Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces

Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10⁻⁴) and oncogenes (Odds Ratio 1.17, P-value < 10⁻³). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10⁻⁸). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.     

葡萄果实中脱落酸结合蛋白的存在及其性质

葡萄果实微粒体上存在高亲和力的脱落酸（ＡＢＡ）结合位点,这些位点与ＡＢＡ的结合具有饱和性,高亲和力及低容量,胰蛋白酶或ＤＴＴ处理可以使该位点的特异结合活性下降约９０％,表明此结合位点是一种蛋白质,故称为ＡＢＡ结合蛋白,它含有维系蛋白质特定构象的二硫键,该蛋白与ＡＢＡ反应的最适ｐＨ为６．０,说明与配基结合部位可能存在带有正电荷的氨基酸残基,结合活性在２５℃高于０℃,结合反应达到动态平衡需要３０ｍｉｎ,３０ｍｉｎ以后结合活性随时间延长而下降。该蛋白与ＡＢＡ结合反应的平衡解离常数为１７．５ｎｍｏｌ／Ｌ,最大结合容量（Ｂｍａｘ）为９８．４ｆｍｏｌ／ｍｇｐｒｏｔｅｉｎ。     

Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks

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Compensatory Hemagglutinin Mutations Alter Antigenic Properties of Influenza Viruses

Influenza viruses routinely acquire mutations in antigenic sites on the globular head of the hemagglutinin (HA) protein. Since these antigenic sites are near the receptor binding pocket of HA, many antigenic mutations simultaneously alter the receptor binding properties of HA. We previously reported that a K165E mutation in the Sa antigenic site of A/Puerto Rico/8/34 (PR8) HA is associated with secondary neuraminidase (NA) mutations that decrease NA activity. Here, using reverse genetics, we show that the K165E HA mutation dramatically decreases HA binding to sialic acid receptors on cell surfaces. We sequentially passaged reverse-genetics-derived PR8 viruses with the K165E antigenic HA mutation in fertilized chicken eggs, and to our surprise, viruses with secondary NA mutations did not emerge. Instead, viruses with secondary HA mutations emerged in 3 independent passaging experiments, and each of these mutations increased HA binding to sialic acid receptors. Importantly, these compensatory HA mutations were located in the Ca antigenic site and prevented binding of Ca-specific monoclonal antibodies. Taken together, these data indicate that HA antigenic mutations that alter receptor binding avidity can be compensated for by secondary HA or NA mutations. Antigenic diversification of influenza viruses can therefore occur irrespective of direct antibody pressure, since compensatory HA mutations can be located in distinct antibody binding sites.     

Predicting the Binding Patterns of Hub Proteins: A Study Using Yeast Protein Interaction Networks

Background

Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations) or party hubs (i.e., simultaneously interact with multiple partners).

Methodology

Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.

Conclusions

Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.

Availability

We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.     

Drug Resistance Missense Mutations in Cancer Are Subject to Evolutionary Constraints

Several tumour types are sensitive to deactivation of just one or very few genes that are constantly active in the cancer cells, a phenomenon that is termed ‘oncogene addiction’. Drugs that target the products of those oncogenes can yield a temporary relief, and even complete remission. Unfortunately, many patients receiving oncogene-targeted therapies relapse on treatment. This often happens due to somatic mutations in the oncogene (‘resistance mutations’). ‘Compound mutations’, which in the context of cancer drug resistance are defined as two or more mutations of the drug target in the same clone may lead to enhanced resistance against the most selective inhibitors. Here, it is shown that the vast majority of the resistance mutations occurring in cancer patients treated with tyrosin kinase inhibitors aimed at three different proteins follow an evolutionary pathway. Using bioinformatic analysis tools, it is found that the drug-resistance mutations in the tyrosine kinase domains of Abl1, ALK and exons 20 and 21 of EGFR favour transformations to residues that can be identified in similar positions in evolutionary related proteins. The results demonstrate that evolutionary pressure shapes the mutational landscape in the case of drug-resistance somatic mutations. The constraints on the mutational landscape suggest that it may be possible to counter single drug-resistance point mutations. The observation of relatively many resistance mutations in Abl1, but not in the other genes, is explained by the fact that mutations in Abl1 tend to be biochemically conservative, whereas mutations in EGFR and ALK tend to be radical. Analysis of Abl1 compound mutations suggests that such mutations are more prevalent than hitherto reported and may be more difficult to counter. This supports the notion that such mutations may provide an escape route for targeted cancer drug resistance.     

