Analysis of cyclic nucleotide binding (CNB) domains shows that they have evolved to sense a wide variety of second messenger signals; a mechanism for allosteric regulation by CNB domains is proposed. The cyclic nucleotide binding (CNB) domain regulates signaling pathways in both eukaryotes and prokaryotes. In this study, we analyze the evolutionary information embedded in genomic sequences to explore the diversity of signaling through the CNB domain and also how the CNB domain elicits a cellular response upon binding to cAMP.Identification and classification of CNB domains in Global Ocean Sampling and other protein sequences reveals that they typically are fused to a wide variety of functional domains. CNB domains have undergone major sequence variation during evolution. In particular, the sequence motif that anchors the cAMP phosphate (termed the PBC motif) is strikingly different in some families. This variation may contribute to ligand specificity inasmuch as members of the prokaryotic cooA family, for example, harbor a CNB domain that contains a non-canonical PBC motif and that binds a heme ligand in the cAMP binding pocket. Statistical comparison of the functional constraints imposed on the canonical and non-canonical PBC containing sequences reveals that a key arginine, which coordinates with the cAMP phosphate, has co-evolved with a glycine in a distal β2-β3 loop that allosterically couples cAMP binding to distal regulatory sites.Our analysis suggests that CNB domains have evolved as a scaffold to sense a wide variety of second messenger signals. Based on sequence, structural and biochemical data, we propose a mechanism for allosteric regulation by CNB domains. The cyclic nucleotide binding (CNB) domain is a conserved signaling module that has evolved to respond to second messenger signals such as cAMP and cGMP ,2. The CCNB domains also occur in prokaryotes. The first characterized family containing a CNB domain in prokaryotes is the CAP (catabolite gene activator protein) family of transcriptional regulators  that conCrystal structures of CNB domains from both eukaryotes and prokaryotes have been determined and their structural comparison reveals a conserved mode of cAMP recognition  and reguAn emerging theme in CNB domain signaling is the allosteric control of CNB domain functions. In the PKA regulatory subunit, for instance, cAMP binding to the beta subdomain causes conformational changes in the distal alpha subdomain, thereby releasing its inhibitory interactions with the catalytic subunit . This prIn the following sections, we first describe the identification and classification of CNB domains to illustrate the diversity of this protein family, and later show how a comparative analysis of CNB domain sequences has provided insights into the evolution of allostery.Cyclic nucleotide binding domains in the National Center for Biotechnology Information's non-redundant amino acid database (NR) and Global Ocean Sampling (GOS) ,20 data Archaeoglobus fulgidus. CNB domains in eukaryotes can be broadly classified into five major categories: the kinase domain associated PKA and PKG families; the guaninine nucleotide exchange factor (Epac's); transmembrane domain containing HCN and Na channels; HCN type channels in protozoans; and CNB domains in metazoans and plants that are fused to functional domains such as PAS domains, PP2C like phosphatases and phospholipases  that contain a DNA binding domain covalently linked to the CNB domain; and a distinct class of DNA binding domain containing proteins  that are activated by second messenger signals such as NO, oxygen and heme . In addilac operon [Most of the GOS sequences, as expected, are prokaryotic in origin since they belong to families that are exclusively prokaryotic Table . In partc operon . In addic operon , is alsoThermotoga maritime has been determined as part of the structural genomics initiative [Until now, the primary function of CNB domains in prokaryotes was believed to be in the transcriptional regulation of genes. However, our analysis suggests that other cellular processes, such as ATP production, protein phosphorylation and NADH production, may also involve CNB domain functions Table . Of partitiative . The occOther novel domains in prokaryotes that are fused to CNB domains include the HisKs that are involved in bacterial two component signaling, and the AAA class of ATPases , which coordinates with the phosphate group of cAMP  from those that bind to other ligands? In order to address this question we used the CHAIN  program, which quantifies the differences between two functionally divergent groups of sequences using statistical methods . Using tThe key residues that distinguish the canonical PBC containing protein families from the ones that diverge from this motif are shown in Figure Recent NMR studies on the PKA regulatory subunit had suggested a key role for the β2-β3 loop in coupling cAMP signals to distal regulatory sites . SpecifiA global analysis of CNB domain containing sequences in the public and GOS data has provided novel insights into the evolution of CNB domain structure and function. Two evolutionary events appear to have contributed to CNB domain functional divergence, domain recombination and sequence variation. The sequence diversity observed within the PBC suggests that the CNB domain has evolved as a scaffold for not only binding cAMP, but also a wide variety of other ligands, many of which are yet to be characterized. Statistical comparison of the evolutionary constraints acting on the canonical PBC motif containing CNB domains with the non-canonical ones reveals that the residues in the PBC region have co-evolved with residues in the β2-β3 loop. Examining these constraints in light of structural and biochemical data provides a model of allosteric regulation, which is likely conserved in all cAMP binding modules. The results described in this study have implications for protein engineering and for the design of allosteric inhibitors.CNB domains in GOS and NR data were identified using a combination of psi-blast  and GibbCNB domains identified from NR  were multiply aligned using the CHAIN analysis program . The aliBecause CNB domains in the GOS data displayed significant sequence similarity to known CNB domains, they were assigned to one of the 30 families by running them against 30 family specific blast profiles. The taxonomic assignment for the GOS sequences was likewise done based on their similarity to known NR sequences . ExaminaIn order to visually examine the evolutionary relationship between the identified sequences, we first constructed a phylogentic tree of all the 7,696 CNB sequences. The resulting tree, however, was very complex and hard to interpret. Therefore, we decided to take an alternative approach where we depicted each family by a consensus sequence. The 30 consensus sequences, corresponding to each of the 30 families, were generated from multiple alignments of individual families. The neighbor joining algorithm as implemented in the Molecular Evolutionary Genetics and Analysis (MEGA) program  was usedThe evolutionary constraints imposed on CNB sequences were measured using the CHAIN program . In brieThe sequence identifiers for the sequences used in alignments Figures CAP/CRP, catabolite activator protein; CBS, cystathionine beta synthase; CNB, cyclic nucleotide binding; GOS, Global Ocean Sampling; HisK, histidine kinase; HTH, helix-turn-helix; NR, National Center for Biotechnology Information's non-redundant amino acid database; PBC, phosphate binding cassette; PK, protein kinase.NK and SST conceived and designed the experiments. NK, JW performed the experiments. NK and SST analyzed the data. AFN, SY, GA and JCV contributed reagents/materials/analysis tools. NK and SST wrote the paper.


