Identification Of Structural Features Research Articles

A deep learning-based optimization framework of two-dimensional hydrofoils for tidal turbine rotor design

Convolutional Neural Network (CNN) is a commonly used deep learning algorithm due to its excellent capability in identification of structural features and parameter predictions in many domains. In addition, it has incomparable advantages of high analysis efficiency and generalization performance. However, it has been questioned in the research community on whether CNN method can be applied to effectively predict hydrofoil performance for hydraulic machinery design. To this end, this paper demonstrates a novel optimization platform using CNN for hydrofoil performance prediction, which can effectively and accurately obtain the optimized hydrofoils results in aid of the structural design of tidal turbine. The prediction model uses signed distance function (SDF) to graphically represent the shape of the hydrofoil which is subsequently imported into CNN as the network input. Three different hydrofoil performance properties including the lift coefficient, drag coefficient and pressure coefficient of surface are used as output to train the neural network. In order to guarantee the accuracy of the forecasting model, Computational Fluid Dynamics (CFD) method characterized by high precision is applied to generate the dataset for neural network training. The results show that it can accurately predict the hydrodynamic parameters at a lower angle of attack with extremely short period of time. On top of the established hydrofoil performance prediction model, the Pareto curve of the optimized hydrofoils is obtained and applied to the design of 3D horizontal axis tidal turbine (HATT) blades. It proves that the optimization platform is effective and versatile in a manner that achieves both accurate and rapid prediction/optimization of the hydrofoil, which greatly facilitates to apply it for the tidal turbine rotor design.

Evidence from Network Analysis application to Innovation Systems and Quintuple Helix

The debate on innovation systems has recently been improved through the development of more dynamic and forward-looking approaches. Scholars and researchers have started to reserve attention for System Dynamics, a widely used methodology for modelling complex socio-economic systems. Moreover, recent innovation models such as the Quintuple Helix framework call for an effort for better understanding and forecasting relationships among ecology, knowledge and innovation, deeply analysing synergies among economy, society, and democracy. This paper focuses on the relationships between the Quintuple Helix and System Dynamics modelling from a quantitative perspective. It discusses how the application of Network Analysis metrics advances the understanding of an innovation system mapped with a Quintuple Helix model. Using Global Innovation Index, a Casual Loop Diagram has been designed representing the process of knowledge generation and innovation dissemination within the Quintuple Helix model. Identification of structural features within Causal Loop Diagrams through Network Analysis is likely to provide new insights into the emergent properties of an innovation system seen as a complex system. Meanwhile, analysing central drivers has the potential to identify leverage points. Results obtained show the benefits of coupling Network Analysis with System Dynamics modelling and valuable evidence of relationships among helices.

In silico functional prediction of hypothetical proteins from the core genome of Corynebacterium pseudotuberculosis biovar ovis.

Corynebacterium pseudotuberculosis is a pathogen of veterinary relevance diseases, being divided into two biovars: equi and ovis; causing ulcerative lymphangitis and caseous lymphadenitis, respectively. The isolation and sequencing of C. pseudotuberculosis biovar ovis strains in the Northern and Northeastern regions of Brazil exhibited the emergence of this pathogen, which causes economic losses to small ruminant producers, and condemnation of carcasses and skins of animals. Through the pan-genomic approach, it is possible to determine and analyze genes that are shared by all strains of a species—the core genome. However, many of these genes do not have any predicted function, being characterized as hypothetical proteins (HP). In this study, we considered 32 C. pseudotuberculosis biovar ovis genomes for the pan-genomic analysis, where were identified 172 HP present in a core genome composed by 1255 genes. We are able to functionally annotate 80 sequences previously characterized as HP through the identification of structural features as conserved domains and families. Furthermore, we analyzed the physicochemical properties, subcellular localization and molecular function. Additionally, through RNA-seq data, we investigated the differential gene expression of the annotated HP. Genes inserted in pathogenicity islands had their virulence potential evaluated. Also, we have analyzed the existence of functional associations for their products based on protein–protein interaction networks, and perform the structural prediction of three targets. Due to the integration of different strategies, this study can underlie deeper in vitro researches in the characterization of these HP and the search for new solutions for combat this pathogen.

