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Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.
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Closed Form Time Response of an Infinite Tree of Mechanical Components Described by an Irrational Transfer Function
In this work we determine the time-domain dynamics of a complex mechanical network of integer-order components, e.g., springs and dampers, with an overall transfer function described by implicitly defined operators. This type of transfer functions can be used to describe very large scale dynamics of robot formations, multi-agent systems or viscoelastic phenomena. Such large-scale integrated systems are becoming increasingly important in modern engineering systems, and an accurate model of their dynamics is very important to achieve their control. We give a time domain representation for the dynamics of the system by using a complex variable analysis to find its impulse response. Furthermore, we validate how our infinite order model can be used to describe dynamics of finite order networks, which can be useful as a model reduction method.
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- Award ID(s):
- 1826079
- PAR ID:
- 10107841
- Date Published:
- Journal Name:
- Proceedings of the ... American Control Conference
- ISSN:
- 2378-5861
- Page Range / eLocation ID:
- 5828-5833
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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