Substrate specificity of protein kinases and computational prediction of substrates

Biochimica et Biophysica Acta 1754 (2005) 200 – 209 http://www.elsevier.com/locate/bba

Review

Substrate specificity of protein kinases and computational prediction of substrates Boxtjan Kobe a,b,*, Thorsten Kampmann a, Jade K. Forwood a,b, Pawel Listwan a,b, Ross I. Brinkworth a a

School of Molecular and Microbial Sciences, University of Queensland, Brisbane, Australia b Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia Received 21 June 2005; received in revised form 13 July 2005; accepted 14 July 2005 Available online 9 September 2005

Abstract To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of ‘‘peptide specificity’’ of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information. D 2005 Elsevier B.V. All rights reserved. Keywords: Bioinformatics; Phosphorylation; Protein kinase; Protein structure; Signal transduction; Substrate specificity

1. Protein kinases Post-translational modification of proteins may be responsible for much of the complexity of higher organisms [1]. Posttranslational modification by phosphorylation is the most abundant type of cellular regulation, affecting essentially every

Abbreviations: AGC, protein kinases A, G, C; AMPK, AMP-activated protein kinase; CaMK, calmodulin-dependent kinase; CDK, cyclin-dependent kinase; CMGC kinases, cyclin-dependent kinases, MAP kinases, glycogen synthase kinase 3 (GSK3) and CK2-related protein kinases; FRET, fluorescence resonance energy transfer; GSK3, glycogen synthase kinase 3; KESTREL, kinase substrate tracking and elucidation; MAP kinase, mitogenactivated protein kinase; PDB, Protein Data Bank; PHK, phosphorylase kinase; PI3 kinase, 3-phosphatidyl inositol kinase; PKA, protein kinase A; PKB, protein kinase B; PKC, protein kinase C; PKG, protein kinase G; PS-OPL, positional scanning of oriented peptide libraries; SDR, specificity-determining residue * Corresponding author. School of Molecular and Microbial Sciences, University of Queensland, Brisbane, Queensland 4072, Australia. Tel.: +61 7 3365 2132; fax: +61 7 3365 4699. E-mail address: [email protected] (B. Kobe). 1570-9639/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.bbapap.2005.07.036

cellular process including metabolism, growth, differentiation, motility, membrane transport, learning and memory [2]. Although almost 2% of all proteins encoded in the human genome are protein kinases [3] and 30– 50% of proteins have been estimated to be phosphorylated [4], only a small fraction of phosphorylation sites has been assigned thus far [5]. Defects in protein kinase function result in a variety of diseases and kinases are major targets for drug design. To ensure signalling fidelity, kinases must be sufficiently specific and act only on a defined subset of cellular targets; this precision of specificity is essential for the integrity of signal transduction. Understanding the basis for this substrate specificity is therefore essential for identifying physiologically relevant substrates of individual protein kinases, and understanding the role of an individual protein kinase in a particular cellular process. In eukaryotes, protein kinases phosphorylate mainly Ser or Thr residues (protein Ser/Thr kinases) or Tyr residues (protein Tyr kinases). Although phosphorylation of His residues, as well as other amino acids, occurs also [6], we will focus in this review on protein Ser/Thr and Tyr kinases. These protein

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kinases have evolved from a common ancestral protein, and are usually divided into three classes: (i) serine/threonine (Ser/Thr) kinases; (ii) CMGC kinases (cyclin-dependent kinases (CDKs), mitogen-activated protein (MAP) kinases, glycogen synthase kinase 3 (GSK3) and CK2-related protein kinases); and (iii) tyrosine (Tyr) kinases [7,8]. The three-dimensional structures are known for a number of protein kinases, some with bound substrates and nucleotides [9– 12]. The catalytic domains of protein kinases have a characteristic three-dimensional structure, which extends to more distantly related kinases such as 3-phosphatidyl inositol (PI3) (lipid) kinases [13]. The characteristic fold consists of a smaller N-terminal ‘‘lobe’’, comprising a five-stranded h-sheet and one or two a-helices, and a larger C-terminal lobe that usually contains six major a-helices and two small h-sheets (Fig. 1). The peptide substrate is held in the groove between the two lobes. The phosphate group is extracted from an ATP molecule located close to the substrate towards the small lobe. A conserved Asp residue is essential for catalysis. The side chains of the bound peptide substrate are located in specific pockets, or subsites, which are shared between the two lobes. The chemistry of the amino acid side chains in each pocket provides the molecular basis of the specificity of a protein kinase. 2. Peptide specificity and substrate recruitment The molecular recognition of the peptide sequence surrounding the phosphorylated residue of a substrate is usually referred to as the ‘‘peptide specificity’’ of a protein kinase. The three-dimensional structures of protein kinases with bound peptide substrates revealed that all three classes

Fig. 1. Schematic representation of the structure of the catalytic subunit of protein kinase A (Protein Data Bank (PDB [93]) code 1JBP [94]). The small lobe is coloured light blue, the large lobe is coloured red, the peptide substrate is coloured yellow, and the ATP molecule is coloured orange. The figure was generated using the program ViewerLite (Accelrys Inc., San Diego, CA).

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of protein kinases bind the peptide substrate in an analogous manner, in an extended conformation and with the same orientation of the substrate peptide chain relative to the protein kinase [14 –19]. We can therefore adopt a notation for the peptide– kinase interaction, analogous to the one commonly used for proteases [20]. If we term the phosphorylated residue P0, the substrate residues immediately N-terminal and C-terminal to the P0 position as P 1 and P+1, respectively, and so on, these substrate residues will bind in their corresponding subsites on the surface of the protein kinases (P 1 residue binds in S 1 subsite, P+2 residue binds in S+2 subsite) (Fig. 2). Early recognition of the peptide specificity in protein kinase phosphorylation [21,22] led to the subsequent tabulation, compilation in databases and rationalization of phosphorylation motifs [4,19,23 – 25]. It is well established that the distribution of the relative ability to phosphorylate different peptide substrates falls sharply after a small number of the optimal peptides, and most peptide sequences show negligible phosphorylation [26]. However, peptide specificity is not the only element that determines the specificity of phosphorylation in the cell. It is well recognized that substrate recruitment plays a critical role in determining substrate preference. Substrate recruitment is any process that can bring a kinase and a substrate in close proximity, thus increasing the effective concentration of the substrate and increasing the chance of forming the enzyme– substrate complex. Typical mechanisms of substrate recruitment include the binding of the substrate to the regulatory domain of a kinase, or to a site distinct from the active site on the catalytic domain of the kinase (e.g., [27]); binding of the kinase and substrate to the same scaffolding protein [28] (a variation of this mechanism is the ligand-induced dimerization of protein kinases [29]); or co-localization of the enzyme and the substrate to a small subregion of the cell. One of the most effective mechanisms of recruitment is auto-phosphorylation, in which case the enzyme and substrate are covalently attached. Equally important is the converse mechanism of substrate sequestration or masking, decreasing the effective concentration of the substrate available for phosphorylation. The relative contributions of peptide specificity and recruitment toward the specificity of phosphorylation in the cell vary among protein kinases, substrates and cellular pathways. However, it is clear that the peptide specificity plays a crucial role and underscores the specificity distribution. If we consider the Michaelis – Menten equation, the maximum rate of the reaction will be achieved if the substrate concentration significantly exceeds the value of the Michaelis constant, but there will be a linear relationship between the rate and the substrate concentration if the substrate concentration is smaller than the Michaelis constant. Recruitment can therefore have a significant effect on the phosphorylation of an individual substrate, transforming a poor substrate into a substrate that can be effectively phosphorylated. However, the relative distribution of the phosphorylated substrates remains determined by the peptide specificity of a kinase; recruitment merely shifts the distribution curve. Ultimately, the fate of

