The Role of cfDNA Biomarkers and Patient Data in the Early Prediction of Preeclampsia: Artificial Intelligence Model.

OBJECTIVE: Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12-16 weeks' gestation when there is evidence for its effectiveness, as well as guiding appropriate pregnancy care pathways and surveillance. The primary objective of this study was to evaluate the performance of artificial neural network models for the prediction of preterm preeclampsia (<37 weeks' gestation) using patient characteristics available at the first antenatal visit and data from prenatal cell-free DNA (cfDNA) screening. Secondary outcomes were prediction of early onset preeclampsia (<34 weeks' gestation) and term preeclampsia (≥37 weeks' gestation). METHODS: This secondary analysis of a prospective, multicenter, observational prenatal cfDNA screening study (SMART) included singleton pregnancies with known pregnancy outcomes. Thirteen patient characteristics that are routinely collected at the first prenatal visit and two characteristics of cfDNA, total cfDNA and fetal fraction (FF), were used to develop predictive models for early-onset (<34 weeks), preterm (<37 weeks), and term (≥37 weeks) preeclampsia. For the models, the 'reference' classifier was a shallow logistic regression (LR) model. We also explored several feedforward (non-linear) neural network (NN) architectures with one or more hidden layers and compared their performance with the LR model. We selected a simple NN model built with one hidden layer and made up of 15 units. RESULTS: Of 17,520 participants included in the final analysis, 72 (0.4%) developed early onset, 251 (1.4%) preterm, and 420 (2.4%) term preeclampsia. Median gestational age at cfDNA measurement was 12.6 weeks and 2,155 (12.3%) had their cfDNA measurement at 16 weeks' gestation or greater. Preeclampsia was associated with higher total cfDNA (median 362.3 versus 339.0 copies/ml cfDNA; p<0.001) and lower FF (median 7.5% versus 9.4%; p<0.001). The expected, cross-validated area under the curve (AUC) scores for early onset, preterm, and term preeclampsia were 0.782, 0.801, and 0.712, respectively for the LR model, and 0.797, 0.800, and 0.713, respectively for the NN model. At a screen-positive rate of 15%, sensitivity for preterm preeclampsia was 58.4% (95% CI 0.569, 0.599) for the LR model and 59.3% (95% CI 0.578, 0.608) for the NN model.The contribution of both total cfDNA and FF to the prediction of term and preterm preeclampsia was negligible. For early-onset preeclampsia, removal of the total cfDNA and FF features from the NN model was associated with a 6.9% decrease in sensitivity at a 15% screen positive rate, from 54.9% (95% CI 52.9-56.9) to 48.0% (95% CI 45.0-51.0). CONCLUSION: Routinely available patient characteristics and cfDNA markers can be used to predict preeclampsia with performance comparable to other patient characteristic models for the prediction of preterm preeclampsia. Both LR and NN models showed similar performance.

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist.

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

Population dynamics, information transfer, and spatial organization in a chemical reaction network under spatial confinement and crowding conditions.

We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.

MARTINI coarse-grained model for crystalline cellulose microfibers.

Commercial-scale biofuel production requires a deep understanding of the structure and dynamics of its principal target: cellulose. However, an accurate description and modeling of this carbohydrate structure at the mesoscale remains elusive, particularly because of its overwhelming length scale and configurational complexity. We have derived a set of MARTINI coarse-grained force field parameters for the simulation of crystalline cellulose fibers. The model is adapted to reproduce different physicochemical and mechanical properties of native cellulose Iß. The model is able not only to handle a transition from cellulose Iß to another cellulose allomorph, cellulose IIII, but also to capture the physical response to temperature and mechanical bending of longer cellulose nanofibers. By developing the MARTINI model of a solid cellulose crystalline fiber from the building blocks of a soluble cellobiose coarse-grained model, we have provided a systematic way to build MARTINI models for other crystalline biopolymers.

ß-sheet propensity controls the kinetic pathways and morphologies of seeded peptide aggregation.

