Stochastic Roadmap Simulation: An Ef cient Representation and Algorithm for Analyzing Molecular Motion

نویسندگان

  • MEHMET SERKAN APAYDIN
  • DOUGLAS L. BRUTLAG
  • CARLOS GUESTRIN
  • DAVID HSU
چکیده

Classic molecular motion simulation techniques, such as Monte Carlo (MC) simulation, generate motion pathways one at a time and spend most of their time in the local minima of the energy landscape deŽ ned over a molecular conformation space. Their high computational cost prevents them from being used to compute ensemble properties (properties requiring the analysis of many pathways). This paper introduces stochastic roadmap simulation (SRS) as a new computational approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways. These pathways are compactly encoded in a graph, which is constructed by sampling a molecular conformation space at random. This computation, which does not trace any particular pathway explicitly, circumvents the localminima problem. Each edge in the graph represents a potential transition of the molecule and is associated with a probability indicating the likelihood of this transition. By viewing the graph as a Markov chain, ensemble properties can be efŽ ciently computed over the entire molecular energy landscape. Furthermore, SRS converges to the same distribution as MC simulation. SRS is applied to two biological problems: computing the probability of folding, an important order parameter that measures the “kinetic distance” of a protein’s conformation from its native state; and estimating the expected time to escape from a ligand–protein binding site. Comparison with MC simulations on protein folding shows that SRS produces arguably more accurate results, while reducing computation time by several orders of magnitude. Computational studies on ligand–protein binding also demonstrate SRS as a promising approach to study ligand–protein interactions.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Stochastic Conformational Roadmaps for Computing Ensemble Properties of Molecular Motion

A key intuition behind probabilistic roadmap planners for motion planning is that many collision-free paths potentially exist between two given robot configurations. Hence the connectivity of a robot’s free space can be captured effectively by a network of randomly sampled configurations. In this paper, a similar intuition is exploited to preprocess molecular motion pathways and efficiently com...

متن کامل

Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis

Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior informat...

متن کامل

The Effects of Different SDE Calculus on Dynamics of Nano-Aerosols Motion in Two Phase Flow Systems

Langevin equation for a nano-particle suspended in a laminar fluid flow was analytically studied. The Brownian motion generated from molecular bombardment was taken as a Wiener stochastic process and approximated by a Gaussian white noise. Euler-Maruyama method was used to solve the Langevin equation numerically. The accuracy of Brownian simulation was checked by performing a series of simulati...

متن کامل

The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty

We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a goal. In many motion planning applications ranging from maneuvering vehicles over unfamiliar terrain to steering flexible medical needles through human tissue, the response of a robot to commanded actions cannot be precis...

متن کامل

Ef"cient Stochastic Source Coding and an Application to a Bayesian Network Source Model

In this paper, we introduce a new algorithm called `bits-back coding' that makes stochastic source codes ef"cient. For a given one-to-many source code, we show that this algorithm can actually be more ef"cient than the algorithm that always picks the shortest codeword. Optimal ef"ciency is achieved when codewords are chosen according to the Boltzmann distribution based on the codeword lengths. ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003