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Membrane Allostery and Unique Hydrophobic Sites Promote Enzyme Substrate Specificity.

ABSTRACT: We demonstrate that lipidomics coupled with molecular dynamics reveal unique phospholipase A2 specificity toward membrane phospholipid substrates. We discovered unexpected headgroup and acyl-chain specificity for three major human phospholipases A2. The differences between each enzyme's specificity, coupled with molecular dynamics-based structural and binding studies, revealed unique binding sites and interfacial surface binding moieties for each enzyme that explain the observed specificity at a hitherto inaccessible structural level. Surprisingly, we discovered that a unique hydrophobic binding site for the cleaved fatty acid dominates each enzyme's specificity rather than its catalytic residues and polar headgroup binding site. Molecular dynamics simulations revealed the optimal phospholipid binding mode leading to a detailed understanding of the preference of cytosolic phospholipase A2 for cleavage of proinflammatory arachidonic acid, calcium-independent phospholipase A2, which is involved in membrane remodeling for cleavage of linoleic acid and for antibacterial secreted phospholipase A2 favoring linoleic acid, saturated fatty acids, and phosphatidylglycerol.

SUBMITTER: Mouchlis VD

PROVIDER: S-EPMC5846079 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Membrane Allostery and Unique Hydrophobic Sites Promote Enzyme Substrate Specificity.

Mouchlis Varnavas D VD Chen Yuan Y McCammon J Andrew JA Dennis Edward A EA

Journal of the American Chemical Society 20180226 9

We demonstrate that lipidomics coupled with molecular dynamics reveal unique phospholipase A<sub>2</sub> specificity toward membrane phospholipid substrates. We discovered unexpected headgroup and acyl-chain specificity for three major human phospholipases A<sub>2</sub>. The differences between each enzyme's specificity, coupled with molecular dynamics-based structural and binding studies, revealed unique binding sites and interfacial surface binding moieties for each enzyme that explain the obs ...[more]

PMID: 29342349

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Project description:BACKGROUND: Protein O-GlcNAcylation, involving the attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues. Elucidation of O-GlcNAcylation sites on proteins is required in order to decipher its crucial roles in regulating cellular processes and aid in drug design. With an increasing number of O-GlcNAcylation sites identified by mass spectrometry (MS)-based proteomics, several methods have been proposed for the computational identification of O-GlcNAcylation sites. However, no development that focuses on the investigation of O-GlcNAcylated substrate motifs has existed. Thus, we were motivated to design a new method for the identification of protein O-GlcNAcylation sites with the consideration of substrate site specificity. RESULTS: In this study, 375 experimentally verified O-GlcNAcylation sites were collected from dbOGAP, which is an integrated resource for protein O-GlcNAcylation. Due to the difficulty in characterizing the substrate motifs by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. To construct the predictive models learned from the identified substrate motifs, we adopted Support Vector Machines (SVMs). A five-fold cross validation was used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 0.76, 0.80, and 0.78, respectively. Additionally, an independent testing set, which was really blind to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (0.94) and outperform three other O-GlcNAcylation site prediction tools. CONCLUSION: This work proposed a computational method to identify informative substrate motifs for O-GlcNAcylation sites. The evaluation of cross validation and independent testing indicated that the identified motifs were effective in the identification of O-GlcNAcylation sites. A case study demonstrated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation. We also anticipated that the revealed substrate motif may facilitate the study of extensive crosstalk between O-GlcNAcylation and phosphorylation. This method may help unravel their mechanisms and roles in signaling, transcription, chronic disease, and cancer.

Project description:Viruses infect humans and progress inside the body leading to various diseases and complications. The phosphorylation of viral proteins catalyzed by host kinases plays crucial regulatory roles in enhancing replication and inhibition of normal host-cell functions. Due to its biological importance, there is a desire to identify the protein phosphorylation sites on human viruses. However, the use of mass spectrometry-based experiments is proven to be expensive and labor-intensive. Furthermore, previous studies which have identified phosphorylation sites in human viruses do not include the investigation of the responsible kinases. Thus, we are motivated to propose a new method to identify protein phosphorylation sites with its kinase substrate specificity on human viruses. The experimentally verified phosphorylation data were extracted from virPTM--a database containing 301 experimentally verified phosphorylation data on 104 human kinase-phosphorylated virus proteins. In an attempt to investigate kinase substrate specificities in viral protein phosphorylation sites, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. The experimental human phosphorylation sites are collected from Phospho.ELM, grouped according to its kinase annotation, and compared with the virus MDD clusters. This investigation identifies human kinases such as CK2, PKB, CDK, and MAPK as potential kinases for catalyzing virus protein substrates as confirmed by published literature. Profile hidden Markov model is then applied to learn a predictive model for each subgroup. A five-fold cross validation evaluation on the MDD-clustered HMMs yields an average accuracy of 84.93% for Serine, and 78.05% for Threonine. Furthermore, an independent testing data collected from UniProtKB and Phospho.ELM is used to make a comparison of predictive performance on three popular kinase-specific phosphorylation site prediction tools. In the independent testing, the high sensitivity and specificity of the proposed method demonstrate the predictive effectiveness of the identified substrate motifs and the importance of investigating potential kinases for viral protein phosphorylation sites.

Dataset Information

Membrane Allostery and Unique Hydrophobic Sites Promote Enzyme Substrate Specificity.

Publications

Membrane Allostery and Unique Hydrophobic Sites Promote Enzyme Substrate Specificity.

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