Transmembrane Protein Structure Research Articles

Conservation of knotted and slipknotted topology in transmembrane transporters

The existence of nontrivial topology is well accepted in globular proteins but not in membrane proteins. Our comprehensive topological analysis of the Protein Data Bank structures reveals 18 families of transmembrane proteins with nontrivial topology, showing that they constitute a significant number of membrane proteins. Moreover, we found that they comprise one of the largest groups of secondary active transporters. We classified them based on their knotted fingerprint into four groups: three slipknotted and one knotted. Unexpectedly, we found that the same protein can possess two distinct slipknot motifs that correspond to its outward- and inward-open conformational state. Based on the analysis of structures and knotted fingerprints, we show that slipknot topology is directly involved in the conformational transition and substrate transfer. Therefore, entanglement can be used to classify proteins and to find their structure-function relationship. Furthermore, based on the topological analysis of the transmembrane protein structures predicted by AlphaFold, we identified new potentially slipknotted protein families.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

To Be or Not to Be an Ion Channel: Cryo-EM Structures Have a Say.

Ion channels are the second largest class of drug targets after G protein-coupled receptors. In addition to well-recognized ones like voltage-gated Na/K/Ca channels in the heart and neurons, novel ion channels are continuously discovered in both excitable and non-excitable cells and demonstrated to play important roles in many physiological processes and diseases such as developmental disorders, neurodegenerative diseases, and cancer. However, in the field of ion channel discovery, there are an unignorable number of published studies that are unsolid and misleading. Despite being the gold standard of a functional assay for ion channels, electrophysiological recordings are often accompanied by electrical noise, leak conductance, and background currents of the membrane system. These unwanted signals, if not treated properly, lead to the mischaracterization of proteins with seemingly unusual ion-conducting properties. In the recent ten years, the technical revolution of cryo-electron microscopy (cryo-EM) has greatly advanced our understanding of the structures and gating mechanisms of various ion channels and also raised concerns about the pore-forming ability of some previously identified channel proteins. In this review, we summarize cryo-EM findings on ion channels with molecular identities recognized or disputed in recent ten years and discuss current knowledge of proposed channel proteins awaiting cryo-EM analyses. We also present a classification of ion channels according to their architectures and evolutionary relationships and discuss the possibility and strategy of identifying more ion channels by analyzing structures of transmembrane proteins of unknown function. We propose that cross-validation by electrophysiological and structural analyses should be essentially required for determining molecular identities of novel ion channels.

Phase state of lipid bilayers modulates ammonia and water permeability through AtTip2;1.

Lipid-protein interactions determine the structure, stability and function of transmembrane proteins. Protein conformation can be influenced by local interactions at the lipid-protein interface or global interactions such as the lateral pressure profile of the membrane, which in turn is influenced by the lipid composition of the membrane. However, despite its methodological importance for in vitro protein characterization and its biological implications the influence of lipid bilayer fluidity on membrane protein activity is largely unknown. Reconstituting the aqua-ammonia channel AtTip2;1, from Arabidopsis thaliana, into OSPC containing large unilamellar vesicles we modulate its unitary water and ammonia permeability switching between the liquid-disordered state and the gel phase state of the bilayer. Quantitative activity characterization utilizing fluorescence correlation spectroscopy, stopped-flow methodology, and dynamic light scattering is complemented by molecular dynamics simulations of AtTIP2;1 in OSPC bilayers. Our in vitro experiments revealed a unitary water permeability in the liquid-disordered phase of 2.5·10−13 cm³s−1. On the contrary, selective transport of ammonia is an order of magnitude lower questioning if ammonia transport is the main function of AtTIP2;1. Moreover, results obtained from our MD simulations show, similar to our in vitro efforts, that AtTIP2;1 exhibits lower activity in the gel state as compared to its activity in the liquid-disordered state. This finding highlights the crucial importance of the lipid matrix in in vitro and in silico protein characterization and is expected to trigger a wealth of future investigations on permeability and regulation of AQPs and other membrane proteins.

Sorption of petroleum hydrocarbons before transmembrane transport and the structure, mechanisms and functional regulation of microbial membrane transport systems

