Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

Tian, Fengchun; Tan, Rui; Guo, Tailin; Zhou, Peng; Li, Yang

doi:10.1016/j.biosystems.2013.04.004

Cited by 39 publications

(8 citation statements)

References 50 publications

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“…The wild‐type H2 peptide ( 27 SDCLLDGLDALVYD 40 for ROCK‐I or 43 VESLLDGLNSLVLD 56 for ROCK‐II) split from the second helical segment of ROCK dimerization domain was used as the initial sequence to start SA iteration. Selectivity. The candidate peptide affinities to ROCK‐I and ROCK‐II were assigned as A I and A II and were predicted using a QSAR‐based statistical potential based on their complex structures. Thus, the peptide selectivity between the two isoforms can be calculated as their difference Δ A = A I − A II , where A > 0 and < 0 indicate I‐over‐II (IoII) and II‐over‐I (IIoI) selectivity. Mutation.…”

Section: Methodsmentioning

confidence: 99%

Rational design and improvement of the dimerization‐disrupting peptide selectivity between ROCK‐I and ROCK‐II kinase isoforms in cerebrovascular diseases

Yan

2020

J of Molecular Recognition

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Human rho-associated coiled-coil forming kinases (ROCKs) ROCK-I and ROCK-II have been documented as attractive therapeutic targets for cerebrovascular diseases.Although ROCK-I and ROCK-II share a high degree of structural conservation and are both present in classic rho/ROCK signaling pathway, their downstream substrates and pathological functions may be quite different. Selective targeting of the two kinase isoforms with traditional small-molecule inhibitors is a great challenge due to their surprisingly high homology in kinase domain (~90%) and the full identity in kinase active site (100%). Here, instead of developing small-molecule drugs to selectively target the adenosine triphosphate (ATP) site of two isoforms, we attempt to design peptide agents to selectively disrupt the homo-dimerization event of ROCK kinases through their dimerization domains which have a relatively low conservation (~60%). Three helical peptides H1, H2, and H3 are split from the kinase dimerization domain, from which the isolated H2 peptide is found to have the best capability to rebind at the dimerization interface. A simulated annealing (SA) iteration method is used to improve the H2 peptide selectivity between ROCK-I and ROCK-II. The method accepts moderate degradation in peptide affinity in order to maximize the affinity difference between peptide binding to the two isoforms. Consequently, hundreds of parallel SA runs yielded six promising peptide candidates with ROCK-I over ROCK-II (I over II [IoII]) calculated selectivity and four promising peptide candidates with ROCK-II over ROCK-I (II over I [IIoI]) calculated selectivity. Subsequent anisotropy assays confirm that the selectivity values range between 13.2-fold and 83.9-fold for IoII peptides, and between 5.8-fold and 21.2-fold for IIoI peptides, which are considerably increased relative to wild-type H2 peptide (2.6-fold for IoII and 2.0-fold for IIoI). The molecular origin of the designed peptide selectivity is also analyzed at structural level; it is revealed that the peptide residues can be classified into conserved, non-conserved, and others, in which the non-conserved residues play a crucial role in defining peptide selectivity, while conserved residues confer stability to kinasepeptide binding. K E Y W O R D S cerebrovascular disease, dimerization domain, dimerization-disrupting peptide, selectivity, rational peptide design, rho-associated coiled-coil forming kinase, ROCK-I and ROCK-II

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Section: Methodsmentioning

confidence: 99%

Rational design and improvement of the dimerization‐disrupting peptide selectivity between ROCK‐I and ROCK‐II kinase isoforms in cerebrovascular diseases

Yan

2020

J of Molecular Recognition

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“…For each kinase‐inhibitor complex interaction, a total of 100 conformational snapshots were evenly extracted from the dynamics trajectory of the production simulation phase, which were then used to derive the binding free energy of inhibitor ligand to kinase receptor by using molecular mechanics/Poisson‐Boltzmann surface area (MM/PBSA) method . The kinase‐inhibitor binding free energy Δ G consists of intermolecular interaction energy (Δ E int ) and desolvation penalty (Δ D dslv ); the former was calculated through fore field approach, while the latter was treated by polar and nonpolar contributions . The polar aspect (∆ D plr ) was analyzed by finite difference solutions to the nonlinear Poisson‐Boltzmann equation, while nonpolar contribution (∆ G nplr ) was determined by summing up the weighted surface area of solute molecule, ie, ∆ G nplr = γ ·∆SASA, where γ = 0.0072 kcal/mol Å 2 and ∆SASA is the change in solute's surface area upon the kinase‐inhibitor binding …”

Section: Methodsmentioning

confidence: 99%

“…34 The kinase-inhibitor binding free energy ΔG consists of intermolecular interaction energy (ΔE int ) and desolvation penalty (ΔD dslv ); the former was calculated through fore field approach, while the latter was treated by polar and nonpolar contributions. 35,36 The polar aspect (ΔD plr ) was analyzed by finite difference solutions to the nonlinear Poisson-Boltzmann equation, while nonpolar contribution (ΔG nplr ) was determined by summing up the weighted surface area of solute molecule, ie, ΔG nplr = γ·ΔSASA, where γ = 0.0072 kcal/mol Å 2 and ΔSASA is the change in solute's surface area upon the kinase-inhibitor binding. 37…”