The effect of seeds in templating the morphology of peptide aggregates is examined using molecular dynamics simulations and a coarse-grained peptide representation. Varying the nature of the aggregate seed between ß-sheet, amorphous, and ß-barrel seeds leads to different aggregation pathways and to morphologically different aggregates. Similar effects are seen by varying the ß-sheet propensity of the free peptides. For a fibrillar seed and free peptides of high ß-sheet propensity, fibrillar growth occurred by means of direct attachment (without structural rearrangement) of free individual peptides and small ordered oligomers onto the seed. For a fibrillar seed and free peptides of low ß-sheet propensity, fibrillar growth occurred through a dock-lock mechanism, in which the free peptides first docked onto the seed, and then locked on, extending and aligning to join the fibril. Amorphous seeds absorbed free peptides into themselves indiscriminately, with any fibrillar rearrangement subsequent to this absorption by means of a condensation-ordering transition. Although the mechanisms observed by varying peptide ß-sheet propensity are diverse, the initial pathways can always be broken down into the following steps: (i) the free peptides diffuse in the bulk and attach individually to the seed; (ii) the free peptides diffuse and aggregate among themselves; (iii) the free peptide oligomers collide with the seed; and (iv) the free oligomers merge with the seed and rearrange in a manner dependent on the backbone flexibility of both the free and seed peptides. Our simulations indicate that it is possible to sequester peptides from amorphous aggregates into fibrils, and also that aggregate morphology (and thus cytoxicity) can be controlled by introducing seeds of aggregate-compatible peptides with differing ß-sheet propensities into the system.

Coarse-grained model for the interconversion between native and liquid ammonia-treated crystalline cellulose.

We present the results of Langevin dynamics simulations on a coarse-grained model for a structural transition in crystalline cellulose pertinent to the cellulose degradation problem. We analyze two different cellulose crystalline forms: cellulose Iß (the natural form of cellulose) and cellulose III(I) (obtained after cellulose Iß is treated with anhydrous liquid ammonia). Cellulose III(I) has been the focus of wide interest in the field of cellulosic biofuels, as it can be efficiently hydrolyzed to readily fermentable glucose (its enzymatic degradation rates are up to 5-fold higher than those of cellulose Iß). The coarse-grained model presented in this study is based on a simplified geometry and on an effective potential mimicking the changes in both intracrystalline hydrogen bonds and stacking interactions during the transition from cellulose Iß to cellulose III(I). The model reproduces both structural and thermomechanical properties of cellulose Iß and III(I). The work presented herein describes the structural transition from cellulose Iß to cellulose III(I) as driven by the change in the equilibrium state of two degrees of freedom in the cellulose chains. The structural transition from cellulose Iß to cellulose III(I) is essentially reduced to a search for optimal spatial arrangement of the cellulose chains.

Effects of surface interactions on peptide aggregate morphology.

The formation of peptide aggregates mediated by an attractive surface is investigated using replica exchange molecular dynamics simulations with a coarse-grained peptide representation. In the absence of a surface, the peptides exhibit a range of aggregate morphologies, including amorphous aggregates, ß-barrels and multi-layered fibrils, depending on the chiral stiffness of the chain (a measure of its ß-sheet propensity). In contrast, aggregate morphology in the presence of an attractive surface depends more on surface attraction than on peptide chain stiffness, with the surface favoring fibrillar structures. Peptide-peptide interactions couple to peptide-surface interactions cooperatively to affect the assembly process both qualitatively (in terms of aggregate morphology) and quantitatively (in terms of transition temperature and transition sharpness). The frequency of ordered fibrillar aggregates, the surface binding transition temperature, and the sharpness of the binding transition all increase with both surface attraction and chain stiffness.

Probing the early events associated with liquid ammonia pretreatment of native crystalline cellulose.

