18th Sir Hans Krebs lecture. Knowledge-based protein modelling and design

Physicochemical and structural characterization, epitope mapping and vaccine potential investigation of a new protein containing Tetratrico Peptide Repeats of Acinetobacter baumannii: An in-silico and in-vivo approach

Acinetobacter baumannii is an opportunistic multidrug-resistant pathogen that causes a significant mortality rate. The proteins containing Tetratrico Peptide Repeats (TPRs) are involved in the pathogenicity and virulence of bacteria and have different roles such as transfer of bacterial virulence factors to host cells, binding to the host cells and inhibition of phagolysosomal maturation. So, in this study, physicochemical properties of a new protein containing TPRs in A. baumannii which was named PcTPRs1 by this study were characterized and its 3D structure was predicted by in-silico tools. The protein B and T cell epitopes were mapped and its vaccine potential was in-silico and in-vivo investigated. Domain analysis indicated that the protein contains the Flp pilus assembly protein TadD domain which has three TPRs. The helix is dominant in the protein structure, and this protein is an outer membrane antigen which, is extremely conserved among A. baumannii strains; thus, has good properties to be applied as a recombinant vaccine. The best-predicted and refined model was applied in ligand-binding sites and conformational epitopes prediction. Based on epitope mapping results, several epitopes were characterized which could stimulate both immune systems. BLAST results showed the introduced epitopes are completely conserved among A. baumannii strains. The in-vivo analysis indicates that a 101 amino acid fragment of the protein which contains the best selected epitope, can produce a good protectivity against A. baumannii as well as the whole TPR protein and thus could be investigated as an effective subunit and potential vaccines.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

Development of a TSR-Based Method for Protein 3-D Structural Comparison With Its Applications to Protein Classification and Motif Discovery

Development of protein 3-D structural comparison methods is important in understanding protein functions. At the same time, developing such a method is very challenging. In the last 40 years, ever since the development of the first automated structural method, ~200 papers were published using different representations of structures. The existing methods can be divided into five categories: sequence-, distance-, secondary structure-, geometry-based, and network-based structural comparisons. Each has its uniqueness, but also limitations. We have developed a novel method where the 3-D structure of a protein is modeled using the concept of Triangular Spatial Relationship (TSR), where triangles are constructed with the C_α atoms of a protein as vertices. Every triangle is represented using an integer, which we denote as “key,” A key is computed using the length, angle, and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. A structure is thereby represented by a vector of integers. Our method is able to accurately quantify similarity of structure or substructure by matching numbers of identical keys between two proteins. The uniqueness of our method includes: (i) a unique way to represent structures to avoid performing structural superimposition; (ii) use of triangles to represent substructures as it is the simplest primitive to capture shape; (iii) complex structure comparison is achieved by matching integers corresponding to multiple TSRs. Every substructure of one protein is compared to every other substructure in a different protein. The method is used in the studies of proteases and kinases because they play essential roles in cell signaling, and a majority of these constitute drug targets. The new motifs or substructures we identified specifically for proteases and kinases provide a deeper insight into their structural relations. Furthermore, the method provides a unique way to study protein conformational changes. In addition, the results from CATH and SCOP data sets clearly demonstrate that our method can distinguish alpha helices from beta pleated sheets and vice versa. Our method has the potential to be developed into a powerful tool for efficient structure-BLAST search and comparison, just as BLAST is for sequence search and alignment.

Generation of Pairwise Potentials Using Multidimensional Data Mining

The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.

Comparative homology model building and docking evaluation for RNA III inhibiting peptide of Multi drug resistant Staphylococcus aureus strain MRSA252

Since last several years, infection caused by Staphylococcus aureus is challenging to cure using conventional antibiotics. The organism is a Gram-positive bacterial pathogen that can cause serious diseases not only in humans but also in animals, such as various skin infections, pneumonia, endocarditis and toxin shock syndrome. This bacterium causes such diseases by producing macromolecules such as hemolysins, enterotoxins, proteases and toxic shock syndrome toxin (TSST-1). This organism had developed the multidrug resistance by acquiring MEC-A gene. This account for made organism to come into the category of Superbug. Several studies showed that, the toxin production is induced by AIP and RAP via the phosphorylation of TRAP. TRAP is a 21 kDa protein and was believed to be associated with the membrane via SvrA Phosphoamino acid analysis revealed that TRAP is histidine phosphorylated in a signal transduction pathway that is activated by RAP. The inhibition of TRAP could be done by RIP (RNAIII-inhibiting peptide). The structure for RIP is still undiscovered to be used as inhibitor. Present work has been carried out to get the structural insight with various online and offline homology modeling techniques such as SWISS-MODEL, MODBASE, GENO3D, CPHmodels and I-TASSER for getting unknown structural information target of RNAIII-activating protein from Staphylococcus aureus strain MRSA252 origin for their future exploration as a target in drug discovery process against MRSA.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Structural biology in antiviral drug discovery

Structural biology has emerged during the last thirty years as a powerful tool for rational drug discovery. Crystal structures of biological targets alone and in complex with ligands and inhibitors provide essential insights into the mechanisms of actions of enzymes, their conformational changes upon ligand binding, the architectures and interactions of binding pockets. Structure-based methods such as crystallographic fragment screening represent nowadays invaluable instruments for the identification of new biologically active compounds. In this context, three-dimensional protein structures have played essential roles for the understanding of the activity and for the design of novel antiviral agents against several different viruses. In this review, the evolution in the resolution of viral structures is analysed, along with the role of crystal structures in the discovery and optimisation of new antivirals.

Data Mining of Macromolecular Structures

The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.

