Computer-Aided Drug Discovery in Plant Pathology

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

[1]  Dev Bukhsh Singh,et al.  Success, Limitation and Future of Computer Aided Drug Designing , 2014 .

[2]  U. Bonas,et al.  Xanthomonas AvrBs3 family-type III effectors: discovery and function. , 2010, Annual review of phytopathology.

[3]  Chemla,et al.  An Enzyme-Bound Bisubstrate Hybrid Inhibitor of Adenylosuccinate Synthetase. , 1999, Angewandte Chemie.

[4]  Ben M. Webb,et al.  Comparative Protein Structure Modeling Using MODELLER , 2016, Current protocols in bioinformatics.

[5]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[6]  Naresh Kandakatla,et al.  Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors , 2014, Adv. Bioinformatics.

[7]  C. Boucher,et al.  Transposon Mutagenesis of Pseudomonas solanacearum: Isolation of Tn5-Induced Avirulent Mutants , 1985 .

[8]  P. Liu,et al.  Resistance Mechanisms and Molecular Docking Studies of Four Novel QoI Fungicides in Peronophythora litchii , 2015, Scientific Reports.

[9]  R. Levesque,et al.  Structure and function of the Mur enzymes: development of novel inhibitors , 2002, Molecular microbiology.

[10]  J. Benen,et al.  Structure and function of pectic enzymes: virulence factors of plant pathogens. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[11]  S. Rees,et al.  Principles of early drug discovery , 2011, British journal of pharmacology.

[12]  David Taylor,et al.  The Pharmaceutical Industry and the Future of Drug Development , 2015 .

[13]  I. Furusawa,et al.  The Colletotrichum lagenarium MAP kinase gene CMK1 regulates diverse aspects of fungal pathogenesis. , 2000, Molecular plant-microbe interactions : MPMI.

[14]  B Rost,et al.  Bridging the protein sequence-structure gap by structure predictions. , 1996, Annual review of biophysics and biomolecular structure.

[15]  T. Yeates,et al.  Verification of protein structures: Patterns of nonbonded atomic interactions , 1993, Protein science : a publication of the Protein Society.

[16]  Sheng-Yong Yang,et al.  Pharmacophore modeling and applications in drug discovery: challenges and recent advances. , 2010, Drug discovery today.

[17]  L. Liping,et al.  In silico characterization and molecular modeling of gntr family regulators in Xanthomonas axonopodis pv. citri: implications for primary metabolism or virulence. , 2012 .

[18]  Santosh A. Khedkar,et al.  Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. , 2010, Current topics in medicinal chemistry.

[19]  Leonardo L. G. Ferreira,et al.  Molecular Docking and Structure-Based Drug Design Strategies , 2015, Molecules.

[20]  M. Modi,et al.  Molecular phylogeny, homology modeling, and molecular dynamics simulation of race-specific bacterial blight disease resistance protein (xa5) of rice: a comparative agriproteomics approach. , 2013, Omics : a journal of integrative biology.

[21]  L. Deslandes,et al.  Catch me if you can: bacterial effectors and plant targets. , 2012, Trends in plant science.

[22]  D. Eisenberg,et al.  VERIFY3D: assessment of protein models with three-dimensional profiles. , 1997, Methods in enzymology.

[23]  Byoung Ryong Jeong,et al.  Macromolecular Docking Simulation to Identify Binding Site of FGB1 for Antifungal Compounds , 2011 .

[24]  Claudio N. Cavasotto,et al.  Homology modeling in drug discovery: current trends and applications. , 2009, Drug discovery today.

[25]  B. Rost.  Twilight zone of protein sequence alignments. , 1999, Protein engineering.

[26]  C. Hansch.  Quantitative approach to biochemical structure-activity relationships , 1969 .

[27]  Pratyoosh Shukla,et al.  Plant Microbe Interactions in Post Genomic Era: Perspectives and Applications , 2016, Front. Microbiol..

