Computational modeling of membrane proteins

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Molecular Insights into Substrate Binding of the Outer Membrane Enzyme OmpT

The enzyme OmpT of the outer membrane of Escherichia coli shows proteolytic activity and cleaves peptides and proteins. Using molecular dynamics simulations in a fully hydrated lipid bilayer on a time scale of hundreds of nanoseconds, we draw a detailed atomic picture of substrate recognition in the OmpT-holo enzyme complex. Hydrogen bonds and salt bridges are essential for maintaining the integrity of the active site and play a central role for OmpT in recognizing its substrate. Electrostatic interactions are critical at all stages from approaching the substrate to docking at the active site. Computational alanine scanning based on the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) approach confirms the importance of multiple residues in the active site that form salt bridges. The substrate fluctuates along the axis of the β-barrel, which is associated with oscillations of the binding cleft formed by the residue pairs D210-H212 and D83-D85. Principal component analysis suggests that substrate and protein movements are correlated. We observe the transient presence of putative catalytic water molecules near the active site, which may be involved in the nucleophilic attack on the cleavable peptide bond of the substrate.

Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling

Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the Protein Data Bank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely, RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Endogenous ion channels expressed in human embryonic kidney (HEK-293) cells

Mammalian expression systems, particularly the human embryonic kidney (HEK-293) cells, combined with electrophysiological studies, have greatly benefited our understanding of the function, characteristic, and regulation of various ion channels. It was previously assumed that the existence of endogenous ion channels in native HEK-293 cells could be negligible. Still, more and more ion channels are gradually reported in native HEK-293 cells, which should draw our attention. In this regard, we summarize the different ion channels that are endogenously expressed in HEK-293 cells, including voltage-gated Na⁺ channels, Ca²⁺ channels, K⁺ channels, Cl^- channels, nonselective cation channels, TRP channels, acid-sensitive ion channels, and Piezo channels, which may complicate the recording of the heterogeneously expressed ion channels to a certain degree. We noted that the expression patterns and channel profiles varied with different studies, which may be due to the distinct originality of the cells, cell culture conditions, passage numbers, and different recording protocols. Therefore, a better knowledge of endogenous ion channels may help minimize potential problems in characterizing heterologously expressed ion channels. Based on this, it is recommended that HEK-293 cells from unknown sources should be examined before transfection for the characterization of their functional profile, especially when the expression level of exogenous ion channels does not overwhelm the endogenous ion channels largely, or the current amplitude is not significantly higher than the native currents.

Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor

Background

The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs.

Results

After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R²) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs.

Conclusion

Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04389-w.

CG2AT2: an Enhanced Fragment-Based Approach for Serial Multi-scale Molecular Dynamics Simulations

Coarse-grained molecular dynamics provides a means for simulating the assembly and interactions of macromolecular complexes at a reduced level of representation, thereby allowing both longer timescale and larger sized simulations. Here, we describe an enhanced fragment-based protocol for converting macromolecular complexes from coarse-grained to atomistic resolution, for further refinement and analysis. While the focus is upon systems that comprise an integral membrane protein embedded in a phospholipid bilayer, the technique is also suitable for membrane-anchored and soluble protein/nucleotide complexes. Overall, this provides a method for generating an accurate and well-equilibrated atomic-level description of a macromolecular complex. The approach is evaluated using a diverse test set of 11 system configurations of varying size and complexity. Simulations are assessed in terms of protein stereochemistry, conformational drift, lipid/protein interactions, and lipid dynamics.

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

Genome-scale metabolic modeling reveals key features of a minimal gene set

Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering.

Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans

Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH ~ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.

In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b

Background: Recent strides in computational structural biology have opened up an opportunity to understand previously uncharacterised proteins.  The under-representation of transmembrane proteins in the Protein Data Bank highlights the need to apply new and advanced bioinformatics methods to shed light on their structure and function.  This study focuses on a family of transmembrane proteins containing the Pfam domain PF09335 ('SNARE_ASSOC'/ 'VTT '/'Tvp38'). One prominent member, Tmem41b, has been shown to be involved in early stages of autophagosome formation and is vital in mouse embryonic development as well as being identified as a viral host factor of SARS-CoV-2. Methods: We used evolutionary covariance-derived information to construct and validate ab initio models, make domain boundary predictions and infer local structural features.  Results: The results from the structural bioinformatics analysis of Tmem41b and its homologues showed that they contain a tandem repeat that is clearly visible in evolutionary covariance data but much less so by sequence analysis.  Furthermore, cross-referencing of other prediction data with covariance analysis showed that the internal repeat features two-fold rotational symmetry.  Ab initio modelling of Tmem41b and homologues reinforces these structural predictions.  Local structural features predicted to be present in Tmem41b were also present in Cl ^-/H ⁺ antiporters.  Conclusions: The results of this study strongly point to Tmem41b and its homologues being transporters for an as-yet uncharacterised substrate and possibly using H ⁺ antiporter activity as its mechanism for transport.

