Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines

In the post-genome era, the prediction of protein function is one of the most demanding tasks in the study of bioinformatics. Machine learning methods, such as the support vector machines (SVMs), greatly help to improve the classification of protein function. In this work, we integrated SVMs, protein sequence amino acid composition, and associated physicochemical properties into the study of nucleic-acid-binding proteins prediction. We developed the binary classifications for rRNA-, RNA-, DNA-binding proteins that play an important role in the control of many cell processes. Each SVM predicts whether a protein belongs to rRNA-, RNA-, or DNA-binding protein class. Self-consistency and jackknife tests were performed on the protein data sets in which the sequences identity was < 25%. Test results show that the accuracies of rRNA-, RNA-, DNA-binding SVMs predictions are approximately 84%, approximately 78%, approximately 72%, respectively. The predictions were also performed on the ambiguous and negative data set. The results demonstrate that the predicted scores of proteins in the ambiguous data set by RNA- and DNA-binding SVM models were distributed around zero, while most proteins in the negative data set were predicted as negative scores by all three SVMs. The score distributions agree well with the prior knowledge of those proteins and show the effectiveness of sequence associated physicochemical properties in the protein function prediction. The software is available from the author upon request.     

Design and evaluation of 3-aminopyrazolopyridinone kinase inhibitors inspired by the natural product indirubin

A lead-like kinase inhibitor screening library containing new 3-aminopyrazolopyridinones and closely related compounds was designed that contained hydrogen-bond donor-acceptor motifs and substitution vectors inspired by the natural product kinase inhibitor indirubin. The solubility of the 3-aminopyrazolopyridinone scaffold was more than 1000-fold greater than that of indirubin itself, and solubility was enhanced by reduction of the proportion of lipophilic aryl substituents or the introduction of basic groups. Several components of the library showed kinase inhibitory activity. A subset of diaryl-substituted analogues preferentially inhibited tyrosine kinases with low micromolar activity and good ligand efficiency, and showed cellular antiproliferative activity. The evaluation of the library shows that new, non-natural compounds with relevant biological activity and improved physicochemical properties can be generated from the natural product indirubin, providing compounds that may be useful for kinase inhibitor drug discovery.     

乌鳖黑色素理化性质及其抗氧化活性研究

本试验以乌鳖肌肉为原料,采用"脱脂-酶解-酸解"三步法制备乌鳖黑色素,研究其结构特性、理化性质和抗氧化活性.采用紫外分光光度计、傅里叶红外光谱仪和元素分析仪对乌鳖肌肉黑色素结构特性进行分析,并进行了颜色检测、溶解性试验、稳定性试验、总抗氧化能力试验、羟基自由基清除能力试验、DPPH自由基清除能力试验、超氧阴离子自由基清...     

Development of a decision support system for the introduction of alternative methods into local irritancy/corrosivity testing strategies. Creation of fundamental rules for a decision support system

The notification procedure of the European Union (EU) for new chemicals requires the application of protocols on physicochemical and toxicological tests for the evaluation of physicochemical properties and probable toxic effects of each notified substance. A computerised database was developed from data sets and toxicological test protocols relating to substance properties responsible for skin and eye irritation/corrosion. To develop specific structure-activity relationship (SAR) models and to find rules for a decision support system (DSS) to predict local irritation/corrosion, physical property data, chemical structure data and toxicological data for approximately 1300 chemicals, each having a purity of 95% or more, were evaluated. The evaluation demonstrated that the lipid solubility and aqueous solubility of a chemical are relevant to, or - in some cases - responsible for, the observed local effects of a substance on the skins and eyes of rabbits. The octanol/water partition coefficient and the measured value of the surface tension of a saturated aqueous solution of the substance give additional information that permits the definition of detailed SAR algorithms that use measured solubility values. Data on melting points and vapour pressure can be used to assess the intensity and duration of local contact with a chemical. Considerations relating to the reactivity of a pure chemical can be based on molecular weight and the nature of the heteroatoms present. With respect to local lesions produced following contact with the skin and eyes of rabbits, the data evaluation revealed that no general "local irritation/corrosion potential" of a chemical can be defined. A variety of mechanisms are responsible for the formation of local lesions on the skin or in the eyes: serious lesions are produced by mechanisms different from those that cause moderate irritation in these organs. In order to develop a DSS that uses the information extracted from the database, chemical main groups were categorised on the basis of their empirical formulae, and rules were defined of the type IF (physicochemical property) A, THEN not (toxic) effect B, based on correlations between specific local effects and measured physicochemical values. Other rules of the type IF substructure A, THEN effect B were developed based on correlations between specific local effects and the submitted structural formulae. Reactive chemical substructures relevant to the formation of local lesions and rules for the prediction of the absence of any skin irritation potential were identified. Proposals are made relating to the development of alternatives to eye irritation testing with rabbits.     

