The rising cost of drug development has necessitated the search for efficient approaches for the discovery of novel drugs. Such difficulty arises from the fact that promising lead compounds would need to be further scrutinized via preclinical and clinical studies so as to characterize their absorption, distribution, metabolism, excretion, and toxicity in order to verify their safety for clinical use. In this respect, drug repositioning/repurposing represents an attractive venue for the discovery of novel therapeutic indications for existing food and drug administration-approved drugs, which in a nutshell repurposes old drugs for treating new diseases for which they were not originally developed. Computational approaches have been instrumental in drug repositioning efforts as they make use of existing biological and chemical data to uncover new therapeutic indications by relying on the concept that similar compounds bind to similar targets. Quantitative structure-activity relationship (QSAR) is a powerful ligand-based approach that correlates structural features of compounds with their observed biological activity against a single target protein. Proteochemometric modeling (PCM) extends QSAR to an extra dimension by allowing several target proteins to be considered in a single model. This chapter briefly explores the concepts of polypharmacology and computational drug discovery and finally examines the use of PCM for drug repositioning.

Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.

Introduction: Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery.Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics.Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.

Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at https://github.com/chaninn/pepbio/.

Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.

Melanocortin signalling in leucocyte subsets elicits anti-inflammatory and immune tolerance inducing effects in animal experimental inflammation. In man, however, the effects of melanocortin signalling in inflammatory conditions have scarcely been examined. We explored the differential reactions of melanocortin 1-5 receptors (MC1-5R) gene expressions in pathogenetic leucocyte subsets in rheumatoid arthritis (RA) to treatment with TNF-α inhibitor adalimumab. Seven patients with active RA donated blood at start and at 3-month treatment. CD4+ T helper (h) lymphocytes (ly), CD8+ T cytotoxic (c) ly, CD19+ B ly and CD14+ monocytes were isolated, using immunomagnetic beads, total RNA extracted and reverse transcription quantitative polymerase chain reaction (RT-qPCR) performed. Fold changes in MC1-5R, Th1-, inflammatory- and regulatory cytokine gene expressions were assessed for correlation. Six patients responded to adalimumab treatment, while one patient was non-responder. In all lymphocyte subtypes, MC1-5R gene expressions decreased in responders and increased in the non-responder. In responders, decrease in MC2R, MC3R and MC4R gene expressions in CD8+ Tc and CD19+ B ly was significant. Fold change in MC1-5R and IFNγ gene expressions correlated significantly in CD8+ Tc ly, while fold change in MC1R, MC3R and MC5R and IL-1β gene expressions correlated significantly in CD4+ Th ly. Our results show regulation of MC2R, MC3R and MC4R gene expressions in CD8+ Tc ly and CD19+ B ly. The correlations between fold change in different MCRs and disease driving cytokine gene expressions in CD8+ Tc ly and CD4+ Th ly point at a central immune modulating function of the melanocortin system in RA.

AIMToxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.MATERIALS & METHODSThis study describes a sequence-based predictor based on a random forest classifier using amino acid composition, dipeptide composition and physicochemical descriptors (named HemoPred).RESULTSThis approach could outperform previously reported method and typical classification methods (e.g., support vector machine and decision tree) verified by fivefold cross-validation and external validation with accuracy and Matthews correlation coefficient in excess of 95% and 0.91, respectively. Results revealed the importance of hydrophobic and Cys residues on α-helix and β-sheet, respectively, on the hemolytic activity.CONCLUSIONA sequence-based predictor which is publicly available as the web service of HemoPred, is proposed to predict and analyze the hemolytic activity of peptides.

