SL2: an interactive webtool for modeling of missing segments in proteins

β-Lactoglobulin Enhances Clay and Activated Carbon Binding and Protection Properties for Cadmium and Lead

The removal of heavy metals from wastewater remains a challenge due to the limitations of current remediation methods. This study aims to develop multicomponent composites as inexpensive and environmentally friendly sorbents with enhanced capture of cadmium (Cd) and lead (Pb). The composites are based on calcium montmorillonite (CM) and activated carbon (AC) because of their proven effectiveness as sorbents for diverse toxins in environmental settings. In this study, we used a combination of computational and experimental methods to delineate that β-lactoglobulin enhances CM and AC binding and protection properties for Cd and Pb. Modeling and molecular dynamics simulations investigated the formation of material systems formed by CM and AC in complex with β-lactoglobulin and predicted their capacity to bind heavy metal ions at neutral pH conditions. Our simulations suggest that the enhanced binding properties of the material systems are attributed to the presence of several binding pockets formed by β-lactoglobulin for the two heavy metal ions. At neutral pH conditions, divalent Cd and Pb shared comparable binding propensities in all material systems, with the former being consistently higher than the latter. To validate the interactions depicted in simulations, two ecotoxicological models (L. minor and H. vulgaris) were exposed to Cd, Pb, and a mixture of the two. The inclusion of CM-lactoglobulin (β-lactoglobulin amended CM) and AC-lactoglobulin (β-lactoglobulin amended AC) at 0.05-0.2% efficiently and dose-dependently reduced the severe toxicity of metals and increased the growth parameters. This high efficacy of protection shown in the ecotoxicological models may result from the numerous possible interaction pockets of the β-lactoglobulin-amended materials depicted in simulations. The ecotoxicological models support the agreement with computations. This study serves as a proof of concept on how computations in tandem with experiments can be used in the design of multicomponent clay- and carbon-based sorbent amended systems with augmented functionalities for particular toxins.

Differential glycosylation in mutant vitamin D-binding protein decimates the binding stability of vitamin D

Vitamin D (VD) is produced by the skin upon exposure to sunlight or is obtained from dietary sources. Several risk factors are associated with VD deficiency including mutations and post-translational modifications in its transport protein known as vitamin D binding protein (VDBP) or GC-globulin. The two common single nucleotide polymorphisms rs7041 and rs4588 create three major isoforms of VDBP, including GC-1F also called wild type, GC1S, and GC-2. The 3D models for both GC-1F and GC-2 were constructed in their glycosylated states to decipher the effect of these mutations on the overall conformational changes and VD-binding affinity. The binding affinities were estimated using the Molecular Mechanics Poison-Boltzmann surface area (MM-PBSA) method and conformational changes were investigated after free energy landscapes estimations. Total free energies suggest that GC-1F exhibits stronger affinity (ΔE = -116.09 kJ/mol) than GC-2 (ΔE = -95 kJ/mol) variant with VD. The GC-1F isoforms had more streamlined motion compared to GC-2 isoforms, predicting a trade-off between cross-talk residues that significantly impacts protein structural stability. The data suggest that glycation at Thr418 plays a vital role in the overall VDBP-VD affinity by stabilizing the N-T loop that holds the domain I (VD-pocket) and domain III intact. The loss of glycation in GC-2 has a pivotal role in the inter-domain conformational stability of VDBP, which may ultimately affect VD transportation and maturation. These findings describe a novel mechanism in how mutations distant from the VD-active site change the overall conformational of the VDBP and abrogate the VDBP-VD interaction.Communicated by Ramaswamy H. Sarma.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Proton Paths in Models of the Hv1 Proton Channel

