Structural bioinformatics study of cyclin-dependent kinases complexed with inhibitors

The present work describes molecular models for the binary complexes CDK9, CDK5 and CDK1 complexed with Flavopiridol and Roscovitine. These structural models indicate that the inhibitors strongly bind to the ATP-binding pocket of CDKs and the structural comparison with the complexes CDK2:Flavopiridol and CDK2:Roscovitine correlates the structural differences with differences in inhibition of these CDKs by the inhibitors. These structures open the possibility of testing new inhibitor families, in addition to new substituents for the already known lead structures such as flavones and adenine derivatives.

Structural bioinformatics analysis of the family of human ubiquitin-specific proteases

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Structural bioinformatics study of EPSP synthase from Mycobacterium tuberculosis

The shikimate pathway is an attractive target for herbicides and antimicrobial agent development because it is essential in algae, higher plants, bacteria, and fungi, but absent from mammals. Homologues to enzymes in the shikimate pathway have been identified in the genome sequence of Mycobacterium tuberculosis. Among them, the EPSP synthase was proposed to be present by sequence homology. Accordingly, in order to pave the way for structural and functional efforts towards anti-mycobacterial agent development, here we describe the molecular modeling of 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase isolated from M. tuberculosis that should provide a structural framework on which the design of specific inhibitors may be based on. Significant differences in the relative orientation of the domains in the two models result in open and closed conformations. The possible relevance of this structural transition in the ligand biding is discussed. (C) 2003 Elsevier B.V. All rights reserved.

Structural bioinformatics study of PNP from Schistosoma mansoni

The parasite Schistosoma mansoni lacks the de novo pathway for purine biosynthesis and depends on salvage pathways for its purine requirements. Schistosomiasis is endemic in 76 countries and territories and amongst the parasitic diseases ranks second after malaria in terms of social and economic impact and public health importance. The PNP is an attractive target for drug design and it has been submitted to extensive structure-based design. The atomic coordinates of the complex of human PNP with inosine were used as template for starting the modeling of PNP from S. mansoni complexed with inosine. Here we describe the model for the complex SmPNP-inosine and correlate the structure with differences in the affinity for inosine presented by human and S. mansoni PNPs. (C) 2004 Elsevier B.V. All rights reserved.

Structural bioinformatics study of cyclin-dependent kinases complexed with inhibitors

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Assessment of the ability to model proteins with leucine-rich repeats in light of the latest structural information

The three-dimensional structures of leucine-rich repeat (LRR) -containing proteins from five different families were previously predicted based on the crystal structure of the ribonuclease inhibitor. using an approach that combined homology-based modeling, structure-based sequence alignment of LRRs, and several rational assumptions. The structural models have been produced based on very limited sequence similarity, which, in general. cannot yield trustworthy predictions. Recently, the protein structures from three of these five families have been determined. In this report we estimate the quality of the modeling approach by comparing the models with the experimentally determined structures. The comparison suggests that the general architecture, curvature, interior/exterior orientations of side chains. and backbone conformation of the LRR structures can be predicted correctly. On the other hand. the analysis revealed that, in some cases. it is difficult to predict correctly the twist of the overall super-helical structure. Taking into consideration the conclusions from these comparisons, we identified a new family of bacterial LRR proteins and present its structural model. The reliability of the LRR protein modeling suggests that it would be informative to apply similar modeling approaches to other classes of solenoid proteins.

From protein structure to function with bioinformatics

It has long been known that amino acids are the building blocks for proteins and govern their folding into specific three-dimensional structures. However, the details of this process are still unknown and represent one of the main problems in structural bioinformatics, which is a highly active research area with the focus on the prediction of three-dimensional structure and its relationship to protein function. The protein structure prediction procedure encompasses several different steps from searches and analyses of sequences and structures, through sequence alignment to the creation of the structural model. Careful evaluation and analysis ultimately results in a hypothetical structure, which can be used to study biological phenomena in, for example, research at the molecular level, biotechnology and especially in drug discovery and development. In this thesis, the structures of five proteins were modeled with templatebased methods, which use proteins with known structures (templates) to model related or structurally similar proteins. The resulting models were an important asset for the interpretation and explanation of biological phenomena, such as amino acids and interaction networks that are essential for the function and/or ligand specificity of the studied proteins. The five proteins represent different case studies with their own challenges like varying template availability, which resulted in a different structure prediction process. This thesis presents the techniques and considerations, which should be taken into account in the modeling procedure to overcome limitations and produce a hypothetical and reliable three-dimensional structure. As each project shows, the reliability is highly dependent on the extensive incorporation of experimental data or known literature and, although experimental verification of in silico results is always desirable to increase the reliability, the presented projects show that also the experimental studies can greatly benefit from structural models. With the help of in silico studies, the experiments can be targeted and precisely designed, thereby saving both money and time. As the programs used in structural bioinformatics are constantly improved and the range of templates increases through structural genomics efforts, the mutual benefits between in silico and experimental studies become even more prominent. Hence, reliable models for protein three-dimensional structures achieved through careful planning and thoughtful executions are, and will continue to be, valuable and indispensable sources for structural information to be combined with functional data.

The ModFOLD server for the quality assessment of protein structural models

The reliable assessment of the quality of protein structural models is fundamental to the progress of structural bioinformatics. The ModFOLD server provides access to two accurate techniques for the global and local prediction of the quality of 3D models of proteins. Firstly ModFOLD, which is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. Secondly ModFOLDclust, which is a more intensive method that carries out clustering of multiple models and provides per-residue local quality assessment.

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

Background: Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC). Results: Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure. Conclusion: We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

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. (C) 2004 Elsevier B.V. All rights reserved.

Molecular models of tryptophan synthase from Mycobacterium tuberculosis complexed with inhibitors

The development of new therapies against infectious diseases is vital in developing countries. Among infectious diseases, tuberculosis is considered the leading cause of death. A target for development of new drugs is the tryptophan pathway. The last enzyme of this pathway, tryptophan synthase (TRPS), is responsible for conversion of the indole 3-glycerol phosphate into indol and the condensation of this molecule with serine-producing tryptophan. The present work describes the molecular models of TRPS from Mycobacterium tuberculosis (MtTRPS) complexed with six inhibitors, the indole 3-propanol phosphate and five arylthioalkyl-phosphonated analogs of substrate of the a-subunit. The molecular models of MtTRPS present good stereochemistry, and the binding of the inhibitors is favorable. Thus, the generated models can be used in the design of more specific drugs against tuberculosis and other infectious diseases.

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.

DBMODELING - A database applied to the study of protein targets from genome projects

Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms. We are now faced with assigning, understanding, and modifying the functions of proteins encoded by these genomes. DBMODELING is a relational database of annotated comparative protein structure models and their metabolic pathway characterization, when identified. This procedure was applied to complete genomes such as Mycobacteritum tuberculosis and Xylella fastidiosa. The main interest in the study of metabolic pathways is that some of these pathways are not present in humans, which makes them selective targets for drug design, decreasing the impact of drugs in humans. In the database, there are currently 1116 proteins from two genomes. It can be accessed by any researcher at http://www.biocristalografia.df.ibilce.unesp.br/tools/. This project confirms that homology modeling is a useful tool in structural bioinformatics and that it can be very valuable in annotating genome sequence information, contributing to structural and functional genomics, and analyzing protein-ligand docking.