Thats why we call peaks (significant binding sites) and overlap them for the different proteins/TF's of interest, to check the similarity.

You can subset them for a specific region of interest or whole genome. Tools like Bedtools Compare Multiple Bed Files? , Macs and in general, R/Bioconductor are helpful to move forward to specifically what you want.

You can also try few papers in regard :

A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples

An effective statistical evaluation of ChIPseq dataset similarity

Edit: Now you have edited the question, so

• If you think the proteins are a part or the potential subunits of the same complex, their binding sites might mostly overlap.
• Regarding shape, you cannot always be completely sure, because of the noise in data, but you can infer cases like, proteinA has normal distribution but proteinB is giving a normal and a small right skewed distribution. It might mean, its extends to genebody (if looking at TSS + depending on the genomic locus you take into account as reference).
• If you make composite profile (abundance profile) of a protein, it can tell you where its mostly abundant (TSS, Genebody, TTS etc.)
• For other deep analysis, you have to play with data more deeply apart from the visual analysis of the peak shapes.

Cheers