HotRegion: a database of predicted hot spot clusters

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion

HotPoint: hot spot prediction server for protein interfaces

The energy distribution along the protein–protein interface is not homogenous; certain residues contribute more to the binding free energy, called ‘hot spots’. Here, we present a web server, HotPoint, which predicts hot spots in protein interfaces using an empirical model. The empirical model incorporates a few simple rules consisting of occlusion from solvent and total knowledge-based pair potentials of residues. The prediction model is computationally efficient and achieves high accuracy of 70%. The input to the HotPoint server is a protein complex and two chain identifiers that form an interface. The server provides the hot spot prediction results, a table of residue properties and an interactive 3D visualization of the complex with hot spots highlighted. Results are also downloadable as text files. This web server can be used for analysis of any protein–protein interface which can be utilized by researchers working on binding sites characterization and rational design of small molecules for protein interactions. HotPoint is accessible at http://prism.ccbb.ku.edu.tr/hotpoint

A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein

Identifying hot-spot residues – residues that are critical to protein–protein binding – can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein–protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36–57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein–protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins.Novartis (Firm)Singapore-MIT Alliance for Research and Technolog

Using machine-learning-driven approaches to boost hot-spot's knowledge

Understanding protein–protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein–protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

Discovery and effects of pharmacological inhibition of the E3 ligase Skp2 by small molecule protein-protein interaction disruptors

Skp2 (S-phase kinase-associated protein 2), one component of the SCF E3 ubiquitin ligase complex, directly interacts with Skp1 and indirectly associates with Cullin1 and Rbx1 to bridge the E2 conjugating enzyme with its protein substrate to execute its E3 ligase activity. Skp2 is an Fbox protein (due to it containing an Fbox domain) and it is the rate-limiting component of the SCF complex. Skp2 targets several cell-cycle regulatory proteins for ubiquitination and degradation; most notable and significant for cancer are the cyclin-dependent kinase inhibitor, p27. Skp2 is an oncogene and studies have shown that over-expression of Skp2 leads to increased degradation of p27 and increased proliferation in several tumor types. Additionally, Skp2 is over-expressed in multiple human cancers. Clearly, Skp2 represents an attractive target for attenuating p27 ubiquitination and subsequent cell cycle progression. However, Skp2 does not have an easily identifiable and druggable “pocket” on which small molecules can bind; it interacts with Skp1 through the Fbox domain and binds to an accessory protein called Cks1 to bind to p27. Despite this hurdle, in this study, two selective small molecule inhibitors of the Skp2 SCF complex were discovered via an in silico screen that disrupt two places: the Skp1/Skp2 interaction site and the p27 binding site via targeting hot-spot residues. The Skp1/Skp2 inhibitor disruption resulted in restoring p27 levels in the nucleus and blocks cancer progression and cancer stem cell traits. Additionally, the inhibitors phenocopy the effects of genetic Skp2 deficiency. Two specific residues on Skp2 were predicted to bind to this Skp1/Skp2 inhibitor: Trp97 and Asp98. When these residues were mutated to alanine, the inhibitor lost its ability to bind to Skp2. To investigate the flexibility and understand the conformational change upon inhibitor binding and dynamics of the SCF complex, molecular dynamics simulations, homology models, and structural analysis was carried out on the complex with and without the inhibitors. These simulations showed that the contributions of the N-terminal tail region of Skp2 does not contribute directly to the binding of these inhibitors; but its conformation is important in the context of the other members of the SCF complex. Further dynamics analysis validated the mutagenesis results, showing that the two Skp2 mutants (Trp97Ala, Asp98Ala) that retained Skp1 binding but blocked inhibitor binding were stable, whereas the mutant that was unable to retain Skp1 binding (Trp127Ala) showed destabilization in the Fbox domain. Finally, active recruitment events after post-translational modifications are shown to be possible by the interaction of phosphorylated Ser256 on Skp2 with Lys104 loop region on Cul1 The model shows that this is due to the significant flexibility in the F-box domain of Skp2, making this interaction very likely. These results show that Skp2 is a promising target on which protein-protein interaction disruptors can be designed, and consideration of the dynamics of protein complexes is required to understand ligand binding

Exploring the structural integrity of a picornavirus capsid

Expected release date-May 202