The voltage-gated proton channel (Hv1) plays an essential role in numerous biological processes, but a detailed molecular understanding of its function is lacking. The lack of reliable structures for the open and resting states is a major handicap. Several models have been built based on homologous voltage sensors and the structure of a chimera between the mouse homologue and a phosphatase voltage sensor, but their validity is uncertain. In addition, differing views exist regarding the mode of proton translocation, the role of specific residues, and the mechanism of pH effects on voltage gating. Here we use classical proton hopping simulations under a voltage biasing force to evaluate some of the proposed structural models and explore the mechanism of proton conduction. Paradoxically, some models proposed for the closed state allow for proton permeation more easily than models for the open state. An open state model with a D112-R211 salt bridge (R3D) allows proton transport more easily than models with a D112-R208 salt bridge (R2D). However, its permeation rate seems too high, considering experimental conductances. In all cases, the proton permeates through a water wire, bypassing the salt-bridged D112 rather than being shuttled by D112. Attempts to protonate D112 are rejected due to its strong interaction with an arginine. Consistent with proton selectivity, no Na+ permeation was observed in the R2D models. As a negative control, simulations with the Kv1.2-Kv2.1 paddle-chimera voltage sensor, which is not expected to conduct protons, did not show proton permeation under the same conditions. Hydrogen bond connectivity graphs show a constriction at D112, but cannot discriminate between open and closed states.

Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction

The inhibition of protein-protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein-protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2's prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.

A short HLA-DRA isoform binds the HLA-DR2 heterodimer on the outer domain of the peptide-binding site

The human leukocyte antigen (HLA) locus encodes a large group of proteins governing adaptive and innate immune responses. Among them, HLA class II proteins form α/β heterodimers on the membrane of professional antigen-presenting cells (APCs), where they display both, self and pathogen-derived exogenous antigens to CD4⁺ T lymphocytes. We have previously shown that a shorter HLA-DRA isoform (sHLA-DRA) lacking 25 amino acids can be presented onto the cell membrane via binding to canonical HLA-DR2 heterodimers. Here, we employed atomistic molecular dynamics simulations to decipher the binding position of sHLA-DRA and its structural impact on functional regions of the HLA-DR2 molecule. We show that a loop region exposed only in the short isoform (residues R69 to G83) is responsible for binding HLA-DR2 on the outer domain of the peptide-binding site, and experimentally validated the critical role of F76 in mediating such interaction. Additionally, sHLA-DRA allosterically modifies the peptide-binding pocket conformation. In summary, this study unravels key molecular mechanisms underlying sHLA-DRA function, providing important insights into the role of full-length proteins in structural modulation of HLA class II receptors.

Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators

Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.

Peptide Sequence Mapping around Bisecting GlcNAc-Bearing N-Glycans in Mouse Brain

N-glycosylation is essential for many biological processes in mammals. A variety of N-glycan structures exist, of which, the formation of bisecting N-acetylglucosamine (GlcNAc) is catalyzed by N-acetylglucosaminyltransferase-III (GnT-III, encoded by the Mgat3 gene). We previously identified various bisecting GlcNAc-modified proteins involved in Alzheimer's disease and cancer. However, the mechanisms by which GnT-III acts on the target proteins are unknown. Here, we performed comparative glycoproteomic analyses using brain membranes of wild type (WT) and Mgat3-deficient mice. Target glycoproteins of GnT-III were enriched with E4-phytohemagglutinin (PHA) lectin, which recognizes bisecting GlcNAc, and analyzed by liquid chromatograph-mass spectrometry. We identified 32 N-glycosylation sites (Asn-Xaa-Ser/Thr, Xaa ≠ Pro) that were modified with bisecting GlcNAc. Sequence alignment of identified N-glycosylation sites that displayed bisecting GlcNAc suggested that GnT-III does not recognize a specific primary amino acid sequence. The molecular modeling of GluA1 as one of the good cell surface substrates for GnT-III in the brain, indicated that GnT-III acts on N-glycosylation sites located in a highly flexible and mobile loop of GluA1. These results suggest that the action of GnT-III is partially affected by the tertiary structure of target proteins, which can accommodate bisecting GlcNAc that generates a bulky flipped-back conformation of the modified glycans.

