Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.The online version of this article (10.1186/s40168-018-0532-2) contains supplementary material, which is available to authorized users. Ecosystem changes are often correlated with the presence of new communities disturbing their stability by importing new metabolic activities –4. Very Computational studies improving the detection of the functional preferences of environmental communities are important for gaining insight into ecosystem changes –15. IdeaOver the past years, a lot of effort has been devoted to the creation of integrated systems for the computational analysis of metagenomic (MG) and metatranscriptomic (MT) datasets. Several pipelines conducting data pre-processing, assembly, taxonomic characterisation, gene finding, protein-coding gene annotation and pathway reconstruction, such as MinPath , ShotgunHere, we introduce MetaCLADE, a new generation method for the annotation of protein domains in MG/MT reads. MetaCLADE uses a multi-source annotation strategy , where eThe great improvement in annotation obtained with the multi-source strategy compared to the mono-source strategy, employed by the two most commonly used annotation tools HMMer  and HHblFor the annotation of MG/MT sequences, the main improvement of MetaCLADE over CLADE lies in the manner clade-centered models are handled. In fact, MetaCLADE employs neither the machine-learning algorithm for domain annotation nor the algorithm constructing the most likely protein domain architecture, characterising the two main algorithmic steps in CLADE. Due to the traits of MG/MT reads compared to full protein sequences , the design of a specific computational method was demanded. Hence, in order to annotate fragmented domains and evaluate much shorter hits, MetaCLADE was designed around two main contributions. First, it introduces a novel definition for a domain-specific bi-dimensional gathering threshold based on a probability space constructed using a naive Bayes classifier. Second, it simplifies the hit selection so that domain annotation would be less sensitive to sequencing errors and hit length.In order to show how MetaCLADE performs with respect to known domain annotation methods, we realised multiple comparative analyses of MG/MT datasets and demonstrated that MetaCLADE improves domain annotation and provides an improved resolution of the functional activity of a community, while clarifying preferences and missing functional features. We compared MetaCLADE against InterProScan , 52, a tProtein domains found in short MG and MT reads can be used as precise markers of the functional activity of an environment. We show that MetaCLADE highly improves current annotation methods and reaches a very fine degree of accuracy in annotation.MetaCLADE workflow is illustrated in Fig. 11111Given a read, a large number of domain hits is produced from the large number of models. MetaCLADE filters them according to three main criteria, applied one after the other to obtain the most likely annotation for each read. The first selection criteria filters out all overlapping hits for the same domain by using the match best score. Note that for any domain identified in a region of the read, it keeps exactly one domain hit per region. Also, note that a read might contain more than one non-overlapping occurrence of the same domain. This filter constitutes the first rough selection eliminating redundant hits and keeping different domains. The second criteria is the heart of the selection step and filters out most hits by keeping only those having a very high probability of being true hits. Probabilities are estimated in MetaCLADE through a pre-computed step that divides the sequence space of each domain in probability regions describing whether a hit (possibly a fragment of a domain) can be accepted with a certain confidence or not. This filter might eliminate all hits associated to a given domain. Finally, the third step selects hits that might be associated to very similar domains. Often, the hit matches produced by models associated to similar domains are overlapping. MetaCLADE carefully evaluates them and selects the hit with a highest sequence identity to the consensus sequence of the model and the highest bit-score see “” sectionA main important methodological point in MetaCLADE concerns the explicit estimation of the likelihood of a hit to be a true domain. This estimation is particularly sensitive to the length of the hit, that in MG and MT data might be very small; namely, for each domain, MetaCLADE defines a two-dimensional gathering threshold (GA) by combining bit-score and mean-bit-score of the domain hit and by identifying multiple regions in the two-dimensional sequence space that, with a high probability, contain reliable annotations for short sequences. All computational details of the approach  are described in the “Annotated MG and MT datasets can be explored to learn about the functional activity of the community. For this, one has to properly evaluate the performance of the methods and this was done on simulated datasets and on several published MG/MT datasets. MetaCLADE’s improvement over HMMer was shown on a simulated dataset, generated from 56 completely sequenced genomes with the addition of sequencing errors, and on five ocean MT samples. Five more environmental samples, produced with 454 GS FLX Titanium technology, demonstrated that MetaCLADE annotation highly improves over InterProScan (run with different libraries). One more environmental sample, produced with Illumina HiSeq 2000 technology, confirmed MetaCLADE’s good performance on datasets of much shorter reads.To compare MetaCLADE against two state-of-the-art annotation tools designed for short sequences, HMM-GRASPx and UProC, we used environmental samples, a simulated dataset previously employed to evaluate HMM-GRASPx, and two datasets simulated directly from MG sequences. These datasets are different in terms of average read length and species coverage, and they highlight the complementarity of MetaCLADE compared to UProC with respect to reads of shorter (100 bp) and longer (200 bp) size. They help to show that a combination of the two tools can improve performances even more.%). This resulted in about 500,000 reads, with an average size of 523 bp, that were given as input to MetaCLADE.We simulated a 454-read dataset from 56 fully sequenced genomes—belonging to archaea and bacteria—and accounting for a total size of 187 Mbp  in NCBI and retained only those also defined by Swiss-Prot (June 2016 release). CDS regions were further enriched with the available Pfam domain annotation (version 29). Then, the set of true positives has been defined by those ORFs overlapping such Pfam annotations.%) than HMMer (74.8%). Instead, if the comparison does not take into account the clans, the F-scores fall down to 95.22 and 98.16 (with 83.3 and 73.8% of recovered domains) for MetaCLADE and HMMer, respectively. In both these analyses, we notice that MetaCLADE presents a higher number of false positives and false negatives with respect to HMMer. This is expected as Swiss-Prot’s annotations—which we used to define the ground-truth—are based on Pfam and hence on HMMer. A synthesis of the comparison is reported in Additional file Predictions made by MetaCLADE and HMMer have been compared on two levels by considering or not as true positives (or true negatives) the predictions which fell into the same Pfam clan  or Inter% of MetaCLADE’s missed domains belonged to very small fragments, with an average length of 38 aa. The distribution of E values for domains annotated by MetaCLADE is plotted in Additional file Finally, it is interesting to point out that the 13.8Domain identification allows to highlight the main functional activities of a community through the identification of the functions supported by the most abundant domains, but also to compare communities and organisms. A more accurate zooming into functional activities hopefully leads to capture missing features of communities’ behaviour.The functional annotation of domains in the five oceans MT datasets in  demonstr22A striking example is the “ion transport” functional class for the EPAC and ANT samples, where HMMer annotation completely misses the large presence of bacteriorhodopsin-like domains in EPAC, as illustrated in Fig. 222222In Fig. 22S-adenosyl-L-homocysteine hydrolase and cyclosome subunit 3—Apc3 domains) covering together the 12% of all EPAC CCM annotations and the 38% of all “bacterial” CCM annotations for EPAC. These are the domains whose “bacterial” CCMs cover alone more than the 1% of all CCM annotations; note that the bacteriorhodopsin-like domain alone covers more than 25% of “bacterial” CCM annotations for EPAC. The list of domains that have been annotated by MetaCLADE with bacterial/eukaryotic/archaea/viral CCMs is given in Additional file Finally, one should notice the distribution of species providing the homologous sequences generating CCMs used by MetaCLADE to annotate domains in the five oceanic samples Fig. e. These MetaCLADE multi-source annotation strategy is used with the purpose of identifying very divergent domain sequences lying in reads. In fact, CCMs are probabilistic models that describe closely specific sequences and they can capture conserved patterns that are specific of homologs niches and that are missed by SCMs. As a consequence, CCMs for a domain have the possibility to describe domain sequences in greater detail and span a greater space of homologous sequences, possibly very divergent. For instance, in Fig. 333333Prorocentrum donghaiense [3Oxyrrhis marina and bacteria. The conserved positions, characteristic of the dinoflagellate sequence [Note that the motif identified by MetaCLADE in the eukaryotic MG sample was recently identified in the dinoflagellate ghaiense  . The performances of InterProScan on the five domain libraries differ greatly Table . The fir44We note that all the 2473 domains annotated exclusively by TIGRFAM correspond to signatures/domains that are unknown to Pfam  and therefore to MetaCLADE.E values for MetaCLADE annotations is shown in Additional file MetaCLADE is also suitable for technologies producing very short reads like Illumina HiSeq 2000 sequencing system. We analysed the predicted ORFs left without annotation by InterPro from one run of the O’Connor lake dataset. The dataset contains 1,315,435 input reads and 1,211,131 predicted ORFs, with an upper bound on the missed ORFs of 104,304. InterProScan annotated 273,903 ORFs, leaving unannotated 937,228 ORFs. The two sets of predicted and unannotated ORFs have a mean ORF length of 123 bp, with a minimum of 100 bp and a maximum of 135 bp. MetaCLADE analysed the 937,228 unannotated ORFs and succeeded to annotate 57,356 of them. The distribution of https://www.ebi.ac.uk/interpro/entry/IPR000515). For this, we considered all 1109 environmental sequences annotated as ABC transporter type 1 by MetaCLADE   the number of eukaryotic sequences in them see the “” section240% the nuE value density distribution curve of UProC annotations . Here again, MetaCLADE outperformed HMM-GRASPx  and 200 bp 