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.

Characterization of aromatic acid/proton symporters in Pseudomonas putida KT2440 toward efficient microbial conversion of lignin-related aromatics

Pseudomonas putida KT2440 (hereafter KT2440) is a well-studied platform bacterium for the production of industrially valuable chemicals from heterogeneous mixtures of aromatic compounds obtained from lignin depolymerization. KT2440 can grow on lignin-related monomers, such as ferulate (FA), 4-coumarate (4CA), vanillate (VA), 4-hydroxybenzoate (4HBA), and protocatechuate (PCA). Genes associated with their catabolism are known, but knowledge about the uptake systems remains limited. In this work, we studied the KT2440 transporters of lignin-related monomers and their substrate selectivity. Based on the inhibition by protonophores, we focused on five genes encoding aromatic acid/H⁺ symporter family transporters categorized into major facilitator superfamily that uses the proton motive force. The mutants of PP_1376 (pcaK) and PP_3349 (hcnK) exhibited significantly reduced growth on PCA/4HBA and FA/4CA, respectively, while no change was observed on VA for any of the five gene mutants. At pH 9.0, the conversion of these compounds by hcnK mutant (FA/4CA) and vanK mutant (VA) was dramatically reduced, revealing that these transporters are crucial for the uptake of the anionic substrates at high pH. Uptake assays using ¹⁴C-labeled substrates in Escherichia coli and biosensor-based assays confirmed that PcaK, HcnK, and VanK have ability to take up PCA, FA/4CA, and VA/PCA, respectively. Additionally, analyses of the predicted protein structures suggest that the size and hydropathic properties of the substrate-binding sites of these transporters determine their substrate preferences. Overall, this study reveals that at physiological pH, PcaK and HcnK have a major role in the uptake of PCA/4HBA and FA/4CA, respectively, and VanK is a VA/PCA transporter. This information can contribute to the engineering of strains for the efficient conversion of lignin-related monomers to value-added chemicals.

Reticulon-like properties of a plant virus-encoded movement protein

Plant viruses encode movement proteins (MPs) that ensure the transport of viral genomes through plasmodesmata (PD) and use cell endomembranes, mostly the endoplasmic reticulum (ER), for delivery of viral genomes to PD and formation of PD-anchored virus replication compartments. Here, we demonstrate that the Hibiscus green spot virus BMB2 MP, an integral ER protein, induces constrictions of ER tubules, decreases the mobility of ER luminal content, and exhibits an affinity to highly curved membranes. These properties are similar to those described for reticulons, cellular proteins that induce membrane curvature to shape the ER tubules. Similar to reticulons, BMB2 adopts a W-like topology within the ER membrane. BMB2 targets PD and increases their size exclusion limit, and these BMB2 activities correlate with the ability to induce constrictions of ER tubules. We propose that the induction of ER constrictions contributes to the BMB2-dependent increase in PD permeability and formation of the PD-associated replication compartments, therefore facilitating the virus intercellular spread. Furthermore, we show that the ER tubule constrictions also occur in cells expressing TGB2, one of the three MPs of Potato virus X (PVX), and in PVX-infected cells, suggesting that reticulon-like MPs are employed by diverse RNA viruses.

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.

Membrane Protein Engineering with Rosetta

Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.

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.

Short Disordered Epitope of CRTAM Ig-Like V Domain as a Potential Target for Blocking Antibodies

