Learning from biological context for protein fitness estimation and design

蛋白质，氨基酸序列，是生命的基本组成部分，是细胞过程的主力。它们的多种功能，从催化化学反应到提供结构支持，支撑着生物体的复杂机制。进化对所有可能的氨基酸序列进行了大规模的实验：编码功能性蛋白质的氨基酸存活了下来；那些不这样做的已经灭绝了。通过观察生命中一系列相关的蛋白质，我们可以开始了解其进化史，这是解开医学、生物技术和生物学各个领域众多进步的关键。该项目属于EPSRC人工智能技术研究领域，由哈佛大学Debbie Marks教授共同担任顾问。计算生物学家已经使用越来越复杂的统计模型来分析蛋白质的进化。最近，通过大型蛋白质语言模型，将模型扩展到整个蛋白质组已经成为可能，这些模型旨在揭示生命的语言。这些模型已经被证明概括了蛋白质的进化，或系统发育，即使相关的同源物的集合很小。该项目旨在利用从上下文学习和非参数建模到蛋白质统计建模领域的新方法。在上下文学习允许模型从上下文中学习，例如通过添加一些示例。非参数建模允许模型从明确的数据点中学习，而不必在参数化权重中记忆整个数据集。通过结合这些方法，目的是更好地利用和检索由蛋白质进化提供的上下文，以相同的计算成本提高性能。这些方法将允许更好地研究不可剥夺的蛋白质序列，如无序区域或抗体，以及模拟序列的插入和删除。这种方法的发展既可以量化给定蛋白质序列的致病性，诊断疾病，也可以优化其功能序列，与用于化学过程和药物开发的蛋白质生物工程相关。

Proteins, sequences of amino acids, are the fundamental building blocks of life, serving as the workhorses of cellular processes. Their diverse functions, from catalysing chemical reactions to providing structural support, underpin the complex machinery of living organisms. Evolution has conducted a massive experiment over the space of all possible amino acid sequences: those that encode a functional protein survive; those that don't are extinct. By looking at a set of related proteins throughout life, we can begin to understand its evolutionary history, key to unlocking numerous advancements in medicine, biotechnology, and various fields of biology. This project falls within the EPSRC Artificial Intelligence Technologies research area and is co advised by Professor Debbie Marks at Harvard University.Computational biologists have used more and more complex statistical models to analyse protein evolution. Extending the models to the whole proteome has recently been made possible by large protein language models, that aim to uncover the language of life. These models have been shown to recapitulate protein evolution, or phylogeny, even when the set of related homologs is small. The project aims to leverage new methodologies from the fields of in-context learning and non-parametric modelling to protein statistical modelling. In context learning allows models to learn from context, for example by the addition of a few examples. Nonparametric modelling allows the model to learn from explicit data points instead of having to memorize an entire dataset in its parametrised weights. By combining these methods, the aim is to better leverage and retrieve the context provided by protein evolution, to improve performance at same compute cost. These methods will allow to better study unalienable protein sequence such as disordered regions or antibodies, as well as to model insertion and deletion of sequences. The development of such method allows to both quantify pathogenicity of a given protein sequence, to diagnose disease as well as optimize a sequence for its function, with relevance to bioengineering of proteins for chemical processes and drug development.