Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants.

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of ß-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.

An In-depth Proteomic Map of Leishmania donovani Isolate from Post Kala-azar Dermal Leishmaniasis (PKDL) Patient.

BACKGROUND: The trypanosomatid protozoan parasite Leishmania donovani is the etiological agent of visceral leishmaniasis (VL) or kala-azar. The patients that have undergone treatment may still harbor the parasite and in a small fraction of the patients the disease re-erupts in the form of post kala-azar dermal leishmaniasis (PKDL). PKDL is a pathological condition found to be intermediate between VL and complete cure of VL. The PKDL disease progression is determined by the host immune response to L. donovani. The majority of the proteomic studies on L. donovani till date have been undertaken on parasites either isolated from kala-azar patients or on established laboratory strains of L. donovani. However, no proteomic information is available on the cutaneous localized isolates of L. donovani from PKDL patients. METHODS: The promastigote stage of L. donovani isolate from PKDL patient was cultured and harvested. The cell lysates were trypsin digested, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The LC-MS/MS raw data were analyzed on Proteome Discoverer. Further bioinformatics analysis was carried out. RESULTS: In the present, we have used high-resolution mass spectrometry to map the global proteome of a L. donovani isolate from PKDL patient. This in-depth study resulted in the identification of 5537 unique proteins from PKDL isolate of L. donovani which covered 64% of its proteome. OUTCOME: This study also identified proteins previously shown to be upregulated in PKDL L. donovani. This is the most in-depth proteome of Leishmania donovani parasite till date.

Functional nanomaterials, synergisms, and biomimicry for environmentally benign marine antifouling technology.

Marine biofouling remains one of the key challenges for maritime industries, both for seafaring and stationary structures. Currently used biocide-based approaches suffer from significant drawbacks, coming at a significant cost to the environment into which the biocides are released, whereas novel environmentally friendly approaches are often difficult to translate from lab bench to commercial scale. In this article, current biocide-based strategies and their adverse environmental effects are briefly outlined, showing significant gaps that could be addressed through advanced materials engineering. Current research towards the use of natural antifouling products and strategies based on physio-chemical properties is then reviewed, focusing on the recent progress and promising novel developments in the field of environmentally benign marine antifouling technologies based on advanced nanocomposites, synergistic effects and biomimetic approaches are discussed and their benefits and potential drawbacks are compared to existing techniques.

Understanding the role of hydrophobic patches in protein disaggregation.

Protein folding is a very complex process and, so far, the mechanism of folding still intrigues the research community. Despite a large conformational space available (O(1047) for a 100 amino acid residue), most proteins fold into their native state within a very short time. While small proteins fold relatively fast (a few microseconds) large globular proteins may take as long as several milliseconds to fold. During the folding process, the protein synthesized in the ribosome is exposed to the crowded environment of the cell and is easily prone to misfolding and aggregation due to interactions with other proteins or biomacromolecules present within the cell. These large proteins, therefore, rely on chaperones for their folding and repair. Chaperones are known to have hydrophobic patchy domains that play a crucial role in shielding the protein against misfolding and disaggregation of aggregated proteins. In the current article, Monte Carlo simulations carried out in the framework of the hydrophobic-polar (H-P) lattice model indicate that hydrophobic patchy domains drastically reduce the inter-protein interactions and are efficient in disaggregating proteins. The effectiveness of the disaggregation depends on the size and distribution of these patches on the surface and also on the strength of the interaction between the protein and the surface. Further, our results indicate that when the patch is complementary to the exposed hydrophobic patch of the protein, protein disaggregation is accompanied by stabilization of the protein even relative to its bulk behavior due to favorable protein-surface interactions. We believe that these findings shed light on the role of the class of chaperones known as heat shock proteins (Hsps) on protein disaggregation and refolding.

Predictive Analytics for Retention in Care in an Urban HIV Clinic.

Consistent medical care among people living with HIV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care.

In-Situ Surface Modification of Terpinen-4-ol Plasma Polymers for Increased Antibacterial Activity.

