The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) in vivo toxicity (meters). Through feature learning-instead engineering-deep neural networks promise outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent discovery. To this end, we present the PotentialNet family graph...

Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret individual features are most salient. While recent work from our group others has demonstrated utility time-lagged covariate models study such systems, linearity assumptions limit compression inherently nonlinear dynamics into just a few characteristic components. Recent in field deep learning led development variational autoencoder...

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible atomic resolution. However, a coarse model must be formulated such that conclusions we draw from it are consistent with would finer level detail. It has been proven force matching scheme defines thermodynamically coarse-grained an atomistic system in variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated existence limit use supervised machine...

Abstract A generalized understanding of protein dynamics is an unsolved scientific problem, the solution which critical to interpretation structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, build a unique dataset unbiased all-atom simulations approximately 9 ms for twelve different proteins with...

Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics fluid mechanics. In the physical sciences, structures such as metastable coherent sets, slow relaxation processes, collective variables dominant transition pathways or manifolds channels probability flow can be great importance for understanding characterizing kinetic, thermodynamic mechanistic properties system. Deeptime a general purpose Python library offering various tools estimate...

Accurate modeling of the solvent environment for biological molecules is crucial computational biology and drug design. A popular approach to achieve long simulation time scales large system sizes incorporate effect in a mean-field fashion with implicit models. However, challenge existing models that they often lack accuracy or certain physical properties compared explicit models, as many-body effects neglected difficult model mean field. Here, we leverage machine learning (ML) multi-scale...

Reaction coordinates are widely used throughout chemical physics to model and understand complex transformations. We introduce a definition of the natural reaction coordinate, suitable for condensed phase biomolecular systems, as maximally predictive one-dimensional projection. then show this criterion is uniquely satisfied by dominant eigenfunction an integral operator associated with ensemble dynamics. present new sparse estimator these eigenfunctions which can search through large...

As molecular dynamics simulations access increasingly longer time scales, complementary advances in the analysis of biomolecular time-series data are necessary. Markov state models offer a powerful framework for this by describing system’s states and transitions between them. A recently established variational theorem now enables modelers to systematically determine best way describe dynamics. In context theorem, we analyze ultra-long folding canonical set twelve proteins [K. Lindorff-Larsen...

Abstract This report advances the hypothesis that multifunctional systems may be associated with multifunnel potential and free energy landscapes, particular focus on biomolecules. It compares exhibit single, double, multiple competing structures, contrasts landscapes misfolded amyloidogenic oligomers, which presumably do not arise as an evolutionary target. In this context, intrinsically disordered proteins could considered molecules, landscapes. Potential landscape theory enables...

The modeling of atomistic biomolecular simulations using kinetic models such as Markov state (MSMs) has had many notable algorithmic advances in recent years. variational principle opened the door for a nearly fully automated toolkit selecting that predict long time-scale kinetics from molecular dynamics simulations. However, one yet-unoptimized step pipeline involves choosing features, or collective variables, which model should be constructed. In order to build intuitive models, these...

The use of coarse-grained (CG) models is a popular approach to study complex biomolecular systems. By reducing the number degrees freedom, CG model can explore long time- and length-scales inaccessible computational at higher resolution. If designed by formally integrating out some system’s one expects multi-body interactions emerge in effective model’s energy function. In practice, it has been shown that inclusion terms indeed improves accuracy model. However, no general proposed...

We developed and validated a next generation sequencing-(NGS) based NIPT assay using quantitative counting template (QCT) technology to detect RhD, C, c, E, K (Kell), Fya (Duffy) fetal antigen genotypes from maternal blood samples in the ethnically diverse U.S. population. Quantitative is utilized enable quantification detection of paternally derived alleles cell-free DNA with high sensitivity specificity. In an analytical validation, status was determined for 1061 preclinical 100% (95% CI...

This tutorial provides an introduction to the construction of Markov models molecular kinetics from dynamics trajectory data with PyEMMA software. Using notebooks, we will guide user through basic functionality as well more common advanced mechanisms. Short exercises self check learning progress and a notebook on troubleshooting complete this introduction.

Markov state models (MSMs) are a powerful framework for analyzing protein dynamics. MSMs require the decomposition of conformation space into states via clustering, which can be cross-validated when prediction method is available clustering method. We present an algorithm predicting cluster assignments new data points with Ward's minimum variance then show that produces better or equivalent folding than other algorithms.

Beta-hairpins are substructures found in proteins that can lend insight into more complex systems. Furthermore, the folding of beta-hairpins is a valuable test case for benchmarking experimental and theoretical methods. Here, we simulate CLN025, miniprotein with beta-hairpin structure, at its melting temperature using range state-of-the-art protein force fields. We construct Markov state models order to examine thermodynamics, kinetics, mechanism, rate-determining step folding....

We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates reproducing kernel Hilbert space (RKHS) operators associated with dynamical systems. In particular, we show that canonical correlation analysis (CCA) can be interpreted in terms transfer it obtained by optimizing the variational approach for Markov processes (VAMP) score. As a result, coherent sets particle trajectories computed CCA. demonstrate...

Markov state models (MSMs) are a powerful framework for the analysis of molecular dynamics data sets, such as protein folding simulations, because their straightforward construction and statistical rigor. The coarse-graining MSMs into an interpretable number macrostates is crucial step connecting theoretical results with experimental observables. Here we present minimum variance clustering approach (MVCA) macrostate models. method utilizes agglomerative Ward's objective function, similarity...

We use the harmonic superposition approach to examine how a single atom substitution affects low-temperature anomalies in vibrational heat capacity (CV) of model nanoclusters. Each anomaly is linked competing solidlike "phases", where crossover corresponding free energies defines solid–solid transition temperature (Ts). For selected Lennard-Jones clusters we show that Ts and CV peak can be tuned over wide range by varying relative atomic size binding strength impurity, but excessive...

The variational principle for conformational dynamics has enabled the systematic construction of Markov state models through optimization hyperparameters by approximating transfer operator. In this note, we discuss why lag time operator being approximated must be held constant in approach.

The output of molecular dynamics simulations is high-dimensional, and the degrees freedom among atoms are related in intricate ways. Therefore, a variety analysis frameworks have been introduced order to distill complex motions into lower-dimensional representations that model system dynamics. These dynamical models developed optimally approximate system's global kinetics. However, separate aims optimizing kinetics modeling process interest diverge when not slowest system. Here, we introduce...

Abstract There are many things—call them ‘experts'—that you should defer to in forming your opinions. The trouble is, experts modest : they're less than certain that they worthy of deference. When this happens, the standard theories deference break down: most popular (“Reflection”‐style) principles collapse inconsistency, while their (“New‐Reflection”‐style) variants allow someone regarding as an anti ‐expert. We propose a middle way: deferring involves preferring make any decision using...