Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Modularity-based parallel protein design algorithm with an implementation using shared memory programming.

Given a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three-dimensional structure. The protein design problem belongs to the non-deterministic polynomial-time-hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity-based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide-and-conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity-based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence-based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure-based comparison of the modeled structures of the design protein with the target structure exhibited an average root-mean-square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity-based protein design algorithm can be extended to protein interaction design as well.

A computational framework for modeling functional protein-protein interactions.

Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.

High-Performance Whole-Cell Simulation Exploiting Modular Cell Biology Principles.

One of the grand challenges of this century is modeling and simulating a whole cell. Extreme regulation of an extensive quantity of model and simulation data during whole-cell modeling and simulation renders it a computationally expensive research problem in systems biology. In this article, we present a high-performance whole-cell simulation exploiting modular cell biology principles. We prepare the simulation by dividing the unicellular bacterium, Escherichia coli (E. coli), into subcells utilizing the spatially localized densely connected protein clusters/modules. We set up a Brownian dynamics-based parallel whole-cell simulation framework by utilizing the Hamiltonian mechanics-based equations of motion. Though the velocity Verlet integration algorithm possesses the capability of solving the equations of motion, it lacks the ability to capture and deal with particle-collision scenarios. Hence, we propose an algorithm for detecting and resolving both elastic and inelastic collisions and subsequently modify the velocity Verlet integrator by incorporating our algorithm into it. Also, we address the boundary conditions to arrest the molecules' motion outside the subcell. For efficiency, we define one hashing-based data structure called the cellular dictionary to store all of the subcell-related information. A benchmark analysis of our CUDA C/C++ simulation code when tested on E. coli using the CPU-GPU cluster indicates that the computational time requirement decreases with the increase in the number of computing cores and becomes stable at around 128 cores. Additional testing on higher organisms such as rats and humans informs us that our proposed work can be extended to any organism and is scalable for high-end CPU-GPU clusters.

Ebola Virus VP35 Protein: Modeling of the Tetrameric Structure and an Analysis of Its Interaction with Human PKR.

The Viral Protein 35 (VP35), a crucial protein of the Zaire Ebolavirus (EBOV), interacts with a plethora of human proteins to cripple the human immune system. Despite its importance, the entire structure of the tetrameric assembly of EBOV VP35 and the means by which it antagonizes the autophosphorylation of the kinase domain of human protein kinase R (PKRK) is still elusive. We consult existing structural information to model a tetrameric assembly of the VP35 protein where 93% of the protein is modeled using crystal structure templates. We analyze our modeled tetrameric structure to identify interchain bonding networks and use molecular dynamics simulations and normal-mode analysis to unravel the flexibility and deformability of the different regions of the VP35 protein. We establish that the C-terminal of VP35 (VP35C) directly interacts with PKRK to prevent it from autophosphorylation. Further, we identify three plausible VP35C-PKRK complexes with better affinity than the PKRK dimer formed during autophosphorylation and use protein design to establish a new stretch in VP35C that interacts with PKRK. The proposed tetrameric assembly will aid in better understanding of the VP35 protein, and the reported VP35C-PKRK complexes along with their interacting sites will help in the shortlisting of small molecule inhibitors.

Boosting phosphorylation site prediction with sequence feature-based machine learning.

Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequence environment, and amino acid-specific features to decipher phosphate binding sites from a protein sequence. Our method, while compared with other existing methods on 2429 protein sequences taken from standard Phospho.ELM (P.ELM) benchmark data set featuring 11 organisms reports a higher F1 score = 0.504 (harmonic mean of the precision and recall) and ROC AUC = 0.836 (area under the curve of the receiver operating characteristics). The computation time of our proposed approach is much less than that of the recently developed deep learning-based framework. Structural analysis on selected protein sequences informs that our prediction is the superset of the phosphorylation sites, as mentioned in P.ELM data set. The foundation of our scheme is manual feature engineering and a decision tree-based classification. Hence, it is intuitive, and one can interpret the final tree as a set of rules resulting in a deeper understanding of the relationships between biophysical features and phosphorylation sites. Our innovative problem transformation method permits more control over precision and recall as is demonstrated by the fact that if we incorporate output probability of the existing deep learning framework as an additional feature, then our prediction improves (F1 score = 0.546; ROC AUC = 0.849). The implementation of our method can be accessed at http://cse.iitkgp.ac.in/~pralay/resources/PPSBoost/ and is mirrored at https://cosmos.iitkgp.ac.in/PPSBoost.