682,380 reads) long, from the Guerrero Negro Hypersaline Microbial Mats dataset :More domains and new models for an improved MetaCLADE annotation. New CCMs could be added to the library with the hope to reach novel and unrepresented evolutionary solutions for a domain. An obvious improvement could be obtained by extending the library with the set of new domains included in Gene3D and TIGRFAM. The motifs represented in PRINTS and ProSite could be also considered and the associated profiles handled in MetaCLADE. Note that MetaCLADE package provides the program to pre-compute gathering thresholds for all domain models. This allows the user to compute appropriate thresholds based on new CCMs.Constructing a library of conserved small motifs. The search for sequence motifs in an environmental sample might be realised with a computationally costly “all against all” read comparison. Alternatively, starting from the most conserved patterns comprised in CCMs, we can generate a repertoire of significant motifs specific of each domain in order to improve hit selection criteria. A systematic classification of these motifs might lead to datasets of motifs that could be used as environmental signatures of metabolic activities.These “environmental patterns” could be also used to find new domains in environmental samples with MetaCLADE. The advantage in this search approach, compared to an “all against all” strategy, is that patterns are constructed starting from domains, possibly functionally annotated and that this annotation could be used to associate a potential functional role to new domains discovered through the pattern.Annotation of longer sequences. Availability of long reads and read assembly in contigs allow reconstructing longer stretches, and possibly entire, ORF sequences. In this case, one could replace the third filter in MetaCLADE with DAMA [ith DAMA , to recoReduction of the number of redundant models in MetaCLADE. Some of the probabilistic models in MetaCLADE library are expected to be redundant, and a suitable handling of these models, after clustering, should help to increase the speed of the method and to preserve the same predictive power. Future development of MetaCLADE will reduce the number of redundant models representing domains.New criteria to filter overlapping hits in MetaCLADE. Different domain hits could be selected by exploiting further the characteristics of the two-dimensional space of sequences pre-computed for the domains. For instance, one could privilege the domain hits with larger bit-score/mean-bit-score distance from the closest negative in the space. These filtering conditions could improve the annotation and need to be tested at large scale.MetaCLADE differences with CLADE. MetaCLADE has been designed with the purpose of annotating MG and MT reads. It exploits the multi-source annotation strategy introduced in CLADE and the CLADE model library, but it handles the models and their output in a different manner. Indeed, the CLADE pipeline combines the output of its rich database of probabilistic models with a machine-learning strategy in order to determine a set of best predictions for each domain sequence. Then, DAMA [en, DAMA  is used CCMs cannot be used with tailored GA thresholds as in CLADE. Instead, we introduce an original bi-dimensional gathering threshold that is specifically designed for evaluating short hits. For each domain, we compute a probability space on which to evaluate hits. This is done with a naïve Bayes classifier. Note that the computation of such a probability space depends on an appropriate generation of positive and negative sequences on which evaluate models for a domain.E value, hit length, consensus on multiple domain hits and clade-specific hits. On the other hand, read annotation should be less sensitive to sequence errors and hit length and should disregard the species the sequence comes from. In MetaCLADE, we do not use a SVM combining the above characteristics but instead we create a simpler pipeline of hit selection.CLADE machine-learning algorithm cannot be used for protein fragment identification. Indeed, CLADE works well with the full domain annotation of known genomes. In its design, it explicitly considers DAMA, the tool used in CLADE to reconstruct protein architectures, cannot be used on short reads. Indeed, reads might be long enough to contain at most one adjacent pair of domains and certainly cannot provide information to evaluate the contextual annotation of a domain within a potential domain architecture. In MetaCLADE, knowledge of adjacent pairs of domains could be considered but we left it for future developments.Neither CLADE machine-learning algorithm nor DAMA are used in MetaCLADE. In fact, the characteristics of MG and MT reads, compared to full protein sequences, are their short lengths and the presence of multiple sequencing errors in them compared to full-length ORFs. Hence, they demand the design of a special computational protocol taking into account the particular nature of the data; namely: MG and MT datasets have been explored mostly to learn about which and in what abundance species are present in the community. Learning about the functional activity of the community and its subcommunities is a crucial step to understand species interactions and large-scale environmental impact. Ecological questions, such as how limited availability of abiotic factors in an ocean shape most abundant genes in a community, or how temperature affects eukaryotic phytoplankton growth strategies, for example, can be approached with an accurate domain annotation and a precise functional mapping. In this respect, one might need to zoom into functional activities and metabolic pathways employed by the environmental communities that might involve non-highly expressed genes. This means searching for lowly abundant domains that, through cooperation, might imply important functional effects. In order to capture common and rare entities in a given environment, functional annotation methods need to be as precise as possible in identifying remote homology.Nowadays, the bottleneck resides in the annotation step, directly influencing an appropriate quantitative estimation of the domains. Here, we show how MetaCLADE, based on a multi-source annotation strategy especially designed for MG/MT data, allows for the discovery of patterns in very divergent sequences and provides a way to overcome this fundamental barrier. With the ongoing generation of new MG/MT data, unknown sequences will augment in number and probabilistic models are expected to play a major role in the annotation of sequences that span unrepresented sequence spaces. This point is clearly shown in our comparison with UProC, which is based on k-mer recognition, and therefore particularly adapted to the identification of already known domain sequences. By construction, UProC approach cannot be successful on unknown diverged domain sequences, a context where probabilistic domain modelling fully reveals its predictive power.This section explains MetaCLADE’s methodology and the datasets used in the analyses. The differences between MetaCLADE and CLADE  are presThe testing datasets were designed to fit current technological characteristics. We considered that Illumina represents nowadays the dominant technology in most sequencing projects. Currently, the HiSeq and MiSeq platforms are able to produce pair-end reads of 150 bp and 300 bp, respectively (with a much higher throughput for the first one). Such fragments in practice might be even shorter after the required low-quality-base trimming. For these reasons, we considered testing datasets of reads of increasing lengths. More precisely, we chose 100 bp and 200 bp as read lengths. However, we also chose to test MetaCLADE on simulated 454 fragments (mean length 523 bp) in order to prove its versatility and to show the annotation improvement as read length increases. This test is particularly important in view of the efforts from current technologies to increase read length.Widely used search methods based on sequence-profile and profile-profile comparison, such as PSI-BLAST , HMMer  and HHblDi, CLADE considers the FULL set of homologous sequences Si in Pfam [Di, and for some representative sequences sj in Si (see below), it constructs a model by retrieving with PSI-BLAST [sj from the NCBI NR database. The probabilistic model generated in this way displays features that are characteristic of the sequence sj and that might be very different for other sequences sk in Si. The more divergent the homologous domain sequences sj and sk are, the more models constructed from these sequences are expected to display different features. It is therefore important for a domain Di to be represented by several models that can characterise its different pathways of evolution within different clades. These probabilistic models are called clade-centered models (CCMs). The multi-source annotation strategy has proven more efficient than the mono-source annotation strategy when applied to full genomes [To overcome this fundamental bottleneck, CLADE , a domai in Pfam  associatSI-BLAST  a set of genomes . In partMetaCLADE is based on the multi-source annotation strategy and employs the CLADE library that includes the Pfam sequence consensus models (SCM) and at most 350 clade-centered models (CCM), with an average of 161 models per domain. The representative sequences associated to these models are selected in order to span most of the tree of life, the underlying idea being that evolutionary patterns can be found in species that are very far apart in the tree. This amounts to more than 2.5 millions probabilistic models.MetaCLADE’s pipeline is illustrated in Fig. MetaCLADE takes as input a set of MG/MT sequences to be annotated and the CLADE model library. More specifically, the input sequences coming from a dataset of reads are expected to be (subsequences of) open reading frames (ORFs). Alternatively, one can use MetaCLADE on the six reading frame translations of the reads.Each sequence is scanned with the model library in order to identify all domain hits. Each hit is defined by a bit-score, that is the PSI-BLAST/HMMer score associated to the match, and by a mean-bit-score, that is the bit-score of the hit divided by its length. These two scores are used to evaluate the likelihood of the hit to represent a true annotation see the “” sectionD, a probabilistic model M associated to D, a bit-score and a mean-bit-score.The output of this first step of MetaCLADE is a set of hits, each one defined by a domain family same domain (i.e. the overlap region covers at least 85% of both hit lengths) are processed with the intention of eliminating their redundancy. Therefore, for each overlapping pair, we retain only the best hit (i.e. with the higher bit score). The filtering is realised independently for CCMs and for SCMs.All pairs of overlapping hits associated to the p of being a true positive is greater than 0.9. More precisely, such a lower bound is defined as the smallest bit-score of the negative sequences used by the classifier during its training  for each annotated sequence.Hits are