Landscape ofπ–πand sugar–πcontacts in DNA–protein interactions

Wilson, Katie A.; Wells, Rachael A.; Abendong, Minette N; Anderson, Colin B.; Kung, Ryan W.; Wetmore, Stacey D.

doi:10.1080/07391102.2015.1013157

Cited by 19 publications

(28 citation statements)

References 60 publications

(97 reference statements)

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“…Previous analyses of nucleic acid-protein crystal structures reveal small distances between the nucleobases and π-containing amino acids (Jones et al 2001;Luscombe et al 2001;Treger and Westhof 2001;Lejeune et al 2005;Morozova et al 2006;Baker and Grant 2007;Ellis et al 2007;Wilson et al 2014;Barik et al 2015;Wilson et al 2015). Indeed, despite the involvement of the DNA nucleobases in stable stacking interactions within the double helix, an abundance of nucleobase-amino acid π-interactions (π-contacts) have been identified in DNA-protein complexes Lejeune et al 2005;Baker and Grant 2007;Wilson et al 2014;Wilson et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, despite the involvement of the DNA nucleobases in stable stacking interactions within the double helix, an abundance of nucleobase-amino acid π-interactions (π-contacts) have been identified in DNA-protein complexes Lejeune et al 2005;Baker and Grant 2007;Wilson et al 2014;Wilson et al 2015). Since most RNA nucleobases are not stacked within duplexes, nucleobase-amino acid π-interactions can occur without requiring unfavorable conformational changes and with the added benefit of sequestering the bases from solvent.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the aromatic amino acids, Morozova et al (2006) and Barik et al (2015) both report significant involvement of R in nucleobase π-π stacking interactions. Furthermore, previous work has illustrated that other relative orientations of nucleobases and π-containing amino acids besides (planar) stacked arrangements (e.g., perpendicular T-shaped orientations) may also contribute significantly to the stability and function of nucleic acidprotein complexes (Rutledge et al 2009;Wilson et al 2014Wilson et al , 2015.…”

Section: Introductionmentioning

confidence: 99%

“…S2; Wilson et al 2014Wilson et al , 2015. In this previous study, 1765 contacts were identified in 672 X-ray crystal structures published prior to January 2, 2014 with a resolution better than 2.0 Å.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the importance of nucleic acid-protein interactions, a number of studies have analyzed experimental Xray crystal structures to understand the types of atomic level interactions between DNA Luscombe and Thornton 2002;Gromiha et al 2004aGromiha et al ,b, 2005Mao et al 2004;Lejeune et al 2005;Prabakaran et al 2006;Baker and Grant 2007;Sathyapriya et al 2008;Wilson et al 2014Wilson et al , 2015 or RNA (Allers and Shamoo 2001;Jones et al 2001;Treger and Westhof 2001;Cheng et al 2003;Jeong et al 2003;Lejeune et al 2005;Morozova et al 2006;Baker and Grant 2007;Ellis et al 2007;Bahadur et al 2008;Barik et al 2015) nucleotides and amino acids that govern nucleic acid recognition and binding. These studies have determined that nucleic acid-protein contacts span hydrogen bonding (direct or water mediated), ionic (salt-bridge or phosphate backbone), van der Waals, and hydrophobic interactions.…”

Section: Introductionmentioning

confidence: 99%

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Topology of RNA–protein nucleobase–amino acid π–π interactions and comparison to analogous DNA–protein π–π contacts

Wilson

Holland

Wetmore

2016

RNA

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The present work analyzed 120 high-resolution X-ray crystal structures and identified 335 RNA-protein π-interactions (154 nonredundant) between a nucleobase and aromatic (W, H, F, or Y) or acyclic (R, E, or D) π-containing amino acid. Each contact was critically analyzed (including using a visual inspection protocol) to determine the most prevalent composition, structure, and strength of π-interactions at RNA-protein interfaces. These contacts most commonly involve F and U, with U:F interactions comprising one-fifth of the total number of contacts found. Furthermore, the RNA and protein π-systems adopt many different relative orientations, although there is a preference for more parallel (stacked) arrangements. Due to the variation in structure, the strength of the intermolecular forces between the RNA and protein components (as determined from accurate quantum chemical calculations) exhibits a significant range, with most of the contacts providing significant stability to the associated RNA-protein complex (up to −65 kJ mol −1 ). Comparison to the analogous DNA-protein π-interactions emphasizes differences in RNA-and DNA-protein π-interactions at the molecular level, including the greater abundance of RNA contacts and the involvement of different nucleobase/amino acid residues. Overall, our results provide a clearer picture of the molecular basis of nucleic acid-protein binding and underscore the important role of these contacts in biology, including the significant contribution of π-π interactions to the stability of nucleic acid-protein complexes. Nevertheless, more work is still needed in this area in order to further appreciate the properties and roles of RNA nucleobaseamino acid π-interactions in nature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Topology of RNA–protein nucleobase–amino acid π–π interactions and comparison to analogous DNA–protein π–π contacts

