A unified hydrophobicity scale for multispan membrane proteins

The Structural Stability of Membrane Proteins Revisited: Combined Thermodynamic and Spectral Phasor Analysis of SDS-induced Denaturation of a Thermophilic Cu(I)-transport ATPase

Assessing membrane protein stability is among the major challenges in protein science due to their inherent complexity, which complicates the application of conventional biophysical tools. In this work, sodium dodecyl sulfate-induced denaturation of AfCopA, a Cu(I)-transport ATPase from Archaeoglobus fulgidus, was explored using a combined model-free spectral phasor analysis and a model-dependent thermodynamic analysis. Decrease in tryptophan and 1-anilino-naphthalene-8-sulfonate fluorescence intensity, displacements in the spectral phasor space, and the loss of ATPase activity were reversibly induced by this detergent. Refolding from the SDS-induced denatured state yields an active enzyme that is functionally and spectroscopically indistinguishable from the native state of the protein. Phasor analysis of Trp spectra allowed us to identify two intermediate states in the SDS-induced denaturation of AfCopA, a result further supported by principal component analysis. In contrast, traditional thermodynamic analysis detected only one intermediate state, and including the second one led to overparameterization. Additionally, ANS fluorescence spectral analysis detected one more intermediate and a gradual change at the level of the hydrophobic transmembrane surface of the protein. Based on this evidence, a model for acquiring the native structure of AfCopA in a membrane-like environment is proposed.

Docking cholesterol to integral membrane proteins with Rosetta

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule's shape, the membrane's hydrophobic environment, and the lipid's orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.

ProteinPrompt: a webserver for predicting protein-protein interactions

Motivation

Protein-protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.

Results

We collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.

Availability and implementation

ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

Predicting the functional impact of KCNQ1 variants with artificial neural networks

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.

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.

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

Membrane partitioning and lipid selectivity of the N-terminal amphipathic H0 helices of endophilin isoforms

Endophilin is an N-BAR protein, which is characterized by a crescent-shaped BAR domain and an amphipathic helix that contributes to the membrane binding of these proteins. The exact function of that H0 helix has been a topic of debate. In mammals, there are five different endophilin isoforms, grouped into A (three members) and B (two members) subclasses, which have been described to differ in their subcellular localization and function. We asked to what extent molecular properties of the H0 helices of these members affect their membrane targeting behavior. We found that all H0 helices of the endophilin isoforms display a two-state equilibrium between disordered and α-helical states in which the helical secondary structure can be stabilized through trifluoroethanol. The helicities in high TFE were strikingly different among the H0 peptides. We investigated H0-membrane partitioning by the monitoring of secondary structure changes via CD spectroscopy. We found that the presence of anionic phospholipids is critical for all H0 helices partitioning into membranes. Membrane partitioning is found to be sensitive to variations in membrane complexity. Overall, the H0 B subfamily displays stronger membrane partitioning than the H0 A subfamily. The H0 A peptide-membrane binding occurs predominantly through electrostatic interactions. Variation among the H0 A subfamily may be attributed to slight alterations in the amino acid sequence. Meanwhile, the H0 B subfamily displays greater specificity for certain membrane compositions, and this may link H0 B peptide binding to endophilin B's cellular function.

Curved or linear? Predicting the 3-dimensional structure of α-helical antimicrobial peptides in an amphipathic environment

α-Helical membrane-active antimicrobial peptides (AMPs) are known to act via a range of mechanisms, including the formation of barrel-stave and toroidal pores and the micellisation of the membrane (carpet mechanism). Different mechanisms imply that the peptides adopt different 3D structures when bound at the water-membrane interface, a highly amphipathic environment. Here, an evolutionary algorithm is used to predict the 3D structure of a range of α-helical membrane-active AMPs at the water-membrane interface by optimising amphipathicity. This amphipathic structure prediction (ASP) is capable of distinguishing between curved and linear peptides solved experimentally, potentially allowing the activity and mechanism of action of different membrane-active AMPs to be predicted. The ASP algorithm is accessible via a web interface at http://atb.uq.edu.au/asp/.

Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.

Development of a Structure-Based, pH-Dependent Lipophilicity Scale of Amino Acids from Continuum Solvation Calculations

Lipophilicity is a fundamental property to characterize the structure and function of proteins, motivating the development of lipophilicity scales. We report a versatile strategy to derive a pH-adapted scale that relies on theoretical estimates of distribution coefficients from conformational ensembles of amino acids. This is accomplished by using an accurately parametrized version of the IEFPCM/MST continuum solvation model as an effective way to describe the partitioning between n-octanol and water, in conjunction with a formalism that combines partition coefficients of neutral and ionic species of residues and the corresponding p K_a values of ionizable groups. Two weighting schemes are considered to derive solvent-like and protein-like scales, which have been calibrated by comparison with other experimental scales developed in different chemical/biological environments and pH conditions as well as by examining properties such as the retention time of small peptides and the recognition of antigenic peptides. A straightforward extension to nonstandard residues is enabled by this efficient methodological strategy.

Integrated Structural Biology for α-Helical Membrane Protein Structure Determination

While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.

Statistically derived asymmetric membrane potentials from α-helical and β-barrel membrane proteins

Modeling membrane protein (MP) folding, insertion, association and their interactions with other proteins, lipids, and drugs requires accurate transfer free energies (TFEs). Various TFE scales have been derived to quantify the energy required or released to insert an amino acid or protein into the membrane. Experimental measurement of TFEs is challenging, and only few scales were extended to depth-dependent energetic profiles. Statistical approaches can be used to derive such potentials; however, this requires a sufficient number of MP structures. Furthermore, MPs are tightly coupled to bilayers that are heterogeneous in terms of lipid composition, asymmetry, and protein content between organisms and organelles. Here we derived asymmetric implicit membrane potentials from β-barrel and α-helical MPs and use them to predict topology, depth and orientation of proteins in the membrane. Our data confirm the ‘charge-outside’ and ‘positive-inside’ rules for β-barrels and α-helical proteins, respectively. We find that the β-barrel profiles have greater asymmetry than the ones from α-helical proteins, as a result of the different membrane architecture of gram-negative bacterial outer membranes and the existence of lipopolysaccharide in the outer leaflet. Our data further suggest that pore-facing residues in β-barrels have a larger contribution to membrane insertion and stability than previously suggested.

Design and characterization of chionodracine-derived antimicrobial peptides with enhanced activity against drug-resistant human pathogens

Design of new chionodracine-derived peptides with potent activity against drug-resistant human pathogens.

Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model

A protocol for predicting the hydrophobic length of membrane proteins using the heterogeneous dielectric generalized Born (HDGB) implicit membrane model is presented. The method involves optimal positioning in the membrane and identification of lipid-facing and inward-facing residues, followed by energy optimization of the implicit membrane model to obtain the hydrophobic length from the optimal membrane width. The latest HDGB version 3 (HDGBv3) and HDGB van der Waals (HDGBvdW) models were applied to a test set containing 15 proteins (seven β-barrel and eight α-helical proteins), for which matching membrane widths are available from experiment, and an additional set contains ten α-helical and ten β-barrel proteins without any experimental data. The results with the HDGB model compare favorably with predictions from methods used in the Orientations of Proteins in Membranes (OPM) and Protein Data Bank of Transmembrane Proteins (PDB-TM) databases.

Molecular mechanisms of Slo2 K+ channel closure

KEY POINTS

Intracellular Na⁺ -activated Slo2 potassium channels are in a closed state under normal physiological conditions, although their mechanisms of ion permeation gating are not well understood. A cryo-electron microscopy structure of Slo2.2 suggests that the ion permeation pathway of these channels is closed by a single constriction of the inner pore formed by the criss-crossing of the cytoplasmic ends of the S6 segments (the S6 bundle crossing) at a conserved Met residue. Functional characterization of mutant Slo2 channels suggests that hydrophobic interactions between Leu residues in the upper region of the S6 segments contribute to stabilizing the inner pore in a non-conducting state. Mutation of the conserved Met residues in the S6 segments to the negatively-charged Glu did not induce constitutive opening of Slo2.1 or Slo2.2, suggesting that ion permeation of Slo2 channels is not predominantly gated by the S6 bundle crossing.

