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1.
González-Díaz H Prado-Prado F Sobarzo-Sánchez E Haddad M Maurel Chevalley S Valentin A Quetin-Leclercq J Dea-Ayuela MA Teresa Gomez-Muños M Munteanu CR José Torres-Labandeira J García-Mera X Tapia RA Ubeira FM 《Journal of theoretical biology》2011,276(1):229-249
There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately most QSAR models predict activity against only one protein target and/or have not been implemented in the form of public web server freely accessible online to the scientific community. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 20:20-15-1:1. This MLP classifies correctly 611 out of 678 DTPs (sensitivity=90.12%) and 3083 out of 3408 nDTPs (specificity=90.46%), corresponding to training accuracy=90.41%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 310 out of 338 DTPs (sensitivity=91.72%) and 1527 out of 1674 nDTP (specificity=91.22%) in validation series, corresponding to total accuracy=91.30% for validation series (predictability). This model favorably compares with other ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. We implemented the present model at web portal Bio-AIMS in the form of an online server called: Non-Linear MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (NL MIND-BEST), which is located at URL: http://miaja.tic.udc.es/Bio-AIMS/NL-MIND-BEST.php. This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally we illustrated two practical uses of this server with two different experiments. In experiment 1, we report by first time Quantum QSAR study, synthesis, characterization, and experimental assay of antiplasmodial and cytotoxic activities of oxoisoaporphine alkaloids derivatives as well as NL MIND-BEST prediction of potential target proteins. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF, and -TOF/TOF MS, MASCOT search, MM/MD 3D structure modeling, and NL MIND-BEST prediction for different peptides a new protein of the found in the proteome of the human parasite Giardia lamblia, which is promising for anti-parasite drug-targets discovery. 相似文献
2.
González-Díaz H Munteanu CR Postelnicu L Prado-Prado F Gestal M Pazos A 《Molecular bioSystems》2012,8(3):851-862
Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense. 相似文献
3.
González-Díaz H Muíño L Anadón AM Romaris F Prado-Prado FJ Munteanu CR Dorado J Sierra AP Mezo M González-Warleta M Gárate T Ubeira FM 《Molecular bioSystems》2011,7(6):1938-1955
Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15?341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms. 相似文献
4.
R. Concu M.A. Dea-Ayuela L.G. Perez-Montoto F.J. Prado-Prado E. Uriarte F. Bolás-Fernández G. Podda A. Pazos C.R. Munteanu F.M. Ubeira H. González-Díaz 《Biochimica et Biophysica Acta - Proteins and Proteomics》2009,1794(12):1784-1794
The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a new and fast Markov chain model (MCM) to predict the enzyme classification (EC) number. We used both linear discriminant analysis (LDA) and/or artificial neural networks (ANN) in order to compare linear vs. non-linear classifiers. The LDA model found is very simple (three variables) and at the same time is able to predict the first EC number with an overall accuracy of 79% for a data set of 4755 proteins (859 enzymes and 3896 non-enzymes) divided into both training and external validation series. In addition, the best non-linear ANN model is notably more complex but has an overall accuracy of 98.85%. It is important to emphasize that this method may help us to predict not only new enzyme proteins but also to select peptide candidates found on the peptide mass fingerprints (PMFs) of new proteins that may improve enzyme activity. In order to illustrate the use of the model in this regard, we first report the 2D electrophoresis (2DE) and MADLI-TOF mass spectra characterization of the PMF of a new possible malate dehydrogenase sequence from Leishmania infantum. Next, we used the models to predict the contribution to a specific enzyme action of 30 peptides found in the PMF of the new protein. We implemented the present model in a server at portal Bio-AIMS (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php). This free on-line tool is based on PHP/HTML/Python and MARCH-INSIDE routines. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification. 相似文献
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6.
The spread of drug resistance through malaria parasite populations calls for the development of new therapeutic strategies.
However, the seemingly promising genomics-driven target identification paradigm is hampered by the weak annotation coverage.
To identify potentially important yet uncharacterized proteins, we apply support vector machines using profile kernels, a
supervised discriminative machine learning technique for remote homology detection, as a complement to the traditional alignment
based algorithms. In this study, we focus on the prediction of proteases, which have long been considered attractive drug
targets because of their indispensable roles in parasite development and infection. Our analysis demonstrates that an abundant
and complex repertoire is conserved in five Plasmodium parasite species. Several putative proteases may be important components in networks that mediate cellular processes, including
hemoglobin digestion, invasion, trafficking, cell cycle fate, and signal transduction. This catalog of proteases provides
a short list of targets for functional characterization and rational inhibitor design.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.
Rui Kuang and Jianying Gu have contributed equally to this work.
An erratum to this article can be found at 相似文献
7.
