Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.1.27.1 (RNase)
 16,360 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A new method for analyzing steady-state enzyme kinetic data is presented. The technique, which is based on the numerical differentiation of the complete reaction curve, has several advantages over initial velocity and integrated Michaelis-Menten equation methods. The differentiated data are fit to the differential equation describing the appropriate kinetic scheme. This approach is particularly valuable in cases of strong competitive product inhibition and of changing concentrations of active enzyme. The method assumes a reversible reaction and is applicable to a very wide variety of steady-state kinetic schemes. A particular advantage of this approach over integrated methods is that it is independent of [S0] and hence of errors in [S0]. The combination of complete progress curve and computer analysis makes this approach very efficient with respect to both time and materials. Running on an IBM PC XT or equivalent microcomputer with an 8087 coprocessor, the analyses are very fast, the complete process usually being complete in a minute or two. The utility of the technique is demonstrated by application to both simulated and real data. We show that the differentiation of the progress curve for the ribonuclease-catalyzed hydrolysis of 2',3'-cyclic cytidine monophosphate reveals strong product inhibition by 3'-CMP, and this product inhibition accounts for the large discrepancies reported in the literature for the value of Km for this substrate. The method was also applied to determine the rate of reactivation of beta-lactamase which had been reversibly inactivated by cloxacillin. Since large numbers of data points are required for the numerical differentiation the method has become practical only with the advent of computer-acquired data systems.
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PMID:The analysis of enzyme progress curves by numerical differentiation, including competitive product inhibition and enzyme reactivation. 312 Jun 22

A way of formulating the protein-folding problem in neural network optimization terms is presented in this paper. This is accomplished by representing the conformation of a protein as an array of the amino acid sequence versus position on a three-dimensional face-centered cubic lattice with an energy function defined in terms of the array variables. The method is called lattice neural network minimization (LNNM). Using the neural network minimization method, the energy function is minimized to locate the global minimum energy for the conformation of the protein. The energy function consisted of site exclusion and bond connectivity penalty terms and a pairwise contact energy potential. The contact energy potential used in the procedure is the united-residue potential of Miyazawa, Jernigan and Covell. The LNNM method found the global minimum for a seven-residue peptide in all of the 15 runs carried out. The time for each run was approximately 30 seconds on one processor of an IBM 3090 computer. For a nine-residue peptide, the global minimum was found in 7 out of 15 runs (47%) in approximately 50 seconds per run. For this peptide, LNNM found the global minimum or the second lowest minimum in 10 of the runs. In the same total CPU times (approximately 750 seconds), a Monte Carlo simulated annealing method found the global minimum or the second lowest minimum in only two runs, demonstrating the superiority of LNNM over the standard Monte Carlo simulated annealing method for this nine-residue peptide. Starting from a uniform array for the protein crambin (46 residues) on the lattice, the energy of the crambin array was minimized and a compact low-energy structure was found in approximately 25 minutes of CPU time. Its energy was much lower than that of the native protein, suggesting that there are inadequacies in the Miyazawa-Jernigan-Covell potential. The LNNM method was applied to the prediction of what was previously called nucleation but more properly called chain-folding initiation sites (CFIS) of a protein. LNNM correctly predicted the CFIS for the two proteins examined, RNase S and T4 lysozyme. The LNNM method was also applied to another chain optimization problem, minimization of the root-mean-square distance error (r.m.s.d.) (a measure similar to r.m.s. deviation) in fitting X-ray structures to a lattice, with good results.
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PMID:Lattice neural network minimization. Application of neural network optimization for locating the global-minimum conformations of proteins. 837 Dec 72