Identification of critical regions within the TIR domain of IL-1 receptor type I

Interleukin-1 receptor type I (IL-1RI) belongs to a superfamily of proteins characterized by an intracellular Toll/IL-1 receptor (TIR) domain. This domain harbors three conserved regions called boxes 1-3 that play crucial roles in mediating IL-1 responses. Boxes 1 and 2 are considered to be involved in binding of adapter molecules. Amino acids possibly crucial for IL-1RI signaling were predicted via homology models of the IL-1RI TIR domain based on the crystal structure of IL-1RAPL. The role of ten of these residues was investigated by site-directed mutagenesis and a functional luciferase assay reflecting NF-κB activity in transiently transfected Jurkat cells. In particular, the mutants E437K/D438K, E472A/E473A and S465A/S470A/S471A/E472A/E473A showed decreased and the mutant E437A/D438A increased IL-1 responsiveness compared to the mouse IL-1RI wild type. In conclusion, the αC' helix (Q469-E473 in mouse IL-1RI) is probably involved in heterotypic interactions of IL-1RI with IL-1RAcP or MyD88.

Carbohydrate-protein interactions: molecular modeling insights

The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses.

Prediction of four kinds of simple supersecondary structures in protein by using chemical shifts

Knowledge of supersecondary structures can provide important information about its spatial structure of protein. Some approaches have been developed for the prediction of protein supersecondary structure. However, the feature used by these approaches is primarily based on amino acid sequences. In this study, a novel model is presented to predict protein supersecondary structure by use of chemical shifts (CSs) information derived from nuclear magnetic resonance (NMR) spectroscopy. Using these CSs as inputs of the method of quadratic discriminant analysis (QD), we achieve the overall prediction accuracy of 77.3%, which is competitive with the same method for predicting supersecondary structures from amino acid compositions in threefold cross-validation. Moreover, our finding suggests that the combined use of different chemical shifts will influence the accuracy of prediction.

In silico determination and validation of baumannii acinetobactin utilization a structure and ligand binding site

Acinetobacter baumannii is a deadly nosocomial pathogen. Iron is an essential element for the pathogen. Under iron-restricted conditions, the bacterium expresses iron-regulated outer membrane proteins (IROMPs). Baumannii acinetobactin utilization (BauA) is the most important member of IROMPs in A. baumannii. Determination of its tertiary structure could help deduction of its functions and its interactions with ligands. The present study unveils BauA 3D structure via in silico approaches. Apart from ab initio, other rational methods such as homology modeling and threading were invoked to achieve the purpose. For homology modeling, BLAST was run on the sequence in order to find the best template. The template was then served to model the 3D structure. All the models built were evaluated qualitatively. The best model predicted by LOMETS was selected for analyses. Refinement of 3D structure as well as determination of its clefts and ligand binding sites was carried out on the structure. In contrast to the typical trimeric arrangement found in porins, BauA is monomeric. The barrel is formed by 22 antiparallel transmembrane β -strands. There are short periplasmic turns and longer surface-located loops. An N-terminal domain referred to either as the cork, the plug, or the hatch domain occludes the β -barrel.

Structural and molecular basis of interaction of HCV non-structural protein 5A with human casein kinase 1α and PKR

Background

Interaction of non-structural protein 5A (NS5A) of Hepatitis C virus (HCV) with human kinases namely, casein kinase 1α (ck1α) and protein kinase R (PKR) have different functional implications such as regulation of viral replication and evasion of interferon induced immune response respectively. Understanding the structural and molecular basis of interactions of the viral protein with two different human kinases can be useful in developing strategies for treatment against HCV.

Results

Serine 232 of NS5A is known to be phosphorylated by human ck1α. A structural model of NS5A peptide containing phosphoacceptor residue Serine 232 bound to ck1α has been generated using the known 3-D structures of kinase-peptide complexes. The substrate interacting residues in ck1α has been identified from the model and these are found to be conserved well in the ck1 family. ck1α – substrate peptide complex has also been used to understand the structural basis of association between ck1α and its other viral stress induced substrate, tumour suppressor p53 transactivation domain which has a crystal structure available.

Interaction of NS5A with another human kinase PKR is primarily genotype specific. NS5A from genotype 1b has been shown to interact and inhibit PKR whereas NS5A from genotype 2a/3a are unable to bind and inhibit PKR efficiently. This is one of the main reasons for the varied response to interferon therapy in HCV patients across different genotypes. Using PKR crystal structure, sequence alignment and evolutionary trace analysis some of the critical residues responsible for the interaction of NS5A 1b with PKR have been identified.

Conclusions

The substrate interacting residues in ck1α have been identified using the structural model of kinase - substrate peptide. The PKR interacting NS5A 1b residues have also been predicted using PKR crystal structure, NS5A sequence analysis along with known experimental results. Functional significance and nature of interaction of interferon sensitivity determining region and variable region 3 of NS5A in different genotypes with PKR which was experimentally shown are also supported by the findings of evolutionary trace analysis. Designing inhibitors to prevent this interaction could enable the HCV genotype 1 infected patients respond well to interferon therapy.

FREAD revisited: Accurate loop structure prediction using a database search algorithm

Loops are the most variable regions of protein structure and are, in general, the least accurately predicted. Their prediction has been approached in two ways, ab initio and database search. In recent years, it has been thought that ab initio methods are more powerful. In light of the continued rapid expansion in the number of known protein structures, we have re-evaluated FREAD, a database search method and demonstrate that the power of database search methods may have been underestimated. We found that sequence similarity as quantified by environment specific substitution scores can be used to significantly improve prediction. In fact, FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than the ab initio methods of MODELLER, PLOP, and RAPPER. Within this subset, FREAD's predictive ability is length independent, in general, producing results within 2A RMSD, compared to an average of over 10A for loop length 20 for any of the other tested methods. We also benchmarked the prediction protocols on a set of 212 loops from the model structures in CASP 7 and 8. An extended version of FREAD is able to make predictions for 127 of these, it gives the best prediction of the methods tested in 61 of these cases. In examining FREAD's ability to predict in the model environment, we found that whole structure quality did not affect the quality of loop predictions.

