Combined Methods for Protein-Structure Prediction

蛋白质结构预测的组合方法

• 批准号: 7450004
• 负责人: Kevin Karplus
• 金额: $ 19.73万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7450004
• 关键词: Amino Acid Sequence; Arts; Cells; Complex; Computer software; Computing Methodologies; Data; Disease; Documentation; Drug Delivery Systems; Drug Design; Freezing; Generations; Goals; Government; Homo; Homologous Gene; Homology Modeling; Internet; Methods; Modeling; Molecular; Molecular Conformation; Numbers; Operative Surgical Procedures; Output; Peptide Sequence Determination; Personal Satisfaction; Phase; Process; Property; Proteins; Research; Sampling; Score; Services; Site; Solutions; Structure; Techniques; Vertebral column; Week; Writing; X-Ray Crystallography; markov model; programs; protein structure; protein structure prediction; rapid growth; relating to nervous system; three dimensional structure; three-dimensional modeling; tool

DESCRIPTION (provided by applicant): Knowing the structure of proteins is key to figuring out their functions and the mechanisms by which they operate. This information is essential for drug target selection and drug design, as well as for fundamental understanding of both disease processes and the normal operation of cells. Unfortunately, experimental methods for determining protein structure cannot keep up with the rapid growth in protein sequence data, so computational methods are needed to predict the structure from the sequence data. The goals of this project are to produce the world's best program for automatic prediction of protein structure from sequence and to make the tool available to biologists, both on the web and as a distributable software package. The project combines three different, but complementary, approaches to protein-structure prediction: 1D prediction of local structural properties using neural nets, fold-recognition using hidden Markov models, and conformation generation and scoring using fragment packing and an emprical energy function.

描述（由申请人提供）：了解蛋白质的结构是找出其功能和操作机制的关键。该信息对于药物靶标和药物设计以及对疾病过程和细胞正常运作的基本了解至关重要。不幸的是，确定蛋白质结构的实验方法无法跟上蛋白质序列数据的快速增长，因此需要计算方法来预测序列数据的结构。该项目的目标是从序列中生成世界上最佳的蛋白质结构预测程序，并将工具用于生物学家，无论是在网络上还是作为可分布的软件包。该项目结合了蛋白质结构预测的三种不同但互补的方法：使用神经网络对局部结构特性的1D预测，使用隐藏的Markov模型进行折叠识别，以及使用片段包装和Emprical Enermal Energy函数的构象产生和评分。

项目成果

Model quality assessment using distance constraints from alignments.

• DOI: 10.1002/prot.22262
• 发表时间: 2009-05-15
• 期刊: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
• 影响因子: 2.9
• 作者: Paluszewski, Martin;Karplus, Kevin
• 通讯作者: Karplus, Kevin

Profile Comparer: a program for scoring and aligning profile hidden Markov models.

• DOI: 10.1093/bioinformatics/btn504
• 发表时间: 2008-11-15
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Madera, Martin

SAM-T08, HMM-based protein structure prediction.

• DOI: 10.1093/nar/gkp403
• 发表时间: 2009-07
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Karplus K

Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8.

• DOI: 10.1002/prot.22570
• 发表时间: 2009
• 期刊: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
• 影响因子: 2.9
• 作者: Krieger, Elmar;Joo, Keehyoung;Lee, Jinwoo;Lee, Jooyoung;Raman, Srivatsan;Thompson, James;Tyka, Mike;Baker, David;Karplus, Kevin
• 通讯作者: Karplus, Kevin