Extension of a protein docking algorithm to membranes and applications to amyloid precursor protein dimerization

Peptide Permeation across a Phosphocholine Membrane: An Atomically Detailed Mechanism Determined through Simulations and Supported by Experimentation

Cell-penetrating peptides (CPPs) facilitate translocation across biological membranes and are of significant biological and medical interest. Several CPPs can permeate into specific cells and organelles. We examine the incorporation and translocation of a novel anticancer CPP in a dioleoylphosphatidylcholine (DOPC) lipid bilayer membrane. The peptide, NAF-1^44-67, is a short fragment of a transmembrane protein, consisting of hydrophobic N-terminal and charged C-terminal segments. Experiments using fluorescently labeled NAF-1^44-67 in ∼100 nm DOPC vesicles and atomically detailed simulations conducted with Milestoning support a model in which a significant barrier for peptide-membrane entry is found at the interface between the aqueous solution and membrane. The initial step is the insertion of the N-terminal segment and the hydrophobic helix into the membrane, passing the hydrophilic head groups. Both experiments and simulations suggest that the free energy difference in the first step of the permeation mechanism in which the hydrophobic helix crosses the phospholipid head groups is -0.4 kcal mol^-1 slightly favoring motion into the membrane. Milestoning calculations of the mean first passage time and the committor function underscore the existence of an early polar barrier followed by a diffusive barrierless motion in the lipid tail region. Permeation events are coupled to membrane fluctuations that are examined in detail. Our study opens the way to investigate in atomistic resolution the molecular mechanism, kinetics, and thermodynamics of CPP permeation to diverse membranes.

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.

Transmembrane Protein Docking with JabberDock

Transmembrane proteins act as an intermediary for a broad range of biological process. Making up 20% to 30% of the proteome, their ubiquitous nature has resulted in them comprising 50% of all targets in drug design. Despite their importance, they make up only 4% of all structures in the PDB database, primarily owing to difficulties associated with isolating and characterizing them. Membrane protein docking algorithms could help to fill this knowledge gap, yet only few exist. Moreover, these existing methods achieve success rates lower than the current best soluble proteins docking software. We present and test a pipeline using our software, JabberDock, to dock membrane proteins. JabberDock docks shapes representative of membrane protein structure and dynamics in their biphasic environment. We verify JabberDock's ability to yield accurate predictions by applying it to a benchmark of 20 transmembrane dimers, returning a success rate of 75.0%. This makes our software very competitive among available membrane protein-protein docking tools.

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.

Protein docking and steered molecular dynamics suggest alternative phospholamban-binding sites on the SERCA calcium transporter

The transport activity of the sarco(endo)plasmic reticulum calcium ATPase (SERCA) in cardiac myocytes is modulated by an inhibitory interaction with a transmembrane peptide, phospholamban (PLB). Previous biochemical studies have revealed that PLB interacts with a specific inhibitory site on SERCA, and low-resolution structural evidence suggests that PLB interacts with distinct alternative sites on SERCA. High-resolution details of the structural determinants of SERCA regulation have been elusive because of the dynamic nature of the regulatory complex. In this study, we used computational approaches to develop a structural model of SERCA-PLB interactions to gain a mechanistic understanding of PLB-mediated SERCA transport regulation. We combined steered molecular dynamics and membrane protein-protein docking experiments to achieve both a global search and all-atom force calculations to determine the relative affinities of PLB for candidate sites on SERCA. We modeled the binding of PLB to several SERCA conformations, representing different enzymatic states sampled during the calcium transport catalytic cycle. The results of the steered molecular dynamics and docking experiments indicated that the canonical PLB-binding site (comprising transmembrane helices M2, M4, and M9) is the preferred site. This preference was even more stringent for a superinhibitory PLB variant. Interestingly, PLB-binding specificity became more ambivalent for other SERCA conformers. These results provide evidence for polymorphic PLB interactions with novel sites on M3 and with the outside of the SERCA helix M9. Our findings are compatible with previous physical measurements that suggest that PLB interacts with multiple binding sites, conferring dynamic responsiveness to changing physiological conditions.

