Matthew Menke

Matt: Local Flexibility Aids Protein Multiple Structure Alignment

Even when there is agreement on what measure a protein multiple structure alignment should be optimizing, finding the optimal alignment is computationally prohibitive. One approach used by many previous methods is aligned fragment pair chaining, where short structural fragments from all the proteins are aligned against each other optimally, and the final alignment chains these together in geometrically consistent ways. Ye and Godzik have recently suggested that adding geometric flexibility may help better model protein structures in a variety of contexts. We introduce the program Matt (Multiple Alignment with Translations and Twists), an aligned fragment pair chaining algorithm that, in intermediate steps, allows local flexibility between fragments: small translations and rotations are temporarily allowed to bring sets of aligned fragments closer, even if they are physically impossible under rigid body transformations. After a dynamic programming assembly guided by these “bent” alignments, geometric consistency is restored in the final step before the alignment is output. Matt is tested against other recent multiple protein structure alignment programs on the popular Homstrad and SABmark benchmark datasets. Matts global performance is competitive with the other programs on Homstrad, but outperforms the other programs on SABmark, a benchmark of multiple structure alignments of proteins with more distant homology. On both datasets, Matt demonstrates an ability to better align the ends of α-helices and β-strands, an important characteristic of any structure alignment program intended to help construct a structural template library for threading approaches to the inverse protein-folding problem. The related question of whether Matt alignments can be used to distinguish distantly homologous structure pairs from pairs of proteins that are not homologous is also considered. For this purpose, a p-value score based on the length of the common core and average root mean squared deviation (RMSD) of Matt alignments is shown to largely separate decoys from homologous protein structures in the SABmark benchmark dataset. We postulate that Matts strong performance comes from its ability to model proteins in different conformational states and, perhaps even more important, its ability to model backbone distortions in more distantly related proteins.

betawrap: Successful prediction of parallel β-helices from primary sequence reveals an association with many microbial pathogens

The amino acid sequence rules that specify β-sheet structure in proteins remain obscure. A subclass of β-sheet proteins, parallel β-helices, represent a processive folding of the chain into an elongated topologically simpler fold than globular β-sheets. In this paper, we present a computational approach that predicts the right-handed parallel β-helix supersecondary structural motif in primary amino acid sequences by using β-strand interactions learned from non-β-helix structures. A program called BETAWRAP (http://theory.lcs.mit.edu/betawrap) implements this method and recognizes each of the seven known parallel β-helix families, when trained on the known parallel β-helices from outside that family. BETAWRAP identifies 2,448 sequences among 595,890 screened from the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/) nonredundant protein database as likely parallel β-helices. It identifies surprisingly many bacterial and fungal protein sequences that play a role in human infectious disease; these include toxins, virulence factors, adhesins, and surface proteins of Chlamydia, Helicobacteria, Bordetella, Leishmania, Borrelia, Rickettsia, Neisseria, and Bacillus anthracis. Also unexpected was the rarity of the parallel β-helix fold and its predicted sequences among higher eukaryotes. The computational method introduced here can be called a three-dimensional dynamic profile method because it generates interstrand pairwise correlations from a processive sequence wrap. Such methods may be applicable to recognizing other beta structures for which strand topology and profiles of residue accessibility are well conserved.

BETASCAN: Probable β-amyloids Identified by Pairwise Probabilistic Analysis

Amyloids and prion proteins are clinically and biologically important β-structures, whose supersecondary structures are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Recent work has indicated the utility of pairwise probabilistic statistics in β-structure prediction. We develop here a new strategy for β-structure prediction, emphasizing the determination of β-strands and pairs of β-strands as fundamental units of β-structure. Our program, BETASCAN, calculates likelihood scores for potential β-strands and strand-pairs based on correlations observed in parallel β-sheets. The program then determines the strands and pairs with the greatest local likelihood for all of the sequences potential β-structures. BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator) in β-structure prediction and amyloid propensity prediction. Accurate prediction is demonstrated for experimentally determined amyloid β-structures, for a set of known β-aggregates, and for the parallel β-strands of β-helices, amyloid-like globular proteins. BETASCAN is able both to detect β-strands with higher sensitivity and to detect the edges of β-strands in a richly β-like sequence. For two proteins (Aβ and Het-s), there exist multiple sets of experimental data implying contradictory structures; BETASCAN is able to detect each competing structure as a potential structure variant. The ability to correlate multiple alternate β-structures to experiment opens the possibility of computational investigation of prion strains and structural heterogeneity of amyloid. BETASCAN is publicly accessible on the Web at http://betascan.csail.mit.edu.

