Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps

BACKGROUND: Protein sequence alignment is one of the basic tools in bioinformatics. Correct alignments are required for a range of tasks including the derivation of phylogenetic trees and protein structure prediction. Numerous studies have shown that the incorporation of predicted secondary structure information into alignment algorithms improves their performance. Secondary structure predictors have to be trained on a set of somewhat arbitrarily defined states (e.g. helix, strand, coil), and it has been shown that the choice of these states has some effect on alignment quality. However, it is not unlikely that prediction of other structural features also could provide an improvement. In this study we use an unsupervised clustering method, the self-organizing map, to assign sequence profile windows to "structural states" and assess their use in sequence alignment. RESULTS: The addition of self-organizing map locations as inputs to a profile-profile scoring function improves the alignment quality of distantly related proteins slightly. The improvement is slightly smaller than that gained from the inclusion of predicted secondary structure. However, the information seems to be complementary as the two prediction schemes can be combined to improve the alignment quality by a further small but significant amount. CONCLUSION: It has been observed in many studies that predicted secondary structure significantly improves the alignments. Here we have shown that the addition of self-organizing map locations can further improve the alignments as the self-organizing map locations seem to contain some information that is not captured by the predicted secondary structure

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

A short survey on protein blocks.

International audienceProtein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications

Probabilistic protein homology modeling

Searching sequence databases and building 3D models for proteins are important tasks for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and time consuming. Fully automatic homology modeling refers to building a 3D model for a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models within a few hours to days. Our group has developed HHpred, which is one of the top performing structure prediction servers in the field. In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4) building a 3D model based on the alignment. In part one of this thesis, we will present improvements of step (2) and (4). Specifically, homology modeling has been shown to work best when multiple templates are selected instead of only a single one. Yet, current servers are using rather ad-hoc approaches to combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions. We find that the query’s atomic distance restraints can be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this approach within HHpred and could significantly improve model quality. Furthermore, we took part in CASP, a community wide competition for structure prediction, where we were ranked first in template based modeling and, at the same time, were more than 450 times faster than all other top servers. Homology modeling heavily relies on detecting and correctly aligning templates to the query sequence (step (1) and (3) from above). But remote homologies are difficult to detect and hard to align on a pure sequence level. Hence, modern tools are based on profiles instead of sequences. A profile summarizes the evolutionary history of a given sequence and consists of position specific amino acid probabilities for each residue. In addition to the similarity score between profile columns, most methods use extra terms that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows. In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that are most conserved in alignments of remotely homologous, structurally aligned proteins. Each so called “context state” in the library consists of a 13-residue sequence profile. We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and improve especially the sensitivity and precision of difficult pairwise alignments significantly. Taken together, we introduced probabilistic methods to improve all four main steps in homology based structure prediction

: Protein Long Local Structure Prediction

International audienceA relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions. Proteins 2009. (c) 2009 Wiley-Liss, Inc

Sequence- and structure-based approaches to deciphering enzyme evolution in the Haloalkonoate Dehalogenase superfamily

Understanding how changes in functional requirements of the cell select for changes in protein sequence and structure is a fundamental challenge in molecular evolution. This dissertation delineates some of the underlying evolutionary forces using as a model system, the Haloalkanoate Dehalogenase Superfamily (HADSF). HADSF members have unique cap-core architecture with the Rossmann-fold core domain accessorized by variable cap domain insertions (delineated by length, topology, and point of insertion). To identify the boundaries of variable domain insertions in protein sequences, I have developed a comprehensive computational strategy (CapPredictor or CP) using a novel sequence alignment algorithm in conjunction with a structure-guided sequence profile. Analysis of more than 40,000 HADSF sequences led to the following observations: (i) cap-type classes exhibit similar distributions across different phyla, indicating existence of all cap-types in the last universal common ancestor, and (ii) comparative analysis of the predicted cap-type and functional diversity indicated that cap-type does not dictate the divergence of substrate recognition and chemical pathway, and hence biological function. By analyzing a unique dataset of core- and cap-domain-only protein structures, I investigated the consequences of the accessory cap domain on the sequence-structure relationship of the core domain. The relationship between sequence and structure divergence in the core fold was shown to be monotonic and independent of the corresponding cap type. However, core domains with the same cap type bore a greater similarity than the core domains with different cap types, suggesting coevolution of the cap and core domains. Remarkably, a few degrees of freedom are needed to describe the structural diversity in the Rossmann fold accounting for the majority of the observed structural variance. Finally, I examined the location and role of conserved residue positions and co-evolving residue pairs in the core domain in the context of the cap domain. Positions critical for function were conserved while non-conserved positions mapped to highly mobile regions. Notably, we found exponential dependence of co-variance on inter-residue distance. Collectively, these novel algorithms and analyses contribute to an improved understanding of enzyme evolution, especially in the context of the use of domain insertions to expand substrate specificity and chemical mechanism

Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers

Background. Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions. Results. In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC. Conclusions. Classifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy. © 2010 Li and Chen; licensee BioMed Central Ltd

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models

<p>Abstract</p> <p>Background</p> <p>Several different methods for contact prediction succeeded within the Sixth Critical Assessment of Techniques for Protein Structure Prediction (CASP6). The most relevant were non-local contact predictions for targets from the most difficult categories: fold recognition-analogy and new fold. Such contacts could provide valuable structural information in case a template structure cannot be found in the PDB.</p> <p>Results</p> <p>We described comprehensive tests of the effectiveness of contact data in various aspects of de novo modeling with CABS, an algorithm which was used successfully in CASP6 by the Kolinski-Bujnicki group. We used the predicted contacts in a simple scoring function for the post-simulation ranking of protein models and as a soft bias in the folding simulations and in the fold-refinement procedure. The latter approach turned out to be the most successful. The CABS force field used in the Replica Exchange Monte Carlo simulations cooperated with the true contacts and discriminated the false ones, which resulted in an improvement of the majority of Kolinski-Bujnicki's protein models. In the modeling we tested different sets of predicted contact data submitted to the CASP6 server. According to our results, the best performing were the contacts with the accuracy balanced with the coverage, obtained either from the best two predictors only or by a consensus from as many predictors as possible.</p> <p>Conclusion</p> <p>Our tests have shown that theoretically predicted contacts can be very beneficial for protein structure prediction. Depending on the protein modeling method, a contact data set applied should be prepared with differently balanced coverage and accuracy of predicted contacts. Namely, high coverage of contact data is important for the model ranking and high accuracy for the folding simulations.</p