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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.

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.

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.

Understanding how T cells discriminate self from non-self is a fundamental question with important implications for immunology, immunotherapy, and vaccine development. Presentation of peptides by human leukocyte antigen I (HLA-I) molecules is necessary but not sufficient for T cell recognition, and peptide features that dictate immunogenicity are obscure. Here, we develop a convolutional neural network that learns features governing peptide immunogenicity, integrating molecular dynamics and sequence representations of humans, pathogen, and tumor peptides presented by HLA-I. Our model identified structural and dynamical properties correlated with immunogenicity and yielded a highly accurate classification of peptides from pathogens versus humans. Furthermore, we applied our model to classify more challenging cancer neoantigens, and it successfully predicted immunogenic neoepitopes from patients with melanomas. These data demonstrate the utility of deep learning models built on molecular dynamics and reveal underlying properties that govern HLA-I peptide immunogenicity.

Objective Viral infections influence intracellular peptide repertoires available for presentation by HLA-I. Alterations in HLA-I/peptide complexes can modulate binding of killer immunoglobuline-like receptors (KIRs) and thereby the function of natural killer (NK) cells. Although multiple studies have provided evidence that HLA-I/KIR interactions play a role in HIV-1 disease progression, the consequence of HIV-1 infection for HLA-I/KIR interactions remain largely unknown. Design We determined changes in HLA-I presented peptides resulting from HIV-1-infection of primary human CD4 T cells and assessed the impact of changes in peptide repertoires on HLA-I/KIR interactions. Methods Liquid chromatography-coupled tandem mass spectrometry to identify HLA-I presented peptides, cell-based in-vitro assays to evaluate functional consequences of alterations in immunopeptidome and atomistic molecular dynamics simulations to confirm experimental data. Results A total of 583 peptides exclusively presented on HIV-1-infected cells were identified, of which only 0.2% represented HIV-1 derived peptides. Focusing on HLA-C*03 : 04/KIR2DL3 interactions, we observed that HLA-C*03 : 04-presented peptides derived from noninfected CD4 T cells mediated stronger binding of inhibitory KIR2DL3 than peptides derived from HIV-1-infected cells. Furthermore, the most abundant peptide presented by HLA-C*03 : 04 on noninfected CD4 T cells (VIYPARISL) mediated the strongest KIR2DL3-binding, while the most abundant peptide presented on HIV-1-infected cells (YAIQATETL) did not mediate KIR2DL3-binding. Molecular dynamics simulations of HLA-C*03 : 04/KIR2DL3 interactions in the context of these two peptides revealed that VIYPARISL significantly enhanced the HLA-C*03 : 04/peptide contact area to KIR2DL3 compared with YAIQATETL. Conclusion These data demonstrate that HIV-1 infection-induced changes in HLA-I-presented peptides can reduce engagement of inhibitory KIRs, providing a mechanism for enhanced activation of NK cells by virus-infected cells.

Nucleic acid aptamers hold great promise for therapeutic applications due to their favorable intrinsic properties, as well as high-throughput experimental selection techniques. Despite the utility of the systematic evolution of ligands by the exponential enrichment (SELEX) method for aptamer determination, complementary in silico aptamer design is highly sought after to facilitate virtual screening and increased understanding of important nucleic acid–protein interactions. Here, with a combined experimental and theoretical approach, we have developed two optimal epithelial cellular adhesion molecule (EpCAM) aptamers. Our structure-based in silico method first predicts their binding modes and then optimizes them for EpCAM with molecular dynamics simulations, docking, and free energy calculations. Our isothermal titration calorimetry experiments further confirm that the EpCAM aptamers indeed exhibit enhanced affinity over a previously patented nanomolar aptamer, EP23. Moreover, our study suggests that EP23 and the de novo designed aptamers primarily bind to EpCAM dimers (and not monomers, as hypothesized in previous published works), suggesting a paradigm for developing EpCAM-targeted therapies.

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.

