Motivation

• pyANCA proposes the use of anharmonicity in organizing the conformational landscapes of proteins and other biomolecules.
• Traditional analysis tools for biomolecular simulations have focused on second-order statistics of atomic fluctuations.
• Anharmonicity in time-dependent conformational fluctuations is noted to be a key feature of functional dynamics of biomolecules. While anharmonic events are rare, long timescale (μs-ms and beyond) simulations facilitate probing of such events.
• This challenge was previously addressed by proposing anharmonicity as an organizing principle for conformational landscapes of proteins and other biomolecules. In particular, we have built quasi-anharmonic analysis (QAA) to resolve higher order spatial correlations (see Ref. 1-7). In this work, we have extended this toolbox to resolve higher order temporal correlations.
• A scalable Python package, anharmonic conformational analysis (pyANCA) is developed as an extensible software framework to characterize anharmonic events and enable a deeper analysis of their functional relevance.