Hot Spots In Protein Research Articles

HotSprint: database of computational hot spots in protein interfaces

We present a new database of computational hot spots in protein interfaces: HotSprint. Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. HotSprint contains data for 35 776 protein interfaces among 49 512 protein interfaces extracted from the multi-chain structures in Protein Data Bank (PDB) as of February 2006. The conserved residues in interfaces with certain buried accessible solvent area (ASA) and complex ASA thresholds are flagged as computational hot spots. The predicted hot spots are observed to correlate with the experimental hot spots with an accuracy of 76%. Several machine-learning methods (SVM, Decision Trees and Decision Lists) are also applied to predict hot spots, results reveal that our empirical approach performs better than the others. A web interface for the HotSprint database allows users to browse and query the hot spots in protein interfaces. HotSprint is available at http://prism.ccbb.ku.edu.tr/hotsprint; and it provides information for interface residues that are functionally and structurally important as well as the evolutionary history and solvent accessibility of residues in interfaces.

Predicting protein interaction sites: binding hot-spots in protein–protein and protein–ligand interfaces

Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding 'hot-spots', and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatic and van der Waals potentials, to predict which are involved in binding in the native complex. The resulting differences between predicting binding-sites at protein-protein and protein-ligand interfaces are striking. There is a high level of prediction accuracy (< or =93%) for protein-ligand interactions, based on the following attributes: van der Waals potential, electrostatic potential, desolvation and surface conservation. Generally, the prediction accuracy for protein-protein interactions is lower, with the exception of enzymes. Our results show that the ease of cleft desolvation is strongly predictive of interfaces and strongly maintained across all classes of protein-binding interface.

Genetic Association, Post-translational Modification, and Protein-Protein Interactions in Type 2 Diabetes Mellitus

Type 2 diabetes mellitus is a complex disorder with a strong genetic component. Inherited complex disease susceptibility in humans is most commonly associated with single nucleotide polymorphisms. The mechanisms by which this occurs are still poorly understood. Here we focus on analyzing the effect of a set of disease-causing missense variations of the monogenetic form of Type 2 diabetes mellitus and a set of disease-associated nonsynonymous variations in comparison with that of nonsynonymous variations without any experimental evidence for association with any disease. Analysis of different properties such as evolutionary conservation status, solvent accessibility, secondary structure, etc. suggests that disease-causing variations are associated with extreme changes in the value of the parameters relating to evolutionary conservation and/or protein stability. Disease-associated variations are rather moderately conserved and have a milder effect on protein function and stability. The majority of the genes harboring these variations are clustered in or near the insulin signaling network. Most of these variations are identified as potential sites for post-translational modifications; certain predictions have already reported experimental evidence. Overall our results indicate that Type 2 diabetes mellitus may result from a large number of single nucleotide polymorphisms that impair modular domain function and post-translational modifications involved in signaling. Our emphasis is more on conserved corresponding residues than the variation alone. We believe that the approach of considering a stretch of peptide sequence involving a polymorphism would be a better method of defining the role of the polymorphism in the manifestation of this disease. Because most of the variations associated with the disease are rare, we hypothesize that this disease is a "mosaic model" of interaction between a large number of rare alleles and a small number of common alleles along with the environment, which is little contrary to the existing common disease common variant model.

The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces

To make information about protein interactive function easily accessible, we are mining the primary scientific literature for detailed data about protein interfaces. The Binding Interface Database (BID) organizes the vast amount of protein interaction information into tables, graphical contact maps and descriptive functional profiles. Currently data on 170 interacting protein pairs are available with over 1300 mutations described. The BID database is freely available at http://tsailab.org/BID/ To have your protein of interest entered, contact Tiffany Fischer (tiffbrink@neo.tamu.edu) or Jerry Tsai at the email below

Use of selective Trp side chain labeling to characterize protein-protein and protein-ligand interactions by NMR spectroscopy.

Recent studies on amino acid occurrence in protein binding sites suggest that only a reduced number of residues are responsible for most interaction energy in protein-protein and protein-ligand interactions. Above all, tryptophan (Trp) seems to be the most frequent residue in protein's hot spots. Here we report a novel, efficient, and cost-effective method to selectively incorporate specific isotope labels into the side chains of Trp residues in recombinant proteins. We show that the method proposed allows selective NMR observation of Trp side chains that enables studies of ligand binding, protein-protein interactions, hydrogen binding, protein folding, and side chain dynamics. Examples with the protein BIR3 will be given.

A simple physical model for binding energy hot spots in protein-protein complexes.

Protein-protein recognition plays a central role in most biological processes. Although the structures of many protein-protein complexes have been solved in molecular detail, general rules describing affinity and selectivity of protein-protein interactions do not accurately account for the extremely diverse nature of the interfaces. We investigate the extent to which a simple physical model can account for the wide range of experimentally measured free energy changes brought about by alanine mutation at protein-protein interfaces. The model successfully predicts the results of alanine scanning experiments on globular proteins (743 mutations) and 19 protein-protein interfaces (233 mutations) with average unsigned errors of 0.81 kcal/mol and 1.06 kcal/mol, respectively. The results test our understanding of the dominant contributions to the free energy of protein-protein interactions, can guide experiments aimed at the design of protein interaction inhibitors, and provide a stepping-stone to important applications such as interface redesign.

Monte Carlo simulations of the peptide recognition at the consensus binding site of the constant fragment of human immunoglobulin G: The energy landscape analysis of a hot spot at the intermolecular interface

Monte Carlo simulations of molecular recognition at the consensus binding site of the constant fragment (Fc) of human immunoglobulin G (Ig) protein have been performed to analyze structural and thermodynamic aspects of binding for the 13-residue cyclic peptide DCAWHLGELVWCT. The energy landscape analysis of a hot spot at the intermolecular interface using alanine scanning and equilibrium-simulated tempering dynamics with the simplified, knowledge-based energy function has enabled the role of the protein hot spot residues in providing the thermodynamic stability of the native structure to be determined. We have found that hydrophobic interactions between the peptide and the Met-252, Ile-253, His-433, and His-435 protein residues are critical to guarantee the thermodynamic stability of the crystallographic binding mode of the complex. Binding free energy calculations, using a molecular mechanics force field and a solvation energy model, combined with alanine scanning have been conducted to determine the energetic contribution of the protein hot spot residues in binding affinity. The conserved Asn-434, Ser-254, and Tyr-436 protein residues contribute significantly to the binding affinity of the peptide-protein complex, serving as an energetic hot spot at the intermolecular interface. The results suggest that evolutionary conserved hot spot protein residues at the intermolecular interface may be partitioned in fulfilling thermodynamic stability of the native binding mode and contributing to the binding affinity of the complex.

Anatomy of hot spots in protein interfaces

Binding of one protein to another is involved in nearly all biological functions, yet the principles governing the interaction of proteins are not fully understood. To analyze the contributions of individual amino acid residues in protein-protein binding we have compiled a database of 2325 alanine mutants for which the change in free energy of binding upon mutation to alanine has been measured (available at http://motorhead.ucsf.edu/∼thorn/hotspot). Our analysis shows that at the level of side-chains there is little correlation between buried surface area and free energy of binding. We find that the free energy of binding is not evenly distributed across interfaces; instead, there are hot spots of binding energy made up of a small subset of residues in the dimer interface. These hot spots are enriched in tryptophan, tyrosine and arginine, and are surrounded by energetically less important residues that most likely serve to occlude bulk solvent from the hot spot. Occlusion of solvent is found to be a necessary condition for highly energetic interactions.