SH2 domains are long known prominent players in the field of phosphotyrosine recognition within signaling protein networks. However, over the years they have been joined by an increasing number of other protein domain families that can, at least with some of their members, also recognise pTyr residues in a sequence-specific context. This superfamily of pTyr recognition modules, which includes substantial fractions of the PTB domains, as well as much smaller, or even single member fractions like the HYB domain, the PKCδ and PKCθ C2 domains and RKIP, represents a fascinating, medically relevant and hence intensely studied part of the cellular signaling architecture of metazoans. Protein tyrosine phosphorylation clearly serves a plethora of functions and pTyr recognition domains are used in a similarly wide range of interaction modes, which encompass, for example, partner protein switching, tandem recognition functionalities and the interaction with catalytically active protein domains. If looked upon closely enough, virtually no pTyr recognition and regulation event is an exact mirror image of another one in the same cell. Thus, the more we learn about the biology and ultrastructural details of pTyr recognition domains, the more does it become apparent that nature cleverly combines and varies a few basic principles to generate a sheer endless number of sophisticated and highly effective recognition/regulation events that are, under normal conditions, elegantly orchestrated in time and space. This knowledge is also valuable when exploring pTyr reader domains as diagnostic tools, drug targets or therapeutic reagents to combat human diseases. Intracellular communication is transmitted via networks of molecules that execute information transfer using protein-mediated interactions. Post-translational modifications (PTMs) such as protein phosphorylation, acetylation, methylation and ubiquitination confer spatiotemporal dynamics to cell signaling . Among tSignaling proteins often possess a cassette-like architecture made up of catalytic domains and/or protein interaction modules . One impThe human genome harbours 90 PTKs -12, and Second, each component of the pTyr signaling circuit, i.e., a PTK, PTP or pTyr-binding module, possesses substrate or ligand recognition specificity to narrow down potential interaction partners -23. SincThis review will focus on the pTyr "reader" proteins contributing to this complex system. In addition to the SH2 and phosphotyrosine-binding (PTB) domains, the two archetype domain families known for pTyr binding, recent studies have shown that at least a handful of additional protein modules are capable of reading out the tyrosine phosphorylation. Here we review and explore the structure, function, specificity and therapeutic potentials of a number of typical and atypical members of the superfamily of pTyr-binding protein modules.vfps/fes oncogene from the Fujinami sarcoma virus [vcrk and in the endogenous cytoplasmic proteins phospholipase Cγ1 (PLCγ1) and the Ras GTPase activating protein (RasGAP) [The Src homology 2 (SH2) domains, a non-catalytic module containing ~100 amino acids, was first discovered by insertion-mutation analysis of the ma virus  (for hisma virus  and Huntma virus ). Soon t(RasGAP) ,29. SH2 (RasGAP) ,11. A re(RasGAP) ,12,30. S(RasGAP) -34, the (RasGAP) . In addi(RasGAP) . A growi(RasGAP) ,36. Thes(RasGAP) .As represented by the v-Src SH2 domain Figure A, the stKD) [SH2 domains are equipped for the specific recognition of a subset of pTyr–containing ligands ,22,53-55KD) -60. ThisKD) . While tKD) , the hydKD) ,40,63,64KD) . The stuKD) ,66.cistrans isomerization, adding another layer of complexity and dynamics to the CTD [in vivo as the CTD contains numerous repeats of the phosphorylated heptad sequence that can increase the effective local concentration of the binding target for the tandem SH2 domains [The yeast genome encodes only one SH2 domain-containing protein, the transcription factor Spt6 . The C-t the CTD ,68. The  the CTD ,11,52,69 the CTD -51. The  the CTD ,50,52. I the CTD ,50. Inte domains ,8,70.Although SH2 domains are the largest group of pTyr-binding modules ,10, it hbona fide SH2-binding motifs revealed that tyrosine phosphorylation sites in human proteome are significantly enriched in the vicinity of the SH2 domain-binding sites [Among the 90 PTKs in the human genome, 32 are cytoplasmic tyrosine kinases . Notablyng sites , which mng sites . As we dInterplay between an SH2 domain and a catalytic domain has also been observed for phosphatases. Pei et al. reported that the SH2 domains of tyrosine phosphatase SHP-1 regulates its catalytic activity via an auto-inhibition mechanism . Since tPost-translational modification such as tyrosine phosphorylation may act as a switch for some proteins. For instance, the presence or absence of phosphorylation may provide a mechanism for alternative binding to distinct protein partners . This tyDictyostelium discoideum[Monosiga brevicollis[M. brevicollis, where multiple tyrosine kinases that contain a PTB domain have been identified [M. brevicollis tyrosine kinase HMTK1 assists in targeting of a pTyr-containing substrate peptide [The phosphotyrosine-binding (PTB) or phosphotyrosine-interacting domain, first identified in the adaptor protein Shc -99, is tiscoideum, and 31 evicollis. The proevicollis, with poentified . Indeed, peptide . While a peptide . PTB dom peptide .Johnson and colleagues divided the PTB domain family into three classes, namely Shc-like, IRS-like and Dab-like, based on the domain structure . AlthougSimilar to the SH2 domain, PTB domains may bind tyrosine phosphorylation sites in cellular proteins. Proteomic studies have revealed that the two domain families may in fact target overlapping pTyr sites with micromolar affinities -59,113. MSF to NAAF resulted in 15-fold increase in binding affinity, implying that the physiological association between the Numb PTB domain and NAK may not be optimized for high affinity [751VYYY motif located in the cytoplasmic region of E-cadherin, but phosphorylation of the motif by Src results in dissociation of the PTB domain, suggesting that Src activation negatively regulates the interaction between Numb and E-cadherin [Numb is an adaptor and endocytic protein that plays an important role in asymmetric cell division and embryogenesis . It contaffinity . Moreoveaffinity . Recentlaffinity ,119. Thecadherin .Unlike SH2 domains, which often coexist with a kinase or phosphatase domain in a protein, the human PTB domain-containing proteins invariably lack a catalytic domain, with the single exception of the presence of a PTP-like domain in the tensin family (see a later section for detail) ,120,121.751VYYY motif on the Tyr residues and acidic residues flanking the motif are necessary for binding to the HYB domain, with a KD value of 7.2 μM for the N751VYpYY containing E-cadherin peptide [Hakai, a protein that binds to E-cadherin in a tyrosine phosphorylation-dependent manner, serves as an E3 ubiquitin ligase for the latter and induces endocytosis and degradation of E-cadherin . Hakai i peptide . Because peptide , it prov peptide . Moreove peptide . Thus, t peptide . The HYBThe PH domain was first described in pleckstrin, a substrate of protein kinase C (PKC) ,132. It KD of 240 nM [The C2 domain belongs to one of the largest domain families with over 200 members in human ,143. Thif 240 nM ,146. CDCf 240 nM . The C2 f 240 nM , was alsf 240 nM . Importaf 240 nM . 5R motif located on the active site loop [Caenorhabditis elegans, contain a PTP-like domain without a catalytic Cys residue  family proteins dephosphorylate a pTyr residue of substrate proteins ("erasers" in the toolkit). There are 107 PTPs in human, of which 61 phosphatases are classified as the dual-specificity phosphatase subfamily, which also possess Ser/Thr phosphatase activity in addition to pTyr dephosphorylation . The PTPite loop -154. Howite loop ,154. Sucite loop ,155. Thiite loop . Since tite loop -159. For338SYY341) regulated by phosphorylation [338pSYpY, with a KD value of 45 μM [bona fide pTyr binder.Raf-1 kinase inhibitory protein (RKIP), also known as phosphatidylethanolamine binding protein 1, suppresses Raf-1 kinase activity in the MAP kinase pathway . RKIP birylation . Substitrylation . The strrylation . NMR titof 45 μM . HoweverIn the above sections, we have explored that, in addition to the SH2 and PTB domains, there are more than a handful of examples of atypical domains that can recognize and bind pTyr-containing target proteins. So does nature have more domain members capable of pTyr-epitope binding that remain to be identified? Recent advances in proteomic technologies are starting to shed light on this question. Christofk et al. employed the SILAC (stable isotope labeling of amino acids in cell culture) technique in combination with a pTyr-peptide library to identify novel pTyr-binding proteins from mammalian cell lysates . WhereasMore recently, Christofk et al. identified a handful of potential pTyr-binding proteins from SILAC experiments. These include the serine/threonine-protein kinase WNK1, 5-formyltetrahydrofolate cyclo-ligase, glycerol-3-phosphate dehydrogenase, vimentin, 2,4-dienoyl-CoA reductase, and the T-complex protein 1 subunit η . AlthougNew insights into the molecular basis of cancer were generated already decades ago by the analysis of SH2-domain containing oncoproteins such as v-Src ,171. SH2in vitro and in vivo[Biochemical and structural studies have demonstrated that direct intramolecular interactions between the SH2 and kinase domains are required for kinase activation in some PTKs ,16,36. Id in vivo.Due to its independently folding nature, a modular domain can often be successfully used as a tool in proteomic research. Jadwin et al. termed this "domainomics” . In partcis, and produces a change in the emission spectrum. Using the Src reporter, the authors successfully visualized kinase activity in cells. In another example, an engineered adenocarcinoma-derived cell line that expresses an EGFR biosensor has been developed [SH2 domains have also been incorporated as a biosensor molecule that functions in live cells. Wang et al. created a bipartite Src reporter biosensor protein that contains an SH2 domain and a Src kinase substrate fused, respectively, with the cyan and green fluorescent proteins . Upon pheveloped . This cea, b, c, d, and e) largely originate from primitive species. Circuits that involve receptor tyrosine kinases which phosphorylate cytoplasmic substrates  mainly originate from bilaterians. Conversely, vertebrate-origin circuits are enriched with membrane protein substrates that are phosphorylated by primitive-origin cytoplasmic kinases  in a tissue-specific manner. This study underlined the importance of network hubs as hotspots for tumourigenesis, as high frequency cancer pTyr sites are involved in more circuits than low frequency sites, and kinase substrate proteins that contain a PTK, SH2 or PTB domain are more frequently recruited for cancer signalling [Lastly, understanding the evolution of phosphotyrosine recognition domains may inform cancer research and treatment. In general, the degree of network complexity in the phosphotyrosine signaling system has increased during the course of evolution, and genetic events, such as gene duplications to create paralog proteins and gene fusions to create multi-domain proteins, played a major role in expanding the network scale ,11. Robugnalling . M. brevicollis, a unicellular choanoflagellate that possesses a PTK signalling system [M. brevicollis contains a plethora of putative PTK toolkit proteins, many of which have no orthologs in humans [Escherichia coli (EPEC) and Helicobacter pylori, are known to hijack pTyr signalling system components of their host by injecting a virulence factor into the host cell during infection [The genome sequence analysis of g system , broughtg system ,100. Numg system ,197, chan humans ,100,196,n humans ,199. Nevnfection ,201. Thenfection . For insnfection ,203. It nfection ,205. Intin vivo. Protein modification reader domains should also be useful in a range of clinical settings, for example for molecular diagnoses and monitoring, but this potential remains untapped in clinical routine settings. As demonstrated for the SH2 domains, e.g., specificity-switching mutants or affinity-enhanced "pTyr superbinders" [Much of what has been learnt in past decades about pTyr reader domains, their interactions and the networks that they serve can now be utilised and also warrants further exploration into several directions. Firstly, we need to make an effort to put more ‘puzzle pieces’ together to start seeing a more complete picture. This includes analysing in much more detail how the compartmentalisation of pTyr writers and readers, i.e. the architecture of the pTyr signalling machinery, generates highly specific signals binders" ,61,63, eThe authors declare that they have no competing interests.RJ and TK drafted the first version of the manuscript. All authors edited and approved the final version.


