Humberto Gonzalez-Diaz
PROFESSOR & RESEARCHER: Years of experience >10, Hirsch's h-index > 30, JCR Papers >120, Teaching and Research experience in: Bioinformatics, Chemo-Informatics, Statistics, Systems Biology, Complex Networks, and Machine Learning. Accreditation: Full Professor of University, ANECA, Spain, Dec 2011.
JOURNAL EDITOR: Co-Editor-in-Chief (EIC) of Curr Top Med Chem (CTMC), 2012. Guest Editor of Curr Pharm Des, Curr Drug Metabolism, Curr Bioinformatics, Curr Comput-Aided Drug Des, and Curr Proteomics.
Research interests
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InterestsSystem Biology, Chemoinformatics, Computational Biology, Computational Chemistry, Computer Aided Drug Design, Proteomics, Molecular Descriptors, Chemical Graph Theory, Bioinformatic Software, Complex Networks, Complex Systems, QSAR, Virtual Screening
Publications
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4.47Impact points
Editorial: QSAR/QSPR Models as Enabling Technologies for Drug & Targets Discovery in: Medicinal Chemistry, Microbiology-Parasitology, Neurosciences, Bioinformatics, Proteomics and Other Biomedical Sciences
Current Topics in Medicinal Chemistry. 04/2012; 12(8):799-801.
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4.47Impact points
From QSAR models of Drugs to Complex Networks: State-of-Art Review and Introduction of New Markov-Spectral Moments Indices
Current Topics in Medicinal Chemistry. 04/2012; 12(8):927-960.
Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In thi... [more] Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Lastly, we report for the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared in response to the kind invitation of the editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.
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3.74Impact points
Patents of Bio-active Compounds based on Computer-Aided Drug Discovery techniques
Frontiers in Bioscience. 03/2012; 17:accepted.
In recent times, there has been an increased use of Computer-Aided Drug Discovery (CADD) techniques in Medicinal Chemistry as auxiliary tools in drug discovery. Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome tha... [more] In recent times, there has been an increased use of Computer-Aided Drug Discovery (CADD) techniques in Medicinal Chemistry as auxiliary tools in drug discovery. Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. The adoption of computational tools by medicinal chemists is sadly, and all too often accompanied, by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products. In the center of picture, which lies in the frontiers of legal, chemistry, and biosciences, we found computational modeling-based drug discovery patents. We refer here to patents that claims new drug candidate compounds and recognized/claim as well computational techniques used in the discovery process. This article aims to review prominent cases of patents of bio-active organic compounds that involved/protect also computational techniques. We put special emphasis on patents based on Quantitative Structure-Activity Relationships (QSAR) models but we include other techniques too. An overview of relevant international issues on drug patenting is also presented. TABLE OF CONTENTS 1. Abstract 2. Introduction 3. CADD Bio-Active compounds patents 3.1. Compounds & Computational method 3.1.1. Aziridinyl quinone antitumor agents 3.1.2 Chalcones for neoplastic disorders 3.1.3. Steroid acting over DNA replication. 3.1.4. Drugs suppressing appetite 3.1.5. Anti-microbial peptidomimetic compounds 3.1.6. Biologically active chalcones 3.1.7. Predictive method for polymers. 3.2. Computational method alone 3.2.1. One-dimensional QSAR models. 3.2.2. Comparative molecular field analysis 3.2.3. Molecular hologram QSAR. 3.2.4. Predictive method for polymers 3.2.5. Molecule Fragmentation Scheme 3.2.6. Method for predicting organic pollutants 3.2.7. Searching compound databases 3.2.8. A 3D-QSAR method 3.2.9. QSAR & pharmacophore fingerprinting 3.2.10. Fast computer data segmenting techniques 4. Notes on Legal issues related to CADD 4.1. Some general remarks 4.2. Short notes on Copyright protection 4.3. A short note on patent protection 5. Conclusions 6. Acknowledgements 7. References
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3.74Impact points
Legal Issues for Chem-Bioinformatics Models at Biosciences Frontiers
Frontiers in Bioscience. 03/2012; 17(Legal Trends in Bioscience: Intellectual Property, Medico-legal Procedures and Regulatory Issues).
