Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors

BACKGROUND: Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens. METHODOLOGY/PRINCIPAL FINDINGS: We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2=0.89 and Q2ext=0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant. CONCLUSIONS/SIGNIFICANCE: Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens

A rule-based approach for discovering effective software team composition

Human aspects in software engineering play a key role in composing effective team members.However, to date there is no general consensus on the effective personality types and diversity based on software team roles.Thus, this paper aims to discover the effective personality types and diversity based on two software team roles – team leader and programmer by using a rule-based approach.The rule-based approach by employing the rough set technique was used to discover patterns of the data selected.In this study, four main steps were involved to discover the patterns – reduct generation rules, rules generation, rules filtering, and rules evaluation.The results show that the rules generated achieved acceptable prediction accuracy with more than 70 per cent accuracy.In addition, the ROC value achieved 0.65, which indicates the rule-based model is valid and useful.The results reveal that the extrovert personality type is dominant for both software team roles and a homogeneous or heterogeneous team plays an equal role to determine an effective team.This study provides useful rules for decision makers to understand and get insight into selecting effective team members that lead to producing high quality software

Uudsete meetodite arendamine ligandide s idumisomaduste uurimiseks melanokortiini 4 retseptorile

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö „Uudsete meetodite arendamine ligandide sidumisomaduste uurimiseks melanokortiini 4 retseptorile“ käsitleb uuringutega, mille üheks eesmärgiks oli fluorestsentsil põhinevate katsesüsteemide väljatöötamine, mida saaks kasutada uute melanokortiini retseptorite spetsiifiliste ligandide avastamiseks. Melanokortiini retseptorid osalevad mitmete oluliste füsioloogiliste funktsioonide regulatsioonil nagu pigmentatsioon, seksuaal- ja toitumiskäitumine, energiatasakaalu reguleerimine, valu ja keha temperatuuri reguleerimine, immuunsed ja põletikuvastased reaktsioonid, jne. Seega on melanokortiini retseptoritele spetsiifilised ligandid perspektiivsed ravimikandidaadid selliste haiguste ravimiseks nagu rasvumine ja anoreksia, melanoom, erektsiooni ja seksuaalsuse häired, aga ka ärevushäired ning depressioon. Uute tõhusate ravimite leidmine sõltub suurel määral ka meie teadmistest retseptor-ligandide vastasmõju mehhanisme kohta ning oskusest kasutada neid teadmisi uudsete efektiivsete raviainete disainimiseks. Lisaks sellele, oleneb tihti ka meetodist, mis on antud juhul meie „silmadeks“ selles katsesüsteemis, millist toimeaine mõju me üldse näeme ja kui hästi me seda detekteerida ning iseloomustada suudame. Siin töös arendatud uudsed fluorestsentsil põhinevad kastesüsteemid võimaldavad loobuda radioaktiivsete ühendite kasutamisest ning jälgida retseptor-ligandi vastasmõjusid reaalajas. See võimaldab saada täiendavat informatsiooni melanokortiinse süsteemi funktsioneerimise kohta, aga ka luua automatiseeritud katsesüsteem uute aktiivsete ühendite leidmiseks.Current PhD thesis “Development of assay systems for characterisation of ligand binding properties to melanocortin 4 receptors” describes our progress of melanocortin receptor studies connected with development of novel assay systems that would facilitate the discovery of novel receptor specific ligands. Melanocortin receptors are involved in regulation of wide variety of physiological functions like pigmentation, sexual behaviour, regulation of energy balance and feeding behaviours, temperature control and pain sense, inflammatory and immune responses and others. Thus, ligands for these receptors have a remarkable therapeutic potential for treatment of several human disorders like obesity and anorexia, melanoma, erectile dysfunction and sexual motivation, as well as anxiety and depression. Success in discovery of new drugs, in many aspects depends from our ability to understand the mechanisms of receptor-ligand interactions and use this to design novel, receptor subtype selective, potent and metabolically suitable drugs. Besides that, assay properties may play a very important role in interpretation of results, as the ability to see the effect of the drug depends on the “eyes” of the assay through which it is monitored. Fluorescence anisotropy-based assay systems we developed avoid the use of radioactive ligands and allow on-line monitoring of receptor-ligand interactions that would improve general understanding of the melanocortin system

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.This work was performed as part of the Smart University Project (SmartUniversity2014) financed by the University of Alicante

