Developing a high-quality scoring function for membrane protein structures based on specific inter-residue interactions

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.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.

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.

TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool

Background

Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence.

Results

Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77 % of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70 %, whether the obtained, lowest energy structure is the native one.

Conclusion

These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses.

Availability

The program is available upon request for academic use.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0638-5) contains supplementary material, which is available to authorized users.

Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)

Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.

Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.

Availability and implementation: Source code and datasets are available on request.

Contact:torsten.schwede@unibas.ch

Supplementary Information:Supplementary data are available at Bioinformatics online.

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.

Improving homology modeling of G-protein coupled receptors through multiple-template derived conserved inter-residue interactions

Evidenced by the three-rounds of G-protein coupled receptors (GPCR) Dock competitions, improving homology modeling methods of helical transmembrane proteins including the GPCRs, based on templates of low sequence identity, remains an eminent challenge. Current approaches addressing this challenge adopt the philosophy of "modeling first, refinement next". In the present work, we developed an alternative modeling approach through the novel application of available multiple templates. First, conserved inter-residue interactions are derived from each additional template through conservation analysis of each template-target pairwise alignment. Then, these interactions are converted into distance restraints and incorporated in the homology modeling process. This approach was applied to modeling of the human β2 adrenergic receptor using the bovin rhodopsin and the human protease-activated receptor 1 as templates and improved model quality was demonstrated compared to the homology model generated by standard single-template and multiple-template methods. This method of "refined restraints first, modeling next", provides a fast and complementary way to the current modeling approaches. It allows rational identification and implementation of additional conserved distance restraints extracted from multiple templates and/or experimental data, and has the potential to be applicable to modeling of all helical transmembrane proteins.

Structural and functional analysis of a platelet-activating lysophosphatidylcholine of Trypanosoma cruzi

Background

Trypanosoma cruzi is the causative agent of the life-threatening Chagas disease, in which increased platelet aggregation related to myocarditis is observed. Platelet-activating factor (PAF) is a potent intercellular lipid mediator and second messenger that exerts its activity through a PAF-specific receptor (PAFR). Previous data from our group suggested that T. cruzi synthesizes a phospholipid with PAF-like activity. The structure of T. cruzi PAF-like molecule, however, remains elusive.

Methodology/Principal findings

Here, we have purified and structurally characterized the putative T. cruzi PAF-like molecule by electrospray ionization-tandem mass spectrometry (ESI-MS/MS). Our ESI-MS/MS data demonstrated that the T. cruzi PAF-like molecule is actually a lysophosphatidylcholine (LPC), namely sn-1 C18:1(delta 9)-LPC. Similar to PAF, the platelet-aggregating activity of C18:1-LPC was abrogated by the PAFR antagonist, WEB 2086. Other major LPC species, i.e., C16:0-, C18:0-, and C18:2-LPC, were also characterized in all T. cruzi stages. These LPC species, however, failed to induce platelet aggregation. Quantification of T. cruzi LPC species by ESI-MS revealed that intracellular amastigote and trypomastigote forms have much higher levels of C18:1-LPC than epimastigote and metacyclic trypomastigote forms. C18:1-LPC was also found to be secreted by the parasite in extracellular vesicles (EV) and an EV-free fraction. A three-dimensional model of PAFR was constructed and a molecular docking study was performed to predict the interactions between the PAFR model and PAF, and each LPC species. Molecular docking data suggested that, contrary to other LPC species analyzed, C18:1-LPC is predicted to interact with the PAFR model in a fashion similar to PAF.

Conclusions/Significance

Taken together, our data indicate that T. cruzi synthesizes a bioactive C18:1-LPC, which aggregates platelets via PAFR. We propose that C18:1-LPC might be an important lipid mediator in the progression of Chagas disease and its biosynthesis could eventually be exploited as a potential target for new therapeutic interventions.

Chagas disease, caused by the parasite Trypanosoma cruzi, was exclusively confined to Latin America but it has recently spread to other regions of the world. Chagas disease affects 8–10 million people and kills thousands of them every year. Lysophosphatidylcholine (LPC) is a major bioactive phospholipid of human plasma low-density lipoproteins (LDL). Platelet-activating factor (PAF) is a phospholipid similar to LPC and a potent intercellular mediator. Both PAF and LPC have been reported to act on mammalian cells through PAF receptor (PAFR). Previous data from our group suggested that T. cruzi produces a phospholipid with PAF activity. Here, we describe the structural and functional analysis of different species of LPC from T. cruzi, including a LPC with a fatty acid chain of 18 carbon atoms and one double bond (C18:1-LPC). We also show that C18:1-LPC is able to induce rabbit platelet aggregation, which is abrogated by a PAFR antagonist. In addition, a three-dimensional model of human PAFR was constructed. Contrary to other T. cruzi LPC molecules, C18:1-LPC is predicted to interact with the PAFR model in a fashion similar to PAF. Further studies are needed to validate the biosynthesis of T. cruzi C18:1-LPC as a potential drug target in Chagas disease.