Class-I Restricted T Cell-Associated Molecule (CRTAM) is a protein that is expressed after T cell activation. The interaction of CRTAM with its ligand, nectin-like 2 (Necl2), is required for the efficient production of IL-17, IL-22, and IFNγ by murine CD4 T cells, and it plays a role in optimal CD8 T and NK cell cytotoxicity. CRTAM promotes the pro-inflammatory cytokine profile; therefore, it may take part in the immunopathology of autoimmune diseases such as diabetes type 1 or colitis. Thus, antibodies that block the interaction between CRTAM and Necl2 would be useful for controlling the production of these inflammatory cytokines. In this work, using bioinformatics predictions, we identified three short disordered epitopes (sDE1-3) that are located in the Ig-like domains of murine CRTAM and are conserved in mammalian species. We performed a structural analysis by molecular dynamics simulations of sDE1 (QHPALKSSKY, Ig-like V), sDE2 (QRNGEKSVVK, Ig-like C1), and sDE3 (CSTERSKKPPPQI, Ig-like C1). sDE1, which is located within a loop of the contact interface of the heterotypic interaction with Nectl2, undergoes an order–disorder transition. On the contrary, even though sDE2 and sDE3 are flexible and also located within loops, they do not undergo order–disorder transitions. We evaluated the immunogenicity of sDE1 and sDE3 through the expression of these epitopes in chimeric L1 virus-like particles. We confirmed that sDE1 induces polyclonal antibodies that recognize the native folding of CRTAM expressed in activated murine CD4 T cells. In contrast, sDE3 induces polyclonal antibodies that recognize the recombinant protein hCRTAM-Fc, but not the native CRTAM. Thus, in this study, an exposed disordered epitope in the Ig-like V domain of CRTAM was identified as a potential site for therapeutic antibodies.

Determinants of Endoplasmic Reticulum-to-Lipid Droplet Protein Targeting

Lipid droplet (LD) formation from the endoplasmic reticulum (ER) is accompanied by the targeting and accumulation of specific hydrophobic, membrane-embedded proteins on LDs. The determinants of this process are unknown. Here, we study the hydrophobic membrane motifs of two Drosophila melanogaster proteins, GPAT4 and ALG14, that utilize this pathway, and we identify crucial sequence features that mediate LD accumulation. Molecular dynamics simulations and studies in cells reveal that LD targeting of these motifs requires deeply inserted tryptophans that have lower free energy in the LD oil phase and positively charged residues near predicted hairpin hinges that become less constrained in the LD environment. Analyzing hydrophobic motifs from similar LD-targeting proteins, it appears that the distribution of tryptophan and positively charged residues distinguishes them from non-LD-targeting membrane motifs. Our studies identify specific sequence features and principles of hydrophobic membrane motifs that mediate their accumulation on LDs.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Finding the Keys to the CAR: Identifying Novel Target Antigens for T Cell Redirection Immunotherapies

Oncology immunotherapy has been a significant advancement in cancer treatment and involves harnessing and redirecting a patient’s immune response towards their own tumour. Specific recognition and elimination of tumour cells was first proposed over a century ago with Paul Erlich’s ‘magic bullet’ theory of therapy. In the past decades, targeting cancer antigens by redirecting T cells with antibodies using either bispecific T cell engagers (BiTEs) or chimeric antigen receptor (CAR) T cell therapy has achieved impressive clinical responses. Despite recent successes in haematological cancers, linked to a high and uniformly expressed CD19 antigen, the efficacy of T cell therapies in solid cancers has been disappointing, in part due to antigen escape. Targeting heterogeneous solid tumours with T cell therapies will require the identification of novel tumour specific targets. These targets can be found among a range of cell-surface expressed antigens, including proteins, glycolipids or carbohydrates. In this review, we will introduce the current tumour target antigen classification, outline existing approaches to discover novel tumour target antigens and discuss considerations for future design of antibodies with a focus on their use in CAR T cells.

Using Schematic Models to Understand the Microscopic Basis for Inverted Solubility in γD-Crystallin

Inverted solubility-melting a crystal by cooling-is observed in a handful of proteins, such as carbomonoxy hemoglobin C and γD-crystallin. In human γD-crystallin, the phenomenon is associated with the mutation of the 23rd residue, a proline, to a threonine, serine, or valine. One proposed microscopic mechanism entails an increase in surface hydrophobicity upon mutagenesis. Recent crystal structures of a double mutant that includes the P23T mutation allow for a more careful investigation of this proposal. Here, we first measure the surface hydrophobicity of various mutant structures of γD-crystallin and discern no notable increase in hydrophobicity upon mutating the 23rd residue. We then investigate the solubility inversion regime with a schematic patchy particle model that includes one of three variants of temperature-dependent patch energies: two of the hydrophobic effect, and one of a more generic nature. We conclude that, while solubility inversion due to the hydrophobic effect may be possible, microscopic evidence to support it in γD-crystallin is weak. More generally, we find that solubility inversion requires a fine balance between patch strengths and their temperature-dependent component, which may explain why inverted solubility is not commonly observed in proteins. We also find that the temperature-dependent interaction has only a negligible impact on liquid-liquid phase boundaries of γD-crystallin, in line with previous experimental observations.

