Improving the topology prediction of α-helical transmembrane proteins with deep transfer learning

Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Oryza sativa)

G protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins in metazoans that mediate the regulation of various physiological responses to discrete ligands through heterotrimeric G protein subunits. The existence of GPCRs in plant is contentious, but their comparable crucial role in various signaling pathways necessitates the identification of novel remote GPCR-like proteins that essentially interact with the plant G protein α subunit and facilitate the transduction of various stimuli. In this study, we identified three putative GPCR-like proteins (OsGPCRLPs) (LOC_Os06g09930.1, LOC_Os04g36630.1, and LOC_Os01g54784.1) in the rice proteome using a stringent bioinformatics workflow. The identified OsGPCRLPs exhibited a canonical GPCR 'type I' 7TM topology, patterns, and biologically significant sites for membrane anchorage and desensitization. Cluster-based interactome mapping revealed that the identified proteins interact with the G protein α subunit which is a characteristic feature of GPCRs. Computational results showing the interaction of identified GPCR-like proteins with G protein α subunit and its further validation by the membrane yeast-two-hybrid assay strongly suggest the presence of GPCR-like 7TM proteins in the rice proteome. The absence of a regulator of G protein signaling (RGS) box in the C- terminal domain, and the presence of signature motifs of canonical GPCR in the identified OsGPCRLPs strongly suggest that the rice proteome contains GPCR-like proteins that might be involved in signal transduction.

DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model

Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pre-trained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the model's effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp).

Experimental and computational approaches for membrane protein insertion and topology determination

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

Molecular dynamics of the human RhD and RhAG blood group proteins

Introduction: Blood group antigens of the RH system (formerly known as "Rhesus") play an important role in transfusion medicine because of the severe haemolytic consequences of antibodies to these antigens. No crystal structure is available for RhD proteins with its partner RhAG, and the precise stoichiometry of the trimer complex remains unknown. Methods: To analyse their structural properties, the trimers formed by RhD and/or RhAG subunits were generated by protein modelling and molecular dynamics simulations were performed. Results: No major differences in structural behaviour were found between trimers of different compositions. The conformation of the subunits is relatively constant during molecular dynamics simulations, except for three large disordered loops. Discussion: This work makes it possible to propose a reasonable stoichiometry and demonstrates the potential of studying the structural behaviour of these proteins to investigate the hundreds of genetic variants relevant to transfusion medicine.

Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre-trained language models

Membrane proteins play a crucial role in various cellular processes and are essential components of cell membranes. Computational methods have emerged as a powerful tool for studying membrane proteins due to their complex structures and properties that make them difficult to analyze experimentally. Traditional features for protein sequence analysis based on amino acid types, composition, and pair composition have limitations in capturing higher-order sequence patterns. Recently, multiple sequence alignment (MSA) and pre-trained language models (PLMs) have been used to generate features from protein sequences. However, the significant computational resources required for MSA-based features generation can be a major bottleneck for many applications. Several methods and tools have been developed to accelerate the generation of MSAs and reduce their computational cost, including heuristics and approximate algorithms. Additionally, the use of PLMs such as BERT has shown great potential in generating informative embeddings for protein sequence analysis. In this review, we provide an overview of traditional and more recent methods for generating features from protein sequences, with a particular focus on MSAs and PLMs. We highlight the advantages and limitations of these approaches and discuss the methods and tools developed to address the computational challenges associated with features generation. Overall, the advancements in computational methods and tools provide a promising avenue for gaining deeper insights into the function and properties of membrane proteins, which can have significant implications in drug discovery and personalized medicine.

Finding functional motifs in protein sequences with deep learning and natural language models

Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machine learning and encoding schemas are available for predicting different structural/functional motifs. Particularly interesting is the adoption of protein language models to encode proteins in addition to evolution information and physicochemical parameters. A thorough analysis of recent predictors developed for annotating transmembrane regions, sorting signals, lipidation and phosphorylation sites allows to investigate the state-of-the-art focusing on the relevance of protein language models for the different tasks. This highlights that more experimental data are necessary to exploit available powerful machine learning methods.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Predicting the Assembly of the Transmembrane Domains of Viral Channel Forming Proteins and Peptide Drug Screening Using a Docking Approach

A de novo assembly algorithm is provided to propose the assembly of bitopic transmembrane domains (TMDs) of membrane proteins. The algorithm is probed using, in particular, viral channel forming proteins (VCPs) such as M2 of influenza A virus, E protein of severe acute respiratory syndrome corona virus (SARS-CoV), 6K of Chikungunya virus (CHIKV), SH of human respiratory syncytial virus (hRSV), and Vpu of human immunodeficiency virus type 2 (HIV-2). The generation of the structures is based on screening a 7-dimensional space. Assembly of the TMDs can be achieved either by simultaneously docking the individual TMDs or via a sequential docking. Scoring based on estimated binding energies (EBEs) of the oligomeric structures is obtained by the tilt to decipher the handedness of the bundles. The bundles match especially well for all-atom models of M2 referring to an experimentally reported tetrameric bundle. Docking of helical poly-peptides to experimental structures of M2 and E protein identifies improving EBEs for positively charged (K,R,H) and aromatic amino acids (F,Y,W). Data are improved when using polypeptides for which the coordinates of the amino acids are adapted to the Cα coordinates of the respective experimentally derived structures of the TMDs of the target proteins.

Modification of phosphoinositides by the Shigella effector IpgD during host cell infection

Shigella, the causative agent of bacillary dysentery, subvert cytoskeletal and trafficking processes to invade and replicate in epithelial cells using an arsenal of bacterial effectors translocated through a type III secretion system. Here, we review the various roles of the type III effector IpgD, initially characterized as phosphatidylinositol 4,5 bisphosphate (PI4,5P2) 4-phosphatase. By decreasing PI4,5P2 levels, IpgD triggers the disassembly of cortical actin filaments required for bacterial invasion and cell migration. PI5P produced by IpgD further stimulates signaling pathways regulating cell survival, macropinosome formation, endosomal trafficking and dampening of immune responses. Recently, IpgD was also found to exhibit phosphotransferase activity leading to PI3,4P2 synthesis adding a new flavor to this multipotent bacterial enzyme. The substrate of IpgD, PI4,5P2 is also the main substrate hydrolyzed by endogenous phospholipases C to produce inositoltriphosphate (InsP3), a major Ca2+ second messenger. Hence, beyond the repertoire of effects associated with the direct diversion of phoshoinositides, IpgD indirectly down-regulates InsP3-mediated Ca2+ release by limiting InsP3 production. Furthermore, IpgD controls the intracellular lifestyle of Shigella promoting Rab8/11 -dependent recruitment of the exocyst at macropinosomes to remove damaged vacuolar membrane remnants and promote bacterial cytosolic escape. IpgD thus emerges as a key bacterial effector for the remodeling of host cell membranes.