Surface modification of thin films is often performed to enhance their properties. In this work, in situ modification of Terpinen-4-ol (T4) plasma polymer is carried out via simultaneous surface functionalization and nanoparticle immobilization. Terpinen-4-ol plasma polymers surface were decorated with a layer of ZnO nanoparticles in an oxygen plasma environment immediately after polymer deposition. A combination of hydrophilic modification and ZnO nanoparticle functionalization of the T4 polymer surface led to an enhancement in antibacterial properties by factor of 3 (from 0.75 to 0.25 CFU.mm-2). In addition, ZnO nanoparticle-modified coatings demonstrated improved UV absorbing characteristics in the region of 300-400 nm by 60% relative to unmodified coatings. The ZnO modified coatings were transparent in the visible region of 400-700 nm. The finding points towards the potential use of ZnO nanoparticle-modified T4 plasma polymers as optically transparent UV absorbing coatings.

Electrically Insulating Plasma Polymer/ZnO Composite Films.

In this report, the electrical properties of plasma polymer films functionalized with ZnO nanoparticles were investigated with respect to their potential applications in biomaterials and microelectronics fields. The nanocomposite films were produced using a single-step method that combines simultaneous plasma polymerization of renewable geranium essential oil with thermal decomposition of zinc acetylacetonate Zn(acac)2. The input power used for the deposition of composites were 10 W and 50 W, and the resulting composite structures were abbreviated as Zn/Ge 10 W and Zn/Ge 50 W, respectively. The electrical properties of pristine polymers and Zn/polymer composite films were studied in metal-insulator-metal structures. At a quantity of ZnO of around ~1%, it was found that ZnO had a small influence on the capacitance and dielectric constants of thus-fabricated films. The dielectric constant of films with smaller-sized nanoparticles exhibited the highest value, whereas, with the increase in ZnO particle size, the dielectric constant decreases. The conductivity of the composites was calculated to be in the in the range of 10-14-10-15 Ω-1 m-1, significantly greater than that for the pristine polymer, the latter estimated to be in the range of 10-16-10-17 Ω-1 m-1.

Surface Patterning for Enhanced Protein Stability: Insights from Molecular Simulations.

Reduced activity of enzymes upon immobilization is a major challenge for the industrial use of enzymes. Enzyme-surface interactions and interactions between the immobilized enzymes are thought of as primary reasons for the reduced activity. In the current paper, we study the thermal and structural stability of proteins on a patterned hydrophobic surface in the framework of a hydrophobic-polar lattice model. Our results indicate that, while a homogeneous hydrophobic surface denatures the proteins, carefully patterned surfaces can dramatically increase the stability of adsorbed proteins. The size, shape, and the distance between surface patterns play a significant role in determining the stability of proteins. When the spacing between the patterns is large, maximum stability is observed when the surface pattern is complementary to the exposed hydrophobic domain of the protein, while at smaller spacing, patterns with lower hydrophobicity stabilize the protein more compared to the complementary pattern. The findings from the paper can be rationalized to design novel enzyme-specific surfaces for immobilization with enhanced enzymatic activity.

Role of confinement, molecular connectivity and flexibility in entropic driven surface segregation of polymer-colloid mixtures.

Relative surface affinity between polymers and colloids is leveraged in many applications like filtration, adhesion, bio-sensing, etc. The surface affinity is governed by both enthalpic (relative interactions between the species and surface) and entropic (excluded volume) effects. Neglecting enthalpic effects, i.e. for purely athermal systems, entropy is the only driving force that controls the surface affinity of the species in a binary or multi-component mixture. Many intensive (relative size of colloids, chain length, equilibrium bond angle, chain flexibility) and extensive (confinement, temperature) factors can dramatically change the entropy of the system and thus enhance or decrease the surface affinities of the constituent species. In this article, we use coarse grained metropolis Monte Carlo simulations to delineate the role of these factors in entropic surface segregation in a binary mixture of polymers and colloids. At low number densities, excluded volume effects are negligible and we do not observe any entropic driven surface segregation. Therefore our system of interest is binary polymer-colloid mixtures at moderate to high number densities where excluded volume effects are predominant. Our results indicate that for flexible polymer chains, the surface is always enriched with colloids compared to polymers and this effect is enhanced for longer polymer chains. The configurational entropy of the flexible polymers is significantly reduced near the surface and therefore they prefer to stay in the bulk (away from the surface). However this behavior can be completely reversed by introducing a large degree of confinement and making the chains relatively rigid (less flexible). Our results show that polymer segregation of long stiff chains in slit pore geometry is driven by nematization near the surface while looping of polymers is observed under a large degree of confinement. We observe that for longer polymer chains with an equilibrium bond angle (θ = π), both confinement and chain stiffness enhance the surface segregation of polymers relative to colloids. However, the segregation behavior within a confinement is dependent on the polymer chain length. The surface segregation of polymers is dramatically decreased for chains with equilibrium bond angles independent of chain length and flexibility due to excluded volume effects and inefficient packing near the surface.

