Large-scale proteomic analysis of membrane proteins

Proteomic analysis of membrane proteins is a promising approach for the identification of novel drug targets and/or disease biomarkers. Despite notable technological developments, obstacles related to extraction and solublization of membrane proteins are encountered. A critical discussion of the different preparative methods of membrane proteins is offered in relation to downstream proteomic applications, mainly gel-based analyses and mass spectrometry. Frequently, unknown proteins are identified by high-throughput profiling of membrane proteins. In search for novel membrane proteins, analysis of protein sequences using computational tools is performed to predict the presence of transmembrane domains. This review also presents these bioinformatic tools with the human proteome as a case study. Along with technological innovations, advancements in the areas of sample preparation and computational prediction of membrane proteins will lead to exciting discoveries.     

Membrane protein structural bioinformatics

Despite the increasing number of recently solved membrane protein structures, coverage of membrane protein fold space remains relatively sparse. This necessitates the use of computational strategies to investigate membrane protein structure, allowing us to further our understanding of how membrane proteins carry out their diverse range of functions, while aiding the development of novel predictive tools with which to probe uncharacterised folds. Analysis of known structures, the application of machine learning techniques, molecular dynamics simulations and protein structure prediction have enabled significant advances to be made in the field of membrane protein research. In this communication, the key bioinformatic methods that allow the characterisation of membrane proteins are reviewed, the tools available for the structural analysis of membrane proteins are presented and the contribution these tools have made to expanding our understanding of membrane protein structure, function and stability is discussed.     

Membrane proteins: the 'Wild West' of structural biology

Historically, the task of determining the structure of membrane proteins has been hindered by experimental difficulties associated with their lipid-embedded domains. Here, we provide an overview of recently developed experimental and predictive tools that are changing our view of this largely unexplored territory - the 'Wild West' of structural biology. Crystallography, single-particle methods and atomic force microscopy are being used to study huge membrane proteins with increasing detail. Solid-state nuclear magnetic resonance strategies provide orientational constraints for structure determination of transmembrane (TM) alpha-helices and accurate measurements of intramolecular distances, even in very complex systems. Longer distance constraints are determined by site-directed spin-labelling electron paramagnetic resonance, but current labelling strategies still constitute some limitation. Other methods, such as site-specific infrared dichroism, enable orientational analysis of TM alpha-helices in aligned bilayers and, combined with novel computational and predictive tools that use evolutionary conservation data, are being used to analyze TM alpha-helical bundles.     

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.     

Estimation of membrane proteins in the human proteome

Genomics and proteomics have added valuable information to our knowledgebase of the human biological system including the discovery of therapeutic targets and disease biomarkers. However, molecular profiling studies commonly result in the identification of novel proteins of unknown localization. A class of proteins of special interest is membrane proteins, in particular plasma membrane proteins. Despite their biological and medical significance, the 3-dimensional structures of less than 1% of plasma membrane proteins have been determined. In order to aid in identification of membrane proteins, a number of computational methods have been developed. These tools operate by predicting the presence of transmembrane segments. Here, we utilized five topology prediction methods (TMHMM, SOSUI, waveTM, HMMTOP, and TopPred II) in order to estimate the ratio of integral membrane proteins in the human proteome. These methods employ different algorithms and include a newly-developed method (waveTM) that has yet to be tested on a large proteome database. Since these tools are prone for error mainly as a result of falsely predicting signal peptides as transmembrane segments, we have utilized an additional method, SignalP. Based on our analyses, the ratio of human proteins with transmembrane segments is estimated to fall between 15% and 39% with a consensus of 13%. Agreement among the programs is reduced further when both a positive identification of a membrane protein and the number of transmembrane segments per protein are considered. Such a broad range of prediction depends on the selectivity of the individual method in predicting integral membrane proteins. These methods can play a critical role in determining protein structure and, hence, identifying suitable drug targets in humans.     

