(485e) A Novel Approach for Alpha-Helical Topology Prediction in Globular Proteins

The protein folding question has developed over the past four decades as one of the most challenging and potentially rewarding problems in computational biology. Three general classes of algorithms have emerged, based on the techniques of comparative modeling, fold recognition, and first principles methods. For a detailed summary of protein structure prediction methods, the reader is directed to two recent reviews[1,2]. Within the field of protein structure prediction, the packing of α-helical proteins has been one of the more difficult problems. The use of distance constraints and topology predictions can be highly useful for reducing the conformational space that must be searched by deterministic algorithms to find a protein structure of minimum conformational energy.

We present a novel first principles framework to predict the structure of α-helical proteins that includes three main stages. 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[3]. Two levels of this formulation allow the prediction of both "primary" contacts between a helical pair as well as the prediction of "wheel" contacts, one helical turn beyond the primary contacts. These predictions are subject to a number of mathematical constraints to disallow sets of contacts that cannot be achieved by a folded protein. The analysis of loop structures with flexible stem regions is then performed by dihedral angle sampling, structure optimization by energy minimization with a physically-based energy function, clustering, and a selection strategy based on discarding conformers that are far from the native structure[4]. The distance restraints from the interhelical contacts are then combined with dihedral angle restraints from the loop analysis to restrict the feasible space of the protein during the prediction of the tertiary structure using a hybrid optimization algorithm[5]. This tertiary structure prediction approach combines torsion angle dynamics with a deterministic global optimization technique (aBB) and a strochastic optimization technique (conformational space annealing) to minimize a detailed atomistic-level energy function. 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 interhelical contact prediction optimization model was evaluated on 26 proteins, where it identified an average contact distance below 11.0 Å for the entire set. The predictions of the proposed overall framework on an extensive test set will be presented.

[1] Floudas CA, Fung HK, McAllister SR, Mönningmann M, and Rajgaria R. Advances in Protein Structure Prediction and De Novo Protein Design: A Review. Chem Eng Sci. 2006;61: 966-988.

[2] Floudas CA. Research Challenges, Opportunities and Synergism in Systems Engineering and Computational Biology. AIChE J. 2005;51:1872-1884.

[3] McAllister SR, Mickus BE, Klepeis JL, and Floudas CA. A Novel Approach for α-Helical Topology Prediction in Globular Proteins: Generation of Interhelical Restraints. 2006. Submitted.

[4] Mönnigmann M and Floudas CA. Protein Loop Structure Prediction with Flexible Stem Geometries. Prot Struct Funct Bioinf. 2005;61:748-762.

[5] Klepeis JL and Floudas CA. ASTRO-FOLD: A Combinatorial and Global Optimization Framework for Ab Initio Prediction of Three-dimensional Structures of Proteins from the Amino Acid Sequence. Biophys J, 2003;85:2119-2146.