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#computational biology

3 public questions tagged with this topic.

Match the items in Group 1 with an appropriate description in Group 2 Group I Group 2 P. UPGMA 1. Protein sequence datab

The principle of enzyme catalysis and lipid structure explains that substrate affinity, transition state stabilization and phospholipid composition govern function. Hence P-2, Q-4, R-1, S-3 fits best because it reflects catalytic efficiency and membrane organization, as documented in authoritative sources on biochemistry.

Ref: Nelson and Cox, Lehninger Principles of Biochemistry, 7th Edition, Chapter 6, Enzyme Kinetics and Regulation, discusses Michaelis-Menten equation, transition state stabilization and allosteric control supporting enzyme function.

Which one of the following proteins can be used as a template for structure prediction by homology modelling?

In biochemistry, enzyme catalysis and lipid structure dictates that substrate affinity, transition state stabilization and phospholipid composition govern function. Therefore pdb|1L9U|H:Identities = 173/333(51%), Positives = 233/333(69%), Expect = 2e-89 is correct as it reflects catalytic efficiency and membrane organization, supported by mechanistic studies and conserved across related systems.

Ref: Nelson and Cox, Lehninger Principles of Biochemistry, 7th Edition, Chapter 6, Enzyme Kinetics and Regulation, discusses Michaelis-Menten equation, transition state stabilization and allosteric control supporting enzyme function.

Needleman-Wunsch algorithm, is an example of dynamic programming, which does not involve

This outcome reflects core biological organization, where structure-function relationships govern cellular processes. Option C captures this correctly because it integrates fundamental principles, consistent with textbook descriptions and experimental observations in general biology.

Ref: Campbell et al., Biology, 11th Edition, Chapter 2, Chemical Basis of Life, discusses fundamental biological principles and molecular organization providing broad foundation for life sciences understanding.