Structure prediction of membrane proteins

Alanine Scanning to Define Membrane Protein-Lipid Interaction Sites Using Native Mass Spectrometry

Lipids surrounding membrane proteins interact with different sites on the protein at varying specificities, ranging from highly specific to weak interactions. These interactions can modulate the structure, function, and stability of membrane proteins. Thus, to better understand membrane protein structure and function, it is important to identify the locations of lipid binding and the relative specificities of lipid binding at these sites. In our previous native mass spectrometry (MS) study, we developed a single and double mutant analysis approach to profile the contribution of specific residues toward lipid binding. Here, we extend this method by screening a broad range of mutants of AqpZ to identify specific lipid binding sites and by measuring binding of different lipid types to measure the selectivity of different lipids at selected binding sites. We complemented these native MS studies with molecular dynamics (MD) simulations to visualize lipid interactions at selected sites. We discovered that AqpZ is selective towards cardiolipins (CL) but only at specific sites. Specifically, CL orients with its headgroup facing the cytoplasmic side, and its acyl chains interact with a hydrophobic pocket located at the monomeric interface within the lipid bilayer. Overall, this integrative approach provides unique insights into lipid binding sites and the selectivity of various lipids towards AqpZ, enabling us to map the AqpZ protein structure based on the lipid affinity.

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.

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.

Sodium laurate, a novel protease- and mass spectrometry-compatible detergent for mass spectrometry-based membrane proteomics

The hydrophobic nature of most membrane proteins severely complicates their extraction, proteolysis and identification. Although detergents can be used to enhance the solubility of the membrane proteins, it is often difficult for a detergent not only to have a strong ability to extract membrane proteins, but also to be compatible with the subsequent proteolysis and mass spectrometric analysis. In this study, we made evaluation on a novel application of sodium laurate (SL) to the shotgun analysis of membrane proteomes. SL was found not only to lyse the membranes and solubilize membrane proteins as efficiently as SDS, but also to be well compatible with trypsin and chymotrypsin. Furthermore, SL could be efficiently removed by phase transfer method from samples after acidification, thus ensuring not to interfere with the subsequent CapLC-MS/MS analysis of the proteolytic peptides of proteins. When SL was applied to assist the digestion and identification of a standard protein mixture containing bacteriorhodoposin and the proteins in rat liver plasma membrane-enriched fractions, it was found that, compared with other two representative enzyme- and MS-compatible detergents RapiGest SF (RGS) and sodium deoxycholate (SDC), SL exhibited obvious superiority in the identification of membrane proteins particularly those with high hydrophobicity and/or multiple transmembrane domains.

Comparative analysis of techniques to purify plasma membrane proteins

The aim of this project was to identify the best method for the enrichment of plasma membrane (PM) proteins for proteomics experiments. Following tryptic digestion and extended liquid chromatography-tandem mass spectrometry acquisitions, data were processed using MaxQuant and Gene Ontology (GO) terms used to determine protein subcellular localization. The following techniques were examined for the total number and percentage purity of PM proteins identified: (a) whole cell lysate (total number, 84-112; percentage purity, 9-13%); (b) crude membrane preparation (104-111; 17-20%); (c) biotinylation of surface proteins with N-hydroxysulfosuccinimydyl-S,S-biotin and streptavidin pulldown (78-115; 27-31%); (d) biotinylation of surface glycoproteins with biocytin hydrazide and streptavidin pulldown (41-54; 59-85%); or (e) biotinylation of surface glycoproteins with amino-oxy-biotin (which labels the sialylated fraction of PM glycoproteins) and streptavidin pulldown (120; 65%). A two- to threefold increase in the overall number of proteins identified was achieved by using stop and go extraction tip (StageTip)-based anion exchange (SAX) fractionation. Combining technique (e) with SAX fractionation increased the number of proteins identified to 281 (54%). Analysis of GO terms describing these proteins identified a large subset of proteins integral to the membrane with no subcellular assignment. These are likely to be of PM location and bring the total PM protein identifications to 364 (68%). This study suggests that selective biotinylation of the cell surface using amino-oxy-biotin in combination with SAX fractionation is a useful method for identification of sialylated PM proteins.

Application of displacement chromatography for the analysis of a lipid raft proteome

Defining membrane proteomes is fundamental to understand the role of membrane proteins in biological processes and to find new targets for drug development. Usually multidimensional chromatography using step or gradient elution is applied for the separation of tryptic peptides of membrane proteins prior to their mass spectrometric analysis. Displacement chromatography (DC) offers several advantages that are helpful for proteome analysis. However, DC has so far been applied for proteomic investigations only in few cases. In this study we therefore applied DC in a multidimensional LC-MS approach for the separation and identification of membrane proteins located in cholesterol-enriched membrane microdomains (lipid rafts) obtained from rat kidney by density gradient centrifugation. The tryptic peptides were separated on a cation-exchange column in the displacement mode with spermine used as displacer. Fractions obtained from DC were analyzed using an HPLC-chip system coupled to an electrospray-ionization ion-trap mass spectrometer. This procedure yielded more than 400 highly significant peptide spectrum matches and led to the identification of more than 140 reliable protein hits within an established rat kidney lipid raft proteome. The majority of identified proteins were membrane proteins. In sum, our results demonstrate that DC is a suitable alternative to gradient elution separations for the identification of proteins via a multidimensional LC-MS approach.

Transmembrane structure predictions with hydropathy index/charge two-dimensional trajectories of stochastic dynamical systems

A novel algorithm is proposed for predicting transmembrane protein secondary structure from two-dimensional vector trajectories consisting of a hydropathy index and formal charge of a test amino acid sequence using stochastic dynamical system models. Two prediction problems are discussed. One is the prediction of transmembrane region counts; another is that of transmembrane regions, i.e. predicting whether or not each amino acid belongs to a transmembrane region. The prediction accuracies, using a collection of well-characterized transmembrane protein sequences and benchmarking sequences, suggest that the proposed algorithm performs reasonably well. An experiment was performed with a glutamate transporter homologue from Pyrococcus horikoshii. The predicted transmembrane regions of the five human glutamate transporter sequences and observations based on the computed likelihood are reported.

Meeting report: Structural determination of environmentally responsive proteins

The three-dimensional structure of gene products continues to be a missing lynchpin between linear genome sequences and our understanding of the normal and abnormal function of proteins and pathways. Enhanced activity in this area is likely to lead to better understanding of how discrete changes in molecular patterns and conformation underlie functional changes in protein complexes and, with it, sensitivity of an individual to an exposure. The National Institute of Environmental Health Sciences convened a workshop of experts in structural determination and environmental health to solicit advice for future research in structural resolution relative to environmentally responsive proteins and pathways. The highest priorities recommended by the workshop were to support studies of structure, analysis, control, and design of conformational and functional states at molecular resolution for environmentally responsive molecules and complexes; promote understanding of dynamics, kinetics, and ligand responses; investigate the mechanisms and steps in posttranslational modifications, protein partnering, impact of genetic polymorphisms on structure/function, and ligand interactions; and encourage integrated experimental and computational approaches. The workshop participants also saw value in improving the throughput and purity of protein samples and macromolecular assemblies; developing optimal processes for design, production, and assembly of macromolecular complexes; encouraging studies on protein-protein and macromolecular interactions; and examining assemblies of individual proteins and their functions in pathways of interest for environmental health.