This paper studies the DNS cache effects that occur on query distribution at the CN top-level domain (TLD) server. We first filter out the malformed DNS queries to purify the log data pollution according to six categories. A model for DNS resolution, more specifically DNS caching, is presented. We demonstrate the presence and magnitude of DNS cache effects and the cache sharing effects on the request distribution through analytic model and simulation. CN TLD log data results are provided and analyzed based on the cache model. The approximate TTL distribution for domain name is inferred quantificationally. The domain name system (DNS) is one of the most critical infrastructures of today's Internet , translaThe scalability of DNS relies heavily on the aggressive use of caching, which helps to cut client-perceived lookup delays and limit the wide-area network bandwidth consumption. For the top-level domain servers at the top of the DNS name space hierarchy, caching is especially advantageous for lowering the otherwise overwhelmingly high load. Compared with noncaching scenarios, DNS cache servers  virtually play as proxies between the Internet users and the answering authoritative servers. Only the DNS requests from users missing the cache eventually arrive at the authoritative servers, while those hitting the cache should never be seen by the authoritative servers. Due to this kind of blinding effects, the original user query behaviors are hardly exposed to the authoritative servers and this tends to complicate or obscure the analysis of query behavior and the retrieval of query patterns.It is commonly agreed that web traffic follows the Zipf-like distribution , which lThere is hardly any previous work on the analytical explanation of DNS query distribution on authoritative servers, especially top-level domain servers. The most relevant work involves measurement studies on the DNS traffic at root servers , analysiThis paper is organized as follows. In The DNS name space is hierarchical, where the upper level domain is administratively responsible for its lower level domains. Different domain levels play different roles, in both technique and policy. Top-level domain (TLD) is the highest level domain category in DNS. TLDs that are delegated for noncountry specific usage are called generic top-level domains (gTLDs). For example,  .com,  .net, and  .org are among the currently most popular gTLDs. Two-letter country code domains reserved for every country or region in the world is another type of TLDs, namely, country code top level domains (ccTLDs). Examples of ccTLDs include  .uk (United Kingdom), .us (United States), .ca (Canada), and  .cn (China). China Internet Network Information Center (CNNIC) takes the responsibility of China's domain name registry to operate and administrate “.CN” country code top-level domain (ccTLD) and Chinese domain Name (CDN) system.There are currently 6 CN TLD name servers specified, with names in the form letter  .dns.cn, where letter ranges from a toe, pulsing ns.cernet.net. However, 4 of these 6 names have physical servers in multiple geographically distributed locations, using anycast address announcements to provide decentralized service. There are 15 anycast instances in 8 sites for CN TLD.BIND is by far the most popular implementation of DNS today . As a meIn this paper, we record query log by BIND at each CN TLD instance for approximate 24 hours from 22 March 2011, 22:54:44.474, to 23 March 2011, 23:06:39.727, and then accumulate them as a one-day's log. The total log file is over 200 GB and contains 1,047,586,432 queries.Previous study observed a variety of problems with DNS queries , 6, 7, aUndelegated TLD. This category refers to a top-level domain that is not officially delegated by ICANN. These invalid TLDs are found in the queries at CN TLD servers. Local misconfiguration is the main source of such TLDs, which is manifested by some local names like localhost, local, and corp. Other undelegated TLDs look like filename extensions, such as txt, html, and c.Recursion Desired. There is a bit allocated in the DNS message, named recursion desired or RD [ed or RD . It specPrivate Source IP. Some IP addresses specified in RFC 1918 are reserved for private usages and should not be seen in the public network [Illegal Characters in Query Name. The only valid characters in DNS names are the letters A–Z, numbers 0–9, and hyphen per the DNS protocol specifications [ications . While sications .Non-CN TLD. As mentioned above, CN TLD server is only responsible for CN domain name resolution. Therefore, resolvers should not request it for other TLDs. But actually we observed a nonignorable amount of non-CN DNS queries in the log. These non-CN TLDs include COM, NET, and ARPA. This would bring a waste of resources, as CN TLD server could do nothing but give a NXDOMAIN reply.Unused Query Class. The DNS specifications only define five query classes: IN (1), CHAOS (3), HS (4), NONE (254), and ANY (255) [NY (255) . As no oAccording to the categories of malformed queries defined in x-axis and the number of queries per source on the y-axis. The number 1 ranking source alone sent 12,287,279 queries during the 24-hour observation period.In this section, we present an overview of DNS, describe the resolution of a DNS system, and then model the DNS system as a whole for further analysis.The major task carried out by a name server is to respond to queries (questions) from a local or remote resolver or another name server acting on behalf of a resolver. A name server may have zone files that define it to be authoritative for some (if any) domains and slaves for others and may be configured to provide caching, forwarding, or other behaviors for other domains or users.There are two types of queries defined for DNS systems.Recursive Queries. The answer to a recursive query is the final answer to the question. This means that the receiving name server will complete all the relevant queries necessary for the final answer. This may involve contacting some authoritative name servers to obtain the intermediate referral information. Due to the security considerations, most authoritative name servers are not required to support recursive queries.Iterative Queries. To answer an iterative query, the name server is not necessarily authoritative for the requested name. Only if the name server has any information useful for the final answer, it will return it. This information is called the referral information. Compared with recursive queries, the receiving name server is not required to make additional requests and return additional information if it has no useful information in itself. All name servers should at least support iterative queries.All DNS agents fall into one of two classes: stub and recursive. Stub resolvers are the simplest resolvers with the minimum functions. They rely on recursive resolvers that understand name server referrals. The implementations of recursive resolvers include the Berkeley Internet Domain Name (BIND)  server aIn this paper, we assume that only caching, recursive name servers talk to the authoritative servers or all queries come from caching servers. This assumption is logical for two reasons. Firstly, an overwhelming proportion of DNS queries do come from their local recursive name servers for these servers are the common Internet infrastructures in most areas. Secondly, most noncaching queries appear to come from stub resolvers and their RD bits are usually set by the stub resolvers. As discussed in According to the assumption of To simplify the analysis of DNS query distribution, we assume that all clients share the common interests. So they produce queries for different CN domain names with the same proportion. At the same time, we allow that clients may have varied DNS traffic. The local caching name server talks to the CN TLD server for clients and thus acts as an agent as to the CN TLD server. Considering the different quantity of clients requesting local caching name servers, the clients' queries accumulated by the servers may also differ. What the CN TLD server sees is queries from a number of local DNS servers with hidden clients' requests.In this section, we study an analytic model of DNS caching and formulate the hit and miss rate as well as the request rate for the CN TLD server. We also demonstrate the cache and cache sharing effects on query distribution.A simple analytic model for the cache query process is presented in . Let thet = 0. Subsequently, three queries come successively, at times S1, S2, and S3. These three queries are cache hits because they occur before the TTL expires at time t = T. The afterward fourth query at time S4 occurs after t = T, and therefore it is a cache miss. According to the renewal process analysis, let N(t) be equal to the number of queries for the given data item in the interval  = 3, and the number of cache hits per miss is 3.H(T) represent the limiting hit rate if the TTL is T. Then, the limiting miss rate, denoted by M(T), can be obtained. So, we haveLet λ is defined to be the expected number of requests in a unit duration. So, we can have closed-form expressions for the hit rate and miss rate as a function of the TTL and λ. Assume there is one client requesting its local name server; then the hit and miss rate can be expressed asWe assume poisson arrivals for the distribution of inter-request times. The request rate n). Let the clients' queries arrive at the local server independently and their request rates be the same as λ. Then according to the property of poisson process, the aggregated query arrival rates at the local server are nλ. Then, the hit rate and miss rate can be expressed asUsually, a local name server has more than one client  and its rank r . This relationship is of the form:c is a constant and β, known as the exponent or scaling parameter, is often close to 1.One common characteristic in web workloads is the highly uneven distribution of references to files. In many cases, Zipf's law has been applied to model file popularity . Zipf's β ≈ 0.91. Here, we analytically study what are the local DNS server's caching effects on the Zipf's-like distribution of popularity of domain names.Previous studies show that the popularity of domain names also follows a Zipf-like distribution with b is a constant. We let b, the request rate for the most popular domain name, be 1,000 request/hour and the TTLs of all domain name resource records are all 1 hour. Let β = 1, 1 ≤ r ≤ 2,000,000. The request rate for a domain name should be in direct proportion to its popularity; that is,The cache DNS name server has the aggregation effect on the original queries from its local clients. A request is not necessarily hit by the one cached from the same clients and it may be alternatively matched by the other clients' previous missed domain names. So, more local clients in a cache name server means higher cache hit rate and therefore more flattening effect on the domain name distribution.We vary the number of clients requesting a local name server from 1, 10 to 100 while holding the other parameters as shown in To deduce how the popularity of names varies in our CN TLD server log file, we plot query counts as a function of the popularity rank of a name. This graph, on a log-log scale, is shown in β. As a function of β, the likelihood function can be expressed as follows:In β ≈ 0.98, which indicates that the popularity of domain names follows a Zipf's distribution. The red line in β = 0.98.A typical DNS caching name server, usually the recursive one, has more than one hundred clients. Some large ISPs and organizations may even have thousands of clients requesting DNS cache service. So, the average conservative estimation of caching group size is 100. However, note that the distribution pattern differs markedly in Figures a is a constant and γ is very close to β.Given that the request rate for a domain name follows a Zipf's-like distribution as  and the According to , 4), an, an4), arTLD display as Zipf's-like distribution, the denominator of the right side of (a is a constant. Therefore, the TTL distribution of domain name is (γ very close to β.In order to make  side of  must be  name is  with γ vThe theorem tells how TTL values are distributed given how frequently each name is accessed. It is plausible that the more popular names have shorter TTL values, and then the corresponding effect on caching would be even more pronounced. Previous study shows that it is indeed the case that shorter-TTL names are more frequently accessed , which iZipf-like distribution mainly comes from the request concentration. The load of CN TLD server is dominated by a small number of hot domain names. As this concentration is caused by the TTL's exponential-like distribution, we can enhance the cache effect by increasing the TLL value for the most popular domain names. However, this solution blocks DNS-based load balancing. Therefore, we suggest that DNS-based load balancing is not recommended for lowering the load of CN TLD DNS servers. Instead, there are alternative ways to perform load balancing by DNS. One possible way is to release the task of load balancing for the local DNS. The authoritative server answers the query with multiple name servers. Local DNS servers can use some algorithm to spread the load and therefore ensure the desired result. For instance, it can provide each name server once in a round-robin sequence weighted by some factors.Our study shows that DNS query for the CN TLD server follows Zipf-like distribution. Through the analytic model and analysis of cache effects, we have inferred the TTL's exponential-like distribution. This distribution lays out a theoretical foundation to improve current DNS systems for lowering the load of DNS CN TLD servers.