Phosphorylation by protein kinases is a common event in many cellular processes. Further, many kinases perform specialized roles and are regulated by non-kinase domains tethered to kinase domain. Perturbation in the regulation of kinases leads to malignancy. We have identified and analysed putative protein kinases encoded in the genome of chimpanzee which is a close evolutionary relative of human.The shared core biology between chimpanzee and human is characterized by many orthologous protein kinases which are involved in conserved pathways. Domain architectures specific to chimp/human kinases have been observed. Chimp kinases with unique domain architectures are characterized by deletion of one or more non-kinase domains in the human kinases. Interestingly, counterparts of some of the multi-domain human kinases in chimp are characterized by identical domain architectures but with kinase-like non-kinase domain. Remarkably, out of 587 chimpanzee kinases no human orthologue with greater than 95% sequence identity could be identified for 160 kinases. Variations in chimpanzee kinases compared to human kinases are brought about also by differences in functions of domains tethered to the catalytic kinase domain. For example, the heterodimer forming PB1 domain related to the fold of ubiquitin/Ras-binding domain is seen uniquely tethered to PKC-like chimpanzee kinase.Though the chimpanzee and human are evolutionary very close, there are chimpanzee kinases with no close counterpart in the human suggesting differences in their functions. This analysis provides a direction for experimental analysis of human and chimpanzee protein kinases in order to enhance our understanding on their specific biological roles. Protein phosphorylation is a core process in many signal transduction pathways which regulates various aspects of cellular processes. Members of the family of protein kinases mediate protein phosphorylation. The family of Ser/Thr and Tyr protein kinases is one of the largest protein families. Members of this family are involved in regulation of a wide variety of cellular processes such as cell growth and differentiation, cell cycle control, apoptosis, transcription, cell motility and cell-cell communication. By covalent tethering of phosphate group from ATP, protein kinases modify the functional activity of the substrates to result in changes in the levels of enzyme activity, cellular location or association with various other proteins in the proximity . The catPan troglodytes) genome by the Chimpanzee Sequencing and Analysis Consortium  in the catalytic loop. Short and truncated sequences, which are less than 200 amino acids long, have not been considered in the present analysis. Hence all the sequences present in the current analysis are likely to be active gene products.Classification of protein kinases into various Hanks and Hunter subfamilies  reveals The objective of this study is to identify, classify and annotate the protein kinases in chimpanzee genome and to compare this preliminary kinome set with human which is the closest relative of chimp.Methods section. Based upon Hanks and Hunter classification scheme, these PPKs are further classified into various protein kinase subfamilies. Accessory domains, transmembrane segments if any, have also been identified. Additional file Chimpanzee and human putative protein kinases (PPKs) are identified using the approach described in the We have identified 587 kinases from chimp genome using various sophisticated sequence analysis and classification procedures described in the section on Methods. We have classified these kinases into various subfamilies and compared the distribution with that of human kinases Table . As can Many chimp kinases are unclassified as the extent of similarity of the catalytic regions to any of the previously known kinase sub-families is very low. However many of these unclassified chimp kinases have corresponding closely-related human kinases. A dendrogram of unclassified chimp and human kinases Figure  demonstrThough many kinases between chimp and human are highly similar the cases of significant difference emerge mainly due to difference in domain architecture of multi-domain kinases of chimp and human. Following sections discuss the differences between some of the chimp and human kinases arising out of difference in domain tethering features. Additional file Many of the protein kinases in higher eukaryote are multi-domain proteins; the kinase domain is tethered to various non-kinase domains. We examined the global domain composition of 587 putative kinases encoded in the chimpanzee genome. Globally, 82 different domains, other than kinase domains and Pkinase_C  domain, are seen in the 587 chimpanzee kinases. A list of the most frequently found accessory domains is provided in Table In the recent Pfam version 22, the protein kinase domain (PF00069) is seen in 771 different architectures, and the protein tyrosine kinase domain (PF07714) is seen in 333 different architectures. In the repertoire of chimpanzee protein kinases, out of 587 PPKs, 286 PPKs have accessory domains tethered to the kinase domain. The rest of the protein kinases do not have any other domain tethered to the kinase domain.Methods section). We have also identified unusual domain architectures. These domain architectures are common only to human and chimpanzee with no or poor representation in other organisms with known genomic data at the present time. We describe here the chimpanzee and human specific architectures and the unusual domain architectures common to chimpanzee and human  implies that the recruitment of diacylglycerol might be achieved by an external interacting module.A protein (ENSPTRP00000000076), classified under PKC subfamily, is composed of a PB1 domain followed by the protein kinase domain which is followed by a protein kinase C terminal domain Figure . The PB1In a chimpanzee PPK classified under the camk1 subfamily (ENSPTRP00000011569), the pleckstrin homology (PH) domain is followed by the protein kinase domain Figure . The PH A chimpanzee PPK belonging to the raf subfamily (ENSPTRP00000037507), a novel peptide displays a domain architecture with a RBD (Ras Binding Domain) followed by the catalytic kinase domain Figure . This paIn the human kinome, we have identified a few multi-domain protein kinases having high sequence identity (more than 57%) with chimp homologues over the entire length. These human kinases and the chimp counterparts share almost the same domain architecture. Most interestingly these chimp homologues are unlikely to function as a protein kinase (PKLNK) as they all lack the catalytic base residue (Asp) Figure  comparedWe identified a human camk1 (ENSP00000311684) with an architecture in which 6 Immunoglobulin domains (I-set) are followed by a fibronectin 3 domain (fn3), followed by two I-set domains, the protein kinase domain, an I-set domain, a fn3 domain, and a second kinase catalytic domain. The close chimp homologue annotated as a known peptide (ENSPTRP00000022133) has PKLNK domain instead of the C-terminal protein kinase domain in the human homologue. This chimp homologue lacks the catalytic aspartate in the catalytic loop Figure . I-set aIn the two human PPKs belonging to ptk8 subfamily (ENSP00000371341 and ENSP00000323216) the protein kinase domain is followed by a Src homology 3 (SH3) domain and a GTPase binding domain. The SH3 domain is seen in signaling proteins related to cytoskeletal organisation . Both thA related and unique architecture, with an additional C-terminal ubiquitin associated domain, is seen in human PPK (ENSP00000329425) which belongs to ptk8 subfamily. The ubiquitin associated domain is seen in many signaling proteins . ENSP000A human putative tyrosine kinase, ENSP00000306717 (belonging to ptk17 subfamily), has an unusual architecture which is human specific, where the protein kinase domain is following an Ldl receptor A domain (Low density lipoprotein receptor domain class A) and a MAM domain Figure . The LdlWe have identified domain architectures in human and chimpanzee kinomes that are poorly or not represented in kinomes of other organisms. These architectures, represented in Figure Two AGC PPKs, ENSPTRP00000001185 from chimpanzee and ENSP00000361078 from human, both annotated as 'novel peptide', display an architecture in which a domain of unknown function (DUF 1908) is followed by the protein kinase domain and the PKC terminal domain. DUF 1908 is present in 29 structural eukaryotic proteins in Pfam. Additionally, 14 sequences in Pfam display a similar architecture with an additional C-terminal PDZ domain. Our current survey identified 3 chimpanzee PPKs and 3 human PPKs with this architecture. The PDZ domain that is present in these 6 sequences, but absent from chimpanzee (ENSPTRP00000001185) and human (ENSP00000361078), is 80 to 90 amino-acid long domain and found tethered to diverse signaling proteins. It is involved in protein-protein interaction, and has the capability to bind the C-terminal part of its partner . This suO15021) which is shown to be highly expressed in the brain [We identified, in human and chimpanzee kinomes, domain architecture of catalytic protein kinase domain which is closely related to AGC group followed by PDZ domain. Such a domain combination is not found in other kinomes. The human PPK (ENSP00000261569), which is the counterpart of ENSPTRP00000029005 of the chimpanzee kinome, is the microtubule-associated serine/threonine-protein kinase 4 (Swiss prot entry: he brain .Q96S38) which is known to be expressed in brain [A combination of PX, MIT and the protein kinase domain is seen in chimpanzee PPK (ENSPTRP00000003319), a novel peptide, and one human PPK (ENSP00000355927), labeled as known-cdds. PX domain is a phosphoinositides binding domain and MIT is the microtubule interacting and transport domain. Human PPK (ENSP00000355927) is highly similar to the Ribosomal protein S6 kinase delta-1  and immunoglobulin I-set domains in a variety of combinations. We identified 4 types of architectures in PPKs which are closely related to camk1 subfamily, and are not present in kinomes of other organisms:'fn3 x 2, I-set, fn3 x 3, I-set, fn3 x 2, I-set, fn3 x 3, I-set, fn3 x 3, I-set x 2, fn3 x 2, I-set x 2, fn3, Pkinase, I-set x 10' : this architecture is seen in a chimpanzee PPK (novel peptide ENSPTRP00000021693) and a human PPK (novel peptide ENSP00000343764).- 'I-set x 8, fn3, Pkinase, I-set': in two chimpanzee PPKs (novel peptides ENSPTRP00000046634 and ENSPTRP00000045642) and two human PKKs . The latter corresponds to myosin light chain kinase (swissprot entry Q15746).- 'I-set x 7, fn3, Pkinase, I-set' : in a chimpanzee PPK (novel peptide ENSPTRP00000026393) and in a human PPK (known peptide ENSP00000320622). The latter sequence corresponds to the entry NP_444254 in the RefSeq database of NCBI and it is the isoform 2 of Myosin light chain kinase.