webPSN is a free web server for high throughput investigation of allosterism in biological systems, which employs a mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA), i.e. PSN-ENM, strategy to investigate the structural communication in proteins and nucleic acids [1-7, 10].
The PSN analysis in general and the PSN-ENM approach in particular proved as valuable tools in a number of studies [1-10]. The webPSN server provides the user with a friendly interface and an immediate feedback through graphical visualizations of the output. Automation and high speed makes this web server suitable for high throughput investigation of the communication pathways in large sets of biomolecular systems in different functional states.
This new version of webPSN server introduces several novelties:
PSNTools User Guide (command line version) (PDF)
PSNToolsGUI User Guide (graphical user interface) (PDF)
This is a list of additional recommended software that, while not strictly necessary, are however highly recommended
Please, refer to your operating system manual and to the website of each software for more details about the installation process:
Total Submitted Jobs | 32223 |
Submitted Single Net Jobs | 28938 |
Submitted Nets Difference Jobs | 1919 |
Submitted Nets Consensus Jobs | 1350 |
Total Generated Labels | 67392 |
Last Submission | Sunday 13-10-2024 @ 14:10:35 |
Average Network Size | 273.05 nodes |
Average Calculation Time | 200.98 sec |
If you have any questions or if you encounter any problems with this server please do not hesitate to contact us at the following email address:
or fill the following form (all fields are required):
Insert the job Id code in the space below and click retrieve button to load your results page
Please note that job Id codes are case sensitive (i.e. abcdef and abCdef are two different codes)
Thank you for using webPSN, we really appreciate it
Please, remember to cite at least one of the following papers in all published works which utilize this webserver:
Follow the step-by-step tutor pages to obtain the results of the three case studies presented in the paper or go directly to the precalculated results
Single PSN | step-by-step submission | example output |
PSN Difference | step-by-step submission | example output |
Consensus PSN | step-by-step submission | example output |
Label Generator | step-by-step submission | example output |
Single PSN | step-by-step submission | example output |
PSN Difference | step-by-step submission | example output |
Consensus PSN | step-by-step submission | example output |
Label Generator | step-by-step submission | example output |
The webPSN web server employs a mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy to investigate allosterism in biological systems [1,2]. PSN is used to compute the interaction strengths and connectivities among nodes while ENM-NMA provides information on system’s dynamics, which serves to compute the cross-correlation of atomic motions for path filtering. The method is hereafter indicated as PSN-ENM.
In synthesis, the first step in the PSN-ENM approach consists in performing the PSN analysis on a single high resolution structure, which serves to derive the network components (e.g. nodes, links, hubs, communities). Nodes interconnectivities represent also the basis to search for all possible shortest communication paths between all nodes in the network. The shortest paths are then filtered out according to the cross-correlation of atomic motions derived from ENM-NMA resulting in a reduced pool of paths composed of highly correlated nodes. Finally, a global metapath made of the most recurrent links in the shortest path pool is computed to infer a coarse picture of the structural communication in the considered system. Single paths can be inferred as well by providing a single node pair as an input of path search.
The first version of webPSN server was designed to compute only Single-structure PSN-ENM analyses. Important novelties concern the possibility to: a) compare Protein Structure Graphs (PSGs) (e.g. nodes, hubs, and links) or metapaths computed on two structures (i.e. difference network) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a set of homologous or analogous systems. Computation of consensus network allows to infer common structural communication features in proteins sharing the same functionality [1, 2, 5, 7, 9] or even sharing only the fold. On the other hand, computation of difference network is particularly useful to infer commonalties and differences in the structural communication of two functionally different states of the same protein either induced by ligand or mutation [1, 2, 6, 7].
The updated version deals not only with proteins but also with nucleic acids and protein-nucleic acid complexes.
Difference and consensus network computations rely on a unique positional labeling and the server provides different options for this task: from a fully automated approach, based on the structural and/or sequence alignment performed by external, state of the art, software [10-14], which does not require any user intervention, to a fully customizable labeling syntax for advanced users.
