Orchestrated actin nucleation by the Candida albicans polarisome complex enables filamentous growth.

Candida albicans is a dimorphic fungus that converts from a yeast form to a hyphae form during infection. This switch requires the formation of actin cable to coordinate polarized cell growth. It's known that nucleation of this cable requires a multiprotein complex localized at the tip called the polarisome, but the mechanisms underpinning this process were unclear. Here, we found that C. albicans Aip5, a homolog of polarisome component ScAip5 in Saccharomyces cerevisiae that nucleates actin polymerization and synergizes with the formin ScBni1, regulates actin assembly and hyphae growth synergistically with other polarisome proteins Bni1, Bud6, and Spa2. The C terminus of Aip5 binds directly to G-actin, Bni1, and the C-terminal of Bud6, which form the core of the nucleation complex to polymerize F-actin. Based on insights from structural biology and molecular dynamic simulations, we propose a possible complex conformation of the actin nucleation core, which provides cooperative positioning and supports the synergistic actin nucleation activity of a tri-protein complex Bni1-Bud6-Aip5. Together with known interactions of Bni1 with Bud6 and Aip5 in S. cerevisiae, our findings unravel molecular mechanisms of C. albicans by which the tri-protein complex coordinates the actin nucleation in actin cable assembly and hyphal growth, which is likely a conserved mechanism in different filamentous fungi and yeast.

Investigating the stability of dengue virus envelope protein dimer using well-tempered metadynamics simulations.

We explored the stability of the dengue virus envelope (E) protein dimer since it is widely assumed that the E protein dimer is stabilized by drug ligands or antibodies in an acidic environment, neutralizing the virus's ability to fuse with human cells. During this process, a large conformational change of the E protein dimer is required. We performed Molecular Dynamics simulations to mimic the conformational change and stability of the dimer in neutral and acidic conditions with the well-tempered metadynamics method. Furthermore, as a few neutralizing antibodies discovered from dengue patients were reported, we used the same simulation method to examine the influence of a selected antibody on the dimer stability in both neutral and acidic conditions. We also investigated the antibody's influence on a point-mutated E protein that had been reported to interrupt the protein-antibody interaction and result in more than 95% loss of the antibody's binding ability. Our simulation results are highly consistent with the experimental conclusion that binding of the antibody to the E protein dimer neutralizes the virus, especially in a low pH condition, while the mutation of W101A or N153A significantly reduces the antibody's ability in stabilizing the E protein dimer. We demonstrate that well-tempered metadynamics can be used to accurately explore the antibody's interaction on large protein complexes such as the E protein dimer, and the computational approach in this work is promising in future antibody development.

ACAP1 assembles into an unusual protein lattice for membrane deformation through multiple stages.

Studies on the Bin-Amphiphysin-Rvs (BAR) domain have advanced a fundamental understanding of how proteins deform membrane. We previously showed that a BAR domain in tandem with a Pleckstrin Homology (PH domain) underlies the assembly of ACAP1 (Arfgap with Coil-coil, Ankryin repeat, and PH domain I) into an unusual lattice structure that also uncovers a new paradigm for how a BAR protein deforms membrane. Here, we initially pursued computation-based refinement of the ACAP1 lattice to identify its critical protein contacts. Simulation studies then revealed how ACAP1, which dimerizes into a symmetrical structure in solution, is recruited asymmetrically to the membrane through dynamic behavior. We also pursued electron microscopy (EM)-based structural studies, which shed further insight into the dynamic nature of the ACAP1 lattice assembly. As ACAP1 is an unconventional BAR protein, our findings broaden the understanding of the mechanistic spectrum by which proteins assemble into higher-ordered structures to achieve membrane deformation.

Incorporating uracil and 5-halouracils into short peptide nucleic acids for enhanced recognition of A-U pairs in dsRNAs.