DR-SIP: protocols for higher order structure modeling with distance restraints- and cyclic symmetry-imposed packing

MOTIVATION

Quaternary structure determination for transmembrane/soluble proteins requires a reliable computational protocol that leverages observed distance restraints and/or cyclic symmetry (Cn symmetry) found in most homo-oligomeric transmembrane proteins.

RESULTS

We survey 118 X-ray crystallographically solved structures of homo-oligomeric transmembrane proteins (HoTPs) and find that ∼97% are Cn symmetric. Given the prevalence of Cn symmetric HoTPs and the benefits of incorporating geometry restraints in aiding quaternary structure determination, we introduce two new filters, the distance-restraints (DR) and the Symmetry-Imposed Packing (SIP) filters. SIP relies on a new method that can rebuild the closest ideal Cn symmetric complex from docking poses containing a homo-dimer without prior knowledge of the number (n) of monomers. Using only the geometrical filter, SIP, near-native poses of 7 HoTPs in their monomeric states can be correctly identified in the top-10 for 71% of all cases, or 29% among 31 HoTP structures obtained through homology modeling, while ZDOCK alone returns 14 and 3%, respectively. When the n is given, the optional n-mer filter is applied with SIP and returns the near-native poses for 76% of the test set within the top-10, outperforming M-ZDOCK's 55% and Sam's 47%. While applying only SIP to three HoTPs that comes with distance restraints, we found the near-native poses were ranked 1st, 1st and 10th among 54 000 possible decoys. The results are further improved to 1st, 1st and 3rd when both DR and SIP filters are used. By applying only DR, a soluble system with distance restraints is recovered at the 1st-ranked pose.

AVAILABILITY AND IMPLEMENTATION

https://github.com/capslockwizard/drsip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

Protein-Protein Docking in Drug Design and Discovery

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

A Membrane Protein Complex Docking Benchmark

We report the first membrane protein-protein docking benchmark consisting of 37 targets of diverse functions and folds. The structures were chosen based on a set of parameters such as the availability of unbound structures, the modeling difficulty and their uniqueness. They have been cleaned and consistently numbered to facilitate their use in docking. Using this benchmark, we establish the baseline performance of HADDOCK, without any specific optimization for membrane proteins, for two scenarios: true interface-driven docking and ab initio docking. Despite the fact that HADDOCK has been developed for soluble complexes, it shows promising docking performance for membrane systems, but there is clearly room for further optimization. The resulting set of docking decoys, together with analysis scripts, is made freely available. These can serve as a basis for the optimization of membrane complex-specific scoring functions.

Structure of APP-C991-99 and implications for role of extra-membrane domains in function and oligomerization

The 99 amino acid C-terminal fragment of Amyloid Precursor Protein APP-C99 (C99) is cleaved by γ-secretase to form Aβ peptide, which plays a critical role in the etiology of Alzheimer's Disease (AD). The structure of C99 consists of a single transmembrane domain flanked by intra and intercellular domains. While the structure of the transmembrane domain has been well characterized, little is known about the structure of the flanking domains and their role in C99 processing by γ-secretase. To gain insight into the structure of full-length C99, REMD simulations were performed for monomeric C99 in model membranes of varying thickness. We find equilibrium ensembles of C99 from simulation agree with experimentally-inferred residue insertion depths and protein backbone chemical shifts. In thin membranes, the transmembrane domain structure is correlated with extra-membrane structural states and the extra-membrane domain structural states become less correlated to each other. Mean and variance of the transmembrane and G₃₇G₃₈ hinge angles are found to increase with thinning membrane. The N-terminus of C99 forms β-strands that may seed aggregation of Aβ on the membrane surface, promoting amyloid formation. In thicker membranes the N-terminus forms α-helices that interact with the nicastrin domain of γ-secretase. The C-terminus of C99 becomes more α-helical as the membrane thickens, forming structures that may be suitable for binding by cytoplasmic proteins, while C-terminal residues essential to cytotoxic function become α-helical as the membrane thins. The heterogeneous but discrete extra-membrane domain states analyzed here open the path to new investigations of the role of C99 structure and membrane in amyloidogenesis. This article is part of a Special Issue entitled: Protein Aggregation and Misfolding at the Cell Membrane Interface edited by Ayyalusamy Ramamoorthy.