Laurent Emmanuel Dardenne

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.

A multiple minima genetic algorithm for protein structure prediction

Protein structure prediction (PSP) has a large potential for valuable biotechnological applications. However the prediction itself encompasses a difficult optimization problem with thousands of degrees of freedom and is associated with extremely complex energy landscapes. In this work a simplified three-dimensional protein model (hydrophobic-polar model, HP in a cubic lattice) was used in order to allow for the fast development of a robust and efficient genetic algorithm based methodology. The new methodology employs a phenotype based crowding mechanism for the maintenance of useful diversity within the populations, which resulted in increased performance and granted the algorithm multiple solutions capabilities. Tests against several benchmark HP sequences and comparative results showed that the proposed genetic algorithm is superior to other evolutionary algorithms. The proposed algorithm was then successfully adapted to an all-atom protein model and tested on poly-alanines. The native structure, an alpha helix, was found in all test cases as a local or a global minimum, in addition to other conformations with similar energies. The results showed that optimization strategies with multiple solutions capability present two advantages for PSP applications. The first one is a more efficient investigation of complex energy landscapes; the second one is an increase in the probability of finding native structures, even when they are not at the global optimum.

Understanding the HIV-1 protease nelfinavir resistance mutation D30N in subtypes B and C through molecular dynamics simulations

A major concern in the antiretroviral (ARV) treatment of HIV infections with protease inhibitors (PI) is the emergence of resistance, which results from the selection of distinct mutations within the viral protease (PR) gene. Among patients who do not respond to treatment with the PI nelfinavir (NFV), the D30N mutation is often observed. However, several reports have shown that D30N emerges with different frequencies in distinct HIV-1 genetic forms or subtypes. In the present work, we analyzed the binding of NFV and the Gag substrate CA/p2 to PR from HIV-1 subtypes B and C through molecular dynamics (MD) simulations. The wild-type and drug-resistant D30N mutants were investigated in both subtypes. The compensatory mutations N83T and N88D, observed in vitro and in vivo when subtype C acquires D30N, were also studied. D30N appears to facilitate conformational changes in subtype B PR, but not in that from subtype C, and this could be associated with disestablishment of an alpha-helical region of the PR. Furthermore, the total contact areas of NFV or the CA/p2 substrate with the mutant PR correlated with changes in the resistance patterns and replicative capacity. Finally, we observed in our MD simulations that mutant PR proteins show different patterns for hydrophobic/van der Waals contact. These findings suggest that different molecular mechanisms contribute to resistance, and we propose that a single mutation has distinct impacts on different HIV-1 subtypes.

A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information

The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasites metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.

A dynamic niching genetic algorithm strategy for docking highly flexible ligands

Abstract Currently, docking methods are very important tools in structure-based drug design (SBDD). However, despite the great advances in the docking area over the last decades, most methods cannot be used to dock highly flexible ligands successfully. It is even harder when the ligand is cross-docked into different ligand-bound receptor structures. In this work, a new multi-solution genetic algorithm method, named Dynamic Modified Restricted Tournament Selection (DMRTS), was developed for the effective docking of highly flexible ligands. The DMRTS method uses an insertion criterion based on similarity and a dynamic tournament size to preserve good, distinct solutions in the genetic algorithm population. The proposed method was implemented in three different versions of a steady-state genetic algorithm and evaluated for the redocking and cross-docking of five HIV-1 protease ligands, with 12–20 rotatable bonds. The DMRTS method was also tested on a more diverse set of 34 protein–ligand complexes covering 18 different protein families. A performance comparison with three of the currently most used docking programs was also done. The proposed method was evaluated for 25 benchmark functions of the CEC2005 test suite. The results indicated that the DMRTS method can adequately sample the conformational search space, producing a diverse set of high quality solutions. Moreover, it might be a powerful tool for docking studies in SBDD practice, increasing the success rate in finding correct ligand conformations and efficiently exploring distinct and valuable ligand binding modes.

Selection-Insertion Schemes in Genetic Algorithms for the Flexible Ligand Docking Problem

In this work we have implemented and analyzed the performance of a new real coded steady-state genetic algorithm (SSGA) for the flexible ligand-receptor docking problem. The algorithm employs a grid-based methodology, considering the receptor rigid, and the GROMOS classical molecular force field to evaluate the energy function. In the implementation we used the restricted tournament selection (RTS) technique in order to find multiple solutions and also introduced a new variation of this technique with an insertion criterion based in the root-mean-square-deviation (RMSD) between the ligand structures. The SSGA was tested in docking four HIV1 protease-ligand complexes with known three-dimensional structures. All ligands tested are highly flexible, having 12 to 20 conformational degrees of freedom. The implemented docking methodology was able to dock successfully all flexible ligands tested with a success ratio higher than 90 % and a mean RMSD lower than 1.3 A with respect to the corresponding experimental structures.

