ABSTRACT
Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in green synthesis has been of great interest, but the high cost of obtaining specific functional enzymes as well as the variety of enzyme types and functions hamper their application. At present, the specific functions of proteins are mainly determined through tedious and time-consuming experimental characterization. With the rapid development of bioinformatics and sequencing technologies, the number of protein sequences that have been sequenced is much larger than those can be annotated, thus developing efficient methods for predicting protein functions becomes crucial. With the rapid development of computer technology, data-driven machine learning methods have become a promising solution to these challenges. This review provides an overview of protein function and its annotation methods as well as the development history and operation process of machine learning. In combination with the application of machine learning in the field of enzyme function prediction, we present an outlook on the future direction of efficient artificial intelligence-assisted protein function research.
Subject(s)
Artificial Intelligence , Machine Learning , Proteins/genetics , Computational Biology/methods , Drug DevelopmentABSTRACT
Glycosidases are widely used in food and pharmaceutical industries due to its ability to hydrolyze the glycosidic bonds of various sugar-containing compounds including glycosides, oligosaccharides and polysaccharides to generate derivatives with important physiological and pharmacological activity. While glycosidases often need to be used under high temperature to improve reaction efficiency and reduce contamination, most glycosidases are mesophilic enzymes with low activity under industrial production conditions. It is therefore critical to improve the thermo-stability of glycosidases. This review summarizes the recent advances achieved in engineering the thermo-stability of glycosidases using strategies such as directed evolution, rational design and semi-rational design. We also compared the pros and cons of various techniques and discussed the future prospects in this area.
Subject(s)
Glycoside Hydrolases/genetics , Oligosaccharides , Polysaccharides , Protein EngineeringABSTRACT
Objectives: To evaluate the differences in dosimetry distribution among three different radiotherapy methods used for the treatment of left breast cancer affecting the total breast, clavicle, and internal breast lymphatic drainage area after breast-conserving surgery. The target coverage and organs at risk (OARs) in each method were compared. Methods: Twenty breast cancer patients who were treated at First Affiliated Hospital of Kunming Medical College, from January 2018 to October 2019 were selected. All patients underwent left-side breast conserving postoperative radiotherapy. Dynamic multi-leaf collimator (dMLC), volumetric modulated arc therapy (VMAT), and helical tomotherapy (HT) plans were designed for each patient. We compared and analyzed the parameters of planning target volume of the tumor bed (PTVtb), planning target volume (PTV), and OARs according to these plans using a paired t-test. Results: The mean dose (Dmean), D1, homogeneity index (HI) of PTVtb, Dmean, and conformity index (CI) of the PTV of the HT plan were better than those of the DMLC and VMAT plans. The Dmean, V5, V10, and V30 of the heart and lungs were significantly decreased in the HT plan. The HT plan had the lowest Dmean and D1 values for the left anterior descending coronary artery and right coronary artery. However, compared with the VMAT and DMLC plans, the HT plan had increased values for D1 and Dmean of the right breast. Conclusions: For patients with left breast cancer who have undergone breast conserving surgery, HT can reduce the dose to the ipsilateral lung and heart. However, the low-dose area of the contralateral lung was larger with the HT plan than with the VMAT and dMLC plans. With the dMLC plan, the dose to the contralateral lung was the lowest, but the doses to the ipsilateral lung and heart were higher than those with the VMAT and HT plans. An appropriate treatment plan should be chosen according to the condition of the patient.
ABSTRACT
Objective:To determine the law of dose variation in nasopharyngeal carcinoma patients treated with helical tomotherapy by observing the difference between the actual and planned doses of targets and at-risk organs in these patients. Methods:Ten naso-pharyngeal carcinoma patients were treated with helical tomotherapy. Each single dose distribution and the corresponding computed tomography (CT) images were transmitted to the commercial software MIMvista 6.50, which was used to perform deformable image registration on the CT images. The sum of the actual dose was then obtained by accumulating the single doses. The image-guided actu-al dose was denoted as Plan 2, and the initial plan was called Plan 1. The dose-volume histogram of the dose distribution of targets and at-risk organs in Plans 1 and 2 were compared. Results:The D98 and D95 doses of the planning gross target volume (PGTV) in Plan 2 were significantly lower than those in Plan 1. Compared with Plan 1, the Dmean and D50 doses of double parotids in Plan 2 were higher by 42.23%and 63.82%(P<0.001, P=0.001) on average, respectively, for the left parotid, as well as by 38.64%and 66.76%(P=0.002, 0.004), respectively, for the right parotid. The D2 dose of spinal cord in Plan 2 was significantly higher than that in Plan 1. The doses were higher by 16.49%on average (P=0.026). Conclusion:To achieve precise and individually adaptive radiotherapy, correcting the error between anatomy and placement during tomotherapy for nasopharyngeal carcinoma patients is necessary.