1,720,971 research outputs found
A semiautomated Nwat-MM-GBSA workflow for fast and accurate predictions of relative binding free energies
No full textDespite the development of high-throughput computational methods able to screen very large libraries in a short time, the reliable prediction of binding free energy can still be important in drug design.1,2 Although quite computationally expensive, molecular dynamics (MD), providing a statistically meaningful conformational ensemble for thermodynamic calculations, are within the most accurate tecqniques to predict interaction free energies of biomolecules. Among MD-based methods, one of the most popular is Molecular Mechanics Poisson−Boltzmann/Generalized Born Surface Area (MM-PB/GBSA).3
We recently reported on how the inclusion of a certain number of explicit waters (Nwat), chosen to be the closest to the ligand atoms, can improve the correlation between MM-PB and GBSA computed binding energy and experimental activities (Fig. 1).4
Fig. : Effect of the inclusion of explicit waters in the correlation of computed and experimental activities for a set of topoisomerase inhibitors
Here, we will present a semiautomated workflow to compute MM-GBSA relative binding energies starting from a set of complexes, either obtained through X-ray crystallography, homology modelling or docking simulations, by taking advantage of GPU calculations and with a minimal effort by the user.
We will also discuss specific examples of application on protein-ligand and protein-protein complexes.
REFERENCES
1. Durrant, J.D.; McCammon, J.A. Molecular dynamics simulations and drug discovery. BMC Biology 2011, 9:71
2. Zhao, H.; Caflish, A. Molecular dynamics in drug design. Eur. J. Med. Chem. 2014, doi:10.1016/j.ejmech.2014.08.004
3. Massova, I.; Kollman, P. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. 2000, 18 (1), 113-135
4. Maffucci, I.; Contini, A. Explicit Ligand Hydration Shells Improve the Correlation between
MM-PB/GBSA Binding Energies and Experimental Activities J. Chem. Theory Comput. 2013, 9, 2706-2717
Behind the helix stabilization and screw sense preferences of chiral Cα-tetrasubstituted α-amino acids
No full textThe theoretical basis behind the ability of chiral constrained Cα-tetrasubstituted amino acids (cCTAAs) to stabilize helical secondary structures[1] or to induce one particular helical screw sense[2] has been investigated theoretically by using Replica Exchange Molecular Dynamics (REMD), Potential of Mean Force (PMF) and Quantum Theory of Atoms In Molecules (QTAIM) calculations.
Two different peptide models were used to evaluate helix stabilization and screw sense preferences: Ac-l-Ala-cCTAA-l-Ala-Aib-l-Ala-NHMe and Ac-Aib2-cCTAA-Aib2-NHMe, respectively. Actually existing cCTAAs, represented in Figure1, as well as some hypothetical derivatives were considered in this study.
We found two alternative mechanisms that contribute to the helix stabilization by limiting the backbone conformational freedom: 1) steric hindrance in the (+x,+y,–z) sector of a right-handed 3D Cartesian space (Figure 2), where the z axis coincides with the helical axis and the Cα of the cCTAA lies on the +y axis, and 2) the establishment of additional and relatively strong C–H···O interactions involving the cCTAA.
Similarly, helical screw sense selectivity is also mediated by steric hindrance, which need to be parallel to the helix axis. However, considering the P-Helix, if the side chain points toward the N-terminus, it also needs to occupy the (−x, +y, +z) sector. Conversely, when the side chain points toward the C-terminus, it have to encumber the (+x, +y, −z) region. In this case also, the behavior of specific cCTAAs is explained by their different ability to affect the noncovalent interaction network by establishing or strengthening C–H···O weak H-bonds
Explicit Ligand hydration shells improve the correlation between MM-PB/GBSA binding energies and experimental activities
No full textMolecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) methods are widely used for drug design/discovery purposes. However, it is not clear if the correlation between predicted and experimental binding affinities can be improved by explicitly considering selected water molecules in the calculation of binding energies, since different and sometimes diverging opinions are found in the literature. In this work, we evaluated how variably populated hydration shells explicitly considered around the ligands may affect the correlation between MM-PB/GBSA computed binding energy and biological activities (IC50 and ΔGbind, depending on the available experimental data). Four different systems—namely, the DNA-topoisomerase complex, α-thrombin, penicillopepsin, and avidin—were considered and ligand hydration shells populated by 10–70 water molecules were systematically evaluated. We found that the consideration of a hydration shell populated by a number of water residues (Nwat) between 30 and 70 provided, in all of the considered examples, a positive effect on correlation between MM-PB/GBSA calculated binding affinities and experimental activities, with a negligible increment of computational cost
Improving the reliability of MM-PBSA and MM-GBSA binding energy predictions by explicitly considering ligand solvation shells
No full textMolecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) are interesting techniques for drug design/discovery applications, but sometimes the correlation between predicted and experimental binding energies might result unsatisfactory.
