RESUMEN
Cyclization of the polypeptide backbone has proven to be a powerful strategy for enhancing protein stability for fundamental research and pharmaceutical application. The use of such an approach is restricted by how well a targeted polypeptide can be efficiently ligated. Recently, an Asx-specific peptide ligase identified from a tropical cyclotide-producing plant and named butelase 1 exhibited excellent cyclization kinetics that cannot be matched by other known ligases, including intein, PATG, PCY1, and sortase A. In this work, we aimed to examine whether butelase 1 facilitated protein conformational stability for structural investigation. First, we successfully expressed recombinant butelase 1 (rBTase) in the yeast Pichia pastoris. Next, rBTase was shown to be highly efficient in the cyclization of the p53-binding domain (N-terminal domain) of murine double minute X (N-MdmX), an important target for designing anticancer drugs. The cyclized N-MdmX (cMdmX) exhibited increased conformational stability and improved interaction with the ligand compared with those of noncyclized N-MdmX. Importantly, the thermal melting process was completely reversible, contrary to noncyclized N-MdmX, and the melting temperature ( Tm) of cMdmX was increased to 47 from 43 °C. This stable conformation of cMdmX was further confirmed by 15N-1H heteronuclear single-quantum coherence nuclear magnetic resonance (NMR) spectroscopy. The complex of cMdmX and the ligand was tested for protein crystallization, and several promising findings were revealed. Therefore, our work not only provides a recombinant version of butelase 1 but also suggests a conventional approach for preparing stable protein samples for both protein crystallization and NMR structural investigation.
Asunto(s)
Fabaceae/enzimología , Ligasas/química , Proteínas Proto-Oncogénicas/química , Secuencia de Aminoácidos , Animales , Cristalización/métodos , Cristalografía por Rayos X/métodos , Ciclización , Ratones , Modelos Moleculares , Unión Proteica , Conformación Proteica , Dominios Proteicos , Estabilidad Proteica , Proteínas Proto-Oncogénicas/metabolismo , Proteínas Recombinantes/química , Proteína p53 Supresora de Tumor/metabolismoRESUMEN
The crystallized ligands in the Protein Data Bank (PDB) can be treated as the inverse shapes of the active sites of corresponding proteins. Therefore, the shape similarity between a molecule and PDB ligands indicated the possibility of the molecule to bind with the targets. In this paper, we proposed a shape similarity profile that can be used as a molecular descriptor for ligand-based virtual screening. First, through three-dimensional (3D) structural clustering, 300 diverse ligands were extracted from the druggable protein-ligand database, sc-PDB. Then, each of the molecules under scrutiny was flexibly superimposed onto the 300 ligands. Superimpositions were scored by shape overlap and property similarity, producing a 300 dimensional similarity array termed the "Three-Dimensional Biologically Relevant Spectrum (BRS-3D)". Finally, quantitative or discriminant models were developed with the 300 dimensional descriptor using machine learning methods (support vector machine). The effectiveness of this approach was evaluated using 42 benchmark data sets from the G protein-coupled receptor (GPCR) ligand library and the GPCR decoy database (GLL/GDD). We compared the performance of BRS-3D with other 2D and 3D state-of-the-art molecular descriptors. The results showed that models built with BRS-3D performed best for most GLL/GDD data sets. We also applied BRS-3D in histone deacetylase 1 inhibitors screening and GPCR subtype selectivity prediction. The advantages and disadvantages of this approach are discussed.
Asunto(s)
Bases de Datos de Proteínas , Simulación por Computador , Descubrimiento de Drogas , Humanos , Ligandos , Modelos Moleculares , Unión Proteica , Dominios y Motivos de Interacción de Proteínas , Receptores Acoplados a Proteínas G/química , Homología Estructural de Proteína , Máquina de Vectores de SoporteRESUMEN
Adenosine receptors (ARs) are potential therapeutic targets for Parkinson's disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2B vs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models' robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2A vs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.
Asunto(s)
Receptores Purinérgicos P1/metabolismo , Humanos , Ligandos , Modelos Moleculares , Unión Proteica , Máquina de Vectores de SoporteRESUMEN
We applied a novel molecular descriptor, three-dimensional biologically relevant spectrum (BRS-3D), in subtype selectivity prediction of dopamine receptor (DR) ligands. BRS-3D is a shape similarity profile calculated by superimposing the objective compounds against 300 template ligands from sc-PDB. First, we constructed five subtype selectivity regression models between DR subtypes D1-D2, D1-D3, D2-D3, D2-D4, and D3-D4. The models' 10-fold cross-validation-squared correlation coefficient (Q2 , for training sets) and determination coefficient (R2 , for test sets) were in the range of 0.5-0.7 and 0.6-0.8, respectively. Then, four pair-wise (D1-D2, D2-D3, D2-D4, and D3-D4) and a multitype (D2, D3, and D4) classification models were developed with the prediction accuracies around or over 90% (for test sets). Lastly, we compared the performances of the models developed on BRS-3D and classical descriptors. The results showed that BRS-3D performed similarly to classical 2D descriptors and better than other 3D descriptors. Combining BRS-3D and 2D descriptors can further improve the prediction performance. These results confirmed the capacity of BRS-3D in the prediction of DR subtype-selective ligands.
Asunto(s)
Receptores Dopaminérgicos/metabolismo , Ligandos , Modelos Químicos , Relación Estructura-Actividad Cuantitativa , Receptores Dopaminérgicos/clasificación , Máquina de Vectores de SoporteRESUMEN
Both recent studies and our calculation suggest that the physicochemical properties of launched drugs changed continuously over the past decades. Besides shifting of commonly used properties, the average biological relevance (BR) and similarity to natural products (NPs) of launched drugs decreased, reflecting the fact that current drug discovery deviated away from NPs. To change the current situation characterized by high investment but low productivity in drug discovery, efforts should be made to improve the BR of the screening library and hunt drugs more effectively in the biologically relevant chemical space. Additionally, a multiple dimensional molecular descriptor, named the biologically relevant spectrum (BRS) was proposed for quantitative structure-activity relationships (QSAR) study or screening library preparation. Prediction models for 43 biological activity categories were developed with BRS and support vector machine (SVM). In most cases, the overall prediction accuracies were around 95% and the Matthew's correlation coefficients (MCC) were over 0.8. Thirty-seven out of 48 drug-activity associations were successfully predicted for drugs that launched from 2006 to 2012, which were not included in the training data set. A web-server named BioRel ( http://ibi.hzau.edu.cn/biorel ) was developed to provide services including BR, BRS calculation, activity class, and pharmacokinetic property prediction.