Fold recognition and accurate sequence–structure alignment of sequences directing β‐sheet proteins

Abstract

The ability to predict structure from sequence is particularly important for toxins, virulence factors, allergens, cytokines, and other proteins of public health importance. Many such functions are represented in the parallel beta-helix and beta-trefoil families. A method using pairwise beta-strand interaction probabilities coupled with evolutionary information represented by sequence profiles is developed to tackle these problems for the beta-helix and beta-trefoil folds. The algorithm BetaWrapPro employs a "wrapping" component that may capture folding processes with an initiation stage followed by processive interaction of the sequence with the already-formed motifs. BetaWrapPro outperforms all previous motif recognition programs for these folds, recognizing the beta-helix with 100% sensitivity and 99.7% specificity and the beta-trefoil with 100% sensitivity and 92.5% specificity, in crossvalidation on a database of all nonredundant known positive and negative examples of these fold classes in the PDB. It additionally aligns 88% of residues for the beta-helices and 86% for the beta-trefoils accurately (within four residues of the exact position) to the structural template, which is then used with the side-chain packing program SCWRL to produce 3D structure predictions. One striking result has been the prediction of an unexpected parallel beta-helix structure for a pollen allergen, and its recent confirmation through solution of its structure. A Web server running BetaWrapPro is available and outputs putative PDB-style coordinates for sequences predicted to form the target folds.

Crystal structure of the Helicobacter pylori vacuolating toxin p55 domain

“…4c Inset) (24). Second, the program BetaWrapPro identifies the stretch of amino acids between residues 120 and 249 as a five-coil ␤-helix and predicts additional ␤-helix strands in the region between 252 and 288 (49). BetaWrapPro uses profile wrapping for prediction and comparative modeling of ␤-helices and has been shown to identify the ␤-helix motif with high sensitivity and selectivity (49).…”

“…Secondary structure prediction analyses suggest that p33 has ␣-helical structural elements between residues 1 and 71. Short ␤-strands are then predicted to begin at residue 87 and extend through a region (residues 120-288) that is predicted to have a ␤-helical structure, based on BetaWrapPro (49). We therefore anticipate that a single subunit of VacA will adopt a roughly symmetric shape within the context of the oligomer and that residues in the N-terminal region of p33 (likely C-terminal to the pore-forming region) would be positioned to mediate oligomerization with the N-terminal region of a neighboring p55 subunit (Fig.…”

Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system

“…Tertiary structure predictors such as Rosetta (12) and LINUS (13), although performing well on all-α-and α/β-proteins, are also challenged by topologically complex all-β-proteins (12, 13). Many threading programs also have particular problems recognizing and then threading β-sheet topologies correctly once sequences fall into the so-called twilight zone (14) of less than 15-20% sequence homology to known structures.Previous methods have been introduced by our group and others to identify β-structural motifs from sequence by capturing pairwise dependencies between residues that come together to form the β-sheets of the motif (8,(15)(16)(17)(18)(19). These methods were shown to be more successful than a variety of competing methods at recognizing the right-handed parallel β-helix fold (8,(15)(16)(17)(18), the β-trefoil fold (18, 19), , and LeucineRich repeat folds (16).…”

“…These methods were shown to be more successful than a variety of competing methods at recognizing the right-handed parallel β-helix fold (8,(15)(16)(17)(18), the β-trefoil fold (18, 19), , and LeucineRich repeat folds (16). These methods have been shown to…”

“…Previous methods have been introduced by our group and others to identify β-structural motifs from sequence by capturing pairwise dependencies between residues that come together to form the β-sheets of the motif (8,(15)(16)(17)(18)(19). These methods were shown to be more successful than a variety of competing methods at recognizing the right-handed parallel β-helix fold (8,(15)(16)(17)(18), the β-trefoil fold (18,19), TM-barrel proteins (20), and LeucineRich repeat folds (16 produce meaningful results on particular β-structural motifs despite the fact that they are throwing away almost all sequence information (of the type learned by a profile HMM).…”