Wilson

Holland

Wetmore

2016

RNA

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Description of noncovalent interactions involving π‐system with high precision: An assessment of RPA, MP2, and DFT‐D methods

Wang

et al. 2019

J Comput Chem

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Efficient approaches with high precision are essential for understanding the formation and stability of noncovalent interaction complexes. Here, 21 noncovalent interaction complexes involving π‐system are selected and grouped in three subsets according to ETS–NOCV method: dispersion‐dominated, electrostatic‐dominated, and mixed. We mainly focus on examining the performance of random‐phase approximation (RPA) on these π systems. The tested RPA‐based method includes standard RPA and its variants including the related single excitations (SEs), renormalized single excitations (rSEs), second‐order screened exchange (SOSEX), and the renormalized second‐order perturbation theory (rPT2). The routine second‐order Møller–Plesset perturbation theory (MP2) and three popular DFT‐D functionals (M06‐2X‐D3, ωB97XD, and PBE‐D3(BJ)) are also assessed for comparison. In this work, besides the calculation of interaction energies at Dunning‐type aug‐cc‐pVDZ and aug‐cc‐pVTZ basis set, we also present a larger database of interaction energies calculated using MP2 and RPA methods with Dunning‐type aug‐cc‐pVQZ basis set. An accurate CCSD(T)/CBS scheme is used to provide benchmark database. In addition to the high‐level results, we also provide potential energy surfaces (PES) of different interaction type. Among all the tested methods, MP2 has a satisfactory performance on electrostatic‐dominated and mixed‐type systems, except for dispersion‐dominated systems. DFT‐D functionals, especially ωB97XD functional, has a balanced performance across all the tested systems. Importantly, for RPA‐based methods, the calculation accuracy can be dramatically improved by taking into account SE or exchange effects, especially in the mixed complexes. We conclude that rPT2 among all the test RPA‐based methods gives an overall satisfactory performance across different interaction types. © 2019 Wiley Periodicals, Inc.

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ComparePD: Improving protein–DNA complex model comparison with hydrogen bond energy‐based metrics

Malik

Guo

2023

Proteins

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Computational modeling of protein-DNA complex structures has important implications in biomedical applications such as structure-based, computer aided drug design.A key step in developing methods for accurate modeling of protein-DNA complexes is similarity assessment between models and their reference complex structures.Existing methods primarily rely on distance-based metrics and generally do not consider important functional features of the complexes, such as interface hydrogen bonds that are critical to specific protein-DNA interactions. Here, we present a new scoring function, ComparePD, which takes interface hydrogen bond energy and strength into account besides the distance-based metrics for accurate similarity measure of protein-DNA complexes. ComparePD was tested on two datasets of computational models of protein-DNA complexes generated using docking (classified as easy, intermediate, and difficult cases) and homology modeling methods. The results were compared with PDDockQ, a modified version of DockQ tailored for protein-DNA complexes, as well as the metrics employed by the community-wide experiment CAPRI (Critical Assessment of PRedicted Interactions). We demonstrated that Com-parePD provides an improved similarity measure over PDDockQ and the CAPRI classification method by considering both conformational similarity and functional importance of the complex interface. ComparePD identified more meaningful models as compared to PDDockQ for all the cases having different top models between ComparePD and PDDockQ except for one intermediate docking case.complex similarity assessment, homology modeling, hydrogen bond energy, hydrogen bonds, protein-DNA complex, protein-DNA docking | INTRODUCTIONKnowledge of protein-DNA complex structures is critical to understanding their roles in important biological processes such as regulation of gene expression. The structures of most protein-DNA complexes, however, remain unsolved due to technical challenges in experimental methods. [1][2][3] To address this issue, in silico prediction of three-dimensional structures of protein-DNA complexes is considered a valuable alternative in applications such as structure-based, computer aided drug discovery. 4,5 Despite efforts by the research community, computational modeling of complex macromolecular interactions remains a challenging problem. [6][7][8][9][10][11]

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Landscape ofπ–πand sugar–πcontacts in DNA–protein interactions

Cited by 19 publications

References 60 publications

Topology of RNA–protein nucleobase–amino acid π–π interactions and comparison to analogous DNA–protein π–π contacts

Topology of RNA–protein nucleobase–amino acid π–π interactions and comparison to analogous DNA–protein π–π contacts

Description of noncovalent interactions involving π‐system with high precision: An assessment of RPA, MP2, and DFT‐D methods

ComparePD: Improving protein–DNA complex model comparison with hydrogen bond energy‐based metrics

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