The webPSN server produces high quality and publication-ready plots and 3D outputs, as PyMol and VMD scripts, as well as a number of easy-to-access data files. Additionally, all these outputs are conveniently zipped for download.
webPSN adopts a modified version of the selection syntax used in Wordom .
This syntax employs a string structured as follows:
/chain/segment/residues
Note: a segment is the 12th field in the pdb (3rd after coordinates). It is a 4-character field, which must not be confused with the chain (single-character) field after the residue-type in the pdb (5th field).
Wild cards such as * (any number of any character), ? (any single character), [abc] (any single character among a, b and c) and [!abc] (any single character except a, b and c) are supported. Ranges can also be defined using - character.
Syntax Meaning /*/*/* selects all residues in all chains and segments (can be omitted, applied by defualt) /C/S/* selects all residues in chain C and segment S /C/S/135 selects only residue 135 from chain C and segment S /C/S?/*/* selects all residues in chain C and segment S1, SB, SC ... /C/S[AB]/* selects all residues in chain C and segment SA, SB /C/S/1-326 selects all residues in the range 1-326 (terminals included) from chain C and segment S
Ranges can be concatenated using the | character
Syntax Meaning /C/S/1-10|15|20-30 selects the following residues from chain C and segment S: from 1 to 10, 15 and from 20 t0 30
Finally, several selections can be concatenated using the ; character
Syntax Meaning /A/Q/* ; /B/W/1-10|15 ; /G/E/20-30 selects all residues from chain A and segment Q residues from 1 to 10 and 15 from chain B and segment W and residues from 20 to 30 from G and segment E
The user can, optionally, provide numerical values to be associated with any number of residues (e.g. conservation scores, mutation effect, etc.). If provided, these values will appear in the output tables (column OutVal) and the user will be able to search and sort output tables on the basis of these values.
The user can submit external values using one of the following file formats:
... C:S:Y10 7 C:S:V11 7 C:S:P12 5 C:S:F13 6 C:S:S14 4 C:S:N15 7 C:S:?100 5 ...
Chain:Segment:ResTypeResNum
Please use one-letter codes for standard amino acids (e.g. P for proline, Y for tyrosine, etc) and the following lower case one-letter codes for standard nucleotides:
Base | Code |
---|---|
A, DA | a |
C, DC | c |
G, DG | g |
DT | t |
U | u |
Use ? character for any other molecule present in your pdb.
Note: When used in difference and consensus networks, the values refer to the selected reference structure and will be applied to all other networks using labels.
This server provides four options for this task:
Although the compilation of label files may require a considerable amount of time depending on the size and number of analyzed structures, label files provide the user with a full control of the labeling process.
The format of a label file is quite similar to the one used in external values files: a text file with 2 columns per line, one-residue definition and one label separated by at least one space or tab character, as in the following excerpt:... C:S:Y10 Tyr10 C:S:V11 Val11 C:S:P12 Pro12 C:S:F13 Phe13 C:S:S14 Ser14 ...
Chain:Segment:ResTypeResNum
Please use one-letter codes for standard amino acids (e.g. P for proline, Y for tyrosine, etc) and the following lower case one-letter codes for standard nucleotides:
Base | Code |
---|---|
A, DA | a |
C, DC | c |
G, DG | g |
DT | t |
U | u |
Use ? character for any other molecule present in your pdb.
... C:S:?100 Ligand ...
Net Summary | |
---|---|
I_{min} | The minimum interaction strength needed to connect two nodes. More details about this value and how it is calculated can be found in the PSN section of the theory page. |
Number of Linked Nodes | Total number of nodes with at least one link. |
Number of Links | Total number of links with an interaction strength ≥ I_{min}. Links with a lower value may have been added to avoid excessive network fragmentation. More details about links with a sub-I_{min} interaction strength can be found in the PSN section of the theory page. |
Number of Hubs | Total number of nodes with at least 4 links. More details about this cutoff can be found in the PSN section of the theory page. |
Number of Links mediated by hubs | Total number of links mediated by hubs. |
Number of Communities | Total number of communities. Communities are sets of highly interconnected nodes that can be viewed as fairly independent compartments of a graph. You can find a more detailed explanation of what a community is and how it is identified in the PSN section of the theory page. |
Number of Nodes involved in Communities | Total number of nodes in a community. |
Number of Links involved in Communities | Total number of links in a community. |
Network Similarities | |
---|---|
Average % Shared Neighbours (Jaccard) | Is the average of the ratio of the intersection over union of each node links.