Connecting peptide physicochemical and antimicrobial properties by a rational prediction model

The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics' strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP's mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays high accuracy and is well correlated with the experimentally reported data. All together, these results suggest that the physicochemical properties of AMPs determine its action. In addition, we conclude that sequence derived parameters are enough to characterize antimicrobial peptides.     

Conformational analysis of apolipoprotein A-I and E-3 based on primary sequence and circular dichroism.

The primary and secondary structure of human plasma apolipoprotein A-I and apolipoprotein E-3 have been analyzed to further our understanding of the secondary and tertiary conformation of these proteins and the structure and function of plasma lipoprotein particles. The methods used to analyze the primary sequence of these proteins used computer programs: (a) to identify repeated patterns within these proteins on the basis of conservative substitutions and similarities within the physicochemical properties of each residue; (b) for local averaging, hydrophobic moment, and Fourier analysis of the physicochemical properties; and (c) for secondary structure prediction of each protein carried out using homology, statistical, and information theory based methods. Circular dichroism was used to study purified lipid-protein complexes of each protein and quantitate the secondary structure in a lipid environment. The data from these analyses were integrated into a single secondary structure prediction to derive a model of each protein. The sequence homology within apolipoproteins A-I, E-3, and A-IV is used to derive a consensus sequence for two 11 amino acid repeating sequences in this family of proteins.     

Fragment based drug design and diversity-oriented synthesis of carboxylic acid isosteres

The medicinal chemist toolbox is plenty of (bio)isosteres when looking for a carboxylic acid replacement. However, systematic assessment of acid surrogates is often time consuming and expensive, while prediction of both physicochemical properties (logP and logD) as well as acidity would be desirable at early discovery stages for a better analog design. Herein in this work, to enable decision making on a project, we have synthesized by employing a Diversity-Oriented Synthetic (DOS) methodology, a small library of molecular fragments endowed with acidic properties. By combining in-silico and experimental methodologies these compounds were chemically characterized and, particularly, with the aim to know their physicochemical properties, the aqueous ionization constants (pKa), partition coefficients logD and logP of each fragment was firstly estimated by using molecular modeling studies and then validated by experimental determinations. A face to face comparison between data and the corresponding carboxylic acid might help medicinal chemists in finding the best replacement to be used. Finally, in the framework of Fragment Based Drug Design (FBDD) the small library of fragments obtained with our approach showed good versatility both in synthetic and physico-chemical properties.     

Evaluation of the Membrane Permeability (PAMPA and Skin) of Benzimidazoles with Potential Cannabinoid Activity and their Relation with the Biopharmaceutics Classification System (BCS)

The permeability of five benzimidazole derivates with potential cannabinoid activity was determined in two models of membranes, parallel artificial membrane permeability assay (PAMPA) and skin, in order to study the relationship of the physicochemical properties of the molecules and characteristics of the membranes with the permeability defined by the Biopharmaceutics Classification System. It was established that the PAMPA intestinal absorption method is a good predictor for classifying these molecules as very permeable, independent of their thermodynamic solubility, if and only if these have a Log P _oct value <3.0. In contrast, transdermal permeability is conditioned on the solubility of the molecule so that it can only serve as a model for classifying the permeability of molecules that possess high solubility (class I: high solubility, high permeability; class III: high solubility, low permeability).     

