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

在解释基因组序列变化对蛋白质产物的影响时，区分有益突变和中性突变和破坏性突变的标准是什么？从实验上测试所有可能的变化是不现实的；可以开发计算规则和方法来准确预测大多数变化的影响，将实验测试保留到边缘情况下。约束功能性蛋白质序列的规则、它们折叠成的结构、结构的稳定性以及发生的转变可以组织成一个信息学框架，帮助区分功能性突变和非功能性突变。蛋白质结构在许多突变情况下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，它们将与原始数据和评估结果一起集成到一个高效的计算平台中。解释新数据并将其与现有结果进行比较的能力不仅允许区分有益的、中性的和有害的突变，而且为基于蛋白质序列结构的进化理解提供了资源。知情的选择规则将使人们能够更好地决定拯救濒危物种个体成员的重要性，以及不同的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，而目前还没有使用这些数据。这个项目将以系统的方式利用这些数据，为进化提供新的线索。蛋白质结构通常以不同的计算方式建模；由于存在大量可能的突变序列，因此对这些结构模型的评估对于扩展可靠的结构集至关重要。蛋白质序列和结构的变化还没有完全被理解。部分困难在于理解它们密集堆积结构中的相互作用，这些结构具有显著的相互依存关系。S的目标之一是开发新的方法来评估密集堆积对蛋白质结构的影响。关注蛋白质结构内的物理相互作用的簇及其序列变体提供了关于氨基酸替换之间的相关性的丰富信息。这些丰富的数据将显著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。这些新方法直接用于评估不同蛋白质突变体的稳定性。评估程序直接来自可用的蛋白质结构集，并与其他已知结构进行了仔细的测试。在一项重要的创新中，现在包括了解释单个蛋白质结构已知变化的熵，即它们的动力学。捕捉这些变化趋势可以显著提高对蛋白质稳定性的评估。对一组突变的应用表明，不利的突变要么比正常情况更稳定，要么比正常情况更不稳定。这些稳定性的变化直接影响蛋白质移动以执行其功能的方式；评估这些变化显著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。这一能力将极大地帮助理解进化的许多方面。

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.