scholarly journals Membrane-mediated ligand unbinding of the PK-11195 ligand from TSPO

2020 ◽  
Author(s):  
Tom Dixon ◽  
Arzu Uyar ◽  
Shelagh Ferguson-Miller ◽  
Alex Dickson
Keyword(s):  
Ligand Binding ◽  
Transition State ◽  
Residence Time ◽  
Protein Interactions ◽  
Lipid Membrane ◽  
Binding Free Energy ◽  
First Passage Time ◽  
Passage Time ◽  
Translocator Protein ◽  
Trajectory Data

ABSTRACTThe translocator protein (TSPO), previously known as the peripheral benzodiazepine receptor, is of longstanding medical interest as both a biomarker for neuroinjury and a potential drug target for neuroinflammation and other disorders. Recently it was shown that ligand residence time is a key factor determining steroidogenic efficacy of TSPO-binding compounds. This spurs interest in simulations of (un)binding pathways of TSPO ligands, which could reveal the molecular interactions governing ligand residence time. In this study, we use a weighted ensemble algorithm to determine the unbinding pathway for different poses of PK-11195, a TSPO ligand used in neuroimaging. In contrast with previous studies, our results show that PK-11195 does not dissociate directly into the solvent but instead dissociates via the lipid membrane by going between the transmembrane helices. We analyze this path ensemble in detail, constructing descriptors that can facilitate a general understanding of membrane-mediated ligand binding. We construct a Markov state model using additional straightforward simulations to determine pose stability and kinetics of ligand unbinding. Together we combine over 40 µs of trajectory data to form a coherent picture of the ligand binding landscape. We find that all poses are able to interconvert before unbinding, leading to single mean first passage time estimate for all starting poses which roughly agrees with the experimental quantity. The ligand binding transition state predicted by our combined model occurs when PK-11195 is already in the membrane and does not involve direct ligand-protein interactions. This has implications for the design of new long residence-time TSPO ligands.SIGNIFICANCEKinetics-oriented drug design is an emerging objective in drug discovery. However, while ligand binding affinity (or the binding free energy) is purely a function of the bound and unbound states, the binding kinetics depends on the nature of the paths by which the (un)binding occurs. This underscores the importance of approaches that can reveal information about the ensemble of (un)binding paths. Here we used advanced molecular dynamics approaches to study the unbinding of PK-11195 from TSPO and find it dissociates from the protein by dissolving into the membrane, and that the transition state occurs after the PK-11195 molecule has already separated from TSPO. These results motivate the design of future long-residence time TSPO ligands that destabilize the membrane-solvated transition state.

scholarly journals APLIKASI MODEL RANTAI MARKOV UNTUK MENGANALISIS TINGKAT KENYAMANAN DI KOTA MAJENE BERDASARKAN TEMPERATURE HUMIDITY INDEX (THI)

2021 ◽  
Vol 15 (1) ◽  
pp. 009-014
Author(s):  
Muhammad Abdy ◽  
Wahidah Sanusi ◽  
Rahmawati Rahmawati
Keyword(s):  
Steady State ◽  
Residence Time ◽  
Mean Residence Time ◽  
First Passage Time ◽  
Passage Time ◽  
Mean First Passage Time ◽  
First Passage ◽  
Temperature Humidity Index ◽  
Humidity Index ◽  
The Mean

This study applied the Markov chain model on the daily temperature and relative humidity data that was collected from the Meteorology and Geophysics Agency station in Majene district for the period 1983 to 2011. This study aims to analyze the comfortable level category in the Majene city based on the Temperature Humidity Index by calculating the probability of steady-state, the mean residence time and the mean first passage time. Categorizing the level of comfortable which is based on the Temperature Humidity Index consists of three categories, namely the comfortable, quite comfortable and uncomfortable. The trend of comfortable levels in the Majene city from 1983 to 2011 was fluctuated in the categories of quite comfortable and uncomfortable. Uncomfortable category occurs in October and November each year. The steady-state probability values indicates that the quite comfortable category has the highest chance of appearance, which is around 70%, and the comfortable category has the smallest chance of appearance, which is only about 5%. Meanwhile, the mean residence time and the mean first passage time indicate that the quite comfortable category have the longest duration of occurrence, which is around 5 days, and has the shortest duration to recur after occurring in the previous event, which is around 1.43 days.

