Hardware Software Synergy in Proteomics: Systematic Evaluation Across Mass Spectrometry Platforms, Search Engines, and Rescoring Methods

Mass spectrometry-based proteomics has advanced through parallel improvements in hardware (instrumentation) and software (data analysis), yet whether these improvements interact synergistically or provide diminishing returns remains unclear. Here, we systematically evaluate hardware-software co-evolution across eight mass spectrometry platforms, three generations of search engines, and multiple rescoring approaches, yielding 72 unique hardware–software combinations spanning from 2004–2024. Our results reveal that hardware and software improvements produce synergistic rather than substitutive benefits. Here, machine learning-based rescoring consistently identifies peptides from low-intensity precursors that produce noisier, more challenging spectra. Crucially, because of increased sensitivity and scan speed, modern instruments detect more low-intensity precursors, increasing the population of challenging spectra where rescoring provides the greatest benefit. However, this expanded detection depth comes at a cost: recovered low-abundance peptides exhibit inherently higher quantification error, creating a fundamental trade-off between proteome coverage and quantification accuracy. Together, these findings provide a systematic overview of how hardware and software advances have jointly shaped proteomics performance over the past two decades.