Covalent Labeling\nAutomated Data Analysis Platform\nfor High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis\nPlatform for Covalent Labeling Experiments

Covalent labeling methods coupled to mass spectrometry\nhave emerged\nin recent years for studying the higher order structure of proteins.\nQuantifying the extent of modification of proteins in multiple states\n(i.e., ligand free vs ligand-bound) can provide information on protein\ninteraction sites and regions of conformational change. Though there\nare several software platforms that are used to quantify the extent\nof modification, the process can still be time-consuming, particularly\nfor proteome-wide studies. Here, we present an open-source software\nfor quantitation called Covalent labeling Automated Data Analysis\nPlatform for high Throughput in R (coADAPTr). coADAPTr tackles the\nneed for more efficient data analysis in covalent labeling mass spectrometry\nfor techniques such as hydroxyl radical protein footprinting (HRPF).\nTraditional methods like Excel’s Power Pivot (PP) are cumbersome\nand time-intensive, posing challenges for large-scale analyses. coADAPTr\nsimplifies analysis by mimicking the functions used in the previous\nquantitation platform using PowerPivot in Microsoft Excel but with\nfewer steps, offering proteome-wide insights with enhanced graphical\ninterpretations. Several features have been added to improve the fidelity\nand throughput compared to those of PowerPivot. These include filters\nto remove any duplicate data and the use of the arithmetic mean rather\nthan the geometric mean for quantitation of the extent of modification.\nValidation studies confirm coADAPTr’s accuracy and efficiency\nwhile processing data up to 200 times faster than conventional methods.\nIts open-source design and user-friendly interface make it accessible\nfor researchers exploring intricate biological phenomena via HRPF\nand other covalent labeling MS methods. coADAPTr marks a significant\nleap in structural proteomics, providing a versatile and efficient\nplatform for data interpretation. Its potential to transform the field\nlies in its seamless handling of proteome-wide data analyses, empowering\nresearchers with a robust tool for deciphering complex structural\nbiology data.