The Role of HSP90 and TRAP1 Targets on Treatment in Hepatocellular Carcinoma.

Hepatocellular Carcinoma (HCC) is the predominant form of liver cancer and arises due to dysregulation of the cell cycle control machinery. Heat Shock Protein 90 (HSP90) and mitochondrial HSP90, also referred to as TRAP1 are important critical chaperone target receptors for early diagnosis and targeting HCC. Both HSP90 and TRAP1 expression was found to be higher in HCC patients. Hence, the importance of HSP90 and TRAP1 inhibitors mechanism and mitochondrial targeted delivery of those inhibitors function is widely studied. This review also focuses on importance of protein-protein interactions of HSP90 and TRAP1 targets and association of its interacting proteins in various pathways of HCC. To further elucidate the mechanism, systems biology approaches and computational biology approach studies are well explored in the association of inhibition of herbal plant molecules with HSP90 and its mitochondrial type in HCC.

TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features.

The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.

PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features.

Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.

R-SIM: A Database of Binding Affinities for RNA-small Molecule Interactions.

Ribonucleic acids (RNAs) are involved in a multitude of crucial cellular functions by acting as a central conduit for information transfer. Due to their essential and versatile functional roles in the cell, RNAs have also been implicated in multiple disease conditions of therapeutic relevance including cancers, bacterial and viral infections and neurodegenerative disorders. Recently, several approaches have emerged to tap into the potentially unexplored regions of the druggable genome, which refers to the genes and gene products that are focused during drug development. For example, considering RNAs as viable alternative therapeutic targets for drug development can potentially expand the range of therapeutic targets. Consequently, the availability of adequate binding affinity measurements for RNA-small molecule interactions is essential to understand target selectivity and design more potent RNA-targeting drug-like molecules. To facilitate this growing need, we have curated a database of experimentally validated RNA-small molecule interactions, called RNA-Small molecule Interaction Miner (R-SIM). Each entry in R-SIM provides comprehensive information on sequence, structure and classification of the RNA target, various physicochemical properties of the small molecule, binding affinity value and corresponding experimental conditions, three-dimensional structure (experimental or modelled) of the RNA-small molecule complex, and the literature source for the data. It also provides a user-friendly web interface with several options for search, display, sorting, visualization, download and upload of the data. R-SIM is freely available at: https://web.iitm.ac.in/bioinfo2/R_SIM/index.html. We envisage that R-SIM has several potential applications in understanding and accelerating the development of novel RNA-targeted small molecule therapeutics.

MPAD: A Database for Binding Affinity of Membrane Protein-protein Complexes and their Mutants.

Membrane protein complexes are crucial for a large variety of biological functions which are mainly dictated by their binding affinity. Due to the intricate nature of their structure, however, the binding affinity of membrane proteins is less explored compared to globular proteins. Mutations in these complexes affect their binding affinity, as well as impair critical functions, and may lead to diseases. Although experimental binding affinity data have expanded in the literature, they are dispersed and it is necessary to compile them into a reliable and comprehensive database. Hence, we developed MPAD (Membrane Protein complex binding Affinity Database), which contains experimental binding affinities of membrane protein-protein complexes and their mutants along with sequence, structure, and functional information, membrane-specific features, experimental conditions, as well as literature information. MPAD has an easy-to-use interface and options to build search queries, display, sort, download, and upload the data are among the other features available to users. This database can be used to understand the factors influencing the binding affinity in membrane proteins when compared to globular proteins as well as the impact of mutations on binding affinity, which may have potential applications to structure-based drug design. MPAD can be freely accessed at https://web.iitm.ac.in/bioinfo2/mpad.

PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes.

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein-carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.

Investigations on the binding specificity of ß-galactoside analogues with human galectin-1 using molecular dynamics simulations.

