Protein–DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins

Amino acid residues, which play important roles in protein function, are often conserved. Here, we analyze thermodynamic and structural data of protein–DNA interactions to explore a relationship between free energy, sequence conservation and structural cooperativity. We observe that the most stabilizing residues or putative hotspots are those which occur as clusters of conserved residues. The higher packing density of the clusters and available experimental thermodynamic data of mutations suggest cooperativity between conserved residues in the clusters. Conserved singlets contribute to the stability of protein–DNA complexes to a lesser extent. We also analyze structural features of conserved residues and their clusters and examine their role in identifying DNA-binding sites. We show that about half of the observed conserved residue clusters are in the interface with the DNA, which could be identified from their amino acid composition; whereas the remaining clusters are at the protein–protein or protein–ligand interface, or embedded in the structural scaffolds. In protein–protein interfaces, conserved residues are highly correlated with experimental residue hotspots, contributing dominantly and often cooperatively to the stability of protein–protein complexes. Overall, the conservation patterns of the stabilizing residues in DNA-binding proteins also highlight the significance of clustering as compared to single residue conservation

Densest subgraph-based methods for protein-protein interaction hot spot prediction

[Background] Hot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots. [Result] In this study, we develop three graph theory-based methods to predict hot spots from only a single residue interaction network. We detect the important residues by finding subgraphs with high densities, i.e., high average degrees. Generally, a high degree implies a high binding possibility between protein chains, and thus a subgraph with high density usually relates to binding sites that have a high rate of hot spots. By evaluating the results on 67 complexes from the SKEMPI database, our methods clearly outperform existing graph theory-based methods on recall and F-score. In particular, our main method, Min-SDS, has an average recall of over 0.665 and an f2-score of over 0.364, while the recall and f2-score of the existing methods are less than 0.400 and 0.224, respectively. [Conclusion] The Min-SDS method performs best among all tested methods on the hot spot prediction problem, and all three of our methods provide useful approaches for analyzing bionetworks. In addition, the densest subgraph-based methods predict hot spots with only one residue interaction network, which is constructed from spatial atomic coordinate data to mitigate the shortage of data from wet-lab experiments

Doctor of Philosophy

dissertationThe dysregulation of proteinâ€"protein interaction (PPI) networks has been implicated in many diseases. Designing therapeutic small-molecule inhibitors of these interactions is a challenging field for medicinal chemistry. This work advances the techniques for discovering more potent PPI inhibitors through integration of computational and biochemical techniques. High-throughput screening using fluorescence polarization and AlphaScreen assays identified an acyl hydrazone-containing inhibitor of the β-catenin/Tcf4 PPI, a key mediator of the canonical Wnt signaling pathway. By removing the undesirable acyl hydrazone moiety, a new compound, 4-(5H-[1,2,5]oxadiazolo[3',4':5,6]pyrazino[2,3-b]indol-5-yl)butanoic acid, was developed to selectively inhibit the β-catenin/Tcf4 interaction. The ethyl ester of this compound was tested in zebrafish embryos and shown to inhibit Wnt signaling in vivo at 2 and 10 μM concentrations. Differences between the PPI interface and the active site of traditional targets add to the difficulty of discovering PPI inhibitors. Herein, the relationship between inhibitor potency and ligand burialâ€"defined as the fraction of the solvent accessible surface areas of the bound over unbound ligand, θlâ€"in the PPI surface was evaluated. A positive correlation between θl and inhibitor potency was discovered. However, this correlation was secondary to the strong nonbonding interactions. A study of five PPI targets with corresponding inhibitor-bound crystal structures also revealed that empirical scoring functions were slightly better at identifying known inhibitors out of the putatively inactive test set, and the Lamarckian genetic algorithm was more successful at pose prediction. Due to the nature of the PPI surface, directly targeting the binding site may be difficult. A novel combination of computational methods explored the druggability, selectivity, and potential allosteric regulation of PPIs. Solvent mapping confirmed that Tcf4, E-cadherin, APC and axin use the same binding site on β-catenin in different ways. Evolutionary trace analysis indicated that the region surrounding W504 of β-catenin might be a potentially allosteric site. Site-directed mutagenesis testing results for a W504I β-catenin mutant resulted in three-fold increased binding of Tcf4 to β-catenin over the wild-type. This new site is promising for the discovery of future allosteric inhibitors of the β-catenin/Tcf4 PPI. The combined results from these studies reveals ways to better design PPI inhibitors

