Hanbing Rao

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi.

A novel electrochemical sensor based on Au@PANI composites film modified glassy carbon electrode binding molecular imprinting technique for the determination of melamine

A novel molecularly imprinted electrochemical sensor for the rapid detection of melamine was reported in this paper. Glassy carbon electrode (GCE) was modified by Au and polyaniline composites (Au@PANI) deposited on the surface of GCE and were used to increase the electrode sensitivity and to amplify the sensor signal. Melamine template molecule was further assembled onto Au@PANI by the formation of hydrogen bonds, can implement the selective detection of melamine. This simple but efficient electrochemistry analysis platform presents a low detection limit of 1.39×10-6µmolL-1 for detection of melamine, which is remarkably lower than the currently used methods and the previous reports. So, this method may open a new way for the determination of melamine which enables low cost, effective and sensitive determination. This shows the sensor can be potentially utilized for the detection of melamine in food, which allows the sensitive and selective determination of melamine from milk and feed.

Prediction of chemical carcinogenicity by machine learning approaches

In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG−) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischers score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG− compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG− compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG− compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG− compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.

Identification of small molecule aggregators from large compound libraries by support vector machines

Small molecule aggregators non‐specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high‐throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries. We used 1319 aggregators and 128,325 non‐aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross‐validation, which showed comparable aggregator and significantly improved non‐aggregator identification rates against earlier studies. The second is the independent test of 17 aggregators discovered independently from the training aggregators, 71% of which were correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non‐aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1.14% of which were predicted as aggregators. SVM showed slightly better overall performance against two other machine learning methods based on five fold cross‐validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM showed substantial capability in identifying aggregators from large libraries at low false‐hit rates.

Visible light-driven photocatalytic degradation performance for methylene blue with different multi-morphological features of ZnS

In this study, four different sphere structures of zinc sulfide (ZnS) materials, dandelion-ZnS, raspberry-ZnS, ball-ZnS and flower-ZnS, were synthesized via a hydro-thermal method. Afterwards, the photocatalytic performances of the produced ZnS powders were investigated using methylene blue (MB) visible light induced photodegradation. X-ray diffraction (XRD), scanning electronic microscopy (SEM), Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FT-IR) and UV-visible diffuse reflectance spectroscopy (UV-vis-DRS) were used for characterization of the as-prepared ZnS powders. The specific surface areas of dandelion-ZnS, raspberry-ZnS, ball-ZnS and flower-ZnS were found to be 112, 119, 5.89 and 37.4 m2 g−1, respectively. The MB photodegradation by ZnS fitted well with a pseudo-first-order model. The high specific surface areas, unique morphology, porous structures and high hydrophilicity contribute to the photocatalytic abilities of ZnS. However, it shows that ZnS with simple structures has better reusability.

Physicochemical Profiles of the Marketed Agrochemicals and Clues for Agrochemical Lead Discovery and Screening Library Development.

Combinatorial chemistry, high‐throughput and virtual screening technologies have been extensively used for discovering agrochemical leads from chemical libraries. The knowledge of the physicochemical properties of the marketed agrochemicals is useful for guiding the design and selection of such libraries. Since the earlier profiling of marketed agrochemicals, the number and types of marketed agrochemicals have significantly increased. Recent studies have shown the change of some physicochemical properties of oral drugs with time. There is a need to also profile the physicochemical properties of the marketed agrochemicals. In this work, we analyzed the key physicochemical properties of 1751 marketed agrochemicals in comparison with the previously‐analyzed herbicides and insecticides, 106 391 natural products and 57 548 diverse synthetic libraries compounds. Our study revealed the distribution profiles and evolution trend of different types of agrochemicals that in many respects are broadly similar to the reported profiles for oral drugs, with the most marked difference being that agrochemicals have a lower number of hydrogen bond donors. The derived distribution patterns provided the rule of thumb guidelines for selecting potential agrochemical leads and also provided clues for further improving the libraries for agrochemical lead discovery.

