Ligand Binding and Circular Permutation Modify Residue Interaction Network in DHFR

Hu, Zhijuan; Bowen, Donnell; Southerland, William M.; Sol, Antonio del; Pan, Yudi; Nussinov, Ruth; Ma, Buyong

doi:10.1371/journal.pcbi.0030117

Cited by 47 publications

(38 citation statements)

References 29 publications

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“…Thel argest changes were observed near residues 50-90 (adjacent to the binding site) and 120-140 (near the active-site loops). These experiments,s upported by network analysis [19] ( Figure S6) and pathway analysis ( Figure S7), point toward am odel whereby the ligand binding event modulates the dynamics of Ubc9 rather than its conformation, and results in rigidification of the protein in general, with pronounced effects near the active-site loops in addition to the binding site.…”

Section: Angewandte Chemiementioning

confidence: 80%

Insights Into the Allosteric Inhibition of the SUMO E2 Enzyme Ubc9

et al. 2016

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Conjugation of the small ubiquitin-like modifier (SUMO) to protein substrates is an important disease-associated posttranslational modification, although few inhibitors of this process are known. Herein, we report the discovery of an allosteric small-molecule binding site on Ubc9, the sole SUMO E2 enzyme.A nX -rayc rystallographic screen was used to identify two distinct small-molecule fragments that bind to Ubc9 at as ite distal to its catalytic cysteine.T hese fragments and related compounds inhibit SUMO conjugation in biochemical assays with potencies of 1.9-5.8 mm.Mechanistic and biophysical analyses,c oupled with molecular dynamics simulations,p oint toward ligand-induced rigidification of Ubc9 as amechanism of inhibition.The posttranslational modification of protein substrates with as mall ubiquitin-like modifier (SUMO) tag occurs through at ightly regulated E1/E2/E3 enzymatic cascade and modulates ab road variety of cellular functions.[1] Ubc9 is the only E2 enzyme involved in the conjugation of all three SUMO isoforms to many diverse substrates within the proteome.As acentral enzyme in the SUMOylation cycle,Ubc9 is required for normal embryonic development, and it is dysregulated in av ariety of disease states such as cancer [2] and ischemia.[3]Ubc9 has been suggested as ap otential anticancer target for small-molecule inhibitors,n otably for MYC-driven [4] and RAS/Raf-driven [5] cancers,a sw ell as multiple myeloma.[6]Despite its relevance to disease,i th as been challenging to identify small molecules that modulate the function of Ubc9.In related ubiquitin-like signaling pathways,inhibitors of E1 [7] and E3 [8] enzymes are now clinically used as anticancer chemotherapeutics.Bycontrast, very few reversible inhibitors of any of the approximately 40 known ubiquitin and ubiquitin-like E2 conjugating enzymes have been discovered, [9] only one of which has been characterized in complex with the protein.[10] Thus,n ew experimentally validated chemical inhibitors of Ubc9 would provide substantial insight into targeting this important enzyme,a nd potentially E2 enzymes in general.One powerful approach to identify ligands for challenging protein targets is fragment-based inhibitor discovery.[11] In general, fragment-based approaches leverage sensitive techniques such as NMR, thermal shift, surface plasmon resonance (SPR), or X-ray crystallography to identify lowmolecular-weight ligands that bind weakly but specifically to target proteins.[12] Weak leads identified through fragmentbased approaches can provide excellent starting points for the development of highly active inhibitors,e ven in cases where the targets are considered to be challenging,such as proteinprotein interactions or so-called "undruggable" targets. [13] As part of al arger program aimed toward identifying chemical inhibitors of Ubc9, we elected to pursue an X-ray crystallographic fragment screening strategy (see the Supporting Information).[14] Ubc9 crystals diffracting to aresolution of 1.12 ( PDB ID:5 F6E) were soaked with as eries ...

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Section: Angewandte Chemiementioning

confidence: 80%

Insights Into the Allosteric Inhibition of the SUMO E2 Enzyme Ubc9

et al. 2016

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“…Mutations affecting these transitions could get the protein trapped, hindering population shift. Topologically, protein structures can be computationally decomposed into energetically independent modules, the building blocks of protein domains (Hu et al, 2007). Inter-modular residues play a key role in signal transmission.…”

Section: The “Old” View the “New” View And The “Updated New” Viewmentioning

confidence: 99%

The Origin of Allosteric Functional Modulation: Multiple Pre-existing Pathways

et al. 2009

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While allostery draws increasing attention, not much is known about allosteric mechanisms. Here we argue that in all proteins, allosteric signals transmit through multiple, pre-existing pathways; which pathways dominate depend on protein topologies, specific binding events, covalent modifications and cellular (environmental) conditions. Further, perturbation events at any site on the protein surface (or in the interior) will not create new pathways but only shift the pre-existing ensemble of pathways. Drugs binding at different sites or mutational events in disease shift the ensemble toward the same conformations; however, the relative populations of the different states will change. Consequently the observed functional, conformational, and dynamic effects will be different. This is the origin of allosteric functional modulation in dynamic proteins: allostery does not necessarily need to invoke conformational rearrangements to control protein activity and pre-existing pathways are always defaulted to during allostery regardless of the stimulant and perturbation site in the protein.

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“…A number of studies have looked at the distances between C α or C β atoms, with appropriately chosen thresholds (e.g., 8.5Å (Dokholyan, et al, 2002), or in the 3–6Å range (Hu, et al, 2007)). An alternative is to look at distances between any two atoms and consider the residues to be in contact if the distance is below 5Å (Greene and Higman, 2003) or the sum of their van der Waals radii plus 0.5Å (Keskin and Nussinov, 2007).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Graphlet Kernels for Prediction of Functional Residues in Protein Structures

Vacic

Iakoucheva²,

Lonardi³

et al. 2010

Journal of Computational Biology

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We introduce a novel graph-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. Each vertex in the graph is then represented as a vector of counts of labeled non-isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicting phosphorylation sites in protein structures. The performance of the graphlet kernel approach was then compared against two alternative methods, a sequence-based predictor and our implementation of the FEATURE framework. On both tasks the graphlet kernel performed favorably; however, the margin of difference was considerably higher on the problem of phosphorylation site prediction. While there is data that phosphorylation sites are preferentially positioned in intrinsically disordered regions, we provide evidence that for the sites that are located in structured regions, neither the surface accessibility alone nor the averaged measures calculated from the residue microenvironments utilized by FEATURE were sufficient to achieve high accuracy. The key benefit of the graphlet representation is its ability to capture neighborhood similarities in protein structures via enumerating the patterns of local connectivity in the corresponding labeled graphs.

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Ligand Binding and Circular Permutation Modify Residue Interaction Network in DHFR

Cited by 47 publications

References 29 publications

Insights Into the Allosteric Inhibition of the SUMO E2 Enzyme Ubc9

Insights Into the Allosteric Inhibition of the SUMO E2 Enzyme Ubc9

The Origin of Allosteric Functional Modulation: Multiple Pre-existing Pathways

Graphlet Kernels for Prediction of Functional Residues in Protein Structures

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