Latest developments in experimental and computational approaches to characterize protein-lipid interactions

Binding equations for the lipid composition dependence of peripheral membrane-binding proteins

The specific recognition of peripheral membrane-binding proteins for their target membranes is mediated by a complex constellation of various lipid contacts. Despite the inherent complexities of the heterogeneous protein-membrane interface, the binding dependence of such proteins is, surprisingly, often reliably described by simple models such as the Langmuir Adsorption Isotherm or the Hill equation. However, these models were not developed to describe associations with two-dimensional, highly concentrated heterogeneous ligands such as lipid membranes. In particular, these models fail to capture the dependence on the lipid composition, a significant determinant of binding that distinguishes target from non-target membranes. In this work, we present a model that describes the dependence of peripheral proteins on lipid composition through an analytic expression for their association. The resulting membrane-binding equation retains the features of these simple models but completely describes the binding dependence on multiple relevant variables in addition to the lipid composition, such as protein and vesicle concentration. Implicit in this lipid composition dependence is a new form of membrane-based cooperativity that significantly differs from traditional solution-based cooperativity. We introduce the Membrane-Hill number as a measure of this cooperativity and describe its unique properties. We illustrate the utility and interpretational power of our model by analyzing previously published data on two peripheral proteins that associate with phosphatidylserine-containing membranes: The transmembrane immunoglobulin and mucin domain-containing protein 3 (TIM3) that employs calcium in its association, and milk fat globulin epidermal growth factor VIII (MFG-E8) which is completely insensitive to calcium. We also provide binding equations for systems that exhibit more complexity in their membrane-binding.

Lipid-protein interactions for ECA1 an N-ANTH domain protein involved in stress signaling in plants

Epsin-like Clathrin Adaptor 1 (ECA1/ PICALM1A) is an A/ENTH domain protein that acts as an adaptor protein in clathrin-mediated endocytosis. ECA1 is recruited to the membrane during salt stress signaling in plants in a phosphatidic acid (PA)-dependent manner. PA is a lipid second messenger that rapidly and transiently increases in concentration under stress stimuli. Upon an increase in PA concentration another lipid, diacylglycerol pyrophosphate (DGPP), starts to accumulate. The accumulation of DGPP is suggested to be a cue for attenuating PA signaling during stress in plants. We showed in vitro that ECA1-PA binding is modulated as a function of membrane curvature stress and charge. In this work, we investigate ECA1 binding to DGPP in comparison with PA. We show that ECA1 has more affinity for the less charged PA, and this binding is pH dependent. Additionally, plant PA binding proteins SnRK2.10, TGD2C, and PDK1-PH2 were investigated for their interaction with DGPP, since no known DGPP binding proteins are available in the literature to date. Our results shed further light on DGPP and its interactions with membrane proteins which brings us closer toward understanding the complexity of protein interactions with anionic lipids, especially the enigmatic anionic lipid DGPP.

A machine learning approach to estimation of phase diagrams for three-component lipid mixtures

The plasma membrane of eukaryotic cells is commonly believed to contain ordered lipid domains. The interest in understanding the origin of such domains has led to extensive studies on the phase behavior of mixed lipid systems. Three-component phase diagrams, composed of a high melting temperature (Tm) lipid, cholesterol, and a low Tm lipid have been valuable in studying lipid phase behavior. However, developing phase diagrams over the entire composition space and with precise tie-lines requires significant experimental effort. In this study, a machine learning approach was used to predict the Tm of lipids and generate phase diagrams from lipid mixtures. First, artificial neural network (ANN) was used for the prediction of Tm. The network was trained using available Tm data and was able to generate Tm values that closely matched literature results for its testing dataset. This model was then used to predict the Tm for lipids that have not yet been experimentally tested. Then, random forests (RF) and support vector machines (SVM) were trained and tested for their ability to predict a test three-component phase diagram. The model from the RF algorithm was able to generate a diagram that closely matched published results. This model was then used to generate phase diagrams for lipid mixtures at various temperatures and various degrees of unsaturation. This machine learning approach to the generation of lipid phase diagrams has the potential to save significant time and resources in studies of lipid phase behavior.