ABSTRACT

Large conductance K⁺ -selective Slo2 channels are in a closed state unless activated by elevated [Na⁺ ]_i . Our previous studies suggested that the pore helix/selectivity filter serves as the activation gate in Slo2 channels. In the present study, we evaluated two other potential mechanisms for stabilization of Slo2 channels in a closed state: (1) dewetting and collapse of the inner pore (hydrophobic gating) and (2) constriction of the inner pore by tight criss-crossing of the cytoplasmic ends of the S6 α-helical segments. Slo2 channels contain two conserved Leu residues in each of the four S6 segments that line the inner pore region nearest the bottom of the selectivity filter. To evaluate the potential role of these residues in hydrophobic gating, Leu267 and Leu270 in human Slo2.1 were each replaced by 15 different residues. The relative conductance of mutant channels was highly dependent on hydrophilicity and volume of the amino acid substituted for Leu267 and was maximal with L267H. Consistent with their combined role in hydrophobic gating, replacement of both Leu residues with the isosteric but polar residue Asn (L267N/L270N) stabilized channels in a fully open state. In a recent cryo-electron microscopy structure of chicken Slo2.2, the ion permeation pathway of the channel is closed by a constriction of the inner pore formed by criss-crossing of the S6 segments at a conserved Met. Inconsistent with the S6 segment crossing forming the activation gate, replacement of the homologous Met residues in human Slo2.1 or Slo2.2 with the negatively-charged Glu did not induce constitutive channel opening.

Outer Membrane Protein Folding and Topology from a Computational Transfer Free Energy Scale

Knowledge of the transfer free energy of amino acids from aqueous solution to a lipid bilayer is essential for understanding membrane protein folding and for predicting membrane protein structure. Here we report a computational approach that can calculate the folding free energy of the transmembrane region of outer membrane β-barrel proteins (OMPs) by combining an empirical energy function with a reduced discrete state space model. We quantitatively analyzed the transfer free energies of 20 amino acid residues at the center of the lipid bilayer of OmpLA. Our results are in excellent agreement with the experimentally derived hydrophobicity scales. We further exhaustively calculated the transfer free energies of 20 amino acids at all positions in the TM region of OmpLA. We found that the asymmetry of the Gram-negative bacterial outer membrane as well as the TM residues of an OMP determine its functional fold in vivo. Our results suggest that the folding process of an OMP is driven by the lipid-facing residues in its hydrophobic core, and its NC-IN topology is determined by the differential stabilities of OMPs in the asymmetrical outer membrane. The folding free energy is further reduced by lipid A and assisted by general depth-dependent cooperativities that exist between polar and ionizable residues. Moreover, context-dependency of transfer free energies at specific positions in OmpLA predict regions important for protein function as well as structural anomalies. Our computational approach is fast, efficient and applicable to any OMP.

Marginally hydrophobic transmembrane α-helices shaping membrane protein folding

Cells have developed an incredible machinery to facilitate the insertion of membrane proteins into the membrane. While we have a fairly good understanding of the mechanism and determinants of membrane integration, more data is needed to understand the insertion of membrane proteins with more complex insertion and folding pathways. This review will focus on marginally hydrophobic transmembrane helices and their influence on membrane protein folding. These weakly hydrophobic transmembrane segments are by themselves not recognized by the translocon and therefore rely on local sequence context for membrane integration. How can such segments reside within the membrane? We will discuss this in the light of features found in the protein itself as well as the environment it resides in. Several characteristics in proteins have been described to influence the insertion of marginally hydrophobic helices. Additionally, the influence of biological membranes is significant. To begin with, the actual cost for having polar groups within the membrane may not be as high as expected; the presence of proteins in the membrane as well as characteristics of some amino acids may enable a transmembrane helix to harbor a charged residue. The lipid environment has also been shown to directly influence the topology as well as membrane boundaries of transmembrane helices-implying a dynamic relationship between membrane proteins and their environment.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Mapping hydrophobicity on the protein molecular surface at atom-level resolution