Structural studies of langerin and Birbeck granule: a macromolecular organization model

Dendritic cells, a sentinel immunity cell lineage, include different cell subsets that express various C-type lectins. For example, epidermal Langerhans cells express langerin, and some dermal dendritic cells express DC-SIGN. Langerin is a crucial component of Birbeck granules, the Langerhans cell hallmark organelle, and may have a preventive role toward HIV, by its internalization into Birbeck granules. Since langerin carbohydrate recognition domain (CRD) is crucial for HIV interaction and Birbeck granule formation, we produced the CRD of human langerin and solved its structure at 1.5 A resolution. On this basis gp120 high-mannose oligosaccharide binding has been evaluated by molecular modeling. Hydrodynamic studies reveal a very elongated shape of recombinant langerin extracellular domain (ECD). A molecular model of the langerin ECD, integrating the CRD structure, has been generated and validated by comparison with hydrodynamic parameters. In parallel, Langerhans cells were isolated from human skin. From their analysis by electron microscopy and the langerin ECD model, an ultrastructural organization is proposed for Birbeck granules. To delineate the role of the different langerin domains in Birbeck granule formation, we generated truncated and mutated langerin constructs. After transfection into a fibroblastic cell line, we highlighted, in accordance with our model, the role of the CRD in the membrane zipping occurring in BG formation as well as some contribution of the cytoplasmic domain. Finally, we have shown that langerin ECD triggering with a specific mAb promotes global rearrangements of LC morphology. Our results open the way to the definition of a new membrane deformation mechanism.

Comparison of Composer and ORCHESTRAR

Although the number of known protein structures is increasing, the number of protein sequences without determined structures is still much larger. Three-dimensional (3D) protein structure information helps in the understanding of functional mechanisms, but solving structures by X-ray crystallography or NMR is often a lengthy and difficult process. A relatively fast way of determining a protein's 3D structure is to construct a computer model using homologous sequence and structure information. Much work has gone into algorithms that comprise the ORCHESTRAR homology modeling program in the SYBYL software package. This novel homology modeling tool combines algorithms for modeling conserved cores, variable regions, and side chains. The paradigm of using existing knowledge from multiple templates and the underlying protein environment knowledgebase is used in all of these algorithms, and will become even more powerful as the number of experimentally derived protein structures increases. To determine how ORCHESTRAR compares to Composer (a broadly used, but an older tool), homology models of 18 proteins were constructed using each program so that a detailed comparison of each step in the modeling process could be carried out. Proteins modeled include kinases, dihydrofolate reductase, HIV protease, and factor Xa. In almost all cases ORCHESTRAR produces models with lower root-mean-squared deviation (RMSD) values when compared with structures determined by X-ray crystallography or NMR. Moreover, ORCHESTRAR produced a homology model for three target sequences where Composer failed to produce any. Data for RMSD comparisons between structurally conserved cores, structurally variable regions, side-chain conformations are presented, as well as analyses of active site and protein-protein interface configurations.

The beta-glucosidases responsible for bioactivation of hydroxynitrile glucosides in Lotus japonicus

Lotus japonicus accumulates the hydroxynitrile glucosides lotaustralin, linamarin, and rhodiocyanosides A and D. Upon tissue disruption, the hydroxynitrile glucosides are bioactivated by hydrolysis by specific beta-glucosidases. A mixture of two hydroxynitrile glucoside-cleaving beta-glucosidases was isolated from L. japonicus leaves and identified by protein sequencing as LjBGD2 and LjBGD4. The isolated hydroxynitrile glucoside-cleaving beta-glucosidases preferentially hydrolyzed rhodiocyanoside A and lotaustralin, whereas linamarin was only slowly hydrolyzed, in agreement with measurements of their rate of degradation upon tissue disruption in L. japonicus leaves. Comparative homology modeling predicted that LjBGD2 and LjBGD4 had nearly identical overall topologies and substrate-binding pockets. Heterologous expression of LjBGD2 and LjBGD4 in Arabidopsis (Arabidopsis thaliana) enabled analysis of their individual substrate specificity profiles and confirmed that both LjBGD2 and LjBGD4 preferentially hydrolyze the hydroxynitrile glucosides present in L. japonicus. Phylogenetic analyses revealed a third L. japonicus putative hydroxynitrile glucoside-cleaving beta-glucosidase, LjBGD7. Reverse transcription-polymerase chain reaction analysis showed that LjBGD2 and LjBGD4 are expressed in aerial parts of young L. japonicus plants, while LjBGD7 is expressed exclusively in roots. The differential expression pattern of LjBGD2, LjBGD4, and LjBGD7 corresponds to the previously observed expression profile for CYP79D3 and CYP79D4, encoding the two cytochromes P450 that catalyze the first committed step in the biosyntheis of hydroxynitrile glucosides in L. japonicus, with CYP79D3 expression in aerial tissues and CYP79D4 expression in roots.

Comparative analysis of protein three-dimensional structures and an approach to the inverse folding problem

Comparative analyses of three-dimensional structures of proteins provide useful rules for protein structure prediction. Sequence templates that define the amino acid substitutions consistent with a known protein fold can be constructed using such rules. Such templates can define sequences with similar three-dimensional structures even where there are no significant sequence identities. In such cases protein structure may be predicted using an extension of the methods developed for comparative modelling of homologous proteins.

Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates

In this contribution, we present an algorithm for protein backbone reconstruction that comprises very high computational efficiency with high accuracy. Reconstruction of the main chain atomic coordinates from the alpha carbon trace is a common task in protein modeling, including de novo structure prediction, comparative modeling, and processing experimental data. The method employed in this work follows the main idea of some earlier approaches to the problem. The details and careful design of the present approach are new and lead to the algorithm that outperforms all commonly used earlier applications. BBQ (Backbone Building from Quadrilaterals) program has been extensively tested both on native structures as well as on near-native decoy models and compared with the different available existing methods. Obtained results provide a comprehensive benchmark of existing tools and evaluate their applicability to a large scale modeling using a reduced representation of protein conformational space. The BBQ package is available for downloading from our website at http://biocomp.chem.uw.edu.pl/services/BBQ/. This webpage also provides a user manual that describes BBQ functions in detail.

ProRegIn: A regularity index for the selection of native-like tertiary structures of proteins

Automated protein tertiary structure prediction from sequence information alone remains an elusive goal to computational prescriptions. Dividing the problem into three stages viz. secondary structure prediction, generation of plausible main chain loop dihedrals and side chain dihedral optimization, considerable progress has been achieved in our laboratory (http://www.scfbio-iitd.res.in/bhageerath/index.jsp) and elsewhere for proteins with less than 100 amino acids. As a part of our on-going efforts in this direction and to facilitate tertiary structure selection/rejection in containing the combinatorial explosion of trial structures for a specified amino acid sequence, we describe here a web-enabled tool ProRegIn (Protein Regularity Index) developed based on the regularity in the Phi, Psi dihedral angles of the amino acids that constitute loop regions. We have analysed the dihedrals in loop regions in a non-redundant dataset of 7351 proteins drawn from the Protein Data Bank and categorized them as helix-like or sheet-like (regular) or irregular. We noticed that the regularity thus defined exceeds 86% for Phi barring glycine and 70% for Psi for all the amino acid side chains including glycine, compelling us to reexamine the conventional view that loops are irregular regions structurally. The regularity index is presented here as a simple tool that finds its application in protein structure analysis as a discriminatory scoring function for rapid screening before the more compute intensive atomic level energy calculations could be undertaken. The tool is made freely accessible over the internet at www.scfbio-iitd.res.in/software/proregin.jsp.

The propeptide of cruzipain--a potent selective inhibitor of the trypanosomal enzymes cruzipain and brucipain, and of the human enzyme cathepsin F

Papain-like cysteine proteases of pathogenic protozoa play important roles in parasite growth, differentiation and host cell invasion. The main cysteine proteases of Trypanosoma cruzi (cruzipain) and of Trypanosoma brucei (brucipain) are validated targets for the development of new chemotherapies. These proteases are synthesized as precursors and activated upon removal of the N-terminal prodomain. Here we report potent and selective inhibition of cruzipain and brucipain by the recombinant full-length prodomain of cruzipain. The propeptide did not inhibit human cathepsins S, K or B or papain at the tested concentrations, and moderately inhibited human cathepsin V. Human cathepsin F was very efficiently inhibited (K(i) of 32 pm), an interesting finding indicating that cruzipain propeptide is able to discriminate cathepsin F from other cathepsin L-like enzymes. Comparative structural modeling and analysis identified the interaction between the beta1p-alpha3p loop of the propeptide and the propeptide-binding loop of mature enzymes as a plausible cause of the observed inhibitory selectivity.

Homology modeling of human Fyn kinase structure: discovery of rosmarinic acid as a new Fyn kinase inhibitor and in silico study of its possible binding modes

Tyrosine phosphorylation represents a unique signaling process that controls metabolic pathways, cell activation, growth and differentiation, membrane transport, apoptosis, neural, and other functions. We present here the three-dimensional structure of Fyn tyrosine kinase, a Src-family enzyme involved in T-cell receptor signal transduction. The structure of Fyn was modeled for homology using the Sybyl-Composer suite of programs for modeling. Procheck and Prosa II programs showed the high quality of the obtained three-dimensional model. Rosmarinic acid, a secondary metabolite of herbal plants, was discovered as a new Fyn kinase inhibitor using immunochemical and in silico methods. Two possible binding modes of rosmarinic acid were evaluated here, i.e., near to or in the ATP-binding site of kinase domain of Fyn. Enzyme kinetic experiments revealed that Fyn is inhibited by a linear-mixed noncompetitive mechanism of inhibition by rosmarinic acid. This indicates that rosmarinic acid binds to the second "non-ATP" binding site of the Fyn tyrosine kinase.

Characterization of a pseudomonad 2-nitrobenzoate nitroreductase and its catabolic pathway-associated 2-hydroxylaminobenzoate mutase and a chemoreceptor involved in 2-nitrobenzoate chemotaxis