Unlabeled lysophosphatidic acid receptor binding in free solution as determined by a compensated interferometric reader

Native interactions between lysophospholipids (LPs) and their cognate LP receptors are difficult to measure because of lipophilicity and/or the adhesive properties of lipids, which contribute to high levels of nonspecific binding in cell membrane preparations. Here, we report development of a free-solution assay (FSA) where label-free LPs bind to their cognate G protein-coupled receptors (GPCRs), combined with a recently reported compensated interferometric reader (CIR) to quantify native binding interactions between receptors and ligands. As a test case, the binding parameters between lysophosphatidic acid (LPA) receptor 1 (LPA₁; one of six cognate LPA GPCRs) and LPA were determined. FSA-CIR detected specific binding through the simultaneous real-time comparison of bound versus unbound species by measuring the change in the solution dipole moment produced by binding-induced conformational and/or hydration changes. FSA-CIR identified K_D values for chemically distinct LPA species binding to human LPA₁ and required only a few nanograms of protein: 1-oleoyl (18:1; K_D = 2.08 ± 1.32 nM), 1-linoleoyl (18:2; K_D = 2.83 ± 1.64 nM), 1-arachidonoyl (20:4; K_D = 2.59 ± 0.481 nM), and 1-palmitoyl (16:0; K_D = 1.69 ± 0.1 nM) LPA. These K_D values compared favorably to those obtained using the previous generation back-scattering interferometry system, a chip-based technique with low-throughput and temperature sensitivity. In conclusion, FSA-CIR offers a new increased-throughput approach to assess quantitatively label-free lipid ligand-receptor binding, including nonactivating antagonist binding, under near-native conditions.

Sensitivity of peripheral membrane proteins to the membrane context: A case study of phosphatidylserine and the TIM proteins

There is a diverse class of peripheral membrane-binding proteins that specifically bind phosphatidylserine (PS), a lipid that signals apoptosis or cell fusion depending on the membrane context of its presentation. PS-receptors are specialized for particular PS-presenting pathways, indicating that they might be sensitive to the membrane context. In this review, we describe a combination of thermodynamic, structural, and computational techniques that can be used to investigate the mechanisms underlying this sensitivity. As an example, we focus on three PS-receptors of the T-cell Immunoglobulin and Mucin containing (TIM) protein family, which we have previously shown to differ in their sensitivity to PS surface density.

Detecting Protein-Glycolipid Interactions Using Glycomicelles and CaR-ESI-MS

This study reports on the use of the catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay, combined with glycomicelles, as a method for detecting specific interactions between water-soluble proteins and glycolipids (GLs) in aqueous solution. The B subunit homopentamers of cholera toxin (CTB₅) and Shiga toxin type 1 B (Stx1B₅) and the gangliosides GM1, GM2, GM3, GD1a, GD1b, GT1b, and GD2 served as model systems for this study. The CTB₅ exhibits broad specificity for gangliosides and binds to GM1, GM2, GM3, GD1a, GD1b, and GT1b; Stx1B₅ does not recognize gangliosides. The CaR-ESI-MS assay was used to analyze solutions of CTB₅ or Stx1B₅ and individual gangliosides (GM1, GM2, GM3, GD1a, GD1b, GT1b, and GD2) or mixtures thereof. The high affinity interaction of CTB₅ with GM1 was successfully detected. However, the apparent affinity, as determined from the mass spectra, is significantly lower than that of the corresponding pentasaccharide or when GM1 is presented in model membranes such as nanodiscs. Interactions between CTB₅ and the low affinity gangliosides GD1a, GD1b, and GT1b, as well as GD2, which served as a negative control, were detected; no binding of CTB₅ to GM2 or GM3 was observed. The CaR-ESI-MS results obtained for Stx1B₅ reveal that nonspecific protein-ganglioside binding can occur during the ESI process, although the extent of binding varies between gangliosides. Consequently, interactions detected for CTB₅ with GD1a, GD1b, and GT1b are likely nonspecific in origin. Taken together, these results reveal that the CaR-ESI-MS/glycomicelle approach for detecting protein-GL interactions is prone to false positives and false negatives and must be used with caution. Graphical Abstract <!-- [INSERT GRAPHICAL ABSTRACT TEXT HERE] -->.