[28]  Martin Buck,et al.  Regulation of the co-evolved HrpR and HrpS AAA+ proteins required for Pseudomonas syringae pathogenicity , 2011, Nature communications.

[29]  Jiang Xiaohua,et al.  Three dimentional quantitative structure-activity relationships of novel 2-heteroaryl-4-chromanone derivatives , 2002 .

[30]  M. Wenk,et al.  TPS1 drug design for rice blast disease in magnaporthe oryzae , 2014, SpringerPlus.

[31]  N. Talbot,et al.  New and Improved Techniques for the Study of Pathogenic Fungi. , 2016, Trends in microbiology.

[32]  Campbell McInnes,et al.  Virtual screening strategies in drug discovery. , 2007, Current opinion in chemical biology.

[33]  Matthias Adam,et al.  Integrating research and development: the emergence of rational drug design in the pharmaceutical industry. , 2005, Studies in history and philosophy of biological and biomedical sciences.

[34]  A. Dinoor,et al.  pH Regulation of Pectate Lyase Secretion Modulates the Attack of Colletotrichum gloeosporioides on Avocado Fruits , 2000, Applied and Environmental Microbiology.

[35]  J. Tyndall,et al.  Structural Insights into Binding of the Antifungal Drug Fluconazole to Saccharomyces cerevisiae Lanosterol 14α-Demethylase , 2015, Antimicrobial Agents and Chemotherapy.

[36]  R. Pathak,et al.  Molecular modeling and docking studies of phytoalexin(s) with pathogenic protein(s) as molecular targets for designing the derivatives with anti-fungal action on Alternaria spp. of Brassica , 2016 .

[37]  H. Leung,et al.  Plant-pathogen interactions: disease resistance in modern agriculture. , 2013, Trends in genetics : TIG.

[38]  W. Grant,et al.  The development, regulation and use of biopesticides for integrated pest management , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[39]  C. Oh,et al.  HrpN of Erwinia amylovora functions in the translocation of DspA/E into plant cells. , 2008, Molecular plant pathology.

[40]  Xiaoqin Zou,et al.  Advances and Challenges in Protein-Ligand Docking , 2010, International journal of molecular sciences.

[41]  Hanna Geppert,et al.  Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation , 2010, J. Chem. Inf. Model..

[42]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[43]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[44]  J L Sussman,et al.  A structure-based design approach to the development of novel, reversible AChE inhibitors. , 2001, Journal of medicinal chemistry.

[45]  H. Hirt,et al.  New insights into an old story: Agrobacterium‐induced tumour formation in plants by plant transformation , 2010, The EMBO journal.

[46]  N. Chandra.  Computational approaches for drug target identification in pathogenic diseases , 2011, Expert opinion on drug discovery.

[47]  A. Mahasneh,et al.  Microbial Growth and Quorum Sensing Antagonist Activities of Herbal Plants Extracts , 2009, Molecules.

[48]  P. Goodford.  A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[49]  Edward W. Lowe,et al.  Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.

[50]  J. Weigelt.  Structural genomics-impact on biomedicine and drug discovery. , 2010, Experimental cell research.

[51]  M. Dunn,et al.  Major roles of isocitrate lyase and malate synthase in bacterial and fungal pathogenesis. , 2009, Microbiology.

[52]  B. Strukelj,et al.  Peptide inhibitors of MurD and MurE, essential enzymes of bacterial cell wall biosynthesis. , 2008, Journal of basic microbiology.

[53]  Matt Nolan,et al.  Comparison of the complete genome sequences of Pseudomonas syringae pv. syringae B728a and pv. tomato DC3000. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Sol Katzman,et al.  Confirmation of the cellular targets of benomyl and rapamycin using next-generation sequencing of resistant mutants in S. cerevisiae. , 2014, Molecular bioSystems.

[55]  Amedeo Caflisch,et al.  Fragment-Based de Novo Ligand Design by Multiobjective Evolutionary Optimization , 2008, J. Chem. Inf. Model..