Engineered nanomaterials promise to transform medicine at the bio–nano interface. However, it is important to elucidate how synthetic nanomaterials interact with critical biological systems before such products can be safely utilized in humans. Past evidence suggests that polyethylene glycol-functionalized (PEGylated) nanomaterials are largely biocompatible and elicit less dramatic immune responses than their pristine counterparts. We here report results that contradict these findings. We find that PEGylated graphene oxide nanosheets (nGO-PEGs) stimulate potent cytokine responses in peritoneal macrophages, despite not being internalized. Atomistic molecular dynamics simulations support a mechanism by which nGO-PEGs preferentially adsorb onto and/or partially insert into cell membranes, thereby amplifying interactions with stimulatory surface receptors. Further experiments demonstrate that nGO-PEG indeed provokes cytokine secretion by enhancing integrin β8-related signalling pathways. The present results inform that surface passivation does not always prevent immunological reactions to 2D nanomaterials but also suggest applications for PEGylated nanomaterials wherein immune stimulation is desired., Polyethylene glycol has been widely utilized to functionalize nanomaterials in order to improve their biocompatibility. Here, the authors demonstrate that PEGylated nano-graphene oxide can elicit an inflammatory response, contradicting current literature.

Lipases, which can be immobilized and reused for many reaction cycles, are important enzymes with many industrial applications. A key challenge in lipase immobilization for catalysis is to open the lipase lid and maintain it in an open conformation in order to expose its active site. Here we have designed “tailor-made” graphene-based nanosupports for effective lipase (QLM) immobilization through molecular engineering, which is in general a grand challenge to control biophysicochemical interactions at the nano–bio interface. It was observed that increasing hydrophobic surface increased lipase activity due to opening of the helical lid present on lipase. The molecular mechanism of lid opening revealed in molecular dynamics simulations highlights the role of hydrophobic interactions at the interface. We demonstrated that the open and active form of lipase can be achieved and tuned with an optimized activity through chemical reduction of graphene oxide. This research is a major step toward designing nanomater...

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 “...

HIV controllers (HCs) are individuals who can naturally control HIV infection, partially due to potent HIV-specific CD8+ T cell responses. Here, we examined the hypothesis that superior function of CD8+ T cells from HCs is encoded by their T cell receptors (TCRs). We compared the functional properties of immunodominant HIV-specific TCRs obtained from HLA-B*2705 HCs and chronic progressors (CPs) following expression in primary T cells. T cells transduced with TCRs from HCs and CPs showed equivalent induction of epitope-specific cytotoxicity, cytokine secretion, and antigen-binding properties. Transduced T cells comparably, albeit modestly, also suppressed HIV infection in vitro and in humanized mice. We also performed extensive molecular dynamics simulations that provided a structural basis for similarities in cytotoxicity and epitope cross-reactivity. These results demonstrate that the differential abilities of HIV-specific CD8+ T cells from HCs and CPs are not genetically encoded in the TCRs alone and must depend on additional factors.

Life is fundamentally a nonequilibrium phenomenon. At the expense of dissipated energy, living things perform irreversible processes that allow them to propagate and reproduce. Within cells, evolution has designed nanoscale machines to do meaningful work with energy harnessed from a continuous flux of heat and particles. As dictated by the Second Law of Thermodynamics and its fluctuation theorem corollaries, irreversibility in nonequilibrium processes can be quantified in terms of how much entropy such dynamics produce. In this work, we seek to address a fundamental question linking biology and nonequilibrium physics: can the evolved dissipative pathways that facilitate biomolecular function be identified by their extent of entropy production in general relaxation processes? We here synthesize massive molecular dynamics simulations, Markov state models (MSMs), and nonequilibrium statistical mechanical theory to probe dissipation in two key classes of signaling proteins: kinases and G-protein-coupled receptors (GPCRs). Applying machinery from large deviation theory, we use MSMs constructed from protein simulations to generate dynamics conforming to positive levels of entropy production. We note the emergence of an array of peaks in the dynamical response (transient analogs of phase transitions) that draw the proteins between distinct levels of dissipation, and we see that the binding of ATP and agonist molecules modifies the observed dissipative landscapes. Overall, we find that dissipation is tightly coupled to activation in these signaling systems: dominant entropy-producing trajectories become localized near important barriers along known biological activation pathways. We go on to classify an array of equilibrium and nonequilibrium molecular switches that harmonize to promote functional dynamics.