A network-based zoning for parallel whole-cell simulation.

Motivation: In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use. Results: In this article, we present a network-based zoning approach that could potentially be utilized in the parallelization of whole-cell simulations. Firstly, we construct the protein-protein interaction graph of the whole-cell of an organism using experimental data from various sources. Based on protein interaction information, we predict protein locality and allocate confidence score to the interactions accordingly. We then identify the modules of strictly localized interacting proteins by performing interaction graph clustering based on the confidence score of the interactions. By applying this method to Escherichia coli K12, we identified 188 spatially localized clusters. After a thorough Gene Ontology-based analysis, we proved that the clusters are also in functional proximity. We then conducted Principal Coordinates Analysis to predict the spatial distribution of the clusters in the simulation space. Our automated computational techniques can partition the entire simulation space (cell) into simulation sub-cells. Each of these sub-cells can be simulated on separate computing units of the High-Performance Computing (HPC) systems. We benchmarked our method using proteins. However, our method can be extended easily to add other cellular components like DNA, RNA and metabolites. Availability and implementation: . Supplementary information: Supplementary data are available at Bioinformatics online.

Estimating the Effect of Single-Point Mutations on Protein Thermodynamic Stability and Analyzing the Mutation Landscape of the p53 Protein.

Nonsynonymous single-nucleotide polymorphisms often result in altered protein stability while playing crucial roles both in the evolution process and in the development of human diseases. Prediction of change in the thermodynamic stability due to such missense mutations will help in protein engineering endeavors and will contribute to a better understanding of different disease conditions. Here, we develop a machine-learning-based framework, viz., ProTSPoM, to estimate the change in protein thermodynamic stability arising out of single-point mutations (SPMs). ProTSPoM outperforms existing methods on the S2648 and S1925 databases and reports a Pearson correlation coefficient of 0.82 (0.88) and a root-mean-squared-error of 0.92 (1.06) kcal/mol between the predicted and experimental ΔΔG values on the long-established S350 (tumor suppressor p53 protein) data set. Further, we estimate the change in thermodynamic stability for all possible SPMs in the DNA binding domain of the p53 protein. We identify single-nucleotide polymorphisms in p53 which are plausibly detrimental to its structural integrity and interaction affinity with the DNA molecule. ProTSPoM with its reliable estimates and time-efficient prediction is well suited to be integrated with existing protein engineering techniques. The ProTSPoM web server is accessible at http://cosmos.iitkgp.ac.in/ProTSPoM/.

Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures.

Insertions/deletions of amino acids in the protein backbone potentially result in altered structural/functional specifications. They can either contribute positively to the evolutionary process or can result in disease conditions. Despite being the second most prevalent form of protein modification, there are no databases or computational frameworks that delineate harmful multipoint deletions (MPD) from beneficial ones. We introduce a positive unlabeled learning-based prediction framework (PROFOUND) that utilizes fold-level attributes, environment-specific properties, and deletion site-specific properties to predict the change in foldability arising from such MPDs, both in the non-loop and loop regions of protein structures. In the absence of any protein structure dataset to study MPDs, we introduce a dataset with 153 MPD instances that lead to native-like folded structures and 7650 unlabeled MPD instances whose effect on the foldability of the corresponding proteins is unknown. PROFOUND on 10-fold cross-validation on our newly introduced dataset reports a recall of 82.2% (86.6%) and a fall out rate (FR) of 14.2% (20.6%), corresponding to MPDs in the protein loop (non-loop) region. The low FR suggests that the foldability in proteins subject to MPDs is not random and necessitates unique specifications of the deleted region. In addition, we find that additional evolutionary attributes contribute to higher recall and lower FR. The first of a kind foldability prediction system owing to MPD instances and the newly introduced dataset will potentially aid in novel protein engineering endeavors.

Analyzing Change in Protein Stability Associated with Single Point Deletions in a Newly Defined Protein Structure Database.

Protein backbone alternation due to insertion/deletion or mutation operation often results in a change of fundamental biophysical properties of proteins. The proposed work intends to encode the protein stability changes associated with single point deletions (SPDs) of amino acids in proteins. The encoding will help in the primary screening of detrimental backbone modifications before opting for expensive in vitro experimentations. In the absence of any benchmark database documenting SPDs, we curate a data set containing SPDs that lead to both folded conformations and unfolded state. We differentiate these SPD instances with the help of simple structural and physicochemical features and eventually classify the foldability resulting out of SPDs using a Random Forest classifier and an Elliptic Envelope based outlier detector. Adhering to leave one out cross validation, the accuracy of the Random Forest classifier and the Elliptic Envelope is of 99.4% and 98.1%, respectively. The newly defined database and the delineation of SPD instances based on its resulting foldability provide a head start toward finding a solution to the given problem.

EvoDesign: De novo protein design based on structural and evolutionary profiles.

Protein design aims to identify new protein sequences of desirable structure and biological function. Most current de novo protein design methods rely on physics-based force fields to search for low free-energy states following Anfinsen's thermodynamic hypothesis. A major obstacle of such approaches is the inaccuracy of the force field design, which cannot accurately describe the atomic interactions or distinguish correct folds. We developed a new web server, EvoDesign, to design optimal protein sequences of given scaffolds along with multiple sequence and structure-based features to assess the foldability and goodness of the designs. EvoDesign uses an evolution-profile-based Monte Carlo search with the profiles constructed from homologous structure families in the Protein Data Bank. A set of local structure features, including secondary structure, torsion angle and solvation, are predicted by single-sequence neural-network training and used to smooth the sequence motif and accommodate the physicochemical packing. The EvoDesign algorithm has been extensively tested in large-scale protein design experiments, which demonstrate enhanced foldability and structural stability of designed sequences compared with the physics-based designing methods. The EvoDesign server is freely available at http://zhanglab.ccmb.med.umich.edu/EvoDesign.

An evolution-based approach to De Novo protein design and case study on Mycobacterium tuberculosis.

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.

MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction.

BACKGROUND AND OBJECTIVE: Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include antibody therapeutics, vaccines, and drug discovery. The problem of sequence-based PPI prediction has been a long-standing issue in computational biology. METHODS: We introduce MaTPIP, a cutting-edge deep-learning framework for predicting PPI. MaTPIP stands out due to its innovative design, fusing pre-trained Protein Language Model (PLM)-based features with manually curated protein sequence attributes, emphasizing the part-whole relationship by incorporating two-dimensional granular part (amino-acid) level features and one-dimensional whole-level (protein) features. What sets MaTPIP apart is its ability to integrate these features across three different input terminals seamlessly. MatPIP also includes a distinctive configuration of Convolutional Neural Network (CNN) with Transformer components for concurrent utilization of CNN and sequential characteristics in each iteration and a one-dimensional to two-dimensional converter followed by a unified embedding. The statistical significance of this classifier is validated using McNemar's test. RESULTS: MaTPIP outperformed the existing methods on both the Human PPI benchmark and cross-species PPI testing datasets, demonstrating its immense generalization capability for PPI prediction. We used seven diverse datasets with varying PPI target class distributions. Notably, within the novel PPI scenario, the most challenging category for Human PPI Benchmark, MaTPIP improves the existing state-of-the-art score from 74.1% to 78.6% (measured in Area under ROC Curve), from 23.2% to 32.8% (in average precision) and from 4.9% to 9.5% (in precision at 3% recall) for 50%, 10% and 0.3% target class distributions, respectively. In cross-species PPI evaluation, hybrid MaTPIP establishes a new benchmark score (measured in Area Under precision-recall curve) of 81.1% from the previous 60.9% for Mouse, 80.9% from 56.2% for Fly, 78.1% from 55.9% for Worm, 59.9% from 41.7% for Yeast, and 66.2% from 58.8% for E.coli. Our eXplainable AI-based assessment reveals an average contribution of different feature families per prediction on these datasets. CONCLUSIONS: MaTPIP mixes manually curated features with the feature extracted from the pre-trained PLM to predict sequence-based protein-protein association. Furthermore, MaTPIP demonstrates strong generalization capabilities for cross-species PPI predictions.

