Solving the molecular distance geometry problem with inaccurate distance data

Souza, Michael; Lavor, Carlile; Muritiba, Albert Einstein Fernandes; Maculan, Nelson

doi:10.1186/1471-2105-14-s9-s7

Cited by 30 publications

(21 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The question of integrating noisy, pairwise distance measurements into an embedding in Euclidean space, referred to variously as the distance geometry problem 14 , global positioning problem 15 , localization The identity of targets as well as the distance between targets is encoded inside barcoded distance records. (4) Distance records are read with next-generation sequencing to obtain length and barcode information, which is used to infer distances between points.…”

Section: Inferring Geometry From Distance Datamentioning

confidence: 99%

A DNA nanoscope that identifies and precisely localizes over a hundred unique molecular features with nanometer accuracy

Gopalkrishnan

Punthambaker

Schaus

et al. 2020

Preprint

View full text Add to dashboard Cite

Techniques that can both spatially map out molecular features and discriminate many targets would be highly valued for their utility in studying fundamental nanoscale processes. In spite of decades of development, no current technique can achieve both nanoscale resolution and discriminate hundreds of targets. Here, we report the development of a novel bottom-up technology that: (a) labels a sample with DNA barcodes, (b) measures pairwise-distances between labeled sites and writes them into DNA molecules, (c) reads the pairwise-distances by sequencing and (d) robustly integrates this noisy information to reveal the geometry of the underlying sample. We demonstrate our technology on DNA origami, which are complex synthetic nanostructures. We both spatially localized and uniquely identified over a hundred densely packed unique elements, some spaced just 6 nm apart, with an average spatial localization accuracy (RMS deviation) of ~2 nm. The bottom-up, sequencing-enabled mechanism of the DNA nanoscope is fundamentally different from top-down imaging, and hence offers unique advantages in precision, throughput and accessibility.

show abstract

Section: Inferring Geometry From Distance Datamentioning

confidence: 99%

A DNA nanoscope that identifies and precisely localizes over a hundred unique molecular features with nanometer accuracy

Gopalkrishnan

Punthambaker

Schaus

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The fundamental problem in Distance Geometry (DG) is the DG Problem (DGP): given an integer K > 0 and a simple, undirected, non-negatively edge-weighted graph G = (V, E, d), with d : E → R + , find positions in R K for each vertex such that each edge, drawn as a segment, has length equal to the weight [25,26,28]. The set of positions of all the vertices in V is called a realization of G. Many variants replace equality with inequalities to address data measurement error and noise [1,2,7,10,14,18,21,33,34]. The DGP has applications to many fields of science and engineering, including clock synchronization protocols, sensor network localization, robotics, nanostructures, and protein structure determination [3,4,9,11,19,31].…”

Section: Introductionmentioning

confidence: 99%

On the polynomiality of finding KDMDGP re-orders

Souza¹,

Carvalho²,

Liberti³

2019

Discrete Applied Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus we are left to solve a variant of the MDGP problem with incomplete and inaccurate data. Many computational approaches have been applied to solve 1 arXiv:1411.4246v1 [cs.NE] 16 Nov 2014 MDGP problem with sparse and inaccurate data on real instances [1,2,3,4,5,6]. However, complete search methods like spatial branch and bound (sBB) and stochastic methods like variable neighborhood search (VNS) have been able to solve the problem for proteins with only upto 50 atoms [7] and fail to converge quickly for larger number of atoms.…”

Section: Introductionmentioning

confidence: 99%

GreMuTRRR: A novel genetic algorithm to solve distance geometry problem for protein structures

Islam

Shatabda

Rahman

2014

8th International Conference on Electrical and Computer Engineering

View full text Add to dashboard Cite

Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to predict the native structure of proteins. However, NMR machines are only able to report approximate and partial distances between pair of atoms. To build the protein structure one has to solve the Euclidean distance geometry problem given the incomplete interval distance data produced by NMR machines. In this paper, we propose a new genetic algorithm for solving the Euclidean distance geometry problem for protein structure prediction given sparse NMR data. Our genetic algorithm uses a greedy mutation operator to intensify the search, a twin removal technique for diversification in the population and a random restart method to recover stagnation. On a standard set of benchmark dataset, our algorithm significantly outperforms standard genetic algorithms.

show abstract

Solving the molecular distance geometry problem with inaccurate distance data

Cited by 30 publications

References 20 publications

A DNA nanoscope that identifies and precisely localizes over a hundred unique molecular features with nanometer accuracy

A DNA nanoscope that identifies and precisely localizes over a hundred unique molecular features with nanometer accuracy

On the polynomiality of finding KDMDGP re-orders

GreMuTRRR: A novel genetic algorithm to solve distance geometry problem for protein structures

Contact Info

Product

Resources

About