Where n is a given node in network A and B, and A_{n} and B_{n} are the links of node n in network A and B, respectively [20]. |
Average % Shared Neighbours (Otsuka version of cosine similarity) | Is the average of the ratio of the intersection over the square root of the product of the number of links made by each node in compared networks..
Where n is a given node in network A and B, and A_{n} and B_{n} are the links of node n in network A and B, respectively [21]. |
Average % Shared Neighbours (Overlap Coefficient) | Is the average of the ratio of the intersection over the smaller list of links made by each node. Also known as overlap coefficient.
Where n is a given node in network A and B, and A_{n} and B_{n} are the links of node n in network A and B, respectively [22]. |
Average % Shared Cliques (k3-6) | Is the average of the ratio of the intersection over union of k=3, k=4, k=5, and k=6 cliques.
Where A_{k} and B_{k} are k-cliques in in network A and B, respectively. |
Graphlets Similarity | Is the Graphlet Degree Distribution Agreement calculated comparing in the two network the distribution of Graphlets small, connected, non-isomorphic subgraphs [23]. |
(Filtered) Path Summary | |
---|---|
Number Of Nodes in Metapath | Total number of nodes in the global/filtered metapath. |
Number Of Links Metapath | Total number of links in the global/filtered metapath. |
Number of Shortest Paths | Total number paths in the global/filtered paths pool. |
Length Of Smallest Path | Number of nodes in the shortest path. |
Average Path Length | Average number of nodes in the global/filtered paths pool. |
Length of Longest Path | Number of nodes in the longest path. |
Minimum Path Force | Lowest average interaction strength of links in the global/filtered path pool. |
Average Path Force | Average of the average interaction strengths of links in the global/filtered path pool. |
Maximum Path Force | Highest average interaction strength of links in the global/filtered path pool. |
Minimum Path Correlation | Lowest average motion correlation between each node and the two extreme nodes in a path in the global/filtered path pool. |
Average Path Correlation | Average of the average motion correlations between each node and the two extreme nodes in a path in the global/filtered path pool. |
Maximum Path Correlation | Highest average motion correlation between each node and the two extreme nodes in a path in the global/filtered path pool. |
Minimum % Of Corr. Nodes | Lowest percentage of internal nodes with a motion correlation ≥ the cutoff with one or both the two extremities in a path in the global/filtered path pool. |
Average % Of Corr. Nodes | Average percentage of internal nodes with a motion correlation ≥ the cutoff with one or both the two extremities in a path in the global/filtered path pool. |
Maximum % Of Corr. Nodes | Highest percentage of internal nodes with a motion correlation ≥ the cutoff with one or both the two extremities in a path in the global/filtered path pool. |
Minimum Path Hubs % | Lowest percentage of hubs in the global/filtered path pool. |
Average Path Hubs % | Average percentage of hub nodes present in the global/filtered paths pool. |
Maximum Path Hubs % | Highest percentage of hubs in the global/filtered path pool. |
CorrPairs | Distribution of correlated node pairs. |
Links | The total number of links in each trajectory frame. |
Hubs | The total number of hubs in each trajectory frame. |
HLinks | The total number links mediated by at least one hub in each trajectory frame. |
CommsNum | The total number of communities in each trajectory frame. |
CommsNodes | The total number of community nodes in each trajectory frame. |
CommsLinks | The total number of community links in each trajectory frame. |
CommsHubs | The total number of community hubs in each trajectory frame. |
CommsHLinks | The total number of community links mediated by at least one hub in each trajectory frame. |
Comm1Nodes | The total number of nodes in the largest community in each trajectory frame. |
Comm1Links | The total number of links in the largest community in each trajectory frame. |
Comm1Hubs | The total number of hubs in the largest community in each trajectory frame. |
Comm1Links | The total number of links mediated by at least one hub in the largest community in each trajectory frame. |
Comm2Nodes | The total number of nodes in the 2^{nd} largest community in each trajectory frame. |
Comm2Links | The total number of links in the 2^{nd} largest community in each trajectory frame. |
Comm2Hubs | The total number of hubs in the 2^{nd} largest community in each trajectory frame. |