New functional assay of P-glycoprotein activity using Hoechst 33342

In this study we describe a simplified, HTS-capable functional assay for the multidrug resistance (MDR) transporter P-glycoprotein (P-gp) based on its substrate Hoechst 33342. The physicochemical properties of Hoechst 33342 and the enormous milieu dependency of its fluorescence intensity allowed performing the assay in a homogeneous manner. This new assay served as an effective tool to estimate the potency of 10 well recognized P-gp substrates and modulators. Further, the potency of these compounds was also estimated in the calcein AM assay. The Hoechst 33342 and calcein AM assays yielded significantly comparable results for all compounds tested. Principal component analysis (PCA) applied to literature data on inhibition of P-gp activity and our results obtained in the Hoechst 33342 and calcein AM assay indicated similarity of compared functional transport assays. However, no correlation could be detected between these functional assays and the ATPase activity assay.     

创新药物体外ADMET 的高通量筛选技术研究进展

目前的新药研发策略已从传统的以活性为主导的筛选模式,转变为在测定药效的同时平行评价化合物的ADMET性质,并建立了系列体外ADMET高通量筛选技术。本文综述了近年来药物体外ADMET高通量筛选技术的研究进展。     

Synthesis of sulfanilamide derivatives and investigation of in vitro inhibitory activities and antimicrobial and physical properties

Novel sulfanilamide derivatives were synthesized and evaluated for carbonic anhydrase inhibitory activity as a target for the treatment of glaucoma, and antibacterial properties for use in chemotherapy. Synthesized compounds were characterized by FT-IR, ¹H NMR, ¹³C NMR and photoluminescence. In vitro inhibitory activities were measured by UV-Vis and some of the compounds were found have greater inhibitory effects than the lead compound sulfanilamide. The correlation between inhibitory activity, biological properties and the physicochemical properties of water solubility and partition coefficients was also investigated. Sulfanilamide derivatives gave intense emissions upon irradiation by UV light and a dimethyl substituted compound and a cyclic analog have photoluminescence quantum yields 42% and 31% and long excited-state lifetimes of 3.92 and 2.91 ns, respectively.     

A screen to identify cellular modulators of soluble levels of an amyotrophic lateral sclerosis (ALS)-causing mutant SOD1

The molecular pathology of many protein misfolding, toxic gain-of-function diseases, such as amyotrophic lateral sclerosis (ALS), is not well understood. Although protein misfolding and aggregation are common themes in these diseases, efforts to identify cellular factors that regulate this process in an unbiased fashion and on a global scale have been lacking. Using an adapted version of an extant β-gal-based protein solubility assay, an expression screen for cellular modulators of solubility of an ALS-causing mutant SOD1 was carried out in mammalian cells. Following fluorescence-activated cell sorting enrichment of a mouse spinal cord cDNA library for gene products that increased SOD1 solubility, high-throughput screening of the cDNA pools from this enriched fraction was employed to identify pools containing relevant modulators. Positive pools, containing approximately 10 cDNA clones each, were diluted and rescreened iteratively until individual clones that improved SOD1 folding/solubility were identified. Genes with profound effects in the solubility assay were selected for validation by independent biochemical assays. Six of 10 validated genes had a significant effect on SOD1 solubility and folding in a SOD1 promoter-driven β-gal assay, indicating that global screening of cellular targets using such protein solubility/folding assay is viable and can be adapted for other misfolding diseases.     

