A machine learning approach to identify carbon dioxide-binding proteins for sustainability and health

二氧化碳是地球上最重要的气体之一，也是生命的绝对需求。地球面临着大气二氧化碳的长期增加，预计这将对农作物产生重大的生理影响。因此，了解二氧化碳生物学具有迫在眉睫的战略意义。然而，用于二氧化碳传感的蛋白质靶标几乎完全未知。我们将开发一种计算方法来识别蛋白质中的所有二氧化碳结合位点。该项目将使我们第一次洞察二氧化碳-蛋白质相互作用的程度，并提供工具来彻底改变我们对二氧化碳生物学的理解。二氧化碳可以通过中性N端赖氨酸e-氨基的氨甲基化自发形成可逆的蛋白质翻译后修饰。我们开发了一种化学蛋白质组学技术，用于捕获蛋白质氨基甲酸酯的共价，使其能够通过质谱学(MS)进行鉴定。虽然有效，但这种技术也是人力和资金成本高昂的。在此背景下，本项目有以下目标：1.开发一个机器学习模型来识别可能的赖氨酸氨甲酰化位点。初步结果表明，用我们的实验数据和文献中的数据训练的机器学习工具，应该能够准确地识别可能形成氨基甲酸酯的赖氨酸。具体地说，我们已经证明了结构依赖赖氨酸pKaH、溶剂可及性和二氧化碳结合之间的联系。对于我们实验数据集中的每个赖氨酸，我们将利用PDB和AlphaFold数据库来提取其他特征(本地氨基酸环境、深度和预测的动态)。这些数据将被用来训练神经网络分类器。为了确定哪些位点是生物相关的，我们将探索纳入序列保护数据的可能性。预测蛋白质组范围内的氨甲基化位点。我们训练的分类器将首先应用于高等植物。最有可能的候选人将接受实验验证。如果这些预测被证明是准确的，我们将把我们的预测扩展到每一种已知的蛋白质。这些最终数据和经过训练的分类器都将免费提供给社区。了解氨甲酰化的生物学作用。我们将选择在二氧化碳代谢中具有已知作用的蛋白质和经过实验验证的氨甲酰化位点。我们将进行多长分子动力学模拟，以表征二氧化碳结合如何影响蛋白质结构/动力学。该项目的输出将是一个所有已知二氧化碳结合位点的数据库(来自现有的结构数据)，以促进对改变的二氧化碳的分子后果的理解，以及一个软件工具(也可以通过网络服务器获得)，以便能够根据未来的结构数据预测二氧化碳结合位点。

Carbon dioxide is one of the most important gases on Earth and an absolute requirement for life. The planet faces long term increases in atmospheric CO2, predicted to have a significant physiological impact on crops. Therefore, understanding CO2 biology is of pressing strategic importance. However, protein targets for CO2 sensing are almost wholly unknown. We will develop a computational approach to identify all CO2-binding sites in proteins. The project will provide our first insight into the extent of CO2-protein interactions and supply tools to revolutionise our understanding of CO2 biology.CO2 can spontaneously form a reversible protein post-translational modification through carbamylation of neutral N-terminal lysine e-amino groups.We have developed a chemical proteomics technology for covalent trapping of protein carbamates that allows their identification by mass spectrometry (MS). While effective, this technique is also labour and capital expensive. In this context, this project has the following aims:1. Developing a machine learning model to identify putative lysine carbamylation sites. Preliminary results indicate that a machine learning tool, trained with our experimental data and data from the literature, should be able to accurately identify lysines likely to form a carbamate. Specifically, we have demonstrated a link between structure-dependent lysine pKaH, solvent accessibility, and CO2-binding. For each lysine in our experimental dataset, we will leverage on the PDB and AlphaFold databases to extract additional features (local amino acid environment, depth, and predicted dynamics). This data will be used to train a neural network classifier. To identify which sites are biologically relevant, we will explore the possibility of incorporating sequence conservation data.2. Predicting proteome-wide carbamylation sites. Our trained classifier will first be applied to higher plants. Most likely candidates will be experimentally verified. Should these predictions be demonstrated as accurate, we will expand our prediction to every known protein. Both this final data and the trained classifier will be made freely available to the community.3. Understanding the biological role of carbamylation. We will select proteins with known roles in CO2 metabolism and experimentally validated carbamylation sites. We will carry out multi-long molecular dynamics simulations to characterise how CO2 binding affects protein structure/dynamics.The project outputs will be a database of all known CO2-binding sites (derived from available structural data) to promote understanding of the molecular consequences of altered CO2 and a software tool (also available via webserver) to enable the prediction of CO2-binding sites from future structural data.