Periplasmic oligopeptide-binding protein (OppA) is the initial receptor in the ATP-binding cassette (ABC) system of bacteria, which exhibits a broad specificity in binding oligopeptides without regard to sequence. Here, we present a computational study on the structural properties and energetic landscapes of OppA protein interacting with its cognate ligands on the basis of 28 structure/affinity-known OppA-tripeptide complexes. By employing a well-designed protocol that couples the hybrid quantum mechanical/molecular mechanical (QM/MM) scheme and the sophisticated Poisson-Boltzmann/surface area (PB/SA) solvent model together to analyze and decompose the energy components associated with the OppA-peptide binding, we demonstrate that the broad specificity of OppA-recognizing peptides is originated from a series of exquisite balances between the free energy contributions from, for example, the direct nonbonded interactions and indirect desolvation effects, the main chains and side chains, and the different residue positions of the tripeptide ligands. We also show that, in a framework of structure-based quantitative structure-activity relationship (SB-QSAR) methodology, the QM/MM-PB/SA-derived energy terms could be used as a good descriptor to characterize the interaction profile of OppA with peptides and correlate pretty well with the experimentally measured affinities of the binding.
Water plays an invaluable role in governing the structure, stability, dynamics, and function of biomolecules, which has also been demonstrated to be critical in mediating biomolecular recognition and association. Accurate determination of the dynamic behavior of water molecules at biological complex interface is important for the understanding of the molecular mechanism of water contributing to the binding between biomolecules and could be exploited as an alternative tool to refine the water's positions in X-ray electron density map. In the present study, a method called local hydrophobic descriptors (LHDs) is used to characterize the hydrophobic landscapes of the hydration sites at protein-DNA interface. The resulting variables of the characterization are then correlated with the experimentally measured B-factor values of 4445 elaborately selected water samples derived from a panel of thematically nonredundant, high-quality protein-DNA interfaces by using a variety of machine learning methods, including PLS, BPNN, SVM, LSSVM, RF, and GP. The results show that the dynamic behavior of interfacial water molecules is primarily governed by the local hydrophobic feature of the hydration sites that water molecules located, and the nonlinear dependence dominates over the linear component in the water B-factor system. We expect that this structured-based approach can be used for predicting the B-factor profile of other biomolecules as well.
The B-factors of crystal structures reflect the atomic fluctuations about their average positions and provide important information about molecular dynamics. Although numerous works have been addressed on theoretical and computational studies of B-factor profile of protein atoms, the methods used for predicting B-factor values of water molecules in protein crystals still remain unexploited. In this article, we describe a new approach that we named local hydrophobic descriptors (LHDs) to characterize the hydrophobic landscapes of protein hydration sites. Using this approach coupled with partial least squares (PLS) regression and least-squares squares support vector machine (LSSVM), we perform a systematic investigation on the linear and nonlinear relationships between the LHDs and water B-factors. Based upon an elaborately selected, large-scale dataset of crystal water molecules, our method predicts B-factor profile with coefficient of determination rpred of 0.554. We demonstrate that (i) the dynamics of water molecules is primarily governed by the local features of hydrophobic potential landscapes, and (ii) the accuracy of predicted B-factor values depends on water packing density.
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