Image processing of biological liquids films for medical diagnostics

In this paper, the development of smart medical autonomous technology is considered. An example of a smart medical autonomous distributed system for diagnostics is also discussed. To develop this system for medical image analysis we review several processing methods. The use of the cuneiform dehydration method for medical diagnosis is considered. The experimental results obtained for blood serum dehydrated films are presented. The author proposes an algorithm for the primary identification of structures formed in the films and their use for automated detection of various structures for diagnostic purposes. The paper describes the first stage of image processing, i.e. the selection of filtering types for the correct identification of structural features and characteristics of the images. The results of filtering and some computational results of various types of structures in the films are presented.

Coordinatively Unsaturated Metal-Nitrogen Active Sites at Twisted Surfaces in Metallic Porous Nitride Single Crystals Delivering Enhanced Electrocatalysis Activity.

To create active sites on surfaces, the identification of structural features that could confine the local-defect structure in the lattice is required. Porous nitride single crystals, combining the advantages of porosity and structural coherence, provide the possibility to create coordinatively unsaturated metal-nitrogen active sites confined on surfaces. For the first time, ordered active sites and tailor the atomically resolved Fe-N and Co-N local structures are created through control of the unsaturated nitrogen coordination at twisted surfaces in porous single-crystalline Fen N (n=2-4) and Con N (n=1-3) nanocubes. The precise tailoring of the electronic structures of these coordinatively unsaturated active sites therefore engineer the catalytic activity. Optimum electrocatalysis performances are observed with the porous Fe4 N and Co3 N nanocubes with highly unsaturated nitrogen coordination for selective nitrate reduction to ammonia and nitrobenzene amination to aminobenzene, while the structural coherence of these porous nitride single crystals delivers excellent durability.

Real-time Crystal Growth Visualization and Quantification by Energy-Resolved Neutron Imaging

Energy-resolved neutron imaging is investigated as a real-time diagnostic tool for visualization and in-situ measurements of “blind” processes. This technique is demonstrated for the Bridgman-type crystal growth enabling remote and direct measurements of growth parameters crucial for process optimization. The location and shape of the interface between liquid and solid phases are monitored in real-time, concurrently with the measurement of elemental distribution within the growth volume and with the identification of structural features with a ~100 μm spatial resolution. Such diagnostics can substantially reduce the development time between exploratory small scale growth of new materials and their subsequent commercial production. This technique is widely applicable and is not limited to crystal growth processes.

Post-translational Claisen Condensation and Decarboxylation en Route to the Bicyclic Core of Pantocin A.

Pantocin A (PA) is a member of the growing family of ribosomally encoded and post-translationally modified peptide natural products (RiPPs). PA is much smaller than most known RiPPs, a tripeptide with a tight bicyclic core that appears to be cleaved from the middle of a larger 30-residue precursor peptide. We show here that the enzyme PaaA catalyzes the double dehydration and decarboxylation of two glutamic acid residues in the 30-residue precursor PaaP. Further truncates of PaaP leader and follower peptide sequences demonstrate the different impacts of these two regions on PaaA-mediated tailoring and delineate an essential role for the follower sequence in the decarboxylation step. The crystal structure of apo PaaA is reported, allowing identification of structural features that set PaaA apart from other homologous enzymes that typically do not catalyze such extended post-translational chemistry. Together, these data reveal how additional chemistry can be extracted from a ubiquitous enzyme family toward ribosomally derived peptide natural product biosynthesis and suggest that more examples of such enzymes likely exist in untapped genomic space.

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis.

Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparseContact: suleiman.khan@aalto.fi or samuel.kaski@aalto.fiSupplementary Information: Supplementary data are available at Bioinformatics online.

Mechanism and inhibition of 1-deoxy-d-xylulose-5-phosphate reductoisomerase

The non-mevalonate or 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway is responsible for generating isoprenoid precursors in plants, protozoa, and bacteria. Because this pathway is absent in humans, its enzymes represent potential targets for the development of herbicides and antibiotics. 1-Deoxy-d-xylulose (DXP) reductoisomerase (DXR) is a particularly attractive target that catalyzes the pathway’s first committed step: the sequential isomerization and NADPH-dependent reduction of DXP to MEP. This article provides a comprehensive review of the mechanistic and structural investigations on DXR, including its discovery and validation as a drug target, elucidation of its chemical and kinetic mechanisms, characterization of inhibition by the natural antibiotic fosmidomycin, and identification of structural features that provide the molecular basis for inhibition of and catalysis.