A Structural Systems Biology Approach for Quantifying the Systemic Consequences of Missense Mutations in Proteins

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell''s behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.     

Molecular Mechanisms of Disease-Causing Missense Mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.     

Rescue of Enzymatic Function for Disease-associated RPE65 Proteins Containing Various Missense Mutations in Non-active Sites

Over 70 different missense mutations, including a dominant mutation, in RPE65 retinoid isomerase are associated with distinct forms of retinal degeneration; however, the disease mechanisms for most of these mutations have not been studied. Although some mutations have been shown to abolish enzyme activity, the molecular mechanisms leading to the loss of enzymatic function and retinal degeneration remain poorly understood. Here we show that the 26 S proteasome non-ATPase regulatory subunit 13 (PSMD13), a newly identified negative regulator of RPE65, plays a critical role in regulating pathogenicity of three mutations (L22P, T101I, and L408P) by mediating rapid degradation of mutated RPE65s via a ubiquitination- and proteasome-dependent non-lysosomal pathway. These mutant RPE65s were misfolded and formed aggregates or high molecular complexes via disulfide bonds. Interaction of PSMD13 with mutant RPE65s promoted degradation of misfolded but not properly folded mutant RPE65s. Many mutations, including L22P, T101I, and L408P, were mapped on non-active sites. Although their activities were very low, these mutant RPE65s were catalytically active and could be significantly rescued at low temperature, whereas mutant RPE65s with a distinct active site mutation could not be rescued under the same conditions. Sodium 4-phenylbutyrate and glycerol displayed a significant synergistic effect on the low temperature rescue of the mutant RPE65s by promoting proper folding, reducing aggregation, and increasing membrane association. Our results suggest that a low temperature eye mask and sodium 4-phenylbutyrate, a United States Food and Drug Administration-approved oral medicine, may provide a promising “protein repair therapy” that can enhance the efficacy of gene therapy by reducing the cytotoxic effect of misfolded mutant RPE65s.     

Single Hemagglutinin Mutations That Alter both Antigenicity and Receptor Binding Avidity Influence Influenza Virus Antigenic Clustering

The hemagglutination inhibition (HAI) assay is the primary measurement used for identifying antigenically novel influenza virus strains. HAI assays measure the amount of reference sera required to prevent virus binding to red blood cells. Receptor binding avidities of viral strains are not usually taken into account when interpreting these assays. Here, we created antigenic maps of human H3N2 viruses that computationally account for variation in viral receptor binding avidities. These new antigenic maps differ qualitatively from conventional antigenic maps based on HAI measurements alone. We experimentally focused on an antigenic cluster associated with a single N145K hemagglutinin (HA) substitution that occurred between 1992 and 1995. Reverse-genetics experiments demonstrated that the N145K HA mutation increases viral receptor binding avidity. Enzyme-linked immunosorbent assays (ELISA) revealed that the N145K HA mutation does not prevent antibody binding; rather, viruses possessing this mutation escape antisera in HAI assays simply by attaching to cells more efficiently. Unexpectedly, we found an asymmetric antigenic effect of the N145K HA mutation. Once H3N2 viruses acquired K145, an epitope involving amino acid 145 became antigenically dominant. Antisera raised against an H3N2 strain possessing K145 had reduced reactivity to H3N2 strains possessing N145. Thus, individual mutations in HA can influence antigenic groupings of strains by altering receptor binding avidity and by changing the dominance of antibody responses. Our results indicate that it will be important to account for variation in viral receptor binding avidity when performing antigenic analyses in order to identify genuine antigenic differences among influenza virus variants.     