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Fig. 2. Substrate binding in protein kinase A. (A) Schematic representation of the binding sites of the substrate side-chains, with the specificity-determining residues (SDRs or determinants) listed in each subsite. The sub-sites are coloured: S 3, red; S 2, yellow, S 1, green; S0, orange-red; S+1, dark blue; S+2, magenta; and S+3, light blue. The same colour scheme for the subsites is used in (B) and (C). (B) Interactions of the heptapeptide region of the substrate (grey; sequence RRASIHD) with the SDRs, coloured according to the subsite. (C) Surface representation highlighting the individual subsites, coloured as in (A), and a heptapeptide region of the substrate (black; sequence RRASIHD). The figure was generated using the program ViewerLite.

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phosphorylation of a substrate in the cell will be determined by a combination of peptide specificity, substrate recruitment/ sequestration and removal of the phosphate group by phosphatases [26]. Cellular phosphatases will set a ‘‘threshold’’, so that proteins remain phosphorylated only if the rate of phosphorylation exceeds the rate of dephosphorylation [26,30]. 3. Structural basis of peptide specificity of protein kinases The discussion above highlights the crucial contribution of peptide specificity to protein phosphorylation. It is therefore important to understand the molecular basis of peptide specificity. The analysis of the structures of protein kinases, in particular their complexes with peptide substrates, in the context of the known peptide specificities of these kinases, allows the description of the substrate specificity of protein kinases [19]. We concentrate here on protein Ser/Thr kinases, for which the structural basis of specificity is understood best. 3.1. Specificity-determining residues of protein Ser/Thr kinases A protein kinase contains a number of peptide substratebinding residues responsible for its specificity (specificitydetermining residues (SDRs) or ‘‘determinants’’). The set of SDRs for protein kinase A (PKA) is listed in Table 1. The different SDRs influence the properties of different subsites or pockets, which bind the side chains of the amino acids surrounding the Ser or Thr to be phosphorylated, and therefore determine the specificity of binding to these subsites. Often, one or two specific subsites have the largest effect on the Table 1 The specificity-determining residues (SDRs or determinants) of protein kinase A and the corresponding specificity as manifested in particular sub-sites SDR

Residue

Role in subsite

Corresponding specificity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Gly52 Ser53 Phe54 Leu82 Gln84 Glu127 Phe129 Ser130 Asp166 Glu170 Phe187 pThr197 Leu198 Thr201 Pro202 Glu203 Tyr204 Leu205 Glu230 Tyr330

S 1 S 1/S+2 S+2 S+2 S+2 S 3 S 3 S 4 S0 S 3/S 2 S+1 S+3 S+3 S 2 S+1 S 2 S 2 (+1) S 2 S 3

Non-specific Hydrophilic Hydrophobic Hydrophobic Ser/Thr Arg Leu (alternative)* Hydrophilic Ser/Thr Arg Hydrophobic Lys/Arg Hydrophobic Ile Hydrophobic Arg Non-specific Leu Glu Glu

* Refers to the alternative preference for a substrate Leu at S-3 by binding to SDR 7 (Phe129), rather than the usual preference for Arg by binding to SDR 6 (Glu127).

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Table 2 Selected specificity-determining residues (SDRs or determinants), and the corresponding preferred substrate residues in some example protein kinases Kinase

SDR

Residue

Subsite

Specificitya

Swiss-prot entryb

PKB/Akt PKB/Akt PKC PKG PKB/Akt CaMK2 AMPK PKB/Akt PHK Chk2 AMPK CaMK2 CK1 CK2 Chk2 Chk2 Chk2 CK2 PKG Titin Chk1 PKC PHK CK2

10 8 6 6 6 6 6 16 16 16 16 16 16 15 18 11 15 15 18 18 15 4, 5 12 4, 5

Glu341 Phe286 Asp423 Glu444 Glu234 Glu96 Glu102 Glu314 Ser188 Thr389 Asn180 Gly178 Arg186 Arg196 Leu391 His371 Pro388 Arg196 Val524 pTyr24917 Leu171 Asp377, Asp379 Glu182 Lys75, Lys78

S 5 S 4 S 3 S 3 S 3 S 3 S 3 S 2c S 2c S 2c S 2c S 2c S 2 S 2 S+1 S+1 S+1 S+1 S+1 S+1 S+1 S+2 S+2 S+2

R P R/K R/K R/K R R G/S/T/R Q/R/L/G/T T/I/M/L/S/R R/S/L/K/T Q/R/S/V/M/L E/D/pS E/D F/M/L D (alternative)d M/L/F E/D V/T/P/A/G R D/A/Q/M K/R R D/E

P31749 P31749 P17252 Q13976 P31749 Q9UQM7 Q13131 P31749 Q16816 O96017 Q13131 Q9UQM7 P48729 P19784 O96017 O96017 O96017 P19784 Q13976 Q10466 O14757 P17252 Q16816 P19784

Only the most specific/characteristic subsites are shown for a chosen kinase. a Based on experimental evidence [74]. One-letter amino acid code is used. b [95]. c Where the substrate contains aliphatic or aromatic residues (Met, Phe, Leu, Ile) or Arg at P-2, these are expected to bind to SDR 17 Tyr instead. d Alternative preference.