A very useful class of models describing structure-property relationships of systems may play an important role to reduce costs in terms of time, human resources, material resources, as well as allow certain laboratory animals replacement in biomedical sciences. Many of these models are in essence Q... [more] A very useful class of models describing structure-property relationships of systems may play an important role to reduce costs in terms of time, human resources, material resources, as well as allow certain laboratory animals replacement in biomedical sciences. Many of these models are in essence Quantitative Structure-Activity or Property Relationships (QSARs/QSPRs). In other words, QSPRs are models that connect the structure of a system with external properties of these systems that are not self-evident after direct inspection of the structure. In particular, QSARs are models that connect the chemical structure of drugs, target (protein, gen, RNA, microorganism, tissue, disease…), or both (drug and target at the same time) with drug biological activity over this target. On the other hand, a systematic judicial framework is needed to provide appropriate and relevant guidance for addressing various computing techniques as applied to scientific research in biosciences frontiers. Bioinformatics and computational biology are two areas within the field of biosciences that require more attention from the legal operators. Taking all the previous aspects into consideration, this article reviews both: the use of the predictions made with models for regulatory purposes in legal and how to legally protect models of molecular sytems systems per se, and the software used to seek them. In this sense, the issues reviewed here are the following. In the first part we review: i) QSAR models as a tool for regulatory purposes, ii) Organizations Involved with Validation of QSARs, iii) Regulatory Guidelines and Documents for QSARs Developing and Application, iv) QSARs for Human Health and Environmental Endpoint, and v) Difficulties to Validation of QSARs. We also discuss in this part: vi) OECD’s Database on Chemical Risk Assessment Models, vii) QSARs Based on Metabolism and In Vitro Data, and viii) Perspectives in QSAR Modeling for Regulatory Use. In the second part we focused on the legal protection of QSAR-like models and software; including: a short summary of topics, and methods for legal protection of computer software like: copyright protection, patent protection, trade secret protection, and trademark protection. We close the review with a section that treat the taxes in software use. TABLE OF CONTENTS 1. Introduction 2. Discussion 2.1. QSAR models as a tool for regulatory purposes 2.1.1 Organizations Involved with Validation of QSARs 2.1.2 Regulatory Guidelines and Documents for QSARs Developing and Application 2.1.3 QSARs for Human Health and Environmental Endpoint 2.1.4 Difficulties to Validation of QSARs 2.1.5 OECD’s Database on Chemical Risk Assessment Models 2.1.6 QSARs Based on Metabolism and In Vitro Data 2.1.7 Perspectives in QSAR Modeling for Regulatory Use 2.2. Legal protection of QSAR-like models and software 2.2.1. Short summary of topics 2.2.2. Methods for legal protection of computer software 2.2.2.1. Copyright protection 2.2.2.2. Patent protection 2.2.2.3. Trade Secret Protection 2.2.2.4. Trademark Protection 2.2.2.5. Copyright and contractual issues. 2.3. Taxes in software use 3. Conclusions
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4.02Impact points
Naïve Bayes QPDR classification based on spiral-graph Shannon entropies for protein biomarkers
Molecular BioSystems. 03/2012;
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2.57Impact points
New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.
Journal of theoretical biology. 01/2012; 293:174-88.
Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are man... [more] Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction: Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry.
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1.69Impact points
Bioinformatics and Quantitative Structure-Property Relationship (QSPR) models
Current Bioinformatics. 01/2012; 7.
