Data Mining Approach for Amino Acid Sequence Classification

Computerized applications are employed all around the world, an enormous amount of data is collected. The essential information contained in large amounts of data is attracting scholars from a variety of disciplines to examine how to extract the hidden knowledge inside them. The technique of obtaining or mining usable and valuable knowledge from enormous amounts of data is known as data mining. Text mining, picture mining, sequential pattern mining, web mining, and so on are all examples of data mining fields. Sequencing mining is one of the most important technologies in this field, as it aids in the discovery of sequential connections in data. Sequence mining is used in a variety of applications, including customers' buying trends analysis, web access trends analysis, atmospheric observation, amino acid sequences, Gene sequencing, and so on. Sequence mining techniques are utilized in protein and DNA analysis for sequence alignment, pattern searching, and pattern categorization. Researchers are exhibiting an interest in the subject of amino acid sequence categorization in the field of amino acid sequence analysis. It has the ability to find recurrent patterns in homologous proteins. This study describes the numerous methods used by numerous studies to categories proteins and gives an overview of the most important sequence classification techniques

How Good Are Simplified Models for Protein Structure Prediction?

Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP

How good are simplified models for protein structure prediction?,” Advances in Bioinformatics

Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP