NVR-BIP: Nuclear vector replacement using binary integer programming for NMR structure-based assignments

Apaydın, Mehmet Serkan; Çatay, Bülent; Patrick, Nicholas; Donald, Bruce Randall

doi:10.1109/iscis.2009.5291840

Cited by 5 publications

(11 citation statements)

References 7 publications

(14 reference statements)

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“…This is the score of assigning contact c to NOESY peak match n, where HSQC peak s ∈ n[0], HSQC peak t ∈ n [1], and NOESY peak p ∈ n [2]. F (c) is a weight on the type of contact.…”

Section: Objective Function Coefficientsmentioning

confidence: 99%

“…It is often the case that the bound structure of the protein is similar across different ligands that can bind to it, so that one bound structure can be used for studying different ligands. Therefore, structurebased resonance assignment methods [11,30,2,36,16,4] are ideal for disambiguating the mappings. Currently, there are no automated backbone resonance assignment methods that use only a series of 15 N-HSQC spectra and ambiguous NOEs from 15 N-NOESY spectra.…”

Section: Introductionmentioning

confidence: 99%

“…NOEnet [30] requires unambiguous NOEs, such as from 4D NOESY. The Nuclear Vector Replacement (NVR) [2,16] approach requires a sparse set of unambiguous NOEs from 3D NOESY, residual dipolar couplings (RDC), and amide exchange rates. The contact replacement (CR) [36] method can handle ambiguous NOEs from 3D 15 N-NOESY , but it also requires 3D 15 N-TOCSY, and 3D HNHA.…”

Section: Introductionmentioning

confidence: 99%

“…NOESY peak p ∈ n[2] of NOESY peak match n is assigned to at most one contact. This is Each pair of HSQC peaks n[0], n[1] of NOESY peak match n has at most one NOESY peak.…”

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confidence: 99%

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Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from ¹⁵ N-NOESY

Jang

Gao

2011

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Chemical shift mapping is an important technique in NMRbased drug screening for identifying the atoms of a target protein that potentially bind to a drug molecule upon the molecule's introduction in increasing concentrations. The goal is to obtain a mapping of peaks with known residue assignment from the reference spectrum of the unbound protein to peaks with unknown assignment in the target spectrum of the bound protein. Although a series of perturbed spectra help to trace a path from reference peaks to target peaks, a one-to-one mapping generally is not possible, especially for large proteins, due to errors, such as noise peaks, missing peaks, missing but then reappearing, overlapped, and new peaks not associated with any peaks in the reference. Due to these difficulties, the mapping is typically done manually or semi-automatically. However, automated methods are necessary for high-throughput drug screening. We present PeakWalker, a novel peak walking algorithm for fast-exchange systems that models the errors explicitly and performs many-to-one mapping. On the proteins: hBclXL, UbcH5B, and histone H1, it achieves an average accuracy of over 95% with less than 1.5 residues predicted per target peak. Given these mappings as input, we present PeakAssigner, a novel combined structure-based backbone resonance and NOE assignment algorithm that uses just 15 N-NOESY, while avoiding TOCSY experiments and 13 C-labeling, to resolve the ambiguities for a one-toone mapping. On the three proteins, it achieves an average accuracy of 94% or better.

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“…This is the score of assigning contact c to NOESY peak match n, where HSQC peak s ∈ n[0], HSQC peak t ∈ n [1], and NOESY peak p ∈ n [2]. F (c) is a weight on the type of contact.…”

Section: Objective Function Coefficientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…NOESY peak p ∈ n[2] of NOESY peak match n is assigned to at most one contact. This is Each pair of HSQC peaks n[0], n[1] of NOESY peak match n has at most one NOESY peak.…”

mentioning

confidence: 99%

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Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from ¹⁵ N-NOESY

Jang

Gao

2011

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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“…NVR is a molecular replacement-like approach for SBA of resonances and sparse Nuclear Overhauser Effects (NOEs). NVR-BIP [4] is a binary integer programming formulation of SBA in NVR framework. It computes the exact solution of an optimization problem subject to NOE constraints and obtains high assignment accuracies on small proteins.…”

Section: Introductionmentioning

confidence: 99%

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

Aslanov

Çatay

Apaydın

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids. Structure Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach NVR-BIP computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-TS extended the applicability of the NVR approach for such proteins, however the accuracies varied significantly from run to run.In this paper, we propose NVR-ACO, an Ant Colony Optimization (ACO) based approach to this problem. NVR-ACO is similar to other ACO algorithms in a way that it also consists of three phases: the construction phase, an optional local search phase and a pheromone update phase. But it has some important differences from other ACO algorithms in terms of solution construction and pheromone update functions and convergence rules. We studied the data set used in NVR-BIP and NVR-TS. Our new method finds optimal solutions for small proteins and achieves perfect assignment on EIN and higher accuracy on MBP compared to NVR-TS. It is also more robust.

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Hash: a program to accurately predict protein Hα shifts from neighboring backbone shifts

2012

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Chemical shifts provide not only peak identities for analyzing NMR data, but also an important source of conformational information for studying protein structures. Current structural studies requiring Hα chemical shifts suffer from the following limitations. (1) For large proteins, the Hα chemical shifts can be difficult to assign using conventional NMR triple-resonance experiments, mainly due to the fast transverse relaxation rate of Cα that restricts the signal sensitivity. (2) Previous chemical shift prediction approaches either require homologous models with high sequence similarity or rely heavily on accurate backbone and side-chain structural coordinates. When neither sequence homologues nor structural coordinates are available, we must resort to other information to predict Hα chemical shifts. Predicting accurate Hα chemical shifts using other obtainable information, such as the chemical shifts of nearby backbone atoms (i.e., adjacent atoms in the sequence), can remedy the above dilemmas, and hence advance NMR-based structural studies of proteins. By specifically exploiting the dependencies on chemical shifts of nearby backbone atoms, we propose a novel machine learning algorithm, called Hash, to predict Hα chemical shifts. Hash combines a new fragment-based chemical shift search approach with a non-parametric regression model, called the generalized additive model, to effectively solve the prediction problem. We demonstrate that the chemical shifts of nearby backbone atoms provide a reliable source of information for predicting accurate Hα chemical shifts. Our testing results on different possible combinations of input data indicate that Hash has a wide rage of potential NMR applications in structural and biological studies of proteins.

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NVR-BIP: Nuclear vector replacement using binary integer programming for NMR structure-based assignments

Cited by 5 publications

References 7 publications

Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from ¹⁵ N-NOESY

Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from ¹⁵ N-NOESY

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

Hash: a program to accurately predict protein Hα shifts from neighboring backbone shifts

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NVR-BIP: Nuclear vector replacement using binary integer programming for NMR structure-based assignments

Cited by 5 publications

References 7 publications

Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from 15 N-NOESY

Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from 15 N-NOESY

An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem

Hash: a program to accurately predict protein Hα shifts from neighboring backbone shifts

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Product

Resources

About

Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from ¹⁵ N-NOESY

Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from ¹⁵ N-NOESY