Using amino acid features to identify the pathogenicity of influenza B virus.

BACKGROUND: Influenza B virus can cause epidemics with high pathogenicity, so it poses a serious threat to public health. A feature representation algorithm is proposed in this paper to identify the pathogenicity phenotype of influenza B virus. METHODS: The dataset included all 11 influenza virus proteins encoded in eight genome segments of 1724 strains. Two types of features were hierarchically used to build the prediction model. Amino acid features were directly delivered from 67 feature descriptors and input into the random forest classifier to output informative features about the class label and probabilistic prediction. The sequential forward search strategy was used to optimize the informative features. The final features for each strain had low dimensions and included knowledge from different perspectives, which were used to build the machine learning model for pathogenicity identification. RESULTS: The 40 signature positions were achieved by entropy screening. Mutations at position 135 of the hemagglutinin protein had the highest entropy value (1.06). After the informative features were directly generated from the 67 random forest models, the dimensions for class and probabilistic features were optimized as 4 and 3, respectively. The optimal class features had a maximum accuracy of 94.2% and a maximum Matthews correlation coefficient of 88.4%, while the optimal probabilistic features had a maximum accuracy of 94.1% and a maximum Matthews correlation coefficient of 88.2%. The optimized features outperformed the original informative features and amino acid features from individual descriptors. The sequential forward search strategy had better performance than the classical ensemble method. CONCLUSIONS: The optimized informative features had the best performance and were used to build a predictive model so as to identify the phenotype of influenza B virus with high pathogenicity and provide early risk warning for disease control.

Calculating degree based multiplicative topological indices of Hyaluronic Acid-Paclitaxel conjugates' molecular structure in cancer treatment.

A large number of medical experiments have confirmed that the features of drugs have a close correlation with their molecular structure. Drug properties can be obtained by studying the molecular structure of corresponding drugs. The calculation of the topological index of a drug structure enables scientists to have a better understanding of the physical chemistry and biological characteristics of drugs. In this paper, we focus on Hyaluronic Acid-Paclitaxel conjugates which are widely used in the manufacture of anticancer drugs. Several multiplicative topological indices of this molecular structure are determined by using of the edge-partition method.Communicated by Ramaswamy H. Sarma.

Rational design of the carbonyl reductase EbSDR8 for efficient biosynthesis of enantiopure (R)-3-chloro-1-phenyl-1-propanol.

(R)-3-Chloro-1-phenyl-1-propanol ((R)-CPPO) is an important chiral intermediate for antidepressants. For its efficient biosynthesis, the carbonyl reductase EbSDR8 was engineered to asymmetrically reduce the unnatural substrate 3-chloro-1-phenyl-1-propanone (3-CPP) at high concentrations. Molecular docking and molecular dynamics simulations of the resulting mutants suggested enlarged substrate binding pocket and more reasonable interactions between the enzyme and the substrate or cofactor as the reasons for the enhanced catalytic activity and thus the remarkably improved conversion of high-concentration 3-CPP. Using the best mutant EbSDR8G94A/L153I/Y188A/Y202M as the whole-cell biocatalyst, reduction of 3-CPP (1.0 M) was conducted using 100% isopropanol as both the solvent and co-substrate for NADH regeneration, delivering (R)-CPPO with ˃ 99% eep and 95.5% conversion. This result suggests EbSDR8G94A/L153I/Y188A/Y202M as a potential biocatalyst for green production of (R)-CPPO at the industrial scale. KEY POINTS: • Rational design of EbSDR8 by modulating steric hindrance and molecular interactions; • Non-aqueous biocatalysis using isopropanol as both the solvent and co-substrate; • Whole-cell catalyzed production of 161 g/L enantiopure (R)-CPPO from 1.0 M of 3-CPP. Graphical Abstract.

Improved enantioselectivity of E. coli BioH in kinetic resolution of methyl (S)-3-cyclohexene-1-carboxylate by combinatorial modulation of steric and aromatic interactions.

As a chiral precursor for the important anticoagulant Edoxaban, enantioselective synthesis of (S)-3-cyclohexene-1-carboxylic acid is of great significance. The complicated procedures and generation of massive solid waste discourage its chemical synthesis, and the alternative biocatalysis route calls for an enzyme capable of asymmetric hydrolysis of racemic methyl-3-cyclohexene-1-carboxylate. To this end, we engineered the E. coli esterase BioH for improved S-enantioselectivity via rational design. By combinatorial modulation of steric and aromatic interactions, a positive mutant Mu3 (L24A/W81A/L209A) with relatively high S-selectivity in hydrolyzing racemic methyl-3-cyclohexene-1-carboxylate was obtained, improving the enantiomeric excess from 32.3% (the wild type) to 70.9%. Molecular dynamics simulation was conducted for both (R)- or (S)- complexes of the wild type and Mu3 to provide hints for the mechanism behind the increased S-selectivity. Moreover, the reaction conditions of Mu3 in methyl-3-cyclohexene-1-carboxylate hydrolysis was optimized to improve the conversion rate to 2 folds.

