Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions

Structural analysis of human G-protein-coupled receptor 17 ligand binding sites

The human G protein coupled membrane receptor (GPR17), the sensor of brain damage, is identified as a biomarker for many neurological diseases. In human brain tissue, GPR17 exist in two isoforms, long and short. While cryo-electron microscopy technology has provided the structure of the long isoform of GPR17 with Gi complex, the structure of the short isoform and its activation mechanism remains unclear. Recently, we theoretically modeled the structure of the short isoform of GPR17 with Gi signaling protein and identified novel ligands. In the present work, we demonstrated the presence of two distinct ligand binding sites in the short isoform of GPR17. The molecular docking of GPR17 with endogenous (UDP) and synthetic ligands (T0510.3657, MDL29950) found the presence of two distinct binding pockets. Our observations revealed that endogenous ligand UDP can bind stronger in two different binding pockets as evidenced by glide and autodock vina scores, whereas the other two ligand's binding with GPR17 has less docking score. The analysis of receptor-UDP interactions shows complexes' stability in the lipid environment by 100 ns atomic molecular dynamics simulations. The amino acid residues VAL83, ARG87, and PHE111 constitute ligand binding site 1, whereas site 2 constitutes ASN67, ARG129, and LYS232. Root mean square fluctuation analysis showed the residues 83, 87, and 232 with higher fluctuations during molecular dynamics simulation in both binding pockets. Our findings imply that the residues of GPR17's two binding sites are crucial, and their interaction with UDP reveals the protein's hidden signaling and communication properties. Furthermore, this finding may assist in the development of targeted therapies for the treatment of neurological diseases.

Intrinsically disordered proteins identified in the aggregate proteome serve as biomarkers of neurodegeneration

A protein's structure is determined by its amino acid sequence and post-translational modifications, and provides the basis for its physiological functions. Across all organisms, roughly a third of the proteome comprises proteins that contain highly unstructured or intrinsically disordered regions. Proteins comprising or containing extensive unstructured regions are referred to as intrinsically disordered proteins (IDPs). IDPs are believed to participate in complex physiological processes through refolding of IDP regions, dependent on their binding to a diverse array of potential protein partners. They thus play critical roles in the assembly and function of protein complexes. Recent advances in experimental and computational analyses predicted multiple interacting partners for the disordered regions of proteins, implying critical roles in signal transduction and regulation of biological processes. Numerous disordered proteins are sequestered into aggregates in neurodegenerative diseases such as Alzheimer's disease (AD) where they are enriched even in serum, making them good candidates for serum biomarkers to enable early detection of AD.

Transcriptomic analysis of glioblastoma multiforme providing new insights into GPR17 signaling communication

Glioblastoma Multiforme (GBM) is one of the most aggressive malignant tumors in the central nervous system, which arises due to the failure or crosstalk in the signaling networks. GPR17, an orphan G protein-coupled receptor is anticipated to be associated with the biology of the GBM disease progression. In the present study, we have identified the differential expressions of around 170 genes along with GPR17 through the RNA-Seq analysis of 169 GBM samples. Coordinated expression patterns of all other gene products with this receptor were analysed using gene ontology and protein-protein interaction data. Several crucial signaling components and genes that play a significant role in tumor progression have been identified among which GPR17 was found to be significantly interacting with about 30 different pathways. High-throughput molecular docking of GPR17 by homology-based model against differentially expressed proteins, showed effective recognition and binding of PX, SH3, and Ig-like domains besides Gi protein. Pathways of PI3, Src, Ptdn, Ras, cytoplasmic tyrosine kinases, phospholipases, nexins and other proteins possessing these structural domains are identified as critical signaling components of the complex GBM signaling network. Our findings also provide a mechanistic insight of GPR17-T0510-3657 interaction, which potentially regulates the interaction of PX domain and helical mPTS recognition domain-containing proteins. Overall, our results demonstrate that GPR17 mediated signaling networks could be used as a therapeutic target for GBM.Communicated by Ramaswamy H. Sarma.

An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein - ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.

Sequence and structural analysis of fibronectin-binding protein reveals importance of multiple intrinsic disordered tandem repeats

The location of certain amino acid sequences like repeats along the polypeptide chain is very important in the context of forming the overall shape of the protein molecule which in fact determines its function. In gram-positive bacteria, fibronectin-binding protein (FnBP) is one such repeat containing protein, and it is a cell wall-attached protein responsible for various acute infections in human. Several studies on sequence, structure, and function of fibronectin-binding regions of FnBPs were reported; however, no detailed study was carried out on the full-length protein sequence. In the present study, we have made a thorough sequence and structure analysis on FnBP_A of Staphylococcus aureus and explored the presence of dual ligand-binding ability of fibrinogen (fg)-binding region and its molecular recognition processes. Multiple sequence alignment and protein-protein docking analysis reveal the regions which are likely involved in dual ligand binding. Further analysis of docking of FnBP_A fg-binding region and fn N-terminal modules suggests that if the latter binds to the fg-binding region of FnBP_A, it would inhibit the subsequent binding of fg because of steric hindrance. The sequence analysis further suggests that the abundance of disorder promoting residue glutamic acid and dual personality (both order/disorder promoting) residue threonine in tandem repeats of FnBP_A and B proteins possibly would help the molecule to undergo a conformational change while binding with fn by β-zipper mechanism. The segment-based power spectral analysis was carried out which helps to understand the distribution of hydrophobic residues along the sequence particularly in intrinsic disordered tandem repeats. The results presented here will help to understand the role of internal repeats and intrinsic disorder in the molecular recognition process of a pathogenic cell surface protein.

Composition-related structural transition of random peptides: insight into the boundary between intrinsically disordered proteins and folded proteins

Previous studies based on bioinformatics showed that there is a sharp distinction of structural features and residue composition between the intrinsically disordered proteins and the folded proteins. What induces such a composition-related structural transition? How do various kinds of interactions work in such processes? In this work, we investigate these problems based on a survey on peptides randomly composed of charged residues (including glutamic acids and lysines) and the residues with different hydrophobicity, such as alanines, glycines, or phenylalanines. Based on simulations using all-atom model and replica-exchange Monte Carlo method, a coil-globule transition is observed for each peptide. The corresponding transition temperature is found to be dependent on the contents of the hydrophobic and charged residues. For several cases, when the mean hydrophobicity is larger than a certain threshold, the transition temperature is higher than the room temperature, and vise versa. These thresholds of hydrophobicity and net charge are quantitatively consistent with the border line observed from the study of bioinformatics. These results outline the basic physical reasons for the compositional distinction between the intrinsically disordered proteins and the folded proteins. Furthermore, the contributions of various interactions to the structural variation of peptides are analyzed based on the contact statistics and the charge-pattern dependence of the gyration radii of the peptides. Our observations imply that the hydrophobicity contributes essentially to such composition-related transitions. Thus, we achieve a better understanding on composition-structure relation of the natural proteins and the underlying physics.