P. Anoosha

Bio: P. Anoosha is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Mutation & Gene. The author has an hindex of 5, co-authored 8 publications receiving 87 citations.

Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer.

Abstract: Cancer is one of the most life-threatening diseases and mutations in several genes are the vital cause in tumorigenesis. Protein kinases play essential roles in cancer progression and specifically, epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this work, we have developed a method to classify single amino acid polymorphisms (SAPs) in EGFR into disease-causing (driver) and neutral (passenger) mutations using both sequence and structure based features of the mutation site by machine learning approaches. We compiled a set of 222 features and selected a set of 21 properties utilizing feature selection methods, for maximizing the prediction performance. In a set of 540 mutants, we obtained an overall classification accuracy of 67.8% with 10 fold cross validation using support vector machines. Further, the mutations have been grouped into four sets based on secondary structure and accessible surface area, which enhanced the overall classification accuracy to 80.2%, 81.9%, 77.9% and 75.1% for helix, strand, coil-buried and coil-exposed mutants, respectively. The method was tested with a blind dataset of 60 mutations, which showed an average accuracy of 85.4%. These accuracy levels are superior to other methods available in the literature for EGFR mutants, with an increase of more than 30%. Moreover, we have screened all possible single amino acid polymorphisms (SAPs) in EGFR and suggested the probable driver and passenger mutations, which would help in the development of mutation specific drugs for cancer treatment.

Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants

Abstract: Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer.

Abstract: Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72–0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.

Patterns of Somatic Mutation in Human Cancer Genomes

Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore PL02-05 All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Intrinsically Disordered Proteins

Abstract: Nothing is solid about proteins. Governing rules and established laws are constantly broken. As an example, the last decade and a half have witnessed the fall of one of the major paradigms in structural biology. Contrarily to the more than a century-old belief that the unique function of a protein is determined by its unique structure, which, in its turn, is defined by the unique amino acid sequence, many biologically active proteins lack stable tertiary and/or secondary structure either entirely or at their significant parts. Such intrinsically disordered proteins (IDPs) and hybrid proteins containing ordered domains and functional IDP regions (IDPRs) are highly abundant in nature, and many of them are associated with various human diseases. Such disordered proteins and regions are very different from ordered and well-structured proteins and domains at a variety of levels and possess well-recognizable biases in their amino acid compositions and amino acid sequences. A characteristic feature of these proteins is their exceptional structural heterogeneity, where different parts of a given polypeptide chain can be ordered (or disordered) to different degrees. As a result, a typical IDP/IDPR contains a multitude of potentially foldable, partially foldable, differently foldable or not foldable structural segments. This distribution of conformers is constantly changing in time, where a given segment of a protein molecule has different structures at different time points. The distribution is also constantly changing in response to changes in the environment. This mosaic structural organization is crucial for their functions and many IDPs are engaged in biological functions that rely on high conformational flexibility and that are not accessible to proteins with unique and fixed structures. As a result, the functional repertoire of IDPs complements that of ordered proteins, with IDPs/IDPRs being often involved in regulation, signaling and control. This Sprenger Briefs volume is dedicated to IDPs and IDPRs and an attempt is made to compress a massive amount of knowledge and into a digest that aims to be of use to those wishing a fast entry into this promising field of structural biology.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.

Abstract: At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.