Corrigendum to “Thermodynamic signatures of fragment binding: Validation of direct versus displacement ITC titrations” [Biochem. Biophys. Acta 1850 (2015) 647–656]

Rühmann, Eggert; Betz, Michael; Fricke, Marie; Heine, A.; Schäfer, Martina; Klebe, G.

doi:10.1016/j.bbagen.2015.12.002

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“…Figure 4 demonstrates the progress of the slowest implied time scale as well as the progress of the standard free energy of binding over 60 epochs of adaptive sampling. Our results of −5.97(±0.50) kcal/mol of binding free energy in epoch 60 compare favorably to the results published by Ruhmann et al 53 (−5.41(±0.22) kcal/mol) using isothermal titration calorimetry for the binding of the ligand. The implied time scale plot demonstrates how the Markov model detects slower processes over increasing epochs.…”

Section: Resultssupporting

confidence: 91%

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery

Doerr

Harvey

Noé

et al. 2016

J. Chem. Theory Comput.

393

432

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Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.

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Section: Resultssupporting

confidence: 91%