Gold nanoclusters (AuNCs) can be primed for biomedical applications through functionalization with peptide coatings. Often anchored by thiol groups, such peptide coronae not only serve as passivators but can also endow AuNCs with additional bioactive properties. In this work, we use molecular dynamics simulations to study the structure of a tridecapeptide-coated Au25 cluster and its subsequent interactions with the enzyme thioredoxin reductase 1, TrxR1. We find that, in isolation, both the distribution and conformation of the coating peptides fluctuate considerably. When the coated AuNC is placed around TrxR1, however, the motion of the highly charged peptide coating (+5e/peptide) is quickly biased by electrostatic attraction to the protein; the asymmetric coating acts to guide the nanocluster's diffusion toward the enzyme's negatively charged active site. After the AuNC comes into contact with TrxR1, its peptide corona spreads over the protein surface to facilitate stable binding with protein. Though individual salt bridge interactions between the tridecapeptides and TrxR1 are transient in nature, the cooperative binding of the peptide-coated AuNC is very stable, overall. Interestingly, the biased corona peptide motion, the spreading and the cooperation between peptide extensions observed in AuNC binding are reminiscent of bacterial stimulus-driven approaching and adhesion mechanisms mediated by cilia. The prevailing AuNC binding mode we characterize also satisfies a notable hydrophobic interaction seen in the association of thioredoxin to TrxR1, providing a possible explanation for the AuNC binding specificity observed in experiments. Our simulations thus suggest this peptide-coated AuNC serves as an adept thioredoxin mimic that extends an array of auxiliary structural components capable of enhancing interactions with the target protein in question.

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.

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.

In the face of chronic cancers and protracted viral infections, human immune cells are known to adopt an exhausted state in which their effector functions are lost. In recent years, a number of inhibitory receptors have been connected to the immune cell exhaustion phenotype; furthermore, ligands capable of activating these receptors have been discovered. The molecular mechanisms by which these ligands affect the exhausted states of immune cells, however, are largely unknown. Here, we present the results of molecular dynamics simulations of one potential exhaustion-associated system: the complex of human inhibitory receptor TIM3 (hTIM3) and its ligand phosphatidylserine (PSF). We find that PSF fundamentally alters the electrostatic environment within hTIM3’s Ca2+ binding site, facilitating the formation of a salt bridge and freeing a tyrosine-containing strand. This liberated tyrosine then collapses into a nearby hydrophobic pocket, anchoring a modified conformational ensemble typified by a β-strand rearrangement. The “electrostatic switching/hydrophobic anchoring” mechanism of conformational modulation reported here suggests a new type of process by which TIM3 activation might be achieved. This work also highlights strategies by which PSF-mediated conformational change could be controlled, either through administration of small molecules, execution of mutations, or modification of receptor phosphorylation states.

Gadolinium-containing fullerenol Gd@C

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.

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.

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 (MSMs). 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.

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.

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.

We report a detailed computational and experimental study of the interaction of single-walled carbon nanotubes (SWCNTs) with the drug-metabolizing cytochrome P450 enzyme, CYP3A4. Dose-dependent inhibition of CYP3A4-mediated conversion of the model compound, testosterone, to its major metabolite, 6β-hydroxy testosterone was noted. Evidence for a direct interaction between SWCNTs and CYP3A4 was also provided. The inhibition of enzyme activity was alleviated when SWCNTs were pre-coated with bovine serum albumin. Furthermore, covalent functionalization of SWCNTs with polyethylene glycol (PEG) chains mitigated the inhibition of CYP3A4 enzymatic activity. Molecular dynamics simulations suggested that inhibition of the catalytic activity of CYP3A4 is mainly due to blocking of the exit channel for substrates/products through a complex binding mechanism. This work suggests that SWCNTs could interfere with metabolism of drugs and other xenobiotics and provides a molecular mechanism for this toxicity. Our study also suggests means to reduce this toxicity, eg., by surface modification.

