From Protein Structure to Function with Bioinformatics.pdf

10 J. Lee et al.Although most knowledge-based force fields contain secondary structure propensitypropensities, it may be that local protein structures are rather difficult toreproduce in the reduced modelling. That is, in nature a variety of protein sequencesprefer either helical or extended structures depending on the subtle differences intheir local and global sequence environment, yet we have not yet found force fieldsthat can reproduce this subtlety properly. One way to circumvent this problem is touse secondary structure fragments, obtained from sequence or profile alignments,directly into 3D model assembly. Another advantage of this approach is that the useof excised secondary structure fragment can significantly reduce the entropy of theconformational search.Here, we introduce two prediction methods utilizing knowledge-based energyfunctions, which are proved to be the most successful in ab initio protein structureprediction (Simons et al. 1997; Zhang and Skolnick 2004a).One of the best-known ideas for ab initio modelling is probably the one pioneeredby Bowie and Eisenberg, who generated protein models by assemblingsmall fragments (mainly 9-mers) taken from the PDB library (Bowie and Eisenberg1994). Based on a similar idea, Baker and coworkers developed ROSETTA (Simonset al. 1997), which was extremely successful for the free modelling (FM) targets inCASP experiments and made the fragment assembly approach popular in the field.In the recent developments of ROSETTA (Bradley et al. 2005; Das et al. 2007), theauthors first generated models in a reduced form with conformations specified withheavy backbone and C βatoms. In the second phase, a set of selected low-resolutionmodels were subject to all-atom refinement procedure using an all-atom physicsbasedenergy function, which includes van der Waals interactions, pair wise solvationfree energy, and an orientation-dependent hydrogen-bonding potential. Theflowchart of the two-phase modelling is shown in Fig. 1.2 and details on the energyfunctions can be found in references (Bradley et al. 2005; Das et al. 2007). For theconformational search, multiple rounds of Monte Carlo minimization (Li andScheraga 1987) are carried out. The most notable example for this two-step protocolis the blind prediction of an ab initio target (T0281 from CASP6, 70 residues),whose C αRMSD from its crystal structure is 1.6 Å (Bradley et al. 2005). In CASP7,a very extensive sampling was carried out using the distributed computing networkof Rosetta@home allowing about 500,000 CPU hours for each target domain.There was one target, T0283, which was a template-based modelling (TBM) targetbut was modeled by the ROSETTA ab initio protocol. It generated a model ofRMSD = 1.8 Å over 92 residues out of the 112 residues (Fig. 1.3, left panel).Despite the significant success, the computational cost of the procedure is ratherexpensive for routine use.Partially because of the notable success of the ROSETTA algorithm, as well asthe limited availability of its energy functions to others, several groups initiateddevelopments of their own energy functions following the idea of ROSETTA.Derivatives of ROSETTA include Simfold (Fujitsuka et al. 2006) and Profesy (Leeet al. 2004); their energy terms include van der Waals interactions, backbone dihedralangle potentials, hydrophobic interactions, backbone hydrogen-bonding potential,rotamer potential, pair wise contact energies, beta-strand pairing, and a term