Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses

Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye–Waller factor or B‐factor. Although numerous works have been addressed on theoretical and computational studies of the B‐factor profiles of proteins, the methods used for predicting B‐factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure–flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B‐factor based on primary sequences (sequence‐based) and advanced structures (structure‐based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B‐factors, we demonstrate that (i) rRNA B‐factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure‐based approach performed much better as compared to sequence‐based methods in modeling of rRNA B‐factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.

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“…[50] r ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi…”

Section: Statistics Usedunclassified

Predicting the Flexibility Profile of Ribosomal RNAs

Tian

Zhang

Xia

et al. 2010

Molecular Informatics

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“…The intermolecular interaction energy (∆ E int ) of kinase–inhibitor complex was estimated with a strategy described by Zhou et al . The Poisson–Boltzmann /surface area method was used to analyze solvent effect associated with kinase–inhibitor binding. The total binding free energy of inhibitor to kinase was computed as follows:

Δ G_{normalttl} = Δ E_{normalint} + Δ G_{normalplr} + Δ G_{normalnplr}

and the change in inhibitor binding free energy upon kinase mutation is as follows:

Δ Δ G_{normalmtt} = Δ G_{ttl}^{normalmt} - Δ G_{ttl}^{normalwt},

where the

Δ G^{normalwt}

and

Δ G^{normalmt}

are total binding free energies of inhibitor ligand to wild‐type and mutant kinase, resp.…”

Section: Methodsmentioning

confidence: 99%

Integrated Exploitation of the Structural Diversity Space of Chemotherapy Drugs to Selectively Inhibit HER2 T798M Mutant in Lung Cancer

Wang

Zhang

et al. 2017

Chemistry & Biodiversity

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An acquired T798M gatekeeper mutation in human epidermal growth factor receptor 2 (HER2) kinase can cause drug resistance to anti-HER2 chemotherapy drugs in lung cancer. Previously, the reversible pan-kinase inhibitor staurosporine has been found to selectively inhibit the HER2 T798M mutant over wild-type kinase, suggesting that the staurosporine scaffold is potentially to develop mutant-selective inhibitors. Here, we systematically evaluated the chemical space of staurosporine scaffold-based compounds in response to HER2 T798M mutation at structural, energetic and molecular levels by using an integrated analysis strategy. With this strategy, we were able to identify several novel wild-type sparing inhibitors with high or moderate selectivity, which are comparable to or even better than that of the parent compound staurosporine. Molecular modeling and structural analysis revealed that noncovalent contacts can form between the side chain of mutated residue Met798 and selective inhibitor ligands, which may improve the favorable interaction energy between the kinase and inhibitor and reduce the unfavorable desolvation penalty upon the kinase-inhibitor binding.

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“…In a previous study, we have described a strategy that employs rigorous QM/MM-PB/SA instead of traditional MM-PB/SA to dissect the free energy profile of OppA protein interacting with its cognate ligands [24]. In addition, considering that the PDZ3 ligands are linear peptides that possess large flexibility and hence would bear significant entropy loss during the binding process, we proposed a method called conformational free energy analysis (CFEA) to account for entropic contributions to the binding energy of peptide to PDZ3.…”

Section: Construction Of Pdz3-peptide Complex Modelsmentioning

confidence: 99%

Characterization of PDZ domain–peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses

Tian

Zhou

et al. 2011

J Comput Aided Mol Des

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Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains. Characterizing the interaction interface of domain-peptide complexes and analyzing binding specificity for modular domains are critical for deciphering protein-protein interaction networks. In this article, we report the successful use of an integrated computational protocol to dissect the energetic profile and structural basis of peptide binding to third PDZ domain (PDZ3) from the PSD-95 protein. This protocol employs rigorous quantum mechanics/molecular mechanics (QM/MM), semi-empirical Poisson-Boltzmann/surface area (PB/SA), and empirical conformational free energy analysis (CFEA) to quantitatively describe and decompose systematic energy changes arising from, respectively, noncovalent interaction, desolvation effect, and conformational entropy loss associated with the formation of 30 affinity-known PDZ3-peptide complexes. We show that the QM/MM-, PB/SA-, and CFEA-derived energy components can work together fairly well in reproducing experimentally measured affinity after a linearly weighting treatment, albeit they are not compatible with each other directly. We also demonstrate that: (1) noncovalent interaction and desolvation effect donate, respectively, stability and specificity to complex architecture, while entropy loss contributes modestly to binding; (2) P(0) and P(-2) of peptide ligand are the most important positions for determining both the stability and specificity of the PDZ3-peptide complex, P(-1) and P(-3) can confer substantial stability (but not specificity) for the complex, and N-terminal P(-4) and P(-5) have only a very limited effect on binding.

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Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses

Cited by 36 publications

References 37 publications

Predicting the Flexibility Profile of Ribosomal RNAs

Predicting the Flexibility Profile of Ribosomal RNAs

Integrated Exploitation of the Structural Diversity Space of Chemotherapy Drugs to Selectively Inhibit HER2 T798M Mutant in Lung Cancer

Characterization of PDZ domain–peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses

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