GPCR-SSFE: A comprehensive database of G-protein-coupled receptor template predictions and homology models

<p>Abstract</p> <p>Background</p> <p>G protein-coupled receptors (GPCRs) transduce a wide variety of extracellular signals to within the cell and therefore have a key role in regulating cell activity and physiological function. GPCR malfunction is responsible for a wide range of diseases including cancer, diabetes and hyperthyroidism and a large proportion of drugs on the market target these receptors. The three dimensional structure of GPCRs is important for elucidating the molecular mechanisms underlying these diseases and for performing structure-based drug design. Although structural data are restricted to only a handful of GPCRs, homology models can be used as a proxy for those receptors not having crystal structures. However, many researchers working on GPCRs are not experienced homology modellers and are therefore unable to benefit from the information that can be gleaned from such three-dimensional models. Here, we present a comprehensive database called the GPCR-SSFE, which provides initial homology models of the transmembrane helices for a large variety of family A GPCRs.</p> <p>Description</p> <p>Extending on our previous theoretical work, we have developed an automated pipeline for GPCR homology modelling and applied it to a large set of family A GPCR sequences. Our pipeline is a fragment-based approach that exploits available family A crystal structures. The GPCR-SSFE database stores the template predictions, sequence alignments, identified sequence and structure motifs and homology models for 5025 family A GPCRs. Users are able to browse the GPCR dataset according to their pharmacological classification or search for results using a UniProt entry name. It is also possible for a user to submit a GPCR sequence that is not contained in the database for analysis and homology model building. The models can be viewed using a Jmol applet and are also available for download along with the alignments.</p> <p>Conclusions</p> <p>The data provided by GPCR-SSFE are useful for investigating general and detailed sequence-structure-function relationships of GPCRs, performing structure-based drug design and for better understanding the molecular mechanisms underlying disease-associated mutations in GPCRs. The effectiveness of our multiple template and fragment approach is demonstrated by the accuracy of our predicted homology models compared to recently published crystal structures.</p

Refinement of the gonadotropin releasing hormone receptor I homology model by applying molecular dynamics

Sexual maturation of human cells in ovaries and prostate is linked to the biochemical cascade initiated by the activation of cell receptors through the binding of Gonadotropin Releasing Hormone (GnRH). The GnRH receptors (GnRHR) are part of the rhodopsin G-protein coupled receptor (GPCR) family and consist of seven trans–membrane helical domains connected via extra– and intra–cellular segments. The GnRH–GnRHR complex has been implicated in various forms of prostate and ovarian cancer. The lack of any structural data about the GnRH receptor impedes the design of antagonists for use in cancer treatment. The aim of the study is to devise a model of GnRHR to be used further for the design of improved peptide/non-peptide GnRH analogues and, to our knowledge provide new structural information regarding the extracellular loop 2 (ECL2) that acts a regulator of ligand entry to GnRHR. The common structural characteristics, of the members of the rhodopsin family of GPCRs, have been employed for the construction of a homology model for GnRHR. Structural information from the human β2–adrenergic receptor, as well as rhodopsins have been used in order to create a theoretical model for GnRHR. Furthermore, molecular dynamics (MD) simulations have been employed for the refinement of the model and to explore the impact of the bilayer membrane in GnRHR conformation

Putative ligand binding sites of two functionally characterized bark beetle odorant receptors

Background: Bark beetles are major pests of conifer forests, and their behavior is primarily mediated via olfaction. Targeting the odorant receptors (ORs) may thus provide avenues towards improved pest control. Such an approach requires information on the function of ORs and their interactions with ligands, which is also essential for understanding the functional evolution of these receptors. Hence, we aimed to identify a high-quality complement of ORs from the destructive spruce bark beetle Ips typographus (Coleoptera, Curculionidae, Scolytinae) and analyze their antennal expression and phylogenetic relationships with ORs from other beetles. Using 68 biologically relevant test compounds, we next aimed to functionally characterize ecologically important ORs, using two systems for heterologous expression. Our final aim was to gain insight into the ligand-OR interaction of the functionally characterized ORs, using a combination of computational and experimental methods. Results: We annotated 73 ORs from an antennal transcriptome of I. typographus and report the functional characterization of two ORs (ItypOR46 and ItypOR49), which are responsive to single enantiomers of the common bark beetle pheromone compounds ipsenol and ipsdienol, respectively. Their responses and antennal expression correlate with the specificities, localizations, and/or abundances of olfactory sensory neurons detecting these enantiomers. We use homology modeling and molecular docking to predict their binding sites. Our models reveal a likely binding cleft lined with residues that previously have been shown to affect the responses of insect ORs. Within this cleft, the active ligands are predicted to specifically interact with residues Tyr84 and Thr205 in ItypOR46. The suggested importance of these residues in the activation by ipsenol is experimentally supported through site-directed mutagenesis and functional testing, and hydrogen bonding appears key in pheromone binding. Conclusions: The emerging insight into ligand binding in the two characterized ItypORs has a general importance for our understanding of the molecular and functional evolution of the insect OR gene family. Due to the ecological importance of the characterized receptors and widespread use of ipsenol and ipsdienol in bark beetle chemical communication, these ORs should be evaluated for their potential use in pest control and biosensors to detect bark beetle infestations