filtered according to a The second step of the pipeline filters the set of hits as follows: Note that in the third point, the ranking score combines the  bit-score and the percentage identity of the match in a product and that these two values are highly correlated. Indeed, one expects higher bit-scores to be associated to higher sequence identities. This means that when two matches differ strongly on their bit-scores, the respective products will not be affected by the percentage identity of the matches. On the contrary, it is when the bit-scores are close to each other that the percentage identity of the matches will play a role by favouring matches with higher sequence identity. Intuitively, MetaCLADE prioritises bit-scores while letting percentage identity play a discriminative role between very close bit-score values.The output of this filtering step is the ORF annotation with non-overlapping domain hits. Due to the short length of the reads, one expects at most two domains per read, possibly flanked by domain fragments on the right and/or the left. Consequently and in contrast to CLADE, there is no reconstruction of the best architecture with DAMA.Also, note that the first filter is used to reduce the size of the set of domain hits, possibly huge at the beginning due to redundant predictions of the high number of models. The second filter is used to identify hits having a high probability to be true hits, and it constitutes the core of the filtering process. The third filter is used to identify the best solution, among the ones with highest bit-score, based on motif conservation.As a consequence of the construction of the probability space for a domain, the second filter asks for domain hits to have a bit-score greater than the smallest bit-score of the negative sequences in the space. This is because negative sequences considered by the classifier are a selected sampling of the space of negatives (see the “Generation of negative sequences” section below); namely, among all negatives generated by the algorithmic procedure, we selected those that lie further away from the origin and that, in consequence, have the highest statistical significance. These selected negative sequences tend to group together further from the origin of the space and to lie at the borderline of regions characterised by positive sequences. Hence, one should properly evaluate the acceptance threshold against this specificity.MG and MT samples demand to annotate domain fragments, possibly of small length. In order to explicitly distinguish small hits from long ones, MetaCLADE directly estimates the likelihood for a small hit to be a positive sequence by considering the bit-score of the hit and also its mean-bit-score; namely, it defines a two-dimensional gathering threshold (GA) for each domain by combining bit-score and mean-bit-score and by identifying multiple regions in the two-dimensional sequence space that, with a high probability, provide reliable annotations for short sequences. Probabilities are estimated with a naive Bayes classifier , and theFor each domain, MetaCLADE estimates bit-score and mean-bit-score domain-sensitive thresholds. More precisely, it constructs a sequence space for each domain, by defining a set of positive sequences  and by generating a set of negative sequences (i.e. sequences wrongly annotated with the domain). Ideally, for each domain, one would like to have a training set comprised of a comparable number of positive and negative sequences.Definition of positive sequences. The training set of positive sequences was constructed as follows. For each domain Di and for each sequence in the Pfam SEED set of homologous sequences for Di, we created a set of prefixes and suffixes of the sequence to simulate small domain portions coming from the beginning or the end of the domain sequence that may be found in MG reads. The maximum size M of prefixes and suffixes was set to 30% of the entire domain sequence length and to a maximum of 100 aa. Hence, fragments were determined by increasing lengths n·L, where L is a constant depending on domain size and n=1,2,3…N is a multiplicative factor where N corresponds to the smallest integer such that M≤N·L. For domains of length between 15 and 75 aa, the constant L was set at 5 aa, for sizes >75 aa it was set to 10 aa, and for sizes <15 aa it was set to 1 aa. For large domains, >270 aa  associated to a domain, we generate a large amount of decoy sequences and select as negatives those where the original domain is identified by the model .A random shuffling of the 2-mers of each SEED sequenceThe reversal of SEED sequences and checks whether they are negatives or not. If the number of negatives reaches at least the 50% of the positive sequences, then the algorithm stops the search. Otherwise, new sequences are generated with a third method:By constructing a Markov model of order 3 for each domain and by using it to generate random sequences with positional probabilitiesThe algorithm generates first sequences with two different methods: 4) 4-tuples is evaluated by assigning a probability to appear in a domain sequence to each 4-tuple. This is done with a pseudo-count, by considering each 4-tuple to appear at least once and by counting the number of occurrences n of the 4-tuple in the SEED sequences of the domain. The probability of a 4-tuple is set to N is the total number of 4-tuple occurrences in the SEED sequences. The Markov model of order 3 is defined on these probability estimations.Note that in 3, the space of 160,000  with a positive bit-score are considered as negative sequences for the MetaCLADE models  and are included in their training sets. The usage of different threshold for the two tools, PSI-BLAST and HMMer, is due to the observation that it is easier to produce negatives with PSI-BLAST than with HMMer; therefore, an E value threshold <1, much more selective than a positive bit-score, would reduce the large number of accepted negatives for CCMs. The statistical significance and the impact of these thresholds on the space of positive and negative sequences is discussed below  with an see the “” sectionn would be too small to produce a significant bias in the 4-mer probability emission. Therefore, n is multiplied by a factor W with initial value at 10 and incremented by one until we reach the generation rate of 1–10 negative sequences out of 10,000 decoys for the domain and its SCM.  This leads to emission probabilities The algorithm estimates the number of decoy sequences that should be generated to obtain the 50% of negative sequences and stops when this estimated number of sequences is generated. For example, supposing there are 100 positive sequences for a domain, then we seek to generate at least 50 negative sequences. If random reshuffling and reversal generate only 10 negative sequences, a Markov model is expected to generate 40 sequences. Since most decoys generated by the Markov model will not be selected as negatives, one estimates the number of decoys that should be generated by the Markov model to obtain 40 negatives and stops the algorithm after such number. The estimation has been realised based on the observation that false positives are found after a very large number of decoy generations: roughly one expects to obtain 1–10 false positives out of 10,000 decoys for SCMs and out of 1000 decoys for CCMs. CCMs lie very close to actual sequences, and for this reason, we expect them to be much more effective in recognising a domain in a random sequence generated by a Markov model of that domain than a SCM. If a domain contains only a few sequences in its SEED set, Positive and negative sequences are described by a bit-score and a mean-bit-score. To avoid an over-representation of sequences with the same bit-score and mean-bit-score, we consider only one representative per fixed values of bit-score and mean-bit-score. This makes the two sets of positive and negative sequences, all domain confounded, to be numerically comparable. The total amount of non-redundant (in terms of their bit-score and mean-bit-score) positive matches is 13,697,142 for SCMs and 7,548,890 for CCMs while the total amount of negatives is 7,569,171 and 6,342,944, for SCMs and CCMs respectively  to construct learning models for each Pfam domain. The discrete version of the naive Bayes classifier provides a finite partition of the sequence space and an estimation of the probability for a sequence to be a positive or a negative hit. Notice that we realise two different analyses, one on CCMs (generated by PSI-BLAST) and the other on SCMs (pHMMs generated by HMMer), because we cannot immediately compare their bit-scores. By so doing, we determine two distinct separation spaces and appropriate parameters for the two model predictions. In particular, only one probability space is estimated for all CCMs of a domain. All positive and negative sequences, generated for all CCMs, are considered in the same sequence space and the associated probability space is computed.Positive and negative sequences are put together and analysed to obtain best separation parameters for CCMs and SCMs; namely, we use a discrete version of the naive Bayes classifier  . The EBI metagenomics pipeline uses InterProScan (v5.0 for pipeline 1.0 and 5.9-50 for pipeline 2.0) as tool to annotate predicted ORF (using FragGeneScan 1.15) using the domain libraries above. HMMer v3.0 was downloaded from http://hmmer.org and run with default parameters and curated inclusion thresholds (option –cut_ga).To compare MetaCLADE to InterProScan, we used several tools and domain model libraries: Pfam , TIGRFAMhttps://github.com/gobics/UProC) is a tool designed for large-scale sequence analysis. Given a read dataset, UProC decides whether a domain is present or not in a sequence, but it does not point out precisely where the domain is localised in the sequence by defining starting and ending positions, nor it associates an E value to the matching. The complementary performance of UProC compared to MetaCLADE suggests to combine the two tools in order to achieve best performance in terms of number of correctly predicted domains. For this reason, we define “MetaCLADE+UProC” by adding to MetaCLADE’s annotations all those obtained by UProC on reads left unannotated by MetaCLADE. In a similar way, we define the symmetric “UProC+MetaCLADE” annotation.UProC   to map it against all Pfam FULL sequences representing D and we selected the best hit, if it existed. The E value associated to this hit was considered as the UProC E value score.In order to evaluate UProC, we looked for the position of the domain in a read and associated an http://sourceforge.net/projects/hmm-graspx); other successive beta versions have been tested, but they did not provide better results. HMM-GRASPx is a profile-based method, like MetaCLADE. It considers profile HMMs of protein families as references and uses them to guide the assembly of complete or near-complete genes and protein sequences related to a particular family. The annotation is realised after the assembly with a mapping of the reads on validated contigs.MetaCLADE was compared to HMM-GRASPx version 0.0.1  . We used Pfam2GO  and annohttp://meme-suite.org/tools/meme.To validate motifs identified by MetaCLADE on the O’Connor lake dataset, we run MEME  with defWe generated a set of fragmented sequences from a set of 11 archaeal and 44 bacterial fully sequenced genomes. The list of species, NCBI accession numbers and genome lengths is reported in Additional file %). The read dataset, which contained sequences of ∼523 bp on average, was finally processed with FragGeneScan [In order to generate a set of 500,000 clones, we first used MetaSim , accordiGeneScan  in orderhttp://www.ebi.ac.uk/genomes/) to identify coding sequences included in the SwissProt database. For these coding sequences, we retrieved annotated domains and their positions from the InterPro site through the UniProtKB proteins file (http://ftp.ebi.ac.uk/pub/databases/interpro/protein2ipr.dat.gz). Domain annotation of coding sequences was used to evaluate MetaCLADE performance. For this simulated dataset, we selected positive domain hits with a probability threshold of 0.85 estimated by the naive Bayes classifier.In parallel, we used information available in the XML file associated to each genome in the EBI site  MG dataset  used in tocol in , we genetocol in . For thetocol in . Both anAlteromonas, Candidatus, Erythrobacter, Flavobacteriales, Nitrosococcus, Photobacterium, Prochlorococcus, Roseobacter, Shewanella, Synechococcus and Vibrio groups. Relative abundances of these bacteria were simulated according to their environmental composition [http://github.com/lh3/wgsim, with error rate parameter -e 0.01) and translated into short peptide reads using FragGeneScan [The comparison between MetaCLADE and HMM-GRASPx was realised on two simulated datasets of reads of length 100 bp and 200 bp, generated from a marine MG dataset containing 23 marine microbial genomes from the position  with an position  using KEGeneScan  , North Pacific (NPAC), Equatorial Pacific (EPAC), Arctic (ARC) and North Atlantic (NATL) . We havehttp://www.ebi.ac.uk/metagenomics/about). These tools are accessible from http://www.ebi.ac.uk/services/proteins. Comparison with UProC was realised on these five sets.Four published MG datasets come from very different environments: soil, ocean, ancient bones and guts. For the gut environment, we also considered a MT sample. These five sets of reads were previously analysed for ORF identification by EBI with FragGeneScan . ORF seqhttps://www.ebi.ac.uk/metagenomics/projects/ERP009498). It was realised with Illumina HiSeq 2000 technology and contains 1,315,435 very short reads (123 nt average length). ORFs were identified by EBI.The O’Connor lake MG dataset (ERP009498) was downloaded from the EBI Metagenomics portal . The F-score is the harmonic mean of precision and recall, defined as In order to evaluate the annotation tools, we used the standard measures of precision , accounting for how many annotations are correct and defined as E values and in UProC runs over UProC scores.In order to plot the precision-recall curves Additional file 2Normalised abundance for all GO-Slim terms/data used to build figure 2A (XLS 74 kb)Additional file 3Total number of nodes in the tree-graph rooted on a specific GO-term (XLS 35 kb)Additional file 4Percentage of domain hits wrt all domain annotations (% tot) and wrt bacterial CCM annotations (% bacteria) (XLS 127 kb)


Members of the tripartite motif family of E3 ubiquitin ligases are characterized by the presence of a conserved N-terminal module composed of a RING domain followed by one or two B-box domains, a coiled-coil and a variable C-terminal region. The RING and B-box are both Zn-binding domains but, while the RING is found in a large number of proteins, the B-box is exclusive to the tripartite motif (TRIM) family members in metazoans. Whereas the RING has been extensively characterized and shown to possess intrinsic E3 ligase catalytic activity, much less is known about the role of the B-box domains. In this study, we adopted an in vitro approach using recombinant point- and deletion-mutants to characterize the contribution of the TRIM32 Zn-binding domains to the activity of this E3 ligase that is altered in a genetic form of muscular dystrophy. We found that the RING domain is crucial for E3 ligase activity and E2 specificity, whereas a complete B-box domain is involved in chain assembly rate modulation. Further, in vitro, the RING domain is necessary to modulate TRIM32 oligomerization, whereas, in cells, both the RING and B-box cooperate to specify TRIM32 subcellular localization, which if altered may impact the pathogenesis of diseases. Ubiquitination is a versatile form of post-translational modification that regulates a large number of processes inside the cell through the regulation of protein turnover and activity. The ubiquitination reaction is a three-step process beginning with the ATP-dependent activation of ubiquitin, a 76-amino-acid peptide, by the E1 activating enzyme. In the following step of the cascade, ubiquitin is transferred to the E2-conjugating enzyme, which then cooperates with an E3 ubiquitin ligase to transfer ubiquitin to the final target substrate ,5,6.2+ ions coordinated by conserved Cysteine and Histidine residues, while the formation of a 4-helix bundle by residues N- and C-terminal to the Zn-binding core allows for the RING dimerization necessary for ubiquitination activity [The RING domain of several TRIM proteins was shown to fold around two Znactivity ,8. Mechaactivity ,10,11. Iactivity ,12,13,14activity ,16,17. Tactivity ,19,20.The TRIM32 tripartite motif is composed of a RING domain followed by a single type 2 B-box and a coiled-coil domain. The C-terminus in TRIM32 is composed of six NHL repeats  shown to be involved in the recognition of various substrates . ImportaHere, we performed a biochemical analysis aimed at investigating TRIM32 cooperation with various E2-conjugating enzymes and the role of the Zn-binding domains in the regulation of its activity.6-MBP-tagged UbE2V2 in pLou3 vector was a kind gift from Prof. Ronald T. Hay (University of Dundee).The mammalian expression plasmids encoding the human TRIM32 sequence in pcDNA3X(+)-Myc-GFP and pcDNA3X(+)-HA were described previously . Briefly6-tagged human TRIM32 (wild-type or mutated forms) were transformed into BL21(DE3) RIPL Escherichia coli cells. Cultures were grown at 37 °C in Terrific Broth until OD600 reached 0.6–0.8 and the expression of recombinant proteins was then induced by addition of 1 mM Isopropyl β-d-1-thiogalactopyranoside (IPTG) and 100 μM ZnCl2 followed by incubation at 16 °C overnight. Bacteria were lysed in lysis buffer ). Bacteria lysis was achieved by addition of lysozyme (250 μg/mL) and sarkosyl (0.3%) followed by sonication. Triton X-100 was then added to a final concentration of 0.5% before centrifugation to remove the insoluble fraction. Recombinant proteins were purified by incubation with Ni-NTA resin  followed by washes with lysis buffer containing 50 mM imidazole. The resin was eluted in elution buffer  and the recombinant proteins were dialyzed overnight against 20 mM TRIS pH 8; 0.5 M NaCl; 10% glycerol and 5 mM β-Mercaptoethanol.Plasmids (pETM11) encoding His6-MBP-tagged UbE2V2 was purified as described in [2). The quantity of E3 added varied in different assays and in assays where different TRIM32 mutants were employed, comparable amounts of each recombinant protein were used. The E2 screening kit was used according to the manufacturer’s instructions. Reactions were stopped by addition of Laemmli buffer containing 100 mM Dithiothreitol (DTT) final concentration.Recombinant proteins used for in vitro ubiquitination assays were purchased from Boston Biochem  , Sigma  (ubiquitin) or UBPBio  (E2 screening kit). Recombinant Hisribed in . Ubiquit2. C2C12 cells were seeded on coverslips and transfected with HA- or Myc-GFP-tagged constructs using Lipofectamine 3000 according to the manufacturer’s instructions the day after seeding.The C2C12 murine myoblast cell line  was grown in high glucose Dulbecco’s Modified Eagle Medium (DMEM)medium without sodium pyruvate supplemented with 20% Fetal Bovine Serum (FBS), 4 mM L-glutamine, 100 U/mL penicillin and 100 μg/mL streptomycin, and maintained at 37 °C in 5% COTwenty-four to forty-eight hours post-transfection, cells were fixed in 4% Paraformaldehyde (PFA) in Phosphate Buffer Saline (PBS) for 10 min at room temperature. For immunofluorescence analysis, cells were permeabilized with 0.1% Triton X-100 in PBS and blocked with 5% Bovine Serum Albumin (BSA) in 0.1% Triton X-100 in PBS. Cells were stained with anti-HA antibody and FITC- or Cy3-conjugated secondary antibodies. Coverslips were mounted with Vectashield mounting medium with 4′,6-diamidino-2-phenylindole (DAPI) and analyzed by epifluorescent microscopy.Samples from the in vitro ubiquitination reactions were boiled and resolved by SDS-PAGE on 4–15% gradient gels (Bio-Rad). For native PAGE, proteins were prepared in Laemmli buffer lacking SDS and DTT and resolved without prior boiling on a 7.5% poly-acrylamide gel without SDS. Following transfer to PVDF membranes, target proteins were detected with anti-ubiquitin  and anti-TRIM32 , and relevant HRP-tagged secondary antibodies. Signal was detected using ECL substrate  and autoradiography films . In some cases, membranes were stripped with mild stripping buffer  prior to re-probing with a different primary antibody.Following the in vitro ubiquitination reaction as described in 2+ ions (C100A/C103A), predicting that the resulting protein would have an unfolded B-box but would maintain the spacing between the TRIM32’s RING and coiled-coil domains . PrevioAs observed in previous in vitro experiments, the deletion of the RING domain abolishes TRIM32 activity in all cases, confirming that the catalytic activity resides in this domain and that the B-box cannot mediate ubiquitination with any of the E2 tested. Of note, with all the UbE2s above, the activity of full-length TRIM32 is comparable with the ΔB-box protein when used as E3 in the reaction. No major difference was observed not only in the auto-poly-ubiquitination reactions (UbE2D1-4), but also when mono-ubiquitination  or formation of free ubiquitin chains  was catalyzed . These dStructural and biochemical studies have demonstrated that the coiled-coil domain in TRIM proteins is necessary for dimerization, while other domains, such as the B-box domain in the case of TRIM5α, are necessary to mediate the higher-order self-association of TRIM proteins ,19,20. ISo far, the experiments have focused on in vitro auto-ubiquitination activity. Thus, to start characterizing the role of different TRIM32 domains belonging to the tripartite module in an in vivo context, we next analyzed the subcellular localization of TRIM32 constructs by expressing the GFP-tagged full-length, RING or B-box domain deletion mutants or the B-box point mutant in the murine myoblast cell line, C2C12 . Like maWe next examined the interplay between the different TRIM32 domains in specifying its subcellular localization by co-expressing constructs lacking the B-box with the full-length or ΔRING mutant. In keeping with previous