Galectin-1 (Gal-1) is the first member of galectin family, which has a carbohydrate recognition domain, specifically binds towards ß-galactoside containing oligosaccharides. Owing its association with carbohydrates, Gal-1 is involved in many biological processes such as cell signaling, adhesion and pathological pathways such as metastasis, apoptosis and increased tumour cell survival. The development of ß-galactoside based inhibitors would help to control the Gal-1 expression. In the current study, we carried out molecular dynamics (MD) simulations to examine the structural and dynamic behaviour Gal-1-thiodigalactoside (TDG), Gal-1-lactobionic acid (LBA) and Gal-1-beta-(1→6)-galactobiose (G16G) complexes. The analysis of glycosidic torsional angles revealed that ß-galactoside analogues TDG and LBA have a single binding mode (BM1) whereas G16G has two binding modes (BM1 and BM2) for interacting with Gal-1 protein. We have computed the binding free energies for the complexes Gal-1-TDG, Gal-1-LBA and Gal-1-G16G using MM/PBSA and are -6.45, -6.22 and -3.08 kcal/mol, respectively. This trend agrees well with experiments that the binding of Gal-1 with TDG is stronger than LBA. Further analysis revealed that the interactions due to direct and water-mediated hydrogen bonds play a significant role to the structural stability of the complexes. The result obtained from this study is useful to formulate a set of rules and derive pharmacophore-based features for designing inhibitors against galectin-1.Communicated by Ramaswamy H. Sarma.

ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins.

Short aggregation prone sequence motifs can trigger aggregation in peptide and protein sequences. Most algorithms developed so far to identify potential aggregation prone regions (APRs) use amino acid residue composition and/or sequence pattern features. In this work, we have investigated the importance of atomic-level characteristics rather than residue level to understand the initiation of aggregation in proteins and peptides. Using atomic-level features an ensemble-classifier, ANuPP has been developed to predict the aggregation-nucleating regions in peptides and proteins. In a dataset of 1279 hexapeptides, ANuPP achieved an area under the curve (AUC) of 0.831 with 77% accuracy on 10-fold cross-validation and an AUC of 0.883 with 83% accuracy in a blind test dataset of 142 hexapeptides. Further, it showed an average SOV of 48.7% on identifying APR regions in 37 proteins. The performance of ANuPP is better than other methods reported in the literature on both amyloidogenic hexapeptide prediction and APR identification. We have developed a web server for ANuPP and it is available at https://web.iitm.ac.in/bioinfo2/ANuPP/. Insights gained from this work demonstrate the importance of atomic and functional group characteristics towards diversity of atomic level origins as well as mechanisms of protein aggregation.

Molecular dynamics simulations of cognate and non-cognate AspRS-tRNAAsp complexes.

Aspartyl tRNA synthetase (AspRS), one of the 20 aminoacyl-tRNA synthetases, plays an important role in protein synthesis by catalyzing the aminoacylation reaction and synthesises Aspartyl-tRNA (tRNAAsp). A typical three-dimensional structure of AspRS comprises three distinct domains for the recognition of cognate tRNA and catalysis, namely, anti-codon binding domain/N-terminal domain, hinge domain and catalytic domain through their interactions with anti-codon loop, D-stem and acceptor arm of cognate tRNA, respectively. In this work, we have studied the structural characteristics of each domain of AspRS to understand the recognition mechanism of tRNAAsp using molecular dynamics simulations. The dynamics of AspRS-tRNAAsp complexes from E.coli (cognate and non-cognate), S.cerevisiae (cognate) and T.thermophilus (non-cognate) were compared to understand the differences in recognition of cognate and non-cognate tRNAs. Our results explain that the conformational changes associated with the recognition of tRNA occur only in the cognate complexes. Among the cognate complexes, the conformational changes in yeast AspRS are highly controlled during tRNAAsp recognition than that of in the E. coli AspRS. Moreover, the functional motions required for the tRNA recognition are observed only in the cognate complexes, and the conformational changes in AspRS and their recognition of tRNAAsp are organism specific.Communicated by Ramaswamy H. Sarma.

ProCaff: protein-carbohydrate complex binding affinity database.

MOTIVATION: Protein-carbohydrate interactions perform several cellular and biological functions and their structure and function are mainly dictated by their binding affinity. Although plenty of experimental data on binding affinity are available, there is no reliable and comprehensive database in the literature. RESULTS: We have developed a database on binding affinity of protein-carbohydrate complexes, ProCaff, which contains 3122 entries on dissociation constant (Kd), Gibbs free energy change (ΔG), experimental conditions, sequence, structure and literature information. Additional features include the options to search, display, visualization, download and upload the data. AVAILABILITY AND IMPLEMENTATION: The database is freely available at http://web.iitm.ac.in/bioinfo2/procaff/. The website is implemented using HTML and PHP and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera. CONTACT: gromiha@iitm.ac.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pred-MutHTP: Prediction of disease-causing and neutral mutations in human transmembrane proteins.

