Rational design of the survivin/CDK4 complex by combining protein–protein docking and molecular dynamics simulations

Survivin, the smallest inhibitor of apoptosis protein (IAP), is a valid target for cancer research. It mediates both the apoptosis pathway and the cell cycle and has been proposed to form a complex with the cyclin-dependent kinase protein CDK4. The resulting complex transports CDK4 from the cytosol to the nucleus, where CDK4 participates in cell division. Survivin has been recognized as a node protein that interacts with several partners; disruption of the formed complexes can lead to new anticancer compounds. We propose a rational model of the survivin/CDK4 complex that fulfills the experimental evidence and that can be used for structure-based design of inhibitors modifying its interface recognition. In particular, the suggested complex involves the alpha helical domain of survivin and resembles the mode of binding of survivin in the survivin/borealin X-ray structure. The proposed model has been obtained by combining protein–protein docking, fractal-based shape complementarity, electrostatics studies and extensive molecular dynamics simulations.

A role for Rad23 proteins in 26S proteasome-dependent protein degradation?

Treatment of cells with genotoxic agents affects protein degradation in both positive and negative ways. Exposure of S. cerevisiae to the alkylating agent MMS resulted in activation of genes that are involved in ubiquitin- and 26S proteasome-dependent protein degradation. This process partially overlaps with the activation of the ER-associated protein degradation pathway. The DNA repair protein Rad23p and its mammalian homologues have been shown to inhibit degradation of specific substrates in response to DNA damage. Particularly the recently identified inhibition of degradation by mouse Rad23 protein (mHR23) of the associated nucleotide excision repair protein XPC was shown to stimulate DNA repair.Recently, it was shown that Rad23p and the mouse homologue mHR23B also associate with Png1p, a deglycosylation enzyme. Png1p-mediated deglycosylation plays a role in ER-associated protein degradation after accumulation of malfolded proteins in the endoplasmic reticulum. Thus, if stabilization of proteins that are associated with the C-terminus of Rad23p is a general phenomenon, then Rad23 might be implicated in the stimulation of ER-associated protein degradation as well. Interestingly, the recently identified HHR23-like protein Mif1 is also thought to play a role in ER-associated protein degradation. The MIF1 gene is strongly activated in response to ER-stress. Mif1 contains a ubiquitin-like domain which is most probably involved in binding to S5a, a subunit of the 19S regulatory complex of the 26S proteasome. On the basis of its localization in the ER-membrane, it is hypothesized that Mif1 could play a role in the translocation of the 26S proteasome towards the ER-membrane, thereby enhancing ER-associated protein degradation.     

胃癌中CDK1和CDK2的表达及预后意义

目的探讨胃癌中CDK1和CDK2的表达情况及预后意义。方法应用免疫组化S-P法对48例各期胃癌和癌旁正常组织中CDK1和CDK2的表达情况进行检测。结果早期胃癌中CDK1和CDK2中表达较低(P0.05),进展期胃癌中表达更高(P0.05)。结论CDK1和CDK2可望成为胃癌早期预后指标和分子治疗的靶标。     

Predicting protein–protein interactions from protein sequences using meta predictor

A novel method is proposed for predicting protein–protein interactions (PPIs) based on the meta approach, which predicts PPIs using support vector machine that combines results by six independent state-of-the-art predictors. Significant improvement in prediction performance is observed, when performed on Saccharomyces cerevisiae and Helicobacter pylori datasets. In addition, we used the final prediction model trained on the PPIs dataset of S. cerevisiae to predict interactions in other species. The results reveal that our meta model is also capable of performing cross-species predictions. The source code and the datasets are available at     

