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Surface features of a Mononegavirales matrix protein indicate sites of membrane interaction.

ABSTRACT: The matrix protein (M) of respiratory syncytial virus (RSV), the prototype viral member of the Pneumovirinae (family Paramyxoviridae, order Mononegavirales), has been crystallized and the structure determined to a resolution of 1.6 A. The structure comprises 2 compact beta-rich domains connected by a relatively unstructured linker region. Due to the high degree of side-chain order in the structure, an extensive contiguous area of positive surface charge covering approximately 600 A(2) can be resolved. This unusually large patch of positive surface potential spans both domains and the linker, and provides a mechanism for driving the interaction of the protein with a negatively-charged membrane surface or other virion components such as the nucleocapsid. This patch is complemented by regions of high hydrophobicity and a striking planar arrangement of tyrosine residues encircling the C-terminal domain. Comparison of the RSV M sequence with other members of the Pneumovirinae shows that regions of divergence correspond to surface exposed loops in the M structure, with the majority of viral species-specific differences occurring in the N-terminal domain.

SUBMITTER: Money VA

PROVIDER: S-EPMC2657372 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Surface features of a Mononegavirales matrix protein indicate sites of membrane interaction.

Money Victoria A VA McPhee Helen K HK Mosely Jackie A JA Sanderson John M JM Yeo Robert P RP

Proceedings of the National Academy of Sciences of the United States of America 20090226 11

The matrix protein (M) of respiratory syncytial virus (RSV), the prototype viral member of the Pneumovirinae (family Paramyxoviridae, order Mononegavirales), has been crystallized and the structure determined to a resolution of 1.6 A. The structure comprises 2 compact beta-rich domains connected by a relatively unstructured linker region. Due to the high degree of side-chain order in the structure, an extensive contiguous area of positive surface charge covering approximately 600 A(2) can be res ...[more]

PMID: 19251668

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Project description:BackgroundMany integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.ResultsWe present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.ConclusionGiven a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.

Project description:MotivationPrediction of protein-protein interaction has become an important part of systems biology in reverse engineering the biological networks for better understanding the molecular biology of the cell. Although significant progress has been made in terms of prediction accuracy, most computational methods only predict whether two proteins interact but not their interacting residues-the information that can be very valuable for understanding the interaction mechanisms and designing modulation of the interaction. In this work, we developed a computational method to predict the interacting residue pairs-contact matrix for interacting protein domains, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact.ResultsOur method is based on supervised learning using support vector machines. For each domain involved in a given domain-domain interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain family, and then each residue position for a member domain sequence is represented as a 20-dimension vector of Fisher scores, characterizing how similar it is as compared with the family profile at that position. Each element of the contact matrix for a sequence pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs, and is tested by leave-one-out cross validation. The performance averaged over a set of 115 DDIs collected from the 3 DID database shows significant improvement (sensitivity up to 85%, and specificity up to 85%), as compared with a multiple sequence alignment-based method (sensitivity 57%, and specificity 78%) previously reported in the literature.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Surface features of a Mononegavirales matrix protein indicate sites of membrane interaction.

Publications

Surface features of a Mononegavirales matrix protein indicate sites of membrane interaction.

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