Recent studies have indicated that graphene and its derivative graphene oxide (GO) engage in a wide range of antibacterial activities with limited toxicity to human cells. Here, we systematically evaluate the dependence of GO toxicity on the size of the nanoparticles used in treatments: we compare the cytotoxic effects of graphene quantum dots (GQDs, μm). We synthesize the results of bacterial colony count assays and SEM-based observations of morphological changes to assess the antibacterial properties that these GOs bring into effect againstE. coli. We also use Live/Dead assays and morphological analysis to investigate changes to mammalian (Murine macrophage-like Raw 264.7) cells induced by the presence of the various GO particle types. Our results demonstrate that LGOs, SGOs, and GQDs possess antibacterial activities and cause mammalian cell cytotoxicity at descending levels of potency. Placing our observations in the context of previous simulation results, we suggest that both the lateral size and surface area of GO particles contribute to cytotoxic effects. We hope that the size dependence elucidated here provides a useful schematic for tuning GO-cell interactions in biomedical applications.

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 ({\alpha}-helix, \b{eta}-hairpin, {\alpha}-helix bundle, and {\alpha}/\b{eta}-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 in influenced by protein size and the flexibility of the native hydrophobic core. For \b{eta}-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 \b{eta}-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., Comment: 7 pages, 5 figures

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.

The adsorption of proteins onto carbon-based nanomaterials (CBNs) is dictated by hydrophobic and π-π interactions between aliphatic and aromatic residues and the conjugated CBN surface. Accordingly, protein adsorption is highly sensitive to topological constraints imposed by CBN surface structure; in particular, adsorption capacity is thought to increase as the incident surface curvature decreases. In this work, we couple Molecular Dynamics (MD) simulations with fluorescence spectroscopy experiments to characterize this curvature dependence in detail for the model protein bovine serum albumin (BSA). By studying BSA adsorption onto carbon nanotubes of increasing radius (featuring descending local curvatures) and a flat graphene sheet, we confirm that adsorption capacity is indeed enhanced on flatter surfaces. Naïve fluorescence experiments featuring multi-walled carbon nanotubes (MWCNTs), however, conform to an opposing trend. To reconcile these observations, we conduct additional MD simulations with MWCNTs that match those prepared in experiments; such simulations indicate that increased mass to surface area ratios in multi-walled systems explain the observed discrepancies. In reduction, our work substantiates the inverse relationship between protein adsorption capacity and surface curvature and further demonstrates the need for subtle consideration in experimental and simulation design.

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 $\ensuremath{\lambda}$ 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 $\ensuremath{\lambda}$ 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.

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.

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.

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.

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.

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.

Ab initio calculations including configuration interaction (CI) are reported for the classical (linear) and the nonclassical (bridged) structures of the vinyl cation (C2H3+), and for the minimum energy path for the rearrangement from one to the other. A ’’double‐zeta plus polarization’’ one‐particle basis of contracted Gaussian functions was used to construct an n‐particle space for the CI calculation consisting of the ground state self‐ consistent field (SCF) configuration and all single and double excitations from it. Critical evaluation of the calculations leads to the chemical predictions that (i) the two structures have the same energy, to within ∼1–2 kcal/mole with probably the bridged structure more stable, (ii) that the minimum energy path for rearrangement keeps a planar configuration of the atomic nuclei, and (iii) that the barrier to rearrangement is low, less than ∼1–3 kcal/mole. The calculations include geometry optimization along the rearrangement path as well as the effects of electronic co...

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.