Server scalability is more important than ever in today's client/server dominated network environments. Recently, researchers
have begun to consider cluster-based computers using commodity hardware as an alternative to expensive specialized hardware
for building scalable Web servers. In this paper, we present performance results comparing two cluster-based Web servers based
on different server architectures: OSI layer two dispatching (LSMAC) and OSI layer three dispatching (LSNAT). Both cluster-based
server systems were implemented as application-space programs running on commodity hardware in contrast to other, similar,
solutions which require specialized hardware/software. We point out the advantages and disadvantages of both systems. We also
identify when servers should be clustered and when clustering will not improve performance.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
8.
Francisco J. Prado-Prado Xerardo García-Mera Humberto González-Díaz 《Bioorganic & medicinal chemistry》2010,18(6):2225-2231
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. 相似文献
9.
Random forests, a novel approach for discrimination of fish populations using parasites as biological tags 总被引:1,自引:0,他引:1
Perdiguero-Alonso D Montero FE Kostadinova A Raga JA Barrett J 《International journal for parasitology》2008,38(12):1425-1434
Due to the complexity of host-parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of fish. This novel approach is applied to a dataset with a complex structure comprising 763 parasite infracommunities in population samples of Atlantic cod, Gadus morhua, from the spawning/feeding areas in five regions in the North East Atlantic (Baltic, Celtic, Irish and North seas and Icelandic waters). The learning behaviour of RF is evaluated in comparison with two other algorithms applied to class assignment problems, the linear discriminant function analysis (LDA) and artificial neural networks (ANN). The three algorithms are used to develop predictive models applying three cross-validation procedures in a series of experiments (252 models in total). The comparative approach to RF, LDA and ANN algorithms applied to the same datasets demonstrates the competitive potential of RF for developing predictive models since RF exhibited better accuracy of prediction and outperformed LDA and ANN in the assignment of fish to their regions of sampling using parasite community data. The comparative analyses and the validation experiment with a 'blind' sample confirmed that RF models performed more effectively with a large and diverse training set and a large number of variables. The discrimination results obtained for a migratory fish species with largely overlapping parasite communities reflects the high potential of RF for developing predictive models using data that are both complex and noisy, and indicates that it is a promising tool for parasite tag studies. Our results suggest that parasite community data can be used successfully to discriminate individual cod from the five different regions of the North East Atlantic studied using RF. 相似文献
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11.
Dea-Ayuela MA Pérez-Castillo Y Meneses-Marcel A Ubeira FM Bolas-Fernández F Chou KC González-Díaz H 《Bioorganic & medicinal chemistry》2008,16(16):7770-7776
The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools. 相似文献
12.
Lian Wang Danling Pan Xihao Hu Jinyu Xiao Yangyang Gao Huifang Zhang Yan Zhang Juan Liu Shanfeng Zhu 《遗传学报》2009,36(5):289-296
Effective identification of major histocompatibility complex (MHC) molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class Ⅱ MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler, ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC (http:∥biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request). Using a standard measure of AUC (Area Under the receiver operating characteristic Curves), we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class Ⅱ MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods. 相似文献
13.
Prado-Prado FJ González-Díaz H de la Vega OM Ubeira FM Chou KC 《Bioorganic & medicinal chemistry》2008,16(11):5871-5880
Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites. 相似文献
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Ponts N Saraf A Chung DW Harris A Prudhomme J Washburn MP Florens L Le Roch KG 《The Journal of biological chemistry》2011,286(46):40320-40330
Malaria is one of the deadliest infectious diseases worldwide. The most severe form is caused by the eukaryotic protozoan parasite Plasmodium falciparum. Recent studies have highlighted the importance of post-translational regulations for the parasite's progression throughout its life cycle, protein ubiquitylation being certainly one of the most abundant. The specificity of its components and the wide range of biological processes in which it is involved make the ubiquitylation pathway a promising source of suitable targets for anti-malarial drug development. Here, we combined immunofluorescent microscopy, biochemical assays, in silico prediction, and mass spectrometry analysis using the multidimensional protein identification technology, or MudPIT, to describe the P. falciparum ubiquitome. We found that ubiquitin conjugates are detected at every morphological stage of the parasite erythrocytic cycle. Furthermore, we detected that more than half of the parasite's proteome represents possible targets for ubiquitylation, especially proteins found to be present at the most replicative stage of the asexual cycle, the trophozoite stage. A large proportion of ubiquitin conjugates were also detected at the schizont stage, consistent with a cell activity slowdown to prepare for merozoite differentiation and invasion. Finally, for the first time in the human malaria parasite, our results strongly indicate the presence of heterologous mixed conjugations, SUMO/UB. This discovery suggests that sumoylated proteins may be regulated by ubiquitylation in P. falciparum. Altogether, our results present the first stepping stone toward a better understanding of ubiquitylation and its role(s) in the biology of the human malaria parasite. 相似文献
16.
