Macromolecular recognition

Deremble, Cyril; Lavery, Richard

doi:10.1016/j.sbi.2005.01.018

Cited by 61 publications

(51 citation statements)

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“…The same threshold of buried area was used in the SPIN-PP database (http://honiglab.cpmc.columbia.edu/SPIN/intro.html). A minimum buried area of 400 Å 2 on one chain has been used in some studies to define biological interfaces (reviewed in [3]). In this study, we also tried a minimum buried area of 400 Å 2 .…”

Section: Selecting Structures For Dataset100 Dataset30 and Dataset30_3mentioning

confidence: 99%

“…One general approach to study the interaction between two proteins is to obtain a crystal structure of the protein-protein complex and then investigate the atomic properties of the protein-protein interface. Many studies have analyzed the characteristics of protein-protein interfaces in an effort to search for the factors that contribute to the affinity and specificity of protein-protein interactions [1][2][3][4][5]. These analyses show that the two surfaces of a protein-protein interface usually show high degrees of geometric and chemical complementarities.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Protein–Protein Interfaces

et al. 2007

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We analyze the characteristics of protein-protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and noninterface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and noninterface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.

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Section: Selecting Structures For Dataset100 Dataset30 and Dataset30_3mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of Protein–Protein Interfaces

et al. 2007

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“…In addition to this probabilistic work, several studies have attempted to construct models that can accurately predict the binding of proteins to DNA via atomistic calculations 16,17 with varying degrees of success.…”

Section: Introductionmentioning

confidence: 99%

Role of aromatic amino acids in protein–nucleic acid recognition

2007

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Statistical analysis of structures from the PBD has been used to examine the role that the aromatic amino acids play in protein-nucleic acid recognition. In protein-DNA complexes, the residues Phe and His are found to bind selectively to the DNA chain--Phe to A and T, and His to T and G. The preferred binding modes are identified, and the interactions involving Phe are shown to be important in the transcription process. In protein-RNA complexes, Phe is found to occur far less often and is instead replaced by Trp, which binds selectively to C and G, offering a possible mechanism for differentiation between the two nucleic acids. SASA analysis of the two sets of complexes suggests that all of the aromatic amino acids are more heavily involved in binding than would be expected on the balance of probability. Phe and Tyr occur approximately equal in both sets of data, whereas the proportions of His and Trp vary considerably, supporting the idea that these residues may be involved in differentiating between the two nucleic acids.

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“…Considerable research has helped clarify many aspects of protein-ligand interaction (7). Various methods enable one to predict such interactions, such as the assessment of packing defects (8), analysis of protein-interface geometry (9), docking dynamics (10), modeling free energy (11), computational molecular probing (12), sequence threading (13), and learning-theory approaches (14).…”

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confidence: 99%

Packing defects as selectivity switches for drug-based protein inhibitors

Fernández

Scott

Berry

2005

Proc. Natl. Acad. Sci. U.S.A.

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The conservation of structure across homolog proteins often diffuses the impact of drug-based inhibition by promoting alternative protein-ligand associations that may lead to toxic side effects. However, sticky packing defects are typically not conserved across homologs, making them valuable a priori targets to enhance specificity. By introducing a homology to quantify packing differences among proteins, we enable a previously undescribed strategy for the design of highly selective drug inhibitors involving ligands that wrap nonconserved packing defects. The selectivity of these ligands is validated by performing affinity assays on a cancer-related pharmacokinome. Minor reengineering of a powerful inhibitor guided by wrapping differences across its target kinome can selectively direct its impact toward a specific kinase. Thus, nonconserved packing defects may be used as selectivity switches across homolog targets, using spatial displacements of packing defects across aligned protein structures.

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Macromolecular recognition

Cited by 61 publications

References 0 publications

Characterization of Protein–Protein Interfaces

Characterization of Protein–Protein Interfaces

Role of aromatic amino acids in protein–nucleic acid recognition

Packing defects as selectivity switches for drug-based protein inhibitors

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