Accurate and automated high-coverage identification of chemically cross-linked peptides with MaxLynx

Cross-linking combined with mass spectrometry (XL-MS) provides a wealth of information about the 3D structure of proteins and their interactions. We introduce MaxLynx, a novel computational proteomics workflow for XL-MS integrated into the MaxQuant environment. It is applicable to non-cleavable and MS-cleavable cross linkers. For both we have generalized the Andromeda peptide database search engine to efficiently identify cross-linked peptides. For non-cleavable peptides, we implemented a novel di-peptide Andromeda score, which is the basis for a computationally efficient N-squared search engine. Additionally, partial scores summarize the evidence for the two constituents of the di-peptide individually. A posterior error probability based on total and partial scores is used to control false discovery rates. For MS-cleavable cross linkers a scoring of signature peaks is combined with the conventional Andromeda score on the cleavage products. The MaxQuant 3D-peak detection was improved to ensure more accurate determination of the monoisotopic peak of isotope patterns for heavy molecules, which cross-linked peptides typically are. A wide selection of filtering parameters can replace manual filtering of identifications, which is often necessary when using other pipelines. On benchmark datasets of synthetic peptides, MaxLynx outperforms all other tested software on data for both types of cross linkers as well as on a proteome-wide dataset of cross-linked D. melanogaster cell lysate. The workflow also supports ion-mobility enhanced MS data. MaxLynx runs on Windows and Linux, contains an interactive viewer for displaying annotated cross-linked spectra and is freely available at https://www.maxquant.org/.

An adaptive inversion algorithm for one-dimensional magnetotelluric problems

<p>      As we know, the traditional one-dimensional (1-D) magnetotelluric (MT) regularization inversion needs the geometry model of the 1-D Earth conductivity model, i.e., the number of layers and the thickness of each layer to be given in advance and cannot be changed during the inversion. In this way, too few layers cannot approximate the 1-D conductivity model accurately, while too many layers will increase the non-uniqueness of the inversion problem and hence may result in unreasonable results. Aiming to solve this issue, an adaptive inversion algorithm has been proposed for 1-D MT problems, where the layer number and the thickness of each layer can be adjusted automatically during the inversion process. To this end, three pseudo a-posterior error estimators has been proposed to guide the adjustment of the 1D geometry model, which are based on the gradient of the data misfit term of the penalty function, the diagonal elements of the model resolution matrix, and the weighted elements of the sensitivity matrix, respectively. The inversion results of the synthetic and field data by using our proposal adaptive inversion algorithm and the traditional regularization inversion not only validate the proposed algorithm, but also show that our proposed algorithm can obtain more accurate and reasonable results than traditional one. Subsequently, the proposed algorithm will be extended for 3-D magnetotelluric inversion problems soon.</p>

Spectral Method with the Tensor-Product Nodal Basis for the Steklov Eigenvalue Problem

This paper discusses spectral method with the tensor-product nodal basis at the Legendre-Gauss-Lobatto points for solving the Steklov eigenvalue problem. A priori error estimates of spectral method are discussed, and based on the work of Melenk and Wohlmuth (2001), a posterior error estimator of the residual type is given and analyzed. In addition, this paper combines the shifted-inverse iterative method and spectral method to establish an efficient scheme. Finally, numerical experiments with MATLAB program are reported.