COMPUTATIONAL LEARNING & DISCOVERY FOR BIOLOGICAL SEQUENCE, STRUCTURE, FUNCTION

本子项目是利用由NIH/NCRR资助的中心赠款提供的资源的众多研究子项目之一。子项目和研究者（PI）可能已经从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。列出的机构是中心的，不一定是研究者的机构。在蛋白质结构领域的七个重点领域已经确定了语言类比方法的应用。这些重点领域是：蛋白质折叠，构象变化，蛋白质-蛋白质相互作用，蛋白质/基因网络和途径，二级结构和重复折叠预测和分割，蛋白质家族分类和基因组比较。最终目标是为每一种语言开发语言模型，以促进对这些领域的理解。在这个过程中遵循的协议包括几个步骤。第一步是利用现有的“基准”数据集或定义适合这些模型的训练和测试的数据集。作为控制，研究重点领域的现有方法（如果可用），并设计一个方案来评估语言模型方法，并将它们与现有的其他方法进行比较。下一步是实现我们的语言方法。该实现最初需要满足两个要求中的一个或两个：(i)系统必须与步骤2中定义的现有系统一样好或更好，和/或（ii）它需要提供可解释的生物学假设。例如，神经网络可能是分类任务中性能最好的算法，但导致这种性能的底层特征可能不清楚。基于语言的方法可能性能较差，但允许研究人员分析导致成功分类的特征类型，可用于在蛋白质序列语言的基本构建块上建立假设。协议的最后一步是设计和实施专门测试这些假设的实验。本文选择以下系统作为语言模型的实验测试用例：视紫红质等G蛋白偶联受体（GPCR）、代谢性谷氨酸受体、表皮生长因子受体、病毒尾刺蛋白、病毒感染过程、肽n-grams。对于这七个重点领域中的每一个，我们都在努力确定或开发用于语言模型训练和测试的基准数据集。学生和博士后参与项目的各个方面。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Seven focus areas in the realm of protein structure have been identified for application of the language analogy approach. These focus areas are: protein folding, conformational changes, protein-protein interactions, protein/gene networks and pathways, secondary structure and repetitive folds prediction and segmentation, protein family classification, and genome comparison. The ultimate goal is to develop linguistic models for each that are capable of advancing the understanding of these areas. The protocol followed in this process consists of several steps. The first step is to utilize existing ¿benchmark ¿ datasets or to define datasets suitable for training and testing of these models. As controls, existing approaches in the focus areas, if available, are studied and a scheme is designed for evaluating the language model approaches and comparing them to existing other approaches. The next step is to implement our language approach. This implementation initially needs to meet one or both of two requirements: (i) the system has to perform equally well or better than existing systems as defined in step 2 and/or (ii) it needs to provide interpretable biological hypotheses. For example, a neural network might be the algorithm with best performance in a classification task, but the underlying features resulting in this performance can be unclear. A language-based approach that might have lesser performance but allows the researcher to analyze the types of features that result in successful classification can be used to build hypotheses on the fundamental building blocks of protein sequence language. The final step in the protocol is to design and carry out experiments that specifically test these hypotheses. The following systems have been chosen as experimental test cases for the language models: G protein coupled receptors (GPCR) such as rhodopsin, metabotropic glutamate receptors, epidermal growth factor receptor, viral tailspike protein, virus infection process, peptide n-grams. For each of the seven focus areas, we are working to identify or develop benchmark datasets for training and testing of linguistic models. Students and postdoctoral fellows participate in all aspects of the projects.