Rational in silico design of aptamers for organophosphates based on the example of paraoxon

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Poisoning by organophosphates (OPs) takes one of the leading places in the total number of exotoxicoses. Detoxication of OPs at the first stage of the poison entering the body could be achieved with the help of DNA- or RNA-aptamers, which are able to bind poisons in the bloodstream. The aim of the research was to develop an approach to rational in silico design of aptamers for OPs based on the example of paraoxon. From the published sequence of an aptamer binding organophosphorus pesticides, its threedimensional model has been constructed. The most probable binding site for paraoxon was determined by molecular docking and molecular dynamics (MD) methods. Then the nucleotides of the binding site were mutated consequently and the values of free binding energy have been calculated using MD trajectories and MM-PBSA approach. On the basis of the energy values, two sequences that bind paraoxon most efficiently have been selected. The value of free binding energy of paraoxon with peripheral anionic site of acetylcholinesterase (AChE) has been calculated as well. It has been revealed that the aptamers found bind paraoxon more effectively than AChE. The peculiarities of paraoxon interaction with the aptamers nucleotides have been analyzed. The possibility of improving in silico approach for aptamer selection is discussed

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

We have performed a systematic study of the entropy term in the MM/GBSA (molecular Mechanics combined with generalized Born and surface area solvation) approach to calculate ligand-binding affinities The entropies are calculated by a normal mode analysis of harmonic frequencies from minimized snapshots of molecular dynamics simulations. For computational reasons, these calculations have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with. distances. larger than 8-16 angstrom to the ligand, including a 4 angstrom shell of fixed protein residues and water molecules, change the absolute entropies by 1-5 kJ/mol on average. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on average. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the Whole protein within statistical uncertainty (172 kJ/mol). We have also tested to use a distance dependent dielectric constant in the minimization and. frequency calculation (epsilon = 4r), but it typically gives slightly different entropies and poorer binding, affinities. Therefore, we recommend entropies calculated with the smallest truncation radius (8 angstrom) and epsilon =1 Such an approach also gives an improved precision for the calculated binding free energies

Comparison of end-point continuum-solvation methods for the calculation of protein-ligand binding free energies.

We have compared the predictions of ligand-binding affinities from several methods based on end-point molecular dynamics simulations and continuum solvation, i.e. methods related to MM/PBSA (molecular mechanics combined with Poisson-Boltzmann and surface area solvation). Two continuum-solvation models were considered, viz. the Poisson-Boltzmann (PB) and generalised Born (GB) approaches. The non-electrostatic energies were also obtained in two different ways, viz. either from the sum of the bonded, van der Waals, non-polar solvation energies, and entropy terms (as in MM/PBSA), or from the scaled protein-ligand van der Waals interaction energy (as in the linear interaction energy approach, LIE). Three different approaches to calculate electrostatic energies were tested, viz. the sum of electrostatic interaction energies and polar solvation energies, obtained either from a single simulation of the complex or from three independent simulations of the complex, the free protein, and the free ligand, or the linear-response approximation (LRA). Moreover, we investigated the effect of scaling the electrostatic interactions by an effective internal dielectric constant of the protein (ε(int) ). All these methods were tested on the binding of seven biotin analogues to avidin and nine 3-amidinobenzyl-1H-indole-2-carboxamide inhibitors to factor Xa. For avidin, the best results were obtained with a combination of the LIE non-electrostatic energies with the MM+GB electrostatic energies from a single simulation, using ε(int) = 4. For fXa, standard MM/GBSA, based on one simulation and using ε(int) = 4-10 gave the best result. The optimum internal dielectric constant seems to be slightly higher with PB than with GB solvation. Proteins 2012. © 2012 Wiley-Liss, Inc

Can System Truncation Speed up Ligand-Binding Calculations with Periodic Free-Energy Simulations?

We have investigated whether alchemical free-energy perturbation calculations of relative binding energies can be sped up by simulating a truncated protein. Previous studies with spherical nonperiodic systems showed that the number of simulated atoms could be reduced by a factor of 26 without affecting the calculated binding free energies by more than 0.5 kJ/mol on average (Genheden, S.; Ryde, U. J. Chem. Theory Comput. 2012, 8, 1449), leading to a 63-fold decrease in the time consumption. However, such simulations are rather slow, owing to the need of a large cutoff radius for the nonbonded interactions. Periodic simulations with the electrostatics treated by Ewald summation are much faster. Therefore, we have investigated if a similar speed-up can be obtained also for periodic simulations. Unfortunately, our results show that it is harder to truncate periodic systems and that the truncation errors are larger for these systems. In particular, residues need to be removed from the calculations, which means that atoms have to be restrained to avoid that they move in an unrealistic manner. The results strongly depend on the strength on this restraint. For the binding of seven ligands to dihydrofolate reductase and ten inhibitors of blood-clotting factor Xa, the best results are obtained with a small restraining force constant. However, the truncation errors were still significant (e.g., 1.5-2.9 kJ/mol at a truncation radius of 10 Å). Moreover, the gain in computer time was only modest. On the other hand, if the snapshots are truncated after the MD simulations, the truncation errors are small (below 0.9 kJ/mol even for a truncation radius of 10 Å). This indicates that postprocessing with a more accurate energy function (e.g., with quantum chemistry) on truncated snapshots may be a viable approach

