Automatic consensus-based fold recognition using Pcons, ProQ, and pmodeller
Automatic consensus-based fold recognition using Pcons, ProQ, and pmodeller
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DOI：
10.1002/prot.10536
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发表时间：
2003-01-01
影响因子：
2.9
通讯作者：
Elofsson, A
中科院分区：
文献类型：
--
作者：
Wallner, B;Fang, HS;Elofsson, A
CASP提供了一个独特的机会，可以将自动折叠识别方法的性能与可能使用这些方法的手动专家的性能进行比较。在这里，我们表明，新颖的自动折叠识别服务器Pmodeller正在接近手动专家的性能。尽管一小群专家仍然表现更好，但参加CASP5的大多数专家实际上表现较差，即使他们可以完全访问所有自动预测。 PMODELLER基于PCON（Lundstrom等，Protein Sci 2001; 10（11）：2354-2365）是使用许多其他服务器的预测的第一个“共识”预测指标。因此，Pmodeller和其他共识服务器的成功应被视为向所有折叠识别服务器的开发人员的集体致敬。此外，我们表明，包括另一种新型方法ProQ（2）来评估蛋白质模型的质量可改善预测。 （c）2003 Wiley-Liss，Inc。
CASP provides a unique opportunity to compare the performance of automatic fold recognition methods with the performance of manual experts who might use these methods. Here, we show that a novel automatic fold recognition server, Pmodeller, is getting close to the performance of manual experts. Although a small group of experts still perform better, most of the experts participating in CASP5 actually performed worse even though they had full access to all automatic predictions. Pmodeller is based on Pcons (Lundstrom et al., Protein Sci 2001; 10(11):2354-2365) the first "consensus" predictor that uses predictions from many other servers. Therefore, the success of Pmodeller and other consensus servers should be seen as a tribute to the collective of all developers of fold recognition servers. Furthermore we show that the inclusion of another novel method, ProQ(2), to evaluate the quality of the protein models improves the predictions. (C) 2003 Wiley-Liss, Inc.