Comm2Links | The total number of links mediated by at least one hub in the 2^{nd} largest community in each trajectory frame. |
Comm3Nodes | The total number of nodes in the 3^{rd} largest community in each trajectory frame. |
Comm3Links | The total number of links in the 3^{rd} largest community in each trajectory frame. |
Comm3Hubs | The total number of hubs in the 3^{rd} largest community in each trajectory frame. |
Comm3Links | The total number of links mediated by at least one hub in the 3^{rd} largest community in each trajectory frame. |
Comm123Nodes | The total number of nodes in the 3 largest communities in each trajectory frame. |
Comm123Links | The total number of links in the 3 largest communities in each trajectory frame. |
Comm123Hubs | The total number of hubs in the 3 largest communities in each trajectory frame. |
Comm123Links | The total number of links mediated by at least one hub in the 3 largest communities in each trajectory frame. |
For each non standard aminoacids/nuclotides in your trajectory, the following statistics will also be present:
LigNameLinks | The total number of links made by the ligand in each trajectory frame. |
LigNameHLinks | The total number of links mediated by at least one hub and the ligand in each trajectory frame. |
LigNameCommNodes | The total number of nodes present in the ligand community in each trajectory frame. |
LigNameCommLinks | The total number of links present in the ligand community in each trajectory frame. |
LigNameCommHubs | The total number of hubs present in the ligand community in each trajectory frame. |
LigNameCommHLinks | The total number of links mediated by at least one hub present in the ligand community in each trajectory frame. |
LigNameShellNodes | The total number of nodes in the interaction shell of the ligand in each trajectory frame. |
LigNameShellLinks | The total number of links in the interaction shell of the ligand in each trajectory frame. |
LigNameShellHubs | The total number of hubs in the interaction shell of the ligand in each trajectory frame. |
LigNameShellHLinks | The total number of links mediated by at least one hub in the interaction shell of the ligand in each trajectory frame. |
where LigName is the label of a given non standard aminoacids/nuclotides
PSN analysis is a product of graph theory applied to protein and nucleic acid structures [4, 16]. A graph is defined by a set of vertices (nodes) and connections (edges) between them. In a PSN, each amino acid residue is represented as a node and these nodes are connected by edges based on the strength of non-covalent interactions between residues. The strength of interaction between residues i and j (I_{ij}) is evaluated as a percentage given by equation 1:
$${I}_{\mathrm{ij}}=\frac{{n}_{\mathrm{ij}}}{\sqrt{{N}_{i}{N}_{j}}}100$$ | (1) |
where n_{ij} is the number of atom-atom pairs between the side chains of residues i and j within a distance cutoff of 4.5 Aring. N_{i} and N_{j} are normalization factors for residue types i and j, which account for the differences in size of the amino acid side chains and their propensity to make the maximum number of contacts with other amino acids in protein structures. Glycines, are now included in the PSN analysis. The webPSN server has an internal database with the normalization factors for the 20 standard amino acids and the 8 standard nucleotides (i.e. dA, dG, dC, dT, A, G, C, and U), as well as for more than 30,000 biologically relevant molecules and ions (ligands, lipids, sugars, etc) from the PDB. Additionally, the server automatically identifies un-parametrized molecules in the submitted PDB files and automatically calculates their normalization factors transparently.
I_{ij} are calculated for all node pairs. At a given interaction strength cutoff, I_{min}, any residue pair ij for which I_{ij} ≥ I_{min} is considered to be interacting and hence is connected. Node interconnectivity is used to highlight node clusters, where a cluster is a set of connected nodes in a graph. Cluster size, i.e., the number of nodes constituting a cluster, varies as a function of the Imin, and the size of the largest cluster is used to calculate the I_{critic} value. The latter is defined as the I_{min}, at which the size of the largest cluster is half the size of the largest cluster at I_{min} = 0.0%. Studies by Vishveshwara's [16] group found that optimal I_{min} corresponds to the one at which the largest cluster undergoes a transition. All resulting clusters are then iteratively connected by the link(s) with the highest sub-I_{critic} interaction strength to compensate, at least in part, for the lack of side chain fluctuations.