With the rapid development of the global industrialization process, as an important energy and strategic resource, petroleum is widely used in various fields. Meanwhile, the leakage of petroleum hydrocarbons (PH) has caused continuous environmental risks. Microbial degradation is an effective and reliable treatment for PH. Although significant progress has been made in the pathways and efficiency of microbial degradation of PH, there is still a lack of understanding of the membrane transport systems involved in the degradation. In this review, the sorption, transmembrane transport processes and their affecting factors of microorganisms in the degradation of PH are summarized, with emphasis on the significant progress in their structures, mechanisms of transmembrane transport, proteins in various membrane transport systems, and functional regulation of membrane transport systems in recent years, including the FadL family, the OmpW family, TonB-dependent transporters (TBDTs), ATP-binding cassette (ABC)-type transporters and major facilitator superfamily (MFS). The possible transmembrane processes involved in the complete degradation of PH by Gram-positive bacteria, Gram-negative bacteria and fungi are proposed. Meanwhile, the gaps in current research on the endocytosis mechanism of filamentous fungi, recognition, functional analysis, structure and transport mechanism of transmembrane proteins, and information acquisition technology of dynamic process are pointed out. • Pre-transmembrane sorption and transmembrane processes of PH. • Structure, mechanism and functional regulation of PH membrane transport systems. • Fungi uptake PH by endocytosis in addition to passive/active transport. • PH are initially degraded between the IM and OM of Gram-negative bacteria. • FadL and OmpW family, TBDTs, ABC and MFS mediate PH transmembrane process.

Molecular Dynamics Simulations of Immune Receptors and Ligands.

Molecular dynamics simulations of immune receptor and ligand proteins in their native membrane environment allow to determine the orientational and structural variability of the proteins and protein complexes. The simulations complement the static, "membrane-free" structural information obtained from cryo-EM structures of transmembrane proteins in detergent micelles or from crystal structures of extracellular protein domains. Here we describe how to set up and perform simulations of transmembrane receptors, ligands, and receptor-ligand complexes.

Formation of extramembrane β-strands controls dimerization of transmembrane helices in amyloid precursor protein C99

The 99-residue C-terminal domain of amyloid precursor protein (APP-C99), precursor to amyloid beta (Aβ), is a transmembrane (TM) protein containing intrinsically disordered N- and C-terminal extramembrane domains. Using molecular dynamics (MD) simulations, we show that the structural ensemble of the C99 monomer is best described in terms of thousands of states. The C99 monomer has a propensity to form β-strand in the C-terminal extramembrane domain, which explains the slow spin relaxation times observed in paramagnetic probe NMR experiments. Surprisingly, homodimerization of C99 not only narrows the conformational ensemble from thousands to a few states through the formation of metastable β-strands in extramembrane domains but also stabilizes extramembrane α-helices. The extramembrane domain structure is observed to dramatically impact the homodimerization motif, resulting in the modification of TM domain conformations. Our study provides an atomic-level structural basis for communication between the extramembrane domains of the C99 protein and TM homodimer formation. This finding could serve as a general model for understanding the influence of disordered extramembrane domains on TM protein structure.

Molecular exposition of broad-spectrum antibacterial efficacy by p-coumaric acid from an edible mushroom Termitomyces heimii: in vitro and in silico approach

P-Coumaric acid was previously reported to contain antioxidant, antidiabetic, anti-inflammatory, antiplatelet, antiulcer and anticancer activities. Along with these, the present work has been conducted to study the antibacterial activity of p-coumaric acid. It could be used to control broad-spectrum microbiome-based inflammation or in cancer control. HPLC analysis of methanolic extract from the mushroom Termitomyces heimii has exhibited a rich fraction of p-coumaric acid (p-CA), which in the pure form showed significant bactericidal potentials. To predict the molecular interactions associated with the bactericidal mechanism of p-CA, the transmembrane protein sequences of Staphylococcus aureus and Escherichia coli were retrieved from the IMG–JGI database, screened and then aligned using Clustal X2 and PHYLIP 3.69 softwares. The common sequences were subjected to tertiary structure prediction using Phyre2 server, followed by quality assessment through the Ramachandran plot. Next, the 3D molecular structure of p-CA was downloaded from PubChem and docked with the selected tertiary structures using Patchdock and showed higher affinity towards 12 common transmembrane protein structures, amongst which CDP-diacylglycerol–glycerol-3-phosphate 3-phosphatidyl transferase (PgsA) exhibited best docking with p-CA on the basis of ACE value (– 249 kcal/mol). This fact revealed that p-CA can block the normal functioning of membrane-bound enzyme PgsA, consequently leading to the interruption in the synthesis and recycling of an essential membrane component phosphatidylglycerol (PG), resulting in membrane disruption followed by cell lysis. Here, for the first time we reported the molecular insights and fundamental biochemical events underlying the bactericidal action by p-CA, to explore new perspective to combat multidrug-resistant bacteria.

The Role of Transmembrane Proteins in Plant Growth, Development, and Stress Responses.

Transmembrane proteins participate in various physiological activities in plants, including signal transduction, substance transport, and energy conversion. Although more than 20% of gene products are predicted to be transmembrane proteins in the genome era, due to the complexity of transmembrane domains they are difficult to reliably identify in the predicted protein, and they may have different overall three-dimensional structures. Therefore, it is challenging to study their biological function. In this review, we describe the typical structures of transmembrane proteins and their roles in plant growth, development, and stress responses. We propose a model illustrating the roles of transmembrane proteins during plant growth and response to various stresses, which will provide important references for crop breeding.

TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures.

AI-driven protein structure prediction, most notably AlphaFold2 (AF2) opens new frontiers for almost all fields of structural biology. As traditional structure prediction methods for transmembrane proteins were both complicated and error prone, AF2 is a great help to the community. Complementing the relatively meager number of experimental structures, AF2 provides 3Dpredictions for thousands of new alpha-helical membrane proteins. However, the lack of reliable structural templates and the fact that AF2 was not trained to handle phase boundaries also necessitates a delicate assessment of structural correctness. In our new database, Transmembrane AlphaFold database (TmAlphaFold database), we apply TMDET, a simple geometry-based method to visualize the likeliest position of the membrane plane. In addition, we calculate several parameters to evaluate the location of the protein into the membrane. This also allows TmAlphaFold database to show whether the predicted 3D structure is realistic or not. The TmAlphaFold database is available at https://tmalphafold.ttk.hu/.

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology.

The number of unique transmembrane (TM) protein structures doubled in the last four years, which can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using the PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

Comparative molecular dynamics simulation studies of realistic eukaryotic, prokaryotic, and archaeal membranes

The OPM (Orientation of Proteins in Membranes) database currently holds, classifies, and annotates around 14,000 three-dimensional structures of transmembrane and peripheral membrane proteins and peptides positioned with respect to membrane boundaries. Developing uniform templates of biological membranes with realistic native-like lipid composition would provide a convenient and accurate platform for researchers who simulate membrane-associated proteins and peptides in natural environments. In this study, for the first time, we performed the standardized molecular dynamic simulations and comparative analysis of structures and properties of 18 native-like membrane models.

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind’s AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

Comparing transmembrane protein structures with ATOLL.

The 3D structure of transmembrane helices plays a key role in the function of membrane proteins. While visual inspection can usually discern the distinctive features of a helix bundle, simply translating them into a 2D diagram can be difficult. ATOLL (Aligned Transmembrane dOmains Layout fLattening) projects the helix bundle onto the lipid bilayer plane, thereby facilitating the comparison of different structures of the same membrane protein or structures of different membrane proteins. ATOLL is a program written in Python3. The source code is freely available on the web at https://github.com/LIT-CCM-lab/ATOLL. ATOLL is implemented into a web server (https://atoll.drugdesign.unistra.fr/). Supplementary data are available at Bioinformatics online.

Prediction of Metal Ion Binding Sites of Transmembrane Proteins.

The metal ion binding of transmembrane proteins (TMPs) plays a fundamental role in biological processes, pharmaceutics, and medicine, but it is hard to extract enough TMP structures in experimental techniques to discover their binding mechanism comprehensively. To predict the metal ion binding sites for TMPs on a large scale, we present a simple and effective two-stage prediction method TMP-MIBS, to identify the corresponding binding residues using TMP sequences. At present, there is no specific research on the metal ion binding prediction of TMPs. Thereby, we compared our model with the published tools which do not distinguish TMPs from water-soluble proteins. The results in the independent verification dataset show that TMP-MIBS has superior performance. This paper explores the interaction mechanism between TMPs and metal ions, which is helpful to understand the structure and function of TMPs and is of great significance to further construct transport mechanisms and identify potential drug targets.

A complex unfolding pathway of α-helical membrane proteins in SDS-containing micelles

The thermodynamic and kinetic principles guiding folding of transmembrane (TM) proteins are still not very well understood, at least when compared with soluble proteins. However, there is a considerable interest in better understanding these processes, not just since membrane proteins are important drug targets. Membrane proteins constitute ∼25–30% of total proteins encoded in any organism, and many diseases are caused by alterations in TM protein structure and function ( 1 Wallin E. von Heijne G. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci. 1998; 7: 1029-1038 Crossref PubMed Scopus (1229) Google Scholar , 2 Marinko J.T. Huang H. Sanders C.R. et al. Folding and misfolding of human membrane proteins in health and disease: from single molecules to cellular proteostasis. Chem. Rev. 2019; 119: 5537-5606 Crossref PubMed Scopus (97) Google Scholar ). The thermodynamic stability of soluble proteins is classically studied via protein denaturation induced by heat or chaotropic salts, typically urea or guanidine hydrochloride, followed by refolding. Yet, TM proteins typically aggregate at increasing temperatures, preventing refolding, and as for in vitro studies, isolated TM proteins are analyzed when incorporated into a lipid bilayer or a detergent micelle, where individual TM helices are shielded from the aqueous environment. Consequently, whereas urea or GdnHCl can unfold extramembranous domains to disrupt TM contacts, the properties of the intimate solvent (here, the membrane or detergent micelles) have to be modified. Cys-labeling kinetics of membrane protein GlpG: a role for specific SDS binding and micelle changes?Otzen et al.Biophysical JournalAugust 6, 2021In BriefEmpirically, α-helical membrane protein folding stability in surfactant micelles can be tuned by varying the mole fraction MFSDS of anionic (sodium dodecyl sulfate (SDS)) relative to nonionic (e.g., dodecyl maltoside (DDM)) surfactant, but we lack a satisfying physical explanation of this phenomenon. Cysteine labeling (CL) has thus far only been used to study the topology of membrane proteins, not their stability or folding behavior. Here, we use CL to investigate membrane protein folding in mixed DDM-SDS micelles. Full-Text PDF