Double-stranded RNA (dsRNA) structures form triplexes and RNA-protein complexes through binding to single-stranded RNA (ssRNA) regions and proteins, respectively, for diverse biological functions. Hence, targeting dsRNAs through major-groove triplex formation is a promising strategy for the development of chemical probes and potential therapeutics. Short (e.g., 6-10 mer) chemically-modified Peptide Nucleic Acids (PNAs) have been developed that bind to dsRNAs sequence specifically at physiological conditions. For example, a PNA incorporating a modified base thio-pseudoisocytosine (L) has an enhanced recognition of a G-C pair in an RNA duplex through major-groove L·G-C base triple formation at physiological pH, with reduced pH dependence as observed for C+·G-C base triple formation. Currently, an unmodified T base is often incorporated into PNAs to recognize a Watson-Crick A-U pair through major-groove T·A-U base triple formation. A substitution of the 5-methyl group in T by hydrogen and halogen atoms (F, Cl, Br, and I) causes a decrease of the pKa of N3 nitrogen atom, which may result in improved hydrogen bonding in addition to enhanced base stacking interactions. Here, we synthesized a series of PNAs incorporating uracil and halouracils, followed by binding studies by non-denaturing polyacrylamide gel electrophoresis, circular dichroism, and thermal melting. Our results suggest that replacing T with uracil and halouracils may enhance the recognition of an A-U pair by PNA·RNA2 triplex formation in a sequence-dependent manner, underscoring the importance of local stacking interactions. Incorporating bromouracils and chlorouracils into a PNA results in a significantly reduced pH dependence of triplex formation even for PNAs containing C bases, likely due to an upshift of the apparent pKa of N3 atoms of C bases. Thus, halogenation and other chemical modifications may be utilized to enhance hydrogen bonding of the adjacent base triples and thus triplex formation. Furthermore, our experimental and computational modelling data suggest that PNA·RNA2 triplexes may be stabilized by incorporating a BrUL step but not an LBrU step, in dsRNA-binding PNAs.

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1.

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.

A Method for Determining Structure Ensemble of Large Disordered Protein: Application to a Mechanosensing Protein.

Structure characterization of intrinsically disordered proteins (IDPs) remains a key obstacle in understanding their functional mechanisms. Due to the highly dynamic feature of IDPs, structure ensembles instead of static unique structures are often derived from experimental data. Several state-of-the-art computational methods have been developed to select an optimal ensemble from a pregenerated structure pool, but they suffer from low efficiency for large IDPs. Here we present a matching pursuit genetic algorithm (MPGA) for structure ensemble determination, which takes advantages from both matching pursuit (MP) to reduce the search space and genetic algorithm (GA) to reduce the restriction on constraint types. The MPGA method is validated using a reference ensemble with predefined structures. In comparison with the conventional GA, MPGA takes much less computational time for large IDPs. The utility of the method is demonstrated by application to structure ensemble determination of a mechanosensing protein domain with 306 amino acids. The structure ensemble determined reveals that the N-terminal region 1-240 is more compact than the C-terminal region 240-306. The unique structural feature explains why only a small portion of YXXP tyrosine residues can be phosphorylated easily by kinases in the absence of extension force and why the phosphorylation is force-dependent.

A Practical Guide to iSPOT Modeling: An Integrative Structural Biology Platform.

Integrative structure modeling is an emerging method for structural determination of protein-protein complexes that are challenging for conventional structural techniques. Here, we provide a practical protocol for implementing our integrated iSPOT platform by integrating three different biophysical techniques: small-angle X-ray scattering (SAXS), hydroxyl radical footprinting, and computational docking simulations. Specifically, individual techniques are described from experimental and/or computational perspectives, and complementary structural information from these different techniques are integrated for accurate characterization of the structures of large protein-protein complexes.

Identifying essential pairwise interactions in elastic network model using the alpha shape theory.

Elastic network models (ENM) are based on the idea that the geometry of a protein structure provides enough information for computing its fluctuations around its equilibrium conformation. This geometry is represented as an elastic network (EN) that is, a network of links between residues. A spring is associated with each of these links. The normal modes of the protein are then identified with the normal modes of the corresponding network of springs. Standard approaches for generating ENs rely on a cutoff distance. There is no consensus on how to choose this cutoff. In this work, we propose instead to filter the set of all residue pairs in a protein using the concept of alpha shapes. The main alpha shape we considered is based on the Delaunay triangulation of the Cα positions; we referred to the corresponding EN as EN(∞). We have shown that heterogeneous anisotropic network models, called αHANMs, that are based on EN(∞) reproduce experimental B-factors very well, with correlation coefficients above 0.99 and root-mean-square deviations below 0.1 Å(2) for a large set of high resolution protein structures. The construction of EN(∞) is simple to implement and may be used automatically for generating ENs for all types of ENMs.

Alzheimer's disease--a panorama glimpse.