Correction: Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways.

[This corrects the article DOI: 10.1371/journal.pcbi.1004568.].

Eco-friendly nanocomposites derived from geranium oil and zinc oxide in one step approach.

Nanocomposites offer attractive and cost-effective thin layers with superior properties for antimicrobial, drug delivery and microelectronic applications. This work reports single-step plasma-enabled synthesis of polymer/zinc nanocomposite thin films via co-deposition of renewable geranium essential oil-derived polymer and zinc nanoparticles produced by thermal decomposition of zinc acetylacetonate. The chemical composition, surfaces characteristics and antimicrobial performance of the designed nanocomposite were systematically investigated. XPS survey proved the presence of ZnO in the matrix of formed polymers at 10 W and 50 W. SEM images verified that the average size of a ZnO nanoparticle slightly increased with an increase in the power of deposition, from approximately 60 nm at 10 W to approximately 80 nm at 50 W. Confocal scanning laser microscopy images showed that viability of S. aureus and E.coli cells significantly reduced on surfaces of ZnO/polymer composites compared to pristine polymers. SEM observations further demonstrated that bacterial cells incubated on Zn/Ge 10 W and Zn/Ge 50 W had deteriorated cell walls, compared to pristine polymers and glass control. The release of ZnO nanoparticles from the composite thin films was confirmed using ICP measurements, and can be further controlled by coating the film with a thin polymeric layer. These eco-friendly nanocomposite films could be employed as encapsulation coatings to protect relevant surfaces of medical devices from microbial adhesion and colonization.

Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs.

The conformational dynamics of proteins is rarely used in methodologies used to predict the impact of genetic mutations due to the paucity of three-dimensional protein structures as compared to the vast number of available sequences. Until now a three-dimensional (3D) structure has been required to predict the conformational dynamics of a protein. We introduce an approach that estimates the conformational dynamics of a protein, without relying on structural information. This de novo approach utilizes coevolving residues identified from a multiple sequence alignment (MSA) using Potts models. These coevolving residues are used as contacts in a Gaussian network model (GNM) to obtain protein dynamics. B-factors calculated using sequence-based GNM (Seq-GNM) are in agreement with crystallographic B-factors as well as theoretical B-factors from the original GNM that utilizes the 3D structure. Moreover, we demonstrate the ability of the calculated B-factors from the Seq-GNM approach to discriminate genomic variants according to their phenotypes for a wide range of proteins. These results suggest that protein dynamics can be approximated based on sequence information alone, making it possible to assess the phenotypes of nSNVs in cases where a 3D structure is unknown. We hope this work will promote the use of dynamics information in genetic disease prediction at scale by circumventing the need for 3D structures.

Plant Secondary Metabolite-Derived Polymers: A Potential Approach to Develop Antimicrobial Films.

The persistent issue of bacterial and fungal colonization of artificial implantable materials and the decreasing efficacy of conventional systemic antibiotics used to treat implant-associated infections has led to the development of a wide range of antifouling and antibacterial strategies. This article reviews one such strategy where inherently biologically active renewable resources, i.e., plant secondary metabolites (PSMs) and their naturally occurring combinations (i.e., essential oils) are used for surface functionalization and synthesis of polymer thin films. With a distinct mode of antibacterial activity, broad spectrum of action, and diversity of available chemistries, plant secondary metabolites present an attractive alternative to conventional antibiotics. However, their conversion from liquid to solid phase without a significant loss of activity is not trivial. Using selected examples, this article shows how plasma techniques provide a sufficiently flexible and chemically reactive environment to enable the synthesis of biologically-active polymer coatings from volatile renewable resources.

Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine.

Aluminium alloyed iron-silicide/silicon solar cells: A simple approach for low cost environmental-friendly photovoltaic technology.

