Collaborative Project: ABI Innovation: Computational Identification & Screening for Deleterious Mutants

合作项目：ABI 创新：计算识别

• 批准号: 1661391
• 负责人: Robert Jernigan
• 金额: $ 88.31万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661391&HistoricalAwards=false
• 关键词: Collaborative; Project; ABI; Innovation; Computational

When interpreting the effect of changes in genome sequences on their protein products, what are the criteria for separating the beneficial from the neutral and destructive mutations? It is not practical to experimentally test all possible changes; it is possible to develop computational rules and methods to accurately predict the effect of most changes, reserving experimental testing for borderline cases. The rules that constrain functional protein sequences, the structures into which they fold, the stability of the structures and the transitions that occur can be organized into an informatics framework that helps distinguish functional from non-functional mutations. The stability and dynamic transitions of protein structures over many mutations are the focus of this research, for which new evaluation methods will be developed and tested. Once the assessment methodologies are validated they will be integrated, along with the raw data and assessment results, within a single, efficient computational platform. The ability to interpret new data and compare it to existing results will not only allow discrimination of beneficial, neutral and deleterious mutations, but provides a resource for a protein-sequence-structure-based understanding of evolution. Informed selection rules will enable better decisions about the importance of saving individual members of endangered species, as well as how different environments affect selection in individual species. There is a large body of data in protein sequences and structures that can aid in understanding evolution, which is not currently being used. This project will utilize this data in systematic ways to shed new light on evolution. Protein structures are often modeled in different computational ways; the evaluation of these structure models is critical for expanding the set of reliable structures, since there are huge numbers of possible mutant sequences. Changes to the protein sequences and structures are not fully understood. Part of the difficulty lies in understanding the interactions within their densely packed structures, which have significant interdependences. Developing new ways to evaluate the effects of dense packing on protein structures is one of the project?s aims. Focusing on the physically interacting clusters within protein structures together with their sequence variants provides rich information about the correlations among amino acid substitutions. This rich data will then significantly advance the ability to distinguish between the important and the unimportant mutations. Progress has been seen with these approaches in the evaluations of predicted structure models at the CASP competitions for protein structure prediction. These new approaches lend themselves directly to the evaluation of the stabilities of different protein mutants. The evaluation procedures are derived directly from the available sets of protein structures and carefully tested against other known structures. In one important innovation these now include entropies that account for known changes in the structures of individual proteins, i.e. their dynamics. Capturing these tendencies for changes significantly improves the evaluation of the stabilities of proteins. Applications to sets of mutants show that unfavorable mutants are either more stable or less stable than the normal cases. These changes in stability directly affect the ways in which the proteins can move to carry out their functions; evaluating these changes significantly aids the understanding of protein mutations. This project will yield a uniform way to reliably assess the effects of protein mutations. This ability will significantly aid in the understanding of many aspects of evolution that remain.

在解释基因组序列变化对其蛋白质产物的影响时，区分有益突变与中性突变和破坏性突变的标准是什么？通过实验测试所有可能的变化是不切实际的;有可能开发计算规则和方法来准确预测大多数变化的影响，保留边界情况的实验测试。限制功能性蛋白质序列的规则，它们折叠的结构，结构的稳定性和发生的转换可以组织成一个信息学框架，有助于区分功能性和非功能性突变。蛋白质结构在许多突变中的稳定性和动态转变是本研究的重点，为此将开发和测试新的评估方法。一旦评估方法得到验证，这些方法将与原始数据和评估结果一起沿着纳入一个单一的高效计算平台。解释新数据并将其与现有结果进行比较的能力不仅可以区分有益的，中性的和有害的突变，而且还为基于蛋白质序列结构的进化理解提供了资源。知情的选择规则将有助于更好地决定拯救濒危物种个体成员的重要性，以及不同环境如何影响个体物种的选择。蛋白质序列和结构中有大量的数据可以帮助理解进化，这些数据目前还没有被使用。该项目将以系统的方式利用这些数据来揭示进化的新观点。 蛋白质结构通常以不同的计算方式建模;这些结构模型的评估对于扩展可靠结构集至关重要，因为存在大量可能的突变序列。蛋白质序列和结构的变化尚未完全了解。部分困难在于理解它们密集结构中的相互作用，这些结构具有显着的相互依赖性。 开发新的方法来评估蛋白质结构的密集包装的影响是项目之一？的目标。关注蛋白质结构中的物理相互作用簇及其序列变体提供了关于氨基酸取代之间相关性的丰富信息。这些丰富的数据将大大提高区分重要和不重要突变的能力。在CASP蛋白质结构预测竞赛中，这些方法在预测结构模型的评估中取得了进展。 这些新方法直接适用于不同蛋白质突变体稳定性的评估。评估程序直接来自可用的蛋白质结构集，并针对其他已知结构进行仔细测试。在一个重要的创新中，这些现在包括熵，它解释了单个蛋白质结构的已知变化，即它们的动力学。 捕捉这些变化趋势显著改善了对蛋白质稳定性的评估。 应用于突变体的集合表明，不利的突变体比正常情况下更稳定或更不稳定。 这些稳定性的变化直接影响蛋白质可以移动以执行其功能的方式;评估这些变化显着有助于理解蛋白质突变。 该项目将产生一种统一的方法来可靠地评估蛋白质突变的影响。 这种能力将大大有助于理解进化的许多方面。

项目成果

Characterizing and Predicting Protein Hinges for Mechanistic Insight

• DOI: 10.1016/j.jmb.2019.11.018
• 发表时间: 2020-01-17
• 期刊: JOURNAL OF MOLECULAR BIOLOGY
• 影响因子: 5.6
• 作者: Khade, Pranav M.;Kumar, Ambuj;Jernigan, Robert L.
• 通讯作者: Jernigan, Robert L.

Robust Sampling of Defective Pathways in Multiple Myeloma.

多发性骨髓瘤缺陷通路的稳健采样。

• DOI: 10.3390/ijms20194681
• 发表时间: 2019
• 期刊: International journal of molecular sciences
• 影响因子: 5.6
• 作者: Fernández-Martínez,JuanLuis;deAndrés-Galiana,EnriqueJ;Fernández-Ovies,FranciscoJavier;Cernea,Ana;Kloczkowski,Andrzej
• 通讯作者: Kloczkowski,Andrzej

Principal component analysis in protein tertiary structure prediction

• DOI: 10.1142/s0219720018500051
• 发表时间: 2018-04-01
• 期刊: JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
• 影响因子: 1
• 作者: Alvarez, Oscar;Luis Fernandez-Martinez, Juan;Kloczkowski, Andrzej
• 通讯作者: Kloczkowski, Andrzej

On the use of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure Prediction

主成分分析和粒子群优化在蛋白质三级结构预测中的应用

• 发表时间: 2018
• 期刊: Lecture notes in computer science
• 作者: Álvarez Ó., Fernández-Martínez J.L.

Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques

• DOI: 10.3390/molecules25112467
• 发表时间: 2020-06-01
• 期刊: MOLECULES
• 影响因子: 4.6
• 作者: Alvarez-Machancoses, Oscar;Luis Fernandez-Martinez, Juan;Kloczkowski, Andrzej
• 通讯作者: Kloczkowski, Andrzej