Differences in protein structural regions that impact functional specificity in GT2 family β-glucan synthases

Most cell wall and secreted β-glucans are synthesised by the CAZy Glycosyltransferase 2 family (www.cazy.org), with different members catalysing the formation of (1,4)-β-, (1,3)-β-, or both (1,4)- and (1,3)-β-glucosidic linkages. Given the distinct physicochemical properties of each of the resultant β-glucans (cellulose, curdlan, and mixed linkage glucan, respectively) are crucial to their biological and biotechnological functions, there is a desire to understand the molecular evolution of synthesis and how linkage specificity is determined. With structural studies hamstrung by the instability of these proteins to solubilisation, we have utilised in silico techniques and the crystal structure for a bacterial cellulose synthase to further understand how these enzymes have evolved distinct functions. Sequence and phylogenetic analyses were performed to determine amino acid conservation, both family-wide and within each sub-family. Further structural analysis centred on comparison of a bacterial curdlan synthase homology model with the bacterial cellulose synthase crystal structure, with molecular dynamics simulations performed with their respective β-glucan products bound in the trans-membrane channel. Key residues that differentially interact with the different β-glucan chains and have sub-family-specific conservation were found to reside at the entrance of the trans-membrane channel. The linkage-specific catalytic activity of these enzymes and hence the type of β-glucan chain built is thus likely determined by the different interactions between the proteins and the first few glucose residues in the channel, which in turn dictates the position of the acceptor glucose. The sequence-function relationships for the bacterial β-glucan synthases pave the way for extending this understanding to other kingdoms, such as plants.

A lipophilicity-based energy function for membrane-protein modelling and design

Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.

Validation of protein backbone structures calculated from NMR angular restraints using Rosetta

Multidimensional solid-state NMR spectra of oriented membrane proteins can be used to infer the backbone torsion angles and hence the overall protein fold by measuring dipolar couplings and chemical shift anisotropies, which depend on the orientation of each peptide plane with respect to the external magnetic field. However, multiple peptide plane orientations can be consistent with a given set of angular restraints. This ambiguity is further exacerbated by experimental uncertainty in obtaining and interpreting such restraints. The previously developed algorithms for structure calculations using angular restraints typically involve a sequential walkthrough along the backbone to find the torsion angles between the consecutive peptide plane orientations that are consistent with the experimental data. This method is sensitive to experimental uncertainty in interpreting the peak positions of as low as ± 10 Hz, often yielding high structural RMSDs for the calculated structures. Here we present a significantly improved version of the algorithm which includes the fitting of several peptide planes at once in order to prevent propagation of error along the backbone. In addition, a protocol has been devised for filtering the structural solutions using Rosetta scoring functions in order to find the structures that both fit the spectrum and satisfy bioinformatics restraints. The robustness of the new algorithm has been tested using synthetic angular restraints generated from the known structures for two proteins: a soluble protein 2gb1 (56 residues), chosen for its diverse secondary structure elements, i.e. an alpha-helix and two beta-sheets, and a membrane protein 4a2n, from which the first two transmembrane helices (having a total of 64 residues) have been used. Extensive simulations have been performed by varying the number of fitted planes, experimental error, and the number of NMR dimensions. It has been found that simultaneously fitting two peptide planes always shifted the distribution of the calculated structures toward lower structural RMSD values as compared to fitting a single torsion-angle pair. For each protein, irrespective of the simulation parameters, Rosetta was able to distinguish the most plausible structures, often having structural RMSDs lower than 2 Å with respect to the original structure. This study establishes a framework for de-novo protein structure prediction using a combination of solid-state NMR angular restraints and bioinformatics.

In silico prediction of prolactin molecules as a tool for equine genomics reproduction

The prolactin hormone is involved in several biological functions, although its main role resides on reproduction. As it interferes on fertility changes, studies focused on human health have established a linkage of this hormone to fertility losses. Regarding animal research, there is still a lack of information about the structure of prolactin. In case of horse breeding, prolactin has a particular influence; once there is an individualization of these animals and equines are known for presenting several reproductive disorders. As there is no molecular structure available for the prolactin hormone and receptor, we performed several bioinformatics analyses through prediction and refinement softwares, as well as manual modifications. Aiming to elucidate the first computational structure of both molecules and analyse structural and functional aspects related to these proteins, here we provide the first known equine model for prolactin and prolactin receptor, which obtained high global quality scores in diverse software's for quality assessment. QMEAN overall score obtained for ePrl was (- 4.09) and QMEANbrane for ePrlr was (- 8.45), which proves the structures' reliability. This study will implement another tool in equine genomics in order to give light to interactions of these molecules, structural and functional alterations and therefore help diagnosing fertility problems, contributing in the selection of a high genetic herd.