We can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems; at least in principle. We see QSPR model as a function that predict the properties of different systems using parameters that numerically describe the structure of the system (like TIs). We referrer ... [more] We can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems; at least in principle. We see QSPR model as a function that predict the properties of different systems using parameters that numerically describe the structure of the system (like TIs). We referrer to almost any class of systems ranging from molecules to social networks such as: Drugs, Proteins and Proteomes, RNA, Diseasomes, Brain cortex Interactome, Disease spreading networks or Internet. There are many QSPR-like terms that fit to these and more specific situations, for instance Quantitative Structure-Activity Relationships (QSAR), Quantitative Structure-Toxicity Relationships (QSPR), Quantitative Proteome-Property Relationships (QPPR), Quantitative Sequence-Action Model (QSAM), or Quantitative Structure-Reactivity Relationships (QSRR), to cite a few examples. In all this cases we can find models that use the TIs of the system as input to predict the properties of this system (output). In addition, some groups published different papers on this topic in different special issues guest-edited by González-Díaz in review journals like Current Topics in Medicinal Chemistry in 2008 [1-10], Current Proteomics in 2009 [11-15], Current Drug Metabolism [16-24] and Current Pharmaceutical Design [25-34] in 2010, as well as Current Bioinformatics in 2011 [35-44]. In this sense, we decided to guest-edit the present issue focused on QSPR-like models with TIs and networks more focused on Bioinformatics. We hope that the present issue may serve as a bridge between theoretical scientists in graph theory and experimentalists in order to suggest new areas of mutual interchange and collaboration. In the first work of this issue KC Chou et al. developed a QSPR-like model to predict Nucleosome positioning based on sequence word composition. In the second paper Chis et al. review and at the same time investigate some stochastic models for tumor-immune systems. To describe these models, we used a Wiener process, as the noise has a stabilization effect. Their dynamics are studied in terms of stochastic stability around the equilibrium points, by constructing the Lyapunov exponent, depending on the parameters that describe the model. Stochastic stability was also proved by constructing a Lyapunov function and the second order moments. They have studied and analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model for tumor-immune systems. The same group also studied Deterministic and stochastic model for the role of the immune response time delay in periodic therapy of the tumors in other work of this issue. In another review of this special issue Riera-Fernandez et al. have made a review of classic Randic TIs. We also analyzed the Kier-Hall indices as the first known generalization of Randic index. Next we studied a more recent generalization reported by Estrada. Last we introduced a new class of indices called the Markov-Randic indices. We applied these indices to seek QSPR-like models useful to re-evaluate the quality of links in known complex networks in Biology, Parasitology, Technology, and Social-Legal sciences. Next, Khan et al. studied Molecular interactions involving naturally occurring steroidal alkaloids from Sarcococca hookeriana against Acetyl- and Butyryl-cholinesterase. The following work after Munteanu et al. is the only in this issue that reviews a new software for QSPR modeling with general application to the construction of complex networks and calculation of TIs from sequence (text or numeric string) data. The work entitled: S2SNet: A Tool for Transforming Characters and Numeric Sequences into Star Network Topological Indices in Chemoinformatics, Bioinformatics, Biomedical, and Social-Legal sciences. In the following paper, Molina et al. analyzed the application of different TIs to seek QSAR models for MicroRNAs (miRNAs), which are non-coding small RNAs, regulate a variety of biological processes and constitute good candidates to scale up the application of QSAR to complex networks of molecular structure. On the other hand, the work of Speck-Planche and Cordeiro focused in the role of QSAR combined with Bioinformatics toward the design of compounds with anti-herpetic activity and also we propose a model based in fragment descriptors for the design, search and prediction of anti-herpes compounds through the inhibition of 5 targets belonging to different herpes viruses. Similarly, Khuntwal