Molecular dynamics simulations are a popular means to study biomolecules, but it is often difficult to gain insights from the trajectories due to their large size, in both time and the number of features. The SAPPHIRE (States And Pathways Projected with HIgh REsolution) plot allows a direct visual inference of the dominant states visited by high-dimensional systems and how they are interconnected in time. Here, we extend this visual inference into a clustering algorithm. Specifically, the automatic procedure derives from the SAPPHIRE plot states that are kinetically homogeneous, structurally annotated, and of tunable granularity. We provide a relative assessment of the kinetic fidelity of this SAPPHIRE-based partitioning in comparison to popular clustering methods. This assessment is carried out on trajectories of a toy model and two polypeptides. We conclude with an application of our approach to a recent 100-microsecond trajectory of the main protease of SARS-CoV-2.
BT - Journal of Chemical Theory and Computation C1 - 0259 DA - 2020 Sep 09 DO - 10.1021/acs.jctc.0c00604 IS - 10 J2 - J. Chem. Theory Comput. LA - eng M3 - Research Article N2 -Molecular dynamics simulations are a popular means to study biomolecules, but it is often difficult to gain insights from the trajectories due to their large size, in both time and the number of features. The SAPPHIRE (States And Pathways Projected with HIgh REsolution) plot allows a direct visual inference of the dominant states visited by high-dimensional systems and how they are interconnected in time. Here, we extend this visual inference into a clustering algorithm. Specifically, the automatic procedure derives from the SAPPHIRE plot states that are kinetically homogeneous, structurally annotated, and of tunable granularity. We provide a relative assessment of the kinetic fidelity of this SAPPHIRE-based partitioning in comparison to popular clustering methods. This assessment is carried out on trajectories of a toy model and two polypeptides. We conclude with an application of our approach to a recent 100-microsecond trajectory of the main protease of SARS-CoV-2.
PY - 2020 SE - 6383 EP - 6383–6396 T2 - Journal of Chemical Theory and Computation TI - SAPPHIRE-based clustering VL - 16 SN - 1549-9626 ER -