细胞质膜蛋白质组学研究技术进展

质膜蛋白在细胞中执行着非常重要的功能。随着蛋白质组学的发展,细胞质膜蛋白质组学成为蛋白质组学研究的重要组成部分,它为质膜蛋白的生物功能研究及药物靶标的发现提供了新的途径。然而,质膜蛋白丰度低、疏水性强,对现有蛋白质组学研究技术提出了挑战。简要综述了近年来质膜蛋白质组研究的相关技术进展,包括富集、提取分离鉴定方法及定量和生物信息学研究方法等。     

A whole genome RNAi screen of Drosophila S2 cell spreading performed using automated computational image analysis

Recent technological advances in microscopy have enabled cell-based whole genome screens, but the analysis of the vast amount of image data generated by such screens usually proves to be rate limiting. In this study, we performed a whole genome RNA interference (RNAi) screen to uncover genes that affect spreading of Drosophila melanogaster S2 cells using several computational methods for analyzing the image data in an automated manner. Expected genes in the Scar-Arp2/3 actin nucleation pathway were identified as well as casein kinase I, which had a similar morphological RNAi signature. A distinct nonspreading morphological phenotype was identified for genes involved in membrane secretion or synthesis. In this group, we identified a new secretory peptide and investigated the functions of two poorly characterized endoplasmic reticulum proteins that have roles in secretion. Thus, this genome-wide screen succeeded in identifying known and unexpected proteins that are important for cell spreading, and the computational tools developed in this study should prove useful for other types of automated whole genome screens.     

Genome-wide structural analysis reveals novel membrane binding properties of AP180 N-terminal homology (ANTH) domains

An increasing number of cytosolic proteins are shown to interact with membrane lipids during diverse cellular processes, but computational prediction of these proteins and their membrane binding behaviors remains challenging. Here, we introduce a new combinatorial computation protocol for systematic and robust functional prediction of membrane-binding proteins through high throughput homology modeling and in-depth calculation of biophysical properties. The approach was applied to the genomic scale identification of the AP180 N-terminal homology (ANTH) domain, one of the modular lipid binding domains, and prediction of their membrane binding properties. Our analysis yielded comprehensive coverage of the ANTH domain family and allowed classification and functional annotation of proteins based on the differences in local structural and biophysical features. Our analysis also identified a group of plant ANTH domains with unique structural features that may confer novel functionalities. Experimental characterization of a representative member of this subfamily confirmed its unique membrane binding mechanism and unprecedented membrane deforming activity. Collectively, these studies suggest that our new computational approach can be applied to genome-wide functional prediction of other lipid binding domains.     

Not Just an Oil Slick: How the Energetics of Protein-Membrane Interactions Impacts the Function and Organization of Transmembrane Proteins

The membrane environment, its composition, dynamics, and remodeling, have been shown to participate in the function and organization of a wide variety of transmembrane (TM) proteins, making it necessary to study the molecular mechanisms of such proteins in the context of their membrane settings. We review some recent conceptual advances enabling such studies, and corresponding computational models and tools designed to facilitate the concerted experimental and computational investigation of protein-membrane interactions. To connect productively with the high resolution achieved by cognate experimental approaches, the computational methods must offer quantitative data at an atomistically detailed level. We show how such a quantitative method illuminated the mechanistic importance of a structural characteristic of multihelical TM proteins, that is, the likely presence of adjacent polar and hydrophobic residues at the protein-membrane interface. Such adjacency can preclude the complete alleviation of the well-known hydrophobic mismatch between TM proteins and the surrounding membrane, giving rise to an energy cost of residual hydrophobic mismatch. The energy cost and biophysical formulation of hydrophobic mismatch and residual hydrophobic mismatch are reviewed in the context of their mechanistic role in the function of prototypical members of multihelical TM protein families: 1), LeuT, a bacterial homolog of mammalian neurotransmitter sodium symporters; and 2), rhodopsin and the β1- and β2-adrenergic receptors from the G-protein coupled receptor family. The type of computational analysis provided by these examples is poised to translate the rapidly growing structural data for the many TM protein families that are of great importance to cell function into ever more incisive insights into mechanisms driven by protein-ligand and protein-protein interactions in the membrane environment.     

Not Just an Oil Slick: How the Energetics of Protein-Membrane Interactions Impacts the Function and Organization of Transmembrane Proteins

The membrane environment, its composition, dynamics, and remodeling, have been shown to participate in the function and organization of a wide variety of transmembrane (TM) proteins, making it necessary to study the molecular mechanisms of such proteins in the context of their membrane settings. We review some recent conceptual advances enabling such studies, and corresponding computational models and tools designed to facilitate the concerted experimental and computational investigation of protein-membrane interactions. To connect productively with the high resolution achieved by cognate experimental approaches, the computational methods must offer quantitative data at an atomistically detailed level. We show how such a quantitative method illuminated the mechanistic importance of a structural characteristic of multihelical TM proteins, that is, the likely presence of adjacent polar and hydrophobic residues at the protein-membrane interface. Such adjacency can preclude the complete alleviation of the well-known hydrophobic mismatch between TM proteins and the surrounding membrane, giving rise to an energy cost of residual hydrophobic mismatch. The energy cost and biophysical formulation of hydrophobic mismatch and residual hydrophobic mismatch are reviewed in the context of their mechanistic role in the function of prototypical members of multihelical TM protein families: 1), LeuT, a bacterial homolog of mammalian neurotransmitter sodium symporters; and 2), rhodopsin and the β1- and β2-adrenergic receptors from the G-protein coupled receptor family. The type of computational analysis provided by these examples is poised to translate the rapidly growing structural data for the many TM protein families that are of great importance to cell function into ever more incisive insights into mechanisms driven by protein-ligand and protein-protein interactions in the membrane environment.     