specificity of a kinase (Table 2). The following discussion includes both the classic protein Ser/Thr kinases and CMGC group. Specificity is best understood for subsites S 3 to S+3. 3.1.1. S 3 subsite (SDRs 6, 7, 8, 10, 16 and 20) The specificity at S 3 primarily depends on SDRs 6 and 16. For kinases in the AGC (protein kinases A, G, C) group, Glu or Asp in these positions results in a preference for substrates containing Arg or Lys at P 3. In the calmodulindependent kinase (CaMK) group, there is Glu at SDR 6 but no acidic residue at 16, increasing the preference for Arg over Lys at P 3. When SDR 6 is a small residue, SDR 7 plays a bigger role in determining the preference in this subsite, for example for hydrophobic residues in the Ste family of kinases. CMGC kinases similarly do not have Glu or Asp at 6 and are less specific; SDR 10 consequently has a bigger influence on specificity. 3.1.2. S 2 subsite (SDRs 10, 14, 15, 16, 17 and 19) Binding in the S 2 pocket of AGC and CaMK kinases primarily involves SDR 17 (Phe, effecting hydrophobic specificity; or Tyr, with the phenolic and phenyl groups allowing binding of Gln/Arg/Lys and Met/Phe, respectively). This specificity is modified in a predictable manner by the nature of SDR 16 (ranging from Glu in PKA (mediates Arg specificity) to Gly in CaMK2 (mediates broad specificity; the

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large size of pocket can accommodate most residues)). SDR 17 is Trp in CMGC kinases and occludes much of the S 2 pocket so that binding is restricted to Val, Pro or Gly; the exception is CK2, where 15 Arg, serving both S 2 and S+1 pockets, causes a preference for Glu at P 2. SDR 19 can be used in subsite S 5 instead, changing the preference at P 2 to smaller residues such as Ser/Thr/Ala [18]. 3.1.3. S 1 subsite (SDRs 1 and 2) The P 1 residue makes few contacts with the enzyme, contacting only the glycine-rich loop and the bound ATP molecule. A small residue such as Ala, Gly or Pro is often found at P 1 although its side chain makes no contacts with the kinase. 3.1.4. S+1 subsite (SDRs 11, 13, 15 and 18) The key SDR is 18 at the deepest end of the pocket. AGC and CaMK groups frequently prefer large hydrophobic residues, e.g., Phe when 11, 13 and 18 are large hydrophobic residues, and 15 is Pro. Variations altering specificity include hydrophilic residues at 11, smaller residues at 13 and 18, and Leu or Ala at 15. Phospho-Tyr at 18 (titin) [31] results in a preference for P+1 Arg. In CDKs and MAP kinases, the conformation of the activation segment creates a suitable hydrophobic pocket for Pro binding in the S+1 subsite [16,32]. GSK3h has many substrates with P+1 Pro but is not exclusively Pro-specific, and CK2 is not Pro-specific at all (prefers Glu or Asp at P+1 instead). 3.1.5. S+2 subsite (SDRs 2, 3, 4 and 5) These SDRs lie in a small loop. In some cases, the binding involves charge– charge interactions, and the specificity is well defined (e.g., protein kinase Ca (for Arg/Lys) due to three consecutive Asp residues). Phosphorylase kinase (PHK) has no small loop so this subsite is not well defined, resulting in broad specificity; in the crystal structure of the peptide complex, the P+2 Arg binds to what is normally the S+3 pocket [15]. SDRs 4 and 5 are involved in cyclin binding in CDKs and therefore do not contribute to specificity [16]. 3.1.6. S+3 subsite (SDRs 12 and 13) This subsite is rarely very specific. SDR 12 in the activation loop is often phosphorylated and can result in a preference for Arg/Lys. SDR 13 that immediately follows 12 is usually hydrophobic and can be shared with the S+1 site. 3.2. Peptide specificity in protein Tyr kinases Although peptide substrates bind protein Ser/Thr and Tyr kinases in an analogous fashion in terms of peptide chain orientation and juxtaposition relative to the kinase catalytic domain, significant differences exist between these classes of enzymes. The increased size of the Tyr residue, compared to a Ser or Thr, results in the peptide backbone being positioned further away from the kinase surface in the vicinity of the phosphorylation site. This means that the side chains of the substrate residues either side of the phosphorylation site bind

to the kinase in a slightly different mode than in Ser/Thr kinases, and the structural basis of specificity changes. For example, the P 1 residue in the bound peptide bypasses the S 1 and S 2 pockets and binds to what normally is the S 3 pocket in Ser/Thr kinases. Furthermore, structural information is only available for the same peptide binding to two closely related protein Tyr kinases, the kinase domains of insulin receptor and insulin-like growth factor 1 receptor [17,33]. The peptide is only ordered in the N-terminal direction up to residue P 2. Structural analyses on further kinase– substrate peptide pairs, in particularly using more optimal peptides, will be required to further understand the specificity. Furthermore, Tyr kinases in general have broad specificities; consensus sequences are difficult to derive and have low predictive value [34]. 4. Experimental methods for determining specificity and identifying substrates When discussing specificity of protein kinases and exploring the possibilities of its prediction, it is important to appreciate the experimental approaches for identifying protein kinase substrates and characterizing their specificities. The discovery of protein kinase substrates has arguably become the rate-limiting step in advancing the knowledge on cell signalling. Physiologically relevant substrates remain to be identified for many protein kinases. Although not the focus of this review, we briefly review the methods for substrate identification of kinases (or kinase identification for substrates) in vitro and in intact cells; the knowledge of protein substrates can be used to derive peptide specificity information for individual kinases. We then review the methods to experimentally determine the peptide specificities of protein kinases directly. 4.1. Identification of protein kinase substrates in vitro A classic demonstration of a protein kinase – substrate relationship in vitro has involved the incubation of a purified candidate substrate with a purified kinase in the presence of [g-32P]ATP and MgCl2. One modification of this method includes in-gel kinase assays, relying on renaturation of the proteins after gel electrophoresis [35]. The use of these approaches in substrate identification requires an educated guess about a kinase– substrate relationship. Therefore, ‘‘fishing’’ approaches have been developed to discover new substrates, for example, through incubation of cell lysates with a purified kinase and radioactive ATP, followed by purification of phosphorylated proteins. The method termed KESTREL (kinase substrate tracking and elucidation) sub-fractionates the proteins before phosphorylation, reducing problems with ATPase activity in cell lysates and facilitating protein identification by mass spectrometry through reduced complexity [36]. Phage expression libraries [37,38] or mRNA display [39] can be used to produce a library of potential kinase substrate proteins. Another interesting method involves the use of an engineered kinase that can accept an ATP analogue

