Bioinformatics become an important part of many areas of biology
Bioinformatics become an important part of many areas of biology. It is an emerging field undergoing rapid growth in the past few decades 12. Every protein has some specific function within the body. Few proteins are involved in bodily movement, while others are involved in structural support. Proteins distinct are in functions as well as structures. One of the important goals persists by bioinformatics and theoretical chemistry is protein structure prediction.
Proteins are being classified according to sequence and structural similarity. The four levels of protein structure are primary, secondary, tertiary, and quaternary structure. Each single protein molecule may contain few of these protein structure types. The structure of protein describes the protein function. The primary structure of a protein is derived from the amino acid sequence of a protein and it is the most fundamental form of information available about the protein.
Proteins perform most of the functions in the cells of living organisms, acting as enzymes to perform complex chemical reactions, recognizing foreign particles, conducting signals, and building cell scaffolds – to name just a few. Their function is dictated by their three-dimensional structure, which can be quite involved, even though proteins are linear polymers composed of only 20 different types of amino acids 20.
Machine learning to target on prediction, based on known properties learned from the training data. In, the field of biology various applications extensively uses methods which are based on machine learning algorithms. Machine learning approaches have found immense importance in numerous bioinformatics prediction methods 12. Most function-prediction methods, both sequence and structure based, rely on inferring relationships between proteins that permit the transfer of functional annotations and binding specificities from one to the other.
Most function-prediction methods, both sequence and structure based, rely on inferring relationships between proteins that permit the transfer of functional annotations and binding specificities from one to the other. A notable challenge here is deciphering the connection between the detected similarities (structural or in sequence) and the level of functional relatedness. Function is often associated with domains, and another problem is the identification of functional domains from sequence alone. The accuracy of current methods for predicting domain boundaries is not yet completely satisfactory. Several methods provide reliable predictions if a structural template for the protein is available, but when this is not the case, one is left with the problem of whether the experimental annotation used for the inference refers to the same domain for which the sequence similarity/motif is established.
Contact Maps are used in protein superimposition and for predicting protein similarity studies as they provide a reduced representation than the 3D coordinate and improve the template-target alignment, thereby increasing the accuracy of structure prediction using primary sequence information.
The accuracy of current methods for predicting domain boundaries is not yet completely satisfactory 15. Several methods provide reliable predictions if a structural template for the protein is available, one is left with the problem of whether the experimental annotation used for the inference refers to the same domain for which the sequence similarity/motif is established.
