Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance

There are many different kinds of pathogenic bacteria species with very different susceptibility profiles to different antibacterial drugs. One limitation of QSAR models is that they consider the biological activity of drugs against only one species of bacteria. In a previous paper, we developed a unified Markov model to describe the biological activity of different drugs tested in the literature against some antimicrobial species. Consequently, predicting the probability with which a drug is active against different species of bacteria with a single unified model is a goal of major importance. The work described here develops the unified Markov model to describe the biological activity of more than 70 drugs from the literature tested against 96 species of bacteria. We applied linear discriminant analysis (LDA) to classify drugs as active or inactive against the different tested bacterial species. The model correctly classified 199 out of 237 active compounds (83.9%) and 168 out of 200 inactive compounds (84%). Overall training predictability was 84% (367 out of 437 cases). Validation of the model was carried out using an external predicting series, with the model classifying 202 out of 243 (i.e., 83.13%) of the compounds. In order to show how the model functions in practice, a virtual screening was carried out and the model recognized as active 84.5% (480 out of 568) antibacterial compounds not used in the training or predicting series. The current study is an attempt to calculate within a unified framework the probabilities of antibacterial action of drugs against many different species.     

Markov state modeling of membrane transport proteins

The flux of ions and molecules in and out of the cell is vital for maintaining the basis of various biological processes. The permeation of substrates across the cellular membrane is mediated through the function of specialized integral membrane proteins commonly known as membrane transporters. These proteins undergo a series of structural rearrangements that allow a primary substrate binding site to be accessed from either side of the membrane at a given time. Structural insights provided by experimentally resolved structures of membrane transporters have aided in the biophysical characterization of these important molecular drug targets. However, characterizing the transitions between conformational states remains challenging to achieve both experimentally and computationally. Though molecular dynamics simulations are a powerful approach to provide atomistic resolution of protein dynamics, a recurring challenge is its ability to efficiently obtain relevant timescales of large conformational transitions as exhibited in transporters. One approach to overcome this difficulty is to adaptively guide the simulation to favor exploration of the conformational landscape, otherwise known as adaptive sampling. Furthermore, such sampling is greatly benefited by the statistical analysis of Markov state models. Historically, the use of Markov state models has been effective in quantifying slow dynamics or long timescale behaviors such as protein folding. Here, we review recent implementations of adaptive sampling and Markov state models to not only address current limitations of molecular dynamics simulations, but to also highlight how Markov state modeling can be applied to investigate the structure–function mechanisms of large, complex membrane transporters.     

Computer simulation of biological systems

The current status of mathematical models of biological systems is reviewed. Advances in supercomputer hardware allows more complex models to be constructed. The new generation of microcomputers are quite adequate for many computer simulations of biological systems. A theory of modeling is being developed to improve the relationship between the real biological system and the model. Deterministic models, stochastic models and applications of control theory and optimization methods are discussed. Examples given include models of molecular structure, of experimental techniques, and of biochemical reactions. It is recommended that experimental biologists consider the use of microcomputers to model the system under study as a part of their research program.     

Combining phylogenetic and hidden Markov models in biosequence analysis.

A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction--for example, for gene finding or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higher-order states in the HMM, which allows for context-dependent models of substitution--that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higher-order states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higher-order states being particularly pronounced.     

Tamoxifen versus aminoglutethimide in advanced breast carcinoma: a randomized cross-over trial.

