Structural assessment of single amino acid mutations: application to TP53 function

Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers

Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein-DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.

Use of the FACTS solvation model for protein-ligand docking calculations. Application to EADock

Protein-ligand docking has made important progress during the last decade and has become a powerful tool for drug development, opening the way to virtual high throughput screening and in silico structure-based ligand design. Despite the flattering picture that has been drawn, recent publications have shown that the docking problem is far from being solved, and that more developments are still needed to achieve high successful prediction rates and accuracy. Introducing an accurate description of the solvation effect upon binding is thought to be essential to achieve this goal. In particular, EADock uses the Generalized Born Molecular Volume 2 (GBMV2) solvent model, which has been shown to reproduce accurately the desolvation energies calculated by solving the Poisson equation. Here, the implementation of the Fast Analytical Continuum Treatment of Solvation (FACTS) as an implicit solvation model in small molecules docking calculations has been assessed using the EADock docking program. Our results strongly support the use of FACTS for docking. The success rates of EADock/FACTS and EADock/GBMV2 are similar, i.e. around 75% for local docking and 65% for blind docking. However, these results come at a much lower computational cost: FACTS is 10 times faster than GBMV2 in calculating the total electrostatic energy, and allows a speed up of EADock by a factor of 4. This study also supports the EADock development strategy relying on the CHARMM package for energy calculations, which enables straightforward implementation and testing of the latest developments in the field of Molecular Modeling.

p53 in head and neck cancer: functional consequences and environmental implications of TP53 mutations

Background

Although TP53 mutations in human tumours generally have been extensively studied, the significance of p53 in the aetiology of head and neck cancers is still incompletely characterized. In recent years, considerable interest has been focused on mutant forms of p53, the abnormal protein product of TP53 alleles with missense mutation that often accumulate in cancer cells.

Methods

We compared the nature of TP53 mutations in primary 46 head and neck squamous cell carcinomas (HNSCC) analyzed by PCR-SSCP and sequencing, immunohistochemistry, and using structural information available at IARC p53 database.

Results

Sequencing confirmed 36 TP53 mutations in 23 tumours of the 39 mutations in 26 tumours found by PCR-SSCP. Only half (17) putatively affect the function of p53 protein. Of these 8 were in the L2 domain, three affected the LSH motif and three the L3 domain. Three were in other domains. Codon 259 (GAC > GAA) which is a very rare mutation was found in 4 samples in our study. There were indications of p53 aberrations being associated with the combined effect of smoking, alcohol and work history. Patients with a negative family history of cancer had more often TP53 mutations than patients with a positive family history (71% vs. 46%).

Conclusions

Our study contributes to the knowledge of cumulative chemical exposure and p53 aberrations in head and neck cancer, an area where literature is scarce.

Fourier transform convolution integrals applied to generalized Born molecular volume

Generalized Born methods are currently among the solvation models most commonly used for biological applications. We reformulate the generalized Born molecular volume method initially described by (Lee et al, 2003, J Phys Chem, 116, 10606; Lee et al, 2003, J Comp Chem, 24, 1348) using fast Fourier transform convolution integrals. Changes in the initial method are discussed and analyzed. Finally, the method is extensively checked with snapshots from common molecular modeling applications: binding free energy computations and docking. Biologically relevant test systems are chosen, including 855-36091 atoms. It is clearly demonstrated that, precision-wise, the proposed method performs as good as the original, and could better benefit from hardware accelerated boards.

MM-GBSA binding free energy decomposition and T cell receptor engineering

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by class I major histocompatibility complexes (MHC) is the key event in the immune response against virus infected cells or tumor cells. The major determinant of T cell activation is the affinity of the TCR for the peptide-MHC complex, though kinetic parameters are also important. A study of the 2C TCR/SIYR/H-2Kb system using a binding free energy decomposition (BFED) based on the MM-GBSA approach had been performed to assess the performance of the approach on this system. The results showed that the TCR-p-MHC BFED including entropic terms provides a detailed and reliable description of the energetics of the interaction (Zoete and Michielin, 2007). Based on these results, we have developed a new approach to design sequence modifications for a TCR recognizing the human leukocyte antigen (HLA)-A2 restricted tumor epitope NY-ESO-1. NY-ESO-1 is a cancer testis antigen expressed not only in melanoma, but also on several other types of cancers. It has been observed at high frequencies in melanoma patients with unusually positive clinical outcome and, therefore, represents an interesting target for adoptive transfer with modified TCR. Sequence modifications of TCR potentially increasing the affinity for this epitope have been proposed and tested in vitro. T cells expressing some of the proposed TCR mutants showed better T cell functionality, with improved killing of peptide-loaded T2 cells and better proliferative capacity compared to the wild type TCR expressing cells. These results open the door of rational TCR design for adoptive transfer cancer therapy.