This work demonstrates the fabrication of silicide/silicon based solar cell towards the development of low cost and environmental friendly photovoltaic technology. A heterostructure solar cells using metallic alpha phase (α-phase) aluminum alloyed iron silicide (FeSi(Al)) on n-type silicon is fabricated with an efficiency of 0.8%. The fabricated device has an open circuit voltage and fill-factor of 240 mV and 60%, respectively. Performance of the device was improved by about 7 fold to 5.1% through the interface engineering. The α-phase FeSi(Al)/silicon solar cell devices have promising photovoltaic characteristic with an open circuit voltage, short-circuit current and a fill factor (FF) of 425 mV, 18.5 mA/cm(2), and 64%, respectively. The significant improvement of α-phase FeSi(Al)/n-Si solar cells is due to the formation p(+-)n homojunction through the formation of re-grown crystalline silicon layer (~5-10 nm) at the silicide/silicon interface. Thickness of the regrown silicon layer is crucial for the silicide/silicon based photovoltaic devices. Performance of the α-FeSi(Al)/n-Si solar cells significantly depends on the thickness of α-FeSi(Al) layer and process temperature during the device fabrication. This study will open up new opportunities for the Si based photovoltaic technology using a simple, sustainable, and los cost method.

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways.

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.

Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics.

The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled.

The Role of Conformational Dynamics and Allostery in the Disease Development of Human Ferritin.

Determining the three-dimensional structure of myoglobin, the first solved structure of a protein, fundamentally changed the way protein function was understood. Even more revolutionary was the information that came afterward: protein dynamics play a critical role in biological functions. Therefore, understanding conformational dynamics is crucial to obtaining a more complete picture of protein evolution. We recently analyzed the evolution of different protein families including green fluorescent proteins (GFPs), ß-lactamase inhibitors, and nuclear receptors, and we observed that the alteration of conformational dynamics through allosteric regulation leads to functional changes. Moreover, proteome-wide conformational dynamics analysis of more than 100 human proteins showed that mutations occurring at rigid residue positions are more susceptible to disease than flexible residue positions. These studies suggest that disease-associated mutations may impair dynamic allosteric regulations, leading to loss of function. Thus, in this study, we analyzed the conformational dynamics of the wild-type light chain subunit of human ferritin protein along with the neutral and disease forms. We first performed replica exchange molecular dynamics simulations of wild-type and mutants to obtain equilibrated dynamics and then used perturbation response scanning (PRS), where we introduced a random Brownian kick to a position and computed the fluctuation response of the chain using linear response theory. Using this approach, we computed the dynamic flexibility index (DFI) for each position in the chain for the wild-type and the mutants. DFI quantifies the resilience of a position to a perturbation and provides a flexibility/rigidity measurement for a given position in the chain. The DFI analysis reveals that neutral variants and the wild-type exhibit similar flexibility profiles in which experimentally determined functionally critical sites act as hinges in controlling the overall motion. However, disease mutations alter the conformational dynamic profile, making hinges more loose (i.e., softening the hinges), thus impairing the allosterically regulated dynamics.

Rigidity loss in disordered systems: three scenarios.

We reveal significant qualitative differences in the rigidity transition of three types of disordered network materials: randomly diluted spring networks, jammed sphere packings, and stress-relieved networks that are diluted using a protocol that avoids the appearance of floppy regions. The marginal state of jammed and stress-relieved networks are globally isostatic, while marginal randomly diluted networks show both overconstrained and underconstrained regions. When a single bond is added to or removed from these isostatic systems, jammed networks become globally overconstrained or floppy, whereas the effect on stress-relieved networks is more local and limited. These differences are also reflected in the linear elastic properties and point to the highly effective and unusual role of global self-organization in jammed sphere packings.

Ring statistics of silica bilayers.

The recent synthesis and characterisation of bilayers of vitreous silica has produced valuable new information on ring sizes and distributions. In this paper, we compare the ring statistics of experimental samples with computer generated samples. The average ring size is fixed at six by topology, but the width, skewness and other moments of the distribution of ring edges are characteristics of particular samples. We examine the Aboav-Weaire law that quantifies the propensity of smaller rings to be adjacent to larger rings, and find similar results for available experimental samples which however differ somewhat from computer-generated bilayers. We introduce a new law for the areas of rings of various sizes.