Altogether 117 patients with advanced breast cancer were treated with either tamoxifen 10 mg by mouth twice daily or aminoglutethimide 250 mg by mouth four times daily with hydrocortisone 20 mg twice daily in a randomised cross-over trial in which patients who failed to respond to the first treatment or relapsed while receiving it were switched to the other. Eighteen (30%) out of 60 patients initially treated with tamoxifen achieved an objective response and 11 (18%) showed stable disease. Seventeen (30%) out of 57 patients treated initially with aminoglutethimide achieved an objective response and 13 (23%) achieved stable disease. Objective responses in bone metastases were achieved more commonly with aminoglutethimide (11 patients (35%)) than with tamoxifen (five (17%)). The predicted median duration of response for tamoxifen was 15 months and for aminoglutethimide over 15 months (no significant difference). Five (15%) out of 34 patients who failed to respond to tamoxifen and four out of six patients who relapsed after responding to tamoxifen subsequently responded to aminoglutethimide. In contrast, only two (6%) out of 31 patients who failed to respond to aminoglutethimide and none out of four patients who relapsed while receiving aminoglutethimide subsequently responded to tamoxifen. The main side effects occurring in the 97 patients who received aminoglutethimide as first- or second-line treatment were lethargy and drowsiness (36 patients) and rash (29); seven patients had to stop treatment because of side effects. In contrast, side effects were rare and mild with tamoxifen and no patient had to stop treatment because of them. Both tamoxifen and aminoglutethimide appeared from this study to be equally effective in the medical endocrine treatment of advanced breast cancer.     

The gastric ulcer protective effect of boswellic acids, a leukotriene inhibitor from Boswellia serrata, in rats

Aim of the study is to evaluate the anti-ulcer efficacy of the boswellic acids (BA), a triterpenoid known as anti-inflammatory/anti-arthritic agent, which is in clinical use. The reason for the study is that, the known non-steroidal anti-inflammatory drugs (NSAIDs) are full of side effects especially ulceration which is at the top. BA, although, used as an anti-arthritic agent yet it is not only devoid of ulcer production but protective also. The activity evaluation was done by the following universally accepted animal models viz., pyloric ligation, ethanol–HCl, acetylsalicylic acid, indomethacin and cold restrained stress-induced ulceration in rats. Results of the present study revealed that BA possess a dose dependent antiulcer effect against different experimental models. It showed different degree of inhibition of the ulcer score towards different ulcerogenic agents. The ulcer score against various ulcer inducing agents viz., pyloric ligation, ethanol/HCl, (acute and chronic) acetylsalicylic acid, indomethacin and cold restraint stress, was inhibited by 39%, 38%, 51%, 31%, 37% and 42% respectively at 250 mg/kg. From the data it is concluded that BA inhibited ulcer production non-specifically in all the experimental models, whereby, it is not possible to propose a single specific mechanism. Nevertheless it is possible that BA might be acting by increasing the gastric mucosal resistance and local synthesis of cytoprotective prostaglandins and inhibiting the leukotriene synthesis.     

Microwave-induced and pharmacological suppression of somatic pain under conditions of the formalin test in mice

We examined pain-related behavioral reactions and non-pain behavioral manifestations in mice under conditions of the formalin test. Levels of analgesia induced by i.p. injections of analgin, microwave irradiation of an antinociceptive acupuncture point (AP), E-36, or combined application of the above factors were measured. The duration of the pain behavioral reaction (licking of the injured limb) decreased due to irradiation of the AP with microwaves and to injection of 8.3 mg/kg analgin by 24.3% and 53.8%, on average, respectively. Combination of injection of analgin in a smaller dose (4.2 mg/kg) and microwave irradiation of the AP suppressed manifestations of the pain behavioral reaction by 43.4%. Thus, combination of pharmacologically induced analgesia with the action of microwaves on the antinociceptive AP allows one to significantly decrease the doses of analgesic preparations necessary to provide a full-level analgesic effect; in such a way, side effects of the respective drugs can be weakened. Neirofiziologiya/Neurophysiology, Vol. 38, No. 1, pp. 46–51, January–February, 2006.     

Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.     

Novel Cancer Chemotherapy Hits by Molecular Topology: Dual Akt and Beta-Catenin Inhibitors

Background and Purpose

Colorectal and prostate cancers are two of the most common types and cause of a high rate of deaths worldwide. Therefore, any strategy to stop or at least slacken the development and progression of malignant cells is an important therapeutic choice. The aim of the present work is the identification of novel cancer chemotherapy agents. Nowadays, many different drug discovery approaches are available, but this paper focuses on Molecular Topology, which has already demonstrated its extraordinary efficacy in this field, particularly in the identification of new hit and lead compounds against cancer. This methodology uses the graph theoretical formalism to numerically characterize molecular structures through the so called topological indices. Once obtained a specific framework, it allows the construction of complex mathematical models that can be used to predict physical, chemical or biological properties of compounds. In addition, Molecular Topology is highly efficient in selecting and designing new hit and lead drugs. According to the aforementioned, Molecular Topology has been applied here for the construction of specific Akt/mTOR and β-catenin inhibition mathematical models in order to identify and select novel antitumor agents.