The histo-aspartic protease (HAP) from the malaria parasite P. falciparum is one of several new promising targets for drug intervention. The enzyme possesses a novel type of active site, but its 3D structure and mechanism of action are still unknown. Here we use a combination of homology modeling, automated docking searches, and molecular dynamics/reaction free energy profile simulations to predict the enzyme structure, conformation of bound substrate, catalytic mechanism, and rate of the peptide cleavage reaction. We find that the computational tools are sufficiently reliable both for identifying substrate binding modes and for distinguishing between different possible reaction mechanisms. It is found that the favored pathway only involves direct participation by the catalytic aspartate, with the neighboring histidine providing critical stabilization (by a factor of approximately 10000) along the reaction. The calculated catalytic rate constant of about 0.1 s(-1) for a hexapeptide substrate derived from the alpha chain of human hemoglobin is in excellent agreement with experimental kinetic data for a similar peptide fragment. 相似文献
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18.
Shams Tabrez Fazlur Rahman Rahat Ali Abdulaziz
S. Alouffi Sajjadul
Kadir Akand Bader
Mohammed Alshehri Fahdah
Ayed Alshammari Aftab Alam Mohammed
A. Alaidarous Saeed Banawas Abdul Aziz
Bin Dukhyil Abdur Rub 《Bioscience reports》2021,41(1)
Cynaroside, a flavonoid, has been shown to have antibacterial, antifungal and anticancer activities. Here, we evaluated its antileishmanial properties and its mechanism of action through different in silico and in vitro assays. Cynaroside exhibited antileishmanial activity in time- and dose-dependent manner with 50% of inhibitory concentration (IC50) value of 49.49 ± 3.515 µM in vitro. It inhibited the growth of parasite significantly at only 20 µM concentration when used in combination with miltefosine, a standard drug which has very high toxicity. It also inhibited the intra-macrophagic parasite significantly at low doses when used in combination with miltefosine. It showed less toxicity than the existing antileishmanial drug, miltefosine at similar doses. Propidium iodide staining showed that cynaroside inhibited the parasites in G0/G1 phase of cell cycle. 2,7-dichloro dihydro fluorescein diacetate (H2DCFDA) staining showed cynaroside induced antileishmanial activity through reactive oxygen species (ROS) generation in parasites. Molecular-docking studies with key drug targets of Leishmania donovani showed significant inhibition. Out of these targets, cynaroside showed strongest affinity with uridine diphosphate (UDP)-galactopyranose mutase with −10.4 kcal/mol which was further validated by molecular dynamics (MD) simulation. The bioactivity, ADMET (absorption, distribution, metabolism, excretion and toxicity) properties, Organisation for Economic Co-operation and Development (OECD) chemical classification and toxicity risk prediction showed cynaroside as an enzyme inhibitor having sufficient solubility and non-toxic properties. In conclusion, cynaroside may be used alone or in combination with existing drug, miltefosine to control leishmaniasis with less cytotoxicity. 相似文献
19.
《Genomics》2021,113(4):2730-2743
Mycoplasma genitalium is an obligate intracellular bacterium that is responsible for several sexually transmitted infections, including non-gonococcal urethritis in men and several inflammatory reproductive tract syndromes in women. Here, we applied subtractive genomics and reverse vaccinology approaches for in silico prediction of potential vaccine and drug targets against five strains of M. genitalium. We identified 403 genes shared by all five strains, from which 104 non-host homologous proteins were selected, comprising of 44 exposed/secreted/membrane proteins and 60 cytoplasmic proteins. Based on the essentiality, functionality, and structure-based binding affinity, we finally predicted 19 (14 novel) putative vaccine and 7 (2 novel) candidate drug targets. The docking analysis showed six molecules from the ZINC database as promising drug candidates against the identified targets. Altogether, both vaccine candidates and drug targets identified here may contribute to the future development of therapeutic strategies to control the spread of M. genitalium worldwide. 相似文献
20.
We have developed a novel method named AlphaTurn to predict alpha-turns in proteins based on the support vector machine (SVM). The prediction was done on a data set of 469 nonhomologous proteins containing 967 alpha-turns. A great improvement in prediction performance was achieved by using multiple sequence alignment generated by PSI-BLAST as input instead of the single amino acid sequence. The introduction of secondary structure information predicted by PSIPRED also improved the prediction performance. Moreover, we handled the very uneven data set by combining the cost factor j with the "state-shifting" rule. This further promoted the prediction quality of our method. The final SVM model yielded a Matthews correlation coefficient (MCC) of 0.25 by a 10-fold cross-validation. To our knowledge, this MCC value is the highest obtained so far for predicting alpha-turns. An online Web server based on this method has been developed and can be freely accessed at http://bmc.hust.edu.cn/bioinformatics/ or http://210.42.106.80/. 相似文献