Identification and physiological function of CsPrip, a new aquaporin in Chilo suppressalis

Aquaporin (AQP) transport solutes across cell membranes in both unicellular and multicellular organisms. In this study, the aquaporin CsPrip was identified in Chilo suppressalis, an important pest of rice. CsPrip was comprised of two variants, CsPrip_v1 and CsPrip_v2; the former variant was <103 bp was shorter than the latter, although both exhibited the same open reading frame (ORF). Transmembrane topology and protein structure analyses showed that CsPrip retained the conserved features of water-selective insect AQPs, including six transmembrane domains, two conserved hydrophobic asparagine–proline–alanine motifs and the aromatic/arginine constriction region. Expression in Xenopus oocytes revealed that CsPrip preferentially transported water and urea instead of trehalose and glycerol. The CsPrip transcript was expressed in multiple organs and tissues of C. suppressalis larvae and was most abundant in the hindgut and Malpighian tubules. CsPrip transcription was highest in male adults and was relatively stable throughout development. CsPrip expression in larvae was significantly altered by thermal stress, and relative humidity levels impacted CsPrip transcription in 3rd and 5th instar larvae. This study confirms that the aquaporin CsPrip performs multiple critical functions in maintaining water equilibrium in C. suppressalis.

Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.

Transmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, namely Prediction of TransMembrane protein Crystallization propensity (PTMC), to predict the propensity of transmembrane protein crystallization. First, we obtained several general sequence features and the specific encoded features of relative solvent accessibility and hydrophobicity. Second, feature selection was employed to filter out redundant and irrelevant features, and the optimal feature subset is composed of hydrophobicity, amino acid composition and relative solvent accessibility. Finally, we chose extreme gradient boosting by comparing with other several machine learning methods. Comparative results on the independent test set indicate that PTMC outperforms state-of-the-art sequence-based methods in terms of sensitivity, specificity, accuracy, Matthew's Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC). In comparison with two competitors, Bcrystal and TMCrys, PTMC achieves an improvement by 0.132 and 0.179 for sensitivity, 0.014 and 0.127 for specificity, 0.037 and 0.192 for accuracy, 0.128 and 0.362 for MCC, and 0.027 and 0.125 for AUC, respectively.

TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence.

Transmembrane protein (TMP) is an important type of membrane protein that is involved in various biological membranes related biological processes. As major drug targets, TMPs’ surfaces are highly concerned to form the structural biases of their material-bindings for drugs or other biological molecules. However, the quantity of determinate TMP structures is still far less than the requirements, while artificial intelligence technologies provide a promising approach to accurately identify the TMP surfaces, merely depending on their sequences without any feature-engineering. For this purpose, we present an updated TMP surface residue predictor TMP-SSurface2 which achieved an even higher prediction accuracy compared to our previous version. The method uses an attention-enhanced Bidirectional Long Short Term Memory (BiLSTM) network, benefiting from its efficient learning capability, some useful latent information is abstracted from protein sequences, thus improving the Pearson correlation coefficients (CC) value performance of the old version from 0.58 to 0.66 on an independent test dataset. The results demonstrate that TMP-SSurface2 is efficient in predicting the surface of transmembrane proteins, representing new progress in transmembrane protein structure modeling based on primary sequences. TMP-SSurface2 is freely accessible at https://github.com/NENUBioCompute/TMP-SSurface-2.0.

Olive Oil Restores Functionality of Intracellularly Retained Mutant CNG Channel

Membrane lipids regulate structure and function of transmembrane proteins through different mechanisms, which depend on the context of the cell type and the inherent characteristics of the protein. For instance, mechanisms that underlie lipid regulation of ion channels depend upon specific lipid-protein interactions required to maintain the proper orientation of channel domains relative to each other. Perturbations of the lipid bilayer structure and packing may have a significant impact on the channel structure and function.