Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Molecular basis of differential HLA class I-restricted T cell recognition of a highly networked HIV peptide

Cytotoxic-T-lymphocyte (CTL) mediated control of HIV-1 is enhanced by targeting highly networked epitopes in complex with human-leukocyte-antigen-class-I (HLA-I). However, the extent to which the presenting HLA allele contributes to this process is unknown. Here we examine the CTL response to QW9, a highly networked epitope presented by the disease-protective HLA-B57 and disease-neutral HLA-B53. Despite robust targeting of QW9 in persons expressing either allele, T cell receptor (TCR) cross-recognition of the naturally occurring variant QW9_S3T is consistently reduced when presented by HLA-B53 but not by HLA-B57. Crystal structures show substantial conformational changes from QW9-HLA to QW9_S3T-HLA by both alleles. The TCR-QW9-B53 ternary complex structure manifests how the QW9-B53 can elicit effective CTLs and suggests sterically hindered cross-recognition by QW9_S3T-B53. We observe populations of cross-reactive TCRs for B57, but not B53 and also find greater peptide-HLA stability for B57 in comparison to B53. These data demonstrate differential impacts of HLAs on TCR cross-recognition and antigen presentation of a naturally arising variant, with important implications for vaccine design.

Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation

The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, data set, and seed bias impact the technology's utility to medicinal and computational chemists. In this work, we analyze the impact of seed and training bias on the output of an activity-conditioned graph-based variational autoencoder (VAE). Leveraging a massive, labeled data set corresponding to the dopamine D2 receptor, our graph-based generative model is shown to excel in producing desired conditioned activities and favorable unconditioned physical properties in generated molecules. We implement an activity-swapping method that allows for the activation, deactivation, or retention of activity of molecular seeds, and we apply independent deep learning classifiers to verify the generative results. Overall, we uncover relationships between noise, molecular seeds, and training set selection across a range of latent-space sampling procedures, providing important insights for practical AI-driven molecule generation.

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction

We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN QSAR architecture, and we observe that such a model can yield performance improvements over both standard gCNN and RF methods on difficult-to-classify test sets. Additionally, we discuss how reductions in convolutional layer dimensions potentially speak to the “anatomical” needs of gCNNs with respect to radial coarse graining of molecular substructure. We augment this simplified architecture with saliency map technology that highlights molecular substructures relevant to activity, and we perform saliency analysis on nearly 100 data-rich protein targets. We show that resultant substructural clusters are useful visualization tools for understanding substructure-activity relationships. We go on to highlight connections between our models’ saliency predictions and observations made in the medicinal chemistry literature, focusing on four case studies of past lead finding and lead optimization campaigns.

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach

We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate subnetworks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that data set bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein-ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased data set is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking data sets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.

Rare Dissipative Transitions Punctuate the Initiation of Chemical Denaturation in Proteins

Protein unfolding dynamics are bound by their degree of entropy production, a quantity that relates the amount of heat dissipated by a nonequilibrium process to a system's forward and time-reversed trajectories. We here explore the statistics of heat dissipation that emerge in protein molecules subjected to a chemical denaturant. Coupling large molecular dynamics datasets and Markov state models with the theory of entropy production, we demonstrate that dissipative processes can be rigorously characterized over the course of the urea-induced unfolding of the protein chymotrypsin inhibitor 2. By enumerating full entropy production probability distributions as a function of time, we first illustrate that distinct passive and dissipative regimes are present in the denaturation dynamics. Within the dissipative dynamical region, we next find that chymotrypsin inhibitor 2 is strongly driven into unfolded states in which the protein's hydrophobic core has been penetrated by urea molecules and disintegrated. Detailed analyses reveal that urea's interruption of key hydrophobic contacts between core residues causes many of the protein's native structural features to dissolve.

Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy

CD8+ T cell-dependent killing of cancer cells requires efficient presentation of tumor antigens by human leukocyte antigen class I (HLA-I) molecules. However, the extent to which patient-specific HLA-I genotype influences response to anti-programmed cell death protein 1 or anti-cytotoxic T lymphocyte-associated protein 4 is currently unknown. We determined the HLA-I genotype of 1535 advanced cancer patients treated with immune checkpoint blockade (ICB). Maximal heterozygosity at HLA-I loci ("A," "B," and "C") improved overall survival after ICB compared with patients who were homozygous for at least one HLA locus. In two independent melanoma cohorts, patients with the HLA-B44 supertype had extended survival, whereas the HLA-B62 supertype (including HLA-B*15:01) or somatic loss of heterozygosity at HLA-I was associated with poor outcome. Molecular dynamics simulations of HLA-B*15:01 revealed different elements that may impair CD8+ T cell recognition of neoantigens. Our results have important implications for predicting response to ICB and for the design of neoantigen-based therapeutic vaccines.

Molecular mechanism of Gd@C82(OH)22 increasing collagen expression: Implication for encaging tumor

Gadolinium-containing fullerenol Gd@C₈₂(OH)₂₂ has demonstrated low-toxicity and highly therapeutic efficacy in inhibiting tumor growth and metastasis through new strategy of encaging cancer, however, little is known about the mechanisms how this nanoparticle regulates fibroblast cells to prison (instead of poison) cancer cells. Here, we report that Gd@C₈₂(OH)₂₂ promote the binding activity of tumor necrosis factor (TNFα) to tumor necrosis factor receptors 2 (TNFR2), activate TNFR2/p38 MAPK signaling pathway to increase cellular collagen expression in fibrosarcoma cells and human primary lung cancer associated fibroblasts isolated from patients. We also employ molecular dynamics simulations to study the atomic-scale mechanisms that dictate how Gd@C₈₂(OH)₂₂ mediates interactions between TNFα and TNFRs. Our data suggest that Gd@C₈₂(OH)₂₂ might enhance the association between TNFα and TNFR2 through a "bridge-like" mode of interaction; by contrast, the fullerenol appears to inhibit TNFα-TNFR1 association by binding to two of the receptor's cysteine-rich domains. In concert, our results uncover a sequential, systemic process by which Gd@C₈₂(OH)₂₂ acts to prison tumor cells, providing new insights into principles of designs of cancer therapeutics.

Directional mechanical stability of Bacteriophage φ29 motor's 3WJ-pRNA: Extraordinary robustness along portal axis

The molecular motor exploited by bacteriophage φ29 to pack DNA into its capsid is regarded as one of the most powerful mechanical devices present in viral, bacterial, and eukaryotic systems alike. Acting as a linker element, a prohead RNA (pRNA) effectively joins the connector and ATPase (adenosine triphosphatase) components of the φ29 motor. During DNA packing, this pRNA needs to withstand enormous strain along the capsid's portal axis-how this remarkable stability is achieved remains to be elucidated. We investigate the mechanical properties of the φ29 motor's three-way junction (3WJ)-pRNA using a combined steered molecular dynamics and atomic force spectroscopy approach. The 3WJ exhibits strong resistance to stretching along its coaxial helices, demonstrating its super structural robustness. This resistance disappears, however, when external forces are applied to the transverse directions. From a molecular standpoint, we demonstrate that this direction-dependent stability can be attributed to two Mg clamps that cooperate and generate mechanical resistance in the pRNA's coaxial direction. Our results suggest that the asymmetric nature of the 3WJ's mechanical stability is entwined with its biological function: Enhanced rigidity along the portal axis is likely essential to withstand the strain caused by DNA condensation, and flexibility in other directions should aid in the assembly of the pRNA and its association with other motor components.

Graphene-Induced Pore Formation on Cell Membranes

Examining interactions between nanomaterials and cell membranes can expose underlying mechanisms of nanomaterial cytotoxicity and guide the design of safer nanomedical technologies. Recently, graphene has been shown to exhibit potential toxicity to cells; however, the molecular processes driving its lethal properties have yet to be fully characterized. We here demonstrate that graphene nanosheets (both pristine and oxidized) can produce holes (pores) in the membranes of A549 and Raw264.7 cells, substantially reducing cell viability. Electron micrographs offer clear evidence of pores created on cell membranes. Our molecular dynamics simulations reveal that multiple graphene nanosheets can cooperate to extract large numbers of phospholipids from the membrane bilayer. Strong dispersion interactions between graphene and lipid-tail carbons result in greatly depleted lipid density within confined regions of the membrane, ultimately leading to the formation of water-permeable pores. This cooperative lipid extraction mechanism for membrane perforation represents another distinct process that contributes to the molecular basis of graphene cytotoxicity.

Sequential protein unfolding through a carbon nanotube pore

An assortment of biological processes, like protein degradation and the transport of proteins across membranes, depend on protein unfolding events mediated by nanopore interfaces. In this work, we exploit fully atomistic simulations of an artificial, CNT-based nanopore to investigate the nature of ubiquitin unfolding. With one end of the protein subjected to an external force, we observe non-canonical unfolding behaviour as ubiquitin is pulled through the pore opening. Secondary structural elements are sequentially detached from the protein and threaded into the nanotube, interestingly, the remaining part maintains native-like characteristics. The constraints of the nanopore interface thus facilitate the formation of stable "unfoldon" motifs above the nanotube aperture that can exist in the absence of specific native contacts with the other secondary structure. Destruction of these unfoldons gives rise to distinct force peaks in our simulations, providing us with a sensitive probe for studying the kinetics of serial unfolding events. Our detailed analysis of nanopore-mediated protein unfolding events not only provides insight into how related processes might proceed in the cell, but also serves to deepen our understanding of structural arrangements which form the basis for protein conformational stability.

Folding and Stabilization of Native-Sequence-Reversed Proteins

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.

Percolation-like phase transitions in network models of protein dynamics

In broad terms, percolation theory describes the conditions under which clusters of nodes are fully connected in a random network. A percolation phase transition occurs when, as edges are added to a network, its largest connected cluster abruptly jumps from insignificance to complete dominance. In this article, we apply percolation theory to meticulously constructed networks of protein folding dynamics called Markov state models. As rare fluctuations are systematically repressed (or reintroduced), we observe percolation-like phase transitions in protein folding networks: whole sets of conformational states switch from nearly complete isolation to complete connectivity in a rapid fashion. We analyze the general and critical properties of these phase transitions in seven protein systems and discuss how closely dynamics on protein folding landscapes relate to percolation on random lattices.

Tunable, Strain-Controlled Nanoporous MoS₂ Filter for Water Desalination

The deteriorating state of global fresh water resources represents one of the most serious challenges that scientists and policymakers currently face. Desalination technologies, which are designed to extract potable water from the planet's bountiful stores of seawater, could serve to alleviate much of the stress that presently plagues fresh water supplies. In recent decades, desalination methods have improved via water-filtering architectures based on nanoporous graphene filters and artificial membranes integrated with biological water channels. Here, we report the auspicious performance (in simulations) of an alternative nanoporous desalination filter constructed from a MoS2 nanosheet. In striking contrast to graphene-based filters, we find that the "open" and "closed" states of the MoS2 filter can be regulated by the introduction of mechanical strain, yielding a highly tunable nanopore interface. By applying lateral strain to the MoS2 filter in our simulations, we see that the transition point between "open" and "closed" states occurs under tension that induces about 6% cross-sectional expansion in the membrane (6% strain); the open state of the MoS2 filter demonstrates high water transparency and a strong salt filtering capability even under 12% strain. Our results thus demonstrate the promise of a controllable nanoporous MoS2 desalination filter, wherein the morphology and size of the central nanopore can be precisely regulated by tensile strain. These findings support the design and proliferation of tunable nanodevices for filtration and other applications.