Pseudomonas putida KT2440 that we have called PepA encodes a two-domain ANP. The alignment of these domains with those of PepA homologues revealed a variable number of insertions with the consensus G-x-D-G-x-x-[GN]-[TN]-x-D-D. This motif has also been detected in the structure of pseudopilin (pdb 3G20), where it was found to be involved in Ca2+ coordination although a sequence analysis did not reveal the presence of any known calcium binding motifs in this protein. Isothermal titration calorimetry revealed that a peptide containing this consensus motif bound specifically calcium ions with affinities ranging between 33–79 µM depending on the pH. Microcalorimetric titrations of the purified N-terminal ANP-like domain of PepA revealed Ca2+ binding with a DK of 12 µM and stoichiometry of 1.25 calcium ions per protein monomer. This domain exhibited peroxidase activity after its reconstitution with heme. These data led to the definition of a novel calcium binding motif that we have termed PERCAL and which was abundantly present in animal peroxidase-like domains of bacterial proteins. Bacterial heme peroxidases thus possess two different types of calcium binding motifs, namely PERCAL and the related hemolysin type calcium binding motif, with the latter being located outside the catalytic domains and in their C-terminal end. A phylogenetic tree of ANP-like catalytic domains of bacterial proteins with PERCAL motifs, including single domain peroxidases, was divided into two major clusters, representing domains with and without PERCAL motif containing insertions. We have verified that the recently reported classification of bacterial heme peroxidases in two families (cd09819 and cd09821) is unrelated to these insertions. Sequences matching PERCAL were detected in all kingdoms of life.Proteins of the animal heme peroxidase (ANP) superfamily differ greatly in size since they have either one or two catalytic domains that match profile PS50292. The orf PP_2561 of  These include hemolysin-type calcium-binding (HTCaB) region Bacterial as well as eukaryotic proteins have evolved to recognize calcium ions by a number of structural motifs which can be identified by specific consensus sequences. In eukaryotic cells CaPseudomonas aeruginosa and the nodulation protein NodO. The alkaline protease adopts a beta-roll structure and calcium ions are bound in the loop regions connecting the strands of the roll. Calcium coordination is primarily achieved by interaction with aspartate side chains and glycin oxo-groups HTCaB containing proteins are extracellular, exported by type I secretion systems Pseudomonas putida KT2440 is sustained by two large extracellular bacterial adhesins, termed LapA and LapF Multicellular behavior of the beneficial bacterium animal heme peroxidases (ANHEMP) and related proteins. The mammalian goat lactoperoxidase bacterial heme peroxidases (BACHEMP) have been defined within this superfamily Pseudomonas putida GB-1 did not compromise Mn (II) oxidation in this strain P. putida. Even though the biological relevance of the HTCaB sites present in PP_2561 is unknown, it has been suggested that it may be related to the Ca2+-mediated modulation of the Mn (II) oxidation activity as observed for a related ANHEMP in AurantimonasIn spite of its designation, proteins of the animal peroxidase_like superfamily are not limited to metazoans and are also found in fungi or plants. This superfamily includes Pseudomonasextracellular peroxidase) and those of its homologues in other species. We were able to identify multiple short inserts of conserved sequence within these catalytic domains. Microcalorimetric titration of a peptide containing this inserts consensus sequence and of the purified N-terminal ANP-like domain of PepA revealed that these inserts bind specifically Ca2+ ions. Since this consensus did not match any of the previously defined sequences of calcium binding motifs . Sequence database searches strongly suggested that the PERCAL motif is abundantly present in pro- and eukaryotes.In this work we have examined in detail the sequence of the ANP-like domains of the protein encoded by PP_2561, renamed here as PepA , which includes PeroxiBase, PROSITE and Pfam databases, categorized this protein as a member of the animal heme peroxidase superfamily. To retrieve sequences of bacterial members of this superfamily, UniProtKB  was scanned using the PROSITE profile PS50292, which defines the animal heme peroxidase superfamily, and by applying a taxonomic filter for bacteria. One hundred and twelve sequences containing 125 hits in total were retrieved. Eight of these hits in seven sequences were excluded from further analysis due to an unusually short size of less than 200 amino acids. Interestingly twelve sequences, including three from P. putida  and nine from different α-proteobacteria, presented two animal peroxidase-like (ANP-like) domains within the same protein. Since these 12 protein sequences differed widely in length (between 2650 and 3619 amino acids), we decided to compare their 24 catalytic domains (matching profile PS50292) instead of the complete protein sequences. A sequence alignment of these ANP-like domains showed for some sequences multiple insertions -G-T/N-A-D-D  at MyHits  and the recruitment of two unrelated ANP-like domains in a single protein (the rest).The 23 full-length protein sequences that contain at least one G-x-D-G-x(5)-D-D motif harbor in total 35 ANP-like domains. A significant number of ANP-like domains contained one of these motifs inserted close to the C-terminal end  and the  strains . This suThe sequences of the protein data bank (pdb) were searched with the motif G-x-D-G-x(5)-D-D. At the time of the search there were 71138 entries in the pdb. In total 6 protein structures were identified which contain this sequence motif. These structures were a lignin peroxidase (pdb 1B80), an acetate kinase (pdb 1G99), an amine dehydrogenase (pdb 1JJU), a domain of unknown function (pdb 2I6E) and two different pseudopilin structures (pdb 1T92 and 3G20). GPDGVPNTEDD (amino acids of the motif are in bold). As shown in In the pseudopilin structure free values of 0. 21–0.22 %. These parameters minimize the probability of a misinterpretation of the final electron density maps. In addition there is functional evidence for a role of calcium in the activity of both proteins. Korotkov and colleagues Structural data strongly suggest that calcium is bound at the consensus motifs present in the lignin peroxidase (pdb ID 1B80) and the pseudopilin (pdb ID 3G20) structures. Both structures Based on the above observation, we hypothesized that the sequence motif identified might correspond to another, yet unidentified binding motif for calcium ions. To verify this hypothesis experimentally and to determine precisely the ligand specificity of this motif we designed a peptide for subsequent microcalorimetric titrations with different cations. Due to the flexible nature of the peptide main chain, peptides can adopt many different conformations. However, the inspection of the pseudopilin structure reveals that the amino acids of the motif need to be present in a defined loop structure in order to establish the contacts detailed in 2+ binding was replaced by the consensus sequence derived from the inserts within the ANP-like domains of 24 bacterial proteins  and the last 3 amino acids (IGN) correspond to the flanking beta strands in the pseudopilin structure. These sections are colored in blue in proteins . This co2+. Initially polybuffer (pH 6) was titrated with 5 mM CaCl2 to assess the dilution heat effects. Peaks resulting from this titration were small and uniform (not shown), indicative of weak dilution heats. Subsequently 50 µM peptide were titrated with CaCl2. Significant exothermic heat changes were observed which diminished as the titration proceeded . An association constant of 30600±10000 M−1 was determined which corresponds to a dissociation constant KD of 33±11 µM.Isothermal titration calorimetry (ITC) roceeded , upper t2+ cation or the Cl− anion with the peptide. To verify this issue the titration was repeated using 5 mM NaCl as ligand. However, no binding heats were measured (not shown), which indicates, firstly, that the above binding parameters represent the interaction of the Ca2+ cation with the peptide and, secondly, that the Na+ cation does not bind to the peptide. Subsequently the interaction of the peptide with Ca2+ was studied at other pH values  and a change in the mode of binding, since binding was driven by favorable enthalpy and entropy changes. However, data illustrate that this interaction also occurs in the presence of physiologic concentrations of monovalent cations like Na+.The next experiment was aimed at