{"title":"酶动力学方法的发展史:从几分钟到几毫秒。","authors":"Kenneth A Johnson","doi":"10.1016/bs.enz.2023.07.005","DOIUrl":null,"url":null,"abstract":"<p><p>The last review on transient-state kinetic methods in The Enzymes was published three decades ago (Johnson, K.A., 1992. The Enzymes, XX, 1-61). In that review the foundations were laid out for the logic behind the design and interpretation of experiments. In the intervening years the instrumentation has improved mainly in providing better sample economy and shorter dead times. More significantly, in 1992 we were just introducing methods for fitting data based on numerical integration of rate equations, but the software was slow and difficult to use. Today, advances in numerical integration methods for data fitting have led to fast and dynamic software, making it easy to fit data without simplifying approximations. This approach overcomes multiple disadvantages of traditional data fitting based on equations derived by analytical integration of rate equations, requiring simplifying approximations. Mechanism-based fitting using computer simulation resolves mechanisms by accounting for the concentration dependence of the rates and amplitudes of the reaction to find a set of intrinsic rate constants that reproduce the experimental data, including details about how the experiment was performed in modeling the data. Rather than discuss how to design and interpret rapid-quench and stopped-flow experiments individually, we now focus on how to fit them simultaneously so that the quench-flow data anchor the interpretation of fluorescence signals. Computer simulation streamlines the fitting of multiple experiments globally to yield a single unifying model to account for all available data.</p>","PeriodicalId":39097,"journal":{"name":"Enzymes","volume":"54 ","pages":"107-134"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"History of advances in enzyme kinetic methods: From minutes to milliseconds.\",\"authors\":\"Kenneth A Johnson\",\"doi\":\"10.1016/bs.enz.2023.07.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The last review on transient-state kinetic methods in The Enzymes was published three decades ago (Johnson, K.A., 1992. The Enzymes, XX, 1-61). In that review the foundations were laid out for the logic behind the design and interpretation of experiments. In the intervening years the instrumentation has improved mainly in providing better sample economy and shorter dead times. More significantly, in 1992 we were just introducing methods for fitting data based on numerical integration of rate equations, but the software was slow and difficult to use. Today, advances in numerical integration methods for data fitting have led to fast and dynamic software, making it easy to fit data without simplifying approximations. This approach overcomes multiple disadvantages of traditional data fitting based on equations derived by analytical integration of rate equations, requiring simplifying approximations. Mechanism-based fitting using computer simulation resolves mechanisms by accounting for the concentration dependence of the rates and amplitudes of the reaction to find a set of intrinsic rate constants that reproduce the experimental data, including details about how the experiment was performed in modeling the data. Rather than discuss how to design and interpret rapid-quench and stopped-flow experiments individually, we now focus on how to fit them simultaneously so that the quench-flow data anchor the interpretation of fluorescence signals. Computer simulation streamlines the fitting of multiple experiments globally to yield a single unifying model to account for all available data.</p>\",\"PeriodicalId\":39097,\"journal\":{\"name\":\"Enzymes\",\"volume\":\"54 \",\"pages\":\"107-134\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Enzymes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/bs.enz.2023.07.005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Enzymes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/bs.enz.2023.07.005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
History of advances in enzyme kinetic methods: From minutes to milliseconds.
The last review on transient-state kinetic methods in The Enzymes was published three decades ago (Johnson, K.A., 1992. The Enzymes, XX, 1-61). In that review the foundations were laid out for the logic behind the design and interpretation of experiments. In the intervening years the instrumentation has improved mainly in providing better sample economy and shorter dead times. More significantly, in 1992 we were just introducing methods for fitting data based on numerical integration of rate equations, but the software was slow and difficult to use. Today, advances in numerical integration methods for data fitting have led to fast and dynamic software, making it easy to fit data without simplifying approximations. This approach overcomes multiple disadvantages of traditional data fitting based on equations derived by analytical integration of rate equations, requiring simplifying approximations. Mechanism-based fitting using computer simulation resolves mechanisms by accounting for the concentration dependence of the rates and amplitudes of the reaction to find a set of intrinsic rate constants that reproduce the experimental data, including details about how the experiment was performed in modeling the data. Rather than discuss how to design and interpret rapid-quench and stopped-flow experiments individually, we now focus on how to fit them simultaneously so that the quench-flow data anchor the interpretation of fluorescence signals. Computer simulation streamlines the fitting of multiple experiments globally to yield a single unifying model to account for all available data.