Side-Chain Conformational Changes upon Protein–Protein Association

Ruvinsky, Anatoly M.; Kirys, Tatsiana; Tuzikov, Alexander V.; Vakser, Ilya A.

doi:10.1016/j.jmb.2011.02.030

Cited by 29 publications

(46 citation statements)

References 72 publications

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“…While several studies have shown the importance of side-chain conformations in flexible protein-protein associations (Beglov et al, 2012; Ehrlich et al, 2005; Rajamani et al, 2004; Ruvinsky et al, 2011), manipulation of backbone flexibility on docking is less well explored. Torsion angle dynamics simulations of the barnase–barstar complex (Ehrlich et al, 2005) showed that barnase could not reach barstar from the unbound starting structures unless the bound-state loop conformation and hot spot residue pair were grafted onto the unbound structures.…”

Section: Discussionmentioning

confidence: 99%

Pushing the Backbone in Protein-Protein Docking

Kuroda

Gray

2016

Structure

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Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics and normal mode analysis. All methods tested rarely sample near-bound-states from the unbound-conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. 70% of docking targets succeed when the unbound-backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer-selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.

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Section: Discussionmentioning

confidence: 99%

Pushing the Backbone in Protein-Protein Docking

Kuroda

Gray

2016

Structure

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“…Previous studies have shown that one-third of all interface residues are observed to undergo conformational changes (87,88). These changes result in an entropic cost by being restricted to one rotamer conformation and an enthalpic cost by breaking the noncovalent intramolecular interactions.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Binding Forces Driving the Tight Interactions between β-Lactamase Inhibitory Protein-II (BLIP-II) and Class A β-Lactamases

Brown¹,

Chow²,

Sankaran

et al. 2011

Journal of Biological Chemistry

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␤-Lactamases hydrolyze ␤-lactam antibiotics to provide drug resistance to bacteria. ␤-Lactamase inhibitory protein-II (BLIP-II) is a potent proteinaceous inhibitor that exhibits low picomolar affinity for class A ␤-lactamases. This study examines the driving forces for binding between BLIP-II and ␤-lactamases using a combination of presteady state kinetics, isothermal titration calorimetry, and x-ray crystallography. The measured dissociation rate constants for BLIP-II and various ␤-lactamases ranged from 10 ؊4 to 10 ؊7 s ؊1 and are comparable with those found in some of the tightest known protein-protein interactions. The crystal structures of BLIP-II alone and in complex with Bacillus anthracis Bla1 ␤-lactamase revealed no significant side-chain movement in BLIP-II in the complex versus the monomer. The structural rigidity of BLIP-II minimizes the loss of the entropy upon complex formation and, as indicated by thermodynamics experiments, may be a key determinant of the observed potent inhibition of ␤-lactamases.Protein-protein interactions govern many cellular processes, and an understanding of the determinants of molecular recognition would facilitate the rationale design of interactions for therapeutic purposes. Detailed examination of kinetic constants and thermodynamic driving forces, however, has been performed for relatively few protein-protein interaction complexes. Although significant progress has been made in the understanding and prediction of association rate constants, the determinants of dissociation rates remain poorly understood (1-5). The ability to predict the kinetic constants would be a significant contribution to the understanding of interaction networks in the systems biology era (6). Several model systems have been developed to analyze the principles of protein-protein interactions, and one such model is the interaction between ␤-lactamase enzymes and a set of ␤-lactamase inhibitory proteins (BLIPs) 3 (7, 8). ␤-Lactamases act by hydrolyzing the four-membered ring of ␤-lactam antibiotics, e.g. penicillins and cephalosporins, rendering the drugs inactive (9, 10). There are four classes of ␤-lactamases (A-D) based on primary amino acid sequences (11,12). Class B ␤-lactamases are metalloenzymes that use a zinc-coordinated catalytic water to hydrolyze the ␤-lactam ring whereas classes A, C, and D are serine hydrolyases (13-15). Class A ␤-lactamases are widespread in both Gram-positive and Gramnegative bacteria and exhibit broad substrate hydrolysis profiles that include penicillins, cephalosporins, and for a few enzymes, carbapenems (12,14).The catalytic mechanism of serine ␤-lactamases is divided into two stages, acylation and deacylation (16). In class A enzymes, the catalytic Ser-70 residue nucleophilically attacks the carbonyl of the ␤-lactam and forms an acyl-intermediate (17,18). An essential Glu-166 residue activates a highly coordinated water for deacylation (18,19). Substitutions at Glu-166 result in an acylated, inactive enzyme (20 -22).Clinically available mechanism-based inhibitor...

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“…15,16 Moreover, in recent analyses, it has been shown that interface residues were more frequently found in high-energy torsion angle state 29 and side-chain conformation changes due to protein-protein association. 30 Therefore, we asked how side-chain conformational changes are associated with multiple binding. To address this point, we identified sets of equivalent residues in the OV and Non-OV regions from multiple sequence alignments (MSAs) and analyzed diversity of sidechain χ1 angles in each such set of residues ( Fig.…”

Section: Residue Compositions In the Ov And Non-ov Regionsmentioning

confidence: 99%

Distinct Roles of Overlapping and Non-overlapping Regions of Hub Protein Interfaces in Recognition of Multiple Partners

DasGupta¹,

Nakamura²,

Kinjo³

2011

Journal of Molecular Biology

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Cellular functions of an organism are maintained by protein-protein interactions. Those proteins that bind multiple partners asynchronously (date hub proteins) are important to make the interaction network coordinated. It is known that many date hub proteins bind different partners at overlapping (OV) interfaces. To understand how OV interfaces of date hub proteins can recognize multiple partners, we analyzed the difference between OV and non-overlapping (Non-OV) regions of interfaces involved in the binding of different partners. By using the structures of 16 date hub proteins with various interaction partners (ranging from 5 to 33), we compared buried surface area, compositions of amino acid residues and secondary structures, and side-chain orientations. It was found that buried interface residues are important for recognizing multiple partners, while exposed interface residues are important for determining specificity to a particular ligand. In addition, our analyses reveal that residue compositions in OV and Non-OV regions are different and that residues in OV region show diverse side-chain torsion angles to accommodate binding to multiple targets.

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Side-Chain Conformational Changes upon Protein–Protein Association

Cited by 29 publications

References 72 publications

Pushing the Backbone in Protein-Protein Docking

Pushing the Backbone in Protein-Protein Docking

Analysis of the Binding Forces Driving the Tight Interactions between β-Lactamase Inhibitory Protein-II (BLIP-II) and Class A β-Lactamases

Distinct Roles of Overlapping and Non-overlapping Regions of Hub Protein Interfaces in Recognition of Multiple Partners

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