Molecular Recognition of Tachykinin Receptor Selective Agonists: Insights from Structural Studies

This Review deals essentially with the elucidation of structural features of Tachykinin family of neuropeptides, which are known to interact through three distinct GPCR subtypes, namely NK1 (Neurokinin 1), NK2 (Neurokinin 2) and NK3 (Neurokinin 3) receptors. In mammals, Tachykinins have been shown to elicit a wide array of activities such as powerful vasodilatation, hypertensive action and stimulation of extravascular smooth muscle and are known to be involved in a variety of clinical conditions including chronic pain, Parkinson's disease, Alzheimer's disease, depression, rheumatoid arthritis, irritable bowel syndrome and asthma. This broad spectrum of action of Tachykinins is attributed to the lack of selectivity of tachykinins to their receptors. All tachykinins interact with all the three-receptor subtypes with SP preferring NK1, NKA preferring NK2 and NKB preferring NK3. This lack of specificity can be accounted for by the conformational flexibility of these short, linear peptides. Hence, identification of structural features of the agonists important for receptor binding and biological activity is of great significance in unraveling the molecular mechanisms involved in tachykinin receptor activation and also in rational design of novel therapeutic agents. Understanding structure of the ligand-receptor complex and analysis of topography of the binding pocket of the tachykinin receptor is also crucial in rational design of drugs.

Identification of Structural Features for 4-Methyl-3-(6-[Phenyl Methylene] Amino} Pyridine-3-yl)-2h Chromen-2-One Derivatives as Clotting Factor XA Inhibitors

Anticoagulants are used to prevent the formation and extension of blood clots in various disorders as prophylactic agents for thrombo-embolic disorders. Designing of specific inhibitors against molecular targets that play a pivotal role in the coagulation cascade is indispensable. Clotting Factor Xa is one such attractive target for the design of new oral anticoagulants because of the unique role factor Xa plays in the coagulation cascade as a connection between the extrinsic and intrinsic pathways. Application of computational techniques in drug discovery process helps in identifying parameters which can lead to achieve better pharmacological profile. The docking interactions and QSAR studies performed on series of 4-methy-3-(6-[phenyl methylene] amino} pyridine-3-yl)-2H chromen-2-one derivatives provide significant insights for designing of better ligands as anticoagulants.

A computational analysis of the binding mode of closantel as inhibitor of the Onchocerca volvulus chitinase: insights on macrofilaricidal drug design

Onchocerciasis is a leading cause of blindness with at least 37 million people infected and more than 120 million people at risk of contracting the disease; most (99%) of this population, threatened by infection, live in Africa. The drug of choice for mass treatment is the microfilaricidal Mectizan(®) (ivermectin); it does not kill the adult stages of the parasite at the standard dose which is a single annual dose aimed at disease control. However, multiple treatments a year with ivermectin have effects on adult worms. The discovery of new therapeutic targets and drugs directed towards the killing of the adult parasites are thus urgently needed. The chitinase of filarial nematodes is a new drug target due to its essential function in the metabolism and molting of the parasite. Closantel is a potent and specific inhibitor of chitinase of Onchocerca volvulus (OvCHT1) and other filarial chitinases. However, the binding mode and specificity of closantel towards OvCHT1 remain unknown. In the absence of a crystallographic structure of OvCHT1, we developed a homology model of OvCHT1 using the currently available X-ray structures of human chitinases as templates. Energy minimization and molecular dynamics (MD) simulation of the model led to a high quality of 3D structure of OvCHIT1. A flexible docking study using closantel as the ligand on the binding site of OvCHIT1 and human chitinases was performed and demonstrated the differences in the closantel binding mode between OvCHIT1 and human chitinase. Furthermore, molecular dynamics simulations and free-energy calculation were employed to determine and compare the detailed binding mode of closantel with OvCHT1 and the structure of human chitinase. This comparative study allowed identification of structural features and properties responsible for differences in the computationally predicted closantel binding modes. The homology model and the closantel binding mode reported herein might help guide the rational development of novel drugs against the adult parasite of O. volvulus and such findings could be extrapolated to other filarial neglected diseases.