et al. recognized the urgent need to develop novel anti-HCV agents has provided an impetus for understanding the structure–activity relationship of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Towards this objective, they developed QSAR-like models based on multiple linear regression (MLR) and support vector machine (SVM). Following this line, García-Pintos et al. revised three servers like ChEMBL, PDB or PubMed to obtain databases of DNA polymerase inhibitors. Next, they reviewed previous works based on 2D-QSAR, 3D-QSAR, CoMFA, CoMSIA and Docking techniques, which studied different compounds to find out the structural requirements. Finally, these authors surveyed the more recent studies of alignments of DNA polymerase. In connection with this previous work, Meysman et al. used the CRoSSeD methodology for describing DNA structural properties to extract common features in the binding sites of different LacI-GalR family members. However, Prado-Prado et al. reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking and new theoretical methodology with different compound to find out the structural requirements. Next, they revised QSAR studies using method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for binding with receptor for AChE inhibitors. Closing this issue, Sanchez et al. give an overview of the different functional and physical interaction networks in bacteria that have been or potentially can be built by the integration of diverse OMICS datasets. Guest Editor González-Díaz H, PhD. Department of Microbiology and Parasitology Faculty of Pharmacy University of Santiago de Compostela 15782, Spain. January, 11, 2011. [1] Caballero, J.; Fernandez, M.Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1) Curr Top Med Chem, 2008, 8, 1580-605. [2] Duardo-Sanchez, A.; Patlewicz, G.; Lopez-Diaz, A.Current topics on software use in medicinal chemistry: intellectual property, taxes, and regulatory issues Curr Top Med Chem, 2008, 8, 1666-75. [3] Gonzalez, M. P.; Teran, C.; Saiz-Urra, L.; Teijeira, M.Variable selection methods in QSAR: an overview Curr Top Med Chem, 2008, 8, 1606-27. [4] Gonzalez-Diaz, H.Quantitative studies on Structure-Activity and Structure-Property Relationships (QSAR/QSPR) Curr Top Med Chem, 2008, 8, 1554. [5] Gonzalez-Diaz, H.; Prado-Prado, F.; Ubeira, F. M.Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach Curr Top Med Chem, 2008, 8, 1676-90. [6] Helguera, A. M.; Combes, R. D.; Gonzalez, M. P.; Cordeiro, M. N.Applications of 2D descriptors in drug design: a DRAGON tale Curr Top Med Chem, 2008, 8, 1628-55. [7] Ivanciuc, O.Weka machine learning for predicting the phospholipidosis inducing potential Curr Top Med Chem, 2008, 8, 1691-709. [8] Vilar, S.; Cozza, G.; Moro, S.Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery Curr Top Med Chem, 2008, 8, 1555-72. [9] Wang, J. F.; Wei, D. Q.; Chou, K. C.Drug candidates from traditional chinese medicines Curr Top Med Chem, 2008, 8, 1656-65. [10] Wang, J. F.; Wei, D. Q.; Chou, K. C.Pharmacogenomics and personalized use of drugs Curr Top Med Chem, 2008, 8, 1573-9. [11] Torrens, F.; Castellano, G.Topological Charge-Transfer Indices: From Small Molecules to Proteins Curr Proteomics, 2009, 204-213. [12] Ivanciuc, O.Machine learning Quantitative Structure-Activity Relationships (QSAR) for peptides binding to Human Amphiphysin-1 SH3 domain Curr Proteomics, 2009, 4, 289-302. [13] Giuliani, A.; Di Paola, L.; Setola, R.Proteins as Networks: A Mesoscopic Approach Using Haemoglobin Molecule as Case Study Curr Proteomics, 2009, 6, 235-245. [14] Chou, K. C.Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology Curr Proteomics, 2009, 6, 262-274. [15] Chen, J.; Shen, B.Computational Analysis of Amino Acid Mutation: a Proteome Wide Perspective Curr Proteomics, 2009, 6, 228-234. [16] Zhong, W. Z.; Zhan, J.; Kang, P.; Yamazaki, S.Gender specific drug metabolism of PF-02341066 in rats--role of sulfoconjugation Curr Drug Metab, 2010, 11, 296-306. [17] Wang, J. F.; Chou, K. C.Molecular modeling of cytochrome P450 and drug metabolism Curr Drug Metab, 2010, 11, 342-6. [18] Mrabet, Y.; Semmar, N.Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts Curr Drug Metab, 2010, 11, 315-41. [19] Martinez-Romero, M.; Vazquez-Naya, J. M.; Rabunal, J. R.; Pita-Fernandez, S.; Macenlle, R.; Castro-Alvarino, J.; Lopez-Roses, L.; Ulla, J. L.; Martinez-Calvo, A. V.; Vazquez, S.; Pereira, J.; Porto-Pazos, A. B.; Dorado, J.; Pazos, A.; Munteanu, C. R.Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network Curr Drug Metab, 2010, 11, 347-68. [20] Khan, M. T.Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches Curr Drug Metab, 2010, 11, 285-95. [21] Gonzalez-Diaz, H.; Duardo-Sanchez, A.; Ubeira, F. M.; Prado-Prado, F.; Perez-Montoto, L. G.; Concu, R.; Podda, G.; Shen, B.Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers Curr Drug Metab, 2010, 11, 379-406. [22] Gonzalez-Diaz, H.Network topological indices, drug metabolism, and distribution Curr Drug Metab, 2010, 11, 283-4. [23] Garcia, I.; Diop, Y. F.; Gomez, G.QSAR & complex network study of the HMGR inhibitors structural diversity Curr Drug Metab, 2010, 11, 307-14. [24] Chou, K. C.Graphic rule for drug metabolism systems Curr Drug Metab, 2010, 11, 369-78. [25] Concu, R.; Podda, G.; Ubeira, F. M.; Gonzalez-Diaz, H.Review of QSAR models for enzyme classes of drug targets: Theoretical background and applications in parasites, hosts, and other organisms Current Pharmaceutical Design, 2010, 16, 2710-23. [26] Estrada, E.; Molina, E.; Nodarse, D.; Uriarte, E.Structural Contributions of Substrates to their Binding to P-Glycoprotein. A TOPS-MODE Approach Current Pharmaceutical Design, 2010, 16, 2676-709. [27] Garcia, I.; Fall, Y.; Gomez, G.QSAR, docking, and CoMFA studies of GSK3 inhibitors Current Pharmaceutical Design, 2010, 16, 2666-75. [28] Gonzalez-Diaz, H.QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer, and neurosciences Current Pharmaceutical Design, 2010, 16, 2598-600. [29] Gonzalez-Diaz, H.; Romaris, F.; Duardo-Sanchez, A.; Perez-Montoto, L. G.; Prado-Prado, F.; Patlewicz, G.; Ubeira, F. M.Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues Current Pharmaceutical Design, 2010, 16, 2737-64. [30] Marrero-Ponce, Y.; Casanola-Martin, G. M.; Khan, M. T.; Torrens, F.; Rescigno, A.; Abad, C.Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds Current Pharmaceutical Design, 2010, 16, 2601-24. [31] Munteanu, C. R.; Fernandez-Blanco, E.; Seoane, J. A.; Izquierdo-Novo, P.; Rodriguez-Fernandez, J. A.; Prieto-Gonzalez, J. M.; Rabunal, J. R.; Pazos, A.Drug discovery and design for complex diseases through QSAR computational methods Current Pharmaceutical Design, 2010, 16, 2640-55. [32] Roy, K.; Ghosh, G.Exploring QSARs with Extended Topochemical Atom (ETA) indices for modeling chemical and drug toxicity Current Pharmaceutical Design, 2010, 16, 2625-39. [33] Speck-Planche, A.; Scotti, M. T.; de Paulo-Emerenciano, V.Current pharmaceutical design of antituberculosis drugs: future perspectives Current Pharmaceutical Design, 2010, 16, 2656-65. [34] Vazquez-Naya, J. M.; Martinez-Romero, M.; Porto-Pazos, A. B.; Novoa, F.; Valladares-Ayerbes, M.; Pereira, J.; Munteanu, C. R.; Dorado, J.Ontologies of drug discovery and design for neurology, cardiology and oncology Current Pharmaceutical Design, 2010, 16, 2724-36. [35] García, I.; Fall, Y.; Gómez, G.Trends in Bioinformatics and Chemoinformatics of Vitamin D analogues and their protein targets Current Bioinformatics, 2011, 6, 16-24. [36] Chiş, O.; Dumitru, O.; Concu, R.; Shen, B.Reviewing Yeast Network and report of new Stochastic-Credibility cell cycle models Current Bioinformatics, 2011, 6, 35-43. [37] Duardo-Sanchez, A.; Patlewicz, G.; González-Díaz, H.A Review of Network Topological Indices from Chem-Bioinformatics to Legal Sciences and back Current Bioinformatics, 2011, 6, 53-70. [38] Bhattacharjee, B.; Jayadeepa, R. M.; Banerjee, S.; Joshi, J.; Middha, S. K.; Mole, J. P.; Samuel, J.Review of Complex Network and Gene Ontology in pharmacology approaches: Mapping natural compounds on potential drug target Colon Cancer network Current Bioinformatics, 2011, 6, 44-52. [39] Prado-Prado, F.; Escobar-Cubiella, M.; García-Mera, X.Review of Bioinformatics and QSAR studies of β-secretase inhibitors Current Bioinformatics, 2011, 6, 3-15. [40] Ivanciuc, T.; Ivanciuc, O.; Klein, D. J.Network-QSAR with Reaction Poset Quantitative Superstructure-Activity Relationships (QSSAR) for PCB Chromatographic Properties Current Bioinformatics, 2011, 6, 25-34. [41] Wan, S. B.; Hu, L. L.; Niu, S.; Wang, K.; Cai, Y. D.; Lu, W. C.; Chou, K. C.Identification of multiple subcellular locations for proteins in budding yeast Current Bioinformatics, 2011, 6, 71-80. [42] Riera-Fernández, P.; Munteanu, C. R.; Pedreira-Souto, N.; Martín-Romalde, R.; Duardo-Sanchez, A.; González-Díaz, H.Definition of Markov-Harary Invariants and Review of Classic Topological Indices and Databases in Biology, Parasitology, Technology, and Social-Legal Networks Current Bioinformatics, 2011, 6, 94-121. [43] Dave, K.; Banerjee, A.Bioinformatics analysis of functional relations between CNPs regions Current Bioinformatics, 2011, 6, 122-128. [44] Speck-Planche, A.; Cordeiro, M. N. D. S.Application of Bioinformatics for the search of novel anti-viral therapies: Rational design of anti-herpes agents Current Bioinformatics, 2011, 6, 81-93.
