The Adaptive Path Collective Variable: A Versatile Biasing Approach to Compute the Average Transition Path and Free Energy of Molecular Transitions

In the past decade, great progress has been made in the development of enhanced sampling methods, aimed at overcoming the time-scale limitations of molecular dynamics (MD) simulations. Many sampling schemes rely on adding an external bias to favor the sampling of transitions and to estimate the underlying free energy landscape. Nevertheless, sampling molecular processes described by many order parameters, or collective variables (CVs), such as complex biomolecular transitions, remains often very challenging. The computational cost has a prohibitive scaling with the dimensionality of the CV-space. Inspiration can be taken from methods that focus on localizing transition pathways: the CV-space can be projected onto a path-CV that connects two stable states, and a bias can be exerted onto a one-dimensional parameter that captures the progress of the transition along the path-CV. In principle, such a sampling scheme can handle an arbitrarily large number of CVs. A standard enhanced sampling technique combined with an adaptive path-CV can then locate the mean transition pathway and obtain the free energy profile along the path. In this chapter, we discuss the adaptive path-CV formalism and its numerical implementation. We apply the path-CV with several enhanced sampling methods—steered MD, metadynamics, and umbrella sampling—to a biologically relevant process: the Watson–Crick to Hoogsteen base-pairing transition in double-stranded DNA. A practical guide is provided on how to recognize and circumvent possible pitfalls during the calculation of a free energy landscape that contains multiple pathways. Examples are presented on how to perform enhanced sampling simulations using PLUMED, a versatile plugin that can work with many popular MD engines

Modulation of Hoogsteen dynamics on DNA recognition

DNA is found in a dynamic equilibrium between standard Watson-Crick (WC) base pairs and non-standard Hoogsteen (HG) base pairs. Here the authors describe the influence of echinomycin and actinomycin D ligands binding on the HG-WC base pair dynamics in DNA

m1A and m1G disrupt A-RNA structure through the intrinsic instability of Hoogsteen base pairs

The B-DNA double helix can dynamically accommodate G–C and A–T base pairs in either Watson-Crick or Hoogsteen configurations. Here, we show that G–C(+) and A–U Hoogsteen base pairs are strongly disfavored in A-RNA. As a result, N(1)-methyl adenosine and N(1)-methyl guanosine, which occur in DNA as a form of alkylation damage, and in RNA as a posttranscriptional modification, have dramatically different consequences. They create G–C(+) and A–U Hoogsteen base pairs in duplex DNA that maintain the structural integrity of the double helix, but block base pairing all together and induce local duplex melting in RNA, providing a mechanism for potently disrupting RNA structure through posttranscriptional modifications. The markedly different propensities to form Hoogsteen base pairs in B-DNA and A-RNA may help meet the opposing requirements of maintaining genome stability on one hand, and dynamically modulating the structure of the epitranscriptome on the other

Serious Asthma Events with Fluticasone plus Salmeterol versus Fluticasone Alone

BACKGROUND: The safe and appropriate use of long-acting beta-agonists (LABAs) for the treatment of asthma has been widely debated. In two large clinical trials, investigators found a potential risk of serious asthma-related events associated with LABAs. This study was designed to evaluate the risk of administering the LABA salmeterol in combination with an inhaled glucocorticoid, fluticasone propionate. METHODS: In this multicenter, randomized, double-blind trial, adolescent and adult patients (age, ≥12 years) with persistent asthma were assigned to receive either fluticasone with salmeterol or fluticasone alone for 26 weeks. All the patients had a history of a severe asthma exacerbation in the year before randomization but not during the previous month. Patients were excluded from the trial if they had a history of life-threatening or unstable asthma. The primary safety end point was the first serious asthma-related event (death, endotracheal intubation, or hospitalization). Noninferiority of fluticasone-salmeterol to fluticasone alone was defined as an upper boundary of the 95% confidence interval for the risk of the primary safety end point of less than 2.0. The efficacy end point was the first severe asthma exacerbation. RESULTS: Of 11,679 patients who were enrolled, 67 had 74 serious asthma-related events, with 36 events in 34 patients in the fluticasone-salmeterol group and 38 events in 33 patients in the fluticasone-only group. The hazard ratio for a serious asthma-related event in the fluticasone-salmeterol group was 1.03 (95% confidence interval [CI], 0.64 to 1.66), and noninferiority was achieved (P=0.003). There were no asthma-related deaths; 2 patients in the fluticasone-only group underwent asthma-related intubation. The risk of a severe asthma exacerbation was 21% lower in the fluticasone-salmeterol group than in the fluticasone-only group (hazard ratio, 0.79; 95% CI, 0.70 to 0.89), with at least one severe asthma exacerbation occurring in 480 of 5834 patients (8%) in the fluticasone-salmeterol group, as compared with 597 of 5845 patients (10%) in the fluticasone-only group (P<0.001). CONCLUSIONS: Patients who received salmeterol in a fixed-dose combination with fluticasone did not have a significantly higher risk of serious asthma-related events than did those who received fluticasone alone. Patients receiving fluticasone-salmeterol had fewer severe asthma exacerbations than did those in the fluticasone-only group