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This report mainly focuses on the link between the principal categorization and the nature of proline cis/trans isomerization. We will understand cis conformation and trans conformation later on in our discussion.
We know that Peptides are short polymers which are formed when amino acids are linked together in a defined order or sequence. The connection between one amino acid filtrate and the next is called a peptide bond. Polypeptides are long chained amino acids covalently bonded through a peptide bond. Proteins, the key functional structures of our body, are composed of Polypeptides molecules, which are multiple in numbers and arranged in specific sequence.
Cis and Trans conformations are the two different arrangements in which a protein molecule is formed and identified. Even though most of the peptide bonds in a protein molecule are found as Trans peptide bonds but we still cannot ignore the importance of Cis peptide bonds, which are not that frequently found bonds but still play a vital role in the structure and function of Proteins and many other major biological processes, for example in the implication of several diseases.
A peptide bond usually occurs in either cis or trans conformation, depending on the position of alpha carbon in the bond. Another thing which plays a part in determining the conformation of the bond is Intrinsic energy. The cis conformation usually holds higher intrinsic energy a compared to trans conformation. But in some cases like X-P amino acid pair, where the difference in intrinsic energy between the two isomers is much smaller, the peptide bonds are in cis conformation.
Cis/Trans Isomerization has drawn amplified attention during the last few years. Modern research technology has made it possible to look into the very details of the biological and chemical processes taking place at micro levels inside the body. Recent studies have explained that Prolyl Isomerization (Cis & trans) can also play their part as timers to help control the intensity and interval of a cellular processes, thus making it a giant leap in clinical involvement.
There are a number of factors reported to affect the nature of the peptide bond. Experiments carried out using Nuclear Magnetic Resonance have shown that the conformation of peptide bonds, Cis or Trans, is based on the codes already present and determined by the sequence of the amino acids closest to proline. But it is also seen that there are certain non random patterns present in the sequence of amino acids and they as well play an important part in the construction of a peptide bond. An identification and evaluation of these patterns will help a great deal in understanding the mechanism behind the peptide bond formation.
In this report we will study in detail the importance of cis peptide bonds in carrying out different biological and chemical processes and the importance of cis prolyl bonds in the structure and function of proteins during the evolutionary phases. We will systematically analyze the detection and occurrence of different pattern and how they influence the conformation of the peptide bonds in various conditions. Study of these patterns will not only give us a biological insight into the structure and the physiochemical characteristics that govern the peptide bond formation but will also be helpful in improving the diagnostic and prediction methods associated with a certain disorder. Since Cis peptide bonds usually form an active part of proteins molecules, they play a major role in the function of proteins. They play an active and role in cell signaling, merging, protein folding and permeable function of cell membranes. So understanding in detail the functional and structural grounds of cis/trans isomerization will open the doors for new treatment and prediction procedures.
Detailed description of Methodology and Techniques
The procedural structure presented in this paper has contributed to the understanding of the isomerization of peptide bonds in a remarkable way. First of all, we apply an organized approach for the origin of frequently occurring patterns in sequence. After that we will further carefully distill the extracted patterns to give us highly selective patterns. These highly selective patterns will accurately identify and describe the considerable protein areas. After getting these highly selective patterns, we will implement an efficient and methodical approach for the functional explanation of uncharacterized protein patterns.
In this procedure, first of all, the regions holding cis proline peptide bond are separated. After that an efficient pattern discovery algorithm is applied which searches through the region for regular type patterns which are frequently repeated in the vicinity of cis peptide bonds. After that in the pattern evaluation phase careful selection and examination of the patterns takes place.
We obtained 3050 high quality protein sequences from the Protein data Bank to make a dataset. The explanation and classification of peptide bonds in the dataset was done Volume Area Dihedral Angle Reporter, which calculates the dihedral angle to distinguish the conformation. If the dihedral angle is between -30 and +30, the conformation of the peptide bond is cis conformation and the bonds with angles outside this range, have trans conformation. So we found that those 3050 sequences of proteins hold 32085 peptide bonds, out of which 1417 are in cis conformation and 30668 in trans conformation. So the two datasets were accumulated having the same residue length. The first one containing 1417 cis proline regions and the second one containing 30668 trans proline regions.
In the pattern discovery or identification phase, we applied another algorithm, TEIRESIAS, to understand the insight of the factors affecting the formation of cis proline bonds. This algorithm is able to identify all given patterns in a set of successions. It operates in two phases. First it scans the given patterns and after that it convolutes. With the help of TIERESIAS, we got four types of patterns, according the authorized amino acid equivalencies.
The Exact Pattern discovery
Pattern identification assuming conservative replacement of chemically equivalent amino acids by one another: [AG], [DE], [FY], [KR], [ST], etc
Pattern discovery based on structural equivalency set: [CS]. [DLN], [EQ] etc
Pattern identification based on the substitution of amino acids sharing certain physiochemical property.
The sequence of patterns resulting from the first three pattern types containing single or group of amino acids are called amino acid patterns. While those belonging to the fourth type of pattern sequence, sharing physiochemical properties are called property patterns.