Experimental Approach

Based on the results obtained by the selected mathematical models, six novel potential inhibitors of the Akt/mTOR and β-catenin pathways were identified. These compounds were then tested in vitro to confirm their biological activity.

Conclusion and Implications

Five of the selected compounds, CAS n° 256378-54-8 (Inhibitor n°1), 663203-38-1 (Inhibitor n°2), 247079-73-8 (Inhibitor n°3), 689769-86-6 (Inhibitor n°4) and 431925-096 (Inhibitor n°6) gave positive responses and resulted to be active for Akt/mTOR and/or β-catenin inhibition. This study confirms once again the Molecular Topology’s reliability and efficacy to find out novel drugs in the field of cancer.     

基于高通量测序的极小种群野生植物长梗杜鹃转录组分析

为加强特有濒危植物长梗杜鹃资源的评价、保护和鉴定工作,及为今后遗传育种和改善其农艺性状提供有益参考。本研究采用新一代高通量测序平台Illumina HiSeq 4000对其转录组测序,得到的数据过滤后进行de novo组装并聚类去冗余,获得74 092个Unigenes,平均长度、N₅₀、Q₂₀、Q₃₀以及GC含量分别为938 nt、1 616 nt、98.22%、95.20%和43.24%,其中1 Kb以上的Unigenes有23 879条。通过与七大功能数据库比对,分别有39 876（NR：53.82%）、38 065（NT：51.38%）、27 384（Swissprot：36.96%）、16 099（COG：21.73%）、30 401（KEGG：41.03%）、17 518（GO：23.64%）以及29 676（Interpro：40.05%）条Unigenes获得功能注释。长梗杜鹃转录组中的Unigenes根据GO功能大致可分为生物学过程、细胞组分和分子功能3大类56亚类,其中执行生物学过程的基因最多,占41.53%;与COG数据库比对,根据其功能大致可分为25类;以KEGG数据库为参考,可归为6个代谢通路大类、32类代谢途径,并发掘出176条与人类疾病相关的Unigenes,包括内分泌及代谢疾病（167条）和抗药性（9条）。根据注释结果共检测出39 418个CDS,未注释上的Unigenes使用ESTScan预测后获得3 194个CDS。同时,预出到1 488个编码TF的Unigenes,以及检测到57 927个SNP多态位点。该转录组分析为今后长梗杜鹃乃至杜鹃属植物功能基因挖掘与利用、基因克隆、抗性机理分析、遗传资源分类和进化、分子标记开发以及分子辅助育种等研究提供了基础数据和重要参考。     

Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60–0.69 and 0.61–0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.     

Non-local concepts and models in biology

In this paper, we consider local and non-local spatially explicit mathematical models for biological phenomena. We show that, when rate differences between fast and slow local dynamics are great enough, non-local models are adequate simplifications of local models. Non-local models thus avoid describing fast processes in mechanistic detail, instead describing the effects of fast processes on slower ones. As a consequence, non-local models are helpful to biologists because they describe biological systems on scales that are convenient to observation, data collection, and insight. We illustrate these arguments by comparing local and non-local models for the aggregation of hypothetical organisms, and we support theoretical ideas with concrete examples from cell biology and animal behavior.     