Various chemicals are being explored for catalyzing efficient lignocellulose deconstruction. In particular, when liquid ammonia is used to convert the naturally occurring cellulose crystalline phase I(ß), to cellulose III(I), the rearrangement of the hydrogen bond network in cellulose III(I) results in enhanced hydrolysis yields. We use molecular dynamics simulations to analyze the interaction between a cellulose I(ß) fibril and ammonia. Our simulations reveal that early structural changes in the fibril are driven by the rapid formation of an extended hydrogen bond network between the solvent-exposed surface chains and ammonia that precedes ammonia penetration into the fibril. The emergence of this hydrogen bond network causes relative shifting of the cellulose layers within the fibril that in turn leads to the formation of channels orthogonal to the (100) and (-100) fibril surfaces. The channels allow ammonia molecules to penetrate into the cellulose fibril. These findings provide avenues for improving existing chemical pretreatments to make them more effective and economical.

Restructuring the crystalline cellulose hydrogen bond network enhances its depolymerization rate.

Conversion of lignocellulose to biofuels is partly inefficient due to the deleterious impact of cellulose crystallinity on enzymatic saccharification. We demonstrate how the synergistic activity of cellulases was enhanced by altering the hydrogen bond network within crystalline cellulose fibrils. We provide a molecular-scale explanation of these phenomena through molecular dynamics (MD) simulations and enzymatic assays. Ammonia transformed the naturally occurring crystalline allomorph I(ß) to III(I), which led to a decrease in the number of cellulose intrasheet hydrogen bonds and an increase in the number of intersheet hydrogen bonds. This rearrangement of the hydrogen bond network within cellulose III(I), which increased the number of solvent-exposed glucan chain hydrogen bonds with water by ~50%, was accompanied by enhanced saccharification rates by up to 5-fold (closest to amorphous cellulose) and 60-70% lower maximum surface-bound cellulase capacity. The enhancement in apparent cellulase activity was attributed to the "amorphous-like" nature of the cellulose III(I) fibril surface that facilitated easier glucan chain extraction. Unrestricted substrate accessibility to active-site clefts of certain endocellulase families further accelerated deconstruction of cellulose III(I). Structural and dynamical features of cellulose III(I), revealed by MD simulations, gave additional insights into the role of cellulose crystal structure on fibril surface hydration that influences interfacial enzyme binding. Subtle alterations within the cellulose hydrogen bond network provide an attractive way to enhance its deconstruction and offer unique insight into the nature of cellulose recalcitrance. This approach can lead to unconventional pathways for development of novel pretreatments and engineered cellulases for cost-effective biofuels production.

Assisted peptide folding by surface pattern recognition.

Natively disordered proteins belong to a unique class of biomolecules whose function is related to their flexibility and their ability to adopt desired conformations upon binding to substrates. In some cases these proteins can bind multiple partners, which can lead to distinct structures and promiscuity in functions. In other words, the capacity to recognize molecular patterns on the substrate is often essential for the folding and function of intrinsically disordered proteins. Biomolecular pattern recognition is extremely relevant both in vivo (e.g., for oligomerization, immune response, induced folding, substrate binding, and molecular switches) and in vitro (e.g., for biosensing, catalysis, chromatography, and implantation). Here, we use a minimalist computational model system to investigate how polar/nonpolar patterns on a surface can induce the folding of an otherwise unstructured peptide. We show that a model peptide that exists in the bulk as a molten globular state consisting of many interconverting structures can fold into either a helix-coil-helix or an extended helix structure in the presence of a complementary designed patterned surface at low hydrophobicity (3.7%) or a uniform surface at high hydrophobicity (50%). However, we find that a carefully chosen surface pattern can bind to and catalyze the folding of a natively unfolded protein much more readily or effectively than a surface with a noncomplementary or uniform distribution of hydrophobic residues.

Relative stability of de novo four-helix bundle proteins: insights from coarse grained molecular simulations.