observations, the results in In this work, we investigated the role of the Zn-binding domains of the tripartite motif of TRIM32. Biochemical studies originally identified a role for the RING domain in TRIM proteins in promoting the direct transfer of ubiquitin from a loaded E2 to the substrate and this feature is also conserved in other RING-type E3 ligases not belonging to the TRIM family, such as Ring Finger protein 4 (RNF4) or Mouse double minute 2 (Mdm2) ,43 and rTo further characterize the potential role of the B-box, we compared the reaction rate of full-length TRIM32 with the ΔB-box and C100A/C103A constructs. One previous report indicated the possibility that the C-terminal NHL domain in TRIM32 is involved in binding of UbE2N . While, We also assessed the potential involvement of the B-box in determining E2 specificity. In our analysis, the protein carrying a deletion of the B-box showed the same E2 specificity of the full-length one, indicating that the B-box is likely not involved in interaction with the E2. Indeed, previous studies have shown that the RING domain of TRIM32 is strictly necessary for interaction with UbE2N, while a TRIM32 mutant lacking the B-box domain maintains the interaction in a Yeast Two-Hybrid setting, further indicating that the B-box is not necessary for association with the E2 ). HoweveThe TRIM5α B-box domain was shown to be necessary to promote the formation of the lattice required for HIV capsid recognition and for the dimerization of the RING domains necessary for activity ,19. LikeWithin a cellular context, however, analysis of the subcellular localization revealed that both the RING and B-box are involved in determining TRIM32 localization, as already observed for other members of the TRIM family . Likewis


The term of neurodegenerative diseases covers a heterogeneous group of disorders that are distinguished by progressive degeneration of the structure and function of the nervous system such as dementias, movement disorders, motor neuron disorders, as well as some prion disorders. In recent years, a paradigm shift started for the diagnosis of neurodegenerative diseases, for which successively clinical testing is supplemented by biomarker information. In research scenarios, it was even proposed recently to substitute the current syndromic by a biological definition of Alzheimer’s diseases. PET examinations with various radiotracers play an important role in providing non-invasive biomarkers and co-morbidity information in neurodegeneration. Information on co-morbidity, e.g. Aβ plaques and Lewy-bodies or Aβ plaques in patients with aphasia or the absence of Aβ plaques in clinical AD patients are of interest to expand our knowledge about the pathogenesis of different phenotypically defined neurodegenerative diseases. Moreover, this information is also important in therapeutic trials targeting histopathological abnormalities.The aim of this review is to present an overview of the currently available radiotracers for imaging neurodegenerative diseases in research and in routine clinical settings. In this context, we also provide a short summary of the most frequent neurodegenerative diseases from a nuclear medicine point of view, their clinical and pathophysiological as well as nuclear imaging characteristics, and the resulting need for new radiotracers. It is well recognized that increased life expectancy results in an increased frequency of neurodegenerative diseases. In the last two to three decades, the development of new diagnostic and therapeutic methods has been intensified in neurodegenerative diseases. Molecular radiopharmaceutical-based neuroimaging is a growing field and provides several new diagnostic methods to investigate and characterize neurodegenerative diseases during life. This review summarizes the facts for the most frequent neurodegenerative diseases from a nuclear medicine point of view. Furthermore, the review provides information on approved radiotracers and ongoing research activities in the development of new radiotracers for imaging neurodegenerative diseases as well as a short passage about the need for novel radiotracers.https://www.sciencedirect.com/science/article/pii/S1552526018300414). Along with the typical clinical features like memory impairment, especially in the semantic and episodic domain and the executive dysfunction  is the most common neurodegenerative disease causing dementia in the elderly. Histopathological characteristics of this disease are a progressive accumulation of β-amyloid (Aβ) plaques and hyperphosphorylated neurofibrillary tau protein (tau). However, only approximately 50 % of the patients have solely Alzheimer’s pathology. Many patients show additional pathologic changes related to other neurodegenerative diseases in autopsy studies ; language disorders - the primary progressive aphasia (PPA): semantic variant (svPPA) and non-fluent/agrammatic variant (nfvPPA); and motor disorders .In general, an individual FTLD disorder can be ascribed to different histopathologies such as tauopathy (FTLD-tau), TDP-43 proteinopathy (FTLD-TDP-43) and the FET protein family that consists of Fused in sarcoma, Ewing sarcoma and TATA-binding protein associated factor 15 proteinopathy (FTLD-FET) Table . Thus, mIn Parkinson’s disease (PD), a degeneration of the nigrostriatal system occurs causing a reduction of the neurotransmitter dopamine. Degeneration of dopaminergic neurons in the substantia nigra pars compacta is an inherent neuropathological sign of PD. Histologically, most patients suffering from PD exhibit intracellular accumulation of protein inclusions mainly constituted of α-synuclein (Lewy bodies). However, some patients with specific genetic forms of PD do not have Lewy body pathology and it remains unclear how Lewy bodies and neuronal loss are connected to each other. The typical clinical criteria of PD consist of symptoms such as bradykinesia, rigidity or resting tremor as cardinal motor manifestations  is an α-synucleinopathy, characterized by widespread accumulation of Lewy bodies and Lewy neurites in the brain-stem, limbic system and cortical areas   Fig. .Fig. 2Ch11C]PK-11195    Fig. a have se-ME Fig. b for ima120 Fig. c for ima120 Fig. . However18F]FDOPA , [123I]IPT) and PET tracers [11C]methylphenidate, [11C]Cocaine, or [18F]FE-PE2I  and [123I]IBZM (for SPECT)  Fig. b was use18F]A-85380, 6-[18F]A-85380 and 5-[123I]A-85380 -[18F]Flubatine), epibatidine  or 3-pyridylether derivatives  -[18F]Flubatine, whereby a displacement study needs to clarify whether these higher distribution volumes are due to specific or nonspecific binding  in patients with AD, PD and DLB  and acetylcholinesterase (AChE) in AD patients compared to healthy controls (HCs) and, further, a correlation between neocortical AChE activity and dementia severity  is a membrane protein that transports monoamines (e.g. dopamine or serotonin) into the presynaptic vesicles. [133 Fig.  is a PET11C]Pittsburgh Compound B (PiB)  Fig. 18F]florbetapir: 96%, [18F]florbetaben: 98%, [18F]flutemetamol: 88%) and specificity  in detecting Aβ plaques in-vivo compared to the postmortem data   and 45 min p.i. for the USA/90 min p.i. for Europe ([18F]florbetaben) is recommended (https://eanm.org/publications/guidelines/Amyloid-Guideline-J_Nucl_Med-2016-Minoshima-1316-22.pdf). Compared to the three approved 18F-labeled radiotracers, [11C]AZD2184 and [18F]NAV4694 seem to show lower white matter binding  Fig. . However694 Fig. , are und694 Fig. . Results18F]FDG PET. However, the validation process of these biomarkers is still incomplete . Although the available data on the second-generation tau PET radiotracers are encouraging, the usefulness of these radiotracers for research and clinical approaches remains to be demonstrated in larger clinical trials.Physiological tau is a phosphoprotein which stabilizes the microtubules. In the brain, six isoforms of tau exist with either three repeats (3R) or four repeats (4R) of the microtubules-binding domain  are mainly intracellularly localized, (ii) appear in a much lower concentration than Aβ plaques , Ewing sarcoma (EWS) and TATA-binding protein associated factor 15 (TAF15) and was first discovered as components of fusion oncogenes causing specific malignancies  the classification of neurodegenerative disorders in clinical routine, (ii) the identification of targets for possible new radiotracers, (iii) the identification of novel radiotracer targets, (iv) the accurate monitoring strategy of such therapy trials. Some of them could be answered by PET studies with new radiotracers. But as important as the development of new radiotracers seems to be, at the moment it is equally important to sum up our gathered pieces of knowledge, combine them and try to get a more comprehensive understanding of the entire spectrum of neurodegenerative disorders Fig. .Fig. 9Ov


Following the publication of this article , the autBacillus group members than cultured phages. We speculate this pattern may reflect gene acquisition from host Firmicutes. Other authors have previously speculated that instances of strong homology observed between B. cereus phage MurNAc-LAA endolysins and host autolysins may reflect horizontal transfer between Bacillus phages and various B. cereus group hosts [30]. The molecular diversity of the BLP lysins and their homologs is exhibited primarily by the presence or absence of the C-terminal amidase_02C domain. Consistent with previous data on lysPBC4 [9] the N-terminal amidase_3 domain of the BLPs show greater conservation among their homologs than the C-terminal amidase_02C domain and linker re- gion that are absent in most phage endolysin homologs.”“Full-length sequences of the putative BLP endolysins display closer overall homology to