A novel form of β-strand assembly observed in Aβ(33-42) adsorbed onto graphene

Peptide assembly plays a seminal role in the fabrication of structural and functional architectures in cells. Characteristically, peptide assemblies are often dominated by β-sheet structures, wherein component molecules are connected by backbone hydrogen bonds in a parallel or an antiparallel fashion. While β-rich peptide scaffolds are implicated in an array of neurodegenerative diseases, the mechanisms by which toxic peptides assemble and mediate neuropathic effects are still poorly understood. In this work, we employ molecular dynamics simulations to study the adsorption and assembly of the fragment Aβ33-42 (taken from the Aβ-42 peptide widely associated with Alzheimer's disease) on a graphene surface. We observe that such Aβ33-42 fragments, which are largely hydrophobic in character, readily adsorb onto the graphitic surface and coalesce into a well-structured, β-strand-like assembly. Strikingly, the structure of such complex is quite unique: hydrophobic side-chains extend over the graphene surface and interact with adjacent peptides, yielding a well-defined mosaic of hydrophobic interaction patches. This ordered structure is markedly depleted of backbone hydrogen bonds. Hence, our simulation results reveal a distinct type of β-strand assembly, maintained by hydrophobic side-chain interactions. Our finding suggests the backbone hydrogen bond is no longer crucial to the peptide assembly. Further studies concerning whether such β-strand assembly can be realized in other peptide systems and in biologically-relevant contexts are certainly warranted.

Reduced Cytotoxicity of Graphene Nanosheets Mediated by Blood-Protein Coating

The advent and pending wide use of nanoscale materials urges a biosafety assessment and safe design of nanomaterials that demonstrate applicability to human medicine. In biological microenvironment, biomolecules will bind onto nanoparticles forming corona and endow nanoparticles new biological identity. Since blood-circulatory system will most likely be the first interaction organ exposed to these nanomaterials, a deep understanding of the basic interaction mechanisms between serum proteins and foreign nanoparticles may help to better clarify the potential risks of nanomaterials and provide guidance on safe design of nanomaterials. In this study, the adsorption of four high-abundance blood proteins onto the carbon-based nanomaterial graphene oxide (GO) and reduced GO (rGO) were investigated via experimental (AFM, florescence spectroscopy, SPR) and simulation-based (molecular dynamics) approaches. Among the proteins in question, we observe competitive binding to the GO surface that features a mélange of distinct packing modes. Our MD simulations reveal that the protein adsorption is mainly enthalpically driven through strong π-π stacking interactions between GO and aromatic protein residues, in addition to hydrophobic interactions. Overall, these results were in line with previous findings related to adsorption of serum proteins onto single-walled carbon nanotubes (SWCNTs), but GO exhibits a dramatic enhancement of adsorption capacity compared to this one-dimensional carbon form. Encouragingly, protein-coated GO resulted in a markedly less cytotoxicity than pristine and protein-coated SWCNTs, suggesting a useful role for this planar nanomaterial in biomedical applications.

Potential-based dynamical reweighting for Markov state models of protein dynamics

As simulators attempt to replicate the dynamics of large cellular components in silico, problems related to sampling slow, glassy degrees of freedom in molecular systems will be amplified manyfold. It is tempting to augment simulation techniques with external biases to overcome such barriers with ease; biased simulations, however, offer little utility unless equilibrium properties of interest (both kinetic and thermodynamic) can be recovered from the data generated. In this Article, we present a general scheme that harnesses the power of Markov state models (MSMs) to extract equilibrium kinetic properties from molecular dynamics trajectories collected on biased potential energy surfaces. We first validate our reweighting protocol on a simple two-well potential, and we proceed to test our method on potential-biased simulations of the Trp-cage miniprotein. In both cases, we find that equilibrium populations, time scales, and dynamical processes are reliably reproduced as compared to gold standard, unbiased data sets. We go on to discuss the limitations of our dynamical reweighting approach, and we suggest auspicious target systems for further application.