Functional Diversity and Structural Disorder in the Human Ubiquitination Pathway

The ubiquitin-proteasome system plays a central role in cellular regulation and protein quality control (PQC). The system is built as a pyramid of increasing complexity, with two E1 (ubiquitin activating), few dozen E2 (ubiquitin conjugating) and several hundred E3 (ubiquitin ligase) enzymes. By collecting and analyzing E3 sequences from the KEGG BRITE database and literature, we assembled a coherent dataset of 563 human E3s and analyzed their various physical features. We found an increase in structural disorder of the system with multiple disorder predictors (IUPred - E1: 5.97%, E2: 17.74%, E3: 20.03%). E3s that can bind E2 and substrate simultaneously (single subunit E3, ssE3) have significantly higher disorder (22.98%) than E3s in which E2 binding (multi RING-finger, mRF, 0.62%), scaffolding (6.01%) and substrate binding (adaptor/substrate recognition subunits, 17.33%) functions are separated. In ssE3s, the disorder was localized in the substrate/adaptor binding domains, whereas the E2-binding RING/HECT-domains were structured. To demonstrate the involvement of disorder in E3 function, we applied normal modes and molecular dynamics analyses to show how a disordered and highly flexible linker in human CBL (an E3 that acts as a regulator of several tyrosine kinase-mediated signalling pathways) facilitates long-range conformational changes bringing substrate and E2-binding domains towards each other and thus assisting in ubiquitin transfer. E3s with multiple interaction partners (as evidenced by data in STRING) also possess elevated levels of disorder (hubs, 22.90% vs. non-hubs, 18.36%). Furthermore, a search in PDB uncovered 21 distinct human E3 interactions, in 7 of which the disordered region of E3s undergoes induced folding (or mutual induced folding) in the presence of the partner. In conclusion, our data highlights the primary role of structural disorder in the functions of E3 ligases that manifests itself in the substrate/adaptor binding functions as well as the mechanism of ubiquitin transfer by long-range conformational transitions. © 2013 Bhowmick et al

Computational protein structure prediction using deep learning

Protein structure prediction is of great importance in bioinformatics and computational biology. Over the past 30 years, many machine learning methods have been developed for this problem in homology-based and ab-initio approaches. Recently, deep learning has been successfully applied and has outperformed previous methods. Deep learning methods could effectively handle high dimensional feature inputs in modeling the complex mapping from protein primary amino acid sequences to protein 2-D or 3-D structures. In this dissertation, new deep learning methods and deep learning networks have been proposed for three problems in protein structure prediction: loop modeling, contact map prediction, and contact map refinement. They have been implemented in the state-of-the-art MUFOLD software and obtained significant performance improvement. The goal of loop modeling is to predict the conformation of a relatively short stretch of protein backbone. A new method based on Generative Adversarial Network (GAN), called MUFOLD-LM, is proposed. The protein 3-D structure can be represented using the 2-D distance map of C [subscript alpha] atoms. The missing region in the structure will be a missing region in the distance map correspondingly. Our network uses the Generator Network to fill in the missing regions in the distance map based on the context, and the Discriminator Network will take both the predicted complete distance map and the ground truth as input to distinguish between them. The method utilizes both the features and context of the missing loop region to make better prediction of the 3-D structure of the loop region. In experiments using commonly used benchmark datasets 8-Res and 12-Res, MUFOLD-LM outperformed previous methods significantly, up to 43.9 [percent] and 4.13 [percent] in RMSD, respectively. To the best of our knowledge, it is the first successful GAN application in protein structure prediction. The goal of contact map prediction is to predict whether the distance between two C [subscript beta] atoms (C [subscript alpha] for Glycine) in a protein falls within a certain threshold. It can help to determine the global s"tructure of a protein in order to assist the 3D modeling process. In this work, a new two-stage multi-branch neural network based on Fully Convolutional Network and Dilated Residual Network, called MUFOLD_Contact, is proposed. It formulates the problem as a pixel-wise regression and classification problem. The first stage predicts distance maps for short-, medium-, and long-range residue pairs. The second stage takes the predicted distances from stage 1 along with other features as input to predict a binary contact map. The method utilizes the distance distribution information in the feature set to improve the binary prediction results. In experiments using CASP13 targets, the new method outperformed single stage networks and is comparable with the best existing tools. In addition to predicting contact directly using deep neural networks, a new method, called TPCref (Template Prediction Correction refinement), is proposed to refine and improve the prediction results of a contact predictor using protein templates. Based on the idea of collaborative filtering from recommendation system, TPCref first finds multiple template sequences based on the target sequence and uses the templates' structures and the templates' predicted contact map generated by a contact predictor to form a target contact map filter using the idea of collaborative filtering. Then the contact-map filter is used to refine the predicted contact map. In experimental results using recently released PDB proteins, TPCref significantly improved the contact prediction results of existing predictors, improving MUFOLD_Contact, MetaPSICOV, and CCMPred by 5.0 [percent], 12.8 [percent], and 37.2 [percent], respectively. The proposed new methods have been implemented in MUFOLD, a comprehensive platform for protein structure prediction. It provides a rich set of functions, including database generation, secondary and supersecondary structure prediction, beta-turn and gamma-turn prediction, contact map prediction and refinement, protein 3D structure prediction, loop modeling, model quality assessment, and model refinement. In this work, a new modularized MUFOLD pipeline has been designed and developed. Each module is decoupled from each other and provides standard communication protocol interfaces for other programs to call. The modularization provides the capability to easily integrate new algorithms and tools to have a fast iteration during research. In addition, a new web portal for MUFOLD has been designed and implemented to provide online services or APIs of our tools to the community