Membrane proteins are unique in that segments thereof concurrently reside in vastly different physicochemical environments: the extracellular space, the lipid bilayer, and the cytoplasm. Accordingly, the effects of missense variants disrupting their sequence depend greatly on the characteristics of the environment of the protein segment affected as well as the function it performs. Because membrane proteins have many crucial roles (transport, signal transduction, cell adhesion, etc.), compromising their functionality often leads to diseases including cancers, diabetes mellitus or cystic fibrosis. Here, we report a suite of sequence-based computational methods "Pred-MutHTP" for discriminating between disease-causing and neutral alterations in their sequence. With a data set of 11,846 disease-causing and 9,533 neutral mutations, we obtained an accuracy of 74% and 78% with 10-fold group-wise cross-validation and test set, respectively. The features used in the models include evolutionary information, physiochemical properties, neighboring residue information, and specialized membrane protein attributes incorporating the number of transmembrane segments, substitution matrices specific to membrane proteins as well as residue distributions occurring in specific topological regions. Across 11 disease classes, the method achieved accuracies in the range of 75-85%. The model designed specifically for the transmembrane segments achieved an accuracy of 85% on the test set with a sensitivity and specificity of 86% and 83%, respectively. This renders our method the current state-of-the-art with regard to predicting the effects of variants in the transmembrane protein segments. Pred-MutHTP allows predicting the effect of any variant occurring in a membrane protein-available at https://www.iitm.ac.in/bioinfo/PredMutHTP/.

Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis.

The selective vulnerability of distinct regions of the brain is a critical factor in neurodegenerative disorders. In Alzheimer's disease (AD), neurons in hippocampus situated in medial temporal lobe are immensely damaged. Identifying tissue-specific variants is essential in order to perceive the selective vulnerability in AD. In current work, we aligned mRNA-seq data with HG19/HG38 genomic assembly and identified specific variations present in temporal, frontal and other lobes of the AD using sequence alignment map tools. We compared the results with the genome-wide association and gene expression quantitative trait loci studies of the various neurological disorders. We also distinguished variants and epitranscriptomic modifications through the RNA-modification database and evaluated the variant effect in the coding/UTR regions. In addition, we developed genetic and functional interaction networks to understand the relationship between predicted vulnerable variations and differentially expressed genes. We found that genes involved in gliogenesis, intermediate filament organization are altered in the temporal lobe. Oxidative phosphorylation, and calcium ion homeostasis are modified in the frontal lobe, and protein degradation, apoptotic signaling are altered in other lobes. From this study, we propose that disruption of glial cell structural integrity, defective gliogenesis, and failure in glia-neuron communication are the primary factors for selective vulnerability.

An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins.

Newly synthesized polypeptides must pass stringent quality controls in cells to ensure appropriate folding and function. However, mutations, environmental stresses and aging can reduce efficiencies of these controls, leading to accumulation of protein aggregates, amyloid fibrils and plaques. In-vitro experiments have shown that even single amino acid substitutions can drastically enhance or mitigate protein aggregation kinetics. In this work, we have collected a dataset of 220 unique mutations in 25 proteins and classified them as enhancers or mitigators on the basis of their effect on protein aggregation rate. The data were analyzed via machine learning to identify features capable of distinguishing between aggregation rate enhancers and mitigators. Our initial Support Vector Machine (SVM) model separated such mutations with an overall accuracy of 69%. When local secondary structures at the mutation sites were considered, the accuracies further improved by 13-15%. The machine-learnt features are distinct for each secondary structure class at mutation sites. Protein stability and flexibility changes are important features for mutations in α-helices. ß-strand propensity, polarity and charge become important when mutations occur in ß-strands and ability to form secondary structure, helical tendency and aggregation propensity are important for mutations lying in coils. These results have been incorporated into a sequence-based algorithm (available at http://www.iitm.ac.in/bioinfo/aggrerate-disc/) capable of predicting whether a mutation will enhance or mitigate a protein's aggregation rate. This algorithm will find several applications towards understanding protein aggregation in human diseases, enable in-silico optimization of biopharmaceuticals and enzymes for improved biophysical attributes and de novo design of bio-nanomaterials.

PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics.

SUMMARY: We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data. AVAILABILITY AND IMPLEMENTATION: The database is freely available at http://www.iitm.ac.in/bioinfo/PROXiMATE/ . The website is implemented in Python, and supports recent versions of major browsers such as IE10, Firefox, Chrome and Opera. CONTACT: gromiha@iitm.ac.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of novel natural inhibitor for NorM - a multidrug and toxic compound extrusion transporter - an insilico molecular modeling and simulation studies.

