Comparative analysis of membrane protein structure databases

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

Oxidative photocatalysis on membranes triggers non-canonical pyroptosis

Intracellular membranes composing organelles of eukaryotes include membrane proteins playing crucial roles in physiological functions. However, a comprehensive understanding of the cellular responses triggered by intracellular membrane-focused oxidative stress remains elusive. Herein, we report an amphiphilic photocatalyst localised in intracellular membranes to damage membrane proteins oxidatively, resulting in non-canonical pyroptosis. Our developed photocatalysis generates hydroxyl radicals and hydrogen peroxides via water oxidation, which is accelerated under hypoxia. Single-molecule magnetic tweezers reveal that photocatalysis-induced oxidation markedly destabilised membrane protein folding. In cell environment, label-free quantification reveals that oxidative damage occurs primarily in membrane proteins related to protein quality control, thereby aggravating mitochondrial and endoplasmic reticulum stress and inducing lytic cell death. Notably, the photocatalysis activates non-canonical inflammasome caspases, resulting in gasdermin D cleavage to its pore-forming fragment and subsequent pyroptosis. These findings suggest that the oxidation of intracellular membrane proteins triggers non-canonical pyroptosis.

EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

A Critical Evaluation of Detergent Exchange Methodologies for Membrane Protein Native Mass Spectrometry

Membrane proteins (MPs) play many critical roles in cellular physiology and constitute the majority of current pharmaceutical targets. However, MPs are comparatively understudied relative to soluble proteins due to the challenges associated with their solubilization in membrane mimetics. Native mass spectrometry (nMS) has emerged as a useful technique to probe the structures of MPs. Typically, nMS studies using MPs have employed detergent micelles to solubilize the MP. Oftentimes, the detergent micelle that the MP was purified in will be exchanged into another detergent prior to analysis by nMS. While methodologies for performing detergent exchange have been extensively described in prior reports, the effectiveness of these protocols remains understudied. Here, we present a critical analysis of detergent exchange efficacy using several model transmembrane proteins and a variety of commonly used detergents, evaluating the completeness of the exchange using a battery of existing protocols. Our data include results for octyl glucoside (OG), octaethylene glycol monododecyl ether (C12E8), and tetraethylene glycol monooctyl ether (C8E4), and these data demonstrate that existing protocols are insufficient and yield incomplete exchange for the proteins under the conditions probed here. In some cases, our data indicate that up to 99% of the measured detergent corresponds to the original pre-exchange detergent rather than the desired post-exchange detergent. We conclude by discussing the need for new detergent exchange methodologies alongside improved exchange yield expectations for studying the potential influence of detergents on MP structures.

Melamine-cored glucosides for membrane protein solubilization and stabilization: importance of water-mediated intermolecular hydrogen bonding in detergent performance

Membrane proteins play essential roles in a number of biological processes, and their structures are important in elucidating such processes at the molecular level and also for rational drug design and development. Membrane protein structure determination is notoriously challenging compared to that of soluble proteins, due largely to the inherent instability of their structures in non-lipid environments. Micelles formed by conventional detergents have been widely used for membrane protein manipulation, but they are suboptimal for long-term stability of membrane proteins, making downstream characterization difficult. Hence, there is an unmet need for the development of new amphipathic agents with enhanced efficacy for membrane protein stabilization. In this study, we designed and synthesized a set of glucoside amphiphiles with a melamine core, denoted melamine-cored glucosides (MGs). When evaluated with four membrane proteins (two transporters and two G protein-coupled receptors), MG-C11 conferred notably enhanced stability compared to the commonly used detergents, DDM and LMNG. These promising findings are mainly attributed to a unique feature of the MGs, i.e., the ability to form dynamic water-mediated hydrogen-bond networks between detergent molecules, as supported by molecular dynamics simulations. Thus, MG-C11 is the first example of a non-peptide amphiphile capable of forming intermolecular hydrogen bonds within a protein-detergent complex environment. Detergent micelles formed via a hydrogen-bond network could represent the next generation of highly effective membrane-mimetic systems useful for membrane protein structural studies.

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.

Infrared Photoactivation Enables Improved Native Top-Down Mass Spectrometry of Transmembrane Proteins

Membrane proteins are often challenging targets for native top-down mass spectrometry experimentation. The requisite use of membrane mimetics to solubilize such proteins necessitates the application of supplementary activation methods to liberate protein ions prior to sequencing, which typically limits the sequence coverage achieved. Recently, infrared photoactivation has emerged as an alternative to collisional activation for the liberation of membrane proteins from surfactant micelles. However, much remains unknown regarding the mechanism by which IR activation liberates membrane protein ions from such micelles, the extent to which such methods can improve membrane protein sequence coverage, and the degree to which such approaches can be extended to support native proteomics. Here, we describe experiments designed to evaluate and probe infrared photoactivation for membrane protein sequencing, proteoform identification, and native proteomics applications. Our data reveal that infrared photoactivation can dissociate micelles composed of a variety of detergent classes, without the need for a strong IR chromophore by leveraging the relatively weak association energies of such detergent clusters in the gas phase. Additionally, our data illustrate how IR photoactivation can be extended to include membrane mimetics beyond micelles and liberate proteins from nanodiscs, liposomes, and bicelles. Finally, our data quantify the improvements in membrane protein sequence coverage produced through the use of IR photoactivation, which typically leads to membrane protein sequence coverage values ranging from 40 to 60%.

A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.

Tuning an efficient Escherichia coli whole-cell catalyst expressing l-pantolactone dehydrogenase for the biosynthesis of d-(-)-pantolactone

d-(-)-Pantolactone (DPL) is a key intermediate for the production of d-(+)-pantothenate (vitamin B5). Deracemization of d,l-pantolactone (D,L-PL) through oxidizing l-(+)-pantolactone (LPL) to ketopantoyl lactone (KPL) and subsequently reducing KPL to DPL is a promising route for synthesizing DPL. Herein, a newly mined l-pantolactone dehydrogenase from Rhodococcus hoagie (RhoLPLDH) was used for the oxidative dehydrogenation of LPL. To alleviate inclusion bodies formed by membrane-bound RhoLPLDH intracellular expression in E. coli, strategies involving chaperone assistance and decreasing induction temperature were used to achieve RhoLPLDH soluble expression. To enhance its activity, directed evolution and hydrophilicity-based engineering yielded increased catalytic activity and thermostability. 1M LPL was efficiently converted to KPL by engineering strain CM5 co-expressing RhoLPLDH^{L254I/V241I/I156L/F224Q/N164K} and chaperone. A "two stages in one-pot" method was employed in deracemization of 1M D,L-PL with 91.2% yield. These results demonstrated that CM5 catalyst exhibits great potential in enzyme cascade deracemization for the production of DPL.

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.

Application of polymersomes in membrane protein study and drug discovery: Progress, strategies, and perspectives

Membrane proteins (MPs) play key roles in cellular signaling pathways and are responsible for intercellular and intracellular interactions. Dysfunctional MPs are directly related to the pathogenesis of various diseases, and they have been exploited as one of the most sought-after targets in the pharmaceutical industry. However, working with MPs is difficult given that their amphiphilic nature requires protection from biological membrane or membrane mimetics. Polymersomes are bilayered nano-vesicles made of self-assembled block copolymers that have been widely used as cell membrane mimetics for MP reconstitution and in engineering of artificial cells. This review highlights the prevailing trend in the application of polymersomes in MP study and drug discovery. We begin with a review on the techniques for synthesis and characterization of polymersomes as well as methods of MP insertion to form proteopolymersomes. Next, we review the structural and functional analysis of the different types of MPs reconstituted in polymersomes, including membrane transport proteins, MP complexes, and membrane receptors. We then summarize the factors affecting reconstitution efficiency and the quality of reconstituted MPs for structural and functional studies. Additionally, we discuss the potential in using proteopolymersomes as platforms for high-throughput screening (HTS) in drug discovery to identify modulators of MPs. We conclude by providing future perspectives and recommendations on advancing the study of MPs and drug development using proteopolymersomes.

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

MOTIVATION

Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task.

RESULTS

We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D.

AVAILABILITY AND IMPLEMENTATION

Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Glyco-steroidal amphiphiles (GSAs) for membrane protein structural study

Integral membrane proteins pose considerable challenges to high resolution structural analysis. Maintaining membrane proteins in their native state during protein isolation is essential for structural study of these bio-macromolecules. Detergents are the most commonly used amphiphilic compounds for stabilizing membrane proteins in solution outside a lipid bilayer. We previously introduced a glyco-diosgenin (GDN) detergent that was shown to be highly effective at stabilizing a wide range of membrane proteins. This steroidal detergent has additionally gained attention due to its compatibility with membrane protein structure study via cryo-EM. However, synthetic inconvenience limits widespread use of GDN in membrane protein study. To improve its synthetic accessibility and to further enhance detergent efficacy for protein stabilization, we designed a new class of glyco-steroid-based detergents using three steroid units: cholestanol, cholesterol and diosgenin. These new detergents were efficiently prepared and showed marked efficacy for protein stabilization in evaluation with a few model membrane proteins including two G protein-coupled receptors. Some new agents were not only superior to a gold standard detergent, DDM, but were also more effective than the original GDN at preserving protein integrity long term. These agents represent valuable alternatives to GDN, and are likely to facilitate structural determination of challenging membrane proteins.

Dependence of Protein Structure on Environment: FOD Model Applied to Membrane Proteins

The natural environment of proteins is the polar aquatic environment and the hydrophobic (amphipathic) environment of the membrane. The fuzzy oil drop model (FOD) used to characterize water-soluble proteins, as well as its modified version FOD-M, enables a mathematical description of the presence and influence of diverse environments on protein structure. The present work characterized the structures of membrane proteins, including those that act as channels, and a water-soluble protein for contrast. The purpose of the analysis was to verify the possibility that an external force field can be used in the simulation of the protein-folding process, taking into account the diverse nature of the environment that guarantees a structure showing biological activity.

BRANEart: Identify Stability Strength and Weakness Regions in Membrane Proteins

Understanding the role of stability strengths and weaknesses in proteins is a key objective for rationalizing their dynamical and functional properties such as conformational changes, catalytic activity, and protein-protein and protein-ligand interactions. We present BRANEart, a new, fast and accurate method to evaluate the per-residue contributions to the overall stability of membrane proteins. It is based on an extended set of recently introduced statistical potentials derived from membrane protein structures, which better describe the stability properties of this class of proteins than standard potentials derived from globular proteins. We defined a per-residue membrane propensity index from combinations of these potentials, which can be used to identify residues which strongly contribute to the stability of the transmembrane region or which would, on the contrary, be more stable in extramembrane regions, or vice versa. Large-scale application to membrane and globular proteins sets and application to tests cases show excellent agreement with experimental data. BRANEart thus appears as a useful instrument to analyze in detail the overall stability properties of a target membrane protein, to position it relative to the lipid bilayer, and to rationally modify its biophysical characteristics and function. BRANEart can be freely accessed from http://babylone.3bio.ulb.ac.be/BRANEart.

The Novel Application of Geometric Morphometrics with Principal Component Analysis to Existing G Protein-Coupled Receptor (GPCR) Structures

The G protein-coupled receptor (GPCR) superfamily is a large group of membrane proteins which, because of their vast involvement in cell signalling pathways, are implicated in a plethora of disease states and are therefore considered to be key drug targets. Despite advances in techniques to study these receptors, current prophylaxis is often limited due to the challenging nature of their dynamic, complex structures. Greater knowledge and understanding of their intricate structural rearrangements will therefore undoubtedly aid structure-based drug design against GPCRs. Disciplines such as anthropology and palaeontology often use geometric morphometrics to measure variation between shapes and we have therefore applied this technique to analyse GPCR structures in a three-dimensional manner, using principal component analysis. Our aim was to create a novel system able to discriminate between GPCR structures and discover variation between them, correlated with a variety of receptor characteristics. This was conducted by assessing shape changes at the extra- and intracellular faces of the transmembrane helix bundle, analysing the XYZ coordinates of the amino acids at those positions. We have demonstrated that GPCR structures can be classified based on characteristics such as activation state, bound ligands and fusion proteins, with the most significant results focussed at the intracellular face. Conversely, our analyses provide evidence that thermostabilising mutations do not cause significant differences when compared to non-mutated GPCRs. We believe that this is the first time geometric morphometrics has been applied to membrane proteins on this scale, and believe it can be used as a future tool in sense-checking newly resolved structures and planning experimental design.

Prediction of amphipathic helix-membrane interactions with Rosetta

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.

Amphipathic helices are important targets as antibacterial peptides and as domains of membrane proteins that play a role in sensing the membrane environment. Understanding how amphipathic helices interact with membrane enables us to design better peptides and understand how membrane proteins use them to interact with their environment. However, there is a limited number of tools available for the modeling of amphipathic helices in membranes. Implicit membrane models can be used for this purpose as simplistic representations of the membrane environment. In this work, we developed the AmphiScan protocol that can be used to predict membrane coordinates of amphipathic helices starting with a helix structure in an implicit membrane environment. We benchmarked the performance of AmphiScan on engineered LK peptides, naturally-occurring amphipathic helices, and hydrophobic and hydrophilic peptides. Our approach provides a reliable and customizable tool to model amphipathic helix–membrane interactions, and pose a platform for the screening of amphipathic helix properties in silico.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

A 10-year meta-analysis of membrane protein structural biology: Detergents, membrane mimetics, and structure determination techniques

Structure determination of membrane proteins is critical to the molecular understanding of many life processes, yet it has historically been a technically challenging endeavor. This past decade has given rise to a number of technological advancements, techniques, and reagents, which have facilitated membrane protein structural biology, resulting in an ever-growing number of membrane protein structures determined. To collate these advances, we have mined available literature to analyze the purification and structure determination specifics for all uniquely solved membrane protein structures from 2010 to 2019. Our analyses demonstrate the strong impact of single-particle cryo-electron microscopy on the field and illustrate how this technique has affected detergent and membrane mimetic usage. Furthermore, we detail how different structure determination methods, taxonomic domains and protein classes have unique detergent/membrane mimetic profiles, highlighting the importance of tailoring their selection. Our analyses provide a quantitative overview of where the field of membrane protein structural biology stands and how it has developed over time. We anticipate that these will serve as a useful tool to streamline future membrane protein structure determination by guiding the choice of detergent/membrane mimetic.

Integrative modeling of membrane-associated protein assemblies

Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins.

On the Detection and Functional Significance of the Protein-Protein Interactions of Mitochondrial Transport Proteins

Protein-protein assemblies are highly prevalent in all living cells. Considerable evidence has recently accumulated suggesting that particularly transient association/dissociation of proteins represent an important means of regulation of metabolism. This is true not only in the cytosol and organelle matrices, but also at membrane surfaces where, for example, receptor complexes, as well as those of key metabolic pathways, are common. Transporters also frequently come up in lists of interacting proteins, for example, binding proteins that catalyze the production of their substrates or that act as relays within signal transduction cascades. In this review, we provide an update of technologies that are used in the study of such interactions with mitochondrial transport proteins, highlighting the difficulties that arise in their use for membrane proteins and discussing our current understanding of the biological function of such interactions.

MemSTATS: A Benchmark Set of Membrane Protein Symmetries and Pseudosymmetries

In membrane proteins, symmetry and pseudosymmetry often have functional or evolutionary implications. However, available symmetry detection methods have not been tested systematically on this class of proteins because of the lack of an appropriate benchmark set. Here we present MemSTATS, a publicly available benchmark set of both quaternary- and internal-symmetries in membrane protein structures. The symmetries are described in terms of order, repeated elements, and orientation of the axis with respect to the membrane plane. Moreover, using MemSTATS, we compare the performance of four widely used symmetry detection algorithms and highlight specific challenges and areas for improvement in the future.

ECOD: identification of distant homology among multidomain and transmembrane domain proteins

The manual classification of protein domains is approaching its 20th anniversary. ECOD is our mixed manual-automatic domain classification. Over time, the types of proteins which require manual curation has changed. Depositions with complex multidomain and multichain arrangements are commonplace. Transmembrane domains are regularly classified. Repeatedly, domains which are initially believed to be novel are found to have homologous links to existing classified domains. Here we present a brief summary of recent manual curation efforts in ECOD generally combined with specific case studies of transmembrane and multidomain proteins wherein manual curation was useful for discovering new homologous relationships. We present a new taxonomy for the classification of ABC transporter transmembrane domains. We examine alternate topologies of the leucine-specific (LS) domain of Leucine tRNA-synthetase. Finally, we elaborate on a distant homologous links between two helical dimerization domains.

Multiscale Simulations of Biological Membranes: The Challenge To Understand Biological Phenomena in a Living Substance

Biological membranes are tricky to investigate. They are complex in terms of molecular composition and structure, functional over a wide range of time scales, and characterized by nonequilibrium conditions. Because of all of these features, simulations are a great technique to study biomembrane behavior. A significant part of the functional processes in biological membranes takes place at the molecular level; thus computer simulations are the method of choice to explore how their properties emerge from specific molecular features and how the interplay among the numerous molecules gives rise to function over spatial and time scales larger than the molecular ones. In this review, we focus on this broad theme. We discuss the current state-of-the-art of biomembrane simulations that, until now, have largely focused on a rather narrow picture of the complexity of the membranes. Given this, we also discuss the challenges that we should unravel in the foreseeable future. Numerous features such as the actin-cytoskeleton network, the glycocalyx network, and nonequilibrium transport under ATP-driven conditions have so far received very little attention; however, the potential of simulations to solve them would be exceptionally high. A major milestone for this research would be that one day we could say that computer simulations genuinely research biological membranes, not just lipid bilayers.

Membrane protein engineering to the rescue

The inherent hydrophobicity of membrane proteins is a major barrier to membrane protein research and understanding. Their low stability and solubility in aqueous environments coupled with poor expression levels make them a challenging area of research. For many years, the only way of working with membrane proteins was to optimise the environment to suit the protein, through the use of different detergents, solubilising additives, and other adaptations. However, with innovative protein engineering methodologies, the membrane proteins themselves are now being adapted to suit the environment. This mini-review looks at the types of adaptations which are applied to membrane proteins from a variety of different fields, including water solubilising fusion tags, thermostabilising mutation screening, scaffold proteins, stabilising protein chimeras, and isolating water-soluble domains.