Pub Date : 2021-07-08DOI: 10.9734/bpi/cacb/v9/1742c
N. Furusawa
The simultaneous measurement of trenbolone acetate and 17(beta)-trenbolone in cattle muscle is described using an affordable, safe, and rapid sample preparation procedure followed by reversed-phase high-performance liquid chromatography (HPLC). HPLC analysis with a photo-diode array detector was carried out on a short C1 column with an isocratic mobile phase. The method was validated through analyses of spiked samples, resulting recoveries ((ge) 87.9%; relative standard deviations (le) 3.4%), analytical total time (< 20 min/sample, where, a batch of 12 samples was completed in 2 hours), and quantitation limits ((le) 1.8 ng/g). The proposed technique for determination of TBa and (beta)-TB in beef (cattle muscle) is a useful tool for the routine residue monitoring in beef and the withdrawal control of beef farm. No harmful organic solvents and reagents were used at all.
同时测量牛肌肉中的醋酸trenbolone和17 (beta) -trenbolone，使用价格合理，安全，快速的样品制备程序，然后是反相高效液相色谱(HPLC)。HPLC分析采用光电二极管阵列检测器，色谱柱为C1短柱，流动相为等等温。通过对加标样品的分析，验证了该方法的有效性，回收率为(ge) 87.9%; relative standard deviations (le) 3.4%), analytical total time (< 20 min/sample, where, a batch of 12 samples was completed in 2 hours), and quantitation limits ((le) 1.8 ng/g). The proposed technique for determination of TBa and (beta)-TB in beef (cattle muscle) is a useful tool for the routine residue monitoring in beef and the withdrawal control of beef farm. No harmful organic solvents and reagents were used at all.
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Pub Date : 2021-07-08DOI: 10.9734/bpi/cacb/v9/9982d
C. D. Carpio, E. Ichiishi
Automatic prediction of bi-molecular protein complexes and biomolecular interactions has been the object of a diversity of computational studies and with different degrees of success. Extrapolating these methodologies to treat the harder problem of computing the structure and function of multi-meric proteins, however, poses several complications. The most relevant stems from the combinatorial aspect of the problem, which involves the prediction of the dynamic order in which the subunits interact (the interaction path). A second, not less important, is the size of these molecules which account for thousands of atoms, and thus require sophisticated computational platforms. In this chapter we entail the efforts of a recent study oriented to the automatic elucidation of protein multimeric configurations and thereby the dynamic order of multimeric protein complex formation. The study is namely based on the development of a genuine approach that requires as unique information that of the isolated structures of each of the subunits constituting the multimeric complex. The method is based on an original protocol we have implemented to infer interaction sites on protein surfaces. Hitherto attempts to solve this relevant problem in protein function elucidation have been limited to three body dockings using conventional docking algorithms and molecular dynamic simulations. Here the aim is to infer complex configurations and dynamic orders of formation from the monomers known to constitute a multimeric complex unveiling active regions on the surfaces of the proteins and intermediate complexes. We present three case studies and show that important insights into the formation mechanisms of this type of multimeric complexes can be gained from the analysis of the surface characteristics of the interacting monomers which can facilitate, in a further stage, the docking and energy calculations involved in the prediction of the configurations of these complexes.
{"title":"Inference of Structure and Dynamic Order of Formation of Multimeric Protein Complexes","authors":"C. D. Carpio, E. Ichiishi","doi":"10.9734/bpi/cacb/v9/9982d","DOIUrl":"https://doi.org/10.9734/bpi/cacb/v9/9982d","url":null,"abstract":"Automatic prediction of bi-molecular protein complexes and biomolecular interactions has been the object of a diversity of computational studies and with different degrees of success. Extrapolating these methodologies to treat the harder problem of computing the structure and function of multi-meric proteins, however, poses several complications. The most relevant stems from the combinatorial aspect of the problem, which involves the prediction of the dynamic order in which the subunits interact (the interaction path). A second, not less important, is the size of these molecules which account for thousands of atoms, and thus require sophisticated computational platforms. \u0000In this chapter we entail the efforts of a recent study oriented to the automatic elucidation of protein multimeric configurations and thereby the dynamic order of multimeric protein complex formation. The study is namely based on the development of a genuine approach that requires as unique information that of the isolated structures of each of the subunits constituting the multimeric complex. The method is based on an original protocol we have implemented to infer interaction sites on protein surfaces. Hitherto attempts to solve this relevant problem in protein function elucidation have been limited to three body dockings using conventional docking algorithms and molecular dynamic simulations. Here the aim is to infer complex configurations and dynamic orders of formation from the monomers known to constitute a multimeric complex unveiling active regions on the surfaces of the proteins and intermediate complexes. We present three case studies and show that important insights into the formation mechanisms of this type of multimeric complexes can be gained from the analysis of the surface characteristics of the interacting monomers which can facilitate, in a further stage, the docking and energy calculations involved in the prediction of the configurations of these complexes.","PeriodicalId":10902,"journal":{"name":"Current Advances in Chemistry and Biochemistry Vol. 9","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88492625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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