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[40,41]. ‘‘Protein kinase chips’’ represent a striking recent development that foreshadows the future potential to analyse the specificity of kinases of the entire proteome [42]. A kinase responsible for phosphorylating a substrate can also be identified using protein kinases in the presence of high concentrations of ADP, facilitating the catalysis of the reverse reaction-dephosphorylation of a phospho-protein [43]. The problems with in vitro approaches include likely difficulties with recombinant protein expression (due to folding problems in the recombinant host or the lack of relevant posttranslational modifications required for activity); phosphorylation conditions may not represent the situation in vivo; and most importantly, the demonstration of effective phosphorylation in vitro does not mean that the substrate may have access to the kinase in vivo [44]. 4.2. Identification of protein kinase substrates in vivo Considering the limitations of protein kinase substrate identification in vitro, it is advantageous, although more challenging, to identify protein kinase substrates in intact cells. Traditionally, this required tracking the transfer of phosphate using large amounts of radioisotopes. Antibody-based approaches are clearly preferable, and have been facilitated by recent advances. Phospho-amino-acid-specific antibodies recognize a single phosphorylated amino acid in the context of a protein; whereas anti-phospho-Tyr antibodies have proved useful, this has not been the case for phospho-Ser and phosphoThr-specific antibodies. Phospho-motif antibodies recognize the phospho-residue with the surrounding sequence; their development therefore requires the knowledge of the peptide specificity of the kinase. Phosphorylated proteins can be detected using immunoblotting or immunoprecipitation and analysed by mass spectrometry [44 –47]. Phospho-proteins can be enriched from cell lysates using affinity chromatography, for example using phospho-protein-binding proteins such as 14 – 3 –3 proteins or FHA domains. Phospho-peptides can also be isolated using metal-affinity chromatography [48], after modification by base-catalysed helimination (and replacement of the phospho-residue by an affinity tag such as biotin) [49], or after modification to phosphoramidate (and attachment of cystamine group) [50]. Phosphorylation mapping by tandem mass spectrometry is challenging because of signal suppression of phosphatecontaining molecules, lability of phosphate groups, and difficulties with achieving full sequence coverage [51,52]. Phospho-peptide mapping on thin layer chromatography plates, site-directed mutagenesis, Edman degradation and phospho-specific proteolysis are therefore attractive alternative methods to characterize phospho-proteins [53 – 55]. Two landmark achievements in phospho-proteome characterization include the analysis of the phospho-proteins in yeast [48] and the HeLa cell nucleus [56]. Proteomics approaches are also suitable for identification of cellular targets of protein kinase inhibitors [57]. Methods that demonstrate a physical interaction between a kinase and a substrate, such as yeast twohybrid screens, tandem affinity purification, phage display

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fusion libraries and cross-linking, suffer from a number of limitations (for example, the interaction is typically transient, and many non-substrate proteins interact with kinases) [58]. An alternative to proteomic methods are genetic screens in model organisms. Several criteria have to be met to identify a protein as a physiological protein kinase substrate with reasonable confidence; these criteria include (i) phosphorylation using purified proteins in vitro with a significant stoichiometry; (ii) phosphorylation in intact cells in response to physiological stimuli on the same site as determined in vitro; (iii) stimulation of the phosphorylation of constitutively active kinase mutants; and (iv) inhibition of the phosphorylation of the substrate by dominant-negative forms of the kinase, specific inhibitors or in cells depleted of the kinase [44]. Methods to monitor phosphorylation in single cells (e.g., based on the use of intracellular substrate peptides [59] or fluorescence-activated cell sorting [60]) and to characterize the dynamics of phosphorylation in cells (e.g., based on fluorescence resonance energy transfer (FRET) [61,62], phosphorylation-dependent translocation of green fluorescence protein-fusion proteins [63] or dye-conjugated proteins [64]) will play an important role in the future to understand the intricacies of signalling in cells. 4.3. Experimental determination of peptide specificity Traditionally, peptide specificity of a kinase was inferred from the analysis of phosphorylation sites of known protein kinase substrates [5,24,25]. The contributions of different peptide positions to the specificity were tested by synthesizing a series of peptides and measuring their ability to be phosphorylated (e.g., [21]). Extending this method led to the development peptide library approaches, such as phage or mRNA display, where the phosphorylated peptides were iteratively selected [39]. These approaches yield optimized peptides, but do not provide information on less strongly phosphorylated peptides. Oriented peptide libraries, on the other hand, rely on a fixed phospho-acceptor site, and the consensus sequence of optimal peptides can be derived from sequencing a mixture of phosphorylated peptides [65]. An oriented peptide library experiment can provide statistical information on the propensities of amino acids at particular positions relative to the phosphorylation site; the method relies on the assumption that the positions are independent from each other. A modification of this approach involves positional scanning of biotinylated oriented peptide libraries (PS-OPL); the resulting position-specific scoring matrices showed improved specificity and sensitivity in predicting phosphorylation sites [66]. An alternative approach involves peptide arrays [67], where the phosphorylation state can be measured even for weak substrates or non-substrates. The results from any of the library or array experiments can be used to derive phosphorylation sequence profiles or ‘‘weight matrices’’ describing the specificity of phosphorylation, or can be processed computationally using the nearest neighbour approach [68].

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5. Prediction of specificity and substrates by computational methods The accumulated experimental information on molecular recognition forms the basis for computational methods for the prediction of interaction specificities [69 – 72]. Current approaches for identifying substrates and phosphorylation sites usually require a prior knowledge of the peptide specificity of a kinase, which in turn is used to search protein databases to identify phosphorylation sites on putative substrates proteins. Only one method is currently available that goes a step further and predicts the peptide specificity of a protein kinases based solely on the amino acid sequence of the kinase (program Predikin [19]). 5.1. Using peptide specificity information to predict substrates Prosite introduced the ability to search for the consensus motif of a given kinase in a protein substrate [73]; however, the consensus motif information was initially available only for a few kinases. A larger number of experimentally verified phosphorylation sites from the literature was subsequently compiled in databases such as Phosphobase (http:// www.cbs.dtu.dk/databases/Phosphobase) [5] and ELM (http:// www.elm.eu.org) [74]. Other relevant databases include Phosphosite (http://www.phosphosite.org/) and the Protein Kinase Resource [75], and more general databases on posttranslational modifications such as HPRD (http:// www.hprd.org/) [76] and RESID (http://pir.georgetown.edu/ pir-www/dbinfo/resid.html) [77]. Such phosphorylation site information was used to train neural networks, resulting in the algorithm NetPhos for identification of phosphorylation sites (http://www.bs.dtu.dk/services/NetPhos) [78]. NetPhos was not specific for individual kinases and could only identify the Ser, Thr and Tyr residues likely to be phosphorylated. NetPhosK (http://www.bs.dtu.dk/services/NetPhosK) was developed more recently to predict PKA phosphorylation sites [79]. Scansite was developed to predict phosphorylation sites for kinases that have been studied using oriented peptide libraries (http://www.scansite.mit.edu/) [80,81]. The specificity and sensitivity of oriented peptide-library-derived weight matrixbased predictions can be enhanced by PS-OLP (http:// www.mpr.nci.nih.gov) [66]. Weight matrices or sequence profiles similar to the ones derived from oriented peptide libraries can also be derived from the libraries based on peptide array experiments [67]. Alternatively, the libraries can be used as a database for the memory-based nearest neighbour prediction approach [68]. Other recent computational approaches include PredPhospho (http://www.pred.ngri.re.kr/PredPhospho.htm) [82] and AutoMotif (http://www.automotif.bioinfo.pl/) [83], which make use of the computational approach termed support vector machines; and GPS (group-based phosphorylation predicting and scoring method; http://www.973-proteinweb.ustc.edu.cn/ gps/gps_web/), which is reported to cover a larger number of protein kinase families and have greater sensitivity and specificity than Scansite and PredPhospho [84].