A novel molecularly imprinted electrochemical sensor based on graphene quantum dots coated on hollow nickel nanospheres with high sensitivity and selectivity for the rapid determination of bisphenol S

In this paper, a novel molecularly imprinted electrochemical sensor (MIECS) based on a glassy carbon electrode (GCE) modified with graphene quantum dots (GQDs) coated on hollow nickel nanospheres (hNiNS) for the rapid determination of bisphenol S (BPS) was proposed for the first time. HNiNS and GQDs as electrode modifications were used to enlarge the active area and electron-transport ability for amplifying the sensor signal, while molecularly imprinted polymer (MIP) film was electropolymerized by using pyrrole as monomer and BPS as template to detect BPS via cyclic voltammetry (CV). Scanning electron microscope (SEM), energy-dispersive spectrometry (EDS), CV and differential pulse voltammetry (DPV) were employed to characterize the fabricated sensor. Experimental conditions, such as molar ratio of monomer to template, electropolymerization cycles, pH, incubation time and elution time were optimized. The DPV response of the MIECS to BPS was obtained in the linear range from 0.1 to 50μM with a low limit of detection (LOD) of 0.03μM (S/N = 3) under the optimized conditions. The MIECS exhibited excellent response towards BPS with high sensitivity, selectivity, good reproducibility, and stability. In addition, the proposed MIECS was also successfully applied for the determination of BPS in the plastic samples with simple sample pretreatment.

Kinetics and thermodynamics studies on the BMP-2 adsorption onto hydroxyapatite surface with different multi-morphological features.

The effect of the surface topography on protein adsorption process is of great significance for designing hydroxyapatite (HA) ceramic material surfaces. In this work, three different topographies of HA materials HA-sheet, HA-rod, and HA-whisker were synthesized and testified by X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Brunauer-Emmett-Teller (BET) and a field emission scanning electron microscopy (FE-SEM). We have systematically investigated the adsorption kinetics and thermodynamics of bone morphogenetic proteins (BMP-2) on the three different topography surfaces of HA, respectively. The results showed that the maximum adsorption capacities of HA-sheet, HA-rod and HA-whisker were (219.96 ± 10.18), (247.13 ± 12.35), and (354.67 ± 17.73) μg · g(-1), respectively. Kinetic parameters, rate constants, equilibrium adsorption capacities and related correlation coefficients, for each kinetic model were calculated as well as discussed. It demonstrated that the adsorption of BMP-2 onto HA could be described by the pseudo second-order equation. Adsorption of BMP-2 onto HA followed the Langmuir isotherm. It confirmed that compared with other samples HA-whisker had more adsorption sites for its high specific surface area which could provide more opportunities for protein molecules. The adsorption processes were endothermic (ΔH > 0), spontaneous (ΔG < 0) and entropy increasing (ΔS > 0). A possible adsorption mechanism has been proposed. In addition, the BMP-2 could be adsorbed to the surface which existed slight conformational changes by FT-IR.

A Sensitive fluorescent assay for trypsin activity in biological samples using BSA-Au nanoclusters

A novel, simple, sensitive, and selective fluorometric method was developed for measuring trypsin in biological samples in this article. The method was based upon measuring the quenching of the fluorescence intensity of the bovine serum albumin (BSA) stabilized Au nanoclusters by enzymatic proteolysis. The calibration plot for trypsin was achieved over the concentration range 1-60 nmol L-1 with a correlation coefficient of 0.995 and a limit of detection of 0.6 nmol L-1. The method was also used satisfactorily for the assessment of the trypsin activity and the results showed that the Michaelis-Menten (Km) and catalytic (Kcat) constant values of trypsin for BSA-Au nanoclusters substrate were 1.6×10-5 mol L-1 and 3.8 s-1 at 37 oC, respectively. This enzyme biosensor is of considerable interest due its promise for simple procedure and the established method has great potential in detection of other proteases in clinical diagnostics of various diseases.

An “on-off-on” fluorescent probe for ascorbic acid based on Cu-ZnCdS quantum dots and α-MnO2 nanorods

This work established a fluorescence approach for detecting ascorbic acid (AA) based on Cu-ZnCdS quantum dots (Cu-ZnCdS QDs) and α-MnO2 nanorods. Cu-ZnCdS QDs and α-MnO2 nanorods were characterized by high-resolution transmission electron microscopy (HRTEM), fluorescence spectroscopy, inductively coupled plasma optical emission spectroscopy (ICP-OES) and X-ray diffraction (XRD). In the presence of α-MnO2 nanorods, the fluorescence of Cu-ZnCdS QDs was greatly quenched through the inner filter effect (IFE). Subsequently, AA can trigger the decomposition of the α-MnO2 nanorods which can reduce α-MnO2 to Mn2+ and recover the fluorescence. Under optimal conditions, a linear relation was obtained over the range 5.02−401.77 μM with a 31.62 μM detection limit. Through applying the fluorescent sensing system for detecting AA, a satisfactory result is obtained with recoveries ranging from 89.23% to 110.99%.