Algorithms for solving the reducts problem in rough sets

This work deals with finding minimal reducts of decision table based on the rough sets theory. Its goal is to develop algorithms capable of finding such reducts. Two algorithms are presented in this report: the first based on Boolean reasoning function, the second based on Genetic Algorithm. Test results on real data are given and conclusions are made

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina

The anti-proliferative activity of drimia altissima and a novel isolated flavonoid glycoside against hela cervical cancer cells

Cancer is one of the leading causes of mortality worldwide. About 44% of all cancer morbidity and 53% of all cancer mortality occur in countries with a low to medium Human Development Index (HDI). Thus, cancer is rapidly emerging as a serious threat to public health in Africa and most especially, sub-Saharan Africa. The International Agency for Research on Cancer (IARC) projects that there will be 1.28 million new cancer cases and 970 000 cancer deaths in Africa by the year 2030 owing to the increase in economic development associated lifestyles. The dominant types of cancer in Africa are those related to infectious diseases such as Kaposi’s sarcoma and cervical, hepatic and urinary bladder carcinomas. The main challenge to cancer treatment in Africa is the unavailability of efficacious anticancer drugs. This is because most developing countries can only afford to procure the most basic anticancer drugs, which are also frequently unavailable due to intermittent supplies. This results in patients progressing to more advanced cancer states. One way of combating this African problem is to focus on research that aims at discovering efficacious and cost effective cancer therapies from available natural resources within the African continent. This study investigated the potential anti-proliferative activity (against HeLa cervical cancer cells) of four plants (Adansonia digitata, Ceiba pentandra, Maytenus senegalensis and Drimia altissima) commonly used in the African traditional treatment of malignancies. After in vitro bio-assay screening using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay, M. senegalensis root extract (MS-R) and D. altissima bulb extract (DA-B) showed anti-proliferative activity against HeLa cervical cancer cells with IC50 values of 25 μg/mL and 1.1 μg/mL respectively. By possessing the strongest anti-proliferative activity among the tested extracts, D. altissima was selected for further studies. Liquid-liquid partitioning of the Drimia altissima bulb extract with n-hexane, ethyl acetate, and n-butanol, yielded partitions 79a – d, with the n-butanol fraction, 79d, exhibiting the strongest cytotoxic activity (IC50 = 0.497 μg/mL). Through High Content Analysis (HCA) screening, fraction 79d was found to induce marked early mitotic cell cycle arrest. Fractionation of 79d using Diaion® HP-20 open column chromatography and a stepwise gradient of reducing polarity (water-methanol-ethanol-ethyl acetate) yielded cytotoxic fractions 82b, 82c, 82d and 82e, all with significant anti-proliferative activities at the tested concentrations of 0.1, 1.0 and 10 μg/mL. Bio-assay guided fractionation of 82c (the most effective fraction at the lowest tested concentration of 0.1 μg/mL) using Sephadex® LH-20 open column chromatography and 50% MeOH led to the isolation of compound 3.17. After structural elucidation using 1D and 2D Nuclear Magnetic Resonance spectroscopy (NMR), High resolution Mass spectrometry (HRMS), Fourier-Transform Infrared spectroscopy (FT-IR), ultraviolet spectroscopy (UV) and Circular Dichroism (CD), compound 3.17 was identified as a novel C-glucosylflavonoid-O-glucoside, 6-C-[-apio-α-D-furanosyl-(1→6)-β-glucopyranosyl]-4′, 5, 7-trihydroxyflavone (Altissimin, 3.17). Compound 3.17 exhibited a dose dependant anti-proliferative activity with an IC50 of 2.44 μM. The mechanism of action for compound 3.17 was investigated through cell cycle arrest, phosphatidylserine translocation (PS), caspase activation and mitochondrial membrane depolarization. The mechanism of cell death elicited by compound 3.17 in HeLa cells was found to involve the induction of M phase cell cycle arrest with consequent activation of apoptotic cell death which was evident from annexin V staining, mitochondrial membrane potential (ΔΨm) collapse and the activation of caspases -8 and -3. In silico computational techniques were employed to virtually determine potential biological targets of compound 3.17. Target fishing using the Similarity Ensemble Approach (SEA) target prediction gave human aldose reductase (hAR, AKR1B1) the highest ranking with a p value of 2.85 x 10-24, a max Tc of 0.35 and a Z-score of 41.8217. Using AutoDock4 and the AutoDock tools suite (ADT), molecular docking of compound 3.17 in the hAR binding pocket was successfully achieved with a lower ΔG free energy binding (-9.4 kcal/mol) than that of positive control ligand 393 (-8.7 kcal/mol). In conclusion, this study identified the genus Drimia and particularly D. altissima as a potential source for novel cytotoxic compounds. The discovery of altissimin (3.17), the first flavonoid glycoside to be isolate from D. altissima, enquires into the possible existence of similar compounds within the species. In addition to the observed in vitro cytotoxic activity against HeLa cells, the potential of altissimin (3.17) as a hAR enzyme inhibitor opens up the possibility of its use as an adjunct to increase cancer cell sensitivity to chemotherapy. Thus, altissimin (3.17) shows promise as a potential anticancer agent