We use a recently developed coarse-grained computational model to investigate the relative stability of two different sets of de novo designed four-helix bundle proteins. Our simulations suggest a possible explanation for the experimentally observed increase in stability of the four-helix bundles with increasing sequence length. In details, we show that both short subsequences composed only by polar residues and additional nonpolar residues inserted, via different point mutations in ad hoc positions, seem to play a significant role in stabilizing the four-helix bundle conformation in the longer sequences. Finally, we propose an additional mutation that rescues a short amino acid sequence that would otherwise adopt a compact misfolded state. Our work suggests that simple computational models can be used as a complementary tool in the design process of de novo proteins.

In silico studies of crystalline cellulose and its degradation by enzymes.

In this report, the current state of computational studies on crystalline cellulose is reviewed. The discussion is focused on fully atomistic molecular-dynamics simulations as well as on other computational approaches which are relevant in the context of enzymatic degradation of cellulose. Finally, possible directions and necessary improvements for future computational studies in this challenging research field are summarized.

Sequence periodicity and secondary structure propensity in model proteins.

We explore the question of whether local effects (originating from the amino acids intrinsic secondary structure propensities) or nonlocal effects (reflecting the sequence of amino acids as a whole) play a larger role in determining the fold of globular proteins. Earlier circular dichroism studies have shown that the pattern of polar, non polar amino acids (nonlocal effect) dominates over the amino acid intrinsic propensity (local effect) in determining the secondary structure of oligomeric peptides. In this article, we present a coarse grained computational model that allows us to quantitatively estimate the role of local and nonlocal factors in determining both the secondary and tertiary structure of small, globular proteins. The amino acid intrinsic secondary structure propensity is modeled by a dihedral potential term. This dihedral potential is parametrized to match with experimental measurements of secondary structure propensity. Similarly, the magnitude of the attraction between hydrophobic residues is parametrized to match the experimental transfer free energies of hydrophobic amino acids. Under these parametrization conditions, we systematically explore the degree of frustration a given polar, non polar pattern can tolerate when the secondary structure intrinsic propensities are in opposition to it. When the parameters are in the biophysically relevant range, we observe that the fold of small, globular proteins is determined by the pattern of polar, non polar amino acids regardless of their instrinsic secondary structure propensities. Our simulations shed new light on previous observations that tertiary interactions are more influential in determining protein structure than secondary structure propensity. The fact that this can be inferred using a simple polymer model that lacks most of the biochemical details points to the fundamental importance of binary patterning in governing folding.

Diversity of kinetic pathways in amyloid fibril formation.

The kinetics of peptide oligomerization was investigated using Langevin Dynamics simulations and a coarse-grained peptide model. The simulations show a rich diversity of aggregation pathways, modulated by the beta-sheet propensity (flexibility) of the peptide. Aggregation into amyloidlike fibrils occurs via three main mechanisms: (i) formation of fibrils directly from the assembly of early ordered oligomers, (ii) fibril formation via the formation of on-pathway, nonfibrillar aggregates high in beta-sheet content, and (iii) formation of amorphous aggregates followed by reorganization to beta-sheet aggregates and to fibrils. beta-sheet, nonfibrillar aggregates also appeared as long-lived, "off-pathway" end-product species.

Effect of beta-sheet propensity on peptide aggregation.

The effect of beta-sheet propensity on the structural features of peptide aggregates was investigated using an off-lattice coarse-grained peptide model. A phase diagram as a function of temperature and beta-sheet propensity reveals a diverse family of supramolecular assemblies. Highly rigid peptides (peptides with high beta-sheet propensity) are seen to assemble predominantly into fibrillar structures. Increasing the flexibility of the peptide (reducing beta-sheet propensity) leads to a variety of structures, including fibrils, beta-barrel structures, and amorphous aggregates. Nonfibrillar entities have been suggested as primary causative agents in amyloid diseases and our simulations indicate that mutations that decrease beta-sheet propensity will decrease fibril formation and favor the formation of such toxic oligomers. Parallels between beta-sheet aggregates and nematic liquid crystals are discussed.