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors

Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists

Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure. © 2012 Koes et al

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

Label-free methods for studying protein-protein interactions

Proteins are the building blocks of life, and mainly perform their roles through protein-protein interactions (PPIs). Thus, PPIs are essential to the normal function of the cells and the body, and disturbances in these interactions are an underlying cause for many diseases. Because PPIs are so vital, multiple methods have been developed to study these interactions. Typically, the methods are based on reporter molecules, although label conjugation may disrupt PPIs. Several label-free methods have also been introduced, which can be categorized into surface-based and solutionbased approaches. Surface-based methods are often very sensitive, but they require protein conjugation to a solid surface. Solution-based methods, on the other hand, are fully conjugation-free but suffer from low sensitivity. In this thesis, a label-free, solution-based Protein-Probe method with nanomolar sensitivity was developed for studying protein thermal stability and interactions. The method is based on an external probe peptide, the Eu-probe, and it does not require conjugation to the interacting proteins, avoiding potential disruption of the reactions. The Eu-probe does not significantly bind to native, intact proteins, and the timeresolved luminescence signal of the free probe is quenched by a modulator in the Protein-Probe solution. When denaturation reveals hydrophobic amino acids or binding events increase the overall surface area, the change in the protein structure enables the Eu-probe binding. Binding to the proteins protects the probe from the modulator and leads to a signal increase. The Protein-Probe was first applied to studying protein-ligand interactions and PPIs by observing their effect on protein thermal stability in thermal shift assays. PPIs were also monitored based on interaction-induced signal increase at elevated temperatures without a thermal shift, and in competitive thermal shift assays with small molecular ligands. The formation of large protein complexes and aggregates was successfully monitored at room temperature. The developed label-free Protein- Probe method has improved sensitivity over the current solution-based PPI detection techniques. The method enables monitoring different interaction types, as the external probe binds to a wide variety of proteins in multiple assay concepts. Thus, the Protein-Probe is a promising new method for PPI studies.Leimavapaat menetelmät proteiinien välisten vuorovaikutusten tutkimiseen Proteiinit ovat yksi elämän rakennuspalikoista, ja ne toimivat pääasiassa proteiinien välisten vuorovaikutusten (PPI:t) kautta. Tämän vuoksi PPI:t ovat elintärkeitä solujen ja kehon normaalille toiminnalle, ja häiriöt vuorovaikututuksissa ovat usean sairauden takana. Koska PPI:t ovat niin keskeisiä, niiden tutkimiseen on kehitetty useita menetelmiä. Tyypillisesti menetelmät perustuvat reportterimolekyyleihin, vaikka niiden kiinnitys saattaa häiritä PPI:tä. On olemassa myös useita leimavapaita menetelmiä, jotka voidaan jakaa pintapohjaisiin ja liuospohjaisiin menetelmiin. Pintapohjaisilla menetelmillä on usein hyvä herkkyys, mutta niiden heikkous on että proteiini täytyy konjugoida kiinteään pintaan. Liuospohjaiset menetelmät taas eivät vaadi ollenkaan konjugaatiota, mutta kärsivät usein huonosta herkkyydestä. Tässä työssä kehitettiin leimavapaa, liuospohjainen ”Protein-Probe” menetelmä, jonka herkkyys on nanomolaarisella alueella. Menetelmä perustuu ulkoiseen koetinpeptidiin, Eu-koettimeen, joten tutkittavia proteiineja ei konjugoida. Eu-koetin ei sitoudu merkittävästi natiivirakenteisiin proteiineihin, ja Protein-Probe-liuoksessa oleva modulaattori sammuttaa vapaan koettimen aikaerotteisen luminesenssisignaalin. Muutokset proteiinien rakenteessa, kuten denaturaatiosta johtuva hydrofobisten aminohappojen paljastuminen ja vuorovaikutuksia seuraava pintaalan kasvu, mahdollistavat Eu-koettimen sitoutumisen. Sitoutuminen proteiineihin suojaa koetinta modulaattorilta ja johtaa näin signaalin kasvuun. Protein-Probe-menetelmällä tutkittiin proteiini-ligandi- ja proteiini-proteiinivuorovaikutuksia seuraamalla niiden vaikutusta proteiinien lämpöstabiilisuuteen ”thermal shift” määrityksissä. Vuorovaikutuksia havainnoitiin myös perustuen sitoutumisen aikaansaamaan signaalin nousuun korkeassa lämpötilassa, ja kilpailevissa thermal shift määrityksissä pienten ligandimolekyylien kanssa. Suurten proteiinikompleksien ja aggregaattien muodostuminen havaittiin huoneenlämmössä. Kehitetty leimavapaa Protein-Probe menetelmä on herkempi kuin nykyiset liuospohjaiset tekniikat, ja sillä on mahdollista tutkia erilaisia vuorovaikutuksia, koska ulkoinen koetin sitoutuu moneen eri proteiiniin useassa määritystyypissä. Protein-Probe-menetelmä on siis lupaava uusi menetelmä PPI-tutkimukseen

生物情報ネットワークのグラフ理論に基づく解析法

京都大学新制・課程博士博士(情報学)甲第24730号情博第818号新制||情||138(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 阿久津 達也, 教授 山本 章博, 教授 岡部 寿男学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability.

We have performed 280 μs of unbiased molecular dynamics (MD) simulations to investigate the effects of 12 different cancer mutations on Kelch-like ECH-associated protein 1 (KEAP1) (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C, R470C, R470H, R470S and G476R), one of the frequently mutated proteins in lung cancer. The aim was to provide structural insight into the effects of these mutants, including a new class of ANCHOR (additionally NRF2-complexed hypomorph) mutant variants. Our work provides additional insight into the structural dynamics of mutants that could not be analyzed experimentally, painting a more complete picture of their mutagenic effects. Notably, blade-wise analysis of the Kelch domain points to stability as a possible target of cancer in KEAP1. Interestingly, structural analysis of the R470C ANCHOR mutant, the most prevalent missense mutation in KEAP1, revealed no significant change in structural stability or NRF2 binding site dynamics, possibly indicating an covalent modification as this mutant\u27s mode of action