verifying whether or to what degree the presence of monovalent cations interferes with the Ca mM NaCl , Table 42+, Cu2+, Fe2+, Cr2+ and Mn2+. For none of these metal ions was a binding heat detected. This finding is exemplified by the titration of the BACHEMP-Cons peptide with MgCl2 as shown in A central question in the study of the motif described concerns the specificity of ligand recognition. To evaluate the specificity of binding, microcalorimetric titrations of the peptide in polybuffer, pH 6.0, supplemented with 100 mM NaCl were conducted using chloride salts of Mg2+ binds with a physiologically relevant affinity to BACHEMP-Cons, secondly, that this binding is specific and, thirdly, that binding also occurs in the presence of physiological concentrations of monovalent anions and cations.In summary, the microcalorimetric titrations reveal, firstly, that Ca2+ with the protein encoded by PP_2561, the DNA fragment coding for the N-terminal ANP-like domain was cloned in the expression vector pET28 and the resulting his-tagged fusion protein was expressed in E. coli. No hemolysin type calcium binding sites are present in this domain , the protein encoded by PP_2561 was renamed as PepA (Pseudomonasextracellular peroxidase).To study the interaction of Cas domain . Spectras domain . From th2+ to the purified domain of PepA was analyzed using ITC. To avoid that the protein sample contains bound Ca2+, the protein was purified under denaturing conditions and then refolded . The microcalorimetric titration of the resulting protein with CaCl2 is shown in Subsequently, the binding of Ca2+ binding motif and derivatives thereof (2+ binding motif named PERCAL (of peroxidase and Ca binding). The database was also scanned against motif 3, which presents glutamic acid in the ninth position and covers 82% of the ANP-like intradomain insertions plus the sequence found in 122 pseudopilin sequences including that of the structure with pdb 3G20. In this case the ratio to the random occurrence increased to 26, as a consequence of the elevated value obtained for bacteria (41) though the ratio in eukaryotes was lower than 1. The same search was performed with motif 4, which presents aspartic acid in this ninth position since it was observed in 15 pseudopilin sequences. Interestingly the ratio of hits observed with motif 4 in comparison to the estimated random occurrence increased to 34 in eukaryotes. Motif 5 is the result of restricting the ninth residue to A, T, D or E in motif 2. The ratio calculated for this motif, which includes more than 80% of insertions in peroxidases and the sequences found in pseudopilins, was in all kingdoms of life significantly superior to 1  and in other homologous proteins from diverse bacterial taxons  in soil varies considerably but even for a soil containing a low ECC of 20 mmol/L Previous work in our laboratory revealed the importance of the bacterial determinant PP_2561 in the beneficial interaction that the bacteria l taxons . The seql taxons , which a2+ binding to the BACHEMP-Cons peptide also occurred, although with reduced affinity, in the presence of physiologic concentrations of monovalent ions. Most importantly, no binding of other bivalent cations was observed, which demonstrates that the PERCAL motif is specific for calcium. Calcium binding occurred also to the recombinant purified N-terminal peroxidase domain of PepA with an affinity superior to the values observed for the peptide. Similarly to our results, an increase in Ca+2 binding affinity to the protein with respect to different peptides have also been observed 2+ saturated. It is tempting to speculate that, high affinity PERCAL sites are already calcium saturated in the samples used, which consequently implies that ITC permitted only the observation of the lower affinity calcium binding events.Ca2+ binding motif here identified is particularly frequent in this kingdom  in ANP-like domains of bacterial proteins PERCAL habitually presents A/T in this position (86%); 2) in the general secretion pathway protein G (or pseudopilin) The consensus proposed for PERCAL corresponds to motif 2 in  kingdom . A simil2+ by the PERCAL motif. There are five direct interactions of which three involve main chain oxygen atoms . Of these, only amino acid at position 8 shows a high degree of conservation. Most interestingly, there are only two side-chain interactions (aspartates at positions 3 and 11). This may suggest that the remaining conserved amino acids of the motif have a structural role and may contribute to the formation of the loop structure. Frequently, ligand recognition is mediated by an indirect interaction with protein bound water. The pseudopilin structure has been solved at a resolution of 1.78 Å and contains 265 water molecules 2+ recognition. Striking parallels exist to other calcium binding motifs, as exemplified by the HTCaB region of the alkaline protease 2+ establishes only two interactions with amino acid side chains. As in the case of PERCAL these side chains are aspartic acid residues. In the structure of the alkaline protease the remaining 4 interactions involve main chain oxygens, of which three are from conserved glycine residues.The analysis of the pseudopilin structure provides insight into the molecular basis of the recognition of CaKM for H2O2 of the His-tagged N-terminal domain (PepA-Nter) after its reconstitution with heme was estimated to be 13±1 mM. KM values higher than 10 mM have been reported previously for the oxidation of 4-aminoantipyrine (same electron donor than the one used in this study) by H2O2 in the presence of cytochrome c kcat values for myoglobin and horseradish perodixase vary as much as three orders of magnitude  We have investigated the catalytic activity of the N-terminal ANP-like domains of PepA, which contains 5 PERCAL insertions. The 2+ binding modules PepA activity, the peroxidase assays were conducted using heme-reconstituted PepA-Nter differentially saturated with Ca2+. The catalytic properties of these differentially Ca2+ saturated proteins did not reveal any major variations, but considering the low magnitude of response a more subtle Ca-mediated modulation of peroxidase activity cannot be excluded. Hence it is plausible that the protein was partially saturated with its ligand previous to the incubation with Ca2+, given the difficulties to generate protein entirely devoid of calcium.The binding of calcium to pseudopilin was found to be essential for protein activity 2+-mediated cell-cell adhesion, adhesion to biotic surfaces and in addition if its mechanism of action in the interkingdom communication is related with oxidative stress resistance.The analysis of the consensus sequences of known calcium binding motifs  shows thP. putida homologues and suggests that PepA is the result of a gene duplication event followed by divergent evolution. However, for the PepA homologues with two ANP-like domains from other species, their N- and C- terminal domains did not cluster together and remarkably in all cases only the N-terminal domains presented PERCAL motives. Therefore, two types of ANP-like domains can be distinguished: those with and those without PERCAL motif. This hence raises the question as to the functional differences between both types of domain. In this context the study of a PepA homologue from AurantimonasP. putida GB1, which has a PepA homologue with PERCAL insertions in both ANP-like domains, lacks the Mn (II) oxidizing activity Aurantimonas PepA homologue may be the cause of the catalytic activity. However it should be pointed out that in Erythrobacter sp. strain SD-21 the Mn-oxidizing protein contains only one ANP-like domain 2+-mediated modulation of the Mn (II) oxidation activity, as observed for the PepA homologue in Aurantimonas, besides the proposition that points to the HTCaB sites The two ANP-like domains of PepA share around 70 % of sequence identity. Thus certain similarities in their catalytic properties are expected. Due to this elevated degree of sequence identity these domains clustered together in a phylogenetic tree of ANP-like domains of bacterial heme peroxidases containing PERCAL . This waThe recent work of Marchler-Bauer and coworkers rd 2010, 11397958 entries) using the PROSITE profile PS50292 to identify proteins containing the animal peroxidase-like (ANP-like) domain. A taxonomy filter for bacteria was used. Segments recognized by profile PS50292, corresponding to the ANP-like domains, were extracted manually. This was followed by a search in a later release of UniProtKB/TrEMBL  using the pattern G-x-D-G-x(5)-D-D and a variety of more stringent derivatives, to determine the ratio of hits in the database compared to the estimated random occurrence. This analysis was initially