Protein residue networks from a local search perspective

Khor, Susan

doi:10.1093/comnet/cnv014

Cited by 8 publications

(21 citation statements)

References 64 publications

(122 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While this result demonstrates the potential of RBC for multi-domain proteins, further study and more tests are required to generalize this method for larger proteins. [16] between the number of native shortcut edges |SC| and the protein chain length N, is observed.…”

Section: Resultsmentioning

confidence: 93%

“…The network of shortcut edges present in a native-state protein (SCN0) is proposed as an effective structural abstraction of protein molecules for folding purposes. Shortcut edges are identified by the EDS algorithm on a Protein Residue Network of a native-state protein (PRN0) [16,24]. The EDS algorithm is based on an intuitive message passing algorithm on a social network [27,28].…”

Section: Resultsmentioning

confidence: 99%

“…The edges of a SCN comprise shortcut edges found by the Euclidean-distance Directed Search (EDS) algorithm on a PRN. PRN construction, the EDS algorithm, and the concept of a shortcut edge, were introduced in [16]. ≥ 5.0. n uv is the number of distinct pairs (i, j) such that i is an atom of residue u, j is an atom of residue v, and the Euclidean distance between atoms i and j is at most 7.5 Å. R u and R v are extracted from a table of normalization values by residue type ( Table 1 in [17]).…”

Section: The Native Shortcut Networkmentioning

confidence: 99%

See 2 more Smart Citations

Folding with a protein's native shortcut network

Khor

2018

Proteins

Self Cite

View full text Add to dashboard Cite

A complex network approach to protein folding is proposed, wherein a protein's contact map is reconceptualized as a network of shortcut edges, and folding is steered by a structural characteristic of this network. Shortcut networks are generated by a known message passing algorithm operating on protein residue networks. It is found that the shortcut networks of native structures (SCN0s) are relevant graph objects with which to study protein folding at a formal level. The logarithm form of their contact order (SCN0_lnCO) correlates significantly with folding rate of two-state and nontwo-state proteins. The clustering coefficient of SCN0s (C ) correlates significantly with folding rate, transition-state placement and stability of two-state folders. Reasonable folding pathways for several model proteins are produced when C is used to combine protein segments incrementally to form the native structure. The folding bias captured by C is detectable in non-native structures, as evidenced by Molecular Dynamics simulation generated configurations for the fast folding Villin-headpiece peptide. These results support the use of shortcut networks to investigate the role protein geometry plays in the folding of both small and large globular proteins, and have implications for the design of multibody interaction schemes in folding models. One facet of this geometry is the set of native shortcut triangles, whose attributes are found to be well-suited to identify dehydrated intraprotein areas in tight turns, or at the interface of different secondary structure elements.

show abstract

Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: The Native Shortcut Networkmentioning

confidence: 99%

See 1 more Smart Citation

Folding with a protein's native shortcut network

Khor

2018

Proteins

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our cutoff distance is 7.5 Å and uv I ≥ 5.0. Values for these parameters were set through trial and error previously in [9], with the goal of creating PRNs that are singly connected without being unnecessarily dense. Ignoring peptide bonds is appropriate since validation of our model relies on results from [14,15].…”

Section: Protein Residue Network (Prn)mentioning

confidence: 99%

“…Hence some positive correlation between sequence distance and Euclidean distance of edges is assumed. The average Spearman correlation between edge Euclidean distance and edge sequence distance for the 166 PRNs in [9] and their randSE and randLE networks are 0.4075 (std. dev.…”

Section: Prn Navigabilitymentioning

confidence: 99%

Comparing local search paths with global search paths on protein residue networks: allosteric communication