Dynamical phase transitions reveal amyloid-like states on protein folding landscapes

Developing an understanding of protein misfolding processes presents a crucial challenge for unlocking the mysteries of human disease. In this article, we present our observations of β-sheet-rich misfolded states on a number of protein dynamical landscapes investigated through molecular dynamics simulation and Markov state models. We employ a nonequilibrium statistical mechanical theory to identify the glassy states in a protein's dynamics, and we discuss the nonnative, β-sheet-rich states that play a distinct role in the slowest dynamics within seven protein folding systems. We highlight the fundamental similarity between these states and the amyloid structures responsible for many neurodegenerative diseases, and we discuss potential consequences for mechanisms of protein aggregation and intermolecular amyloid formation.

Entropy-production-driven oscillators in simple nonequilibrium networks

The development of tractable nonequilibrium simulation methods represents a bottleneck for efforts to describe the functional dynamics that occur within living cells. We here employ a nonequilibrium approach called the λ ensemble to characterize the dissipative dynamics of a simple Markovian network driven by an external potential. In the highly dissipative regime brought about by the λ bias, we observe a dynamical structure characteristic of cellular architectures: The entropy production drives a damped oscillator over state populations in the network. We illustrate the properties of such oscillations in weakly and strongly driven regimes, and we discuss how control structures associated with the "dynamical phase transition" in the system can be related to switches and oscillators in cellular dynamics.

Markov state models provide insights into dynamic modulation of protein function

Protein function is inextricably linked to protein dynamics. As we move from a static structural picture to a dynamic ensemble view of protein structure and function, novel computational paradigms are required for observing and understanding conformational dynamics of proteins and its functional implications. In principle, molecular dynamics simulations can provide the time evolution of atomistic models of proteins, but the long time scales associated with functional dynamics make it difficult to observe rare dynamical transitions. The issue of extracting essential functional components of protein dynamics from noisy simulation data presents another set of challenges in obtaining an unbiased understanding of protein motions. Therefore, a methodology that provides a statistical framework for efficient sampling and a human-readable view of the key aspects of functional dynamics from data analysis is required. The Markov state model (MSM), which has recently become popular worldwide for studying protein dynamics, is an example of such a framework.

In this Account, we review the use of Markov state models for efficient sampling of the hierarchy of time scales associated with protein dynamics, automatic identification of key conformational states, and the degrees of freedom associated with slow dynamical processes. Applications of MSMs for studying long time scale phenomena such as activation mechanisms of cellular signaling proteins has yielded novel insights into protein function. In particular, from MSMs built using large-scale simulations of GPCRs and kinases, we have shown that complex conformational changes in proteins can be described in terms of structural changes in key structural motifs or “molecular switches” within the protein, the transitions between functionally active and inactive states of proteins proceed via multiple pathways, and ligand or substrate binding modulates the flux through these pathways. Finally, MSMs also provide a theoretical toolbox for studying the effect of nonequilibrium perturbations on conformational dynamics. Considering that protein dynamics in vivo occur under nonequilibrium conditions, MSMs coupled with nonequilibrium statistical mechanics provide a way to connect cellular components to their functional environments. Nonequilibrium perturbations of protein folding MSMs reveal the presence of dynamically frozen glass-like states in their conformational landscape. These frozen states are also observed to be rich in β-sheets, which indicates their possible role in the nucleation of β-sheet rich aggregates such as those observed in amyloid-fibril formation. Finally, we describe how MSMs have been used to understand the dynamical behavior of intrinsically disordered proteins such as amyloid-β, human islet amyloid polypeptide, and p53. While certainly not a panacea for studying functional dynamics, MSMs provide a rigorous theoretical foundation for understanding complex entropically dominated processes and a convenient lens for viewing protein motions.

