Abstract
Molecular-docking programs coupled with suitable scoring functions are now established and very useful tools enabling computational chemists to rapidly screen large chemical databases and thereby to identify promising candidate compounds for further experimental processing. In a broader scenario, predicting binding affinity is one of the most critical and challenging components of computer-aided structure-based drug design. The development of a molecular docking scoring function which in principle could combine both features, namely ranking putative poses and predicting complex affinity, would be of paramount importance. Here, we systematically investigated the performance of the MM-PBSA approach, using two different Poisson–Boltzmann solvers (APBS and DelPhi), in the currently rising field of protein-peptide interactions (PPIs), identifying the correct binding conformations of 19 different protein-peptide complexes and predicting their binding free energies. First, we scored the decoy structures from HADDOCK calculation via the MM-PBSA approach in order to assess the capability of retrieving near-native poses in the best-scoring clusters and of evaluating the corresponding free energies of binding. MM-PBSA behaves well in finding the poses corresponding to the lowest binding free energy, however the built-in HADDOCK score shows a better performance. In order to improve the MM-PBSA-based scoring function, we dampened the MM-PBSA solvation and coulombic terms by 0.2, as proposed in the HADDOCK score and LIE approaches. The new dampened MM-PBSA (dMM-PBSA) outperforms the original MM-PBSA and ranks the decoys structures as the HADDOCK score does. Second, we found a good correlation between the dMM-PBSA and HADDOCK scores for the near-native clusters of each system and the experimental binding energies, respectively. Therefore, we propose a new scoring function, dMM-PBSA, to be used together with the built-in HADDOCK score in the context of protein-peptide docking simulations.
Highlights
Molecular docking is a computational method that investigates the intermolecular complexes formed between two or more constituent molecules
Molecular Mechanics (MM)-Poisson–Boltzmann Surface Area (PBSA) is an end-point method devised to estimate binding free energy ( Gcomp) as the difference of the free energy of the complex and those of the unbound receptor and peptide (Massova and Kollman, 1999). It is performed from a set of snapshots obtained from Molecular Dynamics simulation (Hou et al, 2011). This method is significantly less computationally demanding than alternatives such as free energy perturbation (FEP) calculations and it represents a possible alternative to FEP for virtual screening of large chemical libraries
MMPBSA has already been used as a scoring function in the past with various outcomes (Kuhn et al, 2005; Thompson et al, 2008; Zhou et al, 2009; Genheden and Ryde, 2015) but to the best of our knowledge this is the first time it has been used for a set of peptide interactions (PPIs) obtained from docking calculations
Summary
Molecular docking is a computational method that investigates the intermolecular complexes formed between two or more constituent molecules It comprises the process of generating a model of a complex based on the known three-dimensional structures of its components, i.e., the target (protein, or nucleic acids) and the ligand (a peptide, a protein, a small organic molecule), free or bound to other species (Rognan, 2013). The reliability of the scoring functions is probably one of the aspects deserving more attention, since discriminating native pose and obtaining a fair correlation between docking scores and experimental activity data remain difficult tasks. These limitations are responsible for the occurrence of false-positive and false-negative hits in the ranked lists resulting from the screenings performed with standard docking methods. Despite empirical scoring functions are still widely used in drug discovery since they are faster and relatively accurate, first-principle methods for ranking decoy structures and for predicting affinity should be considered the first desirable choice in docking scoring stage
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