112 research outputs found
A Quantum Approach to the Discretizable Molecular Distance Geometry Problem
The Discretizable Molecular Distance Geometry Problem (DMDGP) aims to
determine the three-dimensional protein structure using distance information
from nuclear magnetic resonance experiments. The DMDGP has a finite number of
candidate solutions and can be solved by combinatorial methods. We describe a
quantum approach to the DMDGP by using Grover's algorithm with an appropriate
oracle function, which is more efficient than classical methods that use brute
force. We show computational results by implementing our scheme on IBM quantum
computers with a small number of noisy qubits.Comment: 17 page
Euclidean distance geometry and applications
Euclidean distance geometry is the study of Euclidean geometry based on the
concept of distance. This is useful in several applications where the input
data consists of an incomplete set of distances, and the output is a set of
points in Euclidean space that realizes the given distances. We survey some of
the theory of Euclidean distance geometry and some of the most important
applications: molecular conformation, localization of sensor networks and
statics.Comment: 64 pages, 21 figure
The interval ordering problem
For a given set of intervals on the real line, we consider the problem of
ordering the intervals with the goal of minimizing an objective function that
depends on the exposed interval pieces (that is, the pieces that are not
covered by earlier intervals in the ordering). This problem is motivated by an
application in molecular biology that concerns the determination of the
structure of the backbone of a protein.
We present polynomial-time algorithms for several natural special cases of
the problem that cover the situation where the interval boundaries are
agreeably ordered and the situation where the interval set is laminar. Also the
bottleneck variant of the problem is shown to be solvable in polynomial time.
Finally we prove that the general problem is NP-hard, and that the existence of
a constant-factor-approximation algorithm is unlikely
An algorithm to enumerate all possible protein conformations verifying a set of distance constraints
International audienceBackground: The determination of protein structures satisfying distance constraints is an important problem in structural biology. Whereas the most common method currently employed is simulated annealing, there have been other methods previously proposed in the literature. Most of them, however, are designed to find one solution only. Results: In order to explore exhaustively the feasible conformational space, we propose here an interval Branch-and-Prune algorithm (iBP) to solve the Distance Geometry Problem (DGP) associated to protein structure determination. This algorithm is based on a discretization of the problem obtained by recursively constructing a search space having the structure of a tree, and by verifying whether the generated atomic positions are feasible or not by making use of pruning devices. The pruning devices used here are directly related to features of protein conformations. Conclusions: We described the new algorithm iBP to generate protein conformations satisfying distance constraints, that would potentially allows a systematic exploration of the conformational space. The algorithm iBP has been applied on three α-helical peptides
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