ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-Based Nucleotide Level Profiles.

The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H. sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer.

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry.

Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity of the ternary complex using Hamiltonian replica exchange simulations and small-angle X-ray scattering. (3) We assess the ubiquitination of the POI in the context of the full Cullin-RING Ligase, confirming experimental ubiquitinomics results. Differences in degradation efficiency can be explained by the proximity of lysine residues on the POI relative to ubiquitin.

Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method.

We present an extension of the alchemical transfer method (ATM) for the estimation of relative binding free energies of molecular complexes applicable to conventional, as well as scaffold-hopping, alchemical transformations. Named ATM-RBFE, the method is implemented in the free and open-source OpenMM molecular simulation package and aims to provide a simpler and more generally applicable route to the calculation of relative binding free energies than what is currently available. ATM-RBFE is based on sound statistical mechanics theory and a novel coordinate perturbation scheme designed to swap the positions of a pair of ligands such that one is transferred from the bulk solvent to the receptor binding site while the other moves simultaneously in the opposite direction. The calculation is conducted directly in a single solvent box with a system prepared with conventional setup tools, without splitting of electrostatic and nonelectrostatic transformations, and without pairwise soft-core potentials. ATM-RBFE is validated here against the absolute binding free energies of the SAMPL8 GDCC host-guest benchmark set and against protein-ligand benchmark sets that include complexes of the estrogen receptor ERα and those of the methyltransferase EZH2. In each case the method yields self-consistent and converged relative binding free energy estimates in agreement with absolute binding free energies and reference literature values, as well as experimental measurements.

Controlling the Effects of External Perturbations on a Gene Regulatory Network Using Proportional-Integral-Derivative Controller.

Gene regulatory networks are biologically robust, which imparts resilience to living systems against most external perturbations affecting them. However, there is a limit to this and disturbances beyond this limit can impart unwanted signalling on one or more master regulators in a network. Certain disturbances may affect the functioning of other constituent genes of the same network. In most cases, this phenomenon can have some effect on the functioning of the living organism. In this investigation, we have proposed a methodology to mitigate the effects of external perturbations on a genetic network using a proportional-integral-derivative controller. The proposed controller has been used to perturb one or more of the other unaffected master regulators such that the most affected gene/s of the network revert to their normal state. The only required condition of such type of manoeuvring is that there should be multiple master regulators in a network. The proposed technique has been experimented on a 10-gene DREAM4 benchmark network and also on a larger 20-gene network, where only downregulation has been considered due to data constraints. Simulation results indicate that the most vulnerable genes can be reverted to their normal expression levels in 10 out of the 16 simulations performed.

GenSeg and MR-GenSeg: A Novel Segmentation Algorithm and its Parallel MapReduce Based Approach for Identifying Genomic Regions With Copy Number Variations.

Identifying intragenic as well as intergenic sequences of the DNA, having structural alterations, is a significantly important research area, since this may be the root cause of many neurological and autoimmune diseases, including cancer. Working with whole genome NGS data has provided a new insight in this regard, but has lead to huge explosion of data that is growing exponentially. Hence, the challenges lie in efficient means of storage and processing this big data. In this study, we have developed a novel segmentation algorithm, called GenSeg, and its parallel MapReduce based algorithm, called MR-GenSeg, for detecting copy number variations. In order to annotate CNVs (variants), segments formed by GenSeg/MR-GenSeg have been represented in a novel way using a binary tree, where each node is a CNV event. GenSeg considers each position specific data of whole genome DNA sequence, so that precise identification of breakpoints is possible. GenSeg/MR-GenSeg has been compared with twelve popular CNV detection algorithms, where it has outperformed the others in terms of sensitivity, and has achieved a good F-score value. MR-GenSeg has excelled in terms of SpeedUp, when compared with these algorithms. The effect of CNVs on immunoglobulin (IG) genes has also been analysed in this study. Availability: The source codes are available at https://github.com/rituparna-sinha/MapReduce-GENSEG.

Structural and functional insights into colicin: a new paradigm in drug discovery.

Colicins are agents of allelopathic interactions produced by certain enterobacteria which give them a competitive advantage in the environment. These protein molecules are mostly encoded by plasmids. The colicin operon consists of the activity, immunity and the lysis genes. The activity protein is responsible for the killing activity, the immunity protein protects the producer cell from the lethal action of colicin and the lysis protein facilitates its release. Colicins are primarily composed of three domains, namely the receptor-binding domain, the translocation domain and the cytotoxic domain. The protein molecule binds to its cognate receptor on the target cell via the receptor-binding domain and undergoes translocation into the cell either via the Tol system or the Ton system. After gaining entry into the target cell, there are various mechanisms by which colicins exert their lethality. These comprise DNase activity, RNase activity and pore formation in the target cell membrane or peptidoglycan synthesis inhibition. This review gives a detailed insight into the structural and functional aspect of colicins and their mode of action. This knowledge is of immense significance because colicins are being considered as very useful alternatives to conventional antibiotics in the treatment of multidrug-resistant infections. Besides, they also have a negligible harmful impact on the commensals. Thus, before tapping their therapeutic potential, it is imperative to know their structure and mechanism of action in detail.

New tetrahydroisoquinoline-based D3R ligands with an o-xylenyl linker motif.

