Abstract
Protein-protein docking is a challenging computational problem in
functional genomics, particularly when one or both proteins undergo
conformational change(s) upon binding. The major challenge is to define
scoring function soft enough to tolerate these changes and specific
enough to distinguish between near native and ‘misdocked’ conformations.
Using a linear programming (LP) technique, we derived protein docking
potentials (PDPs) that comply with this requirement. We considered a set
of 63 non-redundant complexes to this aim, and generated 400,000 putative
docked complexes (decoys) based on shape complementarity criterion for
each complex. The PDPs were required to yield for the native (correctly
docked) structure a potential energy lower than those of all the
non-native (misdocked) structures. The energy constraints applied to all
complexes led to a total of ca. 25 million inequalities, the
simultaneous solution of which yielded an optimal set of PDPs that
discriminated the correctly docked (up to 4.0
Å
root-mean-square deviation from known complex structure) structure among
the 85 top-ranking (0.02%) decoys in 59/63 examined bound-bound cases.
The high performance of the potentials was further verified in jackknife
tests and by ranking putative docked conformation submitted to CAPRI. In
addition to their utility in identifying correctly folded complexes, the
PDPs reveal biologically meaningful features that distinguish docking
potentials from folding potentials.
