The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better persp...The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are critical.This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure.Initially,the context in a collection of words is determined using a knowledge graph for feature selection purposes.The proposed prediction is based on an easier and sim-pler logistic regression inferred binary classification technique.This approach can able to obtain a classification accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy changes.Moreover,for each cross-fold validation,the precision,recall,and F-Score are presented.These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.展开更多
文摘The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are critical.This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure.Initially,the context in a collection of words is determined using a knowledge graph for feature selection purposes.The proposed prediction is based on an easier and sim-pler logistic regression inferred binary classification technique.This approach can able to obtain a classification accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy changes.Moreover,for each cross-fold validation,the precision,recall,and F-Score are presented.These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.