ProMod3-A versatile homology modelling toolbox

Computational methods for protein structure modelling are routinely used to complement experimental structure determination, thus they help to address a broad spectrum of scientific questions in biomedical research. The most accurate methods today are based on homology modelling, i.e. detecting a homologue to the desired target sequence that can be used as a template for modelling. Here we present a versatile open source homology modelling toolbox as foundation for flexible and computationally efficient modelling workflows. ProMod3 is a fully scriptable software platform that can perform all steps required to generate a protein model by homology. Its modular design aims at fast prototyping of novel algorithms and implementing flexible modelling pipelines. Common modelling tasks, such as loop modelling, sidechain modelling or generating a full protein model by homology, are provided as production ready pipelines, forming the starting point for own developments and enhancements. ProMod3 is the central software component of the widely used SWISS-MODEL web-server.

The Dynamics of the Neuropeptide Y Receptor Type 1 Investigated by Solid-State NMR and Molecular Dynamics Simulation

We report data on the structural dynamics of the neuropeptide Y (NPY) G-protein-coupled receptor (GPCR) type 1 (Y1R), a typical representative of class A peptide ligand GPCRs, using a combination of solid-state NMR and molecular dynamics (MD) simulation. First, the equilibrium dynamics of Y1R were studied using 15N-NMR and quantitative determination of 1H-13C order parameters through the measurement of dipolar couplings in separated-local-field NMR experiments. Order parameters reporting the amplitudes of the molecular motions of the C-H bond vectors of Y1R in DMPC membranes are 0.57 for the Cα sites and lower in the side chains (0.37 for the CH2 and 0.18 for the CH3 groups). Different NMR excitation schemes identify relatively rigid and also dynamic segments of the molecule. In monounsaturated membranes composed of longer lipid chains, Y1R is more rigid, attributed to a higher hydrophobic thickness of the lipid membrane. The presence of an antagonist or NPY has little influence on the amplitude of motions, whereas the addition of agonist and arrestin led to a pronounced rigidization. To investigate Y1R dynamics with site resolution, we conducted extensive all-atom MD simulations of the apo and antagonist-bound state. In each state, three replicas with a length of 20 μs (with one exception, where the trajectory length was 10 μs) were conducted. In these simulations, order parameters of each residue were determined and showed high values in the transmembrane helices, whereas the loops and termini exhibit much lower order. The extracellular helix segments undergo larger amplitude motions than their intracellular counterparts, whereas the opposite is observed for the loops, Helix 8, and termini. Only minor differences in order were observed between the apo and antagonist-bound state, whereas the time scale of the motions is shorter for the apo state. Although these relatively fast motions occurring with correlation times of ns up to a few µs have no direct relevance for receptor activation, it is believed that they represent the prerequisite for larger conformational transitions in proteins.

Structural basis of BMP-2 and BMP-7 interactions with antagonists Gremlin-1 and Noggin in Glioblastoma tumors

In Glioblastoma (GBM) brain tumors, both Gremlin-1 and Noggin are reported to bind to BMP and inhibit BMP-signaling, thereby allowing the cell to maintain tumorous morphology. Enlisting the interfacial residues important for protein-protein complex formation between BMPs (BMP-2 and BMP-7) and antagonists (Gremlin-1 and Noggin), we analyzed the structural basis of their interactions. We found possible key mutations that destabilize these complexes, which may prevent GBM development. It was also observed that when the interfacial residues were either mutated to histidine or tryptophan, it led to higher destabilization energy values. Besides, our study of the Noggin interactive model of BMP-2 suggested preferential binding at binding site II over binding site I. In the case of Gremlin-1 and BMPs, our research, along with few previous studies, indicates a close-ended cis-trans interactive model.

A splice acceptor variant in HLA-DRA affects the conformation and cellular localization of the class II DR alpha-chain

Class II human leucocyte antigen (HLA) proteins are involved in the immune response by presenting pathogen-derived peptides to CD4⁺ T lymphocytes. At the molecular level, they are constituted by α/β-heterodimers on the surface of professional antigen-presenting cells. Here, we report that the acceptor variant (rs8084) in the HLA-DRA gene mediates the transcription of an alternative version of the α-chain lacking 25 amino acids in its extracellular domain. Molecular dynamics simulations suggest this isoform undergoes structural refolding which in turn affects its stability and cellular trafficking. The short HLA-DRA isoform cannot reach the cell surface, although it is still able to bind the corresponding β-chain. Conversely, it remains entrapped within the endoplasmic reticulum where it is targeted for degradation. Furthermore, we demonstrate that the short isoform can be transported to the cell membrane via interactions with the peptide-binding site of canonical HLA heterodimers. Altogether, our findings indicate that short HLA-DRA functions as a novel intact antigen for class II HLA molecules.