Nowadays, a certain effort is focused on ameliorating the solvation term in MM-PB/GBSA calculations and some strategies were applied to obtain a better correlation between calculations and experiments.
Some authors reported that the predictivity of MM-PB/GBSA calculations might be improved by modulating the internal dielectric constant (εin).1 Unfortunately, a universal εin, suitable for all systems was not found and a thorough analysis of the binding pocket is needed to choose the proper value of εin.
MM-PB/GBSA binding energy predictions might also be improved by explicitly considering selected water molecules in the calculation, however this strategy is controversial.2-5
Herein, we report on how the explicit inclusion of variably populated ligand hydration shells might improve the correlation between MM-PB/GBSA computed binding energy and experimental activities.
DNA-topoisomerase, α-thrombin, penicillopepsin, avidin, and neuraminidase complexes with different ligands were considered as test sets, and ligand hydration shells populated by an increasing number of water molecules were systematically evaluated.
We found that the consideration of a hydration shell populated by a number of water residues (Nwat) between 30 and 70 provided in all the considered examples a positive effect on correlation between MM-PB/GBSA calculated binding affinities and experimental activities, with a negligible increment of computational cost.6
REFERENCES
1. Hou, T.; Wang, J.; Li, Y.; Wang, W., J. Chem. Inf. Model. 2011, 51, 69-82.
2. Wong, S.; Amaro, R. E.; McCammon, J. A., J. Chem. Theory Comput. 2009, 5, 422-429.
3. Hayes, J. M.; Skamnaki, V. T.; Archontis, G.; Lamprakis, C.; Sarrou, J.; Bischler, N.; Skaltsounis, A.-L.; Zographos, S. E.; Oikonomakos, N. G., Proteins 2011, 79, 703-19.
4. Freedman, H.; Huynh, L. P.; Le, L.; Cheatham, I. I. I. T. E.; Tuszynski, J. A.; Truong, T. N., J. Phys. Chem. B 2010, 114, 2227-2237.
5. Checa, A.; Ortiz, A. R.; de Pascual-Teresa, B.; Gago, F., J. Med. Chem. 1997, 40 (25), 4136-45.
6. Maffucci, I.; Contini, A., J. Chem. Theory Comput. 2013, 9 (6), 2706-2717
OPTIMIZATION AND APPLICATION OF COMPUTATIONAL METHODS FOR THE DESIGN OF PROTEIN-PROTEIN INTERACTIONS MODULATORS
Full text linkIn the wide field of PPIs, this PhD project has been focused on the optimization and application of computational methods for the design of PPIs modulators, with a particular interest toward peptide modulators targeting PPIs involving helical motifs.
In this contest, the first part of the project has been aimed to define the rationales behind the helical secondary structure stabilization and the helical screw sense selectivity exerted by chiral Cα-tetrasubstituted amino acids (cCTAAs) through REMD simulations and QTAIM analyses, and the mechanisms responsible of the helical screw sense inversion through PNEB simulations.