Full-atom ab initio protein structure prediction with a Genetic Algorithm using a similarity-based surrogate model

The protein structure prediction problem is one of the most interesting challenges of computational biology. One of its critical facets is the optimization method employed. This is often carried out by metaheuristics, such as Genetic Algorithms (GA). The prediction involves optimization of a complex and computationally expensive energy function. Thus, the usual GA requirements of a large number of function evaluations can ultimately result in prohibitive computational costs. We applied a k-nearest neighbors surrogate modeling strategy, with two different similarity criteria, to improve the quality of proteins structures predicted by a crowding-based steady-state GA, without increasing the number of exact fitness evaluations. Additional protein conformations can be investigated using the surrogate model, potentially increasing the exploratory capability of the algorithm. The results obtained from six test proteins suggest that the surrogate model approach has the potential to improve the performance of the described protein structure prediction method.

Structural modeling and docking studies of ribose 5-phosphate isomerase from Leishmania major and Homo sapiens: A comparative analysis for Leishmaniasis treatment

Leishmaniases are caused by protozoa of the genus Leishmania and are considered the second-highest cause of death worldwide by parasitic infection. The drugs available for treatment in humans are becoming ineffective mainly due to parasite resistance; therefore, it is extremely important to develop a new chemotherapy against these parasites. A crucial aspect of drug design development is the identification and characterization of novel molecular targets. In this work, through an in silico comparative analysis between the genomes of Leishmania major and Homo sapiens, the enzyme ribose 5-phosphate isomerase (R5PI) was indicated as a promising molecular target. R5PI is an important enzyme that acts in the pentose phosphate pathway and catalyzes the interconversion of d-ribose-5-phosphate (R5P) and d-ribulose-5-phosphate (5RP). R5PI activity is found in two analogous groups of enzymes called RpiA (found in H. sapiens) and RpiB (found in L. major). Here, we present the first report of the three-dimensional (3D) structures and active sites of RpiB from L. major (LmRpiB) and RpiA from H. sapiens (HsRpiA). Three-dimensional models were constructed by applying a hybrid methodology that combines comparative and ab initio modeling techniques, and the active site was characterized based on docking studies of the substrates R5P (furanose and ring-opened forms) and 5RP. Our comparative analyses show that these proteins are structural analogs and that distinct residues participate in the interconversion of R5P and 5RP. We propose two distinct reaction mechanisms for the reversible isomerization of R5P to 5RP, which is catalyzed by LmRpiB and HsRpiA. We expect that the present results will be important in guiding future molecular modeling studies to develop new drugs that are specially designed to inhibit the parasitic form of the enzyme without significant effects on the human analog.

Critical Features of Fragment Libraries for Protein Structure Prediction

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

A unique SaeS allele overrides cell-density dependent expression of saeR and lukSF-PV in the ST30-SCCmecIV lineage of CA-MRSA

ST30 (CC30)-SCCmec IV (USA1100) is one of the most common community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) lineages. ST30 isolates typically carry lukSF-PV genes encoding the Panton-Valentine leukocidin (PVL) and are responsible for outbreaks of invasive infections worldwide. In this study, twenty CC30 isolates were analyzed. All were very susceptible to non-β-lactam antimicrobials, 18/20 harbored the lukSF-PV genes, only 1/20 exhibited agr-rnaIII dysfunction, and the majority was not able to form biofilm on inert surfaces. Analysis of lukSF-PV temporal regulation revealed that opposite to other CA-MRSA isolates, these genes were more highly expressed in early log phase than in stationary phase. This inverted lukSF-PV temporal expression was associated with a similar pattern of saeRS expression in the ST30 isolates, namely high level expression in log phase and reduced expression in stationary phase. Reduced saeRS expression in stationary phase was associated with low expression levels of the sae regulators, agr and agr-upregulator sarA, which activate the stationary phase sae-P1 promoter and overexpression of agr-RNAIII restored the levels of saeR and lukSF-PV trancripts in stationary phase. Altered SaeRS activity in the ST30 isolates was attributed to amino acid substitutions (N227S, E268K and S351T) in the HTPase_c domain of SaeS (termed SaeS(SKT)). Complementation of a USA300 saeS mutant with the saeS(SKT) and saeS alleles under the direction of the log phase sae-P3 promoter revealed that saeR and lukSF-PV transcription levels were more significantly activated by saeS(SKT) than saeS. In summary our data identify a unique saeS allele (saeS(SKT)) which appears to override cell-density dependent SaeR and PVL expression in ST30 CA-MRSA isolates. Further studies to determine the contribution of saeS(SKT) allele to the pathogenesis of infections caused by ST30 isolates are merited.