- 'I-set, Pkinase': in a chimpanzee PPK (novel peptide ENSPTRP00000028713) and a human PPK (known peptide ENSP00000339291).- A Filament domain is seen associated to the protein kinase domain of chimpanzee PPK (ENSPTRP00000011929) and human PPK (ENSP00000263802). Filament domains are components of the cytoskeleton and the nuclear envelope . To our P35590), and is specifically known to express in developing vascular endothelial cells.Four domain architectures seen in PTK group are poorly or not represented in kinomes of other organisms. A chimpanzee PPK (the novel peptide ENSPTRP00000001085) and a human PPK (the known-ccds ENSP00000361554) have an architecture composed of an immunoglobulin domain, followed by a Laminin EGF-like domain, an EGF domain, three fibronectin 3 domains and the catalytic protein kinase domain. A transmembrane helix is predicted with high confidence between the fibrnonectin 3 domain and the kinase domain. Laminin EGF-like domain is found in laminin which is the constituent of basement membranes. The functional role of EGF  domain is unclear; it is seen in a variety of membrane and secreted proteins . This arQ02763). This architecture is present in a sequence of mimivirus. A related architecture with only one EGF domain is seen in chimpanzee PPK (ENSPTRP00000035635), and in a human PPK  which is the TEK tyrosine kinase. Such architecture is not yet known to occur in kinomes of other organisms.A combination of two EGF hand domains with three fibronectin 3 domains, followed by a transmembrane domain and the protein kinase domain is seen in a chimpanzee PPK , and in a human PPK  and corresponding to the Tyrosine-protein kinase receptor TIE-2  domain and a protein kinase domain. This architecture has not known to occur in other kinomes. BTK domain is a Zinc-binding motif domain , usuallyIn the current analysis, we have compared 587 PPKs in the chimpanzee genome with human kinases and identified orthologous kinases. To compare genes across these two organisms and to identify the orthologues, we have considered two features: 1) The full length of the two gene products have been compared with the condition that the difference in number of residues of orthologues should not be more than 50. 2) The classification of kinase catalytic domains of orthologues should correspond to same subfamilies of kinases under Hanks and Hunter classification scheme. There are 470 orthologous pairs which we have identified in the current analysis. This information can be found in Additional file et al [The comparison of humans and chimpanzees with respect to differences in expression levels and protein-coding sequences for genes active in brain, heart, liver, kidney, and testis is available . This daet al  who haveThe Additional file In this report we have made an effort to understand the repertoire of protein kinases in the chimpanzee which has close kinship with human. The comprehensive list and classification of protein kinases in chimpanzee genome presented in this report is a valuable resource for future signaling research for these closely related organisms.Identification of various PK subfamilies indicates that the core signaling pathway involving PK is well conserved between chimpanzee and human. This implies conserved steps in the signaling pathways in chimpanzee and human involving protein kinase as central player. However, substantial number of chimpanzee kinases deviates markedly in terms of the domain combination/architecture resulting in unique varieties of functional domain combinations. Considerations of functions of modules which are tethered to the protein kinase domain enable us to predict the functional role of PPKs in the chimpanzee genome. Results obtained from this study adds new facets to the contribution of protein kinases in signal transduction of chimpanzee and comparative kinome gives us insights about evolution of protein kinases in two closely related species. Given the importance of protein kinases in signaling, experimental analysis would be fruitful to dissect the signaling system of chimpanzee and will provide valuable insight into the similarities and differences between chimpanzee and human signaling pathways.In this paper, we have identified and classified chimpanzee protein kinases on the basis of the amino acid sequences of putative proteins encoded in the chimpanzee genome. We have compared them with updated list of human protein kinases.. Chimpanzee proteome is composed of 33,167 protein sequences, generated from an automatic annotation system based on biological evidences [The chimpanzee and human proteome data are retrieved from the ENSEMBL database  release vidences . The humThe human proteome data is based on the NCBI 36 assembly of the human genome, released in November 2005. It is composed of 43,570 protein sequences: 3,614 "novel peptides", 18,642 "known peptides" and 21,314 "known-ccds peptides". The "known-ccds" annotation means that these sequences are part of a core set consistently annotated, high quality data in the frame of the Consensus Coding Sequence (CCDS) project.Protein kinases are identified using a combination of profile-based search methods such as PSI-BLAST  and RPS-On the one hand, the proteome of interest is searched for the protein sequences of Pfam families PF00069  and PF007714 . On the other hand, we use multiple PSSMs created for the 55 protein kinase classes  represen. After reducing the redundancy level to 95%, we obtain a final set of 587 chimpanzee PPKs. These PPKs are classified according to Hanks and Hunter classification scheme [The results of PSI-BLAST and RPS-BLAST searches have been merged into a single list of putative protein kinases which are further refined using several criteria. Sequences shorter than 200 amino-acids are excluded, as it is well known that the usual length of catalytic region is approximately 250 to 300 amino-acids . Redundan scheme . The popn scheme ,50. Trann scheme .organism-specific if it is found to belong to a particular kinase sub-family in an organism but not in the same kinase family of the other organisms. We also define unusual domain architecture as domain architectures identified only in human and chimpanzee kinases, but not present or poorly represented in kinomes of other organisms.Protein kinase domain is commonly found tethered to accessory domains; the ordered list of domains along a sequence defines its domain architecture. Here, we compare domain architectures seen in human and chimpanzee kinomes and extract organism-specific and unusual domain architectures. A domain architecture is said to be Domains are detected using HMMER and Pfam domain library, and families are defined based upon the Hanks and Hunter classification scheme. Unique and unusual architectures are further checked to ensure that the uniqueness  is not the result of family misclassification or domain mis-assignment. Only significant cases are reported here. at the family level. Multiple profiles were generated for each regulatory protein as described in [We searched for the regulatory domains of various PK families. The detection is achieved by RPS-BLAST using multiple profiles of regulatory domains. Sequences of PKA regulatory domains, calmodulins and cyclins were retrieved from the well-annotated sequence data base swiss-prot  using thribed in  with 44,In order to detect orthologous protein kinase pairs between chimpanzee and human, we compared the predicted protein kinases by carrying out all against all BLAST. Firstly, chimpanzee PPK sequences have been used as query against the database of predicted human kinome and secondly predicted human PPK sequences have been used as query against the database of predicted chimpanzee kinome. If the two given sequences are almost of equal length (length difference should not be more than 50 amino acids) and belonging to the same subfamily of protein kinase, then we have considered the two genes as orthologues. Close orthologues have been identified by using a further condition of sequence identity should be greater than 95%.For the phylogenetic analysis MEGA software  has beenAll authors contributed equally to the research. All authors read and approved the final manuscript.Chimpanzee kinases with their subfamily classification, number of residues in the gene product and domains present.Click here for fileSequence alignment between some of the closest chimp and human kinase pairs with difference in domain architecture.Click here for fileList of chimpanzee protein kinases and their putative orthologues in human. The Hanks and Hunter protein kinase subfamily to which chimp kinases belong is also mentioned.Click here for fileThe table representing different gene expression levels for human and chimp protein kinases in five different tissue types.Click here for filePopulation and distribution of chimpanzee protein kinases into various subfamilies.Click here for file


Protein domains are the fundamental units of protein structure, function and evolution. The delineation of different domains in proteins is important for classification, understanding of structure, function and evolution. The delineation of protein domains within a polypeptide chain, namely at the genome scale, can be achieved in several ways but may remain problematic in many instances. Difficulties in identifying the domain content of a given sequence arise when the query sequence has no homologues with experimentally determined structure and searching against sequence domain databases also results in insignificant matches. Identification of domains under low sequence identity conditions and lack of structural homologues acquire a crucial importance especially at the genomic scale.Mycoplasma gallisepticum genome. We could establish 71 new domain relationships and probable 63 putative new domain families through intermediate sequences in the unassigned regions, which importantly represent an overall 10% increase in PfamA domain annotation over the direct assignment in this genome.We have developed a new method for the identification of domains in unassigned regions through indirect connections and scaled up its application to the analysis of 434 unassigned regions in 726 protein sequences of Mycoplasma gallisepticum genome has provided some insight into the possible new domain relationships and putative new domain families. Further investigation of these predicted new domains may prove beneficial in improving the existing domain prediction algorithms.The systematic analysis of the unassigned regions in the  Domain assignment to the protein sequences has paramount importance in the post genomic era. Protein domains are the structural, functional and evolutionary units of proteins. Study of proteins at the domain level has had a profound impact on the study of individual proteins. Experimental and/or computational