GABAA receptor family: overview on structural characterization

The pentameric γ-aminobutyric acid type A receptors are ion channels activated by ligands, which intervene in the rapid inhibitory transmission in the mammalian CNS. Due to their rich pharmacology and therapeutic potential, it is essential to understand their structure and function thoroughly. This deep characterization was hampered by the lack of experimental structural information for many years. Thus, computational techniques have been extensively combined with experimental data, in order to undertake the study of γ-aminobutyric acid type A receptors and their interaction with drugs. Here, we review the exciting journey made to assess the structures of these receptors and outline major outcomes. Finally, we discuss the brand new structure of the α1β2γ2 subtype and the amazing advances it brings to the field.

Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction

Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.

Challenges and advances in atomistic simulations of potassium and sodium ion channel gating and permeation

Ion channels are implicated in many essential physiological events such as electrical signal propagation and cellular communication. The advent of K⁺ and Na⁺ ion channel structure determination has facilitated numerous investigations of molecular determinants of their behaviour. At the same time, rapid development of computer hardware and molecular simulation methodologies has made computational studies of large biological molecules in all-atom representation tractable. The concurrent evolution of experimental structural biology with biomolecular computer modelling has yielded mechanistic details of fundamental processes unavailable through experiments alone, such as ion conduction and ion channel gating. This review is a short survey of the atomistic computational investigations of K⁺ and Na⁺ ion channels, focusing on KcsA and several voltage-gated channels from the K_V and Na_V families, which have garnered many successes and engendered several long-standing controversies regarding the nature of their structure-function relationship. We review the latest advancements and challenges facing the field of molecular modelling and simulation regarding the structural and energetic determinants of ion channel function and their agreement with experimental observations.

A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations

We use the statistics of a large and curated training set of transmembrane helical proteins to develop a knowledge-based potential that accounts for the dependence on both the depth of burial of the protein in the membrane and the degree of side-chain exposure. Additionally, the statistical potential includes depth-dependent energies for unsatisfied backbone hydrogen bond donors and acceptors, which are found to be relatively small, ∼2 RT. Our potential accurately places known proteins within the bilayer. The potential is applied to the mechanosensing MscL channel in membranes of varying thickness and curvature, as well as to the prediction of protein structure. The potential is incorporated into our new Upside molecular dynamics algorithm. Notably, we account for the exchange of protein-lipid interactions for protein-protein interactions as helices contact each other, thereby avoiding overestimating the energetics of helix association within the membrane. Simulations of most multimeric complexes find that isolated monomers and the oligomers retain the same orientation in the membrane, suggesting that the assembly of prepositioned monomers presents a viable mechanism of oligomerization.

A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction

Rhodopsins are seven α-helical membrane proteins that are of great importance in chemistry, biology, and modern biotechnology. Any in silico study on rhodopsin properties and functioning requires a high-quality three-dimensional structure. Due to particular difficulties with obtaining membrane protein structures from the experiment, in silico prediction of the three-dimensional rhodopsin structure based only on its primary sequence is an especially important task. For the last few years, significant progress was made in the field of protein structure prediction, especially for methods based on comparative modeling. However, the majority of this progress was made for soluble proteins and further investigations are needed to achieve similar progress for membrane proteins. In this paper, we evaluate the performance of modern protein structure prediction methodologies (implemented in the Medeller, I-TASSER, and Rosetta packages) for their ability to predict rhodopsin structures. Three widely used methodologies were considered: two general methodologies that are commonly applied to soluble proteins and a methodology that uses constraints that are specific for membrane proteins. The test pool consisted of 36 target-template pairs with different sequence similarities that was constructed on the basis of 24 experimental rhodopsin structures taken from the RCSB database. As a result, we showed that all three considered methodologies allow obtaining rhodopsin structures with the quality that is close to the crystallographic one (root mean square deviation (RMSD) of the predicted structure from the corresponding X-ray structure up to 1.5 Å) if the target-template sequence identity is higher than 40%. Moreover, all considered methodologies provided structures of average quality (RMSD < 4.0 Å) if the target-template sequence identity is higher than 20%. Such structures can be subsequently used for further investigation of molecular mechanisms of protein functioning and for the development of modern protein-based biotechnologies.