[56]  P. R. Scott,et al.  Plant disease: a threat to global food security. , 2005, Annual review of phytopathology.

[57]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[58]  M. Banfield,et al.  Effectors of Filamentous Plant Pathogens: Commonalities amid Diversity , 2017, Microbiology and Molecular Biology Reviews.

[59]  J. Thornton,et al.  PROCHECK: a program to check the stereochemical quality of protein structures , 1993 .

[60]  C. Contreras-Martel,et al.  Dual Inhibitor of MurD and MurE Ligases from Escherichia coli and Staphylococcus aureus. , 2011, ACS medicinal chemistry letters.

[61]  J Hacker,et al.  Common molecular mechanisms of symbiosis and pathogenesis. , 2000, Trends in microbiology.

[62]  Inho Choi,et al.  Computer Aided Drug Design: Success and Limitations. , 2016, Current pharmaceutical design.

[63]  L. Maes,et al.  Anti-infective potential of natural products: how to develop a stronger in vitro 'proof-of-concept'. , 2006, Journal of ethnopharmacology.

[64]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[65]  Yi-Ping Phoebe Chen,et al.  Structure-based drug design to augment hit discovery. , 2011, Drug discovery today.

[66]  Jacob D. Durrant,et al.  Computer-aided drug-discovery techniques that account for receptor flexibility. , 2010, Current opinion in pharmacology.

[67]  Hélène Decornez,et al.  Early phase drug discovery: cheminformatics and computational techniques in identifying lead series. , 2012, Bioorganic & medicinal chemistry.

[68]  T. Raman,et al.  Review on fungal enzyme inhibitors – potential drug targets to manage human fungal infections , 2016 .

[69]  Torsten Schwede,et al.  Protein modeling: what happened to the "protein structure gap"? , 2013, Structure.

[70]  H J Böhm,et al.  Current computational tools for de novo ligand design. , 1996, Current opinion in biotechnology.

[71]  Seon-Woo Lee,et al.  Antifungal Activity Against Colletotrichum spp. of Curcuminoids Isolated from Curcuma longa L. Rhizomes , 2006 .

[72]  I M Kapetanovic,et al.  Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. , 2008, Chemico-biological interactions.

[73]  Xiangjing Wang,et al.  Borrelidin, a potent antifungal agent: insight into the antifungal mechanism against Phytophthora sojae. , 2012, Journal of agricultural and food chemistry.

[74]  David A. Baltrus,et al.  Global virulence regulation networks in phytopathogenic bacteria. , 2007, Trends in microbiology.

[75]  M. Siegel.  Sterol-Inhibiting Fungicides: Effects on Sterol Biosynthesis and Sites of Action , 1981 .

[76]  C. Katiyar,et al.  Drug discovery from plant sources: An integrated approach , 2012, Ayu.

[77]  Antonio Di Pietro,et al.  The Top 10 fungal pathogens in molecular plant pathology. , 2012, Molecular plant pathology.

[78]  M. Yogavel,et al.  Structure-Based Targeting of Orthologous Pathogen Proteins Accelerates Antiparasitic Drug Discovery. , 2017, ACS infectious diseases.

[79]  Magnus W Walter,et al.  Structure-based design of agrochemicals. , 2002, Natural product reports.

[80]  Barna Bordás,et al.  Ligand-based computer-aided pesticide design. A review of applications of the CoMFA and CoMSIA methodologies. , 2003, Pest management science.

[81]  Changbin Chen,et al.  Dominant active Rac and dominant negative Rac revert the dominant active Ras phenotype in Colletotrichum trifolii by distinct signalling pathways , 2004, Molecular microbiology.

[82]  Tudor I. Oprea,et al.  Integrating virtual screening in lead discovery. , 2004, Current opinion in chemical biology.

[83]  G. Salmond,et al.  Top 10 plant pathogenic bacteria in molecular plant pathology. , 2012, Molecular plant pathology.