Residues making four or more edges are referred to as hubs at that particular I_{min}. Such cutoff for hub definition relates to the intrinsic limit in the possible number of non covalent connections made by an amino acid in protein structures due to steric constraints. The cutoff 4 is close to the upper limit. The majority of amino acid hubs indeed make from 4 to 6 links, with 4 being the most frequent value.
Finally, links are then used to highlight network communities, which are sets of highly interconnected nodes such that nodes belonging to the same community are densely linked to each other and poorly connected to nodes outside the community. Communities can be considered as fairly independent compartments of a graph. They are identified using a variant of the clique percolation method, by finding all the k=3-cliques, i.e. sets of three fully interconnected nodes, and then merging all those cliques sharing at least one node.
Back to topThe combination between a coarse grained representation of a protein structure (e.g. ENM) and Normal Mode Analysis (NMA) is ever increasingly used to study the collective dynamics of complex systems. ENM-NMA is a coarse grained normal mode analysis technique able to describe the vibrational dynamics of protein systems around an energy minimum. With this technique, each protein/nucleic acid structure is described by a reduced subset of atoms corresponding to the Cα-atoms, for standard amino acids, and the atom nearest to the geometric center for all other molecules.
The interactions between particle pairs are given by a single term Hookean harmonic potential. The total energy of the system is thus described by the simple Hamiltonian:
$$\sum _{i\ne j}{k}_{\mathrm{ij}}{\left({d}_{\mathrm{ij}}-{d}_{\mathrm{ij}}^{0}\right)}^{2}$$ | (2) |
where d_{ij} and d_{ij}^{0} are the instantaneous and equilibrium distances between particle i and j, respectively, whereas k_{ij} is a force constant, defined as:
$${k}_{\mathrm{ij}}=C{\left(\frac{{d}_{\mathrm{ij}}}{{d}_{\mathrm{ij}}^{0}}\right)}^{6}$$ | (3) |
where C is constant (with a default value of 40 Kcal/mol ·Å^{2}).
The cross-correlations of motions for path filtering are obtained from the covariance matrix C [17]:
$${C}_{\mathrm{ij}}=\frac{\frac{\sum _{l=1}^{M}{\nu}_{\mathrm{il}}{\nu}_{\mathrm{jl}}}{{\lambda}_{l}}}{\sqrt[]{\frac{\sum _{m=1}^{M}{\nu}_{\mathrm{im}}{\nu}_{\mathrm{jm}}}{{\lambda}_{m}}}\sqrt[]{\frac{\sum _{n=1}^{M}{\nu}_{\mathrm{in}}{\nu}_{\mathrm{jn}}}{{\lambda}_{n}}}}$$ | (4) |
where C_{ij} denotes the correlation between particles i and j, M is the number of modes considered for computation (the first 10 non-zero frequency modes), ν_{xy} and λ_{y} are, respectively, the x^{th} element and the associated eigenvalue of the y^{th} mode.
All ENM-NMA calculations are performed by means of the latest realease of our Wordom software [18]
Back to topThe search for all shortest paths relies on Dijkstra’s algorithm [19]. The method first finds all possible communication paths between all node pairs and then filters the results according to cross-correlation of atomic motions, as derived from ENM-NMA analysis.
Filtering consists in retaining only those shortest paths that contain only residues with a correlation ≥ 0.7 with at least one of the two path extremities (i.e. the first and last amino acids in the path).
Finally, filtered paths were used to build the global meta path, which is made of the most recurrent links, i.e. those links present in a number paths ≥ 10% of the number of paths in which the most recurrent link in present.
Such meta path represents a coarse/global picture of the structural communication in the considered system.
In the result page, the user can filter those paths that begin and end at a given residue pair or that pass through a residue. Such a path filtering provides a novel metapath and is particularly recommended when some information on residues involved in structural communication is available.
Back to topPlease stand by while I retrieve your data