Lactobacillus rhamnosus GG strain encodes the operons for two varieties of these pili (SpaCBA and SpaFED), with each structure consisting of backbone, tip, and basal pilin subunits. Although the tertiary structure was recently solved for the backbone subunit (SpaA) of the SpaCBA pilus, no structural information exists for its counterpart in the SpaFED pilus. Here, we report several crystal structures for the SpaD backbone pilin, two of which capture the N-terminal domain in either the closed (linear) or open (bent) conformation. To our knowledge, this is the first observation of the bent conformation in Gram-positive pilin structures. Based on this bent conformation, we suggest a three-stage model, which we call the expose-ligate-seal mechanism, for the docking and assembly of backbone pilins into the sortase-dependent pilus.Effective colonization of host cells by some Gram-positive bacteria often involves using lengthy, adhesive macromolecular structures called sortase-dependent pili. Among commensals, the gut-adapted  Lactobacillus rhamnosus. The observed bent conformation of the N-terminal domain has not been seen in other Gram-positive pilin structures.Priyanka Chaurasia et al. report crystal structures of the SpaD backbone pilin from a gut-adapted bacteria,  These pilus structures are lengthy and limb-like, and often have an adhesive character that fastens readily to cellular surfaces. Moreover, as a direct result of their long and extended peripheral reach, pili facilitate the first interactive contact that some bacteria have with the immediate environment. Although surface pili are not a universal trait of all bacteria, Gram-negative bacteria contain at least five types: chaperone-usher pili; type IV pili; conjugative type IV secretion pili; curli fibers; and type V pili4. In contrast, only two types are known to exist in Gram-positive bacteria: type IV pili5 and sortase-dependent pili6. The presence of sortase-dependent pili was first established among pathogenic species8, but more recently this form of piliation has been identified in gut commensals11. Since sortase-dependent pili are known to adhere to epithelial extracellular matrix proteins and mucus, this enables them to have an important role during host cell colonization as mechanisms of pathogenic virulence and non-pathogenic niche adaptation14.A key contributing factor of bacterial survival is the ability to colonize a host effectively. Among the tools used by bacteria for host-colonization are the easily-recognizable multi-subunit pili15, pilus assembly begins when the sortase severs the threonine-glycine bond within the C-terminal LPXTG sorting motif of a tip pilin. This allows the freed threonine to form an isopeptide bond with the lysine of the YPKN pilin motif in the N-terminal domain of a backbone subunit. This process continues with a succession of backbone pilins, until the pilus structure reaches an adequate length, at which point further polymerization is halted. The arrival of a basal pilin attached to the housekeeping A-type sortase is the signal that initiates termination and anchoring activities16, whereby a C-type sortase catalyzes an isopeptide bond between the basal subunit and the backbone pilin most recently added18. The fully assembled pilus (carried by the A-type sortase) is then anchored to the bacterial cell wall when the LPXTG-motif threonine of the basal pilin covalently attaches to the peptidoglycan layer18. The genes for the backbone, tip, and basal pilins and the C-type sortase are encoded as a pilus operon in the genome, facilitating the coordinated expression of the various proteins involved19.The sortase-dependent pilus structure is usually built from three types of pilin subunits, each with a defined location and purpose. So-called major pilins polymerize head-to-tail into the pilus backbone, with ancillary pilins added at the tip (adhesin) and base (anchor). Typically, these backbone, tip, and basal subunits are held together by covalent bonds catalyzed by a pilin-specific C-type sortase enzyme. According to prevailing models22 by being comprised of the CnaA and CnaB domains. Both domains are variants of the immunoglobulin (Ig)-like domain fold that was first established in the staphylococcal collagen adhesin (Cna) protein25. The topological core of the CnaA and CnaB domains consists of nine and seven β-strands, respectively, and are distinguished by interspersed regions of loops, α-helices, β-strands, and additional domains. In the CnaA and CnaB domains, an autocatalytic internal isopeptide bond can occur between either lysine and aspartate (K–D) or lysine and asparagine (K–N), thus bolstering the structural rigidity of the pilin27. Most solved structures of the three pilin types are for those that comprise the pilus backbone21. Backbone pilins41 consist of between two to four of the CnaA and CnaB domains21, though the domain at the N-terminal region has some unique properties worth studying.Based on their tertiary structures, backbone, tip, and basal pilins share a common structural topologyLactobacillus rhamnosus GG strain29. L. rhamnosus GG is a gut-adapted lactic acid bacterium best known for its advocated use as a probiotic42, but also for being identified as the first Gram-positive commensal capable of producing sortase-dependent pili 9. The SpaCBA pilus, which is encoded by the spaCBA operon and consists of the tip SpaC, basal SpaB, and backbone SpaA pilins, has undergone extensive functional and structural characterization over the past few years, and stems from a strong curiosity in unraveling the molecular aspects of L. rhamnosus GG gut ecology and probiosis14. The L. rhamnosus GG genome also carries the genes for another pilus operon called spaFED9. However, unlike the genes for the L. rhamnosus GG SpaCBA pilus, those for the SpaFED pilus are constitutively inactive under testing conditions43, though their expression might still rely on an unknown stimulus or else be gut-induced in situ44. While the native form of a fully assembled SpaFED pilus has to date only been predicted in L. rhamnosus GG43, the spaFED operon represents an intact translational unit whose cloned expression can be induced in Lactococcus lactis to produce a recombinant version consisting of the tip SpaF, basal SpaE, and backbone SpaD pilins45. To address what structural similarities or differences might exist between the two L. rhamnosus GG pilus types, we sought to solve the tertiary structures of the SpaCBA and SpaFED pilus proteins through X-ray crystallography48. In this present study, we bring new mechanistic insights about the pilin-assembly process for sortase-dependent pili by presenting crystal structures of the SpaD backbone pilin with its N-domain in both a closed (linear) and open (bent) conformation.Recently, we reported the X-ray structural determination of double-domained SpaA, the backbone pilin from the Corynebacterium diphtheriae SpaD33) is a 517-residue protein (UniprotKB ID: A0A179XFF5)  joins together the N- and C-terminal regions (N- and C-domains) of the pilin subunit  Fig. . To crysnit Fig. . The cryC for the closed conformational state. The M- and C-domains are both stabilized by an internal K–N isopeptide bond, normally found in most Gram-positive backbone pilins. The N-domain shows the atypical K–D isopeptide bond, involving the uncommon YPKD pilin motif. The two elongated molecules in the asymmetric unit are packed anti-parallel, with the three domains of each molecule covering a total surface area of 22,030 Å2. The M- and C-domain have an interface area of 567 Å2, with nine hydrogen bonds and one salt bridge. The interface area between the N- and M-domains (787 Å2) involves multiple interactions, including 14 hydrogen bonds and three salt bridges.The N- and C-domains  both consist of four and three-stranded β-sheets and resemble the core fold of the CnaB domain Fig. . The M-dtrans configuration  and last (G) β-strands, which indicates the formation of an isopeptide bond in a ion Fig. . Surrounion Fig. . Residueion Fig. . The FG trans isopeptide bond between Lys186 and Asn337, with a catalytic Asp242 lying nearby  and last (G) β-strands of the CnaB fold   Fig. . Found i1 Å Fig. . In GG-Sins Fig. , taking ain Fig. . Due to 1 Å Fig. . Remarka2) that involves nine hydrogen bonds and four salt bridges. Also, the BC and EF loops of the open N-domain interact with the AB loop and core fold of the M-domain. Some regions of the open N-domain structure appear disordered, e.g., the FG loop with the short β-hairpin and the AB loop with a salt bridge (Glu52-Arg66) covering the linking Lys180 that is now exposed to solvent  analysis of the GG-SpaD protein. Results from the scattering profile showed that GG-SpaD assumes an elongated shape with a radius of gyration (Rg) of 28 Å and a maximal particle dimension (D-max) of 9.23 nm  and a smaller 145-residue mobile domain containing residues 39–183 with a RMSD of 1.53 Å   simulations of models for the GG-SpaDmed Fig. . Values med Fig.  and B-famed Fig.  calculating Fig. .Fig. 6MD2, with eight hydrogen bonds and two salt bridges  of one GG-SpaD subunit docked into a groove-like hydrophobic opening in the N-domain (head) of the neighboring subunit Fig. . Also loThe angle between the N-domain and C-domain of neighboring pilin subunits is 126°, and thus it is rather similar to the N- and M-domain (150°) and M- and C-domain (120°) angles for a single subunit. For the assembled SpaFED pilus structure, the adjacent subunits are each translated 120 Å and rotated ~60° along the axis, with a diameter of 30–50 Å Fig.a, c. PreC  by using the ClusPro protein–protein docking tool50. Among the top ten best low-energy conformational models that were predicted, one shared a strong resemblance to the GG-SpaDC crystal structure  and C-domain (tail) of GG-SpaDure Fig. .51 search of the PDB, a number of three-domain pilins are related to the structure of GG-SpaD  54, these were not among the DALI hits.Incidentally, although the backbone pilins of the Gram-negative chaperone-usher55, with residues 51DEQIQNTGHD60 being identified. A short segment (56NTG58) within this disordered region was further identified as a protein-binding site in the AB loop  using GG-SpaD sequence found that the NTG segment is an invariant feature among other sortase-dependent pilins containing the YPKN pilin motif  within the FG loop  forms an internal isopeptide bond with Lys45. Similar K–D isopeptide bonding has been observed in the C-domain of other backbone pilins, though not in their more flexible N-domain37. Presumably, other amino acids surrounding an internal isopeptide bond should impact whether an asparagine or aspartate residue is to be encoded genetically. Thus, the universal preference for a covalent interaction involving K–D over K–N by gut-adapted bacteria like L. rhamnosus GG might have evolved from the acidic conditions of the digestive tract.Most backbone pilins contain the YPKN pilin motif peptide in their N-domain, wherein during pilus polymerization the lysine and asparagine residues participate in intermolecular and intramolecular isopeptide bond formation, respectively39. Then again, for backbone pilins whose full-length structure has been solved37, the N-domain either lacks or slowly forms an isopeptide bond. Moreover, these full-length structures have revealed that the N-domain is somewhat laterally displaced from the long axis of the other domains, which themselves exhibit strong interface interactions with one another and show only a limited amount of motion. However, whereas this seems the case for three-domain backbone pilins like GG-SpaD, those with a two-domain organization are far more rigid in their structural makeup41. Nonetheless, while on its own or unliganded to the C-terminal tail of an adjoining pilin, the N-domain tends to be unstable in solution primarily due to the flexibility of the AB loop and the apparent absence of an isopeptide bond. Generally, this explains why the N-domain degrades while undergoing crystallization or does not form crystals at all. In this context, it was rather serendipitous that the N-domain structure of GG-SpaA was itself solved, as it appeared that in the crystal unit the C-terminal tail from the symmetry mate was a stabilizing influence on the mobile AB loop29. As for GG-SpaD in our present study, the solution of its full-length structure in the closed conformation was facilitated somewhat similarly.Among the backbone pilins with a triple- or quadruple-domain structure, the N-domain is usually seen as the most flexible in nature, and thus, in