MALDI Deamidation Score (MDS): A Fast and Flexible Method for Assessing Deamidation in ZooMS Data and Its Application to the Denisova Cave Bone Assemblage

Abstract

Estimating deamidation from MALDI-TOF MS spectra of bones has most frequently been achieved using the q2e method due to its high-throughput capacity and ease of use. Despite this accessibility, q2e is only capable of estimating deamidation at the peptide level on a fixed peptide list and does not generate a sample-wide summary. The introduction of the Parchment Glutamine Index (PQI) presented an alternative method for deamidation estimation. Initially designed for a large ZooMS dataset of parchment, it utilises weighted least squares and a linear mixed-effects model (LME) to generate deamidation estimation on both the peptide and sample level. To address the limitations of q2e and expand the applicability of PQI to a wider range of archaeological tissues and MALDI-derived data (such as ZooMS data on bone collagen), we developed the MALDI Deamidation Score (MDS), an iteration of the PQI method optimized for handling large-scale datasets. Compared to PQI, MDS is more streamlined for the analysis of multi-species data with customisable peptide lists, offering a dramatically decreased processing time while being able to normalise the peak intensity for higher accuracy. Through a case study on the published Denisova Cave ZooMS assemblage, we demonstrate that different peptides exhibit varying deamidation patterns over time, making the use of a single peptide to represent overall deamidation potentially biased. Such information is invaluable for investigating key questions such as protein preservation and site formation processes, especially when contextualized with other lines of archaeological evidence.