CDK4和CDK6在甲状腺乳头状癌中的表达及其临床意义

目的探讨细胞周期素依赖性蛋白激酶4(cyclin-dependent kinases 4,CDK4)和细胞周期素依赖性蛋白激酶6(cyclin-dependent kinases 6,CDK6)在甲状腺乳头状癌(papillary thyroid carcinoma,PTC)组织中的表达及其临床意义。方法免疫组织化学检测73例PTC及其癌旁正常组织中CDK4和CDK6的表达,分析两者与PTC患者临床病理特征的关系。结果 CDK4和CDK6在PTC的阳性表达率分别为58.9%和52.1%,显著高于癌旁正常组织的6.8%和11.0%。CDK4和CDK6在Ⅲ+Ⅵ期PTC中的阳性表达率均显著高于Ⅰ+Ⅱ期,在有淋巴结转移的阳性表达率均显著高于无淋巴结转移,而与患者性别、年龄、肿瘤大小、包膜侵犯无关。双变量相关性分析显示CDK4和CDK6具有密切正相关关系。结论 CDK4和CDK6在PTC中高表达,且与临床分期和淋巴结转移存在相关性,可能在肿瘤的恶性转化方面有重要作用。     

CDK9与疾病相关研究进展

细胞周期依赖性蛋白激酶9(cyclin-dependent kinases 9,CDK9)是丝氨酸/苏氨酸激酶家族成员之一,在细胞周期和凋亡中至关重要。CDK9是RNA转录关键延长因子,且通过磷酸化RNA聚合酶Ⅱ的羧基端结构域发挥功能。在这里我们概述了CDK9与炎症因子、流感病毒、艾滋病毒和肿瘤间的密切关系。我们通过对CDK9相关的途径的阐明,指出CDK9是细胞生物学的重要调节剂,CDK9途径的去调节可以为对人类疾病的发病机理和进展提供重要的见解。     

Cycling without CDK2?

Cyclin-dependent kinase 2 (CDK2) regulates diverse aspects of the mammalian cell cycle. Most cancer cells contain mutations in the pathways that control CDK2, and CDK2 activity has received much attention as a target for cancer therapy. However, a recent report demonstrating that some cancer cells can proliferate without CDK2 activity questions the essential role of CDK2 in cell-cycle control, as well as its suitability as a therapeutic target.     

Scoring functions for protein–protein interactions

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Phospho-tyrosine dependent protein–protein interaction network

Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes.     

Insight into TPMT∗23 mutation mis-folding using molecular dynamics simulation and protein structure analysis

Thiopurine S-methyltransferase (TPMT) is an important enzyme that metabolizes thiopurine drugs. This enzyme exhibits a large number of interindividual polymorphism. TPMT^?23 polymorphism has been reported in a few cases in the world in co-dominance with TPMT^?3A. The phenotype has been reported to affect enzyme activity in vivo and in vitro. Its underlying structural basis is not clarified yet. In our study, the wild type (WT) protein structure was analyzed and the amino acids bordering water channels in thiopurine sites were identified. Molecular dynamics of both the WT and TPMT^?23 mutation was carried out. In addition, the effects of this mutation, especially on the thiopurine site which is closed with a pincer like mechanism, were investigated. We focused on explaining how a locally occurred A167G substitution propagated through hydrogen bonds alteration to induce structural modification which affects both thiopurine and S-adenosylmethionine receptors. Finally, a genetic prediction of mutation functional consequences has been conducted confirming altered activity.

An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:20     

Assessment and significance of protein–protein interactions during development of protein biopharmaceuticals

Early development of protein biotherapeutics using recombinant DNA technology involved progress in the areas of cloning, screening, expression and recovery/purification. As the biotechnology industry matured, resulting in marketed products, a greater emphasis was placed on development of formulations and delivery systems requiring a better understanding of the chemical and physical properties of newly developed protein drugs. Biophysical techniques such as analytical ultracentrifugation, dynamic and static light scattering, and circular dichroism were used to study protein–protein interactions during various stages of development of protein therapeutics. These studies included investigation of protein self-association in many of the early development projects including analysis of highly glycosylated proteins expressed in mammalian CHO cell cultures. Assessment of protein–protein interactions during development of an IgG1 monoclonal antibody that binds to IgE were important in understanding the pharmacokinetics and dosing for this important biotherapeutic used to treat severe allergic IgE-mediated asthma. These studies were extended to the investigation of monoclonal antibody–antigen interactions in human serum using the fluorescent detection system of the analytical ultracentrifuge. Analysis by sedimentation velocity analytical ultracentrifugation was also used to investigate competitive binding to monoclonal antibody targets. Recent development of high concentration protein formulations for subcutaneous administration of therapeutics posed challenges, which resulted in the use of dynamic and static light scattering, and preparative analytical ultracentrifugation to understand the self-association and rheological properties of concentrated monoclonal antibody solutions.     