A precise representation of the spatial distribution of hydrophobicity, hydrophilicity and charges on the molecular surface of proteins is critical for the understanding of the interaction with small molecules and larger systems. The representation of hydrophobicity is rarely done at atom-level, as this property is generally assigned to residues. A new methodology for the derivation of atomic hydrophobicity from any amino acid-based hydrophobicity scale was used to derive 8 sets of atomic hydrophobicities, one of which was used to generate the molecular surfaces for 35 proteins with convex structures, 5 of which, i.e., lysozyme, ribonuclease, hemoglobin, albumin and IgG, have been analyzed in more detail. Sets of the molecular surfaces of the model proteins have been constructed using spherical probes with increasingly large radii, from 1.4 to 20 Å, followed by the quantification of (i) the surface hydrophobicity; (ii) their respective molecular surface areas, i.e., total, hydrophilic and hydrophobic area; and (iii) their relative densities, i.e., divided by the total molecular area; or specific densities, i.e., divided by property-specific area. Compared with the amino acid-based formalism, the atom-level description reveals molecular surfaces which (i) present an approximately two times more hydrophilic areas; with (ii) less extended, but between 2 to 5 times more intense hydrophilic patches; and (iii) 3 to 20 times more extended hydrophobic areas. The hydrophobic areas are also approximately 2 times more hydrophobicity-intense. This, more pronounced "leopard skin"-like, design of the protein molecular surface has been confirmed by comparing the results for a restricted set of homologous proteins, i.e., hemoglobins diverging by only one residue (Trp37). These results suggest that the representation of hydrophobicity on the protein molecular surfaces at atom-level resolution, coupled with the probing of the molecular surface at different geometric resolutions, can capture processes that are otherwise obscured to the amino acid-based formalism.

Mechanisms of integral membrane protein insertion and folding

The biogenesis, folding, and structure of α-helical membrane proteins (MPs) are important to understand because they underlie virtually all physiological processes in cells including key metabolic pathways, such as the respiratory chain and the photosystems, as well as the transport of solutes and signals across membranes. Nearly all MPs require translocons--often referred to as protein-conducting channels--for proper insertion into their target membrane. Remarkable progress toward understanding the structure and functioning of translocons has been made during the past decade. Here, we review and assess this progress critically. All available evidence indicates that MPs are equilibrium structures that achieve their final structural states by folding along thermodynamically controlled pathways. The main challenge for cells is the targeting and membrane insertion of highly hydrophobic amino acid sequences. Targeting and insertion are managed in cells principally by interactions between ribosomes and membrane-embedded translocons. Our review examines the biophysical and biological boundaries of MP insertion and the folding of polytopic MPs in vivo. A theme of the review is the under-appreciated role of basic thermodynamic principles in MP folding and assembly. Thermodynamics not only dictates the final folded structure but also is the driving force for the evolution of the ribosome-translocon system of assembly. We conclude the review with a perspective suggesting a new view of translocon-guided MP insertion.

Membrane interacting regions of Dengue virus NS2A protein

The Dengue virus (DENV) NS2A protein, essential for viral replication, is a poorly characterized membrane protein. NS2A displays both protein/protein and membrane/protein interactions, yet neither its functions in the viral cycle nor its active regions are known with certainty. To highlight the different membrane-active regions of NS2A, we characterized the effects of peptides derived from a peptide library encompassing this protein’s full length on different membranes by measuring their membrane leakage induction and modulation of lipid phase behavior. Following this initial screening, one region, peptide dens25, had interesting effects on membranes; therefore, we sought to thoroughly characterize this region’s interaction with membranes. This peptide presents an interfacial/hydrophobic pattern characteristic of a membrane-proximal segment. We show that dens25 strongly interacts with membranes that contain a large proportion of lipid molecules with a formal negative charge, and that this effect has a major electrostatic contribution. Considering its membrane modulating capabilities, this region might be involved in membrane rearrangements and thus be important for the viral cycle.