The single-mutation of genes associated with Alzheimer's disease (AD) increases the production of Aß peptides. An elevated concentration of Aß peptides is prone to aggregation into oligomers and further deposition as plaque. Aß plaques and neurofibrillary tangles are two hallmarks of AD. In this review, we provide a broad overview of the diverses sources that could lead to AD, which include genetic origins, Aß peptides and tau protein. We shall discuss on tau protein and tau accumulation, which result in neurofibrillary tangles. We detail the mechanisms of Aß aggregation, fibril formation and its polymorphism. We then show the possible links between Aß and tau pathology. Furthermore, we summarize the structural data of Aß and its precursor protein obtained via Nuclear Magnetic Resonance (NMR) or X-ray crystallography. At the end, we go through the C-terminal and N-terminal truncated Aß variants. We wish to draw reader's attention to two predominant and toxic Aß species, namely Aß4-42 and pyroglutamate amyloid-beta peptides, which have been neglected for more than a decade and may be crucial in Aß pathogenesis due to their dominant presence in the AD brain.

Refined Bonded Terms in Coarse-Grained Models for Intrinsically Disordered Proteins Improve Backbone Conformations.

Coarse-grained models designed for intrinsically disordered proteins and regions (IDP/Rs) usually omit some bonded potentials (e.g., angular and dihedral potentials) as a conventional strategy to enhance backbone flexibility. However, a notable drawback of this approach is the generation of inaccurate backbone conformations. Here, we addressed this problem by introducing residue-specific angular, refined dihedral, and correction map (CMAP) potentials, derived based on the statistics from a customized coil database. These bonded potentials were integrated into the existing Mpipi model, resulting in a new model, denoted as the "Mpipi+" model. Results show that the Mpipi+ model can improve backbone conformations. More importantly, it can markedly improve the secondary structure propensity (SSP) based on the experimental chemical shift and, consequently, succeed in capturing transient secondary structures. Moreover, the Mpipi+ model preserves the liquid-liquid phase separation (LLPS) propensities of IDPs.

Force distribution analysis of mechanochemically reactive dimethylcyclobutene.

Internal molecular forces can guide chemical reactions, yet are not straightforwardly accessible within a quantum mechanical description of the reacting molecules. Here, we present a force-matching force distribution analysis (FM-FDA) to analyze internal forces in molecules. We simulated the ring opening of trans-3,4-dimethylcyclobutene (tDCB) with on-the-fly semiempirical molecular dynamics. The self-consistent density functional tight binding (SCC-DFTB) method accurately described the force-dependent ring-opening kinetics of tDCB, showing quantitative agreement with both experimental and computational data at higher levels. Mechanical force was applied in two different ways, namely, externally by a constant pulling force and internally by embedding tDCB within a strained macrocycle-containing stiff stilbene. We analyzed the distribution of tDCB internal forces in the two different cases by FM-FDA and found that external force gave rise to a symmetric force distribution in the cyclobutene ring, which also scaled linearly with the external force, indicating that the force distribution was uniquely determined by the symmetric architecture of tDCB. In contrast, internal forces due to stiff stilbene resulted in an asymmetric force distribution within tDCB, which indicated a different geometry of force application and supported the important role of linkers in the mechanochemical reactivity of tDCB. In addition, three coordinates were identified through which the distributed forces contributed most to rate acceleration. These coordinates are mostly parallel to the coordinate connecting the two CH3 termini of tDCB. Our results confirm previous observations that the linker outside of the reactive moiety, such as a stretched polymer or a macrocycle, affects its mechanochemical reactivity. We expect FM-FDA to be of wide use to understand and quantitatively predict mechanochemical reactivity, including the challenging cases of systems within strained macrocycles.

Fitting coarse-grained distribution functions through an iterative force-matching method.

An iterative coarse-graining method is developed for systematically converting an atomistic force field to a model at lower resolution that is able to accurately reproduce the distribution functions defined in the coarse-grained potential. The method starts from the multiscale coarse-graining (MS-CG) approach, and it iteratively refines the distribution functions using repeated applications of the MS-CG algorithm. It is justified on the basis of the force matching normal equation, which can be considered a discrete form of the Yvon-Born-Green equation in liquid state theory. Numerical results for molecular systems involving pairwise nonbonded and three-body bonded interactions are obtained, and comparison with other approaches in literature is provided.

The intrinsically disordered region of coronins fine-tunes oligomerization and actin polymerization.

