The paper "Elucidation of protein–ligand interactions by multiple trajectory analysis methods" by Nian Wu, Ruotian Zhang, Xingang Peng, Lincan Fang, Kai Chen, and Joakim S. Jestil presents a comprehensive framework called Moira (molecular dynamics trajectory analysis) to automate the process of analyzing protein-ligand interactions. The framework integrates molecular docking, molecular dynamics (MD) simulations, and various analysis techniques to explore geometric and energetic aspects of protein-ligand interactions. The authors conducted MD simulations for 400 trajectories, focusing on four initial ligand conformations (native, c_2a, c_5a, c_10a) to assess their stability and binding affinity. They used root-mean square deviation (RMSD), protein-ligand interaction profiler (PLIP), and molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) to analyze the trajectories. The results showed that the native pose exhibited higher stability and binding affinity compared to the other conformations, with the c_2a pose showing a significant advantage over c_5a and c_10a. The study also evaluated the performance of different analysis techniques in distinguishing native poses among the four conformations. The combination of RMSD and MM/PBSA binding affinities improved the accuracy of identifying native poses compared to using single static poses. The framework's effectiveness was demonstrated through its ability to accurately predict binding positions and affinities, providing a valuable tool for drug discovery and computational biology.The paper "Elucidation of protein–ligand interactions by multiple trajectory analysis methods" by Nian Wu, Ruotian Zhang, Xingang Peng, Lincan Fang, Kai Chen, and Joakim S. Jestil presents a comprehensive framework called Moira (molecular dynamics trajectory analysis) to automate the process of analyzing protein-ligand interactions. The framework integrates molecular docking, molecular dynamics (MD) simulations, and various analysis techniques to explore geometric and energetic aspects of protein-ligand interactions. The authors conducted MD simulations for 400 trajectories, focusing on four initial ligand conformations (native, c_2a, c_5a, c_10a) to assess their stability and binding affinity. They used root-mean square deviation (RMSD), protein-ligand interaction profiler (PLIP), and molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) to analyze the trajectories. The results showed that the native pose exhibited higher stability and binding affinity compared to the other conformations, with the c_2a pose showing a significant advantage over c_5a and c_10a. The study also evaluated the performance of different analysis techniques in distinguishing native poses among the four conformations. The combination of RMSD and MM/PBSA binding affinities improved the accuracy of identifying native poses compared to using single static poses. The framework's effectiveness was demonstrated through its ability to accurately predict binding positions and affinities, providing a valuable tool for drug discovery and computational biology.