many instances, its removal from the rest of the protein is needed for obtaining good X-ray-diffracting crystals29. By comparison, the overall structures of GG-SpaD and GG-SpaA bear little resemblance to each other, aside from having the same CnaB fold type for their N- and C-domains, though still lacking strong amino-acid sequence identity and exhibiting opposing kinds of intramolecular isopeptide bonds (K–N or K–D). More pointedly, the additional CnaA domain between the N- and C-domains in GG-SpaD makes this pilin larger than GG-SpaA, indicating that the overall structural topology between the SpaFED and SpaCBA pilus backbones is architecturally distinct.Structurally, the GG-SpaA backbone pilin contains two CnaB domains, where K–N and K–D internal isopeptide bonds are present in the N- and C-domains, respectivelyPerhaps the most interesting aspect of this study was solving the GG-SpaD structure in the open conformational state, whereby the N-domain is no longer aligned with the M- and C-domains in a linear manner (closed conformation), but rather instead is positioned perpendicularly next to the middle region of the M-domain. Since this offers a unique structural perspective to backbone pilins, we suggest that the open conformation represents an intermediary snapshot of the backbone pilin during its sortase-mediated assembly into the SpaFED pilus Fig. . This viS. agalactiae GBS52 basal pilin57.Moreover, we note that the inclusion of ionic NaCl during GG-SpaD crystal formation and SAXS analysis seems to have disrupted the normal interface interactions between the N- and M-domains, thereby favoring a repositioned N-domain. Along with our biophysical analysis for domain motion, such results suggest a flexible state for GG-SpaD, and this might be of mechanistic importance for the intermolecular pilin cross-linking during pilus polymerization. Here it seems that the flexible N-domain, and in particular its AB loop, functions as a gatekeeper to control access to the linking lysine by the C-terminal LPMTG-motif threonine. Our residue identification of a MoRF in the disordered region of AB loop further supports a functional role for the N-domain, which, similarly, is a structural aspect of the Our findings regarding the structural properties observed for the closed and open conformations of GG-SpaD have led us to posit a mechanistic model that outlines the early stages leading up to the assembly of backbone pilins into the SpaFED pilus structure Fig. . Here, tL. rhamnosus GG-SpaD, we conclude that the expose-ligate-seal mechanism defines the process by which an incoming backbone pilin is covalently docked during sortase-dependent pilus assembly and one that is made possible by the coordinated movement of the N-domain and its functional AB loop. We further suggest that this three-stage model of pilin assembly might generally hold true for the sortase-dependent pili of other Gram-positive bacteria.Finally, based on our interpretation of the closed and open conformation structures of L. rhamnosus GG (ATCC 53103) GG-SpaD protein is comprised of 517 amino acids (UniprotKB ID: A0A179XFF5). Recombinant full-length GG GG-SpaD (residues 36–485) was cloned and produced in E. coli BL21 (DE3) pLysS as described earlier58. For this, a putative 34-amino-acid signal peptide at the N-terminus and a 34-amino-acid C-terminal LPXTG-motif sorting signal , respectively. Briefly, GG-SpaD-expressing cells were first disrupted by sonication and afterward the cell-free lysate was loaded onto a Ni2+-charged Hi-Trap chelating column  pre-equilibrated with buffer A . GG-SpaD protein was eluted by a linear gradient of buffer B (buffer A containing 400 mM imidazole), which was followed by size-exclusion chromatography using a Sephacryl S-200 (26/60) column  in buffer C . A stable 33 kDa protein fragment of GG-SpaD was also generated by limited proteolysis using α-chymotrypsin (Sigma-Aldrich) as done previously46. N-terminal sequencing and mass spectrometric analysis confirmed that this 33 kDa protein encompasses residues 177–485, which corresponds to the M- and C-domains of GG-SpaD (GG-SpaDMC). Selenium derivatization of full-length GG-SpaD protein using a selenomethionine (SeMet) kit  was carried out according to the instructions of the manufacturer. GG-SpaDSeMet protein was purified similarly as described above. Mutant proteins of GG-SpaD were made with the primers listed in Supplementary Table E. coli DH5α strain. Transformed plasmids with the desired nucleotide changes were confirmed by DNA sequencing. Mutant constructs were cloned into pET-28 + and expressed in E. coli BL21 DE3 cells. Purification of mutant GG-SpaD was done with essentially the same protocol as used for the wild-type (WT) protein.Unprocessed nal Fig.  were repG ATCC 5303 GG-SpaMC protein generated from the limited proteolysis approach yielded orthorhombic crystals that diffracted to 2.0 Å resolution at the home X-ray source using a GeniX3D Cu HF (high-flux) microbeam X-ray generator set at 0.6 mA and 50 kV  and a MAR 345 image-plate detector 46. X-ray diffraction of the GG-SpaDMC crystal form at the synchrotron source  gave 1.5 Å resolution  datasets of these crystals were collected at the synchrotron source, all of which diffracted anisotropically in the range of 2.3 to 2.9 Å resolution. Cryoprotectants used for the data collections are listed in Supplementary Table 59 and scaled with Aimless60 from the CCP4 program suite61. An anisotropic correction was performed for all the data (except GG-SpaDMC) using the STARANISO Server , and then used in final cycles of model building and refinement with COOT62 and REFMAC563, respectively. Optimal wavelengths for data collection at the synchrotron source (European Synchrotron Radiation Facility) were 0.95372 Å at the BM14 beamline  and 0.97625 Å (GG-SpaDD242A) at the ID30B beamline. Ramachandran statistics for the refined crystal structures showed 96–98% and 1–3% of residues in favored and allowed regions, respectively, with no outliers (0%) detected  using commercially available crystal screening kits. Conditions showing crystals were subsequently optimized manually using the hanging drop-vapor diffusion method. For this, 1 µl aliquots of the protein and reservoir solutions were each mixed and equilibrated against 1 ml reservoir solution at 295 K. Further details about protein concentrations, buffers, and reservoir solutions are provided in Supplementary Table MC), whose crystals diffracted up to 2 Å and 1.5 Å resolution using home46 and synchrotron sources, respectively  with two molecules in the asymmetric unit. Structure solution for GG-SpaDMC was via iodide-SAD phasing with the diffraction data obtained at the home source46. Model refinement of GG-SpaDMC was performed with the high-resolution native data collected at the synchrotron source  and exhibited only one molecule in the asymmetric unit. However, structure solution by MR using GG-SpaDMC as a search model now gave a traceable difference electron density map, though for a differently placed N-domain that is situated perpendicular to the positioning of the M- and C-domains. The N-domain model from the orthorhombic form of GG-SpaD was also used as a reference structure for model building. After final rounds of model building and refinement using the anisotropically truncated data for the single molecule of GG-SpaD in the asymmetric unit, disorderedness was observed in the AB (residues 46–67) and FG (residues 164–171) loops of the N-domain, EF loop (residues 226–233) of the M-domain, and three residues at the C-terminal end (residues 482–485). This full-length GG-SpaD crystal structure is denoted as GG-SpaDO for an open conformation. The model of full-length GG-SpaD (orthorhombic) was used in the MR and refinement of the mutant GG-SpaD structures  was included with the protein-buffer solution in the crystallization screening experiments. These crystals diffracted to 2.5 Å, but belonged to the primitive hexagonal space group  was dissolved in a buffer containing 20 mM HEPES (pH 7) and 150 mM NaCl. To simulate the conditions for obtaining the GG-SpaDO structure, a sample of fresh GG-SpaD protein was also incubated with 1 M NaCl in the same buffer above for 24 h prior to the SAXS analysis. Scattering data were generated with a SAXS Space small-angle X-ray scattering instrument  equipped with a sealed tube (line collimation) X-ray source and a one-dimensional CMOS Mythen detector  at the SAXS facility in the Institute of Microbial Technology (IMTECH), Chandigarh, India. Data for each sample were obtained by a 20-min exposure (two 10-min frames) at 20 °C. Calibration and correction of the scattering patterns for the primary beam position was done with SAXStreat software (Anton Paar). A buffer scan was recorded before the sample runs under the same conditions and subtracted from the collected data using SAXSquant software (Anton Paar). Estimates of the radius of gyration (Rg) and maximal particle dimension (D-max) were obtained using programs  within the ATSAS software package. The program DAMMIF was used to generate low-resolution ab initio envelopes using the pair distribution function P(r), which were further averaged with the program DAMAVER. The normalized spatial discrepancy (NSD) value (computed using SUPCOMB) was 0.6 as an average of ten bead models 66, with AMBER14 force field and TIP3 potential for analyzing proteins and water molecules, respectively. Full-length models of the GG-SpaDC and GG-SpaDO crystal structures were hydrated in a 10 Å cubic water box, with the net charge neutralized by including an appropriate number of Na+ ions. After incremental heating to 310 K, simulation runs lasted 100 ns, with a time step of 10 fs and using the NPT ensemble at 310 K and 1 bar. A Nosé-Hoover thermostat  and a Parrinello-Rahman barostat (tp = 5.0 ps) were used to control the temperature and pressure, respectively. Root mean-square deviation (RMSD) and root mean-square fluctuation (RMSF) calculations per simulation frame that measured backbone flexibility (Cα atoms and amino-acid residues) were analyzed using CPPTRAJ 66 and visualized in VMD (http://www.ks.uiuc.edu/Research/vmd/). Graphical representations of the RMSD and RMSF values were made using Graph Pad Prism software.Molecular dynamics simulations of domain flexibility in GG-SpaD were performed using the AMBER14 program package (49 (http://fizz.cmp.uea.ac.uk/dyndom/). Refined coordinates for the GG-SpaDC and GG-SpaDO crystal structures were uploaded onto the DynDom server and the analyses for conformational change were performed with default settings. Disorder predictions were initially done through the DISOPRED55 program on the XtalPred server  using the sequence of GG-SpaD that was used in protein production. DISOPRED on the PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred) was later used to predict a disordered protein-binding site. A search for GG-SpaD-related sequences in the UniProt Reference Clusters database and their alignment was carried out using tools from the UniprotKB site (http://www.uniprot.org/). PISA 56 at the European Bioinformatics Institute (http://www.ebi.ac.uk/pdbe/prot_int/pistart.html) was