LRRfinder: A web application for the identification of leucine-rich repeats and an integrative Toll-like receptor database

Homology modelling is considered the most accurate technique for computational prediction of protein structure. However, this technique comes with fundamental caveats of dependency on template quality, identification of structural features and accuracy of alignment. Leucine-rich repeats (LRRs) characterise a diverse family of proteins. Recently resolved structures reveal a highly conserved region in LRRs that assemble into the curved parallel β-sheet lining the inner circumference of their solenoid structure. Thus, prediction of these structurally important regions is essential in the comparative modelling of LRR proteins and their interactions. Here, we describe the generation of tLRRdb, a database of selected Toll-like receptor (TLR) sequences with annotated co-ordinates. Derived from this is LRRfinder, a web application for the identification of LRRs within user-defined sequences to facilitate identification of structurally important regions, particularly relevant for protein–protein interaction studies and classification of novel sequences. LRRfinder is available at: www.lrrfinder.com.

Selective Pharmacophore Models of Dopamine D1 and D2 Full Agonists Based on Extended Pharmacophore Features

This study is focused on the identification of structural features that determine the selectivity of dopamine receptor agonists toward D(1) and D(2) receptors. Selective pharmacophore models were developed for both receptors. The models were built by using projected pharmacophoric features that represent the main agonist interaction sites in the receptor (the Ser residues in TM5 and the Asp in TM3), a directional aromatic feature in the ligand, a feature with large positional tolerance representing the positively charged nitrogen in the ligand, and sets of excluded volumes reflecting the shapes of the receptors. The sets of D(1) and D(2) ligands used for modeling were carefully selected from published sources and consist of structurally diverse, conformationally rigid full agonists as active ligands together with structurally related inactives. The robustness of the models in discriminating actives from inactives was tested against four ensembles of conformations generated by using different established methods and different force fields. The reasons for the selectivity can be attributed to both geometrical differences in the arrangement of the features, e.g., different tilt angels of the pi system, as well as shape differences covered by the different sets of excluded volumes. This work provides useful information for the design of new D(1) and D(2) agonists and also for comparative homology modeling of D(1) and D(2) receptors. The approach is general and could therefore be applied to other ligand-protein interactions for which no experimental protein structure is available.

The Global Consciousness Project

The non-mevalonate or 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway is responsible for generating isoprenoid precursors in plants, protozoa, and bacteria. Because this pathway is absent in humans, its enzymes represent potential targets for the development of herbicides and antibiotics. 1-Deoxy-d-xylulose (DXP) reductoisomerase (DXR) is a particularly attractive target that catalyzes the pathway’s first committed step: the sequential isomerization and NADPH-dependent reduction of DXP to MEP. This article provides a comprehensive review of the mechanistic and structural investigations on DXR, including its discovery and validation as a drug target, elucidation of its chemical and kinetic mechanisms, characterization of inhibition by the natural antibiotic fosmidomycin, and identification of structural features that provide the molecular basis for inhibition of and catalysis.

Isomer Stability of N24, N30, and N36 Cages: Cylindrical versus Spherical Structure

Nitrogen molecules have been extensively studied for their potential as high energy density materials (HEDM). One of the major issues in the study of Nx all-nitrogen molecules is the determination of stable structures. In this study, various cage isomers of N24, N30, and N36 are examined for the purposes of determining relative thermodynamic stability. Trends in thermodynamic stability make possible the identification of structural features that lead to stable molecules. All of the molecules in this study have three-coordinate nitrogen with all N−N single bonds. Thermodynamic stability is determined by theoretical calculations employing Hartree−Fock theory (HF), density functional theory (B3LYP), and Moller−Plesset perturbation theory (up to MP4). The correlation-consistent basis sets of Dunning are employed. The major result of this study is the identification of cylinder-shaped molecules that are substantially more stable than more spherically shaped isomers.

Crystal Structure of the Hyperthermophilic Inorganic Pyrophosphatase from the Archaeon Pyrococcus horikoshii

A homolog to the eubacteria inorganic pyrophosphatase (PPase, EC 3.6.1.1) was found in the genome of the hyperthermophilic archaeon Pyrococcus horikoshii. This inorganic pyrophosphatase (Pho-PPase) grows optimally at 88°C. To understand the structural basis for the thermostability of Pho-PPase, we have determined the crystal structure to 2.66 Å resolution. The crystallographic asymmetric unit contains three monomers related by approximate threefold symmetry, and a hexamer is built up by twofold crystallographic symmetry. The main-chain fold of Pho-PPase is almost identical to that of the known crystal structure of the model from Sulfolobus acidocaldarius. A detailed comparison of the crystal structure of Pho-PPase with related structures from S. acidocaldarius, Thermus thermophilus, and Escherichia coli shows significant differences that may account for the difference in their thermostabilities. A reduction in thermolabile residues, additional aromatic residues, and more intimate association between subunits all contribute to the larger thermophilicity of Pho-PPase. In particular, deletions in two loops surrounding the active site help to stabilize its conformation, while ion-pair networks unique to Pho-PPase are located in the active site and near the C-terminus. The identification of structural features that make PPases more adaptable to extreme temperature should prove helpful for future biotechnology applications.

