Lignin is a very abundant biopolymer, which is found in the cell wall of plants along with cellulose and hemicellulose, its structure is very complex and difficult to degrade since it´s composed of phenolic polymers which makes it very resistant, this It´s the main problem in some industries, for example on the production of paper and fuels.
In the paper industry, as lignin is not degraded, some of the susceptible material is no longer used, which means that it is often not profitable or has significant economic losses. In the summer of research, a predictive statistical model was developed to degrade lignin more easily and thus be able to use it in some other process, seeking to make the compounds linked to lignin being more pure.
The research is qualitative and quantitative type, first we perform qualitative research to know the important aspects of lignin, the organisms where it is present, its structure and the properties to be able to determine the reason of why it is so difficult to treat degradation.
Continue with the quantitative research in different platforms such as Punmed National Center for Biotechnology Information (NCBI), PROTEIN DATA BANK (PDB), CHEMBL, to determine the lignolitic enzymes and the most common organisms.
A database was made with the main characteristics, taking into account the data bank from which the information was extracted, the code of the enzyme, etc.
It was necessary to determine the amount of proteins present in each organism, also to use the PseAAC platform to predict the lignin in a specific protein and determine the Shannon entropy.
With the obtained values, work is being done to develop the statistical model in which the most efficient enzyme will be found in order to disintegrate lignin in a way that does not affect cellulose or hemicellulose.
It was found that the most common enzyme is peroxidase that is present in a bacteria kind called Pseudonocardia autotrophica, with this discover the statistical model will be performed, which is expected to be effective and sustainable because if so, it will be very beneficial in industries, The biggest advantage will be in the economic aspect.
The prediction model will help us experiment with different properties of lignin and types of organisms.
In order to later find the most efficient organism for the degradation and to be able to put it into practice at the laboratory level and later at the industrial level, which will bring a great benefits.