8. The pyinteraph program supports the option of writing filtered
graphs directly by specifying a persistence threshold with
options --hc-perco, --hb-perco, and --sb-perco. However, we
usually prefer not to perform any filtering at this stage so
that the original graph can be filtered more than once using
filter_graph, in case different values of P crit need to be tested.
9. xPyder graph analysis uses positive values only from the loaded
matrix. This works when using most of the networks identified
in this protocol, but can be problematic when using the
knowledge-based potential network in which the favorable
interactions are expressed as negative values. In that case we
suggest changing the sign of the energy values in the adjacency
matrix file before loading it into xPyder in order to consider
favored interactions when doing the graph analysis.
10. While this cut-off has been rationalized and validated on some
among the most popular force fields, it might not be the best
for other cases especially in which the definition of the topology changes significantly (as for coarse-grained systems).
Acknowledgments
The authors would like to thank Elena Papaleo and Emmanuelle
Bignon for fruitful comments and suggestions. This work was supported by Carlsberg Foundation Distinguished Fellowship (CF180314), The Danish Council for Independent Research, Natural
Science, Project 1 (102517), Danmarks Grundforskningsfond
(DNRF125) to our group.
References
1. Ribeiro AAST, Ortiz V (2016) A chemical perspective
on
allostery.
Chem
Rev
116:6488–6502. https://doi.org/10.1021/
acs.chemrev.5b00543
2. Papaleo E, Saladino G, Lambrughi M,
Lindorff-Larsen K, Gervasio FL, Nussinov R
(2016) The role of protein loops and linkers in
conformational dynamics and allostery. Chem
Rev 116:6391–6423. https://doi.org/10.
1021/acs.chemrev.5b00623
3. Guo J, Zhou H-X (2016) Protein allostery and
conformational dynamics. Chemical Reviews
116:6503–6515. https://doi.org/10.1021/
acs.chemrev.5b00590
4. del Sol A, Tsai C-J, Ma B, Nussinov R (2009)
The origin of allosteric functional modulation:
multiple pre-existing pathways. Structure
17:1042–1050. https://doi.org/10.1016/j.
str.2009.06.008
5. Feher VA, Durrant JD, Van Wart AT, Amaro
RE (2014) Computational approaches to
mapping allosteric pathways. Curr Opin Struct
Biol 25:98–103. https://doi.org/10.1016/j.
sbi.2014.02.004
6. Hertig S, Latorraca NR, Dror RO (2016)
Revealing atomic-level mechanisms of protein
allostery with molecular dynamics simulations.
PLoS Comput Biol 12:e1004746. https://doi.
org/10.1371/journal.pcbi.1004746
7. Allain A, Chauvot de Beauche ˆne I,
Langenfeld F, Guarracino Y, Laine E, Tchertanov L (2014) Allosteric pathway identification
through network analysis: from molecular
dynamics simulations to interactive 2D and
3D graphs. Faraday Discuss 169:303–321.
https://doi.org/10.1039/c4fd00024b
8. Tiberti M, Invernizzi G, Lambrughi M,
Inbar Y, Schreiber G, Papaleo E (2014)
Interaction Networks with PyInteraph
171
graphs directly by specifying a persistence threshold with
options --hc-perco, --hb-perco, and --sb-perco. However, we
usually prefer not to perform any filtering at this stage so
that the original graph can be filtered more than once using
filter_graph, in case different values of P crit need to be tested.
9. xPyder graph analysis uses positive values only from the loaded
matrix. This works when using most of the networks identified
in this protocol, but can be problematic when using the
knowledge-based potential network in which the favorable
interactions are expressed as negative values. In that case we
suggest changing the sign of the energy values in the adjacency
matrix file before loading it into xPyder in order to consider
favored interactions when doing the graph analysis.
10. While this cut-off has been rationalized and validated on some
among the most popular force fields, it might not be the best
for other cases especially in which the definition of the topology changes significantly (as for coarse-grained systems).
Acknowledgments
The authors would like to thank Elena Papaleo and Emmanuelle
Bignon for fruitful comments and suggestions. This work was supported by Carlsberg Foundation Distinguished Fellowship (CF180314), The Danish Council for Independent Research, Natural
Science, Project 1 (102517), Danmarks Grundforskningsfond
(DNRF125) to our group.
References
1. Ribeiro AAST, Ortiz V (2016) A chemical perspective
on
allostery.
Chem
Rev
116:6488–6502. https://doi.org/10.1021/
acs.chemrev.5b00543
2. Papaleo E, Saladino G, Lambrughi M,
Lindorff-Larsen K, Gervasio FL, Nussinov R
(2016) The role of protein loops and linkers in
conformational dynamics and allostery. Chem
Rev 116:6391–6423. https://doi.org/10.
1021/acs.chemrev.5b00623
3. Guo J, Zhou H-X (2016) Protein allostery and
conformational dynamics. Chemical Reviews
116:6503–6515. https://doi.org/10.1021/
acs.chemrev.5b00590
4. del Sol A, Tsai C-J, Ma B, Nussinov R (2009)
The origin of allosteric functional modulation:
multiple pre-existing pathways. Structure
17:1042–1050. https://doi.org/10.1016/j.
str.2009.06.008
5. Feher VA, Durrant JD, Van Wart AT, Amaro
RE (2014) Computational approaches to
mapping allosteric pathways. Curr Opin Struct
Biol 25:98–103. https://doi.org/10.1016/j.
sbi.2014.02.004
6. Hertig S, Latorraca NR, Dror RO (2016)
Revealing atomic-level mechanisms of protein
allostery with molecular dynamics simulations.
PLoS Comput Biol 12:e1004746. https://doi.
org/10.1371/journal.pcbi.1004746
7. Allain A, Chauvot de Beauche ˆne I,
Langenfeld F, Guarracino Y, Laine E, Tchertanov L (2014) Allosteric pathway identification
through network analysis: from molecular
dynamics simulations to interactive 2D and
3D graphs. Faraday Discuss 169:303–321.
https://doi.org/10.1039/c4fd00024b
8. Tiberti M, Invernizzi G, Lambrughi M,
Inbar Y, Schreiber G, Papaleo E (2014)
Interaction Networks with PyInteraph
171