Pseudomonas fluorescens strain KU-7 is a prototype microorganism that metabolizes 2-nitrobenzoate (2-NBA) via the formation of 3-hydroxyanthranilate (3-HAA), a known antioxidant and reductant. The initial two steps leading to the sequential formation of 2-hydroxy/aminobenzoate and 3-HAA are catalyzed by a NADPH-dependent 2-NBA nitroreductase (NbaA) and 2-hydroxylaminobenzoate mutase (NbaB), respectively. The 216-amino-acid protein NbaA is 78% identical to a plasmid-encoded hypothetical conserved protein of Polaromonas strain JS666; structurally, it belongs to the homodimeric NADH:flavin mononucleotide (FMN) oxidoreductase-like fold family. Structural modeling of complexes with the flavin, coenzyme, and substrate suggested specific residues contributing to the NbaA catalytic activity, assuming a ping-pong reaction mechanism. Mutational analysis supports the roles of Asn40, Asp76, and Glu113, which are predicted to form the binding site for a divalent metal ion implicated in FMN binding, and a role in NADPH binding for the 10-residue insertion in the beta5-alpha2 loop. The 181-amino-acid sequence of NbaB is 35% identical to the 4-hydroxylaminobenzoate lyases (PnbBs) of various 4-nitrobenzoate-assimilating bacteria, e.g., Pseudomonas putida strain TW3. Coexpression of nbaB with nbaA in Escherichia coli produced a small amount of 3-HAA from 2-NBA, supporting the functionality of the nbaB gene. We also showed by gene knockout and chemotaxis assays that nbaY, a chemoreceptor NahY homolog located downstream of the nbaA gene, is responsible for strain KU-7 being attracted to 2-NBA. NbaY is the first chemoreceptor in nitroaromatic metabolism to be identified, and this study completes the gene elucidation of 2-NBA metabolism that is localized within a 24-kb chromosomal locus of strain KU-7.

Enzymatic Properties of Newly Found Green Turtle Egg White Ribonuclease

Egg white ribonuclease was first found in green turtle eggs. The general properties were studied on substrate specificity, the optimum pH and temperature, and the effect of pH and temperature on the RNase activity. The enzyme studied was specific for poly (C) and degraded poly (U) at the lower rate and had the pH optimum at 7.0 and the optimum temperature at 40 degrees C. It was stable at alkaline range (pH 8.0-10.0) and up to 60 degrees C in pH 9.0 for 1 h, and unstable at acidic side for all temperatures. All of the properties studied showed similarity to RNase A. However, the optimum pH, broad range of optimum temperature and pH stability were different from RNase A. To evaluate the relationship of the structure and enzymatic properties, the 3D-structure of this enzyme was engineered by program MODELLER using two RNases (2BWL and 2BLZ) as starting models. The differences found in activity might be affected from the structure of micro environmental changing caused by amino acids deletion and substitution on the molecule.

Binding site-based classification of coronaviral papain-like proteases

The coronavirus replicase gene encodes one or two papain-like proteases (termed PL1pro and PL2pro) implicated in the N-terminal processing of the replicase polyprotein and thus contributing to the formation of the viral replicase complex that mediates genome replication. Using consensus fold recognition with the 3D-JURY meta-predictor followed by model building and refinement, we developed a structural model for the single PLpro present in the severe acute respiratory syndrome coronavirus (SCoV) genome, based on significant structural relationships to the catalytic core domain of HAUSP, a ubiquitin-specific protease (USP). By combining the SCoV PLpro model with comparative sequence analyses we show that all currently known coronaviral PLpros can be classified into two groups according to their binding site architectures. One group includes all PL2pros and some of the PL1pros, which are characterized by a restricted USP-like binding site. This group is designated the R-group. The remaining PL1pros from some of the coronaviruses form the other group, featuring a more open papain-like binding site, and is referred to as the O-group. This two-group, binding site-based classification is consistent with experimental data accumulated to date for the specificity of PLpro-mediated polyprotein processing and PLpro inhibition. It also provides an independent evaluation of the similarity-based annotation of PLpro-mediated cleavage sites, as well as a basis for comparison with previous groupings based on phylogenetic analyses.

Structural biology and drug discovery

It has long been recognized that knowledge of the 3D structures of proteins has the potential to accelerate drug discovery, but recent developments in genome sequencing, robotics and bioinformatics have radically transformed the opportunities. Many new protein targets have been identified from genome analyses and studied by X-ray analysis or NMR spectroscopy. Structural biology has been instrumental in directing not only lead optimization and target identification, where it has well-established roles, but also lead discovery, now that high-throughput methods of structure determination can provide powerful approaches to screening.

Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery

Impressive progress in genome sequencing, protein expression and high-throughput crystallography and NMR has radically transformed the opportunities to use protein three-dimensional structures to accelerate drug discovery, but the quantity and complexity of the data have ensured a central place for informatics. Structural biology and bioinformatics have assisted in lead optimization and target identification where they have well established roles; they can now contribute to lead discovery, exploiting high-throughput methods of structure determination that provide powerful approaches to screening of fragment binding.

Reconstitution of cyanogenesis in barley (Hordeum vulgare L.) and its implications for resistance against the barley powdery mildew fungus

Barley (Hordeum vulgare L.) produces a leucine-derived cyanogenic beta-D-glucoside, epiheterodendrin that accumulates specifically in leaf epidermis. Barley leaves are not cyanogenic, i.e. they do not possess the ability to release hydrogen cyanide, because they lack a cyanide releasing beta-D-glucosidase. Cyanogenesis was reconstituted in barley leaf epidermal cells through single cell expression of a cDNA encoding dhurrinase-2, a cyanogenic beta-D-glucosidase from sorghum. This resulted in a 35-60% reduction in colonization rate by an obligate parasite Blumeria graminis f. sp. hordei, the causal agent of barley powdery mildew. A database search for barley homologues of dhurrinase-2 identified a (1,4)-beta-D-glucan exohydrolase isozyme betaII that is located in the starchy endosperm of barley grain. The purified barley (1,4)-beta-D-glucan exohydrolase isozyme betaII was found to hydrolyze the cyanogenic beta-D-glucosides, epiheterodendrin and dhurrin. Molecular modelling of its active site based on the crystal structure of linamarase from white clover, demonstrated that the disposition of the catalytic active amino acid residues was structurally conserved. Epiheterodendrin stimulated appressoria and appressorial hook formation of B. graminis in vitro, suggesting that loss of cyanogenesis in barley leaves has enabled the fungus to utilize the presence of epiheterodendrin to facilitate host recognition and to establish infection.