methods can be used to identify domains in the given protein sequence. Classification databases such as the Dali Domain Dictionary, CATH2]2], SCOP and DIALMycoplasma gallisepticum causes chronic respiratory disease in chickens and other avian species. The infections result in considerable economic losses in poultry production. This pathogen has a small genome with 726 proteins [Mycoplasma gallisepticum genome as a specific example.proteins , but onlproteins . The gapproteins . Here, wMycoplasma gallisepticum genome; domains were assigned to the genome with HMMpfam program of HMMER package [The procedure that was followed in this study and overall results are described in Figure  package  by searc package ,12 with  package . Only 23Mycoplasma gallisepticum genome; for the remaining 26 domains, one or more copies already exist in the genome. Among the new predictions, 22 domains appear specific to M. gallisepticum genome, only 15 domains were present in other Mycoplasmataceae members and remaining 7 domains are unique to the entire Mycoplasmataceae family members , are powerful tools, these methods often face limitations due to poor sequence similarities and non-uniform sequence dispersion in protein sequence space. Several interesting approaches have been employed in different ways to detect remotely related proteins; one such approach is based on the intermediate sequences. Intermediate sequence procedure substantially increases the ability to recognize the distant evolutionary relationships.There is relatively large number (63) of unassigned regions, which has picked up at least five homologues but not associated with any PfamA domain  in the N-terminal region followed by a C-terminal existing Peptidase_S8 domain. All the PSI-BLAST hits were from prokaryotes. The alignment . 109 homologues could be identified by PSI-BLAST and all homologues belong to prokaryotic organisms  domain alignment and the borderline E-value (0.006 and a cut-off 0.01). Moreover, the 'CRISPR_Cas2' (PF09827) domain family is defined only in Pfam23, but not in Pfam21 database.The results presented in this paper, are based on Pfam21 database with 8957 protein domain families; updated version of Pfam database (Pfam23) has 10340 domain families. Comparison of 71 newly predicted domains in the 62 unassigned regions to the updated Pfam23 database revealed that 67 domains, out of 71, still remain unassigned in the Pfam23 database. Among four Pfam23 annotations, three are the same as our earlier prediction and one annotation differs from our prediction. Comparison of our results with conserved domain database (CDD v2.16) revealedThe nature of sequence/domain searches is such that the databases are constantly going through updates and it is inevitable that our new findings might appear obsolete due to the constant updates of robust databases such as Pfam and CDD. Where there is concurrence with the newer version of a database, they serve to validate the approach. Where there is still new information obtained from PURE approach, this clearly suggests the value and novelty of the protocol due to the early realization of additional domains. When the newer entries are substantially high, this is very encouraging for the development of the approach suggesting that this has promise for discovering hitherto unidentified domains.Mycoplasma gallisepticum. In spite of filters, such as evolutionary conservation and high predicted structural content, about 20% of orphan proteins contained in this genome could be annotated with a known functional domain using our approach. Interestingly, our analysis revealed several meaningful alignments, which could relate to as yet functionally unidentified set of domains. This could be very useful as a starting subset for further functional screening in wet lab experiments. Several improvements of the methodology will be addressed in future. Furthermore, cross-genome comparisons of the results from our procedure between Mycoplasma gallisepticum and other Mycoplasma species are currently being investigated.Here, we present the results of the application of a new method for domain identification to full genome of an avian pathogen Mycoplasma gallisepticum (Strain R) were obtained from National centre for Biotechnology Information website[Complete protein sequences of n website.Mycoplasma gallisepticum protein sequences were scanned against a dataset of Hidden Markov Models (HMMs) obtained from the PfamA database (Pfam version 21.0)[ion 21.0) which coion 21.0), with E-The unassigned sequences thus obtained were subjected to different filtering steps.a. To avoid false positives in the PSI-BLAST  search, b. Transmembrane regions were excluded by using HMMTOP  and coilc. Standalone version of protein secondary-structure prediction program PSIPRED was usedThe unassigned sequences, which have fulfilled the filtering criteria, were used to query non-redundant sequence database  employinOnly regions of homologues that aligned well with the query sequence were obtained from PSI-BLAST output to scan against a dataset of Hidden Markov Models (HMMs), which were obtained from the PfamA database (Pfam version 21.0) which coThe indirect association of query sequence through homologous sequences with HMMs in the Pfam database gave rise to the definitions of full and partially associated domains. At least 75% of HMM should indirectly align with query to be considered as a fully associated domain and rest were considered as partially associated domains.Multiple sequence alignments of the query unassigned region and seed sequences of predicted domains which were obtained from Pfam were perThe authors declare that they have no competing interests.Mycoplasma gallisepticum. SSR compared the results with other domain databases. BO helped to device the experiments and to interpret analysis. RS devised the whole strategy for domain identification using indirect approach and supervised the work of CSRC. CSRC wrote the first draft of the manuscript. Both BO and RS contributed to the writing and critical reading of the manuscript. All authors read and approved the final manuscript.CSRC has developed algorithms and codes used to identify domains and performed analysis on the whole genome of Supplemental Table ST1 Predicted putative new domain families. In our analysis, some of the unassigned regions shared homologues (at least 5 hits in PSI-BLAST search), but failed to associate with any Pfam domain in Hmmpfam search. We performed multiple sequence alignments to assess sequence conservation. 63 unassigned regions showed good conservation which may indicate putative new domain family. In the table, second column indicates the protein-id and third column (UR = Unassigned Region) indicates the start and end points of unassigned regions. Fourth column indicates the percentage secondary structural content in the unassigned region by using PSIPRED program. Fifth column indicates the total number of hits in the PSI-BLAST search and the sixth column indicates the number of PDB hits in PSI-BLAST search. Seventh column indicates the number of different species among the PSI-BLAST hits and eighth column indicates number of transmembrane helices which indicates whether protein is likely to be membrane-bound or not.Click here for fileSupplemental Table ST1 PfamB associations in the unassigned regions. 63 unassigned sequences were used to search against PfamB database (Pfam 24), only 20 unassigned sequences were associated with at least one PfamB domain. Unassigned sequence id along with start and end residues are showed in column two, PfamB start and end residue numbers are shown in column three, PfamB ids are shown in column four and the Expectation values with which PfamB domains associated are shown in the last column.Click here for file


We consider our domains as mobile evolutionary units, which may rearrange during protein evolution. Additionally, they may be visualized as structurally compact and possibly independently folding units. We also found that representing domains as evolutionary and folding units do not always lead to a unique domain definition. However, unlike existing databases, we retain and refine these “alternate” domain definitions after careful inspection of structural similarity, functional sites and automated domain definition methods. We provide domain definitions, including actual residue boundaries, for proteins that well known databases like SCOP and CATH do not attempt to split. Our alternate domain definitions are suitable for sequence and structure searches by automated methods. Additionally, the database can be used for training and testing domain delineation algorithms. Since our domains represent structurally compact evolutionary units, the database may be useful for studying domain properties and evolution.Protein structural domains are necessary for understanding evolution and protein folding, and may vary widely from functional and sequence based domains. Although, various structural domain databases exist, defining domains for some proteins is non-trivial, and definitions of their domain boundaries are not available. Here, we present a novel database of manually defined structural domains for a representative set of proteins from the SCOP “multi-domain proteins” class. ( Although protein domains commonly represent units of protein function Domains are structurally characterized by the presence of isolated hydrophobic cores. Additionally, intra-domain residue contacts are more extensive than between domains One key benefit of a reliable set of reference domains is in similarity searches where the reference domains can be used as a query. Secondly, they can also be a training set for automated domain definition method development. Such reference domains are usually obtained from existing databases like SCOP http://prodata.swmed.edu/multidom/. Our domain definitions will be helpful not only for similarity searches and development of automated methods but possibly also for studying domain properties and evolution.As a result of this work, we developed a database of manual domain definitions with assigned residue ranges based primarily on the combined concepts of structural similarity, compactness and sequence continuity. These domain definitions are available online at http://prodata.swmed.edu/multidom/). Our domain definitions are based on structural, functional, sequence and evolutionary considerations after careful study of relevant literature and inspection of domains defined by existing automated methods. We provide downloadable PyMol We provide a novel database of manually defined domains, with residue boundaries for each domain, for a set of topologically complex proteins . Based on structural overlap of terminal extensions, the same pair of domains could be visualized as being either terminally fused (sequence continuous) or inserted one within the