The emergence of bacterial multidrug resistance is an increasing problem in treatment of infectious diseases. An important cause for the multidrug resistance of bacteria is the expression of multidrug efflux transporters. The multidrug and toxic compound extrusion (MATE) transporters are most recently recognized as unique efflux system for extrusion of antimicrobials and therapeutic drugs due to energy stored in either Na+ or H+ electrochemical gradient. In the present study, high throughput virtual screening of natural compound collections against NorM - a MATE transporter from Neisseria gonorrhea (NorM-NG) has been carried out followed by flexible docking. The molecular simulation in membrane environment has been performed for understanding the stability and binding energetic of top lead compounds. Results identified a compound from the Indian medicinal plant "Terminalia chebula" which has good binding free energy compared to substrates (rhodamine 6 g, ethidium) and more favorable interactions with the central cavity forming active site residues. The compound has restricted movement in TM7, TM8, and TM1, thus blocking the disruption of Na+ - coordination along with equilibrium state bias towards occlude state of NorM transporter. Thus, this compound blocks the effluxing pathway of antimicrobial drugs and provides as a natural bioactive lead inhibitor against NorM transporter in drug-resistant gonorrhea.

Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets.

Somatic mutations developed with missense, silent, insertions and deletions have varying effects on the resulting protein and are one of the important reasons for cancer development. In this study, we have systematically analysed the effect of these mutations at protein level in 41 different cancer types from COSMIC database on different perspectives: (i) Preference of residues at the mutant positions, (ii) probability of substitutions, (iii) influence of neighbouring residues in driver and passenger mutations, (iv) distribution of driver and passenger mutations around hotspot site in five typical genes and (v) distribution of silent and missense substitutions. We observed that R→H substitution is dominant in drivers followed by R→Q and R→C whereas E→K has the highest preference in passenger mutations. A set of 17 mutations including R→Y, W→A and V→R are specific to driver mutations and 31 preferred substitutions are observed only in passenger mutations. These frequencies of driver mutations vary across different cancer types and are selective to specific tissues. Further, driver missense mutations are mainly surrounded with silent driver mutations whereas the passenger missense mutations are surrounded with silent passenger mutations. This study reveals the variation of mutations at protein level in different cancer types and their preferences in cancer genes and provides new insights for understanding cancer mutations and drug development.

Analysis of secondary structural and physicochemical changes in protein-protein complexes.

Conformation switching in protein-protein complexes is considered important for the molecular recognition process. Overall analysis of 123 protein-protein complexes in a benchmark data-set showed that 6.8% of residues switched over their secondary structure conformation upon complex formation. Amino acid residue-wise preference for conformation change has been analyzed in binding and non-binding site residues separately. In this analysis, residues such as Ser, Leu, Glu, and Lys had higher frequency of secondary structural conformation change. The change of helix to coil and sheet to coil conformation and vice versa has been observed frequently, whereas the conformation change of helix to extended sheet occurred rarely in the studied complexes. Influence of conformation change toward the N and C terminal on either side of the binding site residues has been analyzed. Further, analysis on φ and ψ angle variation, conservation, stability, and solvent accessibility have been performed on binding site residues. Knowledge obtained from the present study could be effectively employed in the protein-protein modeling and docking studies.

Identification of efflux proteins using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.

Intermolecular and intramolecular readout mechanisms in protein-DNA recognition.

Protein-DNA recognition plays an essential role in the regulation of gene expression. Regulatory proteins are known to recognize specific DNA sequences directly through atomic contacts (intermolecular readout) and/or indirectly through the conformational properties of the DNA (intramolecular readout). However, little is known about the respective contributions made by these so-called direct and indirect readout mechanisms. We addressed this question by making use of information extracted from a structural database containing many protein-DNA complexes. We quantified the specificity of intermolecular (direct) readout by statistical analysis of base-amino acid interactions within protein-DNA complexes. The specificity of the intramolecular (indirect) readout due to DNA was quantified by statistical analysis of the sequence-dependent DNA conformation. Systematic comparison of these specificities in a large number of protein-DNA complexes revealed that both intermolecular and intramolecular readouts contribute to the specificity of protein-DNA recognition, and that their relative contributions vary depending upon the protein-DNA complexes. We demonstrated that combination of the intermolecular and intramolecular energies derived from the statistical analyses lead to enhanced specificity, and that the combined energy could explain experimental data on binding affinity changes caused by base mutations. These results provided new insight into the relationship between specificity and structure in the process of protein-DNA recognition, which would lead to prediction of specific protein-DNA binding sites.