Structural and Functional Impact of Cancer-Related Missense Somatic Mutations

A number of large-scale cancer somatic genome sequencing projects are now identifying genetic alterations in cancers. Evaluation of the effects of these mutations is essential for understanding their contribution to tumorigenesis. We have used SNPs3D, a software suite originally developed for analyzing nonsynonymous germ-line variants, to identify single-base mutations with a high impact on protein structure and function. Two machine learning methods are used: one identifying mutations that destabilize protein three-dimensional structure and the other utilizing sequence conservation and detecting all types of effects on in vivo protein function. Incorporation of detailed structure information into the analysis allows detailed interpretation of the functional effects of mutations in specific cases.  Data from a set of breast and colorectal tumors were analyzed. In known cancer genes, mutations approaching 100% of mutations are found to impact protein function, supporting the view that these methods are appropriate for identifying driver mutations. Overall, 50-60% of all somatic missense mutations are predicted to have a high impact on structural stability or to more generally affect the function of the corresponding proteins. This value is similar to the fraction of all possible missense mutations that have a high impact and is much higher than the corresponding one for human population single-nucleotide polymorphisms, at about 30%. The majority of mutations in tumor suppressors destabilize protein structure, while mutations in oncogenes operate in more varied ways, including destabilization of less active conformational states. The set of high-impact mutations encompasses the possible drivers.     

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features,Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein''s metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein''s metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.     

Structural and Binding Properties of Two Paralogous Fatty Acid Binding Proteins of Taenia solium Metacestode

Background

Fatty acid (FA) binding proteins (FABPs) of helminths are implicated in acquisition and utilization of host-derived hydrophobic substances, as well as in signaling and cellular interactions. We previously demonstrated that secretory hydrophobic ligand binding proteins (HLBPs) of Taenia solium metacestode (TsM), a causative agent of neurocysticercosis (NC), shuttle FAs in the surrounding host tissues and inwardly transport the FAs across the parasite syncytial membrane. However, the protein molecules responsible for the intracellular trafficking and assimilation of FAs have remained elusive.

Methodology/Principal Findings

We isolated two novel TsMFABP genes (TsMFABP1 and TsMFABP2), which encoded 133- and 136-amino acid polypeptides with predicted molecular masses of 14.3 and 14.8 kDa, respectively. They shared 45% sequence identity with each other and 15–95% with other related-members. Homology modeling demonstrated a characteristic β-barrel composed of 10 anti-parallel β-strands and two α-helices. TsMFABP2 harbored two additional loops between β-strands two and three, and β-strands six and seven, respectively. TsMFABP1 was secreted into cyst fluid and surrounding environments, whereas TsMFABP2 was intracellularly confined. Partially purified native proteins migrated to 15 kDa with different isoelectric points of 9.2 (TsMFABP1) and 8.4 (TsMFABP2). Both native and recombinant proteins bound to 11-([5-dimethylaminonaphthalene-1-sulfonyl]amino)undecannoic acid, dansyl-DL-α-amino-caprylic acid, cis-parinaric acid and retinol, which were competitively inhibited by oleic acid. TsMFABP1 exhibited high affinity toward FA analogs. TsMFABPs showed weak binding activity to retinol, but TsMFABP2 showed relatively high affinity. Isolation of two distinct genes from an individual genome strongly suggested their paralogous nature. Abundant expression of TsMFABP1 and TsMFABP2 in the canal region of worm matched well with the histological distributions of lipids and retinol.

Conclusions/Significance

The divergent biochemical properties, physiological roles and cellular distributions of the TsMFABPs might be one of the critical mechanisms compensating for inadequate de novo FA synthesis. These proteins might exert harmonized or independent roles on lipid assimilation and intracellular signaling. The specialized distribution of retinol in the canal region further implies that cells in this region might differentiate into diverse cell types during metamorphosis into an adult worm. Identification of bioactive systems pertinent to parasitic homeostasis may provide a valuable target for function-related drug design.     

