WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering

Abstract

Motivation: Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post-spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination.

Results: We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on (15)N-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY.

Availability: WaVPeak is an open source program. The source code and two test spectra of WaVPeak are available at http://faculty.kaust.edu.sa/sites/xingao/Pages/Publications.aspx. The online server is under construction.

Contact: statliuzhi@xmu.edu.cn; ahmed.abbas@kaust.edu.sa; majing@ust.hk; xin.gao@kaust.edu.sa.

Fig. 1.

The scaling and wavelet functions of the Daubechies 3 wavelet.

Fig. 2.

The original spectrum and the Daubechies 3 wavelet-smoothed spectrum of ¹⁵N-HSQC of protein VRAR.

Fig. 3.

A peak shape with different spans over the x- and y-dimensions.

Fig. 4.

(a)–(e) ROC curves of WaVPeak and PICKY for NHSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. The curves for WaVPeak are cyan and the curves for PICKY are magenta. The areas under the curve (AUC) for both methods are given in the figures as well. (f) The relationship between the number of top peaks considered and the recall value for the CBCA(CO)NH spectrum of ATC1776. The magenta, black and cyan curves are for PICKY (with the default intensity-based filtering), Daubechies 3 wavelet plus intensity-based filtering and Daubechies 3 wavelet plus volume-based filtering, respectively.