5.2. Prediction of peptide specificity Peptide specificity is determined by the three-dimensional complementarity between the protein kinase and the substrate, and structural information has been used successfully to explain the specificities of kinases [4,14 –17,19,85]. Structural information can therefore form the foundation for predicting peptide specificity of protein kinases. Although computational binding enthalpy calculations showed poor correlation with peptide phosphorylation efficiency [86], molecular dynamics calculations uncovered a correlation between the distance from the substrate nucleophilic oxygen and the ATP phosphorus and kinase –substrate binding specificity [87]. Peptide specificity depends on specificity-determining residues (SDRs) in the protein kinase. These can be identified using a computational procedure where homologous proteins are split into orthologues (assumed to have conserved specificities) and paralogues (assumed to have different specificities), and the functionally important residues are identified from the signatures of conserved residues [88]. We identified the specificity-determining residues through a thorough analysis of known three-dimensional structures of protein kinases and known specificities [19]. Patterns of specificity-determining residues can be used to predict peptide specificities of protein kinases; we have made such predictions possible for protein Ser/Thr kinases in the web-interfaced program Predikin (http://www.smms.uq.edu.au/kinsub). The advantage of this approach over other bioinformatic tools for phosphorylation site prediction is that predictions can be made for any protein Ser/Thr kinase, as long as (i) the specificitydetermining residues can be identified and (ii) their pattern is associated with a particular specificity and catalogued within Predikin. The predictions currently include a heptapeptide sequence spanning positions P 3 to P+3; although peptide specificity can extend beyond these positions [18,89], the residues outside the heptapeptide region usually play a less important role and are less well understood. Several phosphorylation site predictions based on Predikin have been confirmed experimentally, endorsing the utility of this approach [90 – 92]. 5.3. Future prospects for computational approaches Methods for computational prediction of peptide specificities and identification of substrates could be enhanced by combining different approaches and integrating various types of information. For example, approaches such as Scansite, PSOLP, NetPhosK and PredPhospho often show higher specificities and sensitivities than Predikin, but can only make predictions for protein kinases with available experimental information on peptide specificity. On the other hand, Predikin can predict peptide specificities directly from the amino acid sequences and can therefore be used for most kinases, including hypothetical and uncharacterized ones. Other information that can be derived from structural and sequence data could further improve the predictions. For example, we know that phosphorylation sites must be positioned on the surface of proteins, generally in loop regions that are flexible enough to

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access the catalytic residues in the protein kinase active site. Segments with rigid helical structure or highly hydrophobic sequences buried in the core of the protein are not favourable phosphorylation sites. Incorporating such structural information in the prediction algorithms would increase the reliability of predictions. At the end of the day, peptide specificity alone is not sufficient to predict physiological substrates flawlessly, because of the important role of substrate recruitment. For this reason, significant improvements in computational protein kinase substrate identification will require the integration of peptide specificity prediction results with information on the cellular function, localization and interactions from relevant available databases.

[9]

[10]

[11] [12] [13]

[14]

6. Conclusions and perspectives Understanding the molecular and structural basis of peptide specificity of protein kinases forms the foundation of bioinformatic computational approaches for identification of protein kinase substrates, and the functional characterization of these enzymes and the corresponding signal transduction pathways. Phosphorylation specificity is essential for the integrity of signal transduction, and peptide specificity, substrate recruitment/sequestration and the activities of protein phosphatases cooperate strategically in the cell to regulate cellular phosphorylation. Understanding phosphorylation specificity will therefore contribute to understanding the roles of protein kinases in health and disease, and help identifying new therapeutic targets and strategies of protein kinase inhibition and anti-kinase drug development. Acknowledgements

[15]

[16]

[17]

[18]

[19]

[20] [21]

BK is a National Health and Medical Research Council (NHMRC) Senior Research Fellow, and JKF is an NHMRC C.J. Martin Fellow. References [1] J.C. Venter, M.D. Adams, E.W. Myers, P.W. Li, R.J. Mural, G.G. Sutton, H.O. Smith, M. Yandell, C.A. Evans, R.A. Holt, et al., The sequence of the human genome, Science 291 (2001) 1304 – 1351. [2] T. Hunter, Signaling—2000 and beyond, Cell 100 (2000) 113 – 127. [3] G. Manning, D.B. Whyte, R. Martinez, T. Hunter, S. Sudarsanam, The protein kinase complement of the human genome, Science 298 (2002) 1912 – 1934. [4] L.A. Pinna, M. Ruzzene, How do protein kinases recognize their substrates? Biochim. Biophys. Acta 1314 (1996) 191 – 225. [5] A. Kreegipuu, N. Blom, S. Brunak, PhosphoBase, a database of phosphorylation sites: release 2.0, Nucleic Acids Res. 27 (1999) 237 – 239. [6] P.G. Besant, E. Tan, P.V. Attwood, Mammalian protein histidine kinases, Int. J. Biochem. Cell Biol. 35 (2003) 297 – 309. [7] S.K. Hanks, A.M. Quinn, Protein kinase catalytic domain sequence database: identification of conserved features of primary structure and classification of family members, Methods Enzymol. 200 (1991) 38 – 62. [8] R.I. Brinkworth, R.A. Breinl, B. Kobe, Conserved sequence motifs in protein kinase families, in: M.M. Gromiha, S. Selvaraj (Eds.), Recent

[22]

[23] [24]

[25] [26]

[27]

[28]

[29]

[30]

