In silico studies on DARC

Implication of biosignatures in the progression of endometriosis

Endometriosis is an estrogen-dependent chronic inflammatory disorder involving the placement and growth of endometrial tissue outside the uterine cavity. It is the most common multifactorial disease that affects the life quality of women in reproductive age. Due to its multicomponent nature, early diagnosis of the disease is challenging. Since many genetic, epigenetic alterations and non-genetic factors contribute to the pathology of endometriosis, devising a drug therapy that directly acts on the ectopic tissue is extremely difficult. Endometriosis is a hormone-driven disease with estrogen considered as a primary driver for the development of endometriotic lesions. This study aims to identify biosignatures involved in endometriosis with and without gonadotropin releasing hormone agonists (GnRHa). GnRHa is a short peptide analog of GnRH that causes inhibition of estrogen and androgen synthesis. Microarray based-gene expression profiling was performed on total RNA extracted from endometriotic tissue samples with and without GnRHa-treated patients already published in our previous paper. The untreated group were considered as the control. Genes were then selected for validation by quantitative real-time polymerase chain reaction (qRT-PCR). qRT-PCR analysis confirmed significant downregulation in(p < 0.05) expression of DARC (p = 0.0042), CDH1 (p = 0.0027), CDH5 (p = 0.0283), ATP2A3 (p < 0.001), RGS5 (p = 0.0032), and CD36 (p = 0.0162) in endometriosis patients treated with GnRHa analogs. Although, CTNNAL1 (p = 0.0136) also showed significant results but there was upregulation in their expression levels after GnRHa treatment. Thus, an altered expression of these genes makes them a possible candidate determinant of endometriosis treated with GnRHa.

Structural Space of the Duffy Antigen/Receptor for Chemokines' Intrinsically Disordered Ectodomain 1 Explored by Temperature Replica-Exchange Molecular Dynamics Simulations

Plasmodium vivax malaria affects 14 million people each year. Its invasion requires interactions between the parasitic Duffy-binding protein (PvDBP) and the N-terminal extracellular domain (ECD1) of the host's Duffy antigen/receptor for chemokines (DARC). ECD1 is highly flexible and intrinsically disordered, therefore it can adopt different conformations. We computationally modeled the challenging ECD1 local structure. With T-REMD simulations, we sampled its dynamic behavior and collected its most representative conformations. Our results suggest that most of the DARC ECD1 domain remains in a disordered state during the simulated time. Globular local conformations are found in the analyzed local free-energy minima. These globular conformations share an α-helix spanning residues Ser18 to Ser29 and in many cases they comprise an antiparallel β-sheet, whose β-strands are formed around residues Leu10 and Ala49. The formation of a parallel β-sheet is almost negligible. So far, progress in understanding the mechanisms forming the basis of the P. vivax malaria infection of reticulocytes has been hampered by experimental difficulties, along with a lack of DARC structural information. Our collection of the most probable ECD1 structural conformations will help to advance modeling of the DARC structure and to explore DARC-ECD1 interactions with a range of physiological and pathological ligands.

Prospects for targeting ACKR1 in cancer and other diseases

The chemokine network is comprised of a family of signal proteins that encode messages for cells displaying chemokine G-protein coupled receptors (GPCRs). The diversity of effects on cellular functions, particularly directed migration of different cell types to sites of inflammation, is enabled by different combinations of chemokines activating signal transduction cascades on cells displaying a combination of receptors. These signals can contribute to autoimmune disease or be hijacked in cancer to stimulate cancer progression and metastatic migration. Thus far, three chemokine receptor-targeting drugs have been approved for clinical use: Maraviroc for HIV, Plerixafor for hematopoietic stem cell mobilization, and Mogalizumab for cutaneous T-cell lymphoma. Numerous compounds have been developed to inhibit specific chemokine GPCRs, but the complexity of the chemokine network has precluded more widespread clinical implementation, particularly as anti-neoplastic and anti-metastatic agents. Drugs that block a single signaling axis may be rendered ineffective or cause adverse reactions because each chemokine and receptor often have multiple context-specific functions. The chemokine network is tightly regulated at multiple levels, including by atypical chemokine receptors (ACKRs) that control chemokine gradients independently of G-proteins. ACKRs have numerous functions linked to chemokine immobilization, movement through and within cells, and recruitment of alternate effectors like β-arrestins. Atypical chemokine receptor 1 (ACKR1), previously known as the Duffy antigen receptor for chemokines (DARC), is a key regulator that binds chemokines involved in inflammatory responses and cancer proliferation, angiogenesis, and metastasis. Understanding more about ACKR1 in different diseases and populations may contribute to the development of therapeutic strategies targeting the chemokine network.

