854 research outputs found
Techniques for modeling and analyzing RNA and protein folding energy landscapes
RNA and protein molecules undergo a dynamic folding process that is important
to their function. Computational methods are critical for studying this folding pro-
cess because it is difficult to observe experimentally. In this work, we introduce
new computational techniques to study RNA and protein energy landscapes, includ-
ing a method to approximate an RNA energy landscape with a coarse graph (map)
and new tools for analyzing graph-based approximations of RNA and protein energy
landscapes. These analysis techniques can be used to study RNA and protein fold-
ing kinetics such as population kinetics, folding rates, and the folding of particular
subsequences. In particular, a map-based Master Equation (MME) method can be
used to analyze the population kinetics of the maps, while another map analysis tool,
map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways
from the map.
To validate the results, I compared our methods with other computational meth-
ods and with experimental studies of RNA and protein. I first compared our MMC
and MME methods for RNA with other computational methods working on the com-
plete energy landscape and show that the approximate map captures the major fea-
tures of a much larger (e.g., by orders of magnitude) complete energy landscape.
Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental
findings. I present comparisons with two experimental cases to show how I can pre-
dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their
mutants using our MME and MMC tools respectively. I also show that the MME
and MMC tools can be applied to map-based approximations of protein energy energy
landscapes and present kinetics analysis results for several proteins
Major Subject: Computer ScienceTECHNIQUES FOR MODELING AND ANALYZING RNA AND PROTEIN FOLDING ENERGY LANDSCAPES
Major Subject: Computer Scienceiii Techniques for Modeling and Analyzing RNA and Protein Folding Energ
Techniques for modeling and analyzing RNA and protein folding energy landscapes
RNA and protein molecules undergo a dynamic folding process that is important
to their function. Computational methods are critical for studying this folding pro-
cess because it is difficult to observe experimentally. In this work, we introduce
new computational techniques to study RNA and protein energy landscapes, includ-
ing a method to approximate an RNA energy landscape with a coarse graph (map)
and new tools for analyzing graph-based approximations of RNA and protein energy
landscapes. These analysis techniques can be used to study RNA and protein fold-
ing kinetics such as population kinetics, folding rates, and the folding of particular
subsequences. In particular, a map-based Master Equation (MME) method can be
used to analyze the population kinetics of the maps, while another map analysis tool,
map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways
from the map.
To validate the results, I compared our methods with other computational meth-
ods and with experimental studies of RNA and protein. I first compared our MMC
and MME methods for RNA with other computational methods working on the com-
plete energy landscape and show that the approximate map captures the major fea-
tures of a much larger (e.g., by orders of magnitude) complete energy landscape.
Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental
findings. I present comparisons with two experimental cases to show how I can pre-
dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their
mutants using our MME and MMC tools respectively. I also show that the MME
and MMC tools can be applied to map-based approximations of protein energy energy
landscapes and present kinetics analysis results for several proteins
A motion planning approach to protein folding
Protein folding is considered to be one of the grand challenge problems in biology. Protein folding refers to how a protein's amino acid sequence, under certain physiological conditions, folds into a stable close-packed three-dimensional structure known as the native state. There are two major problems in protein folding. One, usually called protein structure prediction, is to predict the structure of the protein's native state given only the amino acid sequence. Another important and strongly related problem, often called protein folding, is to study how the amino acid sequence dynamically transitions from an unstructured state to the native state. In this dissertation, we concentrate on the second problem. There are several approaches that have been applied to the protein folding problem, including molecular dynamics, Monte Carlo methods, statistical mechanical models, and lattice models. However, most of these approaches suffer from either overly-detailed simulations, requiring impractical computation times, or overly-simplified models, resulting in unrealistic solutions.
In this work, we present a novel motion planning based framework for studying protein folding. We describe how it can be used to approximately map a protein's energy landscape, and then discuss how to find approximate folding pathways and kinetics on this approximate energy landscape. In particular, our technique can produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap. The roadmap can be computed in a few hours on a desktop PC using a coarse potential energy function. In addition, our motion planning based approach is the first simulation method that enables the study of protein folding kinetics at a level of detail that is appropriate (i.e., not too detailed or too coarse) for capturing possible 2-state and 3-state folding kinetics that may coexist in one protein. Indeed, the unique ability of our method to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics that are not available to other theoretical techniques
Intelligent Motion Planning and Analysis with Probabilistic Roadmap Methods for the Study of Complex and High-Dimensional Motions
At first glance, robots and proteins have little in common. Robots are commonly
thought of as tools that perform tasks such as vacuuming the floor, while proteins
play essential roles in many biochemical processes. However, the functionality of
both robots and proteins is highly dependent on their motions. In order to study
motions in these two divergent domains, the same underlying algorithmic framework
can be applied. This method is derived from probabilistic roadmap methods (PRMs)
originally developed for robotic motion planning. It builds a graph, or roadmap, where
configurations are represented as vertices and transitions between configurations are
edges. The contribution of this work is a set of intelligent methods applied to PRMs.
These methods facilitate both the modeling and analysis of motions, and have enabled
the study of complex and high-dimensional problems in both robotic and molecular
domains.
In order to efficiently study biologically relevant molecular folding behaviors we
have developed new techniques based on Monte Carlo solution, master equation calculation,
and non-linear dimensionality reduction to run simulations and analysis on
the roadmap. The first method, Map-based master equation calculation (MME), extracts
global properties of the folding landscape such as global folding rates. On the
other hand, another method, Map-based Monte Carlo solution (MMC), can be used to extract microscopic features of the folding process. Also, the application of dimensionality
reduction returns a lower-dimensional representation that still retains the
principal features while facilitating both modeling and analysis of motion landscapes.
A key contribution of our methods is the flexibility to study larger and more complex
structures, e.g., 372 residue Alpha-1 antitrypsin and 200 nucleotide ColE1 RNAII.
We also applied intelligent roadmap-based techniques to the area of robotic motion.
These methods take advantage of unsupervised learning methods at all stages
of the planning process and produces solutions in complex spaces with little cost
and less manual intervention compared to other adaptive methods. Our results show
that our methods have low overhead and that they out-perform two existing adaptive
methods in all complex cases studied
Markov dynamic models for long-timescale protein motion
Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements
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