Structure refinement of membrane proteins via molecular dynamics simulations

A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.

Lipid-Protein Interactions Are Unique Fingerprints for Membrane Proteins

Cell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Our current understanding of the detailed organization of cell membranes remains rather elusive, because of the challenge to study fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of 10 different membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between the leaflets. The simulations detail how each protein modulates its local lipid environment in a unique way, through enrichment or depletion of specific lipid components, resulting in thickness and curvature gradients. Our results provide a molecular glimpse of the complexity of lipid-protein interactions, with potentially far-reaching implications for our understanding of the overall organization of real cell membranes.

Folding a viral peptide in different membrane environments: pathway and sampling analyses

Flock House virus (FHV) is a well-characterized model system to study infection mechanisms in non-enveloped viruses. A key stage of the infection cycle is the disruption of the endosomal membrane by a component of the FHV capsid, the membrane active γ peptide. In this study, we perform all-atom molecular dynamics simulations of the 21 N-terminal residues of the γ peptide interacting with membranes of differing compositions. We carry out umbrella sampling calculations to study the folding of the peptide to a helical state in homogenous and heterogeneous membranes consisting of neutral and anionic lipids. From the trajectory data, we evaluate folding energetics and dissect the mechanism of folding in the different membrane environments. We conclude the study by analyzing the extent of configurational sampling by performing time-lagged independent component analysis.

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.

Eurecon: Equidistant uniform rigid-body ensemble constructor

Conformational ensembles comprise one of the fundamental concepts in statistical bioinformatics and appear in a variety of applications, e.g. molecular docking, virtual screening, searching for pharmacophores, etc. High-throughput applications require billions of conformations to be considered, thus, one often uses the rigid-body representation of molecules or its fragments to cope with the computational cost. Of particular interest is generation of the near-native conformational ensembles, which consist of conformations structurally close to the biologically relevant ones. One possible way to compose such ensembles is to control the root mean square deviation (RMSD) between the original and the generated conformations. To the best of our knowledge there is no computational approach that guarantees that all the generated conformations have the desired RMSD with respect to the reference structure. In this study we presented a fast algorithm for the construction of rigid-body conformational ensembles, which possess two main properties: (i) each generated conformation has a fixed RMSD with respect to the original conformation, (ii) generated conformations are distributed uniformly over the sphere of axes corresponding to the rigid-body motions. The algorithm is very efficient, it does not require any standard RMSD computation between the conformations and has the O(N + M) complexity to generate the required rigid-body transforms, where N is the number of atoms in the system, and M is the size of the conformational ensemble. Eurecon is applicable to an arbitrary atomic system, thus, it could be used for molecular systems of various size and type. We demonstrated Eurecon application by generating near-native conformational ensembles for a ligand placed inside a binding site, a protein dimer embedded into a membrane, and a ribosomal complex. We implemented the developed algorithm in C++ and called it Eurecon, which stands for Equidistant Uniform Rigid-body Ensemble CONstructor. A user-friendly interface allows to define the desired RMSD value, the relative amplitudes for rotation and translation motions by means of the partition parameter, and the set of axes corresponding to the rigid-body motions. Eurecon is available as the SAMSON Element (https://samson-connect.net).

High-resolution structure prediction of β-barrel membrane proteins

[Formula: see text]-Barrel membrane proteins ([Formula: see text]MPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of [Formula: see text]MPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement ([Formula: see text]3 Å) over a recent study. For [Formula: see text]MPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended [Formula: see text]-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by [Formula: see text]%. Our method is general and can be applied to genome-wide structural prediction of [Formula: see text]MPs.

Expanding the toolkit for membrane protein modeling in Rosetta

Motivation

A range of membrane protein modeling tools has been developed in the past 5-10 years, yet few of these tools are integrated and make use of existing functionality for soluble proteins. To extend existing methods in the Rosetta biomolecular modeling suite for membrane proteins, we recently implemented RosettaMP, a general framework for membrane protein modeling. While RosettaMP facilitates implementation of new methods, addressing real-world biological problems also requires a set of accessory tools that are used to carry out standard modeling tasks.

Results

Here, we present six modeling tools, including de novo prediction of single trans-membrane helices, making mutations and refining the structure with different amounts of flexibility, transforming a protein into membrane coordinates and optimizing its embedding, computing a Rosetta energy score, and visualizing the protein in the membrane bilayer. We present these methods with complete protocol captures that allow non-expert modelers to carry out the computations.

