A flexible method to align large numbers of biological sequences

Summary A method for the alignment of two or more biological sequences is described. The method is a direct extension of the method of Taylor (1987) incorporating a consensus sequence approach and allows considerable freedom in the control of the clustering of the sequences. At one extreme this is equivalent to the earlier method (Taylor 1987), whereas at the other, the clustering approaches the binary method of Feng and Doolittle (1987). Such freedom allows the program to be adapted to particular problems, which has the important advantage of resulting in considerable savings in computer time, allowing very large problems to be tackled. Besides a detailed analysis of the alignment of the cytochrome c superfamily, the clustering and alignment of the PIR sequence data bank (3500 sequences approx.) is described.     

Kinetics of the spontaneous organization of microtubules in solution

Optically anisotropic zones occur spontaneously in solutions of microtubules. These tactoids, in which microtubules are arranged in parallel arrays, can be visualized by their birefringence. With microtubules assembled in the presence of associated proteins (MAPs), birefringence appears immediately after nucleation of polymerization, even at relatively low protein concentrations. It is not dependent on whether the assembly is initiated by temperature jump or by isothermal addition of GTP. With pure tubulin, assembled in buffers containing 25% glycerol or 4% dimethylsulfoxide and/or taxol, birefringence appears within a few hours, but it can be speeded up by gentle agitation. With tubulin assembled in the presence of MAPs, spontaneous orientation occurs simultaneously with polymerization. This may be due to the existence of more pronounced repulsive forces between microtubules when they are covered with MAPs. A simple calculation of the covolume, suggests that tactoid formation is expected for microtubules of lengths of 5 to 10 m at protein concentrations in the range 1 to 3 mg/ml (as observed), and that repulsive forces will promote tactoid formation at even lower protein concentrations. Offprint requests to: Y. Engelborghs     

The posterior probability distribution of alignments and its application to parameter estimation of evolutionary trees and to optimization of multiple alignments

How to sample alignments from their posterior probability distribution given two strings is shown. This is extended to sampling alignments of more than two strings. The result is first applied to the estimation of the edges of a given evolutionary tree over several strings. Second, when used in conjunction with simulated annealing, it gives a stochastic search method for an optimal multiple alignment.Correspondence to: L. Allison     

A protein alignment scoring system sensitive at all evolutionary distances

Summary Protein sequence alignments generally are constructed with the aid of a substitution matrix that specifies a score for aligning each pair of amino acids. Assuming a simple random protein model, it can be shown that any such matrix, when used for evaluating variable-length local alignments, is implicitly a log-odds matrix, with a specific probability distribution for amino acid pairs to which it is uniquely tailored. Given a model of protein evolution from which such distributions may be derived, a substitution matrix adapted to detecting relationships at any chosen evolutionary distance can be constructed. Because in a database search it generally is not known a priori what evolutionary distances will characterize the similarities found, it is necessary to employ an appropriate range of matrices in order not to overlook potential homologies. This paper formalizes this concept by defining a scoring system that is sensitive at all detectable evolutionary distances. The statistical behavior of this scoring system is analyzed, and it is shown that for a typical protein database search, estimating the originally unknown evolutionary distance appropriate to each alignment costs slightly over two bits of information, or somewhat less than a factor of five in statistical significance. A much greater cost may be incurred, however, if only a single substitution matrix, corresponding to the wrong evolutionary distance, is employed.     

蛋白质结构成对比较的新方法

介绍一种蛋白质三维结构的快速比较方法.此方法毋需初始联配,而能自动寻找和智能迭代.利用本程序对珠蛋白、丝氨酸蛋白酶、天冬氨酸蛋白酶、钙结合蛋白和溶菌酶作了系统的结构比较,取得了满意的结果.本文也讨论了衡量联配结果好坏的要素问题.     

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top‐Down Mass Spectrometry

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top‐down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top‐down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum‐based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top‐down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome‐level proteoform analyses.     

Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis

PurposeTo assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.MethodsFour different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations.ResultsHighest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms.ConclusionsNone of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.     

C/C++ Coding for Matrix Pseudo Inverses in Clinical Near Infrared Spectroscopy

Near infrared spectroscopy is used clinically to investigate patterns of change in cerebral oxygenation. We have shown that differences reported between authors are likely the result of computer encoding errors in the manipulation of matrices. Current methods compute the inverse of a non-square matrix to derive chromophore concentration values, and solution of another non-square matrix to derive polynomial coefficients of a least squares best fit curve from which the first derivative can be used to estimate blood flow values. Encoding of these pseudo inverses involves too many nested looping steps to easily identify encoding errors. We have given C/C++ source code along with sample numerical values at the termination of each loop within the algorithm. This provides counter checking for future software development by other programmers, and also permits other investigators to report whether the software used for their experiments agrees with previously published material.