Aliphatic ionenes as gene delivery agents: elucidation of structure-function relationship through modification of charge density and polymer length

Polycation-based gene delivery agents are generally polydisperse populations whose properties are averaged among the different molecular weight species. Therefore, to understand the physicochemical properties of polycations and their relationships to cellular gene transfer, one needs to control the molecular weight of the polymer as well as its cationic charge density. To investigate the structure-function correlation of polycations with respect to the degree of polymerization (DP) and charge density, a series of model materials based on aliphatic ionenes was synthesized and fractionated into distinct molecular weight fractions with DP range from 14 to 32. The aliphatic ionene fractions and their polyelectrolyte complexes (PEC) with DNA were studied using physicochemical and biological methods. Ionene polymers were shown to possess low cytotoxicity (minimal viability of the P388D1 murine macrophage cells 80%). DP and charge density of the ionenes were shown to be the factors of effective control of PEC dissociation in water-salt solutions, with a diminished role of charge density upon lengthening the ionene chain. These polymer characteristics were also important for DNA-ionene PEC resistivity to DNase activity and the ability of ionenes to serve as gene delivery vectors in vitro and exhibited good correlation with the results of salt-induced dissociation of PEC. These data may be useful for developing correlations and mathematical models to predict synthetic gene delivery vector efficiency.     

An FPGA implementation to detect selective cationic antibacterial peptides

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides.     

Confidence scores for prediction models

In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer studies, also with high-dimensional predictor space.     

Modeling of phytoextraction efficiency of microbially stimulated Salix dasyclados L. in the soils with different speciation of heavy metals

Bioaugmentation of soils with selected microorganisms during phytoextraction can be the key solution for successful bioremediation and should be accurately calculated for different physicochemical soil properties and heavy metal availability to guarantee the universality of this method. Equally important is the development of an accurate prediction tool to manage phytoremediation process. The main objective of this study was to evaluate the role of three metallotolerant siderophore-producing Streptomyces sp. B1–B3 strains in the phytoremediation of heavy metals with the use of S. dasyclados L. growing in four metalliferrous soils as well as modeling the efficiency of this process based on physicochemical and microbiological properties of the soils using artificial neural network (ANN) analysis. The bacterial inoculation of plants significantly stimulated plant biomass and reduced oxidative stress. Moreover, the bacteria affected the speciation of heavy metals and finally their mobility, thereby enhancing the uptake and bioaccumulation of Zn, Cd, and Pb in the biomass. The best capacity for phytoextraction was noted for strain B1, which had the highest siderophore secretion ability. Finally, ANN model permitted to predict efficiency of phytoextraction based on both the physicochemical properties of the soils and the activity of the soil microbiota with high precision.     

Chemistry and immunomodulatory activity of frankincense oil

The yield of steam distillation of frankincense essential oil (3%); and its physicochemical constants were determined. Capillary GC/MS technique was used for the analysis of the oil. Several oil components were identified based upon comparison of their mass spectral data with those of reference compounds published in literature or stored in a computer library. The oil was found to contain monoterpenes (13.1%), sesquiterpenes (1%), and diterpenes (42.5%). The major components of the oil were duva-3,9,13-trien-1,5alpha-diol-1-acetate (21.4%), octyl acetate (13.4%), o-methyl anisole (7.6%), naphthalene decahydro-1,1,4a-trimethyl-6-methylene-5-(3-methyl-2-pentenyl) (5.7%), thunbergol (4.1%), phenanthrene-7-ethenyl-1,2,3,4,4a,5,6,7,8,9,10,10a-dodecahydro-1,1,4a,7-tetramethyl (4.1%), alpha-pinene (3.1%), sclarene (2.9%), 9-cis-retinal (2.8%), octyl formate (1.4%), verticiol (1.2%) decyl acetate (1.2%), n-octanol (1.1%). The chemical profile of the oil is considered as a chemotaxonomical marker that confirmed the botanical and geographical source of the resin. Biologically, the oil exhibited a strong immunostimulant activity (90% lymphocyte transformation) when assessed by a lymphocyte proliferation assay.