Glyoxalase I from Leishmania donovani: A potential target for anti-parasite drug

Glyoxalases are involved in a ubiquitous detoxification pathway. In pursuit of a better understanding of the biological function of the enzyme, the recombinant glyoxalase I (LdGLOI) protein has been characterized from Leishmania donovani, the most important pathogenic Leishmania species that is responsible for visceral leishmaniasis. A 24kDa protein was heterologously expressed in Escherichia coli. LdGLOI showed a marked substrate specificity for trypanothione hemithioacetal over glutathione hemithioacetal. Antiserum against recombinant LdGLOI protein could detect a band of anticipated size approximately 16kDa in promastigote extracts. Several inhibitors of human GLOI showed that they are weak inhibitors of L. donovani growth. Overexpression of GLOI gene in L. donovani using Leishmania expression vector pspalpha hygroalpha, we detected elevated expression of GLOI RNA and protein. Comparative modelling of the 3-D structure of LDGLOI shows that substrate-binding region of the model involves important differences compared to the homologues, such as E. coli, specific to glutathione. Most notably a substrate-binding loop of LDGLOI is characterized by a deletion of five residues compared to the E. coli homologue. Further, a critical Arg in the E. coli variant at the substrate-binding site is replaced by Tyr in LDGLOI. These major differences result in entirely different shapes of the substrate-binding loop and presence of very different chemical groups in the substrate-binding site of LDGLOI compared to E. coli homologue suggesting an explanation for the difference in the substrate specificity. Difference in the substrate specificity of the human and LDGLOI enzyme could be exploited for structure-based drug designing of selective inhibitors against the parasite.

Matrix Metalloproteinase Target Family Landscape: A Chemometrical Approach to Ligand Selectivity Based on Protein Binding Site Analysis

To gain insight into the structural determinants for the matrix metalloproteinase (MMP) family, we characterized the binding sites of 56 MMP structures and one TACE (tumor necrosis factor alpha converting enzyme) structure using molecular interaction fields (MIFs). These MIFs were produced by two approaches: the GRID force field and the knowledge-based potential DrugScore. The subsequent statistical analysis using consensus principal component analysis (CPCA) for the entire binding site and each subpockets revealed both approaches to encode similar information about discriminating regions. However, the relative importance of the probes varied between both approaches. The CPCA models provided the following ranking of the six subpockets based on the opportunity for selective interactions with different MMPs: S1' > S2, S3, S3' > S1, S2'. The interpretation of these models agreed with experimental binding modes inferred from crystal structures or docking.

Computational approaches to modeling drug transporters

Computational modeling has advanced our understanding of drug absorption, tissue distribution, excretion and toxicity profiles by providing both direct and indirect knowledge of drug-transporter interactions that would otherwise be unavailable using experimental methods. Currently, two complementary approaches are available in modeling transporters: substrate-based and transporter-based methods. The transporter-based approach directly predicts the transporter's three-dimensional structure to assist in understanding the drug transport process, whereas substrate-based models infer such information by studying a group of substrates or inhibitors with measured activities. In this review, the available strategies in both transporter-based and substrate-based approaches are explained and illustrated with applications and case studies. With increasing computational power and continuously improving modeling algorithms, computational techniques can assist in further understanding transporter-substrate interactions as well as, the optimization of transporter-directed drug design.

Toward a better understanding of the basis of the molecular mimicry of polysaccharide antigens by peptides: the example of Shigella flexneri 5a

Protein conjugates of oligosaccharides or peptides that mimic complex bacterial polysaccharide antigens represent alternatives to the classical polysaccharide-based conjugate vaccines developed so far. Hence, a better understanding of the molecular basis ensuring appropriate mimicry is required in order to design efficient carbohydrate mimic-based vaccines. This study focuses on the following two unrelated sets of mimics of the Shigella flexneri 5a O-specific polysaccharide (O-SP): (i) a synthetic branched pentasaccharide known to mimic the average solution conformation of S. flexneri 5a O-SP, and (ii) three nonapeptides selected upon screening of phage-displayed peptide libraries with two protective murine monoclonal antibodies (mAbs) of the A isotype specific for S. flexneri 5a O-SP. By inducing anti-O-SP antibodies upon immunization in mice when appropriately presented to the immune system, the pentasaccharide and peptides p100c and p115, but not peptide p22, were qualified as mimotopes of the native antigen. NMR studies based on transferred NOE (trNOE) experiments revealed that both kinds of mimotopes had an average conformation when bound to the mAbs that was close to that of their free form. Most interestingly, saturation transfer difference (STD) experiments showed that the characteristic turn conformations adopted by the major conformers of p100c and p115, as well as of p22, are clearly involved in mAb binding. These latter experiments also showed that the branched glucose residue of the pentasaccharide was a key part of the determinant recognized by the protective mAbs. Finally, by using NMR-derived pentasaccharide and peptide conformations coupled to STD information, models of antigen-antibody interaction were obtained. Most interestingly, only one model was found compatible with experimental data when large O-SP fragments were docked into one of the mIgA-binding sites. This newly made available system provides a new contribution to the understanding of the molecular mimicry of complex polysaccharides by peptides and short oligosaccharides.

