The inability of A-form RNA to form Hoogsteen base pairs provides a mechanism for how post-transcriptional modifications can disrupt RNA structure and might help explain why DNA is the molecular choice for storing genetic information. 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+ (in which + indicates protonation) and A-U Hoogsteen base pairs are strongly disfavored in A-RNA. As a result,N1-methyladenosine and N1-methylguanosine, which occur in DNA as a form of alkylation damage and in RNA as post-transcriptional modifications, have dramatically different consequences. Whereas they create G-C+ and A-T Hoogsteen base pairs in duplex DNA, thereby maintaining the structural integrity of the double helix, they block base-pairing and induce local duplex melting in RNA. These observations provide a mechanism for disrupting RNA structure through post-transcriptional modifications. The different propensities to form Hoogsteen base pairs in B-DNA and A-RNA may help cells meet the opposing requirements of maintaining genome stability, on the one hand, and of dynamically modulating the structure of the epitranscriptome, on the other.

Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ₁₋₄₀) and its “Arctic” variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.

The milestoning algorithm of Elber and co-workers creates a framework for computing the time scale of processes that are too long and too complex to be studied using simply brute force simulations. The fundamental objects involved in the milestoning algorithm are the first passage time distributions K_AB(τ) between adjacent conformational milestones A and B. The method proposed herein aims to further enhance milestoning (or other interface based sampling methods) by employing an artificially applied force, akin to a wind that blows the trajectories from their initial to their final states, and by subsequently applying corrective weights to the trajectories to yield the true first passage time distributions K_AB(τ) in a fraction of the computation time required for unassisted calculations. The re-weighting method is rooted in the formalism of stochastic path integrals. The theoretical basis for the technique and numerical examples are presented.

In the milestoning framework, and more generally in related transition interface sampling schemes, one significantly enhances the calculation of relaxation rates for complex equilibrium kinetics from molecular dynamics simulations between the milestones or interfaces. The goal of the present paper is to advance milestoning applications into the realm of non-equilibrium statistical mechanics, in particular, to calculate entire time correlation functions. In order to accomplish this, we introduce a novel methodology for obtaining the flux through a given milestone configuration as a function of both time and initial configuration and build upon it with a novel formalism describing autocorrelation for Langevin motion in a discrete configuration space. The method is then applied to three different test systems: a harmonic oscillator, which we solve analytically, a two-well potential, which is solved numerically, and an atomistic molecular dynamics simulation of alanine dipeptide.