Inclusion of persistence length-based secondary structure in replica field theoretic models of heteropolymer freezing

The protein folding problem has long represented a "holy grail" in statistical physics due to its physical complexity and its relevance to many human diseases. While past theoretical work has yielded apt descriptions of protein folding landscapes, recent large-scale simulations have provided insights into protein folding that were impractical to obtain from early theories. In particular, the role that non-native contacts play in protein folding, and their relation to the existence of misfolded, β-sheet rich trap states on folding landscapes, has emerged as a topic of interest in the field. In this paper, we present a modified model of heteropolymer freezing that includes explicit secondary structural characteristics which allow observations of "intramolecular amyloid" states to be probed from a theoretical perspective. We introduce a variable persistence length-based energy penalty to a model Hamiltonian, and we illustrate how this modification alters the phase transitions present in the theory. We find, in particular, that inclusion of this variable persistence length increases both generic freezing and folding temperatures in the model, allowing both folding and glass transitions to occur in a more highly optimized fashion. We go on to discuss how these changes might relate to protein evolution, misfolding, and the emergence of intramolecular amyloid states.

Functional understanding of solvent structure in GroEL cavity through dipole field analysis

Solvent plays a ubiquitous role in all biophysical phenomena. Yet, just how the molecular nature of water impacts processes in biology remains an important question. While one can simulate the behavior of water near biomolecules such as proteins, it is challenging to gauge the potential structural role solvent plays in mediating both kinetic and equilibrium processes. Here, we propose an analysis scheme for understanding the nature of solvent structure at a local level. We first calculate coarse-grained dipole vector fields for an explicitly solvated system simulated through molecular dynamics. We then analyze correlations between these vector fields to characterize water structure under biologically relevant conditions. In applying our method to the interior of the wild type chaperonin complex GroEL+ES, along with nine additional mutant GroEL complexes, we find that dipole field correlations are strongly related to chaperonin function.

Emergence of glass-like behavior in Markov state models of protein folding dynamics

The extent to which glass-like kinetics govern dynamics in protein folding has been heavily debated. Here, we address the subject with an application of space-time perturbation theory to the dynamics of protein folding Markov state models. Borrowing techniques from the s-ensemble method, we argue that distinct active and inactive phases exist for protein folding dynamics, and that kinetics for specific systems can fall into either dynamical regime. We do not, however, observe a true glass transition in any system studied. We go on to discuss how these inactive and active phases might relate to general protein folding properties.

Derivation and assessment of phase-shifted, disordered vector field models for frustrated solvent interactions

The structure and properties of water at biological interfaces differ drastically from bulk due to effects including confinement and the presence of complicated charge distributions. This non-bulk-like behavior generally arises from water frustration, wherein all favorable interactions among water molecules cannot be simultaneously satisfied. While the frustration of interfacial water is ubiquitous in the cell, the role this frustration plays in mediating biophysical processes like protein folding is not well understood. To investigate the impact of frustration at interfaces, we here derive a general field theoretic model for the interaction of bulk and disordered vector fields at an embedded surface. We calculate thermodynamic and correlation functions for the model in two and three dimensions, and we compare our results to Monte Carlo simulations of lattice system analogs. In our analysis, we see that field-field cross correlations near the interface in the model give rise to a loss in entropy like that seen in glassy systems. We conclude by assessing our theory's utility as a coarse-grained model for water at polar biological interfaces.

Characterization and rapid sampling of protein folding Markov state model topologies

Markov state models (MSMs) have proven themselves to be effective statistical and quantitative models for understanding protein folding dynamics. As stochastic networks, MSMs allow for descriptions of parallel folding pathways and facilitate quantitative comparison to experiments conducted at the ensemble level. While this complex network structure is advantageous in many respects, a simple topological description of these graphs is elusive. In this paper, we compare a series of protein folding MSMs to the topology of the Cayley tree, a graph structure on which dynamics are intuitive. We go on to introduce and test new sampling schemes that have potential to improve automated model construction, a critical step toward making Markov state modeling more accessible to general users.

Structure of liquid Y3Al5O12 (YAG)

The total structure factor S(Q) and the radial distribution function G(r) of liquid Y3Al5O12 (YAG) were measured at 1770-2230 K by x-ray scattering from samples under containerless conditions in Ar and O2. Nominal coordination numbers are 4 for Al3+ and 6 for Y3+ ions. The G(r) has peaks at r approximately 1.8 A for Al-O, r approximately 2. 25 A for Y-O, and r approximately 3.3-3.6 A assigned to metal ions in adjacent AlO5-4 and YO9-6 polyhedral ions. Relative to the pure oxides, G(r) for molten YAG has smaller half-widths for the Al-O and Y-O peaks, and an increased sensitivity to temperature and the ambient gas composition.