Life at the border: adaptation of proteins to anisotropic membrane environment

This review discusses main features of transmembrane (TM) proteins which distinguish them from water-soluble proteins and allow their adaptation to the anisotropic membrane environment. We overview the structural limitations on membrane protein architecture, spatial arrangement of proteins in membranes and their intrinsic hydrophobic thickness, co-translational and post-translational folding and insertion into lipid bilayers, topogenesis, high propensity to form oligomers, and large-scale conformational transitions during membrane insertion and transport function. Special attention is paid to the polarity of TM protein surfaces described by profiles of dipolarity/polarizability and hydrogen-bonding capacity parameters that match polarity of the lipid environment. Analysis of distributions of Trp resides on surfaces of TM proteins from different biological membranes indicates that interfacial membrane regions with preferential accumulation of Trp indole rings correspond to the outer part of the lipid acyl chain region-between double bonds and carbonyl groups of lipids. These "midpolar" regions are not always symmetric in proteins from natural membranes. We also examined the hydrophobic effect that drives insertion of proteins into lipid bilayer and different free energy contributions to TM protein stability, including attractive van der Waals forces and hydrogen bonds, side-chain conformational entropy, the hydrophobic mismatch, membrane deformations, and specific protein-lipid binding.

Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales?

The recognition of transmembrane helices by the translocon is primarily guided by the average hydrophobicity of the potential transmembrane helix. However, the exact hydrophobicity of each amino acid can be identified in several different ways. The free energy of transfer for amino acid analogues between a hydrophobic media, for example, octanol and water can be measured or obtained from simulations, the hydrophobicity can also be estimated by statistical properties from known transmembrane segments and finally the contribution of each amino acid type for the probability of translocon recognition has recently been measured directly. Although these scales correlate quite well, there are clear differences between them and it is not well understood which scale represents neither the biology best nor what the differences are. Here, we try to provide some answers to this by studying the ability of different scales to recognize transmembrane helices and predict the topology of transmembrane proteins. From this analysis it is clear that the biological hydrophobicity scale as well scales created from statistical analysis of membrane helices perform better than earlier experimental scales that are mainly based on measurements of amino acid analogs and not directly on transmembrane helix recognition. Using these results we identified the properties of the scales that perform better than other scales. We find, for instance, that the better performing scales consider proline more hydrophilic. This shows that transmembrane recognition is not only governed by pure hydrophobicity but also by the helix preferences for amino acids, as proline is a strong helix breaker.

Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes

Most available antimicrobial peptides (AMP) prediction methods use common approach for different classes of AMP. Contrary to available approaches, we suggest that a strategy of prediction should be based on the fact that there are several kinds of AMP that vary in mechanisms of action, structure, mode of interaction with membrane, etc. According to our suggestion for each kind of AMP, a particular approach has to be developed in order to get high efficacy. Consequently, in this paper, a particular but the biggest class of AMP, linear cationic antimicrobial peptides (LCAP), has been considered and a newly developed simple method of LCAP prediction described. The aim of this study is the development of a simple method of discrimination of AMP from non-AMP, the efficiency of which will be determined by efficiencies of selected descriptors only and comparison the results of the discrimination procedure with the results obtained by more complicated discriminative methods. As descriptors the physicochemical characteristics responsible for capability of the peptide to interact with an anionic membrane were considered. The following characteristics such as hydrophobicity, amphiphaticity, location of the peptide in relation to membrane, charge density, propensities to disordered structure and aggregation were studied. On the basis of these characteristics, a new simple algorithm of prediction is developed and evaluation of efficacies of the characteristics as descriptors performed. The results show that three descriptors, hydrophobic moment, charge density and location of the peptide along the membranes, can be used as discriminators of LCAPs. For the training set, our method gives the same level of accuracy as more complicated machine learning approaches offered as CAMP database service tools. For the test set accuracy obtained by our method gives even higher value than the one obtained by CAMP prediction tools. The AMP prediction tool based on the considered method is available at http://www.biomedicine.org.ge/dbaasp/.