In detail, it has been found that the helical motif is stabilized by two complementary mechanisms: the first depends on the steric hindrance exerted by the cCTAA in an area parallel to the peptide helix axis and downstream of the cCTAA itself, whereas the second consists in the strengthening of the helical H-bond network thanks to peculiar C-H···O=C interactions. Analogously, P-helical screw sense selectivity is ascribable to the cCTAA steric hindrance parallel to the peptide helix axis, without particular preferences for the region downstream and upstream of the cCTAA, together with quite strong noncovalent interactions, consisting of classical N – H···O=C H-bonds and weak C – H···O=C interactions. Furthermore, PNEB simulations performed on achiral peptides of different lengths suggest that the helical screw sense inversion requires the formation of γ-turns, although a preferential screw sense inversion direction was not found.
Therefore, the knowledge gained from these studies could be helpful in designing stable helical peptides, having a preferential screw sense and that can be in principle activated in situ by inducing a conformational switch from P to M helix or vice versa.
Conversely, the second part of the project has been focused on the optimization of an MMGBSA based method, called Nwat-MMGBSA, aimed to improve the correlation between predicted binding energies of PPI complexes and experimental data. This approach, consisting in the inclusion, as part of the receptor, of hydration shells around the ligand during the MMGBSA calculations, was initially tested on classical receptor-ligand complexes and, then, automatized, optimized and tested on PPI complexes.
This approach turned out to be good for the evaluation of PPI modulators activities, from different points of view. First of all, when water played a significant role in mediating protein-ligand interactions, the application of Nwat-MMGBSA improved the correlation between predicted and experimental data. On the other hand, if the solvent does not explicitly participate to the interaction, it did not give detrimental results compared to those obtained with the standard approach. In addition, the protocol proved to be robust and reproducible, giving equivalent results by using different setups. Furthermore, although an optimal number of water molecules to include in the hydration shell could not be found, in the case of PPI interactions inhibited by small molecules the inclusion of 50 – 60 water molecules appears to be a good choice. A non-negligible advantage of this approach is represented by the possibility to automatize it, making it applicable for drug design/discovery purposes.
Therefore, although further evaluations are needed, most of all on larger datasets, the knowledge coming from the combination of both parts of the project can be exploited for the design of stable non-natural peptides targeting PPIs
Tuning the Solvation Term in the MM-PBSA/GBSA Binding Affinity Predictions
No full textMolecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) are widely used methods for the prediction of binding free energies in drug design/discovery. Indeed, their computational efficiency makes them applicable also within virtual screening protocols. Thus, in order to be useful for drug design/discovery purposes, MM-PBSA and MM-GBSA binding energy predictions have to correlate well with experimental activities. Nowadays the global effort to find a way to improve the predictivity of MMPBSA/ GBSA calculations is also focused on the solvation term by using various approaches. This chapter reports on the application of MM-PBSA/GBSA methods within the process of drug discovery and, in particular, on strategies that can be applied to improve the correlation between MM-PBSA/GBSA predicted binding affinities and experimental pharmacological activities by acting on the way the solvent is treated in such calculations. Indeed, in PB and GB models, the solvent is described as a continuous medium with a fixed dielectric constant (i.e. ε = 80 for water), while a low internal dielectric constant is assigned to the solute (generally εin = 1 or 2 for proteins). However, the default approach could in some cases lead to a weak correlation between predicted binding free energies and experimental data. The aim of this chapter is to present and exemplify the ways to improve the prediction of ligand binding affinity by acting on the solvation term. Different methods are observed in the literature, e.g. tuning the εin value depending on the features of the binding site, including a selection of explicit water molecules in order to better describe the solute-solvent interactions, tuning the grid size in PB calculations and/or using different PB solvers, or modifying the non-polar term of the solvation free energy. The pros and cons of the above mentioned methods will be critically discussed in order to help the reader in choosing the most performing protocol in terms of both calculation time and prediction quality, depending on the molecular system under evaluation
Improved Computation of Protein–Protein Relative Binding Energies with the Nwat-MMGBSA Method
No full textA MMGBSA variant (here referred to as Nwat-MMGBSA), based on the inclusion of a certain number of explicit water molecules (Nwat) during the calculations, has been tested on a set of 20 protein–protein complexes, using the correlation between predicted and experimental binding energy as the evaluation metric. Besides the Nwat parameter, the effect of the force field, the molecular dynamics simulation length, and the implicit solvent model used in the MMGBSA analysis have been also evaluated. We found that considering 30 interfacial water molecules improved the correlation between predicted and experimental binding energies by up to 30%, compared to the standard approach. Moreover, the correlation resulted in being rather sensitive to the force field and, to a minor extent, to the implicit solvent model and to the length of the MD simulation
Use of Chiral Cα-Tetrasubstituted Amino Acids For Stabilizing The Geometry and Screw Sense of Helical Secondary Structures.
