What does the resonant recognition model tells us about Myosin Binding Protein C ?

Aim: to compare theoretical predictions with known data about Myosin Binding Protein C (MBPC). Methods: resonant recognition model (RRM) analysis was performed on MBPC from different types in order to identify relevant frequencies. Coarse Grained hot spot Fourier analysis was used for predicting regions of the sequence with major contribution to the function. Results: Two characteristic frequencies were found: f= 0.226, apparently associated to the cardiac isoform and f= 0.3667, non-specifically correspondent to MBPC. Critical areas in the sequence were predicted for the region of phosphorylation as well as for areas where several of the most important disease-causing mutations have been described. An artificial “sequence” with all point mutations reported in literature leads to a significant reduction of the peak at f=0.226. Conclusions: at least two RRM frequencies are associated to MYBPC function, being f=0.226 the most specific for cardiac function. Nearly 15% of the point mutations purportedly associated to CMH4, a cardiac specific insert, two phosphorylation sites and a mutation leading to an irreversible denaturation of the dominion C5 fall into the regions of major contribution to f=0.226. Authors suggest that an RRM based approach could be useful for explaining or predicting other mutation-associated diseases. Introduction Myosin binding protein-C (MYBPC) is an accessory protein from the thick filament that is present in almost every type of vertebrate muscle. MYBPC function has not been yet clarified, but strongly evidence a point to a regulatory component in muscle contractility [1]. It has been shown that cardiac MYBPC is a target site to phosphorylation as response to different inotropic influences, including sympathetic stimuli that induce cardiac MYBPC phosphorylation through cAMP dependent protein kinase (PK). At the same time, there is a group of mutations in the gene for the cardiac isoform of MYBPC associated to Type 4 Familial Hypertrophic Cardiomyopathy (CMH4), a disease related to mutations in proteins participating in cardiac contractile machinery. Familial Hyperthrophic Cardiopathy is the most common cause of sudden cardiac death in young athletes. Its prevalence is believed to be about 0.2%, or 1 in 500[1-4]. However, in spite of the abundant evidence suggesting its importance for cardiac function, little is known about MYBPC function. Thus, several studies deal with effects of PK on cardiac contractility, but the role of cardiac MYBPC as part of the response to PK activation has not yet been elucidated [1-4]. Likewise, the mechanisms leading from MYBPC mutations into cardiac function impairment have not been clarified as yet. Presence of protein truncating mutations has suggested possible expression of truncated proteins in patient’s muscle. The case of point mutations, deletions or insertions has remained unsolved hitherto. The precise mechanism by which many of these mutations cause FHC remains unsolved, but in some cases the degree to which these mutations may affect the structure and function of MYBPC can be inferred from sequence comparisons [4]. In any case, it is worth of notice the relatively large number of point mutations leading to disease. One of the advantages of some bioinformatics methods lies on the possibility to make inferences about a protein, even when little is known about its function. In this study we have employed the Resonant Recognition Model (RRM) approach to the analysis of MYBPC protein sequences. The method is based on the idea that characteristic frequencies must be shared by proteins with common function or interaction. In comparison with other bioinformatics methods used for computational studies of protein structure-function relationships, the RRM brings up a number of advantages, such as [5-10]: • The possibility to support for the presence (or absence) of a common function within a set of proteins. • The possibility to theoretically explore the consequences of a certain mutation for the function of a protein. • The possibility to find out the amino acids in a protein that are crucial for its function. We have identified MYBPC characteristic frequencies corresponding to their common functions, predicted the positions of the critical regions in the amino-acidic sequence that are relevant to these functions and contrasted them with known facts from literature. We regard that the obtained results by means of the RRM analysis are in conformity with current knowledge about MYBPC obtained from both experimental and theoretical evidences. Furthermore, our results provide additional information about specific features of cardiac and muscle MYBPC isoforms. We hypothesize that the application of the RRM may shed some light into the understanding of the functional behavior of MYBPC and its many modifications leading, or not, to pathological conditions. The fact that its implementation is computationally affordable, suggest that this approach may be extended for further clinical and research purposes. Materials and Methods The crucial problem of understanding the rules of encrypting of a protein bioactivity within the amino acid sequence still remains an unsolved goal. Thus, there is an urgent need for robust theoretical approaches for computational analysis of structural and functional performance of different protein families. The RRM is a physic-mathematical approach that interprets protein sequence’s linear information using digital signal processing [11-14]. The RRM suggests that protein interactions represent the transfer of the resonant energy between the interacting molecules at the frequency specific for each observed function/interaction. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein’s biological activity. The RRM hypothesized that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity [13, 14]. Though, it has been found in extensive researches, that protein with the same biological function have a common frequency in their numerical spectra. This frequency was found then to be a characteristic feature for protein biological function or interaction. The RRM procedure involves two stages of calculations. Firstly, the original amino acid sequence is transformed into the numerical sequence by assigning to each amino acid a particular value of the physical parameter relevant to the protein biological function. The assignment of a particular number for each amino acid in the molecule is a crucial step in all these calculations. In this study, the energy of delocalized electrons (calculated as the electron-ion interaction pseudo-potential, EIIP) of each amino acid residue is used. Thus, the resulting numerical series represents the distribution of the free electrons energies along the protein. At the second stage, these numerical series are analyzed by digital signal analysis methods, in particular by Discrete Fourier Transform (DFT), in order to extract information pertinent to the protein biological function. A multiple cross-spectral function is defined and calculated to obtain the common frequency components from the spectra of a group of proteins. Peaks in such function denote common frequency components for all sequences analyzed [5-14]. Through an extensive study the RRM has reached a fundamental conclusion: one RRM characteristic frequency characterizes one particular biological function or interaction. This frequency is related to the biological function provided the following criteria are met: • One peak only exists for a group of protein sequences sharing the same biological function [14]. • No significant peak exists for biologically unrelated protein sequences. • Peak frequencies are different for different biological functions. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM to predict the amino acid regions in the sequence which predominantly contributed to this frequency and consequently to the observed function. For that purpose, we used the short term Fourier transformed approach for a coarse-grained “hot spots analysis”. For coarse-grained hot spot analysis, power spectra were estimated from each window resulting from a sliding pattern along the whole protein sequence. The amplitude corresponding to the frequency of interest was estimated for each window. We hypothesize that those windows with larger values of amplitude are those contributing the most to the given function/interaction. The resolution of the method is limited. On one hand the length of the window allows us to mention only “regions of interest” and not specific positions that are crucial for the function. On the other hand, the short duration of the window makes the frequency precision blurry. However, the fact that the method is straightforward as well as the possibility (in the specific case of MYBPC) to compare theoretical predictions with the details available about the protein, provide motivation to test RRM predictions using this approach. To coarse-grained hot spots analysis we submitted human MYBPC sequences with diverse species and tissue origins. In order to find out specific frequencies corresponding to different putative interactions/functions, we divided the sequences into the following groups Group 1: MYBPC from slow muscle >Q569K7|Q569K7_HUMAN Myosin binding protein C, slow type Homo sapiens (Human) >Q5U3B1|Q5U3B1_DANRE Myosin binding protein C, slow type Danio rerio (Zebrafish) >Myosin-binding protein C, skeletal muscle slow isoform RAT >Q00872|MYPC1_HUMAN Myosin-binding protein C, slow-type Homo sapiens (Human) Group 2 MYBPC from fast muscle >P16419|MYPC2_CHICK Myosin-binding protein C, fast-type Gallus gallus (Chicken) >Q5XKE0|MYPC2_MOUSE Myosin-binding protein C, fast-type