Triage protein fold prediction

Metal-Enhanced Helical Chirality of Coil Macromolecules: Bioinspired by Metal Coordination-Induced Protein Folding

Although the supramolecular helical structures of biomacromolecules have been studied, the examples of supramolecular systems that are assembled using coils to form helical polymer chains are still limited. Inspired by enhanced helical chirality at the supramolecular level in metal coordination-induced protein folding, a series of alanine-based coil copolymers (poly-(l-co-d)-ala-NH₂) carrying (l)- and (d)-alanine pendants were synthesized as a fresh research model to study the cooperative processes between homochirality property and metal coordination. The complexes of poly-(l-co-d)-ala-NH₂ and metal ions underwent a coil-to-helix transition and exhibited remarkable nonlinear effects based on the enantiomeric excess of the monomer unit in the copolymers, affording enhanced helical chirality compared to poly-(l-co-d)-ala-NH₂. More importantly, the synergistic effect of amplification of asymmetry and metal coordination triggered the formation of a helical molecular orbital on the polymer backbone via the coordination with the d orbital of copper ions. Thus, the helical chirality enhancement degree of poly-(l-co-d)-ala-NH₂/Cu²⁺ complexes (31.4) is approximately 3 times higher than that of poly-(l-co-d)-ala-NH₂/Ag⁺ complexes (9.8). This study not only provides important mechanistic insights into the enhancement of helical chirality for self-assembly but also establishes a new strategy for studying the homochiral amplification of asymmetry in biological supramolecular systems.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

Background

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.

Results

The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.

Conclusions

The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

Prediction of protein structural class using novel evolutionary collocation-based sequence representation

Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a relatively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile-based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61-96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered datasets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20-35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html.