Peptides Count (SC) was widely used for protein abundance estimation in proteomics. On the basis of that, Mann and co-workers corrected the SC by dividing spectrum counts by the number of observable peptides per protein and named it PAI. Here we present modified spectral count index (mSCI) for protein abundance estimation, which was defined as the number of observed peptides divided by protein relative identification possibility (RIPpro). RIPpro was derived from 6788 mRNA and protein expression data (collected from human liver samples) and related to proteins' three physical and chemical properties (MW/pI/Hp). For 46 proteins in mouse neuro2a cells, mSCI shows a linear relationship with the actual protein concentration, similar or better than PAI abundance. Also, multiple linear regressions were performed to quantitative assess several factors' impact on the mRNA/protein abundance correlation. Our results shown that the primary factor affecting protein levels was mRNA abundance (32-37%), followed by variability in protein measurement, MW and protein turnover (7-12%,7-9% and 2-3%, respectively). Interestingly, we found that the concordance between mRNA transcripts and protein expression was not consistent among all protein functional categories. This correlation was lower for signaling proteins as compared to metabolism genes. It was determined that RIPpro was the primary factor affecting signaling protein abundance (23% on average), followed by mRNA abundance (17%). In contrast, only 5% (on average) of the variability of metabolic protein abundance was explained by RIPpro, much lower than mRNA abundance (40%). These results provide the impetus for further investigation of the biological significance of mechanisms regulating the mRNA/protein abundance correlation and provide additional insight into the relative importance of the technological parameter (RIPpro) in mRNA/protein correlation research.