Within the field of protein structure prediction, the packing of α-helical proteins has been one of the more difficult problems. Distance constraints and topology predictions can be highly useful for reducing the conformational space that must be searched to find a protein structure of minimum conformational energy. We present a novel first principles framework to predict the structure of α-helical proteins. Given the location of the α-helical regions, a mixed-integer linear optimization model maximizes the interhelical residue contact probabilities to generate distance restraints between α-helices. A hybrid global optimization approach combines torsion angle dynamics with a deterministic global optimization technique (αBB) and a stochastic optimization technique (conformational space annealing) to minimize a detailed atomistic-level energy function subject to these constraints. Several improvements to this hybrid algorithm will also be described, including more robust initial point selection, the incorporation of side chain optimization techniques, and a streamlined parallel implementation. The proposed framework does not assume the form of the helices, so it is applicable to all α-helical proteins, including helices with kinks and irregular helices. The predictions of the proposed overall framework on a number of proteins, including the blind prediction of a four helical bundle designed from a combinatorial library, are presented.
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