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1.69Impact points
Generalized String Pseudo-Folding Lattices in Bioinformatics: State-of-Art Review, New Model for Enzyme Sub-Classes, and Study of ESTs on Trichinella spiralis
Current Bioinformatics. 01/2012; 7(1):7-34.
Several graph representations have been introduced for different data in theoretical biology. For instance, Complex Networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Rand... [more] Several graph representations have been introduced for different data in theoretical biology. For instance, Complex Networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning (sequence pseudo-folding rules) in 2D space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. In fact, we can use this technique to create string pseudo-folding lattice representations for any kind of string data. However, despite the proved efficacy of new Lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. In this work, we review both classic and generalized types of lattice graphs as well as examples that illustrate how to use it in order to represent and compare biological data from different sources. The examples reviewed include the following cases: Protein sequence; Mass Spectra (MS) of protein Peptide Mass Fingerprints (PMF); Molecular Dynamic Trajectory (MDTs) from structural studies; mRNA Microarray data; Single Nucleotide Polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein Polymorphisms and Protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of graph theory in theoretical biology and other areas of biomedical sciences. In addition, we carried out the statistical analysis of 50,000+ cases to seek and validate a new QSAR-like predictor for enzyme sub-classes. The model use as inputs spectral moments of pseudo-folding lattice graphs. Last we illustrated the use of this model to study 4,000+ ESTs of protein sequences present on the parasite Trichinella spiralis.
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1.69Impact points
S2SNet: A Tool for Transforming Characters and Numeric Sequences into Star Network Topological Indices in Chemoinformatics, Bioinformatics, Biomedical, and Social-Legal sciences
Current Bioinformatics. 01/2012; 7.
The study of complex systems such as proteins/DNA/RNA or dynamics of tax law systems can be carried out with the complex network theory. This permits the numerical quantification of the significant information contained by the sequences of amino acids, nucleotides or types of tax laws. In this paper... [more] The study of complex systems such as proteins/DNA/RNA or dynamics of tax law systems can be carried out with the complex network theory. This permits the numerical quantification of the significant information contained by the sequences of amino acids, nucleotides or types of tax laws. In this paper we describe S2SNet, a new Python tool with graphical user interface that can transforms any sequence of characters or numbers into series of invariant star network topological indices. The application is based on Python reusable processing procedures that perform different functions such as reading sequences data, transforming numerical series in character sequences, changing letter codification of strings and drawing the star networks of each sequence by using Graphviz package as graphical back-end. S2SNet was previous used to obtain classification models for natural/random proteins, breast/colon/prostate cancer-related proteins, DNA sequences of Mycobacterial promoters and for early detection of diseases and drug-induced toxicities by using the blood serum proteome mass spectrum. In order to show the extended practical potential of S2SNet, this work presents several examples of application for proteins, DNA/RNA, blood proteome mass spectra and time evolution of the financial law recurrence. The obtained topological indices can be used to characterize the systems by creating classification models, clustering or pattern search with statistics, Neural Network or Machine Learning methods. The free availability of S2SNet, the flexibility of analysing of diverse systems and the Python portability make it an ideal tool in fields such as Bioinformatics, Proteomics, Genomics, and Biomedicine or Social, Economic and Political sciences(http://miaja.tic.udc.es/software/S2SNet/S2SNet3.0.zip).