Structural role of exon-coded fragment of polypeptide chains in selected enzymes

This paper discusses the structural role of fragments encoded by individual exons in proteins. Selected enzymes (hydrolases, transferases, ligases) reveal the presence of at least one exon fragment whose contribution to the protein's hydrophobic core is in line with theoretical expectations. This phenomenon is confirmed by quantitative analysis of the hydrophobicity density distribution in protein molecules. Results are compared with a 3D Gaussian function, treated as an "idealized" distribution of hydrophobicity density, with the highest values observed near the center of the molecule and near-zero values on its surface. At least one accordant exon fragment has been identified in each of the proteins subjected to analysis. On the basis of these results the authors propose that accordant exons are responsible for tertiary structural stabilization of proteins by ensuring the generation of a stable hydrophobic core.

N-terminal AH2 segment of protein NS4B from hepatitis C virus. Binding to and interaction with model biomembranes

HCV NS4B, a highly hydrophobic protein involved in the alteration of the intracellular host membranes forming the replication complex, plays a critical role in the HCV life cycle. NS4B is a multifunctional membrane protein that possesses different regions where diverse and significant functions are located. One of these important regions is the AH2 segment, which besides being highly conserved has been shown to play a significant role in NS4B functioning. We have carried out an in-depth biophysical study aimed at the elucidation of the capacity of this region to interact, modulate and disrupt membranes, as well as to study the structural and dynamic features relevant for that disruption. We show that a peptide derived from this region, NS4BAH2, is capable of specifically binding phosphatidyl inositol phosphates with high affinity, and its interfacial properties suggest that this segment could behave similarly to a pre-transmembrane domain partitioning into and interacting with the membrane depending on the membrane composition and/or other proteins. Moreover, NS4BAH2 is capable of rupturing membranes even at very low peptide-to-lipid ratios and its membrane-activity is modulated by lipid composition. NS4BAH2 is located in a shallow position in the membrane but it is able to affect the lipid environment from the membrane surface down to the hydrophobic core. The NS4B region where peptide NS4BAH2 resides might have an essential role in the membrane replication and/or assembly of the viral particle through the modulation of the membrane structure and hence the replication complex.

Simultaneous prediction of protein secondary structure and transmembrane spans

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org.

Alignment of helical membrane protein sequences using AlignMe

Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments. Existing approaches typically consider membrane-related information either by using membrane-specific substitution matrices or by assigning distinct penalties for gap creation in transmembrane and non-transmembrane regions. Here, we ask whether favoring matching of predicted transmembrane segments within a standard dynamic programming algorithm can improve the accuracy of pairwise membrane protein sequence alignments. We tested various strategies using a specifically designed program called AlignMe. An updated set of homologous membrane protein structures, called HOMEP2, was used as a reference for optimizing the gap penalties. The best of the membrane-protein optimized approaches were then tested on an independent reference set of membrane protein sequence alignments from the BAliBASE collection. When secondary structure (S) matching was combined with evolutionary information (using a position-specific substitution matrix (P)), in an approach we called AlignMePS, the resultant pairwise alignments were typically among the most accurate over a broad range of sequence similarities when compared to available methods. Matching transmembrane predictions (T), in addition to evolutionary information, and secondary-structure predictions, in an approach called AlignMePST, generally reduces the accuracy of the alignments of closely-related proteins in the BAliBASE set relative to AlignMePS, but may be useful in cases of extremely distantly related proteins for which sequence information is less informative. The open source AlignMe code is available at https://sourceforge.net/projects/alignme/, and at http://www.forrestlab.org, along with an online server and the HOMEP2 data set.

Towards a processual microbial ontology

Standard microbial evolutionary ontology is organized according to a nested hierarchy of entities at various levels of biological organization. It typically detects and defines these entities in relation to the most stable aspects of evolutionary processes, by identifying lineages evolving by a process of vertical inheritance from an ancestral entity. However, recent advances in microbiology indicate that such an ontology has important limitations. The various dynamics detected within microbiological systems reveal that a focus on the most stable entities (or features of entities) over time inevitably underestimates the extent and nature of microbial diversity. These dynamics are not the outcome of the process of vertical descent alone. Other processes, often involving causal interactions between entities from distinct levels of biological organisation, or operating at different time scales, are responsible not only for the destabilisation of pre-existing entities, but also for the emergence and stabilisation of novel entities in the microbial world. In this article we consider microbial entities as more or less stabilised functional wholes, and sketch a network-based ontology that can represent a diverse set of processes including, for example, as well as phylogenetic relations, interactions that stabilise or destabilise the interacting entities, spatial relations, ecological connections, and genetic exchanges. We use this pluralistic framework for evaluating (i) the existing ontological assumptions in evolution (e.g. whether currently recognized entities are adequate for understanding the causes of change and stabilisation in the microbial world), and (ii) for identifying hidden ontological kinds, essentially invisible from within a more limited perspective. We propose to recognize additional classes of entities that provide new insights into the structure of the microbial world, namely "processually equivalent" entities, "processually versatile" entities, and "stabilized" entities.

Protein Structure in Membrane Domains

Of great interest to the academic and pharmaceutical research communities, helical transmembrane proteins are characterized by their ability to dissolve and fold in lipid bilayers—properties conferred by polypeptide spans termed transmembrane domains (TMDs). The apolar nature of TMDs necessitates the use of membrane-mimetic solvents for many structure and folding studies. This review examines the relationship between TMD structure and solvent environment, focusing on principles elucidated largely in membrane-mimetic environments with single-TMD protein and peptide models. Following a brief description of TMD sequence and conformational characteristics gleaned from the structural database, we present an overview of the conceptual models used to study folding in vitro. The impact of sequence and solvent context on the incorporation of TMDs into membranes, and its role in measurements of TMD self-assembly strengths, is then described. We conclude with a discussion of the nonspecific effects of membrane components on TMD stability.

Amino acid interaction preferences in helical membrane proteins

Membrane proteins are involved in a number of important biological functions. Yet, they are poorly understood from the structure and folding point of view. The external environment being drastically different from that of globular proteins, the intra-protein interactions in membrane proteins are also expected to be different. Hence, statistical potentials representing the features of inter-residue interactions based exclusively on the structures of membrane proteins are much needed. Currently, a reasonable number of structures are available, making it possible to undertake such an analysis on membrane proteins. In this study we have examined the inter-residue interaction propensities of amino acids in the membrane spanning regions of the alpha-helical membrane (HM) proteins. Recently we have shown that valuable information can be obtained on globular proteins by the evaluation of the pair-wise interactions of amino acids by classifying them into different structural environments, based on factors such as the secondary structure or the number of contacts that a residue can make. Here we have explored the possible ways of classifying the intra-protein environment of HM proteins and have developed scoring functions based on different classification schemes. On evaluation of different schemes, we find that the scheme which classifies amino acids to different intra-contact environment is the most promising one. Based on this classification scheme, we also redefine the hydrophobicity scale of amino acids in HM proteins.

Tyrosine-lipid peroxide adducts from radical termination: para coupling and intramolecular Diels-Alder cyclization

Free radical co-oxidation of polyunsaturated lipids with tyrosine or phenolic analogues of tyrosine gave rise to lipid peroxide-tyrosine (phenol) adducts in both aqueous micellar and organic solutions. The novel adducts were isolated and characterized by 1D and 2D NMR spectroscopy as well as by mass spectrometry (MS). The spectral data suggest that the polyunsaturated lipid peroxyl radicals give stable peroxide coupling products exclusively at the para position of the tyrosyl (phenoxy) radicals. These adducts have characteristic (13)C chemical shifts at 185 ppm due to the cross-conjugated carbonyl of the phenol-derived cyclohexadienone. The primary peroxide adducts subsequently undergo intramolecular Diels-Alder (IMDA) cyclization, affording a number of diastereomeric tricyclic adducts that have characteristic carbonyl (13)C chemical shifts at ~198 ppm. All of the NMR HMBC and HSQC correlations support the structure assignments of the primary and Diels-Alder adducts, as does MS collision-induced dissociation data. Kinetic rate constants and activation parameters for the IMDA reaction were determined, and the primary adducts were reduced with cuprous ion to give a phenol-derived 4-hydroxycyclohexa-2,5-dienone. No products from adduction of peroxyls at the phenolic ortho position were found in either the primary or cuprous reduction product mixtures. These studies provide a framework for understanding the nature of lipid-protein adducts formed by peroxyl-tyrosyl radical-radical termination processes. Coupling of lipid peroxyl radicals with tyrosyl radicals leads to cyclohexenone and cyclohexadienone adducts, which are of interest in and of themselves since, as electrophiles, they are likely targets for protein nucleophiles. One consequence of lipid peroxyl reactions with tyrosyls may therefore be protein-protein cross-links via interprotein Michael adducts.

A comprehensive comparison of transmembrane domains reveals organelle-specific properties

The various membranes of eukaryotic cells differ in composition, but it is at present unclear if this results in differences in physical properties. The sequences of transmembrane domains (TMDs) of integral membrane proteins should reflect the physical properties of the bilayers in which they reside. We used large datasets from both fungi and vertebrates to perform a comprehensive comparison of the TMDs of proteins from different organelles. We find that TMDs are not generic but have organelle-specific properties with a dichotomy in TMD length between the early and late parts of the secretory pathway. In addition, TMDs from post-ER organelles show striking asymmetries in amino acid compositions across the bilayer that is linked to residue size and varies between organelles. The pervasive presence of organelle-specific features among the TMDs of a particular organelle has implications for TMD prediction, regulation of protein activity by location, and sorting of proteins and lipids in the secretory pathway.

On transversal hydrophobicity of some proteins and their modules

Hydrophobicity of proteins encoded in the genomes of diverse organisms was quantified using two novel concepts: (A) amino acid (AA) bulkiness-dependent hydrophobicity profiles and (B) spatial context of hydrophobicity distribution in AA triads. Both concepts were introduced into an algorithm that was used for extracting protein clusters from diverse genomic databases whose sequence attributes were similar to those in the multiple sequence alignment (MSA) of a given family of proteins. The sequences of the G protein-coupled receptors (GPCRs) encoded in different genomes were used as templates for testing the above concepts. The following sequence attributes were used for protein clustering: (A) sequence similarity scores (IDs); (B) amino acid composition (AAC); (C) hydrophobicity; (D) AA-bulkiness; and (E) alpha-helical propensity potentials. Diverse GPCRs display variable distributions of AA bulkiness-dependent buildups and declines in the hydrophobicity profiles that may be related to their function-dependent way of packing and allostery in the membrane. It is shown that intramolecular transversal nonbonded interactions between the TM segments in diverse GPCRs involve about 50% of hydrophobic atoms. Similar interaction networks exist between alpha-helices of tetratricopeptide (TPR) motifs-containing immunophilins and other proteins containing alpha-helical bundles.

Improved prediction of trans-membrane spans in proteins using an Artificial Neural Network

Tools for the identification of trans-membrane spans from the protein sequence are widely used in the experimental community. Computational structural biology seeks to increase the prediction accuracy of such methods since they represent a first step towards membrane protein tertiary structure prediction from the amino acid sequence. We introduce a predictor that is able to identify trans-membrane spans from the sequence of a protein. The novelty of the approach presented here is the simultaneous prediction of trans-membrane spanning α-helices and β-strands within a single tool. An artificial neural network was trained on databases of 102 membrane proteins and 3499 soluble proteins. Prediction accuracies of up to 92% for soluble residues, 75% for residues in the interface, and 73% for TM residues are achieved. On average the algorithm predicts 79% of the residues correctly which is a substantial improvement from a previously published implementation which achieved 57% accuracy (Koehler et al., Proteins: Structure, Function, and Bioinformatics, 2008). The algorithm was applied to four membrane proteins to illustrate the applicability to both α-helical bundles and β-barrels.