Khor

2016

jcomplexnetw

Self Cite

View full text Add to dashboard Cite

Although proteins have been recognized as small-world networks and their small-world network properties of clustering and short paths have been exploited computationally to produce biologically relevant information, they have not been truly explored as such, i.e. as navigable small-world networks in the original spirit of Milgram's work. This research seeks to fill this gap by exploring local search on a network representation of proteins and to probe the source of navigability in proteins. Previously, we confirmed that proteins are navigable small-world networks and observed that local search paths exhibit different characteristics from global search paths. In this paper, we investigate the biological relevance of the differences in path characteristics on a type III receptor tyrosine kinase (KIT). A chief difference that works in favour of local search paths as intra-protein communication pathways is their weaker proclivity, compared to global search paths, for traversing long-range edges. Long-range edges tend to be less stable and their inclusion tends to decrease the communication propensity of a path. The source of protein navigability is traced to clustering provided by short-range edges. The majority of a protein's short-range edges reside within structures deemed important for long-range energy transport and modulation of allosteric communication in proteins. Therefore, the disruption of intra-protein communication as a result of the destruction of these structures via random rewiring is expected. A local search perspective leads us to this expected conclusion while a global search perspective does not. These findings initiate the compilation of a list of path properties that are characteristic of intra-protein pathways and could suggest fresh avenues for evolving and regulating navigable (small-world) networks.

show abstract

Mapping Energy Transport Networks in Proteins

Leitner

Yamato

2018

Reviews in Computational Chemistry

View full text Add to dashboard Cite

INTRODUCTIONThe response of proteins to chemical reactions or impulsive excitation that occurs within the molecule has fascinated chemists for decades. [1][2][3] In recent years ultrafast X-ray studies have provided ever more detailed information about the evolution of protein structural change following ligand photolysis, 4-5 and time-resolved IR and Raman techniques, e.g., have provided detailed pictures of the nature and rate of energy transport in peptides and proteins, [6][7][8][9][10][11] including recent advances in identifying transport through individual amino acids of several heme proteins. [12][13][14] Computational tools to locate energy transport pathways in proteins have also been advancing. 15 Energy transport pathways in proteins have since some time been identified by molecular dynamics (MD) simulations, [16][17] and more recent efforts have focused on the development of coarse graining approaches, 18-29 some of which have exploited analogies to thermal transport in other molecular materials. [30][31][32][33][34][35] With the identification of pathways in proteins and protein 2 complexes, network analysis has been applied to locate residues that control protein dynamics and possibly allostery, [36][37][38] where chemical reactions at one binding site mediate reactions at distance sites of the protein. In this chapter we review approaches for locating computationally energy transport networks in proteins. We present background into energy and thermal transport in condensed phase and macromolecules that underlies the approaches we discuss before turning to a description of the approaches themselves. We also illustrate the application of the computational methods for locating energy transport networks and simulating energy dynamics in proteins with several examples.One of the themes that we address in this chapter is the difference between energy transport in condensed phase generally and in a folded polymer such as a protein specifically. While the approaches that we present and detail are based on linearresponse theory for transport, we apply them to a system, a protein, where energy transport occurs highly anisotropically.We are mainly concerned with the characterization of such anisotropic transport, not simply the calculation of transport coefficients for a particular object, though that information can also be calculated starting with the approaches we present. The focus here then is on local energy transport coefficients, such as local energy conductivities and diffusivities, as discussed below, and how these local transport properties connect into a network that mediates energy dynamics in the protein. It is the network of such local energy transport coefficients that, once identified and located, can be used to model the global energy dynamics in a protein, as we describe.That energy transport pathways exist, i.e., energy does not simply flow isotropically through a globular protein, is an inherent property of the geometry of a 3 folded protein, [62][63][64][65][66][67][68][69][70] which i...

show abstract

Protein residue networks from a local search perspective

Cited by 8 publications

References 64 publications

Folding with a protein's native shortcut network

Folding with a protein's native shortcut network

Comparing local search paths with global search paths on protein residue networks: allosteric communication

Mapping Energy Transport Networks in Proteins

Contact Info

Product

Resources

About