Center of Data Mining and Biomedical Inf Mahidol University, Bangkok 10700, Thai Fax: +66 2 441 4371 ext. 2715; Tel: +66 2 4 Interdisciplinary Graduate Program in B University, Bangkok 10900, Thailand Center for Research and Innovation, Fa University, Bangkok 10700, Thailand Department of Clinical Microbiology and Technology, Mahidol University, Bangkok 10 Department of Pharmaceutical Biosciences Sweden Department of Biomedical Engineering, F Institute of Technology Ladkrabang, Bangko † Electronic supplementary information (E analysis of dipeptide features. See DOI: 10 ‡ Contributed equally to this work. Cite this: RSC Adv., 2017, 7, 35119

Quantitative structure-activity relationship (QSAR) has been instrumental in aiding medicinal chemists and physical scientists in understanding how modification of substituents at different positions on a molecular structure exert its influence on the observed biological activity and physicochemical property, respectively. QSAR has received great attention owing to its predictive capability and as such efforts had been directed toward obtaining models with high prediction performance. However, to be useful QSAR models need to be informative and interpretable in which the underlying molecular features that contribute to the increase or decrease of the biological activity are revealed by the model. Thus, the aim of this chapter is to briefly review the general concepts of QSAR modeling, its development and discussions on key issues influencing and contributing to the interpretability of QSAR models.

BACKGROUND:Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence.RESULTS:After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment.CONCLUSION:osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds13,5and28exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding,π–πstacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

Neobeguea mahafalensis is used as a medicinal plant inMadagascar. A decoction of the stem bark of this species is reported to treat back pain. Recently, it was reported that a decoction of the root bark, containing two novel phragmalin limonoids as identified active constituents, exhibited an extraordinarily high potency and remarkably long duration in augmenting sexual activity in male rodents. From the dichloromethane extract of the root barks of N. mahafalensis, nine phragmalin limonoids were isolated, of which eight were novel compounds. The structures were established mainly by extensive use of 2D NMR spectroscopic techniques and high-resolution mass spectrometry. One of the new compounds named dodoguin displayed sleep-inducing activity in Swiss albino mice. The amount of 3-10 mg/kg of this compound induced sleep 18-22 min after its administration with a duration of 16-18 min.

Background Although biological agents may manage rheumatoid arthritis (RA), curative treatment is still the ”holy grail”. Induction of auto-antigen specific immune tolerance might offer a solution and obviate the need for life-long immunosuppression. Melanocortins are small peptides with considerable immune tolerance inducing, inflammation resolving and tissue preserving qualities (1). In animal experimental, autoimmune conditions, melanocortins have been demonstrated to bind differentially to melanocortin receptor (MCR) 1–5 on immune cells and to transform auto-reactive CD4+ T helper (Th) lymphocytes (ly) as well as sensitized CD8+ T cytotoxic (Tc) ly into regulatory T (Treg) ly and thus clear cell-mediated auto-immunity and delayed type hypersensitivity. In contrast to the situation in organ transplantation, clinical application of Treg therapy in human autoimmune disease is still non-existing, primarily because of a lack of in depth understanding. Objectives To explore if the pro-resolving melanocortin system may offer a pathway to immune tolerance induction, we examined whether the melanocortin system is present and responsive in pathogenic immune cell subsets in RA. To this end, we related changes due to TNFα inhibition (I) in MCR1–5 mRNA levels to changes in Th1 signature-, inflammatory and regulatory cytokine mRNA levels. Methods CD4+ Th, CD8+ Tc ly, CD19+ B-ly and CD14+ monocytes from seven patients with definite RA were isolated by Dynabeads before and three months after the start of TNFαI. Total RNA was extracted and mRNAs for MCR1–5 and a panel of disease driving Th1, inflammatory and regulatory cytokines were measured by real-time qRT-PCR. Fold changes in MCR1–5 gene levels were correlated to changes in cytokine gene levels. Results MCR1–5 gene expressions were reduced in al examined cell types, significantly so in CD8+ Tc ly and CD19+ B ly in RA patients responding to TNFαI. In addition, Th1 and inflammatory cytokine mRNA levels were reduced in all cell types in responders. In a non-responding patient MCR1–5 gene expressions as well as Th1 and inflammatory cytokine gene levels increased substantially. The changes in MCR1–5 gene expressions in CD8+ Tc cells correlated significantly to changes in the Th1 cytokine IFNγ gene level in this cell type. Furthermore, we found significant correlations between changes in MCR 1, 3, 5- and change in IL-1β gene levels in CD4+ Th ly. Conclusions Our results point at a responsive melanocortin system in immune cells in RA. Moreover, its regulation seems intimately connected to the disease driving Th1 response, that is IFNγ production by CD8+ Tc ly. Our results are underlined by the recently reported importance of IFNγ producing CD8+ Tc ly in early RA. Thus our findings indicate that the melanocortin pathway lies open to treatment of auto-reactive effector T ly from RA patients with MCR type specific synthetic ligands in vivo or in vitro to induce Treg transformation. Future curative induction of auto-antigen specific immune tolerance in RA may therefore involve the melanocortin system. References Ahmed TJ, Montero-Melendez T, Peretti M, Pitzalis C. Curbing Inflammation through endogenous pathways: Focus on melanocortin peptides. Int J Inflamm 2013;2013:985815. Disclosure of Interest None declared