What determines the structure and stability of KFFE monomers, dimers, and protofibrils?

The self-assembly of the KFFE peptide was studied using replica exchange molecular dynamics simulations with a fully atomic description of the peptide and explicit solvent. The relative roles of the aromatic residues and oppositely charged end groups in stabilizing the earliest oligomers and the end-products of aggregation were investigated. beta and non-beta-peptide conformations compete in the monomeric state as a result of a balancing between the high beta-sheet propensity of the phenylalanine residues and charge-charge interactions that favor non-beta-conformations. Dimers are present in beta- and non-beta-sheet conformations and are stabilized primarily by direct and water-mediated charge-charge interactions between oppositely charged side chains and between oppositely charged termini, with forces between aromatic residues playing a minor role. Dimerization to a beta-sheet, fibril-competent state, is seen to be a cooperative process, with the association process inducing beta-structure in otherwise non-beta-monomers. We propose a model for the KFFE fibril, with mixed interface and antiparallel sheet and strand arrangements, which is consistent with experimental electron microscopy measurements. Both aromatic and charge-charge interactions contribute to the fibril stability, although the dominant contribution arises from electrostatic interactions.

Computational methods in nanostructure design: replica exchange simulations of self-assembling peptides.

Self-assembling peptides can serve as building blocks for novel biomaterials. Replica exchange molecular dynamics simulations are a powerful means to probe the conformational space of these peptides. We discuss the theoretical foundations of this enhanced sampling method and its use in biomolecular simulations. We then apply this method to determine the monomeric conformations of the Alzheimer amyloid-beta(12-28) peptide that can serve as initiation sites for aggregation.

Structural transitions in model beta-sheet tapes.

We present a molecular-scale simulation study of the structural transitions between helicoidal, helical, and tubular geometries in supramolecular beta-sheet tapes. Such geometries have been observed in different self-assembled amyloid systems (based on either natural or synthetic peptides) for which the beta-sheet tapes represent the simplest fibrillar aggregates. A coarse-grained model for the beta-sheet tapes is proposed, with chiral degrees of freedom and asymmetrical chemical properties, which provides a quantitative characterization of the structural transitions. A quantitative connection is established between the molecular properties and the elastic parameters of the supramolecular tapes.

Structure and stability of amyloid fibrils formed from synthetic beta-peptides.

Synthetic peptides capable of self-assembling into amyloid-like fibrillar structures are emerging as novel building blocks for biomaterials. They also serve as simple model systems to study the aggregation process involved in amyloid diseases. In this paper, we probe the structure and stability of fibrillar assemblies formed by two designed peptides P11-I (CH3-CO-Q2RQ5EQ2-NH2) and P11-II (CH3-CO-Q2RFQWQFEQ2-NH2). Our results suggest that the two peptides assemble by fundamentally different mechanisms to structures of different morphologies. Coulombic interactions between charged residues Arginine and Glutamate drive the self-assembly process for peptide P11-I while the hydrophobic effect appears to be the main driving force in the self-assembly of peptide P11-II.

Self-assembly of beta-sheet forming peptides into chiral fibrillar aggregates.

The authors introduce a novel mid-resolution off-lattice coarse-grained model to investigate the self-assembly of beta-sheet forming peptides. The model retains most of the peptide backbone degrees of freedom as well as one interaction center describing the side chains. The peptide consists of a core of alternating hydrophobic and hydrophilic residues, capped by two oppositely charged residues. Nonbonded interactions are described by Lennard-Jones and Coulombic terms. The influence of different levels of "hydrophobic" and "steric" forces between the side chains of the peptides on the thermodynamics and kinetics of aggregation was investigated using Langevin dynamics. The model is simple enough to allow the simulation of systems consisting of hundreds of peptides, while remaining realistic enough to successfully lead to the formation of chiral, ordered beta tapes, ribbons, as well as higher order fibrillar aggregates.