scholarly journals Kinetics of self-assembly of inclusions due to lipid membrane thickness interactions

2020 ◽  
Author(s):  
Xinyu Liao ◽  
Prashant K. Purohit
Keyword(s):  
Self Assembly ◽  
Time Scale ◽  
Lipid Membranes ◽  
Lipid Membrane ◽  
First Passage Time ◽  
Langevin Dynamics ◽  
Passage Time ◽  
Membrane Thickness ◽  
Kinetics Of ◽  
Two Inclusions

AbstractSelf-assembly of proteins on lipid membranes underlies many important processes in cell biology, such as, exo- and endo-cytosis, assembly of viruses, etc. An attractive force that can cause self-assembly is mediated by membrane thickness interactions between proteins. The free energy profile associated with this attractive force is a result of the overlap of thickness deformation fields around the proteins. The thickness deformation field around proteins of various shapes can be calculated from the solution of a boundary value problem and is relatively well understood. Yet, the time scales over which self-assembly occurs has not been explored. In this paper we compute this time scale as a function of the initial distance between two inclusions by viewing their coalescence as a first passage time problem. The first passage time is computed using both Langevin dynamics and a partial differential equation, and both methods are found to be in excellent agreement. Inclusions of three different shapes are studied and it is found that for two inclusions separated by about hundred nanometers the time to coalescence is hundreds of milliseconds irrespective of shape. Our Langevin dynamics simulation of self-assembly required an efficient computation of the interaction energy of inclusions which was accomplished using a finite difference technique. The interaction energy profiles obtained using this numerical technique were in excellent agreement with those from a previously proposed semi-analytical method based on Fourier-Bessel series. The computational strategies described in this paper could potentially lead to efficient methods to explore the kinetics of self-assembly of proteins on lipid membranes.Author summarySelf-assembly of proteins on lipid membranes occurs during exo- and endo-cytosis and also when viruses exit an infected cell. The forces mediating self-assembly of inclusions on membranes have therefore been of long standing interest. However, the kinetics of self-assembly has received much less attention. As a first step in discerning the kinetics, we examine the time to coalescence of two inclusions on a membrane as a function of the distance separating them. We use both Langevin dynamics simulations and a partial differential equation to compute this time scale. We predict that the time to coalescence is on the scale of hundreds of milliseconds for two inclusions separated by about hundred nanometers. The deformation moduli of the lipid membrane and the membrane tension can affect this time scale.

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

First passage time for the state of exact chemical equilibrium

1980 ◽  
Vol 45 (3) ◽  
pp. 777-782 ◽  
Author(s):  
Milan Šolc
Keyword(s):  
Chemical Equilibrium ◽  
First Passage Time ◽  
Passage Time ◽  
The State ◽  
Recurrence Time ◽  
First Passage ◽  
First Order ◽  
The Mean ◽  
Passage Times ◽  
Number Of Particles

The establishment of chemical equilibrium in a system with a reversible first order reaction is characterized in terms of the distribution of first passage times for the state of exact chemical equilibrium. The mean first passage time of this state is a linear function of the logarithm of the total number of particles in the system. The equilibrium fluctuations of composition in the system are characterized by the distribution of the recurrence times for the state of exact chemical equilibrium. The mean recurrence time is inversely proportional to the square root of the total number of particles in the system.

scholarly journals Credit Gap Risk in a First Passage Time Model with Jumps

2009 ◽  
Author(s):  
Natalie Packham ◽  
Lutz Schloegl ◽  
Wolfgang M. Schmidt
Keyword(s):  
First Passage Time ◽  
Passage Time ◽  
First Passage ◽  
Time Model ◽  
Gap Risk ◽  
Credit Gap

scholarly journals The effect of protein mutations on drug binding suggests ensuing personalised drug selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shunzhou Wan ◽  
Deepak Kumar ◽  
Valentin Ilyin ◽  
Ussama Al Homsi ◽  
Gulab Sher ◽  
...  
Keyword(s):  
Estrogen Receptor ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Personalised Medicine ◽  
Clinical Decision ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Breast Cancer Patients ◽  
Natural Ligand ◽  
Affinity Interaction

AbstractThe advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

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