New methods for protein structure prediction using machine learning and deep learning

Computational protein structure prediction is one of the most challenging problems in bioinformatics area. Due to the widespread use of sampling-and-selection strategy, protein model quality assessment became important. In this dissertation, new machine learning and deep learning methods have been proposed for protein model quality assessment, protein contact prediction, protein model refinement, and loop modeling. The goal of model quality assessment (QA) is to estimate the quality of predicted protein models. First, two new single-model QA methods based on Residual Neural Networks, called PDRN and VDRN, were proposed to achieve state-of-the-art performance. They used a comprehensive set of structure features to predict a quality score in the range of [0, 1]. Next, three single-model QA methods, MMQA-1 MMQA-2 and MMQA-HE, were proposed based on ideas of two-stage learning and hierarchical ensembles. MMQA-1 and MMQA-2 divided the entire feature set into two different sets and used different feature sets and training data in each stage of learning. In addition, MMQA-HE created ensembles of models in the first stage of learning for improved performance. In CASP14, MMQA-1 ranked NO. 2 in terms of average GDT-TS difference. MMQA-2 and MMQA-HE outperformed MMQA-1 consistently across different QA performance metrics in our experiments. Furthermore, a quasi-single-model QA method called INC-QA was proposed using a new method that trained a deep neural network as a QA predictor for each protein target based on template structure information generated from the target sequence. Experimental results using CASP data showed that INC-QA achieved state-of-the-art results, outperforming existing methods on CASP QA stage 2 category on CASP 13 targets. With the release of groundbreaking protein structure prediction software AlphaFold2 and RosettaFold, many research teams start using them to generate highly accurate protein models. We evaluated the performance of different QA methods on models generated by them with random modification by 3DRobot and found that multi-model QA methods were still better than single-model QA methods on these kind of high-performance model pools. Finally, in terms of the prediction of overall folding accuracy and overall interface accuracy for protein complexes in CASP15, we found a strong correlation between the predicted folding accuracy and predicted interface accuracy of protein models. Loop modeling tries to predict the conformation of a relatively short stretch of protein backbone and sidechain. It is a difficult problem due to conformational variability. AlphaFold2 achieved outstanding results in 3-D protein structure prediction and was expected to perform well on loop modeling. We investigated the performances of AlphaFold2 variants on loop modeling benchmark datasets and proposed an efficient constant-time method of using AlphaFold2 for loop modeling, called IAFLoop. To predict the structure of a loop region, IAFLoop ran a fast version of AlphaFold2 with a reduced database without ensembling on an extended segment of the target loop region, and used RMSD based consensus scores to select the top models. Our experimental results showed that IAFLoop generated highly accurate loop models, outperforming basic AlphaFold2 by up to 17 percent in RMSD error, while using less than half of the time. Compared to the previous best method, IAFLoop reduces the RMSD error by more than half. Contact map prediction is to predict whether the Euclidean distance between two C[beta] atoms (C[alpha] for Glycine) in a protein structure is less than 8 angstroms. Contacts information can act as a powerful constraint for determining the overall structural and assist the protein 3D structure prediction process. Based on MUFold-Contact, a new two-stage multi-branch deep neural network based on Residual Network and Inception V3 Network was proposed to improve the performance of MUFold-Contact. In the first stage, distance maps of shortrange, medium-range and long-range residue pairs were predicted, respectively, and the predicted distance along with other features were used as input to predict a binary contact map in the second stage. The role of protein structure refinement is to take models generated by protein structure prediction process and bring them closer to the true native structure. Inspired by AlphaFold in CASP13, a new protein structure refinement process MUFOLD-REFINE based on distance distribution of template pool was developed and achieve improved performance over the MUFOLD refinement method used in CASP13Includes bibliographical references