performed without taxonomy filter and then repeated with taxonomy filters for Bacteria, Eukaryota, Viruses and Archaea.Initial searches were performed against UniProtKB/TrEMBL  and FPxHinR rev (AAGCTTCAGTTGATCTCGATGCCGC-3′5′-). The DNA fragment was cloned into pGEM-T (Promega) and the resulting plasmid pSSH14 was introduced into E. coli DH5α. The restriction sites incorporated by the PCR primers (NheI-HindIII) were used for subsequent digestion with the same enzymes followed by cloning of the insert into pET28a (Novagen). The resulting plasmid was called pSSH15 and transformed in E. coli BL21 (DE3) (Stratagene). The insert and flanking region of this plasmid were verified by DNA sequencing.A 2625 bp-fragment encoding the N-terminal 875 amino acids of PepA (PepA-Nter) was obtained via PCR from cosmid pCSSH1 that contains the wild type E. coli BL21 (DE3) containing pSSH15 was grown in LB culture medium supplied with kanamycin (25 µg/mL) at 30° and 200 rpm. The induction of the protein expression took place by addition of 0.1 mM IPTG at an OD600 of 0.5. Cultures were maintained overnight at the same temperature and harvested by centrifugation. Since SDS-PAGE gels showed that the majority of recombinant protein was present in the insoluble fraction, protein was purified from inclusion bodies. Cells from 50 mL cultures were resuspended in 40 ml of buffer A , disrupted by French Press treatment and centrifugated for 1 h at 20000 × g.−1 cm−1.The supernatant was filtered using a 0.45 µm syringe filter (Millipore), loaded onto a HisTrapHP column  previously equilibrated in buffer A and eluted with a 10 mM to 45 mM imidazol gradient in buffer A. The sample was then diluted to a final concentration of 0.1 mg/ml. Protein renaturation was performed by dialysis against 10 mM Tris-HCl, 50 mM NaCl, 2 M urea, 0.1 mM EGTA, 10% glycerol, pH 7.5 followed by multiple cycles of dilution with buffer 10 mM Tris-HCl, 50 mM NaCl, 10% glycerol, pH 7.5 and subsequent concentration using Amicon Ultra-4 30 K centrifugal filter (Millipore). The protein concentration was determined spectrophotometrically using an extinction coefficient of 59820 M5 M−1 cm−1Spectral analysis of purified PepA-Nter revealed an absence of bound heme. To reconstitute the protein with heme, PepA-Nter at 11 µM was incubated in reconstitution buffer  containing 80 µM hemin (Fluka) at 5°C for 30 min. The excess of heme was removed by gel filtration using PD-10 columns , previously equilibrated with reconstitution buffer. Fractions were collected and analyzed spectrophotometrically at 280 (indicative for protein) and 410 nm (indicative for heme). The molar ratio of heme to PepA-Nterm was calculated using a molar extinction coefficient of 1×10kcat and KM the assays were carried out using hydrogen peroxide concentrations in the range of 0.25–45 mM. Catalytic constants were calculated using the extinction coefficient for the reaction product quinoneimine, which is 6.58 mM−1 cm−1The procedure described by Maehly and Chance Measurements were done on a VP-microcalorimeter  at 25°C.2 solution (5 mM) was placed into the injector syringe. A typical experiment involved the injection of 3.2 µl aliquots of ligand into the peptide solution. The study of the pH optimum was performed in polybuffer adjusted to pH 5.0 to 9.0 with HCl. To determine the binding specificity, experiments were repeated in polybuffer, pH 6.0, containing 100 mM NaCl and ligands CaCl2, MgCl2, CuCl2, FeCl2, CrCl2 or MnCl2, each at a concentration of 5 mM.Calcium binding to the BACHEMP-Cons peptide: The peptide, synthesized by Biomedal S.L. , was dissolved in polybuffer  to a concentration of 50 µM and placed into the ITC sample cell. A freshly prepared CaCl2 solution was prepared using the dialysis buffer and placed into the injector syringe. A typical experiment involved the injection of 6.4 µl aliquots of ligand into the protein solution. Raw data were corrected for dilution effects and concentration was normalized prior to data analysis using the “one binding site” model of the MicroCal version of ORIGIN. During the fitting process KA, ΔH and n were left floating.Calcium binding to the N-terminal ANP-like domain of PepA: The purified protein dialyzed and concentrated as above reported at a final concentration of 11.2 µM in buffer 10 mM Tris-HCl, 50 mM NaCl, 10% glycerol, pH 7.5 was placed into the sample cell. A 1 mM CaClFigure S1Alignment of ANP-like domains of bacterial proteins containing two of these domains. Numbering starts from the amino terminal end of the ANP-like domain according to PS50292. Bold text in boxes corresponds to five insertions found in P. putida sequences matching the consensus G-x-D-x(6)-D-D. Insertions A (2), B (3) and C (5) were firstly identified directly from the alignment whereas insertions 1 and 4 were identified after a second search with the consensus. In the alignment animal heme peroxidases of Fulvimarina pelagi HTCC2506 (Q0G341), Manganese-oxidizing bacterium (strain SI85-9A1) (Q1YMS2), Methylobacterium chloromethanicum CM4 / NCIMB 13688 (B7KW13), M. extorquens DSM 5838 / DM4 (A9W3A5), M. extorquens PA1 (C7CGY0), Pseudomonas putida F1 (A5W572), P. putida GB1 (B0KJL7), P. putida KT2440 (Q88JT6), Rhodopseudomonas palustris BisA53 (Q07SX1), Rhodopseudomonas palustris BisB5 (Q13AU2), Roseobacter sp. MED193 (A3XF15) and Roseovarius sp. TM1035 (A6E280). Letters -A and -B refer to N- and C-terminal ANP-like domains, respectively.(DOCX)Click here for additional data file.Figure S2Alignment of bacterial ANP-like domains with G-x-D-G-x(5)-D-D insertions. Numbering starts from the terminal end of the ANP-like domain according to PS50292. Sequences in the alignment are in Arthrobacter sp. FB24 (A0JUB7), A. chlorophenolicus A6 / ATCC 700700 (B8HDW8), Cyanobium sp. PCC 7001 (B5IMQ8), Erythrobacter sp. SD-21 (A5PER4), Fulvimarina pelagi HTCC2506 (Q0G341), Leptothrix cholodnii ATCC 51168 / LMG 8142 / SP-6 (B1Y442), Manganese-oxidizing bacterium (strain SI85-9A1) (Q1YMS2), Mesorhizobium sp. BNC1 (Q11K84), Methylobacterium chloromethanicum CM4 /NCIMB 13688 , M. extorquens DSM 5838 / DM4 (C7CGY0), M. extorquens PA1 (A9W3A5), Nitrosomonas sp. AL212 (C6MFN2), Pseudomonas putida F1 (A5W572), P. putida GB1 (B0KJL7), P. putida KT2440 (Q88JT6), Rhodopseudomonas palustris BisA53 (Q07SX1), R. palustris BisB5 (Q13AU2), Roseovarius sp. TM1035 (A6E280), Roseobacter sp. AzwK-3b , and Roseobacter sp. MED193 (A3XF15). Letters –A and -B refer to as N- and C-terminal ANP-like domains, respectively. The domain of human myeloperoxidase (P05164) does not present the insertion and is included in this alignment as an out-group member. The alignment was performed with CLUSTAL 2.1. In bold and underlined residues contacting catalytic Ca in human myeloperoxidase.(DOCX)Click here for additional data file.Figure S3Structure of the fragment that contains the G-x-D-G-x(5)-D-D consensus motif in 6 different three dimensional structures (shown in gold). Bound calcium is shown in green. Motif sequence used in the initial search of PDB was G-x-D-G-x(5)-D-D (see the text in the main body of this article). Amino acids that correspond to the consensus defined are shown. pdb 1JJU and pdb 3G20 match PERCAL (G-x-D-G-x-x-[GN]-[TN]-x-D-D) and their motifs are highlighted in yellow. The figure was produced using the program WebLabViewer (http://www.marcsaric.de/index.php/WebLab_Viewer_Lite).(DOCX)Click here for additional data file.Figure S4Preparation of recombinant PepA-Nter. I, SDS-PAGE gel of the purified His-tagged Nter-PepA and II) spectrum of the heme-reconstituted PepA N-terminal domain. The purified N-terminal domain was incubated with hemin and exceeding heme was removed with a Sephadex G-25 column equilibrated with 10 mM Tris-HCl, 50 mM NaCl, 10% DMSO and 10% glycerol buffer (pH = 7.5). Displayed is the optical absorption spectrum of the heme reconstituted protein where (A) is the absorbance exhibited by the protein at 280 nm and (B) is the absorbance of bound heme at 410 nm.(DOCX)Click here for additional data file.Table S1Detailed sequence analysis of the insertions interrupting the ANP-like domains of bacterial proteins.(XLSX)Click here for additional data file.Table S2Random occurrence and hits number of motif G-x-D-G-x(5)-D-D and some of its variants in the databank.(XLSX)Click here for additional data file.Text S1Consensus sequences of the insertions interrupting six P. putida ANP-like domains.(DOCX)Click here for additional data file.Text S2Sequences matching X-X-X-G-X-D-X(6)-[DE]-[DE]-X-X-X used to generate the consensus of .(DOC)Click here for additional data file.Text S3Sequences of the ANP-like domains of bacterial proteins used in this work according to Prosite profile PS50292.(DOCX)Click here for additional data file.


Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases. Based on this assumption, we propose a Bayesian regression approach named "ab initio inference of domain-disease associations and gene-disease associations, and we illustrate the strong agreement between our inferences and the evidences from genome-wide association studies for four common diseases . Finally, we provide a pre-calculated genome-wide landscape of associations between 5,490 protein domains and 5,080 human diseases and offer free access to this resource.Using a compiled dataset containing 1,614 associations between 671 domains and 1,145 disease phenotypes, we demonstrate the effectiveness of the proposed approach through three large-scale leave-one-out cross-validation experiments , and we do so in terms of three criteria . We further show that the proposed approach is robust to the parameters involved and the underlying domain-domain interaction network through a series of permutation tests. Once having assessed the validity of this approach, we show the possibility of ab initio inference of domain-disease associations shows strong agreement with the evidence provided by genome-wide association studies. The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.The proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved. The  Over the past few decades, remarkable success has been achieved for such traditional gene-mapping approaches as family-based linkage analysis ,2 and poIt has been shown that deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that are responsible for a specific disease of interest may change structures of some protein domains, affect functions of corresponding proteins, and further result in the disease under investigation. Therefore, existing associations between domains and diseases can be constructed by bridging protein domains that contain known deleterious nsSNPs and human diseases with which the nsSNPs are associated . FurtherdomainRBF (domain Ranking with Bayes Factor), to calculate Bayes factors that quantify the strength of associations between corresponding domain proximity profiles and phenotype similarity profiles.Recent studies on the modular nature of human genetic diseases have shown that diseases share common clinical characteristics are often caused by functionally related genes ,27. Withab initio way and illustrate the strong agreement of the inference results with evidence of genome-wide association studies for four common human diseases, including type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer. We further demonstrate the possibility of inferring gene-disease associations from domain-disease associations. Finally, we calculate a genome-wide landscape of associations between 5,490 domains and 5,080 human diseases using all known domain-disease associations, and we provide a freely accessible website for this resource.We compile a set of known domain-disease associations using the Pfam database  and annoWe ground the inference of domains that are associated with human inherited diseases on a set of known domain-disease associations that are compiled from the Pfam database  and annop and the pre-calculated pair-wise similarity scores between phenotypes, we extract scores between the query phenotype and all other phenotypes that have at least one associated domain and obtain a phenotype similarity profile for the query phenotype. On the other hand, for a query domain d in a set of candidate domains, we resort to the domain-domain interaction network to calculate proximity scores of the query domain to all domains that are known to be associated with some phenotypes and further calculate a domain proximity profile. With these two profiles, we propose a Bayesian regression approach called domainRBF (domain Ranking with Bayes Factor) to calculate a Bayes factor that quantifies the strength of association between the query domain and the query phenotype, using the phenotype similarity profile as the response variable and the domain proximity profile as the predictor variable. Finally, we rank candidate domains according to their corresponding Bayes factors and obtain a rank list of the candidates.Based on the assumption that phenotypically similar diseases are caused by functionally related domains, we propose a linear regression framework to model the relationship between a domain proximity profile and a phenotype similarity profile, and we resort to a Bayesian approach to solve the linear regression model. As shown in Figure A domain is defined as associated with a disease if the domain contains at least one nonsynonymous single nucleotide polymorphism (nsSNP) associated with the disease . TherefoKnown associations between nsSNPs and diseases are obtained from annotations of nsSNPs in the UniProt database ,35, in wRelationships between human proteins and domains are obtained from the Pfam database , which pUsing the above data sources and having defined a domain as associated with a disease if the domain contains at least one nsSNP associated with the disease, we are able to establish 1,614 associations between 671 domains and 1,145 diseases.Our inference of domain-disease associations is based on domain-domain interaction networks extracted from the DOMINE  and InteThe latest version of DOMINE (released in February 2008) contains a total of 20,513 domain-domain interactions, out of which 4,349 (gold-standard positives) are inferred from PDB entries  contains a total of 148,938 domain-domain interactions, out of which 7,718 are inferred from PDB entries , 143,820In our work, we use two domain-domain interaction networks extracted from these data. First, we discard singletons in the PDB part of the DOMINE database  and obtaet al. [et al. use the anatomy (A) and the disease (C) sections of the medical subject headings vocabulary (MeSH) to extract terms from the OMIM database, thus providing a standard way of presenting the OMIM records as corresponding phenotype feature vectors. As a result, each disease phenotype is characterized by a vector of standardized and weighted phenotypic feature terms mapped from corresponding OMIM records in the full text (TX) and clinical synopsis (CS) fields. Then, for each pair of disease phenotypes, a similarity score is calculated by the cosine of their feature vector angle. The reliability of the phenotype similarity score has been tested [The phenotype similarity network of human diseases is a fully connected network obtained from an earlier work of van Driel et al. , in whicn tested , showingypp' to denote the similarity score between a query disease phenotype p and another disease phenotype p'. We further define the phenotype similarity profile for disease phenotype p as p and all m disease phenotypes p1, p2, ..., pm that have at least one associated domain.Given the phenotype similarity network, we use n nodes, we calculate the proximity between two domains using two measures: (1) shortest path with Gaussian kernel (SG) and (2) diffusion kernel (DK). The shortest path proximity between two domains u and v, SP, is defined as the length of the shortest path between the two domains. Using the Gaussian kernel, the proximity distance measure SG  is obtained as SG = exp{-β)2}, where β is a free parameter. The diffusion kernel for the network is defined as K = (kuv)n×n = e-γL, where 0 < γ < 1 is a free parameter that controls the magnitude of diffusion. The matrix L = D - A is the Laplacian of the network, where D is a diagonal matrix containing node degrees, and A is the adjacency matrix of the domain-domain interaction network. With the diffusion kernel K = (kuv)n×n, we define the diffusion proximity of two domains u and v as DK = kuv, i.e., the corresponding element in the diffusion kernel. Then, let xdd' denote the proximity between domains d and d' in the domain-domain interaction network, and let D(p) denote the set of domains known to be associated with a phenotype p. We define the proximity between domain d to disease phenotype p as the summation of proximity scores between domain d and all domains known to be associated with disease phenotype p, i.e., xdp = ∑d'∈D(p) xdd'. We further define the domain proximity profile for domain d as On the other hand, given a domain-domain interaction network of p and a query domain d, we explain the phenotype similarity profile yp using domain proximity profile xd via a linear regression modelThen, given a query disease phenotype y = yp is the response vector, X =  the design matrix, β = T the coefficient vector, and ε = T the residual vector. Note that the first column of the design matrix being 1s for the purpose of incorporating the intercept. We propose to solve this linear regression model using a Bayesian approach. We choose to take a Bayesian approach because it provides a natural way to consider the uncertainty in estimated parameters, and it provides Bayes factor, a measure of the strength of evidence for an association, which is defined as the ratio of marginal likelihoods for y conditional on X under the alternative and the null hypothesis, respectively, as described below.where y conditional on X is subject to a normal distribution, asFor the alternative model, we assume that 2. We set conjugate prior distributions for β and σ2, aswith residuals independent and identically distributed, following normal density with mean 0 and variance σandμ0 = T is composed of prior means, and σ2Σ0 prior variances with Σ0 = diag being a diagonal matrix. The joint distribution of all random quantities y, β, and σ2 is then given aswhere β and σ2, we obtain the marginal likelihood of y given X asIntegrating out nn = n + n0 and nn σn2 = n0σ02 + yTy + μ0T Σ0-1μ0-μnT Σn-1μn with Σn = (XT X + Σ0-1)-1 and μn = Σn (XT y + Σ0-1μ0).where y is independent of X, the marginal likelihood of y can be derived in a similar way, asOn the other hand, for the null model, where where p1(y|X) and p0(y), asThen, the Bayes factor BF is the ratio of μ2 and 0 for both n0 and σ02, and we obtain the limit value of the Bayes factor asFollowing the literature , we takeμ0 = 0 as in the literature [12 = 1 as the default setting in this paper, although the effect of these parameters are also studied.For simplicity, we further set terature , and we yp and xd, we apply an inverse-normal transform to yp to guarantee that the responsive variable is normally distributed. As illustrated in [Note that before the construction of the Bayesian regression relationship between rated in ,46, the ri is the rank of yp, m the length of yp, and Φ the cumulative distribution function of the standard normal distribution.where On the basis of the domain-domain interaction network and known associations between protein domains and disease phenotypes, we proceed to validate how well the proposed approach performs in recovering these known associations. We adopt three large scale leave-one-out cross-validation experiments for this purpose.First, in the validation of random controls, we prioritize domains that are known to be associated with disease phenotypes  against randomly selected control domains. Specifically, in each run of the validation, we select an association between a domain and a disease phenotype, assume that the association is unknown, and prioritize the domain against a set of 99 randomly selected control domains.Second, in the validation of simulated linkage intervals, we prioritize domains that are known to be associated with disease phenotypes  against domains that are located around the seed domains. Specifically, in each run of the validation, we select an association between a domain and a disease phenotype, assume that the association is unknown, and prioritize the domain against a set of control domains that are located within 10 Mbp upstream and downstream of this domain.Third, in the validation of genome-wide scan, we prioritize seed domains against all known domains. Specifically, in each run of the validation, we select an association between a domain and a disease phenotype, assume that the association is unknown, and prioritize the domain against all other domains in the domain-domain interaction network.In each of the above leave-one-out cross-validation experiments, we repeat the validation run for every known association between a domain and a disease phenotype, and we are able to obtain a number of ranking lists. We further normalize the ranks by dividing them by the total number of candidate domains in the rankling list to obtain rank ratios and calculate the values of three criteria to measure the performance of a prioritization method.The first criterion is termed precision. We consider a prediction as successful if the known disease domain is ranked at the top (with rank 1). Then, the proportion of successful predictions among all predictions is defined as the precision. Obviously, a high precision suggests that a method has high prediction power. The second criterion is termed mean rank ratio, which is simply the average of rank ratios for all known disease domains in a cross-validation experiment. This criterion provides a summary of the ranks of all domains that are known to be associated with disease phenotypes, and the smaller the mean rank ratio, the better a method. The third criterion is termed AUC, which is the area under the receiver operating characteristic curve (ROC). Given a list of rank ratios and a predefined threshold, we define the sensitivity as the percentage of disease domains that are ranked above the threshold and the specificity