ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules

Abstract

The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.

Prediction of allosteric sites and signaling: Insights from benchmarking datasets

“…In the previous benchmarking against ASBench, we only applied the four measures from Amor et al and identified allosteric sites for 102 out of 118 structures. 65 The additional measures enable us to identify allosteric sites of four more protein structures. The reason we have six different measures roots from the unclear definition of allosteric mechanisms.…”

“…The weights of edges correspond to the interaction energies between the atoms with weights derived from relevant interatomic potentials. An indepth procedure for the atomistic protein graph construction has been described in Delmotte et al and Amor et al 58,59 In this work, Biochemical, atomistic graph construction software in Python for proteins (BagPype) 65,74 was used to construct the atomistic protein graph, and Figure 1 illustrates the main features of this process using bovine seminal ribonuclease (PDB: 11BG) 66 as an example. The crystal structures in the PDB files are cleaned by removing water molecules and unwanted ligands followed by adding hydrogen atoms using Reduce (v.3.23), 75 which is incorporated in Bag-Pype.…”

“…Details can be found in Table S2. Note that results on the first four of the six scoring measures were first reported in the supplementary information of Mersmann et al 65 without any analysis. The CASBench database X-ray crystal structures containing various orthosteric and allosteric ligands of 91 allosteric proteins in PDB format were downloaded from the CASBench website together with the corresponding experimentally determined orthosteric and allosteric site residues.…”

“… 62 , 64 It has also been built into an efficient web application, ProteinLens, for the study of allostery. 65 To further benchmark this methodology and provide comparable insights into its performance across as diverse proteins as possible, we apply it here to two recently developed large, encompassing datasets, ASBench and CASBench. ASBench contains 235 allosteric sites, 34 and computational methods such as AlloPred, 50 AllositePro, 51 and SBSMMA 53 have made use of this dataset for method validation.…”

Relative, local and global dimension in complex networks

“…In short, from a pdb file, each atom is represented by a node, and bonds between atoms by an edge weighted by the energy of the bond. The choice of bonds is key to create a meaningful graph representation, and is explained in [23,24], see [25] to access the code.…”