Understanding γ-MnO 2 by molecular modeling

De Wolff disorder, microtwinning, and point defects which are characteristic for γ-MnO 2 have been studied using molecular modeling. Particular attention was paid to the effects these defects have on the X-ray diffraction (XRD) pattern. Comparisons with observed XRD patterns allow identification of structural features in chemical (CMD) and electrochemical (EMD) manganese dioxide. The major factor determining the structure of γ-MnO 2 is de Wolff disorder. CMD materials are characterized by a larger percentage of pyrolusite while EMD materials contain more ramsdellite. Microtwinning occurs to a larger extent in EMD than in CMD materials. EMD materials are also higher in energy.

Identification of Structural Features in the G-protein Regulatory Motif Required for Regulation of Heterotrimeric G-proteins

The G-protein regulatory (GPR) motif, a conserved 25-30 amino acid domain found in multiple mammalian proteins, stabilizes the GDP-bound conformation of Galpha(i), inhibits guanosine 5'-O-(3-thiotriphosphate) (GTPgammaS) binding to Galpha(i) and competes for Gbetagamma binding to Galpha. To define the core GPR motif and key amino acid residues within a GPR peptide (TMGEEDFFDLLAKSQSKRMDDQRVDLAG), we determined the effect of truncation, insertion, and alanine substitutions on peptide-mediated inhibition of GTPgammaS binding to purified Galpha(i1). The bioactive core GPR peptide consists of 17 amino acids ((7)F-R(23)). Within this core motif, two hydrophobic sectors ((7)FF(8) and (10)LL(11)) and Q(22) are required for bioactivity, whereas M19A and R23A increased IC(50) values by 70-fold. Disruption of spatial relationships between the required sectors in the amino and carboxyl regions of the peptide also resulted in a loss of biological activity. Mutation of three charged sectors ((4)EED(6), R(18), (20)DD(21)) within the 28-amino acid GPR decreased peptide affinity by approximately 10-fold. Alanine substitutions of selected residues within the core GPR peptide differently influenced peptide inhibition of GTPgammaS binding to Galpha(i) versus Galpha(o). These data provide a platform for the development of novel, G-protein-selective therapeutics that inhibit Galpha(i)- mediated signaling, selectively activate Gbetagamma-sensitive effectors, and/or disrupt specific regulatory input to G-proteins mediated by GPR-containing proteins.

Identification of structural features and associated mechanisms of action for carcinogens in rats

A set of chemicals tested for carcinogenicity in rats that have been analyzed in the Carcinogenic Potency Database (CPDB) was subjected to CASE/MULTICASE (a computer-automated structure evaluation system) structure-activity relationship (SAR) analyses. This SAR system identifies structural features of chemicals in a learning set that are associated with a predefined activity and produces an SAR model based on these characteristics. The rat CPDB used in this study consisted of 745 chemicals, 383 of which are carcinogens, 14 marginally active carcinogens (i.e., chemicals that require a relatively high dose to induce carcinogenesis) and 348 are non-carcinogens. In an internal prediction analysis where CASE/MULTICASE ‘predicted’ the activity of chemicals in the learning set, the system was able to achieve a concordance between experimental and predicted results of 95%. This indicates that the program is able to adequately assess the chemicals in the database. In a 10-fold cross-validation study where 10 disjoint sets of 10% of the chemicals were removed from the database and the remaining 90% of the chemicals were used as a learning set, CASE/MULTICASE was able to achieve a concordance between experimental and predicted results of 64%. Using a modified validation process designed to investigate the predictivity of a more focused SAR model, the system was able to achieve a concordance of 71% between experimental and predicted results. Among the major biophores identified by CASE/MULTICASE as associated with cancer causation in rats, several are derived from electrophilic or potentially electrophilic compounds (e.g., aromatic amines, nitrogen mustards, isocyanates, epoxides). Other biophores however are derived from chemicals seemingly devoid of actual or potential DNA-reactivity and as such may represent structural features of non-genotoxic carcinogens.