Automatic Feature Selection in Markov State Models Using Genetic Algorithm

Chen, Qihua; Feng, Jiangyan; Mittal, Shriyaa; Shukla, Diwakar

doi:10.22369/issn.2153-4136/9/2/2

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…28,29,32 All the simulation data were used to construct an MSM and the MSM hyperparameters were selected systematically using a genetic algorithm technique (see Methods and Materials for details). 33 In the crystal structure (PDB ID: 4U4W 20 ), Ser56 (TM1) hydrogen bonds with Ala275 (TM7) at the extracellular side and closes the pore tunnel. The hydrophobic interactions between Met151 (TM4) and Phe370 (TM10) act as an intracellular gate.…”

Section: Resultsmentioning

confidence: 99%

“…31,32 All the simulation data were used to construct an MSM and the MSM hyper-parameters were selected systematically using a genetic algorithm technique. 38 The MSM estimation reweighs the MD trajectories such that the equilibrium kinetics and distribution among sampled configurations can be recovered. Simulation and MSM construction details are summarized in Method Details.…”

Section: Resultsmentioning

confidence: 99%

“…To select the optimal hyper-parameters (C α contacts, number of tICA components, and number of clusters) systematically and automatically, we employed a genetic algorithm based technique developed from our lab 38 (Supplementary Table 1 and 2). The source codes and the resulting data associated with this algorithm are available at https://github.com/ShuklaGroup/NarK_Structure_2021_Files.…”

Section: Methods Detailsmentioning

confidence: 99%

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How antiporters exchange substrates across the cell membrane? An atomic-level description of the complete exchange cycle in NarK

Feng

Selvam

Shukla

2020

Preprint

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Major facilitator superfamily (MFS) proteins operate via three different mechanisms: uniport, symport, and antiport. Despite extensive investigations, molecular understanding of antiporters is less advanced than other transporters due to the complex coupling between two substrates and the lack of distinct structures. We employ extensive (∼300 µs) all-atom molecular dynamics simulations to dissect the complete substrate exchange cycle of the bacterial NO − 3 /NO − 2 antiporter, NarK. We show that paired basic residues in the binding site prevent the closure of unbound protein and ensure the exchange of two substrates. Conformational transition only occurs in the presence of substrate, which weakens the electrostatic repulsion and stabilizes the transporter by ∼1.5 kcal/mol. Furthermore, we propose a state-dependent substrate exchange model, in which the relative spacing between the paired basic residues determines whether NO − 3 and NO − 2 bind simultaneously or sequentially. Overall, this work presents a general working model for the antiport mechanism within MFS family.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methods Detailsmentioning

confidence: 99%

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How antiporters exchange substrates across the cell membrane? An atomic-level description of the complete exchange cycle in NarK

Feng

Selvam

Shukla

2020

Preprint

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“…To select the “best” MSM automatically, we combined the genetic algorithm and Osprey variational cross-validation package to optimize the set of Cα atom distances between residue pairs along with two critical hyperparameters (number of tICA components and number of clusters) in MSM construction. 39 The quality of MSMs is quantified with the generalized matrix Raleigh quotient (GMRQ). 40 GMRQ is the sum of the eigenvalues of the transition matrix, indicating that the higher the GMRQ, the better the MSM.…”

Section: Methodsmentioning

confidence: 99%

Atomistic Insights Into The Mechanism of Dual Affinity Switching In Plant Nitrate Transporter NRT1.1

