The paper addresses the challenge of identifying slow molecular order parameters for constructing accurate Markov models of macromolecular systems. The authors propose using the variational principle of conformation dynamics to identify the "slow subspace" of a set of prior order parameters, such as distances and dihedral angles. They demonstrate that time-lagged independent component analysis (TICA) can be used to identify optimal indicators of slow transitions, which serve as reaction coordinates. The method is applied to two molecular dynamics simulations: the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue natively disordered peptide KID. The results show that TICA coordinates effectively resolve the slowest processes and provide accurate timescales, while PCA and minimal RMSD metrics often fail to capture the slowest relaxation timescales. The authors also propose a method to select the optimal indicators of slow processes, which helps in understanding the structural changes associated with these processes.The paper addresses the challenge of identifying slow molecular order parameters for constructing accurate Markov models of macromolecular systems. The authors propose using the variational principle of conformation dynamics to identify the "slow subspace" of a set of prior order parameters, such as distances and dihedral angles. They demonstrate that time-lagged independent component analysis (TICA) can be used to identify optimal indicators of slow transitions, which serve as reaction coordinates. The method is applied to two molecular dynamics simulations: the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue natively disordered peptide KID. The results show that TICA coordinates effectively resolve the slowest processes and provide accurate timescales, while PCA and minimal RMSD metrics often fail to capture the slowest relaxation timescales. The authors also propose a method to select the optimal indicators of slow processes, which helps in understanding the structural changes associated with these processes.