Acquired immunity to Trichophyton mentagrophytes in thymus-grafted or peritoneal exudate cell-injected nude rats

Congenitally athymic "nude" (RNU/RNU) rats were grafted with syngeneic neonatal thymus glands and 30 days later cutaneously inoculated with Trichophyton mentagrophytes. Nude rats and thymus-grafted nude rats were susceptible to infection with T. mentagrophytes but only thymus-grafted nude rats cleared the dermatophytosis. Clearance of the fungal infection took twice as long (approximately 60 days) in thymus-grafted nude rats when compared with heterozygous euthymic (+/RNU) controls (approximately 35 days). Following clearance of the primary dermatophytosis, peritoneal exudate cells (PEC) were adoptively transferred from either thymus-grafted or heterozygous rats to nude rats. Eight of 12 PEC-recipient nude rats acquired the capacity to resolve T. mentagrophytes-induced dermatophytosis (mean clearance time was approximately 40 days). All heterozygous (+/RNU) rats, thymus-grafted nude rats (4/4), and 8 of 12 PEC-recipient nude rats, which had cleared a primary dermatophytosis also expressed delayed-type hypersensitivity and elevated serum antibody titers to trichophytin antigen. These results demonstrate that immunity to T. mentagrophytes can be acquired in congenitally athymic nude rats following thymus grafting or injection of PEC from syngeneic +/RNU rats; however, injection of PEC from trichophytin-sensitized +/RNU donor rats to nude recipient rats did not result in transfer of specific adoptive immunity to T. mentagrophytes. Interestingly, PEC transfer from non-sensitized +/RNU rats was comparable to thymus grafting in effecting clearance of T. mentagrophytes dermatophytosis. These results suggest that acquired immunity to T. mentagrophytes in the rat is T cell-dependent, and that the absence of functional T lymphocytes and not an epithelial defect results in chronic dermatophytosis in the nude rat.

The thymus dependency of acquired resistance to Trichophyton mentagrophytes dermatophytosis in rats

Congenitally athymic "nude" (RNU/RNU) rats and euthymic (+/RNU) rats were cutaneously inoculated with Trichophyton mentagrophytes. Dermatophytosis, as evidenced by erythema and scaling, was observed in both athymic and euthymic rats by day 7 postinfection. Macroscopic lesions in +/RNU rats became intensely erythematous (climax days 10-14), were limited in spread and alopecia (days 16-20), and healed with hair regrowth by day 35. In nude rats, however, erythema peaked early (days 8-10) and a persistent, mild erythema and scaling spread over the animals' backs. Viable T. mentagrophytes was cultured from the skin of all infected nude rats for the duration of each experiment (90 days), while +/RNU rats became culture-negative by day 35. Following clearance of primary lesions, +/RNU rats manifest a delayed-type hypersensitivity skin test response to soluble trichophytin and an accelerated cutaneous inflammation and enhanced resistance to reinfection. Although T. mentagrophytes primarily invaded the keratinized layers of the epidermis in both nude and +/RNU rats, hyphae and arthrospores were also observed within the nucleated layers of the internal root sheath of hair follicles. Our observations are consistent with the hypothesis that thymus-dependent cell-mediated immunity is required to limit cutaneous spread and terminate cutaneous T. mentagrophytes infection. This acquired immunity against T. mentagrophytes in +/RNU rats was characterized histologically by: (1) an intense inflammatory migration of lymphocytes, monocytes, and macrophages into the epidermis, dermis, and follicular epithelium; (2) hyperplasia of the epidermis and follicular epithelium; and (3) elimination of arthrospores and hyphae from T. mentagrophytes-infected skin.