Genetic selection for and molecular dynamic modeling of a protein transmembrane domain multimerization motif from a random Escherichia coli genomic library

In order to identify new transmembrane helix packing motifs in naturally occurring proteins, we have selected transmembrane domains from a library of random Escherichia coli genomic DNA fragments and screened them for homomultimerization via their abilities to dimerize the bacteriophage lambda cI repressor DNA-binding domain. Sequences were isolated using a modified lambda cI headpiece dimerization assay system, which was shown previously to measure transmembrane helix-helix association in the E. coli inner membrane. Screening resulted in the identification of several novel sequences that appear to mediate helix-helix interactions. One sequence, representing the predicted sixth transmembrane domain (TM6) of the E. coli protein YjiO, was chosen for further analysis. Using site-directed mutagenesis and molecular dynamics, a small set of models for YjiO TM6 multimerization interface interactions were generated. This work demonstrates the utility of combining in vivo genetic tools with computational systems for understanding membrane protein structure and assembly.     

CLIPSeqTools—a novel bioinformatics CLIP-seq analysis suite

Immunoprecipitation of RNA binding proteins (RBPs) after in vivo crosslinking, coupled with sequencing of associated RNA footprints (HITS-CLIP, CLIP-seq), is a method of choice for the identification of RNA targets and binding sites for RBPs. Compared with RNA-seq, CLIP-seq analysis is widely diverse and depending on the RBPs that are analyzed, the approaches vary significantly, necessitating the development of flexible and efficient informatics tools. In this study, we present CLIPSeqTools, a novel, highly flexible computational suite that can perform analysis from raw sequencing data with minimal user input. It contains a wide array of tools to provide an in-depth view of CLIP-seq data sets. It supports extensive customization and promotes improvization, a critical virtue, since CLIP-seq analysis is rarely well defined a priori. To highlight CLIPSeqTools capabilities, we used the suite to analyze Ago-miRNA HITS-CLIP data sets that we prepared from human brains.     

Quaternary structure predictions of transmembrane proteins starting from the monomer: a docking-based approach

Background  

We introduce a computational protocol for effective predictions of the supramolecular organization of integral transmembrane proteins, starting from the monomer. Despite the demonstrated constitutive and functional importance of supramolecular assemblies of transmembrane subunits or proteins, effective tools for structure predictions of such assemblies are still lacking. Our computational approach consists in rigid-body docking samplings, starting from the docking of two identical copies of a given monomer. Each docking run is followed by membrane topology filtering and cluster analysis. Prediction of the native oligomer is therefore accomplished by a number of progressive growing steps, each made of one docking run, filtering and cluster analysis. With this approach, knowledge about the oligomerization status of the protein is required neither for improving sampling nor for the filtering step. Furthermore, there are no size-limitations in the systems under study, which are not limited to the transmembrane domains but include also the water-soluble portions.     

HMPAS: Human Membrane Protein Analysis System

Background

Membrane proteins perform essential roles in diverse cellular functions and are regarded as major pharmaceutical targets. The significance of membrane proteins has led to the developing dozens of resources related with membrane proteins. However, most of these resources are built for specific well-known membrane protein groups, making it difficult to find common and specific features of various membrane protein groups.

Methods

We collected human membrane proteins from the dispersed resources and predicted novel membrane protein candidates by using ortholog information and our membrane protein classifiers. The membrane proteins were classified according to the type of interaction with the membrane, subcellular localization, and molecular function. We also made new feature dataset to characterize the membrane proteins in various aspects including membrane protein topology, domain, biological process, disease, and drug. Moreover, protein structure and ICD-10-CM based integrated disease and drug information was newly included. To analyze the comprehensive information of membrane proteins, we implemented analysis tools to identify novel sequence and functional features of the classified membrane protein groups and to extract features from protein sequences.

