MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information.
MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information.
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作者：
Wu S;Zhang Y
我们开发了一个新的线程算法MUSTER通过扩展以前的序列轮廓轮廓比对方法，PPA。它将各种序列和结构信息组合成单体术语，可以方便地用于动态编程搜索：（1）序列概况;（2）二级结构;（3）结构片段概况;（4）溶剂可及性;（5）二面角扭转角;（6）疏水评分矩阵。通过基于111个训练蛋白质的平均TM分数的分级搜索来优化加权参数的平衡，该分级搜索显示出比使用基于PROTEINS数据库的常规优化方法更好的性能。该算法进行了测试500非同源蛋白质的独立的训练集。在去除与靶序列同一性> 30%的同源模板后，在224种情况下，第一模板比对具有正确的拓扑结构，TM评分>0.5。即使通过去除序列同一性>20%或可通过PSI-BLAST检测到的E值<0.05的模板来进行更严格的截止，MUSTER也能够在137个病例中鉴定正确的折叠，其中第一个模型的TM评分>0.5。依赖于同源性截断，通过MUSTER的第一个线程比对的平均TM-得分比通过PPA的高5.1-6.3%。通过Wilcoxon符号秩检验，这一改进具有统计学显著性，P值< 1.0 × 10−13，这证明了额外的结构信息对蛋白质折叠识别的影响。MUSTER服务器可在http://zhang.bioinformatics.ku.edu/MUSTER上免费提供给学术界。
We develop a new threading algorithm MUSTER by extending the previous sequence profile–profile alignment method, PPA. It combines various sequence and structure information into single-body terms which can be conveniently used in dynamic programming search: (1) sequence profiles; (2) secondary structures; (3) structure fragment profiles; (4) solvent accessibility; (5) dihedral torsion angles; (6) hydrophobic scoring matrix. The balance of the weighting parameters is optimized by a grading search based on the average TM-score of 111 training proteins which shows a better performance than using the conventional optimization methods based on the PROSUP data-base. The algorithm is tested on 500 nonhomologous proteins independent of the training sets. After removing the homologous templates with a sequence identity to the target >30%, in 224 cases, the first template alignment has the correct topology with a TM-score >0.5. Even with a more stringent cutoff by removing the templates with a sequence identity >20% or detectable by PSI-BLAST with an E-value <0.05, MUSTER is able to identify correct folds in 137 cases with the first model of TM-score >0.5. Dependent on the homology cutoffs, the average TM-score of the first threading alignments by MUSTER is 5.1–6.3% higher than that by PPA. This improvement is statistically significant by the Wilcoxon signed rank test with a P-value < 1.0 × 10−13, which demonstrates the effect of additional structural information on the protein fold recognition. The MUSTER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/MUSTER.
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DOI：
10.1002/prot.340230412
发表时间：
1995-12-01
期刊：
PROTEINS-STRUCTURE FUNCTION AND GENETICS
影响因子：
--
作者：
Frishman, D;Argos, P
通讯作者：
Argos, P
影响因子：
5.6
作者：
GODZIK, A;KOLINSKI, A;SKOLNICK, J
通讯作者：
SKOLNICK, J
影响因子：
14.9
作者：
Ginalski, K;Pas, J;Rychlewski, L
通讯作者：
Rychlewski, L
影响因子：
2.9
作者：
Battey, James N. D.;Kopp, Jurgen;Schwede, Torsten
通讯作者：
Schwede, Torsten
DOI：
10.1073/pnas.78.6.3824
发表时间：
1981-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
HOPP, TP;WOODS, KR
通讯作者：
WOODS, KR