  Physical interactions between proteins are often difficult to decipher. The
aim of this paper is to present an algorithm designed to recognize binding
patches and supporting structural scaffolds of interacting heterodimer protein
chains using the Gaussian Network Model (GNM). The recognition is based on the
(self)adjustable identification of kinetically hot residues, i.e., residues
with highest contributions to the weighted sum of fastest modes per chain
extracted via GNM, and their connection to possible binding scaffolds. The
algorithm adjusts the number of modes used in the GNM's weighted sum
calculation using the ratio of predicted and expected numbers of target
residues (contact and first layer residues). This approach produces very good
results when applied to chains forming heterodimers, especially with dimers
with high chain length ratios. The protocol's ability to recognize near native
decoys was compared to the ability of the statistical potential of Lu and
Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical
potential produced better overall results, but in a number of cases its
predicting ability was comparable, or even worse than the ability of the
adjustable GNM approach. The results presented in this paper suggest that in
heterodimers, at least one partnering chain has interacting scaffold determined
by the immovable kinetically hot residues. In many cases interacting chains
(especially if being of noticeably different sizes), either behave as rigid
lock and key, or exhibit opposite dynamic behaviors. While the binding surface
of one of the chains is rigid and stable, its partner's interacting scaffold is
more flexible and adaptable.
  Authors note: The approach described here was initially given as a rough
draft in 2013 [1]. The next manuscript will describe the behavior of protein
dimers incorrectly characterized with the present approach.
