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Statistical Analysis of Protein-Ligand Interaction Patterns in Nuclear Receptor RORγ.

ABSTRACT: The receptor RORγ belongs to the nuclear receptor superfamily that senses small signaling molecules and regulates at the gene transcription level. Since RORγ has a high basal activity and plays an important role in immune responses, inhibitors targeting this receptor have been a focus for many studies. The receptor-ligand interaction is complex, and often subtle differences in ligand structure can determine its role as an inverse agonist or an agonist. We examined more than 130 existing RORγ crystal structures that have the same receptor complexed with different ligands. We reported the features of receptor-ligand interaction patterns and the differences between agonist and inverse agonist binding. Specific changes in the contact interaction map are identified to distinguish active and inactive conformations. Further statistical analysis of the contact interaction patterns using principal component analysis reveals a dominant mode which separates allosteric binding vs. canonical binding and a second mode which may indicate active vs. inactive structures. We also studied the nature of constitutive activity by performing a 100-ns computer simulation of apo RORγ. Using constitutively active nuclear receptor CAR as a comparison, we identified a group of conserved contacts that have similar contact strength between the two receptors. These conserved contact interactions, especially a couple key contacts in H11-H12 interaction, can be considered essential to the constitutive activity of RORγ. These protein-ligand and internal protein contact interactions can be useful in the development of new drugs that direct receptor activity.

SUBMITTER: Pham B

PROVIDER: S-EPMC9240913 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Most of the biological processes are governed through specific protein-ligand interactions. Discerning different components that contribute toward a favorable protein- ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of ?68?000 protein-ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein-ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites (84?846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein-ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database-protein-ligand interaction clusters (PLIC). Database URL: http://proline.biochem.iisc.ernet.in/PLIC.

Project description:BACKGROUND: A novel human nuclear receptor interaction protein (NRIP) has recently been discovered by Chen SL et al, which may play a role in enhancing the transcriptional activity of steroid nuclear receptors in prostate (LNCaP) and cervical (C33A) cancer cell lines. However, knowledge about the biological functions and clinical implications of NRIP, is still incomplete. Our aim was to determine the distribution of NRIP expression and to delineate the cell types that express NRIP in various malignant tumors and healthy non-pathological tissues. This information will significantly affect the exploration of its physiological roles in healthy and tumor cells. METHODS: By using tissue microarray (TMA) technology and an anti-NRIP monoclonal antibody immunohistochemical (IHC) survey, NRIP expression was examined in 48 types of tumors and in a control group of 48 matched or unmatched healthy non-neoplastic tissues. RESULTS: Our survey results showed that ten cases were revealed to express the NRIP in six malignancies (esophageal, colon, breast, ovarian, skin, and pancreatic cancers), but not all of these specific tumor types consistently showed positive NRIP expression. Moreover, malignant tumors of the stomach, prostate, liver, lung, kidney, uterine cervix, urinary bladder, lymph node, testis, and tongue revealed no NRIP expression. Among the control group of 48 matched and unmatched non-neoplastic tissues, all of them demonstrated IHC scores less than the cut-off threshold of 3. In addition, ten cores out of thirty-six carcinomatous tissues revealed positive NRIP expression, which indicated that NRIP expression increases significantly in carcinoma tissue cores, comparing to the matched controlled healthy tissues. CONCLUSION: This is the first study to use a human TMA and IHC to validate the nuclear localization for this newly identified NRIP expression. In considering the use of NRIP as a potential diagnostic tool for human malignancies survey, it is important to note that NRIP expression carries a sensitivity of only 23%, but has a specificity of 100%. There is also a significant difference in positive NRIP expression between primary carcinomatous tissues and matched controlled healthy tissues. Although further large-scale studies will merit to be conducted to evaluate its role as a potential adjunct for cancer diagnosis, data from this study provides valuable references for the future investigation of the biological functions of NRIP in humans.

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Statistical Analysis of Protein-Ligand Interaction Patterns in Nuclear Receptor RORγ.

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