Coronins play critical roles in actin network formation. The diverse functions of coronins are regulated by the structured N-terminal ß propeller and the C-terminal coiled coil (CC). However, less is known about a middle "unique region" (UR), which is an intrinsically disordered region (IDR). The UR/IDR is an evolutionarily conserved signature in the coronin family. By integrating biochemical and cell biology experiments, coarse-grained simulations, and protein engineering, we find that the IDR optimizes the biochemical activities of coronins in vivo and in vitro. The budding yeast coronin IDR plays essential roles in regulating Crn1 activity by fine-tuning CC oligomerization and maintaining Crn1 as a tetramer. The IDR-guided optimization of Crn1 oligomerization is critical for F-actin cross-linking and regulation of Arp2/3-mediated actin polymerization. The final oligomerization status and homogeneity of Crn1 are contributed by three examined factors: helix packing, the energy landscape of the CC, and the length and molecular grammar of the IDR.

B-cell epitope prediction through a graph model.

BACKGROUND: Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist. RESULTS: In this paper, we propose to divide an antigen surface graph into subgraphs by using a Markov Clustering algorithm, and then we construct a classifier to distinguish these subgraphs as epitope or non-epitope subgraphs. This classifier is then taken to predict epitopes for a test antigen. On a big data set comprising 92 antigen-antibody PDB complexes, our method significantly outperforms the state-of-the-art epitope prediction methods, achieving 24.7% higher averaged f-score than the best existing models. In particular, our method can successfully identify those epitopes with a non-planarity which is too small to be addressed by the other models. Our method can also detect multiple epitopes whenever they exist. CONCLUSIONS: Various protrusive and planar patches at the surface of antigens can be distinguishable by using graphical models combined with unsupervised clustering and supervised learning ideas. The difficult problem of identifying multiple epitopes from an antigen can be made easied by using our subgraph approach. The outstanding residue combinations found in the supervised learning will be useful for us to form new hypothesis in future studies.

The multiscale coarse-graining method. X. Improved algorithms for constructing coarse-grained potentials for molecular systems.

The multiscale coarse-graining (MS-CG) method uses simulation data for an atomistic model of a system to construct a coarse-grained (CG) potential for a coarse-grained model of the system. The CG potential is a variational approximation for the true potential of mean force of the degrees of freedom retained in the CG model. The variational calculation uses information about the atomistic positions and forces in the simulation data. In principle, the resulting MS-CG potential will be an accurate representation of the true CG potential if the basis set for the variational calculation is complete enough and the canonical distribution of atomistic states is well sampled by the data set. In practice, atomistic configurations that have very high potential energy are not sampled. As a result there usually is a region of CG configuration space that is not sampled and about which the data set contains no information regarding the gradient of the true potential. The MS-CG potential obtained from a variational calculation will not necessarily be accurate in this unsampled region. A priori considerations make it clear that the true CG potential of mean force must be very large and positive in that region. To obtain an MS-CG potential whose behavior in the sampled region is determined by the atomistic data set, and whose behavior in the unsampled region is large and positive, it is necessary to intervene in the variational calculation in some way. In this paper, we discuss and compare two such methods of intervention, which have been used in previous MS-CG calculations for dealing with nonbonded interactions. For the test systems studied, the two methods give similar results and yield MS-CG potentials that are limited in accuracy only by the incompleteness of the basis set and the statistical error of associated with the set of atomistic configurations used. The use of such methods is important for obtaining accurate CG potentials.

A teamwork promotion of formin-mediated actin nucleation by Bud6 and Aip5 in Saccharomyces cerevisiae.

Actin nucleation is achieved by collaborative teamwork of actin nucleator factors (NFs) and nucleation-promoting factors (NPFs) into functional protein complexes. Selective inter- and intramolecular interactions between the nucleation complex constituents enable diverse modes of complex assembly in initiating actin polymerization on demand. Budding yeast has two formins, Bni1 and Bnr1, which are teamed up with different NPFs. However, the selective pairing between formin NFs and NPFs into the nucleation core for actin polymerization is not completely understood. By examining the functions and interactions of NPFs and NFs via biochemistry, genetics, and mathematical modeling approaches, we found that two NPFs, Aip5 and Bud6, showed joint teamwork effort with Bni1 and Bnr1, respectively, by interacting with the C-terminal intrinsically disordered region (IDR) of formin, in which two NPFs work together to promote formin-mediated actin nucleation. Although the C-terminal IDRs of Bni1 and Bnr1 are distinct in length, each formin IDR orchestrates the recruitment of Bud6 and Aip5 cooperatively by different positioning strategies to form a functional complex. Our study demonstrated the dynamic assembly of the actin nucleation complex by recruiting multiple partners in budding yeast, which may be a general feature for effective actin nucleation by formins.