No full textProtein-protein interactions are involved in many biological events and the development of molecules targeting PPIs is one of the main goals of contemporary medicinal chemistry. Recently, much attention has been paid to the design of peptides containing non-natural amino acids (nnAAs) able to stabilize a certain secondary structure, often the right-handed helical one, in order to combine the high selectivity and specificity and low toxicity of well-designed peptides with the stability toward peptidases and proteases provided by the nnAAs. One of the most exploited classes of nnAAs is that of chiral Cα-tetrasubistuted amino acids (cCTAAs), which stabilize the helical secondary structure by limiting the backbone conformational freedom and induce a preferential helical screw sense in otherwise achiral peptides thanks to their side chains. However, although these cCTAAs are commonly used, rationales explaining the mechanisms of their helical stabilization and stereoselectivity are not well clarified. We started to fill this knowledge gap by performing REMD simulations, PMF and QTAIM calculations on selected Ac-L-Ala-cCTAA-L-Ala-Aib-L-Ala-NHMe and Ac-Aib2-cCTAA-Aib2-NHMe model peptides for the study of the mechanism of helical stabilization and helical stereoselectivity, respectively. We found that the inclusion of the selected cCTAAs in the former peptide model limits the backbone conformational freedom thanks to a steric hindrance predominantly located in the (+x,+y,-z) sector of a right-handed 3D-Cartesian space, where the +z → -z axis coincides with the N → C helical axis and the Cα of the cCTAA lies on the +y axis (0,+y,0), and the generation of additional intramolecular C-H···O interactions. Analogously, the stereoselectivity toward a particular screw sense of Ac-Aib2-cCTAA-Aib2-NHMe peptides is achieved thanks to the positioning of the cCTAAs' side chains in the (-x,+y,+z) and, at minor extent, in the (+x,+y,-z) sectors and to the presence of a strong C-H···O H-bonds network
Automatization of the Nwat-MMGBSA method to rescore docking results in medium-throughput virtual screening applications
No full textNwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory.[1] This method can increase the correlation between predicted and experimental binding energies in both ligand-receptor and protein-protein complexes,[2] compared to standard MM-GBSA. The protocol for molecular dynamic (MD) simulations, preparatory to subsequent Nwat-MMGBSA calculations, has now been optimized to maximize efficacy and efficiency, thus making the calculations practical in low-to-medium throughput virtual screenings. Three systems, penicillopepsin, HIV1-protease and BCL-XL, have been used as test cases. Calculations have been performed in triplicates on both classic HPC environments as well as on workstations equipped by GPU cards, evidencing no statistical differences in the results, but a dramatic decrease of the “cost per nanosecond” for the latter systems. With the optimized protocol, the whole calculation, from equilibration to production MD and subsequent Nwat-MMGBSA rescoring, averagely took about one hour per ligand using a single GPU.
A set of scripts for automatic structure based virtual screening, from library setup to docking and rescoring, has also been designed and tested within a retrospective virtual screening for inhibitors of the Rac1-Tiam1 protein-protein interaction. The screening library has been built using compounds experimentally tested, with a ratio between actives and real inactives of 1 to 10, and different protocols were used to process the library prior to docking (DB-A, DB-B and DB-C, Figure 1).
The results, summarized in Figure 1, confirmed the benefit of including explicit water molecules MM-GBSA calculation and the validity of Nwat-MMGBSA to rescore virtual screening results
- …