Determination of catalytic key amino acids and UDP sugar donor specificity of the cyanohydrin glycosyltransferase UGT85B1 from Sorghum bicolor. Molecular modeling substantiated by site-specific mutagenesis and biochemical analyses

Plants produce a plethora of structurally diverse natural products. The final step in their biosynthesis is often a glycosylation step catalyzed by a family 1 glycosyltransferase (GT). In biosynthesis of the cyanogenic glucoside dhurrin in Sorghum bicolor, the UDP-glucosyltransferase UGT85B1 catalyzes the conversion of p-hydroxymandelonitrile into dhurrin. A structural model of UGT85B1 was built based on hydrophobic cluster analysis and the crystal structures of two bacterial GTs, GtfA and GtfB, which each showed approximately 15% overall amino acid sequence identity to UGT85B1. The model enabled predictions about amino acid residues important for catalysis and sugar donor specificity. p-Hydroxymandelonitrile and UDP-glucose (Glc) were predicted to be positioned within hydrogen-bonding distance to a glutamic acid residue in position 410 facilitating sugar transfer. The acceptor was packed within van der Waals distance to histidine H23. Serine S391 and arginine R201 form hydrogen bonds to the pyrophosphate part of UDP-Glc and hence stabilize binding of the sugar donor. Docking of UDP sugars predicted that UDP-Glc would serve as the sole donor sugar in UGT85B1. This was substantiated by biochemical analyses. The predictive power of the model was validated by site-directed mutagenesis of selected residues and using enzyme assays. The modeling approach has provided a tool to design GTs with new desired substrate specificities for use in biotechnological applications. The modeling identified a hypervariable loop (amino acid residues 156-188) that contained a hydrophobic patch. The involvement of this loop in mediating binding of UGT85B1 to cytochromes P450, CYP79A1, and CYP71E1 within a dhurrin metabolon is discussed.

Crystallographic Refinement by Knowledge-Based Exploration of Complex Energy Landscapes

Although X-ray crystallography remains the most versatile method to determine the three-dimensional atomic structure of proteins and much progress has been made in model building and refinement techniques, it remains a challenge to elucidate accurately the structure of proteins in medium-resolution crystals. This is largely due to the difficulty of exploring an immense conformational space to identify the set of conformers that collectively best fits the experimental diffraction pattern. We show here that combining knowledge-based conformational sampling in RAPPER with molecular dynamics/simulated annealing (MD/SA) vastly improves the quality and power of refinement compared to MD/SA alone. The utility of this approach is highlighted by the automated determination of a lysozyme mutant from a molecular replacement solution that is in congruence with a model prepared independently by crystallographers. Finally, we discuss the implications of this work on structure determination in particular and conformational sampling and energy minimization in general.

Structural analysis of isoform-specific inhibitors targeting the tetrahydrobiopterin binding site of human nitric oxide synthases

Nitric oxide synthesized from l-arginine by nitric oxide synthase isoforms (NOS-I-III) is physiologically important but also can be deleterious when overproduced. Selective NOS inhibitors are of clinical interest, given their differing pathophysiological roles. Here we describe our approach to target the unique NOS (6R,1'R,2'S)-5,6,7,8-tetrahydrobiopterin (H(4)Bip) binding site. By a combination of ligand- and structure-based design, the structure-activity relationship (SAR) for a focused set of 41 pteridine analogues on four scaffolds was developed, revealing selective NOS-I inhibitors. The X-ray crystal structure of rat NOS-I dimeric-oxygenase domain with H(4)Bip and l-arginine was determined and used for human isoform homology modeling. All available NOS structural information was subjected to comparative analysis of favorable protein-ligand interactions using the GRID/concensus principal component analysis (CPCA) approach to identify the isoform-specific interaction site. Our interpretation, based on protein structures, is in good agreement with the ligand SAR and thus permits the rational design of next-generation inhibitors targeting the H(4)Bip binding site with enhanced isoform selectivity for therapeutics in pathology with NO overproduction.

Molecular modeling and site-directed mutagenesis of plant chloroplast monogalactosyldiacylglycerol synthase reveal critical residues for activity

Monogalactosyldiacylglycerol (MGDG), the major lipid of plant and algal plastids, is synthesized by MGD (or MGDG synthase), a dimeric and membrane-bound glycosyltransferase of the plastid envelope that catalyzes the transfer of a galactosyl group from a UDP-galactose donor onto a diacylglycerol acceptor. Although this enzyme is essential for biogenesis, and therefore an interesting target for herbicide design, no structural information is available. MGD monomers share sequence similarity with MURG, a bacterial glycosyltransferase catalyzing the transfer of N-acetyl-glucosamine on Lipid 1. Using the x-ray structure of Escherichia coli MURG as a template, we computed a model for the fold of Spinacia oleracea MGD. This structural prediction was supported by site-directed mutagenesis analyses. The predicted monomer architecture is a double Rossmann fold. The binding site for UDP-galactose was predicted in the cleft separating the two Rossmann folds. Two short segments of MGD (beta2-alpha2 and beta6-beta7 loops) have no counterparts in MURG, and their structure could not be determined. Combining the obtained model with phylogenetic and biochemical information, we collected evidence supporting the beta2-alpha2 loop in the N-domain as likely to be involved in diacylglycerol binding. Additionally, the monotopic insertion of MGD in one membrane leaflet of the plastid envelope occurs very likely at the level of hydrophobic amino acids of the N-terminal domain.

