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Organic chem and enzyme review game
Organic chem and enzyme review game

... becomes a polymer.  Describe how a polymer becomes a monomer. ...
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... Ag1+ + NO31- + Na1+ + Cl1-  AgCl + Na1+ + NO31Note that the AgCl did not ionize, because it is a “precipitate” ...
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... If the solution turns pale green and a grey deposit forms on the iron metal, then the solution contains lead ions, as Fe is higher on the activity series than Pb. The pale green solution is due to iron(II) ions being formed. The grey deposit is lead. Fe + Pb2+  Fe2+ + Pb If the solution remains col ...
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... Homology modeling is based on the assumption that, if two proteins have similar sequences then they will have similar structures. If the sequence identity is greater than 90% then the predicted structure is practically the same as the template structure. If the sequence identity is lower than 25% th ...
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... They are generally equally active on D and L isomers of a given substrate. They can increase the equilibrium constant for a given reaction by a thousand fold or more. They can increase the reaction rate for a given reaction by a thousand fold or more. To be effective, they must be present at the sam ...
Chapter 11 Chemical Reactions
Chapter 11 Chemical Reactions

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... A. because proteins (enzymes) are made of chiral subunits they usually react with only one stereoisomer of a molecule. B. because proteins (enzymes) are not made of chiral subunits they react with all stereoisomers of a molecule. C. because proteins (enzymes) are made of chiral subunits they react w ...
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Balancing Chemical Equations

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... absorbance) contains no protein, while the blue sample (higher absorbance) contains protein. The amount of protein in the second sample can be determined by comparison to a standard curve ...
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Multi-state modeling of biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behaviour of biological molecules or complexes that can adopt a large number of possible functional states.Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: The problem of how to describe and specify a multi-state system (the ""specification problem"") and the problem of how to use a computer to simulate the progress of the system over time (the ""computation problem""). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states, and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators further fall into two categories: Non-spatial simulators such as StochSim, DYNSTOC, RuleMonkey, and NFSim and spatial simulators, including Meredys, SRSim and MCell. Modelers can thus choose from a variety of tools; the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.
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