Stochastic modelling of a single ion channel: an alternating renewal approach with application to limited time resolution

Stochastic models of ion channels have been based largely on Markov theory where individual states and transition rates must be specified, and sojourn-time densities for each state are constrained to be exponential. This study presents an approach based on random-sum methods and alternating-renewal theory, allowing individual states to be grouped into classes provided the successive sojourn times in a given class are independent and identically distributed. Under these conditions Markov models form a special case. The utility of the approach is illustrated by considering the effects of limited time resolution (modelled by using a discrete detection limit, xi) on the properties of observable events, with emphasis on the observed open-time (xi-open-time). The cumulants and Laplace transform for a xi-open-time are derived for a range of Markov and non-Markov models; several useful approximations to the xi-open-time density function are presented. Numerical studies show that the effects of limited time resolution can be extreme, and also highlight the relative importance of the various model parameters. The theory could form a basis for future inferential studies in which parameter estimation takes account of limited time resolution in single channel records. Appendixes include relevant results concerning random sums and a discussion of the role of exponential distributions in Markov models.     

Simple stochastic models and their power-law type behaviour

A power-law relationship between the mean and variance of ecological time series has been shown to hold for a vast number of species. Here we examine the behaviour of single-species stochastic models and concentrate in particular on the mean-variance relationship as the carrying capacity becomes large. Single-species stochastic models can be written as Markov chains, and the long-term distribution of population sizes and hence power-law scaling can be found analytically. The various power-law scalings that arise have very different biological implications for the effects of stochasticity and the departure from the deterministic paradigm. Finally we extend our analysis to consider the complicating factors of spatial heterogeneity, nontrivial deterministic dynamics, and multispecies models.     

A Segmental Semi Markov Model for protein secondary structure prediction

Hidden Markov Models (HMMs) are practical tools which provide probabilistic base for protein secondary structure prediction. In these models, usually, only the information of the left hand side of an amino acid is considered. Accordingly, these models seem to be inefficient with respect to long range correlations. In this work we discuss a Segmental Semi Markov Model (SSMM) in which the information of both sides of amino acids are considered. It is assumed and seemed reasonable that the information on both sides of an amino acid can provide a suitable tool for measuring dependencies. We consider these dependencies by dividing them into shorter dependencies. Each of these dependency models can be applied for estimating the probability of segments in structural classes. Several conditional probabilities concerning dependency of an amino acid to the residues appeared on its both sides are considered. Based on these conditional probabilities a weighted model is obtained to calculate the probability of each segment in a structure. This results in 2.27% increase in prediction accuracy in comparison with the ordinary Segmental Semi Markov Models, SSMMs. We also compare the performance of our model with that of the Segmental Semi Markov Model introduced by Schmidler et al. [C.S. Schmidler, J.S. Liu, D.L. Brutlag, Bayesian segmentation of protein secondary structure, J. Comp. Biol. 7(1/2) (2000) 233-248]. The calculations show that the overall prediction accuracy of our model is higher than the SSMM introduced by Schmidler.     

Mouse interleukin-2 structure-function studies: substitutions in the first alpha-helix can specifically inactivate p70 receptor binding and mutations in the fifth alpha-helix can specifically inactivate p55 receptor binding.

The function of two alpha-helical regions of mouse interleukin-2 were analyzed by saturation substitution analysis. The functional parts of the first alpha-helix (A) was defined as residues 31-39 by the observation that proline substitutions within this region inactivate the protein. Four residues within alpha-helix A, Leu31, Asp34, Leu35 and Leu38, were found to be crucial for biological activity. Structural modeling suggested that these four residues are clustered on one face of alpha-helix A. Residues 31 and 35 had to remain hydrophobic for the molecule to be functional. At residue 38 there was a preference for hydrophobic side chain residues, while at residue 34 some small side chain residues as well as acidic or amide side chain residues were functionally acceptable. Inactivating changes at residue 34 had no effect upon the ability of the protein to interact with the p55 receptor. Disruption of the fifth alpha-helix (E), which had little effect upon biological activity, resulted in an inability of the protein to interact with the p55 receptor. Mutagenesis of the alpha-helix E region demonstrated that alpha-helicity and the nature of the side chain residues in this region were unimportant for biological activity. The region immediately proximal to alpha-helix E was important only for the single intramolecular disulfide linkage.