人乳腺癌cAMP结合蛋白

建立了测定人乳腺癌胞浆ｃＡＭＰ结合蛋白（ｃＡＭＰｂ．ｐ．）方法。综合研究了其温度、保温时间、配体浓度、稳定性等条件。ｃＡＭＰｂ．ｐ．的Ｋ_Ｄ值为２．９０×１０~（－８）ｍｏｌ／Ｌ．并测定了６０例雌激素受体（ＥＲ）Ｆｕ性乳腺癌标本的ｃＡＭＰｂ．ｐ．含量。此组病人术后均接受系统的内分泌治疗,ＥＲ／ｃＡＭＰｂｐ,比值范围为７．７～３６２×１０~（－３）,ＥＲ／ｃＡＭＰｂ．ｐ．比值≥４０×１０~（－３）的五年生存率明显高于比值＜４０×１０~（－３）组,（ｐ＜０．００５）．表明测定ＥＲ／ｃＡＭＰｂ．ｐ．比值对预测患者内分泌治疗疗效,优于单独测定ＥＲ．     

Only Missense Mutations Affecting the DNA Binding Domain of P53 Influence Outcomes in Patients with Breast Carcinoma

The presence of a TP53 gene mutation can influence tumour response to some treatments, especially in breast cancer. In this study, we analysed p53 mRNA expression, LOH at 17p13 and TP53 mutations from exons 2 to 11 in 206 patients with breast carcinoma and correlated the results with disease-free and overall survival. The observed mutations were classified according to their type and location in the three protein domains (transactivation domain, DNA binding domain, oligomerization domain) and correlated with disease-free and overall survival. In our population, neither p53 mRNA expression nor LOH correlated with outcome. Concerning TP53 mutations, 27% of tumours were mutated (53/197) and the presence of a mutation in the TP53 gene was associated with worse overall survival (p = 0.0026) but not with disease-free survival (p = 0.0697), with median survival of 80 months and 78 months, respectively. When alterations were segregated into mutation categories and locations, and related to survival, tumours harbouring mutations other than missense mutations in the DNA binding domain of P53 had the same survival profiles as wild-type tumours. Concerning missense mutations in the DNA binding domain, median disease-free and overall survival was 23 months and 35 months, respectively (p = 0.0021 and p<0.0001, respectively), compared with 78 and 80 months in mutated tumours overall. This work shows that disease-free and overall survival in patients with a frameshift mutation of TP53 or missense mutation in the oligomerization domain are the same as those in wild-type TP53 patients.     

Mutations in microRNA Binding Sites of CEP Genes Involved in Cancer

The CEP genes play a pivotal role in the replication of the cell. CEP family proteins form the major constituents of the centrosome and play a prominent role in centriole biogenesis and in cell replication. Alteration in CEP genes will result in disruption of cell cycle that may in turn cause cancer. In our study, we found that 16 of the CEP genes are a potential target to miRNA that binds to complementary sequences in 3′untranslated regions (UTR) of mRNA and stop them from translation. Single nucleotide polymorphisms (SNPs) occurring naturally in such miRNA binding site can alter the miRNA: mRNA interaction and can significantly alter gene expression. We developed a systematic computational pipeline that integrates data from well-established databases, followed stringent selection criteria and identified a panel of 44 high-confidence SNPs that may impair miRNA target sites in the 3′UTR of 16 genes. Further we performed expression analysis to shed light on the potential tissues that might be affected by mutation, enrichment analysis to find the metabolic functions of the gene, and network analysis to highlight the important interactions of CEP genes with other genes to provide insight that complex network will be disturbed upon mutation. In this study, we explored and prioritised the SNPs in CEP gene which could act as a potential target in centrosome-associated human disease. Our analysis would provide a thoughtful insight to wet lab researches to understand the expression pattern of CEP genes and binding phenomenon of mRNA and miRNA upon mutation, which is responsible for inhibition of translation process at genomic levels.