DaReUS-Loop: a web server to model multiple loops in homology models

Loop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.

Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis

The DNA damage response (DDR) comprises multiple functions that collectively preserve genomic integrity and suppress tumorigenesis. The Mre11 complex and ATM govern a major axis of the DDR and several lines of evidence implicate that axis in tumor suppression. Components of the Mre11 complex are mutated in approximately five percent of human cancers. Inherited mutations of complex members cause severe chromosome instability syndromes, such as Nijmegen Breakage Syndrome, which is associated with strong predisposition to malignancy. And in mice, Mre11 complex mutations are markedly more susceptible to oncogene- induced carcinogenesis. The complex is integral to all modes of DNA double strand break (DSB) repair and is required for the activation of ATM to effect DNA damage signaling. To understand which functions of the Mre11 complex are important for tumor suppression, we undertook mining of cancer genomic data from the clinical sequencing program at Memorial Sloan Kettering Cancer Center, which includes the Mre11 complex among the 468 genes assessed. Twenty five mutations in MRE11 and RAD50 were modeled in S. cerevisiae and in vitro. The mutations were chosen based on recurrence and conservation between human and yeast. We found that a significant fraction of tumor-borne RAD50 and MRE11 mutations exhibited separation of function phenotypes wherein Tel1/ATM activation was severely impaired while DNA repair functions were mildly or not affected. At the molecular level, the gene products of RAD50 mutations exhibited defects in ATP binding and hydrolysis. The data reflect the importance of Rad50 ATPase activity for Tel1/ATM activation and suggest that inactivation of ATM signaling confers an advantage to burgeoning tumor cells.

GPCR-SSFE 2.0-a fragment-based molecular modeling web tool for Class A G-protein coupled receptors

G-protein coupled receptors (GPCRs) are key players in signal transduction and therefore a large proportion of pharmaceutical drugs target these receptors. Structural data of GPCRs are sparse yet important for elucidating the molecular basis of GPCR-related diseases and for performing structure-based drug design. To ameliorate this problem, GPCR-SSFE 2.0 (http://www.ssfa-7tmr.de/ssfe2/), an intuitive web server dedicated to providing three-dimensional Class A GPCR homology models has been developed. The updated web server includes 27 inactive template structures and incorporates various new functionalities. Uniquely, it uses a fingerprint correlation scoring strategy for identifying the optimal templates, which we demonstrate captures structural features that sequence similarity alone is unable to do. Template selection is carried out separately for each helix, allowing both single-template models and fragment-based models to be built. Additionally, GPCR-SSFE 2.0 stores a comprehensive set of pre-calculated and downloadable homology models and also incorporates interactive loop modeling using the tool SL2, allowing knowledge-based input by the user to guide the selection process. For visual analysis, the NGL viewer is embedded into the result pages. Finally, blind-testing using two recently published structures shows that GPCR-SSFE 2.0 performs comparably or better than other state-of-the art GPCR modeling web servers.

DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins

Despite efforts during the past decades, loop modeling remains a difficult part of protein structure modeling. Several approaches have been developed in the framework of crystal structures. However, for homology models, the modeling of loops is still far from being solved. We propose DaReUS-Loop, a data-based approach that identifies loop candidates mining the complete set of experimental structures available in the Protein Data Bank. Candidate filtering relies on local conformation profile-profile comparison, together with physico-chemical scoring. Applied to three different template-based test sets, DaReUS-Loop shows significant increase in the number of high-accuracy loops, and significant enhancement for modeling long loops. A special advantage is that our method proposes a prediction confidence score that correlates well with the expected accuracy of the loops. Strikingly, over 50% of successful loop models are derived from unrelated proteins, indicating that fragments under similar constraints tend to adopt similar structure, beyond mere homology.