In silico strategies to study polypharmacology of G-protein-coupled receptors

The development of drugs that simultaneously target multiple receptors in a rational way (i.e., 'magic shotguns') is regarded as a promising approach for drug discovery to treat complex, multi-factorial and multi-pathogenic diseases. My major goal is to develop and employ different computational approaches towards the rational design of drugs with selective polypharmacology towards guanine nucleotide-binding protein (G-protein)-coupled receptors (GPCRs) to treat central nervous system diseases. Our methodologies rely on the advances in chemocentric informatics and chemogenomics to generate experimentally testable hypotheses that are derived by fusing independent lines of evidence. We posit that such hypothesis fusion approach allows us to improve the overall success rates of in silico lead identification efforts. We have developed an integrated computational approach that combines Quantitative Structure-Activity Relationships (QSAR) modeling, model-based virtual screening (VS), gene expression analysis and mining of the biological literature for drug discovery. The dissertation research described herein is focused on: (1) The development of robust data-driven Quantitative Structure-Activity Relationship (QSAR) models of single target GPCR datasets that will amount to the compendium of GPCR predictors: the GPCR QSARome; (2) The development of robust data-driven QSAR models for families of GPCRs and other trans-membrane molecular targets (i.e., sigma receptors) and the application of models as virtual screening tools for the quick prioritization of compounds for biological testing across receptor families; (3) The development of novel integrative chemocentric informatics approaches to predict receptor-mediated clinical effects of chemicals. Results indicated that our computational efforts to establish a compendium of computational predictors and devise an integrative chemocentric informatics approach to study polypharmacology in silico will eventually lead to useful and reliable tools aimed at identifying and enriching chemical libraries with compounds that have the desired activities for more than one molecular target of interest

Integration and analysis of protein evolutionary relationships and small molecule bioactivity data

RT-158623Interactions of small organic molecules and proteins have been studied extensively in the search of therapeutic drugs. Historically, the interaction partners have been attributed to separate scientific disciplines: small organic molecules to the domain of chemistry, proteins to the domain of biology. Likewise, chemical and biological data have been stored and maintained separately. The aim of my thesis was to integrate the ChEMBL database, a public repository of small molecule bioactivity measurements, with resources of protein evolutionary relationships, and exploit these new links to further our understanding of small molecule bioactivity. In order to link biological assays via the evolutionary relationships of their protein targets, I established a mapping of small molecule binding to specific structural protein domains - the fundamental building blocks of protein architecture and evolution. By mapping small molecule binding to protein domains, I was able to examine links between the properties of small molecules and the evolutionary units that mediate their binding. I used domain definitions from Pfam, a database of protein domains derived from conserved sequence blocks. The mapping is now an integral part of the ChEMBL database and can be used to limit sequence-based queries to sequence partitions that are relevant to small molecule binding. Further, I integrated information from the homology resource EnsemblCompara Genetrees with bioactivity data from ChEMBL to examine the conservation of small molecule potency between homologous proteins within and across species. Potency differences between related proteins are a useful indicator of small molecule specificity. Specificity is an early milestone for most drug discovery projects as it allows for the manipulation of a desired process in a targeted manner, with side effects reduced to a minimum. I examined pairs of closely related human proteins and found that potency differences were overall greater than the estimated background noise. Using the outlined integration approach in a cross-species comparison, I also observed that potency differences between pairs of related proteins in human and rat were overall no greater than the background noise. This is relevant to the use of model organisms for drug discovery, which relies on extrapolation from a measured response in one species to a therapeutic effect in humans. Taken together I have integrated small molecule bioactivity and protein evolutionary data from two resources, Pfam and EnsemblCompara Genetrees. This has provided a framework for studying small molecule binding in the context of protein evolution