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.

Rheumatoid arthritis (RA) is caused by complex interactions between immune cells and sustained by Th1 response cytokines. Resistin [resistance to insulin; (RETN)] is an inflammatory cytokine, first discovered in murine adipocytes. In man, RETN is mainly secreted by monocytes. The distinct role of RETN in the immune reaction is uncertain; however, RETN has pro-inflammatory, profibrotic and possibly tolerogenic properties. The aim was to assess the reaction of RETN gene expression to TNF-alpha inhibition (I) in pathogenetic immune cell subsets in RA, in the context of Th1, inflammatory and regulatory cytokine gene expressions. Accordingly, we measured RETN, IFN-gamma, TNF-beta, IL-1 beta, TNF-alpha, TGF-beta and IL-10 gene expressions in CD14(+) monocytes, CD4(+) T helper (Th) lymphocytes (ly), CD8(+) T cytotoxic (Tc) ly and CD19(+) B ly in active RA before and 3 months after start of TNF-alpha I. Leucocyte subsets were separated by specific monoclonal antibody-covered beads, RNA extracted and levels of RETN, Th1 response, inflammatory and regulatory cytokine mRNAs measured by quantitative reverse transcription-polymerase chain reaction technique. We found that TNF-alpha I caused a significant downregulation of RETN gene expression in CD14(+) monocytes and CD4(+) Th ly and was unchanged in CD8(+) Tc ly and CD19(+) B ly. Both in active RA and during TNF-alpha I, RETN mRNA levels were significantly higher in CD14(+) monocytes than in all other examined cell types. In monocytes, fold change in RETN and TGF-beta gene expressions upon TNF-alpha I correlated significantly. Our findings indicate that RETN has pro-inflammatory as well as proresolving roles in active RA.

Contamination of ground water by industrial chemicals presents a major environmental and health problem. Soil sorption plays an important role in the transport and movement of such pollutant chemicals. In this study, proteochemometric (PCM) modeling was used to unravel the origins of interactions of 17 phthalic acid esters (PAEs) against 3 soil types by predicting the organic carbon content normalized sorption coefficient (log Koc) values as a function of fingerprint descriptors of 17 PAEs and physical and textural properties of 3 soils. The results showed that PCM models provided excellent predictivity (R2=0.94, Q2=0.89,QExt2=0.85). In further validation of the model, our proposed PCM model was assessed by leave-one-compound-out (QLOCO2=0.86) and leave-one-soil-out (QLOSO2=0.86) cross-validations. The transparency of the PCM model allowed interpretation of the underlying importance of descriptors, which potentially contributes to a better understanding on the outcome of PAEs in the environment. A thorough analysis of descriptor importance revealed the contribution of secondary carbon atoms on the hydrophobicity and flexibility of PAEs as significant properties in influencing the soil sorption capacity.

Host defense peptides (HDPs) are positively-charged and amphipathic components of the innate immune system that have demonstrated great potential to become the next generation of broad spectrum therapeutic agents effective against a vast array of pathogens and tumor. As such, many approaches have been taken to improve the therapeutic efficacy of HDPs. Amongst these methods, the incorporation of d-amino acids (d-AA) is an approach that has demonstrated consistent success in improving HDPs. Although, virtually all HDP review articles briefly mentioned about the role of d-AA, however it is rather surprising that no systematic review specifically dedicated to this topic exists. Given the impact that d-AA incorporation has on HDPs, this review aims to fill that void with a systematic discussion of the impact of d-AA on HDPs.