Availability and Implementation

The presented tools are part of the Rosetta software suite, available at www.rosettacommons.org .

Contact

julia.koehler.leman@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Fusion pore in exocytosis: More than an exit gate? A β-cell perspective

Secretory vesicle exocytosis is a fundamental biological event and the process by which hormones (like insulin) are released into the blood. Considerable progress has been made in understanding this precisely orchestrated sequence of events from secretory vesicle docked at the cell membrane, hemifusion, to the opening of a membrane fusion pore. The exact biophysical and physiological regulation of these events implies a close interaction between membrane proteins and lipids in a confined space and constrained geometry to ensure appropriate delivery of cargo. We consider some of the still open questions such as the nature of the initiation of the fusion pore, the structure and the role of the Soluble N-ethylmaleimide-sensitive-factor Attachment protein REceptor (SNARE) transmembrane domains and their influence on the dynamics and regulation of exocytosis. We discuss how the membrane composition and protein-lipid interactions influence the likelihood of the nascent fusion pore forming. We relate these factors to the hypothesis that fusion pore expansion could be affected in type-2 diabetes via changes in disease-related gene transcription and alterations in the circulating lipid profile. Detailed characterisation of the dynamics of the fusion pore in vitro will contribute to understanding the larger issue of insulin secretory defects in diabetes.

Rosetta Broker for membrane protein structure prediction: concentrative nucleoside transporter 3 and corticotropin-releasing factor receptor 1 test cases

Background

Membrane proteins are difficult targets for structure prediction due to the limited structural data deposited in Protein Data Bank. Most computational methods for membrane protein structure prediction are based on the comparative modeling. There are only few de novo methods targeting that distinct protein family. In this work an example of such de novo method was used to structurally and functionally characterize two representatives of distinct membrane proteins families of solute carrier transporters and G protein-coupled receptors. The well-known Rosetta program and one of its protocols named Broker was used in two test cases. The first case was de novo structure prediction of three N-terminal transmembrane helices of the human concentrative nucleoside transporter 3 (hCNT3) homotrimer belonging to the solute carrier 28 family of transporters (SLC28). The second case concerned the large scale refinement of transmembrane helices of a homology model of the corticotropin-releasing factor receptor 1 (CRFR1) belonging to the G protein-coupled receptors family.

Results

The inward-facing model of the hCNT3 homotrimer was used to propose the functional impact of its single nucleotide polymorphisms. Additionally, the 100 ns molecular dynamics simulation of the unliganded hCNT3 model confirmed its validity and revealed mobility of the selected binding site and homotrimer interface residues. The large scale refinement of transmembrane helices of the CRFR1 homology model resulted in the significant improvement of its accuracy with respect to the crystal structure of CRFR1, especially in the binding site area. Consequently, the antagonist CP-376395 could be docked with Autodock VINA to the CRFR1 model without any steric clashes.

Conclusions

The presented work demonstrated that Rosetta Broker can be a versatile tool for solving various issues referring to protein biology. Two distinct examples of de novo membrane protein structure prediction presented here provided important insights into three major areas of protein biology. Namely, the dynamics of the inward-facing hCNT3 homotrimer system, the structural changes of the CRFR1 receptor upon the antagonist binding and finally, the role of single nucleotide polymorphisms in both, hCNT3 and CRFR1 proteins, were investigated.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-017-0078-8) contains supplementary material, which is available to authorized users.

A Central Small Amino Acid in the VAMP2 Transmembrane Domain Regulates the Fusion Pore in Exocytosis

Exocytosis depends on cytosolic domains of SNARE proteins but the function of the transmembrane domains (TMDs) in membrane fusion remains controversial. The TMD of the SNARE protein synaptobrevin2/VAMP2 contains two highly conserved small amino acids, G₁₀₀ and C₁₀₃, in its central portion. Substituting G₁₀₀ and/or C₁₀₃ with the β-branched amino acid valine impairs the structural flexibility of the TMD in terms of α-helix/β-sheet transitions in model membranes (measured by infrared reflection-absorption or evanescent wave spectroscopy) during increase in protein/lipid ratios, a parameter expected to be altered by recruitment of SNAREs at fusion sites. This structural change is accompanied by reduced membrane fluidity (measured by infrared ellipsometry). The G₁₀₀V/C₁₀₃V mutation nearly abolishes depolarization-evoked exocytosis (measured by membrane capacitance) and hormone secretion (measured biochemically). Single-vesicle optical (by TIRF microscopy) and biophysical measurements of ATP release indicate that G₁₀₀V/C₁₀₃V retards initial fusion-pore opening, hinders its expansion and leads to premature closure in most instances. We conclude that the TMD of VAMP2 plays a critical role in membrane fusion and that the structural mobility provided by the central small amino acids is crucial for exocytosis by influencing the molecular re-arrangements of the lipid membrane that are necessary for fusion pore opening and expansion.

