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1 BBSP – Building Blocks Structure Predictor

Secondary structures identified by HCAM method, as well as HFUs and closed loops collected in a database, may provide fingerprinted maps for the identification of several building blocks in both prokaryotic and eukaryotic proteins. Thus, we linked HCAM profiles to an own made hybrid combinatorial fragment assembly algorithm that is based on dynamic programming and leads toward the prediction of protein three-dimensional structures through generation of hypothetical assembled models, followed by structural comparisons between such models and database building blocks. The template-based algorithm described here was named Building Blocks Structure Predictor (BBSP) (see Figure 1).

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Figure 1. Schematic representation of BBSP algorithm. First, fragments stored in the BBSP matrix are checked and assembled recursively in order to build hypothetical structural models. Finally, structural models generated are compared with the database building blocks and refined by secondary structure and physicalchemical profiles in order to calculate scores and to associate the best structural matches.

1.2 BBSP Results

Predictive tests on an independent protein test set varying in length from 90 to 250 aa demonstrated the capacity of BBSP to identify folding motifs with a high grade of structural homology, though with weak sequence identity (usually < 20%). Overlapping of structural models built by the BBSP assembly algorithm showed that such method can provide structural information on the type of folding identified, since backbone RMSD values of building blocks belonging to the same structural category and having similar lengths often fall within the crystallographic range. In many cases, motifs belonging to the unclassified/disordered sub-set also revealed a high grade of overlap with target native models. Secondary structure prediction rate of matched models refined by use of HCAM score was estimated at about 82%. Nevertheless, hydropathy clustering assistance allowed the detection of super-secondary models having RMSD closer to target resolution (see sample results downloadable at Tools section). For each set of results obtained, it was almost always possible to find at least one structural model having RMSD below 1.5 Å. Furthermore, 74% of matched fragments fall within the PDB range resolution; whereas the mean RMSD of the whole population was estimated at 2.3 Å. Moreover, in each generated sub-set, it was often possible to detect at least one full length predictive model that cover target sequences corresponding to a super-secondary motif, though for building blocks over 45 amino acids there is a low probability to find a full size assembled counterpart. In many cases, it is worthy to note that when main chain trajectories of several predictive segments are joined together, it was possible to obtain accurate prediction of higher order architectures. The secondary structure profiles generated by HCAM and used to aid the alignment procedure often made it possible to more correctly identify protein fragments having backbone trajectories that replicate target super-secondary folds. Our matches were compared with the ones detected by HHsearch/HHpred, which is one of most cited protein remote homologues detection methods in the scientific literature. The search was conducted on the standard PDB database, using 8 PSI-BLAST iterations and local alignment mode (HHpred web server at: http://toolkit.tuebingen.mpg.de/hhpred). The average number of predictive matches found by HHpres was, as expected, higher than that found by our method: HHpred can perform an extensive search on the whole updated PDB database by position-specific scoring matrices (PSSMs), while BBSP performs search through our in-house database only. Average RMSD of models detected by HHpred reveals a better accuracy than BBSpred ones. However, both methods detected backbone trajectories enclosed within the PDB resolution of native targets. On the contrary, BBSpred was able in many cases to detect remote homologues having sequence identity significantly lower than HHpred counterparts (see Table 1). Therefore, we argued that our method can improve sensitivity in the detection of remotely related sequences because of the building of hypothetical models by linkage of different sized fragments that are excised from unrelated structures. Instead, HHpred performs a multiple sequence alignment of related protein sequences stored in public databases, with no mixing of detected models. Hence, our search strategy might have extended coverage of conformational space required for the detection of almost sequence-independent related structures.

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Table 1. Secondary structure prediction rate (Q3), Sequence identity (Seq. id.) and backbone Root Mean Square Deviation (RMSD) for super-secondary structure models detected by both BBSP and HHpred methods. Sequence positional numbers are enclosed within brackets. Symbol * is referred on HHpred results having gaps in matched sequences.

1.3 Detection of protein domain superfamilies/superfolds

For the purposes of our work, the only detection of short motifs would have been reductive. Accordingly, we tested our method on five whole protein sequences in order to detect domain superfamilies/superfolds by the merging of trajectories of contiguous motifs (see Figure 2). Our results were compared with the ones obtained by the protein fold-recognition servers HHPred, COMPASS, and PHYRE2 .
Proteins used in our test set were represented by Streptococcus agalactiae hyaluronate lyase (PDB ID: 1F1S), Pseudomonas putida muconate lactonizing enzyme (PDB ID: 1BKH), Acinetobacter glutaminasificans glutaminase-asparaginase (PDB ID: 1AGX), Salmonella typhimurium N-acetylgamma-glutamyl-phosphate reductase (PDB ID: 2G17), and Lactococcus lactis 6-phosphogluconate dehydrogenase (PDB ID: 2IZ0). Despite the reduced size of our library, BBSP was able to detect six folds sharing significant remote homology with respect of the targets. Particularly, both Enolase C-terminal domain of Pseudomonas putida (D)-glucarate dehydratase (PDB ID: 1BQG) and NAD (P)-binding Rossmann-fold of Sinorhizobium morelense 1, 5-anhydrod-fructose reductase (PDB ID: 2GLX), revealed the lowest sequence identity. Moreover, GaGLyase fold of Streptococcus pneumoniae hyaluronate lyase (PDB ID: 1EGU) shown the best accuracy in terms of both RMSD and TM-score. Furthermore, BBSP was able to detect two architectures corresponding to spatially contiguous folds of 1AGX protein, represented by one NAD(P)-binding Rossmann fold of Escherichia coli Succinyl-CoA synthetase (PDB ID: 1CQI), and one L-asparaginase-like superfamily domain of Escherichia coli L-asparaginase (PDB ID: 3ECA). In four cases, COMPASS was not able to detect templates, whereas HHPred outperformed in two cases by the detection of remote homology domains, as well as PHYRE2. Overall, BBSP results resembled those obtained by the other methods. Finally, the search strategy of BBSP algorithm demonstrated that domain-building motifs of our library could be successfully used as a probe to detect higher order protein architectures.

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Figure 2. Domain superfamilies/superfolds detected by BBSP algorithm by concatenation of trajectories of domain building motifs. Domains are represented as black ribbon, whereas motifs are represented as color-coded Q3 cartoon.

1.4 Conclusions

Despite the time consuming calculation needed, the method discussed in this section has been completely automated and does not require expensive computational resources. Moreover, BBSP can be easily parameterized to run parallel execution and to define matching parameters (read instructions contained in the BBSP zip file downloadable in the Tools section). The secondary structure prediction module takes only few seconds for generating the results, whereas it was estimated that BBSP requires from 3 to 8 hours of parallel execution (depending from the complexity of the dynamic matrix) for a 150 mers sequence if tested by Intel® Core Duo 3.2 GHz CPU and 4 GB RAM. Structural results were obtained solely by rigid transformation protocols and could provide guidelines for the design of both globular and transmembrane proteins. Indeed, BBSP can generate its own models of great structural relevance and can therefore be a valid starting point for the development of molecular statistical mechanics protocols. Furthermore, the predictive skills of such method will be refined by adding new statistics. Nonetheless, filters to detect false positive matches will be built. In short, BBSP could be considered as a remarkable complementary tool in the field of fold-recognition.

Reference

· Gullotto D, Nolassi SM, Bernini A, Spiga O, Niccolai N. Probing the protein space for extending the detection of weak homology folds. J Theor Biol 320, 152-158 (2013).