FragFit: a web-application for interactive modeling of protein segments into cryo-EM density maps

Cryo-electron microscopy (cryo-EM) is a standard method to determine the three-dimensional structures of molecular complexes. However, easy to use tools for modeling of protein segments into cryo-EM maps are sparse. Here, we present the FragFit web-application, a web server for interactive modeling of segments of up to 35 amino acids length into cryo-EM density maps. The fragments are provided by a regularly updated database containing at the moment about 1 billion entries extracted from PDB structures and can be readily integrated into a protein structure. Fragments are selected based on geometric criteria, sequence similarity and fit into a given cryo-EM density map. Web-based molecular visualization with the NGL Viewer allows interactive selection of fragments. The FragFit web-application, accessible at http://proteinformatics.de/FragFit, is free and open to all users, without any login requirements.

A fragment based method for modeling of protein segments into cryo-EM density maps

BACKGROUND

Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded.

RESULTS

Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map.

CONCLUSION

Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps.

Ligand-binding characterization of simulated β-adrenergic-like octopamine receptor in Schistocerca gregaria via progressive structure simulation

It is important to design insecticides having both low drug resistance and less undesirable toxicity for desert locust control. Specific GPCRs of Schistocerca gregaria, especially β-adrenergic-like octopamine receptor (SgOctβR), can be considered as its potential effective insecticide targets. However, either the unavailability of SgOctβR's structure or the inadequate capability of its sequence lead the development of insecticide for Schistocerca gregaria meets its plateau. To relax this difficulty, this paper develops a promising progressive structure simulation from SgOctβR's sequence, to its predicted structure of SgOctβR in vacuum, to its conformation as well as its complex with endogenous ligand octopamine in a solvent-membrane system. The combined approach of multiple sequence alignment, static structural characterization, and dynamic process of conformational change during binding octopamine reveal three important aspects. The first one is the characterization of SgOctβR's active pocket, including the attending secondary structure elements, its hydrophobic residues and nonpolar surface. The second one is the interaction with octopamine, especially the involved hydrogen bonds and an aromatic stacking of pi-pi interactions. The third one is the potential binding sites, including six highly conserved residues and one highly variable residue for locust insecticide design. This work is definitely helpful for the further structure-based drug design for efficient and eco-friendly insecticides, as well as site-directed mutagenesis biochemical research of SgOctβR.

Structural-Functional Features of the Thyrotropin Receptor: A Class A G-Protein-Coupled Receptor at Work

The thyroid-stimulating hormone receptor (TSHR) is a member of the glycoprotein hormone receptors, a sub-group of class A G-protein-coupled receptors (GPCRs). TSHR and its endogenous ligand thyrotropin (TSH) are of essential importance for growth and function of the thyroid gland and proper function of the TSH/TSHR system is pivotal for production and release of thyroid hormones. This receptor is also important with respect to pathophysiology, such as autoimmune (including ophthalmopathy) or non-autoimmune thyroid dysfunctions and cancer development. Pharmacological interventions directly targeting the TSHR should provide benefits to disease treatment compared to currently available therapies of dysfunctions associated with the TSHR or the thyroid gland. Upon TSHR activation, the molecular events conveying conformational changes from the extra- to the intracellular side of the cell across the membrane comprise reception, conversion, and amplification of the signal. These steps are highly dependent on structural features of this receptor and its intermolecular interaction partners, e.g., TSH, antibodies, small molecules, G-proteins, or arrestin. For better understanding of signal transduction, pathogenic mechanisms such as autoantibody action and mutational modifications or for developing new pharmacological strategies, it is essential to combine available structural data with functional information to generate homology models of the entire receptor. Although so far these insights are fragmental, in the past few decades essential contributions have been made to investigate in-depth the involved determinants, such as by structure determination via X-ray crystallography. This review summarizes available knowledge (as of December 2016) concerning the TSHR protein structure, associated functional aspects, and based on these insights we suggest several receptor complex models. Moreover, distinct TSHR properties will be highlighted in comparison to other class A GPCRs to understand the molecular activation mechanisms of this receptor comprehensively. Finally, limitations of current knowledge and lack of information are discussed highlighting the need for intensified efforts toward TSHR structure elucidation.