Results

We constructed HMPAS with 28,509 collected known membrane proteins and 8,076 newly predicted candidates. This system provides integrated information of human membrane proteins individually and in groups organized by 45 subcellular locations and 1,401 molecular functions. As a case study, we identified associations between the membrane proteins and diseases and present that membrane proteins are promising targets for diseases related with nervous system and circulatory system. A web-based interface of this system was constructed to facilitate researchers not only to retrieve organized information of individual proteins but also to use the tools to analyze the membrane proteins.

Conclusions

HMPAS provides comprehensive information about human membrane proteins including specific features of certain membrane protein groups. In this system, user can acquire the information of individual proteins and specified groups focused on their conserved sequence features, involved cellular processes, and diseases. HMPAS may contribute as a valuable resource for the inference of novel cellular mechanisms and pharmaceutical targets associated with the human membrane proteins. HMPAS is freely available at http://fcode.kaist.ac.kr/hmpas.

     

Recent findings and technological advances in phosphoproteomics for cells and tissues

Site-specific phosphorylation is a fast and reversible covalent post-translational modification that is tightly regulated in cells. The cellular machinery of enzymes that write, erase and read these modifications (kinases, phosphatases and phospho-binding proteins) is frequently deregulated in different diseases, including cancer. Large-scale studies of phosphoproteins – termed phosphoproteomics – strongly rely on the use of high-performance mass spectrometric instrumentation. This powerful technology has been applied to study a great number of phosphorylation-based phenotypes. Nevertheless, many technical and biological challenges have to be overcome to identify biologically relevant phosphorylation sites in cells and tissues. This review describes different technological strategies to identify and quantify phosphorylation sites with high accuracy, without significant loss of analysis speed and reproducibility in tissues and cells. Moreover, computational tools for analysis, integration and biological interpretation of phosphorylation events are discussed.     

Modification-specific proteomics of plasma membrane proteins: identification and characterization of glycosylphosphatidylinositol-anchored proteins released upon phospholipase D treatment

Plasma membrane proteins are displayed through diverse mechanisms, including anchoring in the extracellular leaflet via glycosylphosphatidylinositol (GPI) molecules. GPI-anchored membrane proteins (GPI-APs) are a functionally and structurally diverse protein family, and their importance is well-recognized as they are candidate cell surface biomarker molecules with potential diagnostic and therapeutic applications in molecular medicine. GPI-APs have also attracted interest in plant biotechnology because of their role in root development and cell remodeling. Using a shave-and-conquer concept, we demonstrate that phospholipase D (PLD) treatment of human and plant plasma membrane fractions leads to the release of GPI-anchored proteins that were identified and characterized by capillary liquid chromatography and tandem mass spectrometry. In contrast to phospholipase C, the PLD enzyme is not affected by structural heterogeneity of the GPI moiety, making PLD a generally useful reagent for proteomic investigations of GPI-anchored proteins in a variety of cells, tissues, and organisms. A total of 11 human GPI-APs and 35 Arabidopsis thaliana GPI-APs were identified, representing a significant addition to the number of experimentally detected GPI-APs in both species. Computational GPI-AP sequence analysis tools were investigated for the characterization of the identified GPI-APs, and these demonstrated that there is some discrepancy in their efficiency in classification of GPI-APs and the exact assignment of omega-sites. This study highlights the efficiency of an integrative proteomics approach that combines experimental and computational methods to provide the selectivity, specificity, and sensitivity required for characterization of post-translationally modified membrane proteins.     

Tailoring Proteins to Re-Evolve Nature: A Short Review

Proteins are key biomolecules for most biological processes, their function is related to their conformation that is also dictated by their sequence of amino acids. Through evolution, nature has produced an immense variety of enzymatic tools of high efficiency and selectivity, and thanks to the understanding of the molecular basis of life and the technological advances, scientists have learned to introduce mutations and select mutant enzymes, to optimize and control their molecular fitness characteristics mainly for industrial, medical and environmental applications. The relationship between protein structure and enzymatic functionality is essential, and there are various experimental and instrumental techniques for unravelling the molecular changes, activities and specificities. Protein engineering applies computational tools, in hand with experimental tools for mutations, like directed evolution and rational design, along with screening methods to obtain protein variations with the desired properties under a short time frame. With innovations in technology, it is possible to fine tune properties in proteins and reach new frontiers in their applications. The present review will briefly discuss these points and methods, with a glimpse on their strengths and pitfalls, while giving an overview of the versatility of synthetic proteins and their huge potential for biotechnological and biomedical fields.