Multiscale coarse-graining of the protein energy landscape.

A variety of coarse-grained (CG) models exists for simulation of proteins. An outstanding problem is the construction of a CG model with physically accurate conformational energetics rivaling all-atom force fields. In the present work, atomistic simulations of peptide folding and aggregation equilibria are force-matched using multiscale coarse-graining to develop and test a CG interaction potential of general utility for the simulation of proteins of arbitrary sequence. The reduced representation relies on multiple interaction sites to maintain the anisotropic packing and polarity of individual sidechains. CG energy landscapes computed from replica exchange simulations of the folding of Trpzip, Trp-cage and adenylate kinase resemble those of other reduced representations; non-native structures are observed with energies similar to those of the native state. The artifactual stabilization of misfolded states implies that non-native interactions play a deciding role in deviations from ideal funnel-like cooperative folding. The role of surface tension, backbone hydrogen bonding and the smooth pairwise CG landscape is discussed. Ab initio folding aside, the improved treatment of sidechain rotamers results in stability of the native state in constant temperature simulations of Trpzip, Trp-cage, and the open to closed conformational transition of adenylate kinase, illustrating the potential value of the CG force field for simulating protein complexes and transitions between well-defined structural states.

The multiscale coarse-graining method. VII. Free energy decomposition of coarse-grained effective potentials.

The potential of mean force (PMF) with respect to coarse-grained (CG) coordinates is often calculated in order to study the molecular interactions in atomistic molecular dynamics (MD) simulations. The multiscale coarse-graining (MS-CG) approach enables the computation of the many-body PMF of an atomistic system in terms of the CG coordinates, which can be used to parameterize CG models based on all-atom configurations. We demonstrate here that the MS-CG method can also be used to analyze the CG interactions from atomistic MD trajectories via PMF calculations. In addition, MS-CG calculations at different temperatures are performed to decompose the PMF values into energetic and entropic contributions as a function of the CG coordinates, which provides more thermodynamic information regarding the atomistic system. Two numerical examples, liquid methanol and a dimyristoylphosphatidylcholine lipid bilayer, are presented. The results show that MS-CG can be used as an analysis tool, comparable to various free energy computation methods. The differences between the MS-CG approach and other PMF calculation methods, as well as the characteristics and advantages of MS-CG, are also discussed.

The multiscale coarse-graining method. VI. Implementation of three-body coarse-grained potentials.

Many methodologies have been proposed to build reliable and computationally fast coarse-grained potentials. Typically, these force fields rely on the assumption that the relevant properties of the system under examination can be reproduced using a pairwise decomposition of the effective coarse-grained forces. In this work it is shown that an extension of the multiscale coarse-graining technique can be employed to parameterize a certain class of two-body and three-body force fields from atomistic configurations. The use of explicit three-body potentials greatly improves the results over the more commonly used two-body approximation. The method proposed here is applied to develop accurate one-site coarse-grained water models.

Systematic coarse-graining of a multicomponent lipid bilayer.

A solvent-free coarse-grained model for a 1:1 mixed dioleoylphosphatidylcholine (DOPC) and a dioleoylphospatidylethanolamine (DOPE) bilayer is developed using the multiscale coarse-graining (MS-CG) approach. B-spline basis functions are implemented instead of the original cubic spline basis functions in the MS-CG method. The new B-spline basis functions are able to dramatically reduce memory requirements and increase computational efficiency of the MS-CG calculation. Various structural properties from the CG simulations are compared with their corresponding all-atom counterpart in order to validate the CG model. The resulting CG structural properties agree well with atomistic results, which shows that the MS-CG force field can reasonably approximate the many-body potential of mean force in the coarse-grained coordinates. Fast lipid lateral diffusion in the CG simulations, as a result of smoother free energy landscape, makes the study of phase behavior of the binary mixture possible. Small clusters of distinct lipid composition are identified by analyzing the DOPC/DOPE lipid lateral distribution, indicating a nonuniform distribution for the mixed bilayer. The results of lipid phase behavior are compared to experimental results, and connections between the experimental and simulation conclusions are discussed.