Evaluation of molecular modelling methods to predict the sequence-selectivity of DNA minor groove binding ligands

Literature Information

Publication Date 2009-10-14
DOI 10.1039/B911702D
Impact Factor 3.676
Authors

Hao Wang, Charles A. Laughton


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Abstract

The accurate prediction of ligand–receptor interaction energies by molecular modelling methods remains challenging. Predicting and understanding the sequence-selectivity of DNA minor groove-binding ligands constitutes a particularly interesting and potentially valuable aspect of this. Here, we use experimental data on the binding of Hoechst 33258 to the minor groove of various A/T-rich DNA duplexes to evaluate the reliability of a popular class of molecular modelling methods based on the energetic analysis of molecular dynamics (MD) simulations. We examine how performance depends on the use of explicit versus implicit solvent models, on the use of generalised Born versus Poisson–Boltzmann models to evaluate solvent-associated energetic terms, and the use of normal mode analysis to evaluate entropic factors. Quantitatively evaluating many different combinations of methodologies, we find that the most reliable results are obtained when the MD simulations are performed in explicit solvent, when the data is processed using the MM-PB/SA approach, and when normal mode analysis is used to estimate configurational entropy changes.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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