The Cartesian Product and Join Graphs on Edge-Version Atom-Bond Connectivity and Geometric Arithmetic Indices.

The Cartesian product and join are two classical operations in graphs. Let dL(G)(e) be the degree of a vertex e in line graph L(G) of a graph G. The edge versions of atom-bond connectivity (ABCe) and geometric arithmetic (GAe) indices of G are defined as ∑ef∈E(L(G))dL(G)(e)+dL(G)(f)-2dL(G)(e)×dL(G)(f) and ∑ef∈E(L(G))2dL(G)(e)×dL(G)(f)dL(G)(e)+dL(G)(f), respectively. In this paper, ABCe and GAe indices for certain Cartesian product graphs (such as Pn□Pm, Pn□Cm and Pn□Sm) are obtained. In addition, ABCe and GAe indices of certain join graphs (such as Cm+Pn+Sr, Pm+Pn+Pr, Cm+Cn+Cr and Sm+Sn+Sr) are deduced. Our results enrich and revise some known results.

On Metric Dimension in Some Hex Derived Networks.

The concept of a metric dimension was proposed to model robot navigation where the places of navigating agents can change among nodes. The metric dimension m d ( G ) of a graph G is the smallest number k for which G contains a vertex set W, such that | W | = k and every pair of vertices of G possess different distances to at least one vertex in W. In this paper, we demonstrate that m d ( H D N 1 ( n ) ) = 4 for n ≥ 2 . This indicates that in these types of hex derived sensor networks, the least number of nodes needed for locating any other node is four.

Synergistic effects of boron-doped silicone resin and a layered double hydroxide modified with sodium dodecyl benzenesulfonate for enhancing the flame retardancy of polycarbonate.

To improve the flame retardancy of polycarbonate (PC), a novel and environmentally friendly flame retardant was synthesized by combining boron-doped silicone resin (BSR) with a layered double hydroxide (LDH) modified with sodium dodecyl benzenesulfonate (SDBS) which was denoted as DBS-LDH/BSR. The structure of the hybrid was characterized by Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS), which indicated that BSR was successfully combined with DBS-LDH. X-ray diffraction (XRD) studies showed that the reaction of BSR occurred only on the surface of DBS-LDH. In addition, scanning electron microscopy (SEM) was used to further verify the combination of DBS-LDH with BSR. PC exhibited the optimum flame retardancy following the incorporation of 10 wt% DBS-LDH/BSR (5 wt% DBS-LDH and 5 wt% BSR). Based on thermogravimetric analysis, the char residue of this PC composite in air at 750 °C increased to 3.60 wt%. Mechanical test showed that the DBS-LDH/BSR could affect the mechanical properties after incorporation into PC. According to the UL-94 vertical burning test, the flame retardant rating of the PC composite improved to V-0. Furthermore, the limiting oxygen index (LOI) value of the PC composite increased to 34%. According to the cone calorimeter test, the peak heat release rate (PHRR) dramatically decreased by 44%. The morphology of the PC composite after combustion was characterized by SEM, which revealed that the pores of the composite were smaller than those of pure PC. This result was attributed to the limited spread of oxygen and heat permeation. Thus, both DBS-LDH and BSR contributed to the synergistic effects of reducing the fire hazard of PC.

Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles.

The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.

L(2,1)-labeling of the strong product of paths and cycles.

An L(2,1)-labeling of a graph G = (V, E) is a function f from the vertex set V(G) to the set of nonnegative integers such that the labels on adjacent vertices differ by at least two and the labels on vertices at distance two differ by at least one. The span of f is the difference between the largest and the smallest numbers in f(V). The λ-number of G, denoted by λ(G), is the minimum span over all L(2,1)-labelings of G. We consider the λ-number of Pn⊠C m and for n ≤ 11 the λ-number of Cn⊠Cm. We determine λ-numbers of graphs of interest with the exception of a finite number of graphs and we improve the bounds on the λ-number of Cn⊠Cm, m ≥ 24 and n ≥ 26.