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1.69Impact points
Markov-Randic indices for QSPR Re-evaluation of Metabolic, Parasite-Host, Fasciolosis Spreading, Brain Cortex and Legal-Social Complex Networks
Current Bioinformatics. 01/2012; 7.
Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are man... [more] Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, many of these methods are expensive in terms of time or resources. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of the major interest. The Randić index is a well known topological index (TI) used in QSAR/QSPR studies to quantify the molecular structure represented by a graph. In this work we review some aspects of this TI with special emphasis on the generalizations introduced by Kier & Hall and more recently by Estrada. Next, we introduced a new generalization using a Markov chain in order to obtain a new family of TIs called the Markov-Randić indices of order k-th (1χk). Last, we apply these new indices to seek QSPR models useful to calculate numerical quality scores S(Lij) for network links Lij (connectivity) in known complex networks. The method may be summarized as follows: (i) first, the χk(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (Lij = 1) pairs of nodes experimentally confirmed from non-linked ones (Lij = 0); (iii) The new model is validated with external series of pairs of nodes, (iv) The equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network re-construction: Metabolic networks (70.48%), Parasite-Host networks (90.86%), CoCoMac brain cortex co-activation network (81.59%), NW Spain Fasciolosis spreading network (99.04%). Spanish financial law network (71.83%). This work opens a new door to the computational re-evaluation of network connectivity quality (collation) in different complex systems.
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3.27Impact points
2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.
European journal of medicinal chemistry. 12/2011; 46(12):5838-51.
There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interact... [more] There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC(50) = 0.00083 μM, notably better than the control drug Clorgyline.
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1.68Impact points
From chemical graphs in computer-aided drug design to general Markov-Galvez indices of drug-target, proteome, drug-parasitic disease, technological, and social-legal networks.
Current computer-aided drug design. 10/2011; 7(4):315-37.
Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures to large systems. We can cite for instance, drug-t... [more] Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures to large systems. We can cite for instance, drug-target protein interaction networks, drug policy legislation networks, or drug treatment in large geographical disease spreading networks. In any case, all these networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and edges (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for different kind of problems in Computer-Aided Drug Design (CADD). Taking into account all the above-mentioned aspects, the present work is aimed at offering a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most common types of complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. Next, we use for the first time a Markov chain model to generalize Galvez TIs to higher order analogues coined here as the Markov-Galvez TIs of order k (MGk). Lastly, we illustrate the calculation of MGk values for different classes of networks found in drug research, nature, technology, and social-legal sciences.
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1.68Impact points
QSPR models for computer-aided drug design in microbiology, parasitology, and pharmacology.
Current computer-aided drug design. 10/2011; 7(4):228-30.
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1.68Impact points
Review of computer-aided models for predicting collagen stability.
Current computer-aided drug design. 10/2011; 7(4):287-303.
Collagen is the most abundant protein in the whole human body and its instability is involved in many important diseases, such as Osteogenesis imperfecta, Ehlers-Danlos syndrome, and collagenopathy. The stability of the collagen triple helix is strictly related to its amino acid sequence, especially... [more] Collagen is the most abundant protein in the whole human body and its instability is involved in many important diseases, such as Osteogenesis imperfecta, Ehlers-Danlos syndrome, and collagenopathy. The stability of the collagen triple helix is strictly related to its amino acid sequence, especially the main Gly-X-Y motif. Many groups have used computational methods to investigate collagen's structure and the relationship between its stability and structure. In this study, we initially reviewed the most important computational methods that have been applied in this field. We then assembled data on a large number of collagen-like peptides to build the first Markov chain model for predicting the stability of the collagen at different temperatures, simply by analyzing the amino acid sequence. We used the literature to assemble a set of 102 peptides and their relative melting temperatures were determined experimentally, indicating a great variance with the main motif of the collagen. This dataset was then split in two classes, stable and unstable, according to their melting temperatures and the dataset was then used to build artificial neural network (ANN) models to predict collagen stability. We built models to predict stability at temperatures of 38°C, 35°C, 30°C, and 25°C degrees, and all models had an accuracy between 82% and 92%. Several cross-validation procedures were performed to validate the model. This method facilitates fast and accurate predictions of collagen stability at different temperatures.