other (sequence discontinuous). For proteins with a large number of domains such an alternate definition might be possible with regard to only a few, or even just a pair, of the constituent domains. Additionally, domain-modularity concepts bar certain structurally compact regions from having arisen by a single evolutionary event. Typically these consist of a structurally compact group of inserts into other domains. Rather than omitting these regions from our database, we define them as yet another alternate domain definition (composite domains). These alternate domain definitions are explained below.A common occurrence in multi-domain protein structures was the presence of relatively short non-globular extensions at the N- or the C-terminus of a domain that interacted with a neighboring domain. Such terminal extensions are known to stabilize associated domains , while “In addition to terminal extensions, some domains included a relatively short non-globular extension that protruded from within the domain  yet interacted with another domain. For example, a β-hairpin extending from the middle of a profillin-like α/β/α sandwich domain in penicillin-binding protein 2× extends the β-sheet of an N-terminal domain . This β-In summary, our database provides four categories of definitions: “By structure”, “by sequence” and composite domains, and extensions. The “by structure” definitions are more applicable to structure similarity searches, as the terminal extensions associated with the spatially closest domain may be important in finding remote homologs. “By-sequence” definitions that attribute terminal extensions to the sequence-continuous polypeptide segment (similar to SCOP) are more useful for sequence search strategies; as such regions may contain conserved sequence motifs. The “extensions” category specifies extended regions  that differentiate between the “by structure” and “by sequence” definitions. Finally, “composite” domains do not represent evolutionary units and are purely geometric, but may be useful in studies of convergent evolution.Manually defined domains in our database are of three alternate categories . Additionally, the database separately defines 83 inserts, linkers and terminal extensions that also are part of the domain definitions. CATH Despite more domains being defined manually for a single chain in contrast to automated methods, manual definition produced a marginally lower number of polypeptide segments per domain . This inFor analysis of domain size, our manual definitions were comparable to PDP, SCOP and CATH in the percentage of defined domains ranging between 95 and 215 residues in length, with DOMAK shifting towards longer domains . HoweverDuring our visual assessment, we found that automated methods failed to provide consistent domain definitions for most chains in the dataset. Automated methods reached a consensus only on very simple cases, where structurally compact domains displayed few inter domain interactions. In almost every case, the domain boundaries from these programs required further refinement, even if the domain number and their general locations were correct. Domain definitions obtained from CATH, although incomplete, were more reliable. However we did notice stray inconsistencies in domain definitions between similar proteins. These problems and limitations of domain definition were well documented by others For protein families with a large number of available structures, combining structural similarity with evolutionary considerations was especially useful while defining domains. Our modular domains could be positively identified and boundaries refined by structural similarity of conserved domains, insertion position and general topology of various associated domains, and interactions between them. We illustrate our general line of thought and difficulties with domain definition using DNA/RNA polymerases as an example. The common architecture of polymerases includes three functional domains commonly referred to as “palm”, “thumb” and “finger”, with the ubiquitous palm domain providing the catalytic activity for the enzymes In polymerase structures from all groups, the catalytic palm domain incorporated a ferredoxin-like fold , green tWhile the palm domain was the most conserved, and hence represented an evolutionary core unit of the polymerase structures, the thumb and finger domains exhibited significant topological variation. Structural position of the thumb domain was conserved with respect to the palm , wheat, Composite domains were also observed among polymerases. Together with an N-terminal domain , cyan, iApart from the palm, thumb and fingers, polymerase structures included various additional domains. One such domain resembled a Ribonuclease H-like (RNAseH-like) fold topology , orange.Although biologists agree that proteins are composed of domains and that analysis of constituent domains are important for studying the whole protein, there is widespread disagreement regarding the properties and definition of the individual domains themselves. Thus, the concept of a domain (what is a domain?) and the methods to delineate them in a given protein (how to find domain boundaries?) must be addressed, and are a pre-requisite for automated analysis of various protein properties. Additionally, questions arise concerning the possibility of providing consistent domain definitions for proteins based on a few general principles. This work, based on a set of topologically complex proteins, represents a step towards these important goals.Functional domains are characterized by functional independence, and form units sufficient for a certain, mainly enzymatic, activity. Since functional characterization of a protein domain is often a primary objective, protein domains are usually thought of as functional domains. 2) Sequence domains are regions of polypeptide chain that can be detected by sequence similarity and can be found in combination with other sequence domains. These domains are widely used in sequence analysis where incorrect boundaries imply erroneous delineation of conserved regions. Since conserved regions are relied upon by sequence-profile based search methods such as PSI-BLAST Evolutionary domains are modules that can shuffle between proteins via recombination, transposition, exon shuffling and other mutational events. Thus their occurrence in dissimilar domain contexts, provided homology can be detected by sequence or structural similarity, forms the basis of evolutionary domain definition. 4) Domains as folding units were defined in the early days of protein studies Structural domains are defined geometrically by structural compactness, presence of a hydrophobic core and more extensive amino acid interactions intra-domain rather than inter-domain. Such domains are essential for structural similarity search. For many proteins, e.g. those that look like “beads on a string” with domains that are well separated from each other, all 5 concepts may lead to the same domain definition. However, the criteria used to formulate these five domain concepts are quite different, and bringing these concepts together consistently may not always be possible. When domains interact more closely, different concepts inevitably lead to differing definitions. This work attempts to reconcile several of these domain concepts on some of the most topologically complex examples of protein chains.Perhaps the greatest difficulty in domain studies is the absence of a uniform answer to the question: What is a domain? Interestingly, several domain “concepts” can be proposed. Among them, the following five widespread concepts are worth noting. 1) Domains may be considered as evolutionary units that can potentially shuffle between proteins, and are a natural viewpoint for biodiversity studies. This evolutionary consideration provides an easy guide to domain identification when sequence similarity between domains from different proteins is high, even if the domain structures differ. However, when sequence or even structural similarity to other proteins is harder to ascertain, domain definition becomes especially difficult. This difficulty is particularly noticeable at domain boundaries, where domain definition tends to be possible only with additional consideration of geometric properties like structural compactness and interactions between residues.As a result of biological complexity and uncertainty in evolutionary deductions, a single domain definition might fail to address the real process of a domain's origin. Additionally, due to the limitations of contemporary automated sequence and structure analysis methods, such a single domain definition may not even be desirable. Instead, we treat domain definitions in 3 categories; two of them (“by sequence” and “by structure”) represent different plausible mechanisms of domain origin based on the same evolutionary considerations. A third category of “composite” domains is based only on geometric properties and not on our evolutionary considerations. We also provide an additional category of “extensions” to list the causative polypeptide segments that differentiate the “by sequence” and “by structure” definitions. Despite this special treatment, the majority of domains do not differ in “by sequence” and “by structure” definitions due to the absence of extensions. Nonetheless, we list them separately in our database for ease of use by automated methods. Thus, our work manages to bring forth a complete and consistent picture of domain structure from an evolutionary perspective for many topologically complex proteins.First, in definitions “by structure”, the extensions are assigned to the interacting domain, by structure. This results in a discontinuous sequence for the “ancestral” domain  Detect structurally similar proteins using structure and sequence methods. Similar but non-identical proteins (homologs) showed differing domain arrangements. Alignments of these homologs indicated domain boundaries for at least some of the domains, making delineation of remaining domains easier. Dissimilar proteins (with a low similarity score) were sometimes observed to contain a similar domain, which we could then align and delineate. Some homologous structures showed additional domains at various stages of growth. To be consistent, we defined these additional domains only if a hydrophobic core was discernable, otherwise the polypeptide segments were treated as loops. 2) Locate possible duplications within a given protein. Structural repeats are likely formed by duplications and thus represented separate evolutionary domains. 3) Attempt to recognize canonical domains like Rossmann fold, ferredoxin-like fold, 4-helical bundle, immunoglobulin, SH3 and OB barrels, etc. This step was limited by our experience. However, frequent referral to relevant literature alleviated some of the problem. The only solution to these limitations is to actually have a reference set of domains, exactly what this current work takes a step towards addressing. Such a reference set could even be used during a structure search in step 1 above. 