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Life
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Computational Modeling of Kinetics in Biological Systems
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Computational Modeling of Kinetics in Biological Systems

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 14705

Special Issue Editor

Prof. Dr. Ron Elber

E-Mail Website
Guest Editor
Department of Chemistry, Oden Institute, The University of Texas at Austin, Austin, TX 78712, USA
Interests: computer simulations; computational biophysics; membranes; membrane permeation; conformational transitions of proteins; molecular dynamics; reaction pathways; long time dynamics; RNA folding

Special Issue Information

Dear Colleagues,

Biological systems respond to changes in the environment, such as the depletion of ingredients, temperature reduction, increase in light intensity, and many other transient variations using timely molecular actions. The speed of cellular responses is critical for living systems. It depends on the rates of physical and chemical processes, making the study of the dynamics of these systems particularly relevant for life. Diffusion and searches in crowd environments, enzymatic reactions, signaling, translocation, organization, and reorganization of biological matter are a few examples of biomolecular dynamics that shape cellular life and are frequently conducted at conditions far from equilibrium. Computer simulations in molecular biophysics and biochemistry have considerable potential to shed light on these complex phenomena. Enhanced sampling techniques for molecular kinetics in biology expanded considerably in the last decade and are currently mature enough to produce useful and consistent models for atomic and molecular scale biology. Computational studies of the kinetics of biomolecular events are the focus of the current issue.

Prof. Dr. Ron Elber
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-equilibrium
  • computer simulations
  • molecular dynamics
  • biomolecular dynamics
  • enzymes
  • membrane transport
  • molecular activation

Published Papers (7 papers)