207

Research Developments in Protein Folding, Stability and Design, Research Signpost, Trivandrum, Kerala, India, 2002, pp. 67 – 77. D.R. Knighton, J. Zheng, L.F. Ten Eyck, V.A. Ashford, N.-H. Xuong, S.S. Taylor, J.M. Sowadski, Crystal structure of the catalytic subunit of cyclic adenosine monophosphate-dependent protein kinase, Science 253 (1991) 407 – 414. B. Kobe, T. Gleichmann, T. Teh, J. Heierhorst, B.E. Kemp, Crystallization of protein kinases and phosphatases, in: D.G. Hardie (Ed.), Protein Phosphorylation: A Practical Approach, vol. 2, Oxford Univ. Press, Oxford, 1999, pp. 387 – 421. M. Huse, J. Kuriyan, The conformational plasticity of protein kinases, Cell 109 (2002) 275 – 282. G.M. Cheetham, Novel protein kinases and molecular mechanisms of autoinhibition, Curr. Opin. Struct. Biol. 14 (2004) 700 – 705. E.H. Walker, O. Perisic, C. Ried, L. Stephens, R.L. Williams, Structural insights into phosphoinositide 3-kinase catalysis and signalling, Nature 402 (1999) 313 – 320. D.R. Knighton, J. Zheng, L.F. Ten Eyck, N.-H. Xuong, S.S. Taylor, J.M. Sowadski, Structure of a peptide inhibitor bound to the catalytic subunit of cyclic adenosine monophosphate-dependent protein kinase, Science 253 (1991) 414 – 420. E.D. Lowe, M.E. Noble, V.T. Skamnaki, N.G. Oikonomakos, D.J. Owen, L.N. Johnson, The crystal structure of a phosphorylase kinase peptide substrate complex: kinase substrate recognition, EMBO J. 16 (1997) 6646 – 6658. N.R. Brown, M.E. Noble, J.A. Endicott, L.N. Johnson, The structural basis for specificity of substrate and recruitment peptides for cyclindependent kinases, Nat. Cell Biol. 1 (1999) 438 – 443. S.R. Hubbard, Crystal structure of the activated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog, EMBO J. 16 (1997) 5572 – 5581. J. Yang, P. Cron, V.M. Good, V. Thompson, B.A. Hemmings, D. Barford, Crystal structure of an activated Akt/protein kinase B ternary complex with GSK3-peptide and AMP-PNP, Nat. Struct. Biol. 9 (2002) 940 – 944. R.I. Brinkworth, R.A. Breinl, B. Kobe, Structural basis and prediction of substrate specificity in protein serine/threonine kinases, Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 74 – 79. I. Schechter, A. Berger, On the size of the active site in proteases I. Papain, Biochem. Biophys. Res. Commun. 27 (1967) 157 – 162. B.E. Kemp, D.J. Graves, E. Benjamini, E.G. Krebs, Role of multiple basic residues in determining the substrate specificity of cyclic AMP-dependent protein kinase, J. Biol. Chem. 252 (1977) 4888 – 4894. G.W. Tessmer, J.R. Skuster, L.B. Tabatabai, D.J. Graves, Studies on the specificity of phosphorylase kinase using peptide substrates, J. Biol. Chem. 252 (1977) 5666 – 5671. B.E. Kemp, R.B. Pearson, Protein kinase recognition sequence motifs, Trends Biochem. Sci. 15 (1990) 342 – 346. R.B. Pearson, B.E. Kemp, Protein kinase phosphorylation site sequences and consensus specificity motifs: tabulations, Methods Enzymol. 200 (1991) 62 – 81. A. Kreegipuu, N. Blom, S. Brunak, J. Jarv, Statistical analysis of protein kinase specificity determinants, FEBS Lett. 430 (1998) 45 – 50. G. Zhu, Y. Liu, S. Shaw, Protein kinase specificity. A strategic collaboration between kinase peptide specificity and substrate recruitment, Cell Cycle 4 (2005) 52 – 56. T. Lee, A.N. Hoofnagle, Y. Kabuyama, J. Stroud, X. Min, E.J. Goldsmith, L. Chen, K.A. Resing, N.G. Ahn, Docking motif interactions in MAP kinases revealed by hydrogen exchange mass spectrometry, Mol. Cell 14 (2004) 43 – 55. M.C. Faux, J.D. Scott, More on target with protein phosphorylation: conferring specificity by location, Trends Biochem. Sci. 21 (1996) 312 – 315. M.A. Lemmon, J. Schlessinger, Regulation of signal transduction and signal diversity by receptor oligomerization, Trends Biochem. Sci. 19 (1994) 459 – 463. H.M. Sauro, B.N. Kholodenko, Quantitative analysis of signaling networks, Prog. Biophys. Mol. Biol. 86 (2004) 5 – 43.

208

B. Kobe et al. / Biochimica et Biophysica Acta 1754 (2005) 200 – 209

[31] O. Mayans, P.F.M. van der Ven, M. Wilm, A. Mues, P. Young, D. Furst, M. Wilmanns, M. Gautel, Structural basis for activation of the titin kinase domain during myofibrillogenesis, Nature 395 (1998) 863 – 869. [32] N.R. Brown, M.E. Noble, A.M. Lawrie, M.C. Morris, P. Tunnah, G. Divita, L.N. Johnson, J.A. Endicott, Effects of phosphorylation of threonine 160 on cyclin-dependent kinase 2 structure and activity, J. Biol. Chem. 274 (1999) 8746 – 8756. [33] S. Favelyukis, J.H. Till, S.R. Hubbard, W.T. Miller, Structure and autoregulation of the insulin-like growth factor 1 receptor kinase, Nat. Struct. Biol. 8 (2001) 1058 – 1063. [34] Z. Songyang, L.C. Cantley, Recognition and specificity in protein tyrosine kinase-mediated signalling, Trends Biochem. Sci. 20 (1995) 470 – 475. [35] M.W. Wooten, In-gel kinase assay as a method to identify kinase substrates, Science STKE 153 (2002) PL15. [36] A. Knebel, N. Morrice, P. Cohen, A novel method to identify protein kinase substrates: eEF2 kinase is phosphorylated and inhibited by SAPK4/p38delta, EMBO J. 20 (2001) 4360 – 4369. [37] R. Fukunaga, T. Hunter, Identification of MAPK substrates by expression screening with solid-phase phosphorylation, Methods Mol. Biol. 250 (2004) 211 – 236. [38] R. Fukunaga, T. Hunter, MNK1, a new MAP kinase-activated protein kinase, isolated by a novel expression screening method for identifying protein kinase substrates, EMBO J. 16 (1997) 1921 – 1933. [39] T.P. Cujec, P.F. Medeiros, P. Hammond, C. Rise, B.L. Kreider, Selection of v-abl tyrosine kinase substrate sequences from randomized peptide and cellular proteomic libraries using mRNA display, Chem. Biol. 9 (2002) 253 – 264. [40] A.C. Bishop, J.A. Ubersax, D.T. Petsch, D.P. Matheos, N.S. Gray, J. Blethrow, E. Shimizu, J.Z. Tsien, P.G. Schultz, M.D. Rose, et al., A chemical switch for inhibitor-sensitive alleles of any protein kinase, Nature 407 (2000) 395 – 401. [41] K. Shah, K.M. Shokat, A chemical genetic approach for the identification of direct substrates of protein kinases, Methods Mol. Biol. 233 (2003) 253 – 271. [42] H. Zhu, J.F. Klemic, S. Chang, P. Bertone, A. Casamayor, K.G. Klemic, D. Smith, M. Gerstein, M.A. Reed, M. Snyder, Analysis of yeast protein kinases using protein chips, Nat. Genet. 26 (2000) 283 – 289. [43] H.K. Kole, M. Abdel-Ghany, E. Racker, Specific dephosphorylation of phosphoproteins by protein-serine and -tyrosine kinases, Proc. Natl. Acad. Sci. U. S. A. 85 (1988) 5849 – 5853. [44] D.C. Berwick, J.M. Tavare, Identifying protein kinase substrates: hunting for the organ-grinder’s monkeys, Trends Biochem. Sci. 29 (2004) 227 – 232. [45] H. Zhang, X. Zha, Y. Tan, P.V. Hornbeck, A.J. Mastrangelo, D.R. Alessi, R.D. Polakiewicz, M.J. Comb, Phosphoprotein analysis using antibodies broadly reactive against phosphorylated motifs, J. Biol. Chem. 277 (2002) 39379 – 39387. [46] A. Pandey, A.V. Podtelejnikov, B. Blagoev, X.R. Bustelo, M. Mann, H.F. Lodish, Analysis of receptor signaling pathways by mass spectrometry: identification of vav-2 as a substrate of the epidermal and plateletderived growth factor receptors, Proc. Natl. Acad. Sci. U. S. A. 97 (2000) 179 – 184. [47] M. Gronborg, T.Z. Kristiansen, A. Stensballe, J.S. Andersen, O. Ohara, M. Mann, O.N. Jensen, A. Pandey, A mass spectrometry-based proteomic approach for identification of serine/threonine-phosphorylated proteins by enrichment with phospho-specific antibodies: identification of a novel protein, Frigg, as a protein kinase A substrate, Mol. Cell. Proteomics 1 (2002) 517 – 527. [48] S.B. Ficarro, M.L. McCleland, P.T. Stukenberg, D.J. Burke, M.M. Ross, J. Shabanowitz, D.F. Hunt, F.M. White, Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae, Nat. Biotechnol. 20 (2002) 301 – 305. [49] Y. Oda, T. Nagasu, B.T. Chait, Enrichment analysis of phosphorylated proteins as a tool for probing the phosphoproteome, Nat. Biotechnol. 19 (2001) 379 – 382. [50] H. Zhou, J.D. Watts, R. Aebersold, A systematic approach to the analysis of protein phosphorylation, Nat. Biotechnol. 19 (2001) 375 – 378.

