing selected algorithm model. Next, it proceeds by iteratively adding one encoding/embedding to the current best performing features and evaluating the performance of the classification. The procedure is halted when performance worsens and the best combination of embeddings/encodings is retained. A schematic representation of this approach given in The determination of the sub-localisation of peroxisomal (membrane Support Vector Machines (SVM) is an algorithm for two-group classification which aims to find the maximal margin hyperplane separating the points in the feature space ,43.Random Forest ,45 is anY contains dummy variables indicating class labels (0–1 in this case). Sample predicted with Y by projecting the data in a k-dimensional space with k possibly much smaller than the original number of variables.Partial least squares discriminant analysis (PLS-DA) is a partial least squares regression ,47 whereLogistic Regression (LR). We used a penalised implementation of multivariable logistic regression .i) optimising the hyper-parameters of the different classification algorithm used  and  ,50 for grid search: for each set of hyperparameters, the average classification score is computed across the folds. The hyperparameters corresponding to the best classification score are then used to fit a classification model whose quality is assessed on the Validation set obtaining unbiased model evaluation since the validation data has not been used to train the classification model.The DCV strategy consists of two nested cross-validation loops. In the outer loop data is first split in The step forward feature selection procedure described in Given the unbalance of the two class of proteins, different weights were applied to the two class. Class weights were considered to be metaparameters and optimised in the inner calibration loop.We used several metrics to quantify the quality of the classification models, namely: accuracy (ACC), F1 score , balanceAccuracy (The F1 score :(2)F1=2×The The balanced accuracy BACC :(5)BACC=The Matthews correlation coefficient (MCC) :(7)MCC=Tnbalance .http://www.cbs.dtu.dk/services/TMHMM/ (accessed on 17 May 2020)We used TMHMM (Trans-Membrane Hidden Markov Model) for the prediction of trans-membrane proteins ,56 availTMHMM returns the most probable location and orientation of trans-membrane helices in the protein sequence, summarised in several output parameters: ExpAA, the expected number of amino acids in transmembrane helices. If this number is larger than 18 it is very likely to be a transmembrane protein; First60, the expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein; PredHel, the number of predicted transmembrane helices. We refer the reader to the original publications for more details.We used TMHMM to predict the localisation of peroxisomal protein with unknown sub-localisation  . We obta