Abstract
Continuing improvements in algorithms and computer speeds promise that an increasing number of biomolecular phenomena can be simulated by molecular dynamics to produce accurate "trajectories" of their molecular motions on the nanosecond to microsecond time scale. An important target for such simulations will be non-equilibrium biochemical processes, such as protein folding, but existing tools for analyzing molecular dynamics trajectories are not well suited to non-equilibrium processes and progress will require improvements in tools for classifying the range and types of dynamics exhibited by these systems. An extreme example of a non-equilibrium biochemical process is the function of "intrinsically disordered" proteins - proteins that function without ever folding into a unique structure. In this paper, we demonstrate the use of spectral clustering methods to analyze the data produced from simulations of several forms from one class of intrinsically disordered proteins, the phenylalanine-glycine nucleoporins (FG-Nups). We explain why such methods are well-suited for the data produced by our simulations and show that clustering methods provide a direct, quantitative measure of how effectively single simulations independently sample regions of structural phase space. Moreover, our clustering results show distinct dynamical behavior in different forms of the FGNups, which may provide insights into their biological function.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW |
| Pages | 17-24 |
| Number of pages | 8 |
| DOIs | |
| State | Published - 2008 |
| Externally published | Yes |
| Event | 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW - Philadelphia, PA, United States Duration: Nov 3 2008 → Nov 5 2008 |
Other
| Other | 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW |
|---|---|
| Country | United States |
| City | Philadelphia, PA |
| Period | 11/3/08 → 11/5/08 |
Fingerprint
ASJC Scopus subject areas
- Molecular Biology
- Information Systems
- Biomedical Engineering
Cite this
Analyzing dynamical simulations of intrinsically disordered proteins using spectral clustering. / Phillips, Joshua L.; Lau, Edmond Y; Colvin, Michael E.; Newsam, Shawn.
Proceedings - 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW. 2008. p. 17-24 4686204.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Analyzing dynamical simulations of intrinsically disordered proteins using spectral clustering
AU - Phillips, Joshua L.
AU - Lau, Edmond Y
AU - Colvin, Michael E.
AU - Newsam, Shawn
PY - 2008
Y1 - 2008
N2 - Continuing improvements in algorithms and computer speeds promise that an increasing number of biomolecular phenomena can be simulated by molecular dynamics to produce accurate "trajectories" of their molecular motions on the nanosecond to microsecond time scale. An important target for such simulations will be non-equilibrium biochemical processes, such as protein folding, but existing tools for analyzing molecular dynamics trajectories are not well suited to non-equilibrium processes and progress will require improvements in tools for classifying the range and types of dynamics exhibited by these systems. An extreme example of a non-equilibrium biochemical process is the function of "intrinsically disordered" proteins - proteins that function without ever folding into a unique structure. In this paper, we demonstrate the use of spectral clustering methods to analyze the data produced from simulations of several forms from one class of intrinsically disordered proteins, the phenylalanine-glycine nucleoporins (FG-Nups). We explain why such methods are well-suited for the data produced by our simulations and show that clustering methods provide a direct, quantitative measure of how effectively single simulations independently sample regions of structural phase space. Moreover, our clustering results show distinct dynamical behavior in different forms of the FGNups, which may provide insights into their biological function.
AB - Continuing improvements in algorithms and computer speeds promise that an increasing number of biomolecular phenomena can be simulated by molecular dynamics to produce accurate "trajectories" of their molecular motions on the nanosecond to microsecond time scale. An important target for such simulations will be non-equilibrium biochemical processes, such as protein folding, but existing tools for analyzing molecular dynamics trajectories are not well suited to non-equilibrium processes and progress will require improvements in tools for classifying the range and types of dynamics exhibited by these systems. An extreme example of a non-equilibrium biochemical process is the function of "intrinsically disordered" proteins - proteins that function without ever folding into a unique structure. In this paper, we demonstrate the use of spectral clustering methods to analyze the data produced from simulations of several forms from one class of intrinsically disordered proteins, the phenylalanine-glycine nucleoporins (FG-Nups). We explain why such methods are well-suited for the data produced by our simulations and show that clustering methods provide a direct, quantitative measure of how effectively single simulations independently sample regions of structural phase space. Moreover, our clustering results show distinct dynamical behavior in different forms of the FGNups, which may provide insights into their biological function.
UR - http://www.scopus.com/inward/record.url?scp=58049152329&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049152329&partnerID=8YFLogxK
U2 - 10.1109/BIBMW.2008.4686204
DO - 10.1109/BIBMW.2008.4686204
M3 - Conference contribution
AN - SCOPUS:58049152329
SN - 9781424428908
SP - 17
EP - 24
BT - Proceedings - 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW
ER -