[51] M. Mann, S.E. Ong, M. Gronborg, H. Steen, O.N. Jensen, A. Pandey, Analysis of protein phosphorylation using mass spectrometry: deciphering the phosphoproteome, Trends Biotechnol. 20 (2002) 261 – 268. [52] D.T. McLachlin, B.T. Chait, Analysis of phosphorylated proteins and peptides by mass spectrometry, Curr. Opin. Chem. Biol. 5 (2001) 591 – 602. [53] D.G. Campbell, N.A. Morrice, Identification of protein phosphorylation sites by a combination of mass spectrometry and solid phase Edman sequencing, J. Biomol. Tech. 13 (2002) 119 – 130. [54] A.J. Mackey, T.A. Haystead, W.R. Pearson, CRP: cleavage of radiolabeled phosphoproteins, Nucleic Acids Res. 31 (2003) 3859 – 3861. [55] Z.A. Knight, B. Schilling, R.H. Row, D.M. Kenski, B.W. Gibson, K.M. Shokat, Phosphospecific proteolysis for mapping sites of protein phosphorylation, Nat. Biotechnol. 21 (2003) 1047 – 1054. [56] S.A. Beausoleil, M. Jedrychowski, D. Schwartz, J.E. Elias, J. Villen, J. Li, M.A. Cohn, L.C. Cantley, S.P. Gygi, Large-scale characterization of HeLa cell nuclear phosphoproteins, Proc. Natl. Acad. Sci. U. S. A. 101 (2004) 12130 – 12135. [57] K. Godl, J. Wissing, A. Kurtenbach, P. Habenberger, S. Blencke, H. Gutbrod, K. Salassidis, M. Stein-Gerlach, A. Missio, M. Cotton, et al., An efficient proteomics method to identify the cellular targets of protein kinase inhibitors, Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 15434 – 15439. [58] S.A. Johnson, T. Hunter, Kinomics: methods for deciphering the kinome, Nat. Methods 2 (2005) 17 – 25. [59] C.E. Sims, N.L. Allbritton, Single-cell kinase assays: opening a window onto cell behavior, Curr. Opin. Biotechnol. 14 (2003) 23 – 28. [60] O.D. Perez, G.P. Nolan, Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry, Nat. Biotechnol. 20 (2002) 155 – 162. [61] P.J. Verveer, F.S. Wouters, A.R. Reynolds, P.I. Bastiaens, Quantitative imaging of lateral ErbB1 receptor signal propagation in the plasma membrane, Science 290 (2000) 1567 – 1570. [62] A. Miyawaki, Visualization of the spatial and temporal dynamics of intracellular signaling, Dev. Cell 4 (2003) 295 – 305. [63] E. Oancea, M.N. Teruel, A.F. Quest, T. Meyer, Green fluorescent protein (GFP)-tagged cysteine-rich domains from protein kinase C as fluorescent indicators for diacylglycerol signaling in living cells, J. Cell Biol. 140 (1998) 485 – 498. [64] A. Toutchkine, V. Kraynov, K. Hahn, Solvent-sensitive dyes to report protein conformational changes in living cells, J. Am. Chem. Soc. 125 (2003) 4132 – 4145. [65] Z. Songyang, S. Blechner, N. Hoagland, M.F. Hoekstra, H. PiwnicaWorms, L.C. Cantley, Use of an oriented peptide library to determine the optimal substrates of protein kinases, Curr. Biol. 4 (1994) 973 – 982. [66] K. Fujii, G. Zhu, Y. Liu, J. Hallam, L. Chen, J. Herrero, S. Shaw, Kinase peptide specificity: improved determination and relevance to protein phosphorylation, Proc. Natl. Acad. Sci. U. S. A. 101 (2004) 13744 – 13749. [67] U. Reimer, U. Reineke, J. Schneider-Mergener, Peptide arrays: from macro to micro, Curr. Opin. Biotechnol. 13 (2002) 315 – 320. [68] L. Rychlewski, M. Kschischo, L. Dong, M. Schutkowski, U. Reimer, Target specificity analysis of the Abl kinase using peptide microarray data, J. Mol. Biol. 336 (2004) 307 – 311. [69] A.M. Wollacott, J.R. Desjarlais, Virtual interaction profiles of proteins, J. Mol. Biol. 313 (2001) 317 – 342. [70] B. Brannetti, A. Via, G. Cestra, G. Cesareni, M. Helmer-Citterich, SH3SPOT: an algorithm to predict preferred ligands to different members of the SH3 gene family, J. Mol. Biol. 298 (2000) 313 – 328. [71] P. Aloy, R.B. Russell, Interrogating protein interaction networks through structural biology, Proc. Natl. Acad. Sci. U. S. A. 99 (2002) 5896 – 5901. [72] T. Kortemme, D. Baker, Computational design of protein – protein interactions, Curr. Opin. Chem. Biol. 8 (2004) 91 – 97. [73] A. Bairoch, P. Bucher, K. Hofmann, The PROSITE database, its status in 1997, Nucleic Acids Res. 25 (1997) 217 – 221. [74] P. Puntervoll, R. Linding, C. Gemund, S. Chabanis-Davidson, M. Mattingsdal, S. Cameron, D.M. Martin, G. Ausiello, B. Brannetti, A. Costantini, et al., ELM server: a new resource for investigating short

