scholarly journals Catalysis: transition-state molecular recognition?

2010 ◽  
Vol 6 ◽  
pp. 1026-1034 ◽  
Author(s):  
Ian H Williams
Keyword(s):  
Free Energy ◽  
Molecular Recognition ◽  
Transition State ◽  
Computational Modelling ◽  
Catalytic Antibodies ◽  
Single Atom ◽  
Boat Conformation ◽  
Methyl Transferase ◽  
Modelling Studies ◽  
Dynamics Simulations

The key to understanding the fundamental processes of catalysis is the transition state (TS): indeed, catalysis is a transition-state molecular recognition event. Practical objectives, such as the design of TS analogues as potential drugs, or the design of synthetic catalysts (including catalytic antibodies), require prior knowledge of the TS structure to be mimicked. Examples, both old and new, of computational modelling studies are discussed, which illustrate this fundamental concept. It is shown that reactant binding is intrinsically inhibitory, and that attempts to design catalysts that focus simply upon attractive interactions in a binding site may fail. Free-energy changes along the reaction coordinate for SN2 methyl transfer catalysed by the enzyme catechol-O-methyl transferase are described and compared with those for a model reaction in water, as computed by hybrid quantum-mechanical/molecular-mechanical molecular dynamics simulations. The case is discussed of molecular recognition in a xylanase enzyme that stabilises its sugar substrate in a (normally unfavourable) boat conformation and in which a single-atom mutation affects the free-energy of activation dramatically.

scholarly journals Computational Modelling of the Interaction of Gold Nanoparticle with Lung Surfactant Monolayer

MRS Advances ◽  
2019 ◽  
Vol 4 (20) ◽  
pp. 1177-1185 ◽  
Author(s):  
Sheikh I. Hossain ◽  
Neha S. Gandhi ◽  
Zak E. Hughes ◽  
Suvash C. Saha
Keyword(s):  
Respiratory Diseases ◽  
Lung Surfactant ◽  
Computational Modelling ◽  
Fine Tuning ◽  
Coarse Grained ◽  
Normal Lung ◽  
Open Questions ◽  
Compression And Expansion ◽  
Modelling Studies ◽  
Dynamics Simulations

ABSTRACTLung surfactant (LS), a thin layer of phospholipids and proteins inside the alveolus of the lung is the first biological barrier to inhaled nanoparticles (NPs). LS stabilizes and protects the alveolus during its continuous compression and expansion by fine-tuning the surface tension at the air-water interface. Previous modelling studies have reported the biophysical function of LS monolayer and its role, but many open questions regarding the consequences and interactions of airborne nano-sized particles with LS monolayer remain. In spite of gold nanoparticles (AuNPs) having a paramount role in biomedical applications, the understanding of the interactions between bare AuNPs (as pollutants) and LS monolayer components still unresolved. Continuous inhalation of NPs increases the possibility of lung ageing, reducing the normal lung functioning and promoting lung malfunction, and may induce serious lung diseases such as asthma, lung cancer, acute respiratory distress syndrome, and more. Different medical studies have shown that AuNPs can disrupt the routine lung functions of gold miners and promote respiratory diseases. In this work, coarse-grained molecular dynamics simulations are performed to gain an understanding of the interactions between bare AuNPs and LS monolayer components at the nanoscale. Different surface tensions of the monolayer are used to mimic the biological process of breathing (inhalation and exhalation). It is found that the NP affects the structure and packing of the lipids by disordering lipid tails. Overall, the analysed results suggest that bare AuNPs impede the normal biophysical function of the lung, a finding that has beneficial consequences to the potential development of treatments of various respiratory diseases.

scholarly journals Understanding the Binding Transition State After the Conformational Selection Step: The Second Half of the Molecular Recognition Process Between NS1 of the 1918 Influenza Virus and Host p85β

2021 ◽  
Vol 8 ◽  
Author(s):  
Alyssa Dubrow ◽  
Iktae Kim ◽  
Elias Topo ◽  
Jae-Hyun Cho
Keyword(s):  
Free Energy ◽  
Molecular Recognition ◽  
Transition State ◽  
Hydrophobic Interactions ◽  
Nonstructural Protein ◽  
Conformational Dynamics ◽  
Linear Free Energy Relationship ◽  
Conformational Selection ◽  
Nonstructural Protein 1 ◽  
1918 Influenza

Biomolecular recognition often involves conformational changes as a prerequisite for binding (i.e., conformational selection) or concurrently with binding (i.e., induced-fit). Recent advances in structural and kinetic approaches have enabled the detailed characterization of protein motions at atomic resolution. However, to fully understand the role of the conformational dynamics in molecular recognition, studies on the binding transition state are needed. Here, we investigate the binding transition state between nonstructural protein 1 (NS1) of the pandemic 1918 influenza A virus and the human p85β subunit of PI3K. 1918 NS1 binds to p85β via conformational selection. We present the free-energy mapping of the transition and bound states of the 1918 NS1:p85β interaction using linear free energy relationship and ϕ-value analyses. We find that the binding transition state of 1918 NS1 and p85β is structurally similar to the bound state with well-defined binding orientation and hydrophobic interactions. Our finding provides a detailed view of how protein motion contributes to the development of intermolecular interactions along the binding reaction coordinate.