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4.02Impact points
MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens.
Molecular bioSystems. 04/2011; 7(6):1938-55.
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 theoreti... [more] 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.
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3.27Impact points
Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors.
European journal of medicinal chemistry. 03/2011; 46(6):2185-92.
Tyrosine kinases constitute an eligible class of target for novel drug discovery. They resulted often overexpressed and/or deregulated in several cancer diseases. Thus, the development of novel tyrosine kinases inhibitors is of value, as well as the finding of novel cheminformatic tools for their de... [more] Tyrosine kinases constitute an eligible class of target for novel drug discovery. They resulted often overexpressed and/or deregulated in several cancer diseases. Thus, the development of novel tyrosine kinases inhibitors is of value, as well as the finding of novel cheminformatic tools for their design. Among the different ways to rationally design novel compounds, the Quantitative Structure-Activity Relationship (QSAR) plays a key role. The QSAR approach, in fact, allow the prediction of activity against a number of targets (multi-target QSAR), thus leading to models able to predict not only the activity of a compound, but also its selectivity versus a set of targets. Despite it is well known that tyrosine kinase inhibitors have to show multi-kinases inhibitory potency to be useful in anticancer therapy, only few multi-target computational tools have been developed to help medicinal chemists in the design of novel compounds. Herein we present the development of several multi-target classification QSAR (mtc-QSAR) models useful to assess the activity profile of the tyrosine kinases inhibitors.
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Definition of Markov-Harary Invariants and Review of Classic Topological Indices and Databases in Biology, Parasitology, Technology, and Social-Legal Networks
Current Bioinformatics. 01/2011; 6(1):94-121.
Graph and Complex Network theories are applied to different levels of matter organization such as genome networks, protein-protein networks, sexual disease transmission networks, linguistic networks, law and social networks, power electric networks or Internet. A very important fact is that we can u... [more] Graph and Complex Network theories are applied to different levels of matter organization such as genome networks, protein-protein networks, sexual disease transmission networks, linguistic networks, law and social networks, power electric networks or Internet. A very important fact is that we can use the numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems, at least in principle. In any case, there is a lack of manuscripts or issues focused on QSAR-like models with TIs and of networks more focused on Bioinformatics. In this sense, the present issue provides state-of-the-art reviews of some of these new computational approaches in this rapidly expanding area of Bioinformatics. Taking into account all the above-mentioned aspects, the present work intends to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of classic TIs and Databases of Biology, Parasitology, Technology, and Social-Legal Networks. After that, we report a definition of a new class of TIs, coined here as Markov-Harary invariants. We also present the calculation of this class of TIs for different classes of networks. Next, we carry out a comparative study of these networks using the values of the new Markov-Harary TIs. Finally, we compare these new indices with another new class of TIs called Markov entropy values, which has been previously developed.
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2.57Impact points
NL MIND-BEST: a web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum.
Journal of theoretical biology. 01/2011; 276(1):229-49.
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 Re... [more] 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.
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3.27Impact points
Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica.
European journal of medicinal chemistry. 01/2011; 46(4):1074-94.
There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very... [more] There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. 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. 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 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.
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4.02Impact points
LIBP-Pred: Web Server for Lipid Binding Proteins using Structural Network Parameters; PDB Mining of Human Cancer Biomarkers and Drug Targets in Parasites and Bacteria.
Molecular BioSystems. 01/2011;
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 ... [more] 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 3,000+ 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 http://miaja.tic.udc.es/Bio-AIMS/LIBPpred.php, 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.
Following (90)
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Juan M. Ruso
Universidad de Santiago de Compostela -
Alberto Del Rio
Alma Mater Studiorum Università di Bologna -
Helena Niño
Universidad de Santiago de Compostela -
Mukesh Yadav
Softvision College, Indore, M.P. INDIA- 452010 -
Maria Cerezo
Universidad de Santiago de Compostela