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Peripheral membrane proteins: Tying the knot between experiment and computation

Experimental biology has contributed to answer questions about the morphology of a system and how molecules organize themselves to maintain a healthy functional cell. Single-molecule techniques, optical and magnetic experiments, and fluorescence microscopy have come a long way to probe structural and dynamical information at multiple scales. However, some details are simply too small or the processes are too short-lived to detect by experiments. Computational biology provides a bridge to understand experimental results at the molecular level, makes predictions that have not been seen in vivo, and motivates new fields of research. This review focuses on the advances on peripheral membrane proteins (PMPs) studies; what is known about their interaction with membranes, their role in cell biology, and some limitations that both experiment and computation still have to overcome to gain better structural and functional understanding of these PMPs. As many recent reviews have acknowledged, interdisciplinary efforts between experiment and computation are needed in order to have useful models that lead future directions in the study of PMPs. We present new results of a case study on a PMP that behaves as an intricate machine controlling lipid homeostasis between cellular organelles, Osh4 in yeast Saccharomyces cerevisiae. Molecular dynamics simulations were run to examine the interaction between the protein and membrane models that reflect the lipid diversity of the endoplasmic reticulum and trans-Golgi membranes. Our study is consistent with experimental data showing several residues that interact to smaller or larger extent with the bilayer upon stable binding (~200 ns into the trajectory). We identified PHE239 as a key residue stabilizing the protein-membrane interaction along with two other binding regions, the ALPS-like motif and the β6-β7 loops in the mouth region of the protein. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

The increasing role of phosphatidylethanolamine as a lipid receptor in the action of host defence peptides

Host defence peptides (HDPs) are antimicrobial agents produced by organisms across the prokaryotic and eukaryotic kingdoms. Many prokaryotes produce HDPs, which utilise lipid and protein receptors in the membranes of bacterial competitors to facilitate their antibacterial action and thereby survive in their niche environment. As a major example, it is well established that cinnamycin and duramycins from Streptomyces have a high affinity for phosphatidylethanolamine (PE) and exhibit activity against other Gram-positive organisms, such as Bacillus. In contrast, although eukaryotic HDPs utilise membrane interactive mechanisms to facilitate their antimicrobial activity, the prevailing view has long been that these mechanisms do not involve membrane receptors. However, this view has been recently challenged by reports that a number of eukaryotic HDPs such as plant cyclotides also use PE as a receptor to promote their antimicrobial activities. Here, we review current understanding of the mechanisms that underpin the use of PE as a receptor in the antimicrobial and other biological actions of HDPs and describe medical and biotechnical uses of these peptides, which range from tumour imaging and detection to inclusion in topical microbicidal gels to prevent the sexual transmission of HIV.

Picodiscs for facile protein-glycolipid interaction analysis

Protein interactions with glycolipids are implicated in diverse cellular processes. However, the study of protein-glycolipid complexes remains a significant experimental challenge. Here, we describe a powerful new assay that combines electrospray ionization mass spectrometry (ESI-MS) and picodiscs, which are composed of human sphingolipid activator protein saposin A and a small number of phospholipids, to display glycolipids in a lipid environment for protein-glycolipid interaction studies in aqueous solution. Time-resolved measurements of enzyme catalyzed hydrolysis of glycolipid substrates and the detection of low, moderate, and high affinity protein-glycolipid interactions serve to demonstrate the reliability and versatility of the assay.

