Molecular Modeling Applied to Nucleic Acid-Based Molecule Development

Abstract

Molecular modeling by means of docking and molecular dynamics (MD) has become an integral part of early drug discovery projects, enabling the screening and enrichment of large libraries of small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based molecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically with the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique, which results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to their targets. Herein, we discuss the role and contribution of docking and MD to the development and optimization of new nucleic acid-based molecules. This review focuses on the different approaches currently available for molecular modeling applied to NA interaction with proteins. We discuss topics ranging from structure prediction to docking and MD, highlighting their main advantages and limitations and the influence of flexibility on their calculations.

Rationally Designed Anti-CRISPR Nucleic Acid Inhibitors of CRISPR-Cas9

“…Nucleic acid inhibitors are significantly smaller than known natural anti-CRISPR proteins [27,28] and should benefit from the lessons learned during therapeutic development of antisense oligonucleotides and small interfering RNAs [51], some of which have been recently FDA approved [52,53]. Rational design of SNuBs will also benefit from the growing availability of CRISPR-Cas structural and biochemical data [32], molecular modeling [54,55], and chemical modifications for tuning therapeutic nucleic acid properties [51].…”

Rational design of an XNA ligase through docking of unbound nucleic acids to toroidal proteins

“…Compared to molecular modelling on protein–protein complexes, computational approaches on the protein–nucleic acid prediction and calculation (21–24) is lagging behind. Most software that is applicable to calculate models for DNA-protein complexes was originally developed for proteins and later adapted to accept nucleic acids as input structures (25). Two main categories of docking algorithms exist: first, machine learning algorithms to predict molecule interactions based on sequence-based and/or structure-based information; and second, data-driven algorithms to calculate interactions with information from known crystal structures (26).…”

A Bottom-Up Approach for Developing Aptasensors for Abused Drugs: Biosensors in Forensics

“…The secondary structure of the aptamer can be computationally predicted by using Mfold [68], RNAstructure [69], and ValFold [70]. Circular dichroism (CiD) [71], nuclear magnetic resonance (NMR) [72], X-ray crystallography [73] as well as molecular modeling [74] are useful to probe aptamer-target interactions. Using the structural information, the full-length aptamer can be subjected to post-SELEX to obtain the optimal sequence [75][76][77][78][79][80].…”