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

SAMPL7 TrimerTrip Host-Guest Binding Poses and Binding Affinities from Spherical-Coordinates-Biased Simulations

2020 ◽  
Author(s):  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Computational Modelling ◽  
Free Energy Calculations ◽  
Energy Estimates ◽  
Binding Affinities ◽  
Spherical Coordinates ◽  
Collective Variables ◽  
Guest Binding ◽  
Starting Point ◽  
Sampl Challenge

Host-guest binding remains a major challenge in modern computational modelling. The newest 7<sup>th</sup> statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and predict the binding affinities in all three host-guest binding cases of the 6<sup>th</sup> SAMPL challenge. In this work, we employed the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The predicted binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations.

Molecular Basis of Glucose Transport Mechanism in Plants

2019 ◽  
Author(s):  
Balaji Selvam ◽  
Ya-Chi Yu ◽  
Liqing Chen ◽  
Diwakar Shukla
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Conformational Changes ◽  
Transport Mechanism ◽  
Conformational Dynamics ◽  
Site Directed Mutagenesis ◽  
Free Energy Barrier ◽  
Transport Cycle ◽  
Dynamics Simulations

<p>The SWEET family belongs to a class of transporters in plants that undergoes large conformational changes to facilitate transport of sugar molecules across the cell membrane. However, the structures of their functionally relevant conformational states in the transport cycle have not been reported. In this study, we have characterized the conformational dynamics and complete transport cycle of glucose in OsSWEET2b transporter using extensive molecular dynamics simulations. Using Markov state models, we estimated the free energy barrier associated with different states as well as 1 for the glucose the transport mechanism. SWEETs undergoes structural transition to outward-facing (OF), Occluded (OC) and inward-facing (IF) and strongly support alternate access transport mechanism. The glucose diffuses freely from outside to inside the cell without causing major conformational changes which means that the conformations of glucose unbound and bound snapshots are exactly same for OF, OC and IF states. We identified a network of hydrophobic core residues at the center of the transporter that restricts the glucose entry to the cytoplasmic side and act as an intracellular hydrophobic gate. The mechanistic predictions from molecular dynamics simulations are validated using site-directed mutagenesis experiments. Our simulation also revealed hourglass like intermediate states making the pore radius narrower at the center. This work provides new fundamental insights into how substrate-transporter interactions actively change the free energy landscape of the transport cycle to facilitate enhanced transport activity.</p>

scholarly journals Shifting the scaling relations of single-atom catalysts for facile methane activation by tuning the coordination number

2021 ◽  
Author(s):  
Changhyeok Choi ◽  
Sungho Yoon ◽  
Yousung Jung
Keyword(s):  
Transition State ◽  
Coordination Number ◽  
Active Sites ◽  
Methane Activation ◽  
Single Atom ◽  
Scaling Relations ◽  
Scaling Relationship ◽  
Relationship Of

The scaling relationship of methane activation via a radical-like transition state shifts toward a more reactive region with decreasing coordination number of the active sites.

scholarly journals Reconstructing the Free Energy Profiles Describing the Switching Mechanism of a pH-Dependent DNA Nanodevice from ABMD Simulations

2021 ◽  
Vol 11 (9) ◽  
pp. 4052
Author(s):  
Alice Romeo ◽  
Mattia Falconi ◽  
Alessandro Desideri ◽  
Federico Iacovelli
Keyword(s):  
Free Energy ◽  
Double Helix ◽  
Minimum Energy ◽  
Switching Mechanism ◽  
Linker Length ◽  
Energy Profiles ◽  
Functional Dna ◽  
Triplex Forming Oligonucleotide ◽  
Dynamics Simulations ◽  
Free Energy Profiles

The pH-responsive behavior of six triple-helix DNA nanoswitches, differing in the number of protonation centers (two or four) and in the length of the linker (5, 15 or 25 bases), connecting the double-helical region to the single-strand triplex-forming region, was characterized at the atomistic level through Adaptively Biased Molecular Dynamics simulations. The reconstruction of the free energy profiles of triplex-forming oligonucleotide unbinding from the double helix identified a different minimum energy path for the three diprotic nanoswitches, depending on the length of the connecting linker and leading to a different per-base unbinding profile. The same analyses carried out on the tetraprotic switches indicated that, in the presence of four protonation centers, the unbinding process occurs independently of the linker length. The simulation data provide an atomistic explanation for previously published experimental results showing, only in the diprotic switch, a two unit increase in the pKa switching mechanism decreasing the linker length from 25 to 5 bases, endorsing the validity of computational methods for the design and refinement of functional DNA nanodevices.

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