Binding affinities in the SAMPL3 trypsin and host-guest blind tests estimated with the MM/PBSA and LIE methods

We have estimated affinities for the binding of 34 ligands to trypsin and nine guest molecules to three different hosts in the SAMPL3 blind challenge, using the MM/PBSA, MM/GBSA, LIE, continuum LIE, and Glide score methods. For the trypsin challenge, none of the methods were able to accurately predict the experimental results. For the MM/GB(PB)SA and LIE methods, the rankings were essentially random and the mean absolute deviations were much worse than a null hypothesis giving the same affinity to all ligand. Glide scoring gave a Kendall's τ index better than random, but the ranking is still only mediocre, τ = 0.2. However, the range of affinities is small and most of the pairs of ligands have an experimental affinity difference that is not statistically significant. Removing those pairs improves the ranking metric to 0.4-1.0 for all methods except CLIE. Half of the trypsin ligands were non-binders according to the binding assay. The LIE methods could not separate the inactive ligands from the active ones better than a random guess, whereas MM/GBSA and MM/PBSA were slightly better than random (area under the receiver-operating-characteristic curve, AUC = 0.65-0.68), and Glide scoring was even better (AUC = 0.79). For the first host, MM/GBSA and MM/PBSA reproduce the experimental ranking fairly good, with τ = 0.6 and 0.5, respectively, whereas the Glide scoring was considerably worse, with a τ = 0.4, highlighting that the success of the methods is system-dependent

Computational modeling of protein ligand systems

Biomoleküle können auf Basis ihrer Struktur, ihrer Dynamik oder der von ihnen eingegangenen Wechselwirkungen bzw. Funktion betrachtet werden. Die drei klassischen Verfahren der biomolekularen Modellierung, die für solche Untersuchungen verwendet werden sind die Homologie Modellierung, das Docking unddie Molekulardynamiksimulation (MD). In dieser Promotionsarbeit sollen diese drei Verfahren etabliert, angewendet und mit anderen Verfahren, wie elektrostatischen Modellen, Signifikanzanalysen, Clusteranalysen und Varianten der Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) Methode, weiterentwickelt werden, um die nachfolgenden Fragestellungen in drei biomolekularen Systemen zu untersuchen. Im ersten Fall werden verschiedene Glucose Dehydrogenase Isoenzyme des hyperthermophilen Archaeon Sulfolobus solfataricus untersucht und ihre Substratspezifität wird miteinander verglichen. Das Basisverfahren ist hier die Homologie Modellierung. Sie wird durch ein elektrostatisches Modell und ein Docking erweitert, um ein tieferes Verständnis ihrer Wechselwirkungen mit den Substraten zu erhalten. Im zweiten Fall wird die Chitinase B des Enterobakteriums Serratia marcescens mit bekannten Inhibitoren untersucht. Hier bildet das Docking die Grundlage und wird unter anderem mit dem MMPBSA Methode erweitert. Im letzten Fall werden verschiedene SH3 Domänen mit ihren Peptidliganden mittels MD untersucht. Die Simulationen werden dabei mit Signifikanzanalysen statistisch verglichen und geclustert. Daraus ergeben sich Strukturmodelle, die mit höherer Wahrscheinlichkeit zutreffen als Modelle, die mit gängigen Verfahren erzeugt werden.Biomolecules can be analyzed with regard to their structure, dynamics, interactions, and function. Three standard methods for the analysis of these molecules are homology modeling, docking, and molecular dynamics (MD) simulation. In this dissertation, these three methods are established, tested and combined with additional techniques, such as electrostatic models, significance analysis, cluster analysis, and variants of the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method. I examine with these computational modeling methods three biomolecular systems: First, various glucose dehydrogenase isoenzymes of the hyperthermophilic archaeon Sulfolobus solfataricus are studied and their respective substrate specificity are compared. Here, the main method is homology modeling. It is enhanced by electrostatic models and docking in order to obtain a deeper understanding of the interactions between enzymes and ligands. Second, chitinase B of the enterobacterium Serratia marcescens is investigated, including its interactions with known inhibitors. The results from an initial docking simulations are refined by subsequent calculations with the MMPBSA method. Third, different SH3 domains are examined in complex with their peptide ligands by MD simulations. The simulations are compared statistically with a significance analysis method and clustering. The outcome of these analyses promise to be more realistic models than models developed by conventional methods