as the percentage of control domains that are ranked below the threshold. By varying the threshold values, we are able to plot a receiver operating characteristic curve, which shows the relationship between sensitivity and 1-specificity. Calculating the area under the ROC curve (AUC), we are able to obtain the AUC score, which provides an overall measure for the performance of the prioritization approach.p-value is smaller than 2.2 × 10-16, indicating that the similarities of disease phenotypes have a strong relationship with the proximities of associated domains.The DomainRBF approach is based on the assumption that similarities of disease phenotypes can be explained by proximities of domains associated with the phenotypes within a domain-domain interaction network via a regression model. In order to validate this assumption, we discard singletons in the PDB part of the DOMINE database  and obtaTo further substantiate this point, we perform a series of permutations towards disease-disease, domain-disease, and domain-domain relationships. First, we break the disease-disease relationship by permuting the phenotype similarity profile. Second, we break the domain-disease relationship by two methods: (1) permuting domain-disease associations and 2) replacing domains in known disease-domain associations with randomly selected domains. Third, we break the domain-domain relationship by permuting connections in the underlying domain-domain interaction network, while keeping node degrees and recalculating the diffusion kernel. For each of the above permutations, we calculate Bayes factors of disease domains and present the results in Figure  replacinp-value of the Wilcoxon signed rank test is smaller than 2.2 × 10-16. We further perform a series of permutation tests and present the results [Additional file We also perform similar studies using the large domain-domain interaction network  that includes the entire DOMINE database  and the Since interactions from the PDB entries have the highest confidence of domain-domain interactions, we first test the validity of our approach on the PDB part of the DOMINEdatabase . We implFor each of the three validation experiments, using either the diffusion kernel or the shortest path with Gaussian kernel, we draw a histogram of rank ratios for the entire 1,066 known associations, as shown in Figure We then assess the performance of the proposed approach using the three criteria  and summarize the results in Table Second, we conjecture from these results that the diffusion kernel measure is slightly better than the shortest path measure with Gaussian kernel, because the mean rank ratios obtained using the diffusion kernel are in general smaller, and the precisions and AUC scores are in general larger, than those obtained using the shortest path with Gaussian kernel. This phenomenon might be explained by the fact that diffusion kernel is a global network-distance measure. As such, the distance between two domains not only depends on the relative location of the candidate domain to all other domains (as the shortest path with Gaussian kernel does), but also relies on the graph structure of the entire network. Thus, for interaction networks with different graph structure, two nodes with the same shortest path distance usually have different diffusion kernel distance, and it is possible that this difference makes the diffusion kernel distance more reasonable and precise in the description of similarities between two domains in the interaction network. This point has also been explicitly illustrated in literature .Third, we conjecture from these results that the domainRBF approach with some proper defined priors can achieve higher performance than the non-Bayesian linear regression method. We compare the performance of the (Bayesian) domainRBF approach with the (non-Bayesian) ordinary linear regression method through the three large-scale leave-one-out cross-validation experiments, and we also list the results in Table The above validation results suggest that the domainRBF approach can successfully prioritize candidate domains and put the domain that is truly associated with the query disease phenotype at the top of the candidates. However, it is still necessary to determine whether the correct prioritization of disease domains is due to the connectivity information that includes in the domain-domain interactions, domain-phenotype associations, and phenotype-phenotype similarities. To accomplish this, we artificially destroy informative interactions in the above three networks and see what performances will turn out. It is expected that both the mean rank ratios and the AUC scores will be around 50%, together with very low precisions. With this understanding, we perform three permutation experiments: 1) shuffling interactions among domains while fixing the node degree (number of direct neighbours) distribution of the entire interaction network, 2) shuffling interactions among domain-phenotype associations while fixing the number of associated domains for each of the phenotypes, and 3) shuffling the phenotype-phenotype similarity while fixing the distribution of phenotype similarities, respectively. Then we repeat the leave-one-out cross-validation experiments using the shuffled networks, which contain no informative interactions among domains, among domain and phenotypes, or among phenotypes, respectively. As shown in Figure We notice that the two compiled domain-domain interaction networks have different properties. For example, the average degree of the smaller network that includes only PDB entries is 2.32, while that of the larger network that includes predicted interactions from both DOMINE and InterDom is 17.77. It is possible that many predicted interactions may actually be noise and thus negatively affect the prioritization of disease domains. Hence, it is necessary to validate the robustness of the proposed approaches to the underlying domain-domain interactions. For this purpose, we implement the same validation process based on the large compiled domain-domain interaction network that is composed of all interactions in the DOMINE database and high-confidence interactions in the InterDom database. Results are presented in Table β in the shortest path measure with Gaussian kernel and the parameter γ in the diffusion kernel are free parameters that need to be pre-determined . In the above cross-validation experiments we set these parameters as 1 and 0.05, respectively, for simplicity. However, it is necessary to show whether the prioritization methods are sensitive to these parameters. For this purpose, we select several values across the range of these parameters, perform the cross-validation experiments, and see how the results change accordingly. We take the prioritization results using the domainRBF approach against random controls , the values in the curve becomes fairly stable. Even so, we find that the peak performance is obtained at β = 3.7 , and the worst performance is obtained at β = 0.1 . From these results, we conclude that the prioritization methods are not sensitive to this free parameter when β is greater than 1. Similarly, we find that the prioritization methods are not sensitive to the free parameter γ when it is smaller than 0.15 (data not shown). The corresponding changes in precision, mean rank ratio, and AUC score are shown in Figure γ = 0.03 , and the worst performance is obtained at γ = 0.93 . From the results, we can see that the proposed approach is quite robust when β in the shortest path with Gaussian kernel is greater than 1 or when γ in the diffusion kernel is smaller than 0.15.We further notice that the parameter μ0, σ12, n0, and σ02, all of which are included in the priors of the domainRBF approach . In the real implementation we set μ0 = 0, n0 = 0, and σ02 = 0, for the reason explained in the literature [12 = 1, for simplicity. Therefore, we only need to test the robustness of the approach when different values of σ12 are used. To achieve this objective, we set σ12 as 0.001, 0.01, 0.1, 1, 10, and 100, respectively, and we apply the approach to the same cross validation process. We list the results in Table 12 is smaller than 1, the domainRBF approach is quite robust to the change of σ12, with the change of precision within 1.12%, change of mean rank ratios within 0.53%, and change of AUC scores within 0.54%. On the other hand, when σ12 is larger than 1, the decrease in performances becomes slightly conspicuous, but remains within the scope of 3.04% for precisions, 2.22% for mean rank ratios and 1.77% for AUC scores. Hence we can see that our domainRBF approach is generally robust to the change of parameters.Besides the two parameters in the distance measures, there are also four parameters in the domainRBF approach that need to be pre-determined, namely terature , and we In order to test the influence of the size of seed, or known associations on the prioritization results, we select at random 100%, 90%, 80%, 70%, 60%, and 50% of the original seed associations, respectively, and we repeat the leave-one-out validation processes. We only calculate the performance using the domainRBF approach based on diffusion kernel measure, and we choose the PDB part of the DOMINE database as the domain-domain interaction network. Results show that with the percentage of seed associations decreases from 100% to 50%, performance also slightly decreases in terms of precision, mean rank ratio and AUC score, despite some exceptions , apply the domainRBF method to score candidate domains, and then prioritize the candidates. We again perform random control, linkage interval, and genome-wide validation experiments and evaluate the performance of our approach in terms of precision, mean rank ratio, and AUC scores. We perform this ab initio inference using the large network that is composed of entire interactions in DOMINE and high-confidence interactions in InterDom (with diffusion kernel), and we summarize the results in Table Above we have used several large scale leave-one-out cross-validation experiments to evaluate the performance and robustness of the proposed domainRBF approach. However, it might be argued that a disease may be associated with more than one domain and that, consequently, the inclusion of domains already known to be associated with the query disease in the calculation of the domain proximity profile may ease the identification of novel associations. Following this line of reasoning, we demonstrate the capability of the proposed domainRBF approach in the prediction of novel associations for query diseasesby performing the following In comparison with the results in Table ab initio prediction experiments. We observe from the results [Additional file ab initio prediction tends to give higher ranks to domains that occur more frequently in human proteins. We merge the frequency of occurrence of domains in all human proteins into 11 bins , and we look at how ranks of domains that are known to be associated with diseases distribute in different bins. From the results [Additional file We also study the contribution of seed domain-disease associations by keeping a fraction of disease phenotypes that have the highest similarity scores to the query disease and repeating ab initio inference of domains and diseases, we further propose the following application of the proposed domainRBF approach in the inference of genes that are associated with diseases, by combining predicted domain-disease associations and known domain-protein relations. As shown in Figure Inspired by the success of a% of all candidates in b% validation runs, the fold enrichment is b/a on average). In other words, our domainRBF approach can also be effectively used as an intermediate step to infer associations between genes and diseases.We validate our gene prioritization approach using known gene-disease associations extracted from the BioMart database ,49. Afteab initio inference of associations between domains and diseases are consistent with these GWAS results.The identification of susceptible single nucleotide polymorphisms (SNPs) conferring risk for common human diseases is one of the main tasks of genome-wide association studies (GWAS). Since the study that identified the association of complement factor H (CFH) with age-related macular degeneration (AMD) in 2005, over 450 GWAS have been performed and more than 2,000 susceptible SNPs or genetic loci have been reported [ab initio inference, which uses all domains in the domain-domain interaction network as candidates. We further implement the following permutation test to check whether the number of such susceptible SNPs is significantly enriched within these regions, asGiven a disease of interest, we collect from SNPedia ,52 or otab initio inference. Record this number as N0.1. Count the number of reported SNPs that appear within 5 Mbp of domains that are ranked among the top 10 in the genome-wide i-th permutation, select 10 domains at random from all domains in the genome-wide ab initio inference. Count the number of reported SNPs that appear within 5 Mbp of these domains. Record this number as Ni.2. For the M times . Count the number of times that Ni  is greater than or equal to N0. Record this number as m.3. Repeat the above random selection p-value as p = m/M.4. Calculate a p-value indicates that the reported susceptible SNPs tend to be closer to the high ranking domains. In other words, high ranking domains are more likely to be associated with