used to analyze the interaction and interface between the two GG-SpaDC molecules in the crystal unit. Multiple protein structural alignments were performed using MUSTANG (http://lcb.infotech.monash.edu.au/mustang), with the corresponding sequence alignment diagram prepared by using Chimera (https://www.cgl.ucsf.edu/chimera).In silico analysis of domain motion in GG-SpaD was performed with the DynDom programC was done using the ClusPro50 web server (https://cluspro.bu.edu/). The N- and C-domains from the crystal structure of GG-SpaDC were used as the receptor and ligand, respectively. Representative models from the top ten most favored clusters were predicted by different scoring modes  and analyzed visually. The third and eighth models from the electrostatic-favored and balanced, respectively, scoring modes had resembled the docked molecules in the crystal structures.Computational blind docking of the C-domain to the N-domain of GG-SpaDKD pilin motif peptide in the N-domain of GG-SpaD is predicted to form an intermolecular K–T isopeptide bond, this was tested by making the corresponding K180A substitution in a nisin-inducible recombinant SpaFED-piliated L. lactis clone (GRS1189)45 and observing the effect on pilus assembly. In addition, Asp181 of the YPKD peptide was examined similarly (D181A), as this residue forms an internal isopeptide bond with Lys45, though this position is typically occupied by an asparagine in the more commonly found YPKN pilin motif. The construction of the mutant clones GRS1234 (K180A) and GRS1232 (D181A) was largely done as described previously45. For this, the expression plasmid (pKTH5393) containing the L. rhamnosus GG spaFED pilus genes acted as the template for adding the needed nucleotide changes (AAA → GCG for K180A or GAC → GCG for D181A) into the spaD gene by the overlap extension PCR technique67. Mutagenic PCR primers for the site-directed modifications were as follows: for K180A, forward 5'-CATCTTTATCCTGCGGACAGTCTTGTTAC-3' and reverse 5'-GTAACAAGACTGTCCGCAGGATAAAGATG-3' and for D181A, forward 5'-CATCTTTATCCTAAAGCGAGTCTTGTTAC-3' and reverse 5'-GTAACAAGACTCGCTTTAGGATAAAGATG-3'. Mutant plasmids pKTH5456 (for K180A) and pKTH5455 (for D181A) were introduced into L. lactis NZ9000 by standard electroporation68. Afterward, the K180A and D181A substitutions were corroborated by DNA sequencing. Growth and nisin-induced expression of lactococcal cells were carried out as done before45. Included as positive and negative controls were lactococcal clones GRS1189 and GRS1052 , respectively. Immunoblotting experiments were performed using established methods. Briefly, cell pellets were recovered by centrifugation, and then washed (once) and resuspended with phosphate-buffered saline. Lactococcal cell suspensions were mixed with Laemmli buffer (1:1), incubated at 100 °C for at least 5 min, and later clarified centrifugally. Protein content was resolved electrophoretically on a precast 4–20% SDS-polyacrylamide gel (Bio-Rad) and followed by electroblotting onto a 0.45-μm nitrocellulose membrane (Bio-Rad). The membrane was then probed with primary (1:5000 diluted rabbit anti-GG-SpaD serum58) and secondary  antibodies. GG-SpaD bands were detected by using a chemiluminescent kit  according to manufacturer instructions.As Lys180 of the YPwww.pdb.org) with the entry codes 5YU5 (WT full-length in closed/linear conformation), 5YXO (WT full-length in open/bent conformation), 5YXG , 5Z0Z (D242A), and 5Z24 (K365A). All other data not present in the published paper or supplementary files are available from the authors upon reasonable request.The atomic coordinates of the models and their corresponding structure factors have been deposited in the Protein Data Bank (Supplementary Information


Plant homeodomain (PHD) fingers are central “readers” of histone post-translational modifications (PTMs) with > 100 PHD finger-containing proteins encoded by the human genome. Many of the PHDs studied to date bind to unmodified or methylated states of histone H3 lysine 4 (H3K4). Additionally, many of these domains, and the proteins they are contained in, have crucial roles in the regulation of gene expression and cancer development. Despite this, the majority of PHD fingers have gone uncharacterized; thus, our understanding of how these domains contribute to chromatin biology remains incomplete.We expressed and screened 123 of the annotated human PHD fingers for their histone binding preferences using reader domain microarrays. A subset (31) of these domains showed strong preference for the H3 N-terminal tail either unmodified or methylated at H3K4. These H3 readers were further characterized by histone peptide microarrays and/or AlphaScreen to comprehensively define their H3 preferences and PTM cross-talk.The high-throughput approaches utilized in this study establish a compendium of binding information for the PHD reader family with regard to how they engage histone PTMs and uncover several novel reader domain–histone PTM interactions . This study highlights the usefulness of high-throughput analyses of histone reader proteins as a means of understanding how chromatin engagement occurs biochemically. Histone proteins are fundamental to genome organization and packaging, and are chemically modified by a wide range of “writer” or “eraser” enzymes that, respectively, install or remove histone post-translational modifications (PTMs) , 2. ThesHistone PTMs often have either activating or repressive effects on gene transcription depending on the type of PTM  and the position being modified . In general, distinct classes of reader domains bind to specific types of PTMs; for example, bromodomains recognize lysine acetylation , and thePlant homeodomain fingers comprise one of the largest families of reader domains, with over 100 human proteins containing this module . PHD finDespite great progress in uncovering the role of a subset of PHD fingers, many (over 100) of the annotated domain family remain uncharacterized. In this report, we set out to close the gap in our understanding of this reader domain class. Using a combination of complementary approaches , we show (31/123) of the PHD-containing query proteins to bind histone H3 N-terminal peptides, with the majority of these preferring H3K4me3 over unmodified H3K4. Furthermore, a number of unreported histone PTM–PHD protein interactions were uncovered, with the PHD regions of PHRF1 and TRIM66 binding preferentially to an unmodified H3 N-terminal tail peptide. Given that many of these PHD fingers are mutated in diseases such as breast cancer and leukemia , 20–24, E. coli. The recombinant proteins consisted of either PHD fingers in isolation, or as tandem domains if a given PHD finger was located adjacent to another reader domain   bound specifically with methylated H3K4; 2) interacted only with unmethylated H3K4; or (3) bound without preference to the H3K4 methylated state. Many of the PHD fingers found to only bind H3K4 methylation have previously been described and include the well characterized domains from the ING and PHF protein families , 24. The interact bound spTo more comprehensively define the histone interactions of the 31 PHD readers identified from the domain microarray analyses, we probed each on an alternate microarray platform containing a library of 293 synthetic histone peptides with single or combinatorial PTMs   showed no preference for the H3K4 methyl state on the domain array but strong preference for H3K4me0 on peptide microarray. Furthermore, KDM5B (PHD3), is reported to bind H3K4me3 , and shoDuring the course of this study, we observed that domain binding to H3 peptides tended to be enhanced when neighboring lysine residues were additionally acetylated  Fig. . While adCypher®) to provide a third and orthogonal approach to analyzing the histone binding preferences for a subset of the 31 PHD proteins with respect to various histone tail PTMs. In this assay, biotinylated peptides are bound to streptavidin “donor” beads and the GST-tagged reader domains bound to Glutathione “acceptor” beads. The donor beads are excited by 680 nm light, releasing a singlet oxygen which causes light emission (520–570 nm) in proximal acceptor beads (within 200 nm); emission intensity is then correlated to binding strength  and added to each well. Plates were incubated at 23 °C in subdued lighting for 60 min and AlphaLISA signal measured on a PerkinElmer 2104 EnVision .The dCypher peptide screen assay was performed as previously described . BrieflyAdditional file 1: Table S1. PHD finger Domain Array Constructs and Map. Excel Tab PHD Proteins is a compiled list of the 123 GST-tagged proteins used for the domain array. The list contains information on the domains, UniProt accession numbers, sequence coverage, and amino acid sequence for each protein. Excel Tab Array Map represents the pattern in which each of the GST-tagged proteins has been printed for the domain microarray.Additional file 2: Figure S1. Positive and negative controls of PHD finger domain arrays. Figure S2. PHD finger domain array with H3 (1-20) K4me1 and K4me2. Figure S3. Domain architecture of the 31 human PHD-containing proteins identified as hits for H3K4me0 or H3K4me3 via protein domain array .Additional file 3: Table S2. Histone Peptide Microarray Results. Excel Tab Peptide List is a compiled list of the entire 293 histone peptide library with amino acid sequence ranges. Excel Tab Peptide Array Grid Map is a table representing the pattern in which peptides have been printed for the peptide microarray. Each number corresponds to the peptide number designated in Peptide List. Excel Tab Average Signals is a compiled set of binding data: chromatin reader at 0.5 μM vs. entire 293 peptide library. Data is presented as the average and standard deviation of 4 replicates. The average signals are colored to indicate signal intensity with green being strong and white being weak/low. Excel Tab Array Heatmap contains a condensed form of Average Signals data displaying an average of 4 replicates for each chromatin reader tested and individually formatted to demonstrate binding specificity. Key: red= stronger relative binding; blue = weaker relative binding. Excel Tab Array info summary is a table summarizing the domains, sequence coverage, array signal range, and noise for each of the GST-tagged readers assayed on the peptide microarrays.Additional file 4: Table S3. dCypher Results. Excel Tab Peptide Phase A is a compiled set of binding data: chromatin reader titration vs. several peptide targets. Data is presented as raw Alpha counts in duplicates at each concentration tested. Excel Tab Peptide Phase B is a compiled set of binding data: chromatin reader at optimized concentrations vs. entire 293 peptide library. Data is presented as the average and standard deviation of duplicates. Excel Tab Peptide Phase B Heatmap contains a condensed form of Peptide Phase B data displaying an average of duplicates for each chromatin reader tested and individually formatted to demonstrate binding specificity. Key: red = strong binding; blue = weak/no binding.Additional file 5: Table S4. dCypher screen reader domain concentrations.