Structure of the Complex between Plastocyanin and Cytochrome f from the Cyanobacterium Nostoc sp. PCC 7119 as Determined by Paramagnetic NMR

The complex between cytochrome f and plastocyanin from the cyanobacterium Nostoc has been characterized by NMR spectroscopy. The binding constant is 16 mM(-1), and the lifetime of the complex is much less than 10 ms. Intermolecular pseudo-contact shifts observed for the plastocyanin amide nuclei, caused by the heme iron, as well as the chemical-shift perturbation data were used as the sole experimental restraints to determine the orientation of plastocyanin relative to cytochrome f with a precision of 1.3 angstroms. The data show that the hydrophobic patch surrounding tyrosine 1 in cytochrome f docks the hydrophobic patch of plastocyanin. Charge complementarities are found between the rims of the respective recognition sites of cytochrome f and plastocyanin. Significant differences in the relative orientation of both proteins are found between this complex and those previously reported for plants and Phormidium, indicating that electrostatic and hydrophobic interactions are balanced differently in these complexes.

Inhibitor-based validation of a homology model of the active-site of tripeptidyl peptidase II

A homology model of the active site region of tripeptidyl peptidase II (TPP II) was constructed based on the crystal structures of four subtilisin-like templates. The resulting model was subsequently validated by judging expectations of the model versus observed activities for a broad set of prepared TPP II inhibitors. The structure-activity relationships observed for the prepared TPP II inhibitors correlated nicely with the structural details of the TPP II active site model, supporting the validity of this model and its usefulness for structure-based drug design and pharmacophore searching experiments.

Molecular models of NS3 protease variants of the Hepatitis C virus

BACKGROUND

Hepatitis C virus (HCV) currently infects approximately three percent of the world population. In view of the lack of vaccines against HCV, there is an urgent need for an efficient treatment of the disease by an effective antiviral drug. Rational drug design has not been the primary way for discovering major therapeutics. Nevertheless, there are reports of success in the development of inhibitor using a structure-based approach. One of the possible targets for drug development against HCV is the NS3 protease variants. Based on the three-dimensional structure of these variants we expect to identify new NS3 protease inhibitors. In order to speed up the modeling process all NS3 protease variant models were generated in a Beowulf cluster. The potential of the structural bioinformatics for development of new antiviral drugs is discussed.

RESULTS

The atomic coordinates of crystallographic structure 1CU1 and 1DY9 were used as starting model for modeling of the NS3 protease variant structures. The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain. The protease domain exhibits the dual beta-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related beta-alpha-beta subdomains and a third subdomain of seven helices and three short beta strands. The latter domain is usually referred to as the helicase alpha-helical subdomain. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are presented for NS3 protease variant structures.

CONCLUSIONS

This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus. The structural models will be used to guide future efforts in the structure-based drug design of a new generation of NS3 protease variants inhibitors. All models in the database are publicly accessible via our interactive website, providing us with large amount of structural models for use in protein-ligand docking analysis.

Modeling the Similarity and Divergence of Dopamine D2-like Receptors and Identification of Validated Ligand−Receptor Complexes

Focusing on the similarity and divergence of GPCR subtypes and their ligand interactions, we generated dopamine D2, D3, and D4 receptor models based on the rhodopsin crystal structure and refined these with an extensive MM/MD protocol. After validation by diagnostic experimental data, subtype-specific relative positions of TM1, 2, 6, and 7 and bending angles of TM7 were found. To sample the conformational space of the complex, we performed simulated-annealing runs of the receptor protein with the sub-nanomolar antagonist spiperone. Docking a representative set of ligands, we were able to identify one superior model for each subtype when excellent correlations between predicted energies of binding and experimental affinities (r2 = 0.72 for D2, 0.91 for D3 and 0.77 for D4) could be observed. Further analysis revealed general ligand interactions with ASP3.32 and aromatic residues in TM6/7 and individual key interactions with TM1 and TM2 residues of the D3 and D4 receptor models, respectively.

Biology and chemistry of the inhibition of nitric oxide synthases by pteridine-derivatives as therapeutic agents

Inhibitors of the family of nitric oxide synthases (NOS-I-III; EC 1.14.13.39) are of interest as pharmacological agents to modulate pathologically high nitric oxide (NO) levels in inflammation, sepsis, and stroke. In this article, we discuss the approach for targeting the unique (6R)-5,6,7,8-tetrahydro-L-biopterin (H4Bip) binding site of NOS by appropriate inhibitors. This binding site maximally increases enzyme activity and NO production from the substrate L-arginine upon cofactor binding. The first generation of H4Bip-based NOS inhibitors was based on 4-amino H4Bip derivatives in analogy to anti-folates such as methotrexate. In addition, we discuss the structure-activity relationship of a related series of 4-oxo-pteridine derivatives. Furthermore, molecular modeling studies provide an understanding of pterin antagonism on a structural level based on favorable and unfavorable interactions between protein binding site and ligands. These techniques include 3D-QSAR (CoMFA, CoMSIA) to understand ligand affinity and GRID/consensus principal component analysis (CPCA) to learn about selectivity requirements. Collectively these approaches, in combination with the presented SAR and structural data, provide useful information for the design of novel NOS inhibitors with increased isoform selectivity.

Molecular models for shikimate pathway enzymes of Xylella fastidiosa

The Xylella fastidiosa is a bacterium that is the cause of citrus variegated chlorosis (CVC). The shikimate pathway is of pivotal importance for production of a plethora of aromatic compounds in plants, bacteria, and fungi. Putative structural differences in the enzymes from the shikimate pathway, between the proteins of bacterial origin and those of plants, could be used for the development of a drug for the control of CVC. However, inhibitors for shikimate pathway enzymes should have high specificity for X. fastidiosa enzymes, since they are also present in plants. In order to pave the way for structural and functional efforts towards antimicrobial agent development, here we describe the molecular modeling of seven enzymes of the shikimate pathway of X. fastidiosa. The structural models of shikimate pathway enzymes, complexed with inhibitors, strongly indicate that the previously identified inhibitors may also inhibit the X. fastidiosa enzymes.