the disease under investigation.The null hypothesis in the above permutation test is that the number of the reported susceptible SNPs within 5 Mbp regions of the high ranking domains (top 10) is not different from that of randomly selected domains. Therefore, a small We then select four disease examples , apply the above permutation test method to these diseases, and analyze the results in detail. We choose these four diseases because they are common and have GWAS results available. It has been shown that diabetes had affected 2.8% of the population worldwide by 2000 , with tyβ cell destruction usually leads to absolute insulin deficiency [p-value of 0.0313, which is smaller than 0.05. From these results we conjecture that domains ranked among the top 10 do, indeed, tend to be closer to, or even include, known susceptible SNPs for this disease.Type 1 diabetes, formerly called juvenile diabetes or insulin-dependent diabetes, is a condition in which pancreatic ficiency . The genficiency . In our et al. [In addition, we also examine the 4 domains that are not close to susceptible SNPs reported by GWAS. For domain HNF-1B_C (PF04812), we notice that Urhammer et al.  have poiet al. . Therefop-value of 0.0363, which is smaller than 0.05. From these results we conjecture that domains ranked among the top 10 do, indeed, tend to be closer to known susceptible SNPs for this disease.Type 2 diabetes, formerly called adult-onset diabetes or noninsulin-dependent diabetes, is the most common form of diabetes. It usually begins with insulin resistance, a condition in which fat, muscle, and liver cells do not use insulin properly . Numerouet al. [In addition, we also examine the 3 domains that are not close to susceptible SNPs reported by GWAS. We notice that both domains HNF-1A_C (PF04813) and HNF-1_N (PF04814) contain mutations that may cause the type 3 form of maturity-onset diabetes of the young (MODY3), as pointed out by Urhammer et al. . Althougp-value of 0.0029, which is far smaller than 0.05. From these results we conjecture that domains ranked among the top 10 do, indeed, tend to be closer to, or even include, known susceptible SNPs for this disease.Crohn's disease, a chronic inflammatory disorder of the gastrointestinal tract, is thought to result from the combination of effect of environmental factors and genetic predisposition ,67. Recep-value of 0.0159, which is smaller than 0.05. From these results we conjecture that domains ranked among the top 10 do, indeed, tend to be closer to, or even include, known susceptible SNPs for this disease.Breast cancer, the most common malignancy in women in the Western world , exhibitp-values are smaller than 0.05, and are also numerically close to those obtained using all phenotypes. We therefore conjecture that our approach is robust to the seed domain-disease associations in the inference for these disease examples.For each of the four diseases examples, we further evaluate the contribution of seed domain-disease associations by keeping 10%, 20%, 30%, 40%, and 50% disease phenotypes that have the highest similarity scores to the query disease, obtaining a rank list of all domains, and then using the permutation test to check whether the number of known susceptible SNPs is still significantly enriched around the top ranking domains. As shown in [Additional file http://bioinfo.au.tsinghua.edu.cn/domainRBF/domain. All domains on the webpage are linked to the DOMINE database and the InterDom database, from which further information can be obtained.With the above validation results demonstrating the possibility of recovering the associations between protein domains and disease phenotypes, we further apply the domainRBF approach to all available protein domains and human disease phenotypes and predict a genome-wide landscape of the associations between protein domains and human disease phenotypes. There are a total of 5,080 phenotypes in the phenotype similarity network and 5,490 protein domains in the domain-domain interaction network (the union of the entire DOMINE and InterDom network). For each phenotype, we perform a prioritization of all domains with the use of the domainRBF approach (using the diffusion kernel measure). The prioritization results, together with a freely accessible web interface, are provided at On the basis of the above prioritization results, we aggregate the Bayes factors between all the 5,490 domains and 1,145 phenotypes, and obtain a matrix of altogether 6,286,050 elements. Here we first make a log (base 10) transform of original matrix, and then implement clustering while removing the rows in which the values are all smaller than 0.1. Since phenotypes clustered together generally have similar molecular basis, or share significant genetic overlaps , we implhttp://bioinfo.au.tsinghua.edu.cn/domainRBF/gene/. All genes on the webpage are linked to the Ensembl database [We further apply the above prioritization method to all human disease phenotypes and obtain a landscape of gene-phenotype associations that include 5,080 disease phenotypes and 14,944 human genes. The prioritization results, together with a freely accessible web interface, are provided at database , from whab initio inference of domain-disease associations and gene-disease associations. Finally, we calculated a landscape between 5,490 protein domains and 5,080 disease phenotypes.In this paper, we studied the problem of identifying domains that are associated with human inherited diseases under a prioritization framework. We proposed an approach called domainRBF from the perspective of Bayesian regression, verified its superior performance through three large-scale cross-validation experiments, and demonstrated the robustness of this approach via a series of permutation tests. We further proposed to perform In comparison with previous studies that rely on phenotype similarity and protein-protein interaction data to infer gene-disease associations , our appHowever, our method has the following limitations. First, our method can only be applied to diseases that are included in phenotype similarity data and domains that are included in domain interaction data. In the case of phenotype similarity, a possible solution would involve the development of a visualization and annotation system such as the one in  that canSecond, our method currently only considers conjugate priors in the Bayesian regression model. Although such formulation results in analytic solutions and thus alleviates the computational burden in the calculation of Bayes factors, it is known that the specification of prior is intrinsically complicated and subjective . The maiOur approach can be further studied from the following aspects. First, in addition to the domain-domain interaction network, information such as annotations of Pfam domains in the Gene Ontology (GO) can also provide a means for calculating similarities between domains. Recently, methods for calculating semantic similarities between GO terms have been packed into user-friendly software . It is tSecond, it is conceptually straightforward to extend the domainRBF model to infer interactive effects of multiple domains on a query disease. For example, given a query disease and a set of candidate domains, we can enumerate all two-way combinations of the domains and then use the DomainRBF model to infer possible associations between the disease and interactions of two domains. Nevertheless, such brute force method is computationally intensive and not quite feasible in application to the study of three-way or even higher order interactive effects of candidate domains.p-values of SNPs in a GWA study, we can obtain the statistical significance of candidate domains in the GWA study by combining the p-values of SNPs located in the domains and then prioritize the candidates to obtain their ranks. With these two ranks, we can resort to statistical methods, such as the one described in [Third, with the accumulation of publicly available data in genome-wide association (GWA) studies, we can consider the integration of our method and GWA studies. For example, given a disease of interest and a set of candidate domains, we can prioritize the candidate domains using our method and obtain the ranks of the domains. On the other hand, given ribed in , to obtaThe authors declare that they have no competing interests.WZ derived the model, implemented the method, collected the results and drafted the manuscript. YC participated in the design of the study. FS designed the research and revised the manuscript. RJ designed the research, drafted, and revised the manuscript. All authors read and approved the final manuscript.Supplemental Figures. Supplemental Figure S1 shows the results of a series of permutation test using the large domain-domain interaction network. Supplemental Figure S2 shows the mean ranks for domains with different frequency of occurrence in human proteins.Click here for fileSupplemental Tables. Supplemental Table S1 lists contributions of seed domain-disease associations . Supplemental Table S2 lists contributions of seed domain-disease associations . Supplemental Table S3 lists contributions of seed domain-disease associations (ab initio prediction experiments using the large domain-domain interaction network). Supplemental Table S4 lists the genome-wide evidence of associations between domains and type 1 diabetes. Supplemental Table S5 lists the genome-wide evidence of associations between domains and type 2 diabetes. Supplemental Table S6 lists the genome-wide evidence of associations between domains and Crohn's disease. Supplemental Table S7 lists the genome-wide evidence of associations between domains and breast cancer. Supplemental Table S8 lists contributions of seed domain-disease associations in the analysis of the four disease examples.Click here for file


The proteasome-assembly chaperone Nas2 binds to the proteasome subunit Rpt5 using its PDZ domain. The structure of the Nas2 PDZ domain has been determined. The 26S proteasome is a 2.5 MDa protease dedicated to the degradation of ubiquitinated proteins in eukaryotes. The assembly of this complex containing 66 polypeptides is assisted by at least nine proteasome-specific chaperones. One of these, Nas2, binds to the proteasomal AAA-ATPase subunit Rpt5. The PDZ domain of Nas2 binds to the C-terminal tail of Rpt5; however, it does not require the C-terminus of Rpt5 for binding. Here, the 1.15 Å resolution structure of the PDZ domain of Nas2 is reported. This structure will provide a basis for further insights regarding the structure and function of Nas2 in proteasome assembly. The CP is a cylindrically shaped structure with the proteolytic active sites on the inner surface of this complex  column.The open reading frame of 2.2.−1 in 50 mM NaCl, 50 mM Tris–HCl pH 6.8, 1 mM DTT, 1 mM EDTA was used for crystallization screening. All crystallization experiments were conducted by sitting-drop vapor diffusion in Compact Jr plates  using equal volumes of protein and crystallization solutions equilibrated against 75 µl of the latter. After two months, prismatic crystals of ∼0.2 mm in length were obtained at 4°C from Wizard Classic 4 screen  condition No. 26 .A purified sample of Nas2LND Table 1 concentr2.3.v/v) PEG 400 and stored in liquid nitrogen. Diffraction data were collected in-house using a Rigaku RU-H3R rotating-anode generator (Cu Kα) equipped with Osmic Blue confocal mirrors and an R-AXIS IV++ image-plate detector. High-resolution data were collected on beamline 17-ID at the Advanced Photon Source using a Dectris Pilatus 6M pixel-array detector. Intensities were integrated using XDS . Structure solution was conducted by molecular replacement with BALBES  which was used from this point forward. Additional structure refinement and manual model building with the high-resolution synchrotron data were conducted with PHENIX and Coot . Nas2LND produced well diffracting crystals after two months, enabling us to determine a structure comprised of amino acids Gln129–Leu220 in the asymmetric unit . The second region was observed between Lys119 and Lys120 and was assigned as a sulfate ion .The C-terminal half of Nas2 constitutes a PDZ domain, while the N-terminal half has no predicted domain structure . Several conserved residues are clustered on the surface of the PDZ domain . Interestingly, there is a group of identical hydrophobic residues covering the region between α-helix 2 and β-sheet 5 , a region that is known to be the peptide-binding groove in other PDZ domains . Similar to what was reported for GRASP55 (PDB entry 3rle), the groove was formed by the final, instead of the second, β-strand , indicating a similar circular permutation from a typical eukaryotic PDZ domain . Interestingly, a similar motif is found in this region in Nas2LND . Next, the structure of Nas2LND was also compared with the top structures obtained from a DALI search  and 1.68 Å with PDB entry 3gdv –3f).To compare the structure of Nas2 PDZ with a typical PDZ domain that utilizes a GLGF motif for binding the C-terminus of other proteins (Lee & Zheng, 20104.et al., 2011The 1.15 Å resolution crystal structure of the C-terminal region of Nas2 shows it contains a PDZ domain. Nas2 lacks the GLGF motif commonly found just prior to β-strand 2 in PDZ domains. This motif interacts with the carboxy-termini of PDZ-binding partners (Lee & Zheng, 2010proteasome-assembly chaperone Nas2 PDZ domain, 4o06PDB reference: 10.1107/S2053230X14003884/hv5251sup1.pdfSupplementary figures. DOI: