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Small-molecule binding sites to explore protein-protein interactions in the cancer proteome.

ABSTRACT: The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeutic agents (druggable). These binding sites were classified as enzyme active sites (ENZ), protein-protein interaction sites (PPI), or other sites whose function is unknown (OTH). Interestingly, the overwhelming majority of binding sites were classified as OTH. We find that ENZ, PPI, and OTH binding sites often occurred on the same structure suggesting that many of these OTH cavities can be used for allosteric modulation of enzyme activity or protein-protein interactions with small molecules. We discovered several ENZ (PYCR1, QPRT, and HSPA6) and PPI (CASC5, ZBTB32, and CSAD) binding sites on proteins that have been seldom explored in cancer. We also found proteins that have been extensively studied in cancer that have not been previously explored with small molecules that harbor ENZ (PKMYT1, STEAP3, and NNMT) and PPI (HNF4A, MEF2B, and CBX2) binding sites. All binding sites were classified by the signaling pathways to which the protein that harbors them belongs using KEGG. In addition, binding sites were mapped onto structural protein-protein interaction networks to identify promising sites for drug discovery. Finally, we identify pockets that harbor missense mutations previously identified from analysis of TCGA data. The occurrence of mutations in these binding sites provides new opportunities to develop small-molecule probes to explore their function in cancer.

SUBMITTER: Xu D

PROVIDER: S-EPMC5030169 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Small-molecule binding sites to explore protein-protein interactions in the cancer proteome.

Xu David D Jalal Shadia I SI Sledge George W GW Meroueh Samy O SO

Molecular bioSystems 20160725 10

The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeuti ...[more]

PMID: 27452673

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Project description:The identification of proteins that interact with small bioactive molecules is a critical but often difficult and time-consuming step in understanding cellular signaling pathways or molecular mechanisms of drug action. Numerous methods for identifying small molecule-interacting proteins have been developed and utilized, including affinity-based purification followed by mass spectrometry analysis, protein microarrays, phage display, and three-hybrid approaches. Although all these methods have been used successfully, there remains a need for additional techniques for analyzing small molecule-protein interactions. A promising method for identifying small molecule-protein interactions is affinity-based selection of yeast surface-displayed human proteome libraries. Large and diverse libraries displaying human protein fragments on the surface of yeast cells have been constructed and subjected to FACS-based enrichment followed by comprehensive exon microarray-based output analysis to identify protein fragments with affinity for small molecule ligands. In a recent example, a proteome-wide search has been successfully carried out to identify cellular proteins binding to the signaling lipids PtdIns(4,5)P2 and PtdIns(3,4,5)P3. Known phosphatidylinositide-binding proteins such as pleckstrin homology domains were identified, as well as many novel interactions. Intriguingly, many novel nuclear phosphatidylinositide-binding proteins were discovered. Although the existence of an independent pool of nuclear phosphatidylinositides has been known about for some time, their functions and mechanism of action remain obscure. Thus, the identification and subsequent study of nuclear phosphatidylinositide-binding proteins is expected to bring new insights to this important biological question. Based on the success with phosphatidylinositides, it is expected that the screening of yeast surface-displayed human proteome libraries will be of general use for the discovery of novel small molecule-protein interactions, thus facilitating the study of cellular signaling pathways and mechanisms of drug action or toxicity.

Project description:Background Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand. Results To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome. Conclusions We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Graphical abstract Mapping binding site space. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0180-0) contains supplementary material, which is available to authorized users.

Project description:BackgroundThe 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.ResultsWe 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.ConclusionsA 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

Small-molecule binding sites to explore protein-protein interactions in the cancer proteome.

Publications

Small-molecule binding sites to explore protein-protein interactions in the cancer proteome.

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