B. Kobe et al. / Biochimica et Biophysica Acta 1754 (2005) 200 – 209

[75]

[76]

[77] [78]

[79]

[80]

[81]

[82] [83]

[84]

[85]

functional sites in modular eukaryotic proteins, Nucleic Acids Res. 31 (2003) 3625 – 3630. C.M. Smith, I.N. Shindyalov, S. Veretnik, M. Gribskov, S.S. Taylor, L.F. Ten Eyck, P.E. Bourne, The protein kinase resource, Trends Biochem. Sci. 22 (1997) 444 – 446. S. Peri, J.D. Navarro, R. Amanchy, T.Z. Kristiansen, C.K. Jonnalagadda, V. Surendranath, V. Niranjan, B. Muthusamy, T.K. Gandhi, M. Gronborg, et al., Development of human protein reference database as an initial platform for approaching systems biology in humans, Genome Res. 13 (2003) 2363 – 2371. J.S. Garavelli, The RESID database of protein modifications: 2003 developments, Nucleic Acids Res. 31 (2003) 499 – 501. N. Blom, S. Gammeltoft, S. Brunak, Sequence and structure-based prediction of eukaryotic protein phosphorylation sites, J. Mol. Biol. 294 (1999) 1351 – 1362. M. Hjerrild, A. Stensballe, T.E. Rasmussen, C.B. Kofoed, N. Blom, T. Sicheritz-Ponten, M.R. Larsen, S. Brunak, O.N. Jensen, S. Gammeltoft, Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry, J. Proteome Res. 3 (2004) 426 – 433. M.B. Yaffe, G.G. Leparc, J. Lai, T. Obata, S. Volinia, L.C. Cantley, A motif-based profile scanning approach for genome-wide prediction of signaling pathways, Nat. Biotechnol. 19 (2001) 348 – 353. J.C. Obenauer, L.C. Cantley, M.B. Yaffe, Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs, Nucleic Acids Res. 31 (2003) 3635 – 3641. J.H. Kim, J. Lee, B. Oh, K. Kimm, I. Koh, Prediction of phosphorylation sites using SVMs, Bioinformatics 20 (2004) 3179 – 3184. D. Plewczynski, A. Tkacz, L.S. Wyrwicz, L. Rychlewski, AutoMotif server: prediction of single residue post-translational modifications in proteins, Bioinformatics 21 (2005) 2525 – 2527. F.F. Zhou, Y. Xue, G.L. Chen, X. Yao, GPS: a novel group-based phosphorylation predicting and scoring method, Biochem. Biophys. Res. Commun. 325 (2004) 1443 – 1448. Z. Songyang, K.P. Lu, Y.T. Kwon, L.-H. Tsai, O. Filhol, C. Cochet, D.A. Brickey, T.R. Soderling, C. Bartleson, D.J. Graves, et al., A structural basis for substrate specificities of protein Ser/Thr kinases: primary sequence preference of casein kinases I and II, NIMA, phosphorylase

[86]

[87]

[88]

[89]

[90]

[91]

[92]

[93]

[94]

[95]

209

kinase, calmodulin-dependent kinase II, CDK5, and Erk1, Mol. Cell. Biol. 16 (1996) 6486 – 6493. R.I. Brinkworth, J. Horne, B. Kobe, A computational analysis of substrate binding strength by phosphorylase kinase and protein kinase A, J. Mol. Recognit. 15 (2002) 104 – 111. M. Sun, X.H. Liu, S.H. Ji, Y.F. Zhao, Modeling kinase-substrate specificity: implication of the distance between substrate nucleophilic oxygen and attacked phosphorus of ATP analog on binding affinity, J. Mol. Graphics Modell. 23 (2005) 433 – 438. L. Li, E.I. Shakhnovich, L.A. Mirny, Amino acids determining enzymesubstrate specificity in prokaryotic and eukaryotic protein kinases, Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 4463 – 4468. J.W. Scott, D.G. Norman, S.A. Hawley, L. Kontogiannis, D.G. Hardie, Protein kinase substrate recognition studied using the recombinant catalytic domain of AMP-activated protein kinase and a model substrate, J. Mol. Biol. 317 (2002) 309 – 323. A. Dinudom, A.B. Fotia, R.J. Lefkowitz, J.A. Young, S. Kumar, D.I. Cook, The kinase Grk2 regulates Nedd4/Nedd4-2-dependent control of epithelial Na+ channels, Proc. Natl. Acad. Sci. U. S. A. 101 (2004) 11886 – 11890. A. Kelner, I. Pekala, S. Kaczanowski, G. Muszynska, D.G. Hardie, G. Dobrowolska, Biochemical characterization of the tobacco 42-kD protein kinase activated by osmotic stress, Plant Physiol. 136 (2004) 3255 – 3265. N.W. Court, I. Kuo, O. Quigley, M.A. Bogoyevitch, Phosphorylation of the mitochondrial protein Sab by stress-activated protein kinase 3, Biochem. Biophys. Res. Commun. 319 (2004) 130 – 137. J.L. Sussman, D. Lin, J. Jiang, N.O. Manning, J. Prilusky, O. Ritter, E.E. Abola, Protein data bank (PDB): database of three-dimensional structural information of biological macromolecules, Acta Crystallogr., D Biol. Crystallogr. 54 (1998) 1078 – 1084. Madhusudan, E.A. Trafny, N.-H. Xuong, J.A. Adams, L.F. Ten Eyck, S.S. Taylor, J.M. Sowadski, cAMP-dependent protein kinase: crystallographic insights into substrate recognition and phosphotransfer, Protein Sci. 3 (1994) 176 – 187. B. Boeckmann, A. Bairoch, R. Apweiler, M.C. Blatter, A. Estreicher, E. Gasteiger, M.J. Martin, K. Michoud, C. O’Donovan, I. Phan, et al., The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003, Nucleic Acids Res. 31 (2003) 365 – 370.