Statistical method on nonrandom clustering with application to somatic mutations in cancer

Background

Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.

Results

We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.

Conclusions

Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.

Investigation and prediction of the severity of p53 mutants using parameters from structural calculations

A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers.

P53 transcriptional activities: a general overview and some thoughts

P53 is a master transcriptional regulator controlling several main cellular pathways. Its role is to adapt gene expression programs in order to maintain cellular homeostasis and genome integrity in response to stresses. P53 is found mutated in about half of human cancers and most mutations are clustered within the DNA-binding domain of the protein resulting in altered p53 transcriptional activity. This illustrates the importance of the gene regulations achieved by p53. The aim of this review is to provide a global overview of the current understanding of p53 transcriptional activities and to discuss some ongoing questions and unresolved points about p53 transcriptional activity.

Predicted effects of missense mutations on native-state stability account for phenotypic outcome in phenylketonuria, a paradigm of misfolding diseases

Phenylketonuria (PKU) is a genetic disease caused by mutations in human phenylalanine hydroxylase (PAH). Most missense mutations result in misfolding of PAH, increased protein turnover, and a loss of enzymatic function. We studied the prediction of the energetic impact on PAH native-state stability of 318 PKU-associated missense mutations, using the protein-design algorithm FoldX. For the 80 mutations for which expression analyses have been performed in eukaryote systems, in most cases we found substantial overall correlations between the mutational energetic impact and both in vitro residual activities and patient metabolic phenotype. This finding confirmed that the decrease in protein stability is the main molecular pathogenic mechanism in PKU and the determinant for phenotypic outcome. Metabolic phenotypes have been shown to be better predicted than in vitro residual activities, probably because of greater stringency in the phenotyping process. Finally, all the remaining 238 PKU missense mutations compiled at the PAH locus knowledgebase (PAHdb) were analyzed, and their phenotypic outcomes were predicted on the basis of the energetic impact provided by FoldX. Residues in exons 7-9 and in interdomain regions within the subunit appear to play an important structural role and constitute hotspots for destabilization. FoldX analysis will be useful for predicting the phenotype associated with rare or new mutations detected in patients with PKU. However, additional factors must be considered that may contribute to the patient phenotype, such as possible effects on catalysis and interindividual differences in physiological and metabolic processes.

Structure-function-rescue: the diverse nature of common p53 cancer mutants

The tumor suppressor protein p53 is inactivated by mutation in about half of all human cancers. Most mutations are located in the DNA-binding domain of the protein. It is, therefore, important to understand the structure of p53 and how it responds to mutation, so as to predict the phenotypic response and cancer prognosis. In this review, we present recent structural and systematic functional data that elucidate the molecular basis of how p53 is inactivated by different types of cancer mutation. Intriguingly, common cancer mutants exhibit a variety of distinct local structural changes, while the overall structural scaffold is largely preserved. The diverse structural and energetic response to mutation determines: (i) the folding state of a particular mutant under physiological conditions; (ii) its affinity for the various p53 target DNA sequences; and (iii) its protein-protein interactions both within the p53 tetramer and with a multitude of regulatory proteins. Further, the structural details of individual mutants provide the basis for the design of specific and generic drugs for cancer therapy purposes. In combination with studies on second-site suppressor mutations, it appears that some mutants are ideal rescue candidates, whereas for others simple pharmacological rescue by small molecule drugs may not be successful.

Breast cancer prognostication and prediction in the postgenomic era

Expanding knowledge, together with implementation of new techniques, has fuelled the area of translational medical research aiming at improving prognostication as well as prediction in cancer therapy. At the same time, new discoveries have revealed a biological complexity we were unaware of only a decade ago. Thus, we are faced with novel challenges with respect to how we may explore issues such as prognostication and predict drug resistance in vivo. While microarray analysis exploring expression of thousands of genes in concert represents a major methodological advancement, discoveries such as the finding of different mechanisms of epigenetic silencing, intronic mutations, that most gene transcripts in the human genome are subject to alternative splicing and that hypersplicing seems to be a tumour-related phenomenon, exemplifies a complex pathology that may not be explored with use of single analytical methods only. This paper discusses clinical settings for studying drug resistance in vivo together with a discussion of contemporary biology in this field. Notably, each individual parameter which has been found correlated to drug resistance in vivo so far represents either a direct drug target or a factor involved in DNA repair or apoptosis. On the basis of these findings, we suggest drug resistance may be explored on the basis of upfront biological hypotheses.