Molecular glues to stabilise protein–protein interactions

Targeting protein–protein interactions (PPIs) has become a common approach to tackle various diseases whose pathobiology is driven by their mis-regulation in important signalling pathways. Modulating PPIs has tremendous untapped therapeutic potential and different approaches can be used to modulate PPIs. Initially, therapeutic effects were mostly sought by inhibiting PPIs. However, by gaining insight in the mode of action of certain therapeutic compounds, it became clear that stabilising (i.e. enhancing) PPIs can also be useful. The latter strategy is recently gaining a lot of attention, as stabilising physiologic, or even inducing novel interactions of a target protein with E3 ubiquitin ligases forms the basis of the targeted protein degradation (TPD) approach. An emerging additional example for drug discovery based on PPI stabilisation are the 14-3-3 proteins, a family of regulatory proteins, which engages in many protein–protein interactions, some of which might become therapeutical targets.     

共转染CDK1、CDK2siRNA对肿瘤细胞周期和细胞凋亡的影响

目的研究共转染CDK1、CDK2siRNA同时抑制CDKI、CDK2蛋白表达对肿瘤细胞周期和细胞凋亡的影响,探讨细胞周期主要调控分子在肿瘤细胞凋亡中的作用。方法以人宫颈癌细胞株HeLa细胞为研究对象,用脂质体lipofectamine2000同时转染CDKl和CDK2siRNA。在转染后48、60h收集细胞,用Western印迹检测CDKl、CDK2蛋白的表达,AnnexinV／PI检测转染细胞的凋亡,流式细胞术DNA含量检测分析细胞周期。转染细胞进行瑞氏一姬姆萨染色（Wright—Giemsa）后在显微镜下观察其形态变化i结果共转染CDKl、CDK2siRNA后48和60h,Western印迹结果显示CDKl和CDK2蛋白的表达都同时降低。共转染CDKl、CDK2siRNA后,细胞周期S期和G1／M期比例与对照相比有明显增加;共转染细胞经瑞氏一姬姆萨染色后在显微镜下可见双核或多核细胞增多;AnnexinV／PI检测结果显示共转染CDK1、CDK2siRNA的细胞在48和60h细胞凋亡率与对照相比有显著的升高。结论siRNA干扰导致的CDKI、CDK2表达同时降低不仅导致细胞周期s期和G1／M期的阻滞,也诱导了肿瘤细胞的凋亡。     

PCS-based structure determination of protein–protein complexes

A simple and fast nuclear magnetic resonance method for docking proteins using pseudo-contact shift (PCS) and ¹H^N/¹⁵N chemical shift perturbation is presented. PCS is induced by a paramagnetic lanthanide ion that is attached to a target protein using a lanthanide binding peptide tag anchored at two points. PCS provides long-range (~40 Å) distance and angular restraints between the lanthanide ion and the observed nuclei, while the ¹H^N/¹⁵N chemical shift perturbation data provide loose contact-surface information. The usefulness of this method was demonstrated through the structure determination of the p62 PB1-PB1 complex, which forms a front-to-back 20 kDa homo-oligomer. As p62 PB1 does not intrinsically bind metal ions, the lanthanide binding peptide tag was attached to one subunit of the dimer at two anchoring points. Each monomer was treated as a rigid body and was docked based on the backbone PCS and backbone chemical shift perturbation data. Unlike NOE-based structural determination, this method only requires resonance assignments of the backbone ¹H^N/¹⁵N signals and the PCS data obtained from several sets of two-dimensional ¹⁵N-heteronuclear single quantum coherence spectra, thus facilitating rapid structure determination of the protein–protein complex.     

Structure-based method for analyzing protein–protein interfaces

Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (G1.5 kcal mol^-1), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation. Figure Key interaction grids at the interface between barnase and barstar. Key interaction grid for barnase and barstar are presented in one figure according to their coordinates. In order to distinguish the two proteins, different icons were assigned. Crosses represent key grids for barstar and dots represent key grids for barnase. The four residues in ball and stick are Asp40 in barstar and Arg83, Arg87, His102 in barnase.