Predicting the β-helix fold from protein sequence data

A method is presented that uses β-strand interactions to predict the right-handed β-helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method, and is shown to score known β-helices above non-β-helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP β-helix families, when trained on the the known β-helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown structure that play a role in human infectious disease to β-helices; in particular, these proteins serve as virulence factors, adhesins and toxins in bacterial pathogenesis, and include cell surface proteins from Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other β structures for which strand topology and profiles of residue accessibility are well conserved.

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

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 β‐helix and β‐trefoil families. A method using pairwise β‐strand interaction probabilities coupled with evolutionary information represented by sequence profiles is developed to tackle these problems for the β‐helix and β‐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 β‐helix with 100% sensitivity and 99.7% specificity and the β‐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 β‐helices and 86% for the β‐trefoils accurately (within four residues of the exact positon) 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 β‐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. Proteins 2006.

Predicting the beta-helix fold from protein sequence data.

A method is presented that uses beta-strand interactions to predict the parallel right-handed beta-helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method and is shown to score known beta-helices above non-beta-helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP beta-helix families, when trained primarily on beta-structures that are not beta-helices, together with structural features of known beta-helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown structure to be beta-helices; in particular, these proteins serve as virulence factors, adhesins, and toxins in bacterial pathogenesis and include cell surface proteins from Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other beta-structures for which strand topology and profiles of residue accessibility are well conserved.

STITCHER: Dynamic assembly of likely amyloid and prion β‐structures from secondary structure predictions

The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Previous work has demonstrated that probability‐based prediction of discrete β‐strand pairs can offer insight into these structures. Here, we devise a system of energetic rules that can be used to dynamically assemble these discrete β‐strand pairs into complete amyloid β‐structures. The STITCHER algorithm progressively ‘stitches’ strand‐pairs into full β‐sheets based on a novel free‐energy model, incorporating experimentally observed amino‐acid side‐chain stacking contributions, entropic estimates, and steric restrictions for amyloidal parallel β‐sheet construction. A dynamic program computes the top 50 structures and returns both the highest scoring structure and a consensus structure taken by polling this list for common discrete elements. Putative structural heterogeneity can be inferred from sequence regions that compose poorly. Predictions show agreement with experimental models of Alzheimers amyloid beta peptide and the Podospora anserina Het‐s prion. Predictions of the HET‐s homolog HET‐S also reflect experimental observations of poor amyloid formation. We put forward predicted structures for the yeast prion Sup35, suggesting N‐terminal structural stability enabled by tyrosine ladders, and C‐terminal heterogeneity. Predictions for the Rnq1 prion and alpha‐synuclein are also given, identifying a similar mix of homogenous and heterogeneous secondary structure elements. STITCHER provides novel insight into the energetic basis of amyloid structure, provides accurate structure predictions, and can help guide future experimental studies. Proteins 2012.

Wrap-and-pack: a new paradigm for beta structural motif recognition with application to recognizing beta trefoils

A method is presented that uses β-strand interactions at both the sequence and the atomic level, to predict the beta-structural motifs in protein sequences. A program called Wrap-and-Pack implements this method, and is shown to recognize β-trefoils, an important class of globular β-structures, in the Protein Data Bank with 92% specificity and 92.3% sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP β-trefoil families, when trained primarily on β-structures that are not β-trefoils, together with 3D structures of known β-trefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be β-trefoils. The computational method used here may generalize to other β-structures for which strand topology and profiles of residue accessibility are well conserved.

Touring protein space with matt

Using the Matt structure alignment program, we take a tour of protein space, producing a hierarchical clustering scheme that divides protein structural domains into clusters based on geometric dissimilarity. While it was known that purely structural, geometric, distance-based metrics of structural similarity, such as Dali/FSSP, could largely replicate hand-curated schemes such as SCOP at the family level, it was an open question as to whether any such scheme could approximate SCOP at the more distant superfamily and fold levels. We partially answer this question in the affirmative, by designing a clustering scheme based on Matt that approximately matches SCOP at the superfamily level. Implications for the debate over the organization of protein fold space are discussed.

Wrap-and-Pack: a new paradigm for beta structural motif recognition with application to recognizing beta trefoils.

A method is presented that uses beta-strand interactions at both the sequence and the atomic level, to predict beta-structural motifs of protein sequences. A program called Wrap-and- Pack implements this method and is shown to recognize beta-trefoils, an important class of globular beta-structures, in the Protein Data Bank with 92% specificity and 92.3% sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP beta-trefoil families, when trained primarily on beta-structures that are not beta-trefoils, together with three-dimensional structures of known beta-trefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be beta-trefoils. The computational method used here may generalize to other beta-structures for which strand topology and profiles of residue accessibility are well conserved.