4) Identify potential domains based on the presence of separate β-sheets. Individual domains most often contained β-sheets in their entirety. In rare cases domains contained more than one sheet, which were however, sequence-intertwined (e.g. beta-sandwich). 5) Identify potential domains based on spatial aggregations of α-helices. Identification of a domain core via step 4 and 5 was usually easy. However position of a domain interface was sometimes unclear. Difference between Intra and inter-domain residue contacts was sometimes difficult to perceive. Side-chain orientation of residues was of some help in this regard. In rare cases we turned to structural similarity detection (step 1 and 3) for help. 6) Treat domains as evolutionary modules by noting sequence continuity and domain insertion events. A pre-requisite for our domain definitions was that all sequence-discontinuous domains could only originate due to other domain insertions. Most automated domain definition methods were observed to fail at this step. 7) Attribute any unassigned peptide segments to the already identified domain cores. Short (<20 residue) segments were attributed to existing domains. Longer segments were either defined as novel domains or attributed to existing domains based on the presence of a perceived hydrophobic core. 8) Exact domain boundaries were refined based on side-chain orientations and interactions as well as superposition of structurally similar domains.Several applications of our domain database are possible. 1) The database can be used to train and test automated domain definition algorithms as it is the only database that provides domain definitions for the, topologically, most challenging protein chains. 2) The database can be used to study domain interactions, interfaces and topology. Additionally the variation in domain combinations allows the database to be a reference for possible domain architectures. 3) Our manual domain definition criteria, observations and pitfalls may be helpful in the design of an automated domain definition algorithm that considers modular evolutionary units as domains. 4) The compact nature of our structural domains indicates possible folding units or nuclei, and their analysis could enhance understanding of protein folding and structure prediction techniques. 5) Biologists interested in specific proteins catalogued in our database can infer functional and evolutionary units that could be isolated for biochemical studies. 6) Although our composite domains are assembled from several sequence segments that do not suggest mobile evolutionary units, such assembled domains might resemble evolutionary domains in other proteins. As a future work, such findings may yield analogous domain pairs useful for the analysis of convergence in protein evolution.A dataset of 157 PDB Domain boundaries defined by CATH (version 3.0) were obtained for reference and comparisons. Structural domain assignments by PDP Visual inspection of the structure coordinates was essential in identifying properties important for domain definition. A number of structural characteristics helped to define the domain boundaries. The most important of these characteristics were secondary structure packing and topology, globularity Our method can be broadly split into two steps; an initial step (step 1) of identifying the number and general position of the domains, and (step 2) a later refinement of the domain boundaries. Since refinement of domain boundaries (step 2) also involves assignment of structural extensions to a domain, this step influenced the sequential arrangement and modular representation of our domains. This sequential rearrangement sometimes necessitated changes in domain numbers (step 1) due to our view of domains being capable of modular rearrangement. Thus, we followed an iterative method of domain definition via the above- mentioned two broad steps.The putative domain dataset (described above) was helpful in informing us of potential domains. In most cases structurally distinct compact regions were readily split into domains by automated methods. We considered these putative domains as domain cores and attempted to re-define domains arising from these cores. However, automated methods sometimes showed obvious errors in domain detection. Large and easily recognizable folds like Rossmann, and PIN domain were sometimes over split. For these recognizable folds, our consideration of domain cores was altered from those suggested by the automated methods. In complement, smaller folds in close proximity such as ferredoxin-like, RNaseH, 4-helical bundle, immunoglobulin, SH3 and OB barrels were merged by automated methods and more than one fold was assigned a single domain. For these folds, we relied on visual assessment of secondary structure packing and perceived presence of hydrophobic regions for identification of structurally compact and globular domain cores. Small zinc binding domains were detected by the presence of metal ions and proximal histidine and cysteine residues. Neighboring folds were scrutinized with respect to topology for evidence of duplication. Duplicated domains within a structure were defined by manual observation and sequence and structural alignment.Domain boundaries were refined to assign domains that were modular both by sequence and structure. The possibility that these modules may rearrange during protein evolution was considered, and is expressed in our definitions. Boundary refinement was aided by comparing all similar domains within the same SCOP n domains inserted into a domain A, domain A was defined to be composed of n+1 polypeptide segments. However, for some pairs of neighboring domains one of the domains was observed to contain terminal structural-extensions that interacted with the neighboring domain. Alternate definitions have been provided wherever these extensions were identified as a secondary structural element (α helix or β sheet). We defined the terminal extension “by structure” ensuring structural compactness of the extension to the domain core. This consideration led to a definition where one domain was inserted into the other. Additionally, we defined the terminal extenson “by sequence” ensuring sequence continuity of each domain. This alternative led to a definition where the domains appeared terminally fused by sequence, with no discontinuity in either. Composite domains form a third category of our domain definitions and are based only on structural compactness. In rare cases, secondary structural extensions and inserts to domains, as well as non-globular domains formed a structurally compact region, wherein a hydrophobic core could be perceived. We define these regions as composite domains although they are sequence discontinuous but do not contain inserted domains.Our adherence towards modular domain definitions ensured sequence continuity for individual domains; unless additional inserted domains also were present (except for composite domains described later in this section). Thus for


Shuffling of modular protein domains is an important source of evolutionary innovation. Formins are a family of actin-organizing proteins that share a conserved FH2 domain but their overall domain architecture differs dramatically between opisthokonts (metazoans and fungi) and plants. We performed a phylogenomic analysis of formins in most eukaryotic kingdoms, aiming to reconstruct an evolutionary scenario that may have produced the current diversity of domain combinations with focus on the origin of the angiosperm formin architectures.Dictyostelium formins was found in all lineages except plants, suggesting its ancestral character. Instead, mosses and vascular plants possess the two formin classes known from angiosperms: membrane-anchored Class I formins and Class II formins carrying a PTEN-like domain. PTEN-related domains were found also in stramenopile formins, where they have been probably acquired independently rather than by horizontal transfer, following a burst of domain rearrangements in the chromalveolate lineage. A novel RhoGAP-related domain was identified in some algal, moss and lycophyte (but not angiosperm) formins that define a specific branch (Class III) of the formin family.The Rho GTPase-binding domain (GBD/FH3) reported from opisthokont and We propose a scenario where formins underwent multiple domain rearrangements in several eukaryotic lineages, especially plants and chromalveolates. In plants this replaced GBD/FH3 by a probably inactive RhoGAP-like domain, preserving a formin-mediated association between (membrane-anchored) Rho GTPases and the actin cytoskeleton. Subsequent amplification of formin genes, possibly coincident with the expansion of plants to dry land, was followed by acquisition of alternative membrane attachment mechanisms present in extant Class I and Class II formins, allowing later loss of the RhoGAP-like domain-containing formins in angiosperms. Domain shuffling in modular proteins is considered one of the major sources of evolutionary innovation . PhylogeFormins (or FH2 domain-containing proteins) may serve as a good example of an ancient protein family with a likely role in morphogenesis (at least on the cellular level) whose evolution included extensive domain rearrangements. The well-conserved FH2 (formin homology 2) domain  can nucleate new actin filaments by a unique „leaky barbed-end cap“ mechanism, and in some situations acts as a capping protein (for a review see ). BesideDictyostelium , and the GBD/FH3 motif is recognized by a C-terminally located autoinhibitory domain . This domain architecture, which is believed to mediate control of the formin's actin-nucleating activity by Rho-related GTPases , was oriostelium .FH2-containing proteins tend to form extensive families of paralogs , and thHowever, no GBD/FH3-containing formins have been identified in plants so far. Formins from angiosperms can be unequivocally assigned to one out of two classes, based on primary structure of their FH2 domains ,8; none Arabidopsis and suggested within-class functional differences reflected e.g. in varying tissue- or organ-specific expression patterns or in protein localization to specific subcellular destinations , reserving the full "GBD/FH3-FH1-FH2-DAD" description to cases where this motif was found unambiguously. Nevertheless, we believe that presence of GBD/FH3 is likely to indicate regulation by means of GTPase-dependent release of intramolecular inhibition via a canonical or variant DAD that may be detected upon visual inspection , ZnF – zinc finger [Smart: SM00355], PHD – PHD Zinc Finger [Smart: SM00249], HDAC_int – histone deacetylase