In-silico Screening of Phytoconstituents on Wound Healing Targets-Approaches and Current Status

Over recent years, there has been tremendous research focused on the effective utilization of natural products in wound management. Natural or herbal products contain several phytoconstituents that may act on various stages in wound healing and thereby provide a multi-targeted approach especially in the treatment of chronic wounds. Currently, attempts have been made to screen the phytoconstituents present in herbs on various targets involved in wound healing. This review includes a systematic evaluation of scientific reports by various groups of researchers on the herbals evaluated for wound management, their phytochemical profiling, pre-clinical studies, and molecular modeling studies. Various wound targets discussed include Interleukin-1, Interleukin-6, Tumor necrosis factor-α (TNF-α), Thymosin beta-4 (Tβ-4) that regulate the early inflammatory stage and the novel T cell immune response cDNA 7(TIRC7) that regulates angiogenesis. Also, neuropeptides P and Y act on the inflammatory, migratory, and proliferation phases, and growth factors like vascular endothelial growth factor family (VEGF) and placental growth factor family (PGF) are involved in angiogenesis, while the role of Fibroblast growth factor in tissue remodeling is discussed. As many of the natural products include polyherbal systems, this approach can help in the judicious selection of a combination of herbs that will act on multiple targets in the wound healing process and provide a multi-factorial approach in wound management.

Discrete analyses of protein dynamics

Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how β-strand, β-turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β-turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. AbbreviationsN_eqnumber of equivalentPBProtein BlocksPDBProtein DataBankRMSfroot mean square fluctuationsCommunicated by Ramaswamy H. Sarma.

Repository of Enriched Structures of Proteins Involved in the Red Blood Cell Environment (RESPIRE)

The Red Blood Cell (RBC) is a metabolically-driven cell vital for processes such a gas transport and homeostasis. RBC possesses at its surface exposing antigens proteins that are critical in blood transfusion. Due to their importance, numerous studies address the cell function as a whole but more and more details of RBC structure and protein content are now studied using massive state-of-the art characterisation techniques. Yet, the resulting information is frequently scattered in many scientific articles, in many databases and specialized web servers. To provide a more compendious view of erythrocytes and of their protein content, we developed a dedicated database called RESPIRE that aims at gathering a comprehensive and coherent ensemble of information and data about proteins in RBC. This cell-driven database lists proteins found in erythrocytes. For a given protein entry, initial data are processed from external portals and enriched by using state-of-the-art bioinformatics methods. As structural information is extremely useful to understand protein function and predict the impact of mutations, a strong effort has been put on the prediction of protein structures with a special treatment for membrane proteins. Browsing the database is available through text search for reference gene names or protein identifiers, through pre-defined queries or via hyperlinks. The RESPIRE database provides valuable information and unique annotations that should be useful to a wide audience of biologists, clinicians and structural biologists.

Database URL:http://www.dsimb.inserm.fr/respire

Protein flexibility in the light of structural alphabets

Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.

A short survey on protein blocks

Protein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications.

Multiple interests in structural models of DARC transmembrane protein

Duffy Antigen Receptor for Chemokines (DARC) is an unusual transmembrane chemokine receptor which (i) binds the two main chemokine families and (ii) does not transduct any signal as it lacks the DRY consensus sequence. It is considered as silent chemokine receptor, a tank useful for chemiotactism. DARC had been particularly studied as a major actor of malaria infection by Plasmodium vivax. It is also implicated in multiple chemokine inflammation, inflammatory diseases, in cancer and might play a role in HIV infection and AIDS. In this review, we focus on the interest to build structural model of DARC to understand more precisely its abilities to bind its physiological ligand CXCL8 and its malaria ligand. We also present innovative development on VHHs able to bind DARC protein. We underline difficulties and limitations of such bioinformatics approaches and highlight the crucial importance of biological data to conduct these kinds of researches.

Protein short loop prediction in terms of a structural alphabet

Loops connect regular secondary structures. In many instances, they are known to play crucial biological roles. To bypass the limitation of secondary structure description, we previously defined a structural alphabet composed of 16 structural prototypes, called Protein Blocks (PBs). It leads to an accurate description of every region of 3D protein backbones and has been used in local structure prediction. In the present study, we used our structural alphabet to predict the loops connecting two repetitive structures. Thus, we showed interest to take into account the flanking regions, leading to prediction rate improvement up to 19.8%, but we also underline the sensitivity of such an approach. This research can be used to propose different structures for the loops and to probe and sample their flexibility. It is a useful tool for ab initio loop prediction and leads to insights into flexible docking approach.