Applications of contact predictions to structural biology

Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallo-graphy, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.

Computational protein structure refinement: Almost there, yet still so far to go

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

Discovery of Novel Leptospirosis Vaccine Candidates Using Reverse and Structural Vaccinology

Leptospira spp. are diderm (two membranes) bacteria that infect mammals causing leptospirosis, a public health problem with global implications. Thousands of people die every year due to leptospirosis, especially in developing countries with tropical climates. Prophylaxis is difficult due to multiple factors, including the large number of asymptomatic hosts that transmit the bacteria, poor sanitation, increasing numbers of slum dwellers, and the lack of an effective vaccine. Several leptospiral recombinant antigens were evaluated as a replacement for the inactivated (bacterin) vaccine; however, success has been limited. A prospective vaccine candidate is likely to be a surface-related protein that can stimulate the host immune response to clear leptospires from blood and organs. In this study, a comprehensive bioinformatics approach based on reverse and structural vaccinology was applied toward the discovery of novel leptospiral vaccine candidates. The Leptospira interrogans serovar Copenhageni strain L1-130 genome was mined in silico for the enhanced identification of conserved β-barrel (βb) transmembrane proteins and outer membrane (OM) lipoproteins. Orthologs of the prospective vaccine candidates were screened in the genomes of 20 additional Leptospira spp. Three-dimensional structural models, with a high degree of confidence, were created for each of the surface-exposed proteins. Major histocompatibility complex II (MHC-II) epitopes were identified, and their locations were mapped on the structural models. A total of 18 βb transmembrane proteins and 8 OM lipoproteins were identified. These proteins were conserved among the pathogenic Leptospira spp. and were predicted to have epitopes for several variants of MHC-II receptors. A structural and functional analysis of the sequence of these surface proteins demonstrated that most βb transmembrane proteins seem to be TonB-dependent receptors associated with transportation. Other proteins identified included, e.g., TolC efflux pump proteins, a BamA-like OM component of the βb transmembrane protein assembly machinery, and the LptD-like LPS assembly protein. The structural mapping of the immunodominant epitopes identified the location of conserved, surface-exposed, immunogenic regions for each vaccine candidate. The proteins identified in this study are currently being evaluated for experimental evidence for their involvement in virulence, disease pathogenesis, and physiology, in addition to vaccine development.

Nanodiscs in Membrane Biochemistry and Biophysics

Membrane proteins play a most important part in metabolism, signaling, cell motility, transport, development, and many other biochemical and biophysical processes which constitute fundamentals of life on the molecular level. Detailed understanding of these processes is necessary for the progress of life sciences and biomedical applications. Nanodiscs provide a new and powerful tool for a broad spectrum of biochemical and biophysical studies of membrane proteins and are commonly acknowledged as an optimal membrane mimetic system that provides control over size, composition, and specific functional modifications on the nanometer scale. In this review we attempted to combine a comprehensive list of various applications of nanodisc technology with systematic analysis of the most attractive features of this system and advantages provided by nanodiscs for structural and mechanistic studies of membrane proteins.

Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP

BACKGROUND

Membrane proteins are underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Although programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins.

RESULTS

Here we present mp_lipid_acc, the first method to identify lipid accessible residues from the protein structure, implemented in the RosettaMP framework and available as a webserver. Our method uses protein structures transformed in membrane coordinates, for instance from PDBTM or OPM databases, and a defined membrane thickness to classify lipid accessibility of residues. mp_lipid_acc is applicable to both α-helical and β-barrel membrane proteins of diverse architectures with or without water-filled pores and uses a concave hull algorithm for surface-residue classification. We further provide a manually curated benchmark dataset that can be used for further method development.

CONCLUSIONS

We present a novel tool to classify lipid accessibility from the protein structure, which is applicable to proteins of diverse architectures and achieves prediction accuracies of 90% on a manually curated database. mp_lipid_acc is part of the Rosetta software suite, available at www.rosettacommons.org . The webserver is available at http://rosie.graylab.jhu.edu/mp_lipid_acc/submit and the benchmark dataset is available at http://tinyurl.com/mp-lipid-acc-dataset .