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Research

15 pages, 7817 KiB  
Article
The Structures of Heterogeneous Membranes and Their Interactions with an Anticancer Peptide: A Molecular Dynamics Study
by Ghulam Abbas, Alfredo E. Cardenas and Ron Elber
Life 2022, 12(10), 1473; https://doi.org/10.3390/life12101473 - 22 Sep 2022
Cited by 3 | Viewed by 1636
Abstract
We conduct molecular dynamics simulations of model heterogeneous membranes and their interactions with a 24-amino acid peptide—NAF-144–67. NAF-144–67 is an anticancer peptide that selectively permeates and kills malignant cells; it does not permeate normal cells. We examine three membranes with [...] Read more.
We conduct molecular dynamics simulations of model heterogeneous membranes and their interactions with a 24-amino acid peptide—NAF-144–67. NAF-144–67 is an anticancer peptide that selectively permeates and kills malignant cells; it does not permeate normal cells. We examine three membranes with different binary mixtures of lipids, DOPC–DOPA, DOPC–DOPS, and DOPC–DOPE, with a single peptide embedded in each as models for the diversity of biological membranes. We illustrate that the peptide organization in the membrane depends on the types of nearby phospholipids and is influenced by the charge and size of the head groups. The present study sheds light on early events of permeation and the mechanisms by which an amphiphilic peptide crosses from an aqueous solution to a hydrophobic membrane. Understanding the translocation mechanism is likely to help the design of new permeants. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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25 pages, 19211 KiB  
Article
Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison
by Jianan Sun, Mark Anthony V. Raymundo and Chia-En A. Chang
Life 2022, 12(1), 116; https://doi.org/10.3390/life12010116 - 13 Jan 2022
Cited by 4 | Viewed by 1781
Abstract
Understanding non-covalent biomolecular recognition, which includes drug–protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease [...] Read more.
Understanding non-covalent biomolecular recognition, which includes drug–protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand–protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand–protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand–protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand–protein recognition mechanisms and drug discovery. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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20 pages, 3593 KiB  
Article
A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
by Satwik Pasani and Shruthi Viswanath
Life 2021, 11(11), 1183; https://doi.org/10.3390/life11111183 - 05 Nov 2021
Cited by 3 | Viewed by 1853
Abstract
Integrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid [...] Read more.
Integrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid bodies, for sampling to be efficient and accurate. Currently, these parameters are tuned manually. To automate this process, we developed a general heuristic for derivative-free, global, stochastic, parallel, multiobjective optimization, termed StOP (Stochastic Optimization of Parameters) and applied it to optimize sampling-related parameters for the Integrative Modeling Platform (IMP). Given an integrative modeling setup, list of parameters to optimize, their domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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10 pages, 1536 KiB  
Article
Evolution of Thyroglobulin Loop Kinetics in EpCAM
by Serena H. Chen and David R. Bell
Life 2021, 11(9), 915; https://doi.org/10.3390/life11090915 - 03 Sep 2021
Cited by 3 | Viewed by 2111
Abstract
Epithelial cell-activating molecule (EpCAM) is an important cancer biomarker and therapeutic target given its elevated expression in epithelial cancers. EpCAM is a type I transmembrane protein that forms cis-dimers along the thyroglobulin type-1A-like domain (TYD) in the extracellular region. The thyroglobulin loop [...] Read more.
Epithelial cell-activating molecule (EpCAM) is an important cancer biomarker and therapeutic target given its elevated expression in epithelial cancers. EpCAM is a type I transmembrane protein that forms cis-dimers along the thyroglobulin type-1A-like domain (TYD) in the extracellular region. The thyroglobulin loop (TY loop) within the TYD is structurally dynamic in the monomer state of human EpCAM, binding reversibly to a TYD site. However, it is not known if this flexibility is prevalent across different species. Here, we conduct over 17 μs of all-atom molecular dynamics simulations to study EpCAM TY loop kinetics of five different species, including human, mouse, chicken, frog, and fish. We find that the TY loop remains dynamic across evolution. In addition to the TYD binding site, we discover a second binding site for the TY loop in the C-terminal domain (CTD). Calculations of the dissociation rate constants from the simulation trajectories suggest a differential binding pattern of fish EpCAM and other organisms. Whereas fish TY loop has comparable binding for both TYD and CTD sites, the TY loops of other species preferably bind the TYD site. A hybrid construct of fish EpCAM with human TY loop restores the TYD binding preference, suggesting robust effects of the TY loop sequence on its dynamic behavior. Our findings provide insights into the structural dynamics of EpCAM and its implication in physiological functions. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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18 pages, 4010 KiB  
Article
Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances
by Armin Shayesteh Zadeh and Baron Peters
Life 2021, 11(6), 570; https://doi.org/10.3390/life11060570 - 17 Jun 2021
Cited by 3 | Viewed by 1973
Abstract
Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the [...] Read more.
Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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16 pages, 3899 KiB  
Article
Length Dependent Folding Kinetics of Alanine-Based Helical Peptides from Optimal Dimensionality Reduction
by Krzysztof Kuczera, Robert Szoszkiewicz, Jinyan He and Gouri S. Jas
Life 2021, 11(5), 385; https://doi.org/10.3390/life11050385 - 24 Apr 2021
Cited by 4 | Viewed by 1944
Abstract
We present a computer simulation study of helix folding in alanine homopeptides (ALA)n of length n = 5, 8, 15, and 21 residues. Based on multi-microsecond molecular dynamics simulations at room temperature, we found helix populations and relaxation times increasing from about 6% [...] Read more.
We present a computer simulation study of helix folding in alanine homopeptides (ALA)n of length n = 5, 8, 15, and 21 residues. Based on multi-microsecond molecular dynamics simulations at room temperature, we found helix populations and relaxation times increasing from about 6% and ~2 ns for ALA5 to about 60% and ~500 ns for ALA21, and folding free energies decreasing linearly with the increasing number of residues. The helix folding was analyzed with the Optimal Dimensionality Reduction method, yielding coarse-grained kinetic models that provided a detailed representation of the folding process. The shorter peptides, ALA5 and ALA8, tended to convert directly from coil to helix, while ALA15 and ALA21 traveled through several intermediates. Coarse-grained aggregate states representing the helix, coil, and intermediates were heterogeneous, encompassing multiple peptide conformations. The folding involved multiple pathways and interesting intermediate states were present on the folding paths, with partially formed helices, turns, and compact coils. Statistically, helix initiation was favored at both termini, and the helix was most stable in the central region. Importantly, we found the presence of underlying universal local dynamics in helical peptides with correlated transitions for neighboring hydrogen bonds. Overall, the structural and dynamical parameters extracted from the trajectories are in good agreement with experimental observables, providing microscopic insights into the complex helix folding kinetics. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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19 pages, 1867 KiB  
Article
Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics
by Justin Spiriti and Chung F. Wong
Life 2021, 11(2), 74; https://doi.org/10.3390/life11020074 - 20 Jan 2021
Cited by 14 | Viewed by 2235
Abstract
Most early-stage drug discovery projects focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. Since many approved drugs have nonequilibrium binding characteristics, there has been increasing interest in optimizing binding kinetics early in the drug discovery process. As [...] Read more.
Most early-stage drug discovery projects focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. Since many approved drugs have nonequilibrium binding characteristics, there has been increasing interest in optimizing binding kinetics early in the drug discovery process. As focal adhesion kinase (FAK) is an important drug target, we examine whether steered molecular dynamics (SMD) can be useful for identifying drug candidates with the desired drug-binding kinetics. In simulating the dissociation of 14 ligands from FAK, we find an empirical power–law relationship between the simulated time needed for ligand unbinding and the experimental rate constant for dissociation, with a strong correlation depending on the SMD force used. To improve predictions, we further develop regression models connecting experimental dissociation rate with various structural and energetic quantities derived from the simulations. These models can be used to predict dissociation rates from FAK for related compounds. Full article
(This article belongs to the Special Issue Computational Modeling of Kinetics in Biological Systems)
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