Selvam

Feng

Shukla

2022

Preprint

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Improving nitrogen use efficiency is critical to enhancing agricultural productivity and to mitigate environmental pollution. To overcome the fluctuations in soil nitrate concentration, plants have evolved an elaborate nitrate transporting mechanism that switches between high and low affinity. In plants, NRT1.1, a root-associated nitrate transporter, switches its affinity upon phosphorylation at Thr101. However, the molecular basis of this unique functional behavior known as dual-affinity switching remains elusive. Crystal structures of the NRT1.1 nitrate transporter have provided evidence for the two competing hypotheses to explain the origin of dual-affinity switching. It is not known how the interplay between transporter phosphorylation and dimerization regulates the affinity switching. To reconcile the different hypotheses, we have performed extensive simulations of nitrate transporter in conjunction with Markov state models to elucidate the molecular origin for a dual-affinity switching mechanism. Simulations of monomeric transporter reveal that phosphorylation stabilizes the outward-facing state and accelerates dynamical transitions for facilitating transport. On the other hand, phosphorylation of the transporter dimer decouples dynamic motions of dimer into independent monomers and thus facilitates substrate transport. Therefore, the phosphorylation-induced enhancement of substrate transport and dimer decoupling not only reconcile the competing experimental results but also provide an atomistic view of how nitrate transport is regulated in plants.

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“…The second approach is typically more systematic and generalizable and will normally be the best choice if we know little about the system beforehand. Several methods provide automated feature selection specifically designed with MSM building in mind: Scherer, Husic et al illustrate use of VAMP in this respect [56], and Chen et al use a genetic algorithm based method for feature selection [57]. The former method works directly on the features, whether the latter approach relies sub-sequent modeling steps to evaluate the selected features.…”

Section: Feature Selectionmentioning

confidence: 99%

Markov State Models of protein-protein encounters

Olsson¹

2021

Preprint

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When applying molecular dynamics simulations, we aim to understand biomolecular processes. Ideally, our understanding must build on statistically robust scientific observations. The key observables of interest:1. Important structures, 2. their thermodynamic weights, 3. and the transition probabilities amongst them, or their inter-conversion rates.Robust identification of these three properties allows for MD results' direct connection to experimental data, including NMR spectroscopy and sm-FRET [33][34][35][36]. Comparisons such as these may serve as an important complementary means of validating the simulation models and can help drive robust scientific hypotheses and models.Analysis of MD simulations, however, often relies on visually inspecting simulation trajectories one-by-one. Alternatively, we follow the simulation trajectories projected onto a few order parameters (or collective variables) derived from chemical intuition about the process of interest or some global structural property [37][38][39][40][41]. Inspecting structures and following certain order parameters is an integral part of any analysis of molecular dynamics simulations. However, these strategies alone do not guarantee a statistical relevance of events observed, and the overall approach becomes increasingly time-consuming with growing data-sets. Furthermore, limiting ourselves to these analyses may still overlook rare events important for biological function. So ultimately, conclusions drawn from these kinds of analyses may be misleading [30].Statistical models to analyze data from MD simulations are enjoying increased attention in recent years [42][43][44][45][46][47][48][49][50]. This popularity is a necessary consequence of growing datasets enabled by improvements in software efficiency and large-scale investment into consumer-grade GPU (graphical processing units) based compute resources by many academic groups. Another important factor is community-driven, cloud-based super-computers such as Folding@Home [51] and GPUgrid (www.gpugrid.net) that generate enormous volumes of simulation data whose analysis critically relies on a systematic and principled framework. Markov state models (MSM) are one prominent example of statistical models for analyzing molecular dynamics simulation, which fits the bill [30,42,44,52].This section will briefly discuss the motivation and theoretical basis of MSMs and some important mathematical properties of MSM, motivating subsequent sections. With this text, I do not attempt to discuss these topics comprehensively but instead, provide a guiding primer into the following sections and enable the reader to build some intuition about the theory -in general, the text is based upon the references cited in this section. However, I intentionally minimize technical language and equations and avoid specific details in the notation for clarity. For a more detailed MSM theory treatment, I refer to the excellent review by Prinz et al. [30]. For a more comprehensive historical overview of MSMs, I refer to Brooke and Pande'...

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Automatic Feature Selection in Markov State Models Using Genetic Algorithm

Cited by 7 publications

References 21 publications

How antiporters exchange substrates across the cell membrane? An atomic-level description of the complete exchange cycle in NarK

How antiporters exchange substrates across the cell membrane? An atomic-level description of the complete exchange cycle in NarK

Atomistic Insights Into The Mechanism of Dual Affinity Switching In Plant Nitrate Transporter NRT1.1

Markov State Models of protein-protein encounters

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