Biology subjects

Yaliraki, S. N.

Publications and source records attributed to Yaliraki, S. N..

4 recordsLinked to original sources

bioRxiv2021

Prediction of allosteric sites and signalling: insights from benchmarking datasets

AO_SCPLOWBSTRACTC_SCPLOWAllostery is a pervasive mechanism which regulates the activity of proteins in living systems through binding of a molecule at a distant site from the orthosteric site of the protein. The universality of allosteric regulation complemented by the benefits of highly specific, potentially non-toxic and protein activity modulating allosteric drugs makes uncovering allosteric sites on proteins invaluable for drug discovery. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis, a recently developed method, has successfully predicted allosteric sites for a diverse group of proteins with only the knowledge of the orthosteric sites and the corresponding ligands in 19 of 20 cases. The method is based on an energy-weighted atomistic protein graph and allows for computationally highly efficient analysis in atomistic detail. We here extended the analysis onto 432 structures of 146 proteins from two existing benchmarking datasets for allosteric proteins: ASBench and CASBench. We further refined the metrics to account for the cumulative effect of residues with high propensities and the crucial residues in a given site with two additional measures. The allosteric site is recovered for 95/113 proteins (99/118 structures) from ASBench and 32/33 proteins (304/314 structures) from CASBench, with the only a priori knowledge being the orthosteric site residues. Knowing the orthosteric ligands of the protein, the allosteric site is identified for 32/33 proteins (308/314 structures) from CASBench.

bioinformatics↗

bioRxiv2020

Allosteric hotspots in the main protease of SARS-CoV-2

AO_SCPLOWBSTRACTC_SCPLOWInhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph theoretical methods: Bond-to-bond propensity analysis, which has been previously successful in identifying allosteric sites without a priori knowledge in benchmark data sets, and, Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. We further score the highest ranking sites against random sites in similar distances through statistical bootstrapping and identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

bioinformatics↗

bioRxiv2019

Unsupervised graph-based learning predicts mutations that alter protein dynamics

AO_SCPLOWBSTRACTC_SCPLOWProteins exhibit complex dynamics across a vast range of time and length scales, from the atomistic to the conformational. Adenylate kinase (ADK) showcases the biological relevance of such inherently coupled dynamics across scales: single mutations can affect large-scale protein motions and enzymatic activity. Here we present a combined computational and experimental study of multiscale structure and dynamics in proteins, using ADK as our system of choice. We show how a computationally efficient method for unsupervised graph partitioning can be applied to atomistic graphs derived from protein structures to reveal intrinsic, biochemically relevant substructures at all scales, without re-parameterisation or a priori coarse-graining. We subsequently perform full alanine and arginine in silico mutagenesis scans of the protein, and score all mutations according to the disruption they induce on the large-scale organisation. We use our calculations to guide Forster Resonance Energy Transfer (FRET) experiments on ADK, and show that mutating residue D152 to alanine or residue V164 to arginine induce a large dynamical shift of the protein structure towards a closed state, in accordance with our predictions. Our computations also predict a graded effect of different mutations at the D152 site as a result of increased coherence between the core and binding domains, an effect confirmed quantitatively through a high correlation (R2 = 0.93) with the FRET ratio between closed and open populations measured on six mutants.

biophysics↗

bioRxiv2019

An edge-based formulation of elastic network models

We present an edge-based framework for the study of geometric elastic network models to model mechanical interactions in physical systems. We use a formulation in the edge space, instead of the usual node-centric approach, to characterise edge fluctuations of geometric networks defined in d-dimensional space and define the edge mechanical embeddedness, an edge mechanical susceptibility measuring the force felt on each edge given a force applied on the whole system. We further show that this formulation can be directly related to the infinitesimal rigidity of the network, which additionally permits three- and four-centre forces to be included in the network description. We exemplify the approach in protein systems, at both the residue and atomistic levels of description.

biophysics↗