Manifold Learning in Atomistic Simulations: A Conceptual Review

Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Such practice is needed in atomistic simulations of complex systems where even thousands of degrees of freedom are sampled. An abundance of such data makes gaining insight into a specific physical problem strenuous. Our primary aim in this review is to focus on unsupervised machine learning methods that can be used on simulation data to find a low-dimensional manifold providing a collective and informative characterization of the studied process. Such manifolds can be used for sampling long-timescale processes and free-energy estimation. We describe methods that can work on datasets from standard and enhanced sampling atomistic simulations. Unlike recent reviews on manifold learning for atomistic simulations, we consider only methods that construct low-dimensional manifolds based on Markov transition probabilities between high-dimensional samples. We discuss these techniques from a conceptual point of view, including their underlying theoretical frameworks and possible limitations

Manifold learning in atomistic simulations: a conceptual review

Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Such practice is needed in atomistic simulations of complex systems where even thousands of degrees of freedom are sampled. An abundance of such data makes gaining insight into a specific physical problem strenuous. Our primary aim in this review is to focus on unsupervised machine learning methods that can be used on simulation data to find a low-dimensional manifold providing a collective and informative characterization of the studied process. Such manifolds can be used for sampling long-timescale processes and free-energy estimation. We describe methods that can work on datasets from standard and enhanced sampling atomistic simulations. Unlike recent reviews on manifold learning for atomistic simulations, we consider only methods that construct low-dimensional manifolds based on Markov transition probabilities between high-dimensional samples. We discuss these techniques from a conceptual point of view, including their underlying theoretical frameworks and possible limitations

Toward Overcoming Pyrethroid Resistance in Mosquito Control: The Role of Sodium Channel Blocker Insecticides

Diseases spread by mosquitoes lead to the death of 700,000 people each year. The main way to reduce transmission is vector control by biting prevention with chemicals. However, the most commonly used insecticides lose efficacy due to the growing resistance. Voltage-gated sodium channels (VGSCs), membrane proteins responsible for the depolarizing phase of an action potential, are targeted by a broad range of neurotoxins, including pyrethroids and sodium channel blocker insecticides (SCBIs). Reduced sensitivity of the target protein due to the point mutations threatened malaria control with pyrethroids. Although SCBIs—indoxacarb (a pre-insecticide bioactivated to DCJW in insects) and metaflumizone—are used in agriculture only, they emerge as promising candidates in mosquito control. Therefore, a thorough understanding of molecular mechanisms of SCBIs action is urgently needed to break the resistance and stop disease transmission. In this study, by performing an extensive combination of equilibrium and enhanced sampling molecular dynamics simulations (3.2 μs in total), we found the DIII-DIV fenestration to be the most probable entry route of DCJW to the central cavity of mosquito VGSC. Our study revealed that F1852 is crucial in limiting SCBI access to their binding site. Our results explain the role of the F1852T mutation found in resistant insects and the increased toxicity of DCJW compared to its bulkier parent compound, indoxacarb. We also delineated residues that contribute to both SCBIs and non-ester pyrethroid etofenprox binding and thus could be involved in the target site cross-resistance

Enhancing the Inhomogeneonus Photodynamics of Canonical Bacteriophytochrome

The ability of phytochromes to act as photoswitches in plants and microorganisms depends on interactions between a bilin-like chromophore and a protein. The interconversion occurs between the spectrally distinct red (Pr) and far-red (Pfr) conformers. This conformational change is triggered by the photoisomerization of the chromophore D-ring pyrrole. In this study, as a representative example of a phytochrome-bilin system, we take biliverdin IXα (BV) bound to bacteriophytochrome (BphP) from Deinococcus radiodurans. In the absence of light, we use an enhanced sampling molecular dynamics (MD) method to overcome the photoisomerization energy barrier. We find that the calculated free energy (FE) barriers between essential metastable states agree with spectroscopic results. We show that the enhanced dynamics of the BV chromophore in BphP triggers nanometer-scale conformational movements that propagate by two experimentally determined signal transduction pathways. Most importantly, we describe how the metastable states enable a thermal transition known as the dark reversion between Pfr and Pr, through a previously unknown intermediate state of Pfr. Here, for the first time, the heterogeneity of temperature-dependent Pfr states is presented at the atomistic level. This work paves a way toward understanding the complete mechanism of the photoisomerization of a bilin-like chromophore in phytochromes