PRUNE and PROBE--two modular web services for protein-protein docking.

The protein-protein docking programs typically perform four major tasks: (i) generation of docking poses, (ii) selecting a subset of poses, (iii) their structural refinement and (iv) scoring, ranking for the final assessment of the true quaternary structure. Although the tasks can be integrated or performed in a serial order, they are by nature modular, allowing an opportunity to substitute one algorithm with another. We have implemented two modular web services, (i) PRUNE: to select a subset of docking poses generated during sampling search (http://pallab.serc.iisc.ernet.in/prune) and (ii) PROBE: to refine, score and rank them (http://pallab.serc.iisc.ernet.in/probe). The former uses a new interface area based edge-scoring function to eliminate >95% of the poses generated during docking search. In contrast to other multi-parameter-based screening functions, this single parameter based elimination reduces the computational time significantly, in addition to increasing the chances of selecting native-like models in the top rank list. The PROBE server performs ranking of pruned poses, after structure refinement and scoring using a regression model for geometric compatibility, and normalized interaction energy. While web-service similar to PROBE is infrequent, no web-service akin to PRUNE has been described before. Both the servers are publicly accessible and free for use.

A sequence space search engine for computational protein design to modulate molecular functionality.

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.

Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India.

India had witnessed unprecedented surge in SARS-CoV-2 infections and its dire consequences during the second wave of COVID-19, but the detailed report of the epidemiological based spatiotemporal incidences of the disease is missing. In the manuscript, we have applied various statistical approaches (correlation, hierarchical clustering) to decipher the pattern of pathogenesis of the circulating VoCs responsible for surge in the incidences. B.1.617.1 (Kappa) was the predominant VoC during the early phase of the second wave, whereas, Delta (B.1.617.2) or Delta-like (AY.x) VoC constitutes majority ([Formula: see text]%) of the cases during the peak of the second wave. The correlation plot of Delta/Delta-like lineage demonstrates inverse correlation with other lineages including B.1.617.1, B.1.1.7, B.1, B.1.36.29 and B.1.36. The spatiotemporal analysis shows that most of the Indian states were affected during the peak of the second wave due to the Delta surge, and fall under the same cluster. The second cluster populated mostly by north-eastern states and the islands of India were minimally affected. The presence of signature mutations (T478K, D950N, E156G) along with L452K, D614G and P681R within the spike protein of Delta or Delta-like might cause elevation in the host cell attachment, increased transmission and altered antigenicity which in due course of time has replaced the other circulating variants.The timely assessment of new VoCs including Delta-like will provide a rationale for updating the diagnostic, vaccine development by medical industries and decision making by various agencies including government, educational institutions, and corporate industries.

A novel computational predictive biological approach distinguishes Integrin ß1 as a salient biomarker for breast cancer chemoresistance.

Chemoresistance is a primary cause of breast cancer treatment failure, and protein-protein interactions significantly contribute to chemoresistance during different stages of breast cancer progression. In pursuit of novel biomarkers and relevant protein-protein interactions occurring during the emergence of breast cancer chemoresistance, we used a computational predictive biological (CPB) approach. CPB identified associations of adhesion molecules with proteins connected with different breast cancer proteins associated with chemoresistance. This approach identified an association of Integrin ß1 (ITGB1) with chemoresistance and breast cancer stem cell markers. ITGB1 activated the Focal Adhesion Kinase (FAK) pathway promoting invasion, migration, and chemoresistance in breast cancer by upregulating Erk phosphorylation. FAK also activated Wnt/Sox2 signaling, which enhanced self-renewal in breast cancer. Activation of the FAK pathway by ITGB1 represents a novel mechanism linked to breast cancer chemoresistance, which may lead to novel therapies capable of blocking breast cancer progression by intervening in ITGB1-regulated signaling pathways.

