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Concentration-Dependent Binding of Small Ligands to Multiple Saturable Sites in Membrane Proteins.

ABSTRACT: Membrane proteins are primary targets for most therapeutic indications in cancer and neurological diseases, binding over 50% of all known small molecule drugs. Understanding how such ligands impact membrane proteins requires knowledge on the molecular structure of ligand binding, a reasoning that has driven relentless efforts in drug discovery and translational research. Binding of small ligands appears however highly complex involving interaction to multiple transmembrane protein sites featuring single or multiple occupancy states. Within this scenario, looking for new developments in the field, we investigate the concentration-dependent binding of ligands to multiple saturable sites in membrane proteins. The study relying on docking and free-energy perturbation provides us with an extensive description of the probability density of protein-ligand states that allows for computation of thermodynamic properties of interest. It also provides one- and three-dimensional spatial descriptions for the ligand density across the protein-membrane system which can be of interest for structural purposes. Illustration and discussion of the results are shown for binding of the general anesthetic sevoflurane against Kv1.2, a mammalian ion channel for which experimental data are available.

SUBMITTER: Stock L

PROVIDER: S-EPMC5516019 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Concentration-Dependent Binding of Small Ligands to Multiple Saturable Sites in Membrane Proteins.

Stock Letícia L Hosoume Juliana J Treptow Werner W

Scientific reports 20170718 1

Membrane proteins are primary targets for most therapeutic indications in cancer and neurological diseases, binding over 50% of all known small molecule drugs. Understanding how such ligands impact membrane proteins requires knowledge on the molecular structure of ligand binding, a reasoning that has driven relentless efforts in drug discovery and translational research. Binding of small ligands appears however highly complex involving interaction to multiple transmembrane protein sites featurin ...[more]

PMID: 28720769

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Project description:BACKGROUND: Many 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. RESULTS: We 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. CONCLUSION: Given 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:The study of protein-small molecule interactions is vital for understanding protein function and for practical applications in drug discovery. To benefit from the rapidly increasing structural data, it is essential to improve the tools that enable large scale binding site prediction with greater emphasis on their biological validity.We have developed a new method for the annotation of protein-small molecule binding sites, using inference by homology, which allows us to extend annotation onto protein sequences without experimental data available. To ensure biological relevance of binding sites, our method clusters similar binding sites found in homologous protein structures based on their sequence and structure conservation. Binding sites which appear evolutionarily conserved among non-redundant sets of homologous proteins are given higher priority. After binding sites are clustered, position specific score matrices (PSSMs) are constructed from the corresponding binding site alignments. Together with other measures, the PSSMs are subsequently used to rank binding sites to assess how well they match the query and to better gauge their biological relevance. The method also facilitates a succinct and informative representation of observed and inferred binding sites from homologs with known three-dimensional structures, thereby providing the means to analyze conservation and diversity of binding modes. Furthermore, the chemical properties of small molecules bound to the inferred binding sites can be used as a starting point in small molecule virtual screening. The method was validated by comparison to other binding site prediction methods and to a collection of manually curated binding site annotations. We show that our method achieves a sensitivity of 72% at predicting biologically relevant binding sites and can accurately discriminate those sites that bind biological small molecules from non-biological ones.A new algorithm has been developed to predict binding sites with high accuracy in terms of their biological validity. It also provides a common platform for function prediction, knowledge-based docking and for small molecule virtual screening. The method can be applied even for a query sequence without structure. The method is available at http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi.

Dataset Information

Concentration-Dependent Binding of Small Ligands to Multiple Saturable Sites in Membrane Proteins.

Publications

Concentration-Dependent Binding of Small Ligands to Multiple Saturable Sites in Membrane Proteins.

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