Section: Molecular Dynamics Simulation and Binding Energetics Analysismentioning

confidence: 99%

Statistical analysis and heuristic identification of unexpected interactions from the neurokinase‐inhibitor interactome in trigeminal neuralgia pharmacological intervention

Liu

Chen

Liu

et al. 2019

Journal of Chemometrics

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The cell signaling pathways of human trigeminal neuralgia (TN) are orchestrated by a variety of protein kinase‐elicited phosphorylation events. However, a majority of TN‐related neurokinases have not yet been revealed and still remain largely unexplored. Here, a systematic kinase‐inhibitor interactome for 601 small‐molecule inhibitors across 51 human neurokinases is created via a high‐throughput molecular docking procedure; the inhibitors are reversible, ATP competitive, and commercially available, while the kinases are enriched by gene ontology (GO) to be semantically relevant with TN and have cocrystallized structures with their cognate ligands. Heuristic clustering and statistical analysis identify 35 hit inhibitors from the interactome profile, which are potential to target TN‐related kinases. The intermolecular interactions of four promising inhibitors with the array of 51 TN‐related kinases are investigated in detail at molecular level using dynamics simulation and energetics analysis. Consequently, a total of nine protein kinases are inferred as high‐affinity cotargets of these potent inhibitors, in which fours have already been established as sophisticated targets for TN therapy, while others are still unclear about their therapeutic implications underlying the disease. Some candidates are selected as representatives to perform kinase assay. It is found that some putative kinase targets can be inhibited effectively by noncognate inhibitors; the activity values are comparable with or even better than known cognate inhibitors. Structural examination of a potent kinase‐inhibitor complex identifies a number of noncovalent interactions such as hydrogen bonds, cation‐π stacking, and hydrophobic contacts at the tightly packed complex interface, conferring both stability and specificity to the complex recognition and interaction.

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“…[ 18,19 ] Previously, we have successfully performed the molecular design and genetic optimization, and in vitro susceptibility test of unnatural antimicrobial peptides to combat antibiotic‐resistant bacterial infections, [ 20 ] which have been involved in the structural characterization, statistical modeling, and biophysical exploration of protein–peptide binding phenomena. [ 21–25 ] In the current study, we systematically profiled the molecular response of SSBIs to drug‐resistant kinase mutations by integrating bioinformatics analysis and experimental assay, aiming to give a comprehensive understanding of such response and to provide a useful strategy for developing mutant‐sensitive inhibitors. [ 13 ] With the profiling, we were able to identify a number of potential mutations that may sensitize or cause resistance to certain SSBIs.…”

Section: Introductionmentioning

confidence: 99%

Systematic response of staurosporine scaffold‐based inhibitors to drug‐resistant cancer kinase mutations

2020

Archiv der Pharmazie

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Human protein kinases have been established as promising druggable targets in cancer therapy. However, a large number of acquired drug-resistant kinase mutations are observed after first-and second-line kinase inhibitor treatments, largely limiting the application of small-molecule inhibitors in the targeted cancer therapy.Previously, the pan-kinase inhibitor staurosporine and its derivatives have been reported to selectively inhibit gatekeeper mutants over wild-type kinases, suggesting that the staurosporine scaffold is potentially helpful in developing wild-typesparing inhibitors of drug-resistant kinase mutants. Here, a systematic response profile of 32 staurosporine scaffold-based inhibitors (SSBIs) for 61 ontologyenriched drug-resistant cancer kinase mutations is created using a combination of in silico analysis and in vitro assay, from which it is possible to identify those mutations that have the potential to cause resistance or confer sensitivity to SSBIs. The profile reveals that SSBIs exhibit distinct responses to kinase gatekeeper and nongatekeeper mutations, and SSBIs bearing p7 substituents can considerably influence their response to kinase gatekeeper mutations, particularly for the mutations of the Ile residue, which possesses a Cβ methyl group that tends to cause steric clash with bound SSBIs. Nongatekeeper mutations generally have a moderate and unfavorable effect on SSBI activity, as most of them are outside the kinase active site and do not directly contact inhibitor ligands. In addition, it is found that resistance is commonly caused by mutation-induced hindrance effects, whereas sensitivity is primarily conferred by mutation-established additional interactions. K E Y W O R D Sdrug-resistant kinase mutation, inhibitor selectivity, protein kinase, staurosporine scaffold-based inhibitor, targeted cancer therapy

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Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

Cited by 39 publications

References 50 publications

Rational design and improvement of the dimerization‐disrupting peptide selectivity between ROCK‐I and ROCK‐II kinase isoforms in cerebrovascular diseases

Rational design and improvement of the dimerization‐disrupting peptide selectivity between ROCK‐I and ROCK‐II kinase isoforms in cerebrovascular diseases

Statistical analysis and heuristic identification of unexpected interactions from the neurokinase‐inhibitor interactome in trigeminal neuralgia pharmacological intervention

Systematic response of staurosporine scaffold‐based inhibitors to drug‐resistant cancer kinase mutations

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