ProMoCell and ProModb: Web services for analyzing interaction-based functionally localized protein modules in a cell.

The modular organization of a cell which can be determined by its interaction network allows us to understand a mesh of cooperation among the functional modules. Therefore, cellular-level identification of functional modules aids in understanding the functional and structural characteristics of the biological network of a cell and also assists in determining or comprehending the evolutionary signal. We develop ProMoCell that performs real-time Web scraping for generating clusters of the cellular level functional units of an organism. ProMoCell constructs the Protein Locality Graphs and clusters the cellular level functional units of an organism by utilizing experimentally verified data from various online sources. Also, we develop ProModb, a database service that houses precomputed whole-cell protein-protein interaction network-based functional modules of an organism using ProMoCell. Our Web service is entirely synchronized with the KEGG pathway database and allows users to generate spatially localized protein modules for any organism belonging to the KEGG genome using its real-time Web scraping characteristics. Hence, the server will host as many organisms as is maintained by the KEGG database. Our Web services provide the users a comprehensive and integrated tool for an efficient browsing and extraction of the spatial locality-based protein locality graph and the functional modules constructed by gathering experimental data from several interaction databases and pathway maps. We believe that our Web services will be beneficial in pharmacological research, where a novel research domain called modular pharmacology has initiated the study on the diagnosis, prevention, and treatment of deadly diseases using functional modules.

Human prion protein: exploring the thermodynamic stability and structural dynamics of its pathogenic mutants.

Human familial prion diseases are known to be associated with different single-point mutants of the gene coding for prion protein with a primary focus at several locations of the globular domain. We have identified 12 different single-point pathogenic mutants of human prion protein (HuPrP) with the help of extensive perturbations/mutation technique at multiple locations of HuPrP sequence related to potentiality towards conformational disorders. Among these, some of the mutants include pathogenic variants that corroborate well with the literature reported proteins while majority include some unique single-point mutants that are either not explicitly studied early or studied for variants with different residues at the specific position. Primarily, our study sheds light on the unfolding mechanism of the above mentioned mutants in depth. Besides, we could identify some mutants under investigation that demonstrates not only unfolding of the helical structures but also extension and generation of the ß-sheet structures and or simultaneously have highly exposed hydrophobic surface which is assumed to be linked with the production of aggregate/fibril structures of the prion protein. Among the identified mutants, Q212E needs special attention due to its maximum exposure of hydrophobic core towards solvent and E200Q is found to be important due to its maximum extent of ß-content. We are also able to identify different respective structural conformations of the proteins according to their degree of structural unfolding and those conformations can be extracted and further studied in detail. Communicated by Ramaswamy H. Sarma.

Therapeutic targeting of RBPJ, an upstream regulator of ETV6 gene, abrogates ETV6-NTRK3 fusion gene transformations in glioblastoma.

Fusion genes are abnormal genes resulting from chromosomal translocation, insertion, deletion, inversion, etc. ETV6, a rather promiscuous partner forms fusions with several other genes, most commonly, the NTRK3 gene. This fusion leads to the formation of a constitutively activated tyrosine kinase which activates the Ras-Raf-MEK and PI3K/AKT/MAPK pathways, leading the cells through cycles of uncontrolled division and ultimately resulting in cancer. Targeted therapies against this ETV6-NTRK3 fusion protein are much needed. Therefore, to find a targeted approach, a transcription factor RBPJ regulating the ETV6 gene was established and since the ETV6-NTRK3 fusion gene is downstream of the ETV6 promoter/enhancer, this fusion protein is also regulated. The regulation of the ETV6 gene via RBPJ was validated by ChIP analysis in human glioblastoma (GBM) cell lines and patient tissue samples. This study was further followed by the identification of an inhibitor, Furamidine, against transcription factor RBPJ. It was found to be binding with the DNA binding domain of RBPJ with antitumorigenic properties and minimal organ toxicity. Hence, a new target RBPJ, regulating the production of ETV6 and ETV6-NTRK3 fusion protein was found along with a potent RBPJ inhibitor Furamidine.