Now in order to ensure the selectivity and reliability of the selected patterns we must compare them with negative control sets. We rate them precisely to explain which pattern type describes cis regions and which describes trans regions. This rating is based on the comparison and proportionality of both the datasets of cis and trans patterns. Based on pattern ratings, if the score range is zero, it means that the respective pattern is only observed in trans areas and if the score range is one, it means that the pattern is seen only in cis region. Whereas a 0.5 score range will indicate a random pattern. Property patterns are more repeatedly observed and are more evenly distributed among the two conformations. This blend of scoring functions indicates that preserved patterns are not random but indicate a strong penchant for cis regions.
A systematic study of the correlation between the cis patterns and PROSITE patterns will give us some functional outlook of the cis proline peptide bond. PROSITE is a key storage in which the patterns of proteins are determined according to their functional attributes. But PROSITE does not have specific category of cis areas. When patterns are being compared, until we get the exact match, all residues are treated equally.
Presentation of the Results:
After we systematically analyzed the patterns in the above given procedure, we identified several cis proline peptide bonds. But the two types of extracted and selected patterns we identified are amino acid patterns and property patterns.
We reached some qualitative result about the nature and function of cis prolyl peptide bonds. So in the table 1 given in the paper, we analyzed twenty selected patterns.
We saw from the scores we got from different patterns, set in order, that some specific structural and chemical properties of amino acid plays an important role in differentiating the two conformations of peptide bonds.
It is notable here that the scores from every pattern stand for the ratio of matches in the two regionsââ‚¬â„¢ dataset, cis and trans.
It can also be seen that in the three types of amino acid patterns, certain patterns are completely common or with little modifications.
After the efficient implication of pattern discovery algorithm we identified four types of pattern discoveries, exact pattern discovery, pattern discover having chemical equivalency set, pattern discovery using structural equivalency set and pattern discovery using certain physiochemical properties of amino acids.
The specially formulated and implemented scoring function was carefully used to validate the extracted patterns. The score verge for the first three types of patterns discovery came out to be 0.90 while the score ratio for the last type of pattern discovery came out to be 0.80. These scores measures significantly proved that it is extremely dubious that these patterns would have emerged by chance as all the patterns gave readings ranging from -9 ,31.
Many new patterns are also indentified along with the highest scoring patterns confirming the neighborhood of cis proline peptide bonds. PROSITE helped a great deal in gaining insight into the practical insinuation of cis prolyl bonds when the highly selected patterns were evaluated against the PROSITE list of previously identified patterns.
PROSITE encloses protein fragments, which play a vital role in the protein functions and the maintenance of their three dimensional structures, are classified according to the similarities in their sequence and functions. When cis patterns matched with the PROSITE database patterns, they largely were classified into two main categories, Protein signatures and Family Signatures.
This observation does highlight the importance of cis peptide bond in structure and function of protein during evolutionary phase of life and thus improves the understanding and reliability of our results.
It is important to note here that our study has again found some purposeful practical associations already present in the scientific literature which helped a great deal in the validation of the work done.
In this way the nature of isomerization and the interactions in the vicinity of any considerable protein region can be examined.
Short Discussion about the Paper:
The procedural examination we followed and did in this study has shown that it can be utilized for the discovery and generalization of any major protein type. The authors started with the set of uneven protein sequences and extorted a list of frequently repeating protein patterns. They further refined it after passing it through more complex and analytical processes. By doing so, they investigated the nature and connections in the neighborhood of any key protein region. They after that classified and filtered highly selective protein patterns and functionally explained them by comparing them to the record of a biological database called PROSITE. They explained the functional occurrence of cis peptide bond by significantly analyzing the role it plays in the function and structure of protein molecules.
Since cis/trans isomerization has been an active topic in the scientific circles for the past many years, the work done by the authors in understanding this concept and the functionalities associated with it is highly practical and significant. The results they achieved make a lot of sense in unfolding the hidden brackets of protein synthesis and their functional implications. It will also help in better understanding of the causes of many sever diseases and their relation to the certain complex biological and chemical processes occurring inside our body. Their results can be very helpful in uncovering the mechanism of cis/trans proline peptide bond conformation and the factors affecting its occurrence. In this study, the authors carried out a systematic evaluation and analysis of the regions in protein molecules which contain cis proline peptide bonds. They came across a number of non random amino acid and property patterns, which were captured and analyzed to understand the mechanism and nature of cis prolyl conformations.
Besides the qualitative assessments they did about the characteristics of cis peptide bond, a broad and helpful list of carefully selected regular patterns was achieved too, which, like the authors are assuming, will prove to be very helpful in quantitative judgments. They will give an insight into new protein patterns and sequences and will help in better understanding of underlying causes of certain protein related disorders. As the consequent patterns were compared against the biological database, PROSITE, it will help in improving and maximizing the functional performance of the database by introducing it to those new patterns which were never parts of it before. Since new patterns obtained from the distinctive sequences of the amino acid can be characterized into some new protein family, it would be quite interesting to redefine the number of protein families and extract the specific patterns of each family individually. Since proline isomerization (cis/trans) is a complex phenomenon, the efforts made by the authors in unlocking the functional implications of proline cis peptide bonds and proline trans peptide bonds will further help bringing out great results.