Molecular Determinants for ATP-binding in Proteins: A Data Mining and Quantum Chemical Analysis

Mao, Lisong; Wang, Yanli; Liu, Yuemin; Hu, Xiche

doi:10.1016/j.jmb.2003.12.056

Cited by 120 publications

(103 citation statements)

References 120 publications

(49 reference statements)

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“…Because the aromatic ring of Tyr 103 is important for binding BITC; the aromatic rings of both Tyr 103 and BITC have been positioned appropriately for Pi-Pi stacking interactions, i.e. the rings should be Ͻ5.6 Å apart (45). The distance between the reactive carbon of BITC and the -SH of glutathione is ϳ2.4 Å, and the distance between the two aromatics is ϳ4 Å.…”

Section: Discussionmentioning

confidence: 99%

“…Ahn et al (38) studied Y108A and Y108F, showing little change in the kinetic parameters for CDNB as substrate. In peak IIa, masses containing BITC correspond to Cys 47 (271.3 atomic mass units) and 45 ASC 47 (429.6 atomic mass units). From these data, the modified peptide from peak IIa is 45 ASC 47 .…”

Section: Table II Extent Of Inactivation Of Hgst P1-1 By 100 M Bitc Imentioning

confidence: 99%

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Glutathione S-Transferase Pi Has at Least Three Distinguishable Xenobiotic Substrate Sites Close to Its Glutathione-binding Site

Ralat

Colman

2004

Journal of Biological Chemistry

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Benzyl isothiocyanate (BITC), present in cruciferous vegetables, is an efficient substrate of human glutathione S-transferase P1-1 (hGST P1-1). BITC also acts as an affinity label of hGST P1-1 in the absence of glutathione, yielding an enzyme inactive toward BITC as substrate. As monitored by using BITC as substrate, the dependence of k of inactivation (K I ) of hGST P1-1 on [BITC] is hyperbolic, with K I ‫؍‬ 66 ؎ 7 M. The enzyme incorporates 2 mol of BITC/mol of enzyme subunit upon complete inactivation. S-Methylglutathione and 8-anilino-1-naphthalene sulfonate (ANS) each yield partial protection against inactivation and decrease reagent incorporation, whereas S-(N-benzylthiocarbamoyl)glutathione or S-methylglutathione ؉ ANS protects completely. Mapping of proteolytic digests of modified enzyme by using mass spectrometry reveals that Tyr 103 and Cys 47 are modified equally. S-Methylglutathione reduces modification of Cys 47 , indicating this residue is at/near the glutathione binding region, whereas ANS decreases modification of Tyr 103 , suggesting this residue is at/near the BITC substrate site, which is also near the binding site of ANS. The Y103F and Y103S mutant enzymes were generated, expressed, and purified. Both mutants handle substrate 1-chloro-2,4-dinitrobenzene normally; however, Y103S exhibits a 30-fold increase in K m for BITC and binds ANS poorly, whereas Y103F has a normal K m for BITC and K d for ANS. These results indicate that an aromatic residue at position 103 is essential for the binding of BITC and ANS. This study provides evidence for the existence of a novel xenobiotic substrate site in hGST P1-1, which can be occupied by benzyl isothiocyanate and is distinct from that of monobromobimane and 1-chloro-2,4 dinitrobenzene.

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Section: Discussionmentioning

confidence: 99%

Section: Table II Extent Of Inactivation Of Hgst P1-1 By 100 M Bitc Imentioning

confidence: 99%

Glutathione S-Transferase Pi Has at Least Three Distinguishable Xenobiotic Substrate Sites Close to Its Glutathione-binding Site

Ralat

Colman

2004

Journal of Biological Chemistry

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“…89 Innovative novel approaches have been proposed, that is, the use of hydrophobicity distribution on protein structures using the fuzzy oil drop model, 59 the destabilization of limited protein regions, 90 phylogenomic classification of protein sequences, 91 or the classification of known protein catalytic sites. 92 Prediction of protein functional sites is an important step in identifying small-molecule interactions for drug discovery 93 and to optimize the drugs targeting these sites. 94 Another valuable application is as a preprocessing step to reduce search space for rigorous computational docking algorithms.…”

Section: Methods To Compare Binding Sitesmentioning

confidence: 99%

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

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“…The molecular basis for the recognition of the adenine portion of adenylate molecules, including ATP, has recently been systematically analysed based on 68 crystal structures of at least 2.5 Å resolution available in the Protein Data Bank (Mao et al, 2004). This analysis has revealed that protein-adenine interactions are dominated by hydrogen bonds, stacking (p-p interactions) between aromatic residues (Phe, Tyr and Trp) with the adenine base, and interactions between the positively charged side-chains of Lys or Arg with the adenine aromatic ring (cation-p interactions; Mao et al, 2003).…”

Section: +mentioning

confidence: 99%

“…This analysis has revealed that protein-adenine interactions are dominated by hydrogen bonds, stacking (p-p interactions) between aromatic residues (Phe, Tyr and Trp) with the adenine base, and interactions between the positively charged side-chains of Lys or Arg with the adenine aromatic ring (cation-p interactions; Mao et al, 2003). On average, each adenineprotein interaction involves 2.7 hydrogen bonds, 1 p-p stacking interaction and 0.8 cation-p interactions (Mao et al, 2004). In the case of hydrogen-bonding interactions, these most often involve main-chain atoms, rather than side-chains, resulting in adenine-binding structural motifs that are not easily recognizable at the sequence level alone.…”

Section: +mentioning

confidence: 99%

ATP ‐binding Motifs

Matte

Delbaere

2010

Encyclopedia of Life Sciences

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Adenosine 5′‐triphosphate (ATP) binds to a great number of proteins to elicit a wide variety of effects, including energy production and molecular signalling. Proteins have evolved different strategies to specifically recognize ATP, utilizing different ways of binding the phosphoryl moieties as well as the adenine base. The most common, conserved sequence and structural motif for binding ATP is the Walker‐A motif, or P‐loop, found in many different protein structural families. Greater variation in the sequence of the P‐loop is being recognized, as more ATP‐binding proteins are being structurally and functionally characterized. In contrast to the P‐loop, recognition of the adenine base often makes use of conserved structural motifs of main‐chain atoms via hydrogen‐bonding interactions, or side‐chains in stacking interactions, without a definitive amino acid sequence pattern. Key concepts: A major class of ATP‐binding proteins are those that contain a P‐loop or Walker‐A motif. P‐loops or glycine‐rich loops function by binding the phosphoryl groups of ATP. Several sequence variations on the Walker‐A motif are now known and have been functionally characterized. The Walker‐B motif contains a conserved acidic residue (Glu/Asp) that functions to bind directly or indirectly a metal ion important in catalysis. Adenine‐binding does not occur through specific sequence motifs, but rather uses a conserved pattern of polar and nonpolar interactions within a structural motif. Both main‐chain hydrogen bonding and aromatic residue stacking contribute to adenine‐binding by proteins.

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Molecular Determinants for ATP-binding in Proteins: A Data Mining and Quantum Chemical Analysis

Cited by 120 publications

References 120 publications

Glutathione S-Transferase Pi Has at Least Three Distinguishable Xenobiotic Substrate Sites Close to Its Glutathione-binding Site

Glutathione S-Transferase Pi Has at Least Three Distinguishable Xenobiotic Substrate Sites Close to Its Glutathione-binding Site

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

ATP ‐binding Motifs

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