The effect of rigidification of the n-butyl linker region of tetrahydroisoquinoline-containing D3R ligands via inclusion of an o-xylenyl motif was examined in this study. Generally, rigidification with an o-xylenyl linker group reduces D3R affinity and negatively impacts selectivity versus D2R for compounds possessing a 6-methoxy-1,2,3,4,-tetrahydroisoquinolin-7-ol primary pharmacophore group. However, D3R affinity appears to be regulated by the primary pharmacophore group and high affinity D3R ligands with 6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline and 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline primary pharmacophore groups were identified. The results of this study also indicate that D3R selectivity versus the σ2R is dictated by the benzamide secondary pharmacophore group, this being facilitated with 4-substituted benzamides. Compounds 5s and 5t were identified as high affinity (Ki < 4 nM) D3R ligands. Docking studies revealed that the added phenyl ring moiety interacts with the Cys181 in D3R which partially accounts for the strong D3R affinity of the ligands.

Modified Half-System Based Method for Reverse Engineering of Gene Regulatory Networks.

The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system. Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3. The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases.

Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes.

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.

Perturbation potentials to overcome order/disorder transitions in alchemical binding free energy calculations.

We investigate the role of order/disorder transitions in alchemical simulations of protein-ligand absolute binding free energies. We show, in the context of a potential of mean force description, that for a benchmarking system (the complex of the L99A mutant of T4 lysozyme with 3-iodotoluene) and for a more challenging system relevant for medicinal applications (the complex of the farnesoid X receptor with inhibitor 26 from a recent D3R challenge) that order/disorder transitions can significantly hamper Hamiltonian replica exchange sampling efficiency and slow down the rate of equilibration of binding free energy estimates. We further show that our analytical model of alchemical binding combined with the formalism developed by Straub et al. for the treatment of order/disorder transitions of molecular systems can be successfully employed to analyze the transitions and help design alchemical schedules and soft-core functions that avoid or reduce the adverse effects of rare binding/unbinding transitions. The results of this work pave the way for the application of these techniques to the alchemical estimation with explicit solvation of hydration free energies and absolute binding free energies of systems undergoing order/disorder transitions.

New Dopamine D3-Selective Receptor Ligands Containing a 6-Methoxy-1,2,3,4-tetrahydroisoquinolin-7-ol Motif.

A series of analogues featuring a 6-methoxy-1,2,3,4-tetrahydroisoquinolin-7-ol unit as the arylamine "head" group of a classical D3 antagonist core structure were synthesized and evaluated for affinity at dopamine D1, D2, and D3 receptors (D1R, D2R, D3R). The compounds generally displayed strong affinity for D3R with very good D3R selectivity. Docking studies at D2R and D3R crystal structures revealed that the molecules are oriented such that their arylamine units are positioned in the orthosteric binding pocket of D3R, with the arylamide "tail" units residing in the secondary binding pocket. Hydrogen bonding between Ser 182 and Tyr 365 at D3R stabilize extracellular loop 2 (ECL2), which in turn contributes to ligand binding by interacting with the "tail" units of the ligands in the secondary binding pocket. Similar interactions between ECL2 and the "tail" units were absent at D2R due to different positioning of the D2R loop region. The presence of multiple H-bonds with the phenol moiety of the headgroup of 7 and Ser192 accounts for its stronger D3R affinity as compared to the 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline-containing analogue 8.

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives.

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

A combined treatment of hydration and dynamical effects for the modeling of host-guest binding thermodynamics: the SAMPL5 blinded challenge.

As part of the SAMPL5 blinded experiment, we computed the absolute binding free energies of 22 host-guest complexes employing a novel approach based on the BEDAM single-decoupling alchemical free energy protocol with parallel replica exchange conformational sampling and the AGBNP2 implicit solvation model specifically customized to treat the effect of water displacement as modeled by the Hydration Site Analysis method with explicit solvation. Initial predictions were affected by the lack of treatment of ionic charge screening, which is very significant for these highly charged hosts, and resulted in poor relative ranking of negatively versus positively charged guests. Binding free energies obtained with Debye-Hückel treatment of salt effects were in good agreement with experimental measurements. Water displacement effects contributed favorably and very significantly to the observed binding affinities; without it, the modeling predictions would have grossly underestimated binding. The work validates the implicit/explicit solvation approach employed here and it shows that comprehensive physical models can be effective at predicting binding affinities of molecular complexes requiring accurate treatment of conformational dynamics and hydration.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm.

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

Synthetic α-Hydroxytropolones as Inhibitors of HIV Reverse Transcriptase Ribonuclease H Activity.

HIV Reverse Transcriptase-associated ribonuclease H activity is a promising enzymatic target for drug development that has not been successfully targeted in the clinic. While the α-hydroxytropolone-containing natural products ß-thujaplicinol and manicol have emerged as some of the most potent leads described to date, structure-function studies have been limited to the natural products and semi-synthetic derivatives of manicol. Thus, a library of α-hydroxytropolones synthesized through a convenient oxidopyrylium cycloaddition/ring-opening sequence have been tested in in vitro and cell-based assays, and have been analyzed using computational support. These studies reveal new synthetic α-hydroxytropolones that, unlike the natural product leads they are derived from, demonstrate protective antiviral activity in cellular assays.