interacting domain [Smart: SM00761], BROMO [Smart: SM00297], C1 [Smart: SM00109], FHA – Forkhead-associated [Smart: SM00240], TPR – tetratricopeptide repeats [Smart: SM00028], ARM – Armadillo/beta-catenin repeats [Smart: SM00185], 1i84 – contractile protein [PDB: 1i84], KISc – kinesin motor [Smart: SM00129], SAP [Smart: SM00513], FYVE [Smart: SM00064], PAN_1 [Pfam: PAN_1], WW – a conserved domain containing two tryptophan residues [Smart: SM00456]. For commonly appearing domains, the table is color-coded as follows: green – present in all formins of the species, yellow – present in some formins, orange – absent, grey – incomplete data. Format: Adobe portable document (*.pdf).Click here for fileDiversity and domain structure of plant FH2 proteins (MG_A5.pdf). For each species, the total number of complete formin sequences is given as FH2, and the number of incomplete sequences that did not allow conclusive analysis of domain architecture is as FH2 . Class I, Class II, Class III and Other FH2 denote numbers of formins carrying FH2 domains from the respective clades (or unassigned ones); the remaining abbreviations denote additional domains and motifs found in plant formins: Sec10 – Exocyst complex component Sec10 [Pfam: Sec10]; for remaining domain abbreviations and database accessions see legend to Table Click here for fileModel of the RhoGAP-related domain from Phypa7 (MG_Phypa7.pdb). Coordinates of the model in the PDB format (*. pdb) which can be viewed using e.g. the free Deep View Viewer [w Viewer .Click here for fileSummary of PTEN domains analyzed in this study (MG_A7.xls). List of taxa and PTEN protein sequences included in this study, in addition to FH2 – containing sequences that are listed in Additional file Click here for filePredicted protein sequences of selected PTEN-containing proteins (MG_A8.txt). Modified protein sequence predictions corresponding to some of the genes listed in Additional file Click here for filePhylogenetic tree of 90 PTEN domains (MG_A9.pdf). An unrooted maximum likelihood tree of 90 PTEN domains constructed as described in Materials and Methods. For a full list of list of genes, see Additional file Click here for file


Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products.Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive  in silico genome mining. These results also have interesting implications for rational design of novel natural products using a biosynthetic engineering approach.Polyketide synthases (PKSs) form a large family of multifunctional proteins involved in the biosynthesis of diverse classes of therapeutically important natural products. These enzymes biosynthesize natural products with enormous diversity in chemical structures by combinatorial use of a limited number of catalytic domains. Therefore, deciphering the rules for relating the amino acid sequence of these domains to the chemical structure of the polyketide product remains a major challenge. We have carried out bioinformatics analysis of a large number of PKS clusters with known metabolic products to correlate the chemical structures of these metabolites to the sequence and structural features of the PKS proteins. The remarkable conservation observed in the PKS sequences across organisms, combined with unique structural features in their active sites and contact surfaces, allowed us to formulate a comprehensive set of predictive rules for deciphering metabolic products of uncharacterized PKS clusters. Our work thus represents a major milestone in natural product research, demonstrating the feasibility of discovering novel metabolites by  A number of recent theoretical studies have demonstrated the utility of in silico analysis in providing novel insights into the mechanistic details of polyketide biosynthesis as well as in identifying novel natural products by genome mining. Computational analysis of polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) proteins have provided valuable clues for development of knowledge-based methods for identification of catalytic domains in PKS Mycobacterium tuberculosisIt is well known that polyketide synthase (PKS) gene clusters can generate enormously diverse array of polyketide products by making use of various biosynthetic paradigms like, modular organization of sets of catalytic domains or iterative catalysis of condensation steps using single set of catalytic domains in silico identification of polyketide products of uncharacterized PKS clusters, the computational method should also take into consideration various different paradigms employed by PKS biosynthetic machinery in silico methods should be capable of predicting from sequence information, whether a given PKS cluster is iterative, the number of iterative chain condensation steps catalyzed by it and crucial amino acids which control the number of iterations.Thus, these studies have established that knowledge based computational approaches can play a powerful role in elucidation of novel secondary metabolite biosynthetic pathways. However, for cat) of chain transfer of otherwise disfavored substrates by as much as 100-fold et alIn contrast to type I iterative PKSs where a single multifunctional enzyme is involved in biosynthesis of the polyketide product, biosynthesis in type I modular PKS clusters often involve multiple ORFs, each containing several modules. Therefore, predicting the correct order of substrate channeling between various ORFs is crucial for deciphering the final metabolic product of a modular PKS cluster. Several lines of experimental evidence reveal that inter subunit interactions between C-terminal docking domain region of the upstream ORF and N-terminal docking domain region of the downstream ORF, play a crucial role in channeling of substrates from upstream domains to downstream domains in silico prediction of polyketide products. Is it possible to distinguish between modular and iterative PKS from their sequence alone? Can we predict the number of iterations a given iterative PKS protein would catalyze and identify crucial amino acid residues that control the number of iterations? Is it possible to predict the correct order of substrate channeling between various ORFs in a modular PKS cluster? We have carried out profile Hidden Markov Model (HMM) analysis of KS domains to identify signature profiles which can decipher whether a PKS protein is modular or iterative. Structural modeling of KS domains of iterative PKS proteins and analysis of their active site pockets have given novel insight into the structural features that dictate the number of iterations catalyzed by a PKS protein and crucial amino acids which control them. Similarly, comparative analysis of crucial inter polypeptide contacts between cognate and non-cognate pairs of ORFs based on the three dimensional structure of the docking domains have given novel clues for prediction of the correct order of substrate channeling.In this work, we have carried out a detailed comparative analysis of the experimentally characterized modular and iterative PKS clusters with known polyketide products to address following major questions relating to KS domains are the most conserved among various catalytic PKS domains and are responsible of catalysis of the chain condensation step. We have analyzed them in detail to identify class specific conserved patterns which distinguish modular and iterative PKS systems. For KS domains, the total dataset comprised of 217 pure modular KS domains, 82 pure iterative domains, 19 enediyne, 43 trans-type and 34 KS domains from hybrid NRPS-PKS clusters. Apart from the sequences of 20 experimentally characterized bacterial type I modular clusters included in our earlier analysis E. coli KAS-II protein  were used as the templates for modeling these iterative KS domains. Since 1B3N was a ligand bound structure  belong to the MSAS type PKSs that perform three iterations. Intermediate sized cavities (∼800Å3) belong to the napthopyrone (NAP) like PKSs that iterate from five to eight times. The largest cavities, 1780Å3, were observed for the T-Toxin models that perform 20 iterations. 3) and surround the ligand analogue cerulenin. A comparison of the modeled structures with the template FAS KS structure revealed that in case of MSAS and NAP, the backbones of the models had not altered significantly during modeling Click here for additional data file.Figure S2Superposition of backbones of iterative KS domain models on structural templates(0.24 MB DOC)Click here for additional data file.Figure S3Genomic order vs biosynthetic order(0.09 MB DOC)Click here for additional data file.Figure S4The four helix bundle structure of DEBS docking domain(0.21 MB DOC)Click here for additional data file.Table S1Scoring scheme for docking domain interactions(0.07 MB DOC)Click here for additional data file.


B. thetaiotaomicron reveals a two-domain protein with a putative carbohydrate-binding site in the C-terminal domain.The crystal structure of BT2081 from  Bacteroides thetaiotaomicron (GenBank accession code NP_810994.1) is a member of a novel protein family consisting of over 160 members, most of which are found in the different classes of Bacteroidetes. Genome-context analysis lends support to the involvement of this family in carbohydrate metabolism, which plays a key role in B. thetaiotaomicron as a predominant bacterial symbiont in the human distal gut microbiome. The crystal structure of BT2081 at 2.05 Å resolution represents the first structure from this new protein family. BT2081 consists of an N-terminal domain, which adopts a β-sandwich immunoglobulin-like fold, and a larger C-terminal domain with a β-sandwich jelly-roll fold. Structural analyses reveal that both domains are similar to those found in various carbohydrate-active enzymes. The C-terminal β-jelly-roll domain contains a potential carbohydrate-binding site that is highly conserved among BT2081 homologs and is situated in the same location as the carbohydrate-binding sites that are found in structurally similar glycoside hydrolases (GHs). However, in BT2081 this site is partially occluded by surrounding loops, which results in a deep solvent-accessible pocket rather than a shallower solvent-exposed cleft.BT2081 from  This bacterial species is essential for the metabolism and uptake of dietary plant polysaccharides by the human host  and a calculated isoelectric point of 4.9. It contains a predicted N-terminal signalling peptide, suggesting that it is secreted from the cytoplasm. Sequence and genomic context analysis suggest that it is a putative carbohydrate-processing protein that is part of the extracellular polysaccharide-processing machinery. Its N-terminal region (residues 1–114) shares high sequence similarity to the carbohydrate-binding domains of endo-1,4-β-xylanase from Streptomyces griseoflavus (35% sequence identity), β-xylosidase from Magnetospirillum magnetotacticum (29% sequence identity) and a putative β-fructosidase from Sarcoptes scabiei (39% sequence identity), all of which are glycoside hydrolases (GHs), which catalyze the cleavage of the glycosidic bonds in monosaccharide, disaccharides and polysaccharides into their constituent sugar units. Moreover, genome-context analysis shows that BT2081 and several of its paralogs belong to characteristic PULs (polysaccharide-utilization loci) of B. thetaiotaomicron and are associated with transmembrane porin domains that are homologous to the B. thetaiotaomicron outer membrane protein transporter SusC. SusC is part of the well characterized eight-component sus (starch-utilization system) operon used by B. thetaiotaomicron in carbohydrate metabolism. Furthermore, in the sus operon two other BT2081 paralogs (BT0450 and BT1761) are immediately followed by GHs. To further investigate the role that BT2081 may play in carbohydrate metabolism, we have determined its crystal structure using the semi-automated high-throughput pipeline of the Joint Center for Structural Genomics  and I-PIPE (Insert) primers  that included sequences for the predicted 5′ and 3′ ends. The expression vector pSpeedET, which encodes an amino-terminal tobacco etch virus (TEV) protease-cleavable expression and purification tag (MGSDKIHHHHHHENLYFQ/G), was PCR-amplified with V-PIPE (Vector) primers . V-PIPE and I-PIPE PCR products were mixed to anneal the amplified DNA fragments together. Escherichia coli GeneHogs (Invitrogen) competent cells were transformed with the I-PIPE/V-PIPE mixture and dispensed onto selective LB–agar plates. The cloning junctions were confirmed by DNA sequencing. Using the PIPE method, the gene segment encoding Met1–Ile20 was deleted because it was predicted to be a signal peptide. Expression was performed in selenomethionine-containing medium at 310 K with suppression of normal methionine synthesis. At the end of fermentation, lysozyme was added to the culture to a final concentration of 250 µg ml−1 and the cells were harvested and frozen. After one freeze–thaw cycle, the cells were sonicated in lysis buffer  and the lysate was clarified by centrifugation at 32 500g for 30 min. The soluble fraction was passed over nickel-chelating resin  pre-equilibrated with lysis buffer, the resin was washed with wash buffer  and the protein was eluted with elution buffer . The eluate was buffer-exchanged with TEV buffer  using a PD-10 column  and incubated with 1 mg TEV protease per 15 mg eluted protein. The protease-treated eluate was passed over nickel-chelating resin  pre-equilibrated with HEPES crystallization buffer  and the resin was washed with the same buffer. The flowthrough and wash fractions were combined and concentrated to 13.5 mg ml−1 by centrifugal ultrafiltration (Millipore) for crystallization trials. BT2081 was crystallized by mixing 100 nl protein solution with 100 nl crystallization solution in a sitting drop over a 50 µl reservoir volume using the nanodroplet vapor-diffusion method  polyethylene glycol 600, 0.25 M calcium acetate and 0.1 M sodium cacodylate pH 6.33. PEG 400 was added to the crystal as a cryoprotectant to a final concentration of 5%(v/v). A triangular prism-shaped crystal of approximately 200 × 200 × 200 µm in size was harvested after 43 d at 277 K for data collection. Initial screening for diffraction was carried out using the Stanford Automated Mounting system . The mobile phase consisted of 20 mM Tris pH 8.0, 150 mM NaCl and 0.02%(w/v) sodium azide. The molecular weight was calculated using ASTRA v.5.1.5 software (Wyatt Technology). Protein concentrations were determined using the Coomassie Plus assay (Pierce).Clones were generated using the Polymerase Incomplete Primer Extension (PIPE) cloning method (Klock 2.2.1) of a selenium SAD experiment. The data set was collected at 100 K using a MAR Mosaic 325 mm CCD detector (Rayonix) using the Blu-Ice data-collection environment  of 0.28/0.09 with 11 anomalous scatterers per asymmetric unit ; Vonrhein et al., 2007ARP/wARP  and the absence of nickel.Single-wavelength anomalous diffraction (SAD) data were collected on beamline 11-1 at the SSRL at a wavelength corresponding to the peak . This server processes the coordinates and data through a variety of validation tools including AutoDepInputTool  was adapted from PDBsum . Atomic coordinates and experimental structure factors for BT2081 from B. thetaiotaomicron at 2.05 Å resolution were deposited in the PDB (http://www.pdb.org) under code 3hbz.The quality of the crystal structure was analyzed using the 3.3.1.V               M; Matthews, 19683 Da−1 and the estimated solvent content is 67%. The Ramachandran plot produced by MolProbity  , four α-helices  and three 37) Fig. 1. The totet al., 2004The BT2081 monomer consists of two structural domains Fig. 1. The N-t3.2.et al., 2005et al., 2009et al., 1998et al., 1995Carbohydrate-active enzymes are quite modular, often containing various carbohydrate-binding and catalytic domains in different combinations. Glycoside hydrolases (GHs), for example, often contain both catalytic and noncatalytic carbohydrate-binding modules (CBMs) which can be assembled in different orders  and CBM family 44 (CBM44) domains  that are absent from BT2081.The Ig-like and jelly-roll domains of BT2081 have previously been observed as modules in carbohydrate-active enzymes. ty Fig. 2. An impoMicromonospora viridifaciens  of the outer membrane protein usher from Yersinia pestis  of human obscurin , with an r.m.s.d. of 3.07 Å between 59 Cα atoms and a sequence identity of 2.5%. Although none of the top structural neighbors from this search matched carbohydrate-active enzymes, the Ig-like fold has been observed in a number of these proteins. In addition to CtCel9D-Cel44A and NedA, structures from CBM families 9, 20, 25, 26, 31, 33 and 34 have also been found to adopt Ig-like folds .A structural similarity search of the N-terminal domain of BT2081 only performed using the BLAST revealed significant sequence similarity to portions of several GHs, including endo-1,4-β-xylanase from St. griseoflavus (35% sequence identity over 90 residues), β-xylosidase from M. magnetotacticum (29% sequence identity over 94 residues) and a putative β-fructosidase from Sa. scabiei (39% sequence identity over 76 residues). In all of these sequences, the putative Ig-like domain immediately precedes a putative GH43 catalytic domain, which is known to adopt a fivefold β-propeller fold. The role of the putative Ig-like domain in these GHs is still currently unknown.Sequence analysis of the BT2081 N-terminal domain with Thermotoga maritima xylanase 10A .Analysis with et al., 2002e.g. Phe133, Trp145, Lys237, Trp300 and Phe331) are also highly conserved. Comparison of this site in BT2081 and its structural neighbors reveals that it is most similar, in terms of residue conservation, to the corresponding sites in members from the CBM11 (PDB code 1v0a) and CBM16 (PDB code 2zew) families.Despite the low sequence identity between the C-terminal domain of BT2081 and its top structural neighbors, they share the same core β-jelly-roll topology Fig. 4. Another3.5.10-helices. As a consequence of these extended loop regions, a significant distinction between BT2081 and its structural counterparts is the presence of a deep solvent-accessible pocket in BT2081 formed by highly conserved residues from the concave surface of the β-jelly-roll core and five of the loop regions extending from this core. The concave surface of the β-jelly-roll core forms the base of this pocket, with loop 1 (residues 144–165) forming the pocket terminus, loop 2 (residues 182–199) and loop 3 (residues 203–228) comprising the middle section of the pocket, and loop 4 (residues 267–284) and loop 5 (residues 333–346) forming the side walls near the pocket entrance.The C-terminal domain of BT2081 contains extended solvent-exposed loop regions  that are not found in its closest structural neighbors. Several of these loops also contain additional secondary-structural elements including β-strands, α-helices and 3PSI-BLAST-derived sequence homologs, lending support to this pocket being functionally significant.The pocket is lined with a combination of both hydrophobic and hydrophilic residues  whose side chains are positioned for potential hydrogen bonding with a bound polysaccharide, in another characteristic feature which helps to define carbohydrate-binding specificity in type B CBMs.Similar to the carbohydrate-binding sites found in type B CBMs, the putative binding pocket of BT2081 contains several highly conserved aromatic residues arranged in such a way that they form a twisted platform to which a potential polysaccharide could bind. These aromatic residues include Phe202, Phe206, Phe221, Tyr261, Phe280 and Phe340, of which Phe202, Phe206, Tyr261 and Phe340 are strictly conserved among sequence homologs. Moreover, the BT2081 pocket contains a number of polar residues , a member of GH family 16 (GHF16) and one of the top structural neighbors of the BT2081 C-terminal domain, Glu105 and Glu109 act as the nucleophile and acid/base in a retaining mechanism for the glycosidic bond cleavage , while Asp338 (12/20 = 60%) is less so  and in this regard would be similar to other exopolysaccharidases, such as glucoamylase and β-amylase  into the BT2081 pocket reveals that the pocket can accommodate a polysaccharide as large as a pentaose, with three of the sugar units fitting into the section of the pocket extending from the bend to the pocket terminus and the other two sugar units fitting into the portion that extends from the bend to the pocket entrance. Consequently, if BT2081 were indeed proven to be a GH with Asp259 and Asp338 being the catalytic residues, it would be feasible that up to three terminal sugar units of a bound polysaccharide chain could be cleaved off at one time.Manual docking of various polysaccharides (4.BT2081 is the first structural representative of a new protein family which is likely to play a role in carbohydrate metabolism in the distal human gut based on structural and genomic context analyses. BT2081 contains Ig-like and jelly-roll domains, which have been observed to be modules in carbohydrate-active enzymes, such as GHs. The N-terminal Ig-like domain may act as a CBM and/or a linker region between the outer membrane and the C-terminal domain. The C-terminal domain contains a pocket that is similar in many respects to carbohydrate-binding sites in CBMs and catalytic modules of GHs. This domain may be catalytic, with Asp259 and Asp338 acting as the catalytic residues in glycosidic bond cleavage. Further biochemical investigation of BT2081 is needed to ascertain whether this is indeed the case.TOPSAN (Krishna et al., 2010http://www.topsan.org/explore?PDBid=3hbz.Additional information about BT2081 is available from putative glycoside hydrolase, 3hbz            PDB reference: