Solving the mechanism of a chemical reaction requires determining the structures of all the ground and transition states on its pathway. 2024 is the centenary of Brønsted’s landmark paper introducing the β-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium free energies. Protein engineering was introduced for an analogous procedure, Φ value analysis, to analyse noncovalent interactions central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis and then applied to study transition states in the pathway of protein folding – “part (b) of the protein folding problem”. This review describes the development of Φ value analysis of transition states and compares and contrasts the interpretation of β- and Φ values and their limitations. Φ analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.