Stressor-induced proteome alterations in zebrafish: a meta-analysis of response patterns

Proteomics approaches are being increasingly applied in ecotoxicology on the premise that the identification of specific protein expression changes in response to a particular chemical would allow elucidation of the underlying molecular pathways leading to an adverse effect. This in turn is expected to promote the development of focused testing strategies for specific groups of toxicants. Although both gel-based and gel-free global characterization techniques provide limited proteome coverage, the conclusions regarding the cellular processes affected are still being drawn based on the few changes detected. To investigate how specific the detected responses are, we analyzed a set of studies that characterized proteome alterations induced by various physiological, chemical and biological stressors in zebrafish, a popular model organism. Our analysis highlights several proteins and protein groups, including heat shock and oxidative stress defense proteins, energy metabolism enzymes and cytoskeletal proteins, to be most frequently identified as responding to diverse stressors. In contrast, other potentially more specifically responding protein groups are detected much less frequently. Thus, zebrafish proteome responses to stress reported by different studies appear to depend mostly on the level of stress rather than on the specific stressor itself. This suggests that the most broadly used current proteomics technologies do not provide sufficient proteome coverage to allow in-depth investigation of specific mechanisms of toxicant action. We suggest that the results of any differential proteomics experiment performed with zebrafish should be interpreted keeping in mind the list of the most frequent responders that we have identified. Similar reservations should apply to any other species where proteome responses are analyzed by global proteomics methods. Careful consideration of the reliability and significance of observed changes is necessary in order not to over-interpret the experimental results and to prevent the proliferation of false positive linkages between the chemical and the cellular functions it perturbs. We further discuss the implications of the identified "top lists" of frequently responding proteins and protein families, and suggest further directions for proteomics research in ecotoxicology. Apart from improving the proteome coverage, further research should focus on defining the significance of the observed stress response patterns for organism phenotypes and on searching for common upstream regulators that can be targeted by specific assays.

The Ras-Membrane Interface: Isoform-specific Differences in The Catalytic Domain

The small GTPase Ras is mutated in about 20% of human cancers, primarily at active site amino acid residues G12, G13, and Q61. Thus, structural biology research has focused on the active site, impairment of GTP hydrolysis by oncogenic mutants, and characterization of protein-protein interactions in the effector lobe half of the protein. The C-terminal hypervariable region has increasingly gained attention due to its importance in H-Ras, N-Ras, and K-Ras differences in membrane association. A high-resolution molecular view of the Ras-membrane interaction involving the allosteric lobe of the catalytic domain has lagged behind, although evidence suggests that it contributes to isoform specificity. The allosteric lobe has recently gained interest for harboring potential sites for more selective targeting of this elusive "undruggable" protein. The present review reveals critical insight that isoform-specific differences appear prominently at these potentially targetable sites and integrates these differences with knowledge of Ras plasma membrane localization, with the intent to better understand the structure-function relationships needed to design isoform-specific Ras inhibitors.

Avanti lipid tools: connecting lipids, technology, and cell biology

Lipid research is challenging owing to the complexity and diversity of the lipidome. Here we review a set of experimental tools developed for the seasoned lipid researcher, as well as, those who are new to the field of lipid research. Novel tools for probing protein-lipid interactions, applications for lipid binding antibodies, enhanced systems for the cellular delivery of lipids, improved visualization of lipid membranes using gold-labeled lipids, and advances in mass spectrometric analysis techniques will be discussed. Because lipid mediators are known to participate in a host of signal transduction and trafficking pathways within the cell, a comprehensive lipid toolbox that aids the science of lipidomics research is essential to better understand the molecular mechanisms of interactions between cellular components. This article is part of a Special Issue entitled Tools to study lipid functions.

Global approaches for the elucidation of phosphoinositide-binding proteins

Phosphoinositide lipids (PIPns) control numerous critical biological pathways, typically through the regulation of protein function driven by non-covalent protein-lipid binding interactions. Despite the importance of these systems, the unraveling of the full scope of protein-PIPn interactions has represented a significant challenge due to the massive complexity associated with these events, including the large number of diverse proteins that bind to these lipids, variations in the mechanisms by which proteins bind to lipids, and the presence of multiple distinct PIPn isomers. As a result of this complexity, global methods in which numerous proteins that bind PIPns can be identified and characterized simultaneously from complex samples, which have been enabled by key technological advancements, have become popular as an efficient means for tackling this challenge. This review article provides an overview of advancements in large-scale methods for profiling protein-PIPn binding, including experimental methods, such as affinity enrichment, microarray analysis and activity-based protein profiling, as well as computational methods, and combined computational/experimental efforts.