SInCRe—structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding

Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

Control of quantum phenomena: Past, present, and future

Quantum control is concerned with active manipulation of physical and chemical processes on the atomic and molecular scale. This work presents a perspective of progress in the field of control over quantum phenomena, tracing the evolution of theoretical concepts and experimental methods from early developments to the most recent advances. The current experimental successes would be impossible without the development of intense femtosecond laser sources and pulse shapers. The two most critical theoretical insights were (1) realizing that ultrafast atomic and molecular dynamics can be controlled via manipulation of quantum interferences and (2) understanding that optimally shaped ultrafast laser pulses are the most effective means for producing the desired quantum interference patterns in the controlled system. Finally, these theoretical and experimental advances were brought together by the crucial concept of adaptive feedback control, which is a laboratory procedure employing measurement-driven, closed-loop optimization to identify the best shapes of femtosecond laser control pulses for steering quantum dynamics towards the desired objective. Optimization in adaptive feedback control experiments is guided by a learning algorithm, with stochastic methods proving to be especially effective. Adaptive feedback control of quantum phenomena has found numerous applications in many areas of the physical and chemical sciences, and this paper reviews the extensive experiments. Other subjects discussed include quantum optimal control theory, quantum control landscapes, the role of theoretical control designs in experimental realizations, and real-time quantum feedback control. The paper concludes with a prospective of open research directions that are likely to attract significant attention in the future.Comment: Review article, final version (significantly updated), 76 pages, accepted for publication in New J. Phys. (Focus issue: Quantum control

Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field

Molecular Considerations In The Design Of Novel Alpha/Beta Hydrolase Inhibitors

Alpha/beta hydrolases (ABHs) are a superfamily of hydrolytic enzymes that process a wide variety of substrates. A subfamily of ABHs called carboxylesterases (CEs) are important enzymes that catalyze biological detoxification, hydrolysis of certain pesticides, and metabolism of many esterified drugs. The chemotherapy drug irinotecan used for treatment of colorectal cancer is metabolized to SN-38, the active drug metabolite, by two CE isozymes CES1 (localized in the liver) and CES2 (localized in the small intestines). CES2\u27s ability to activate irinotecan at a faster rate than CES1 creates a localization of activated SN-38 in the gut epithelium, resulting in the dose limiting side effect of delayed diarrhea. Development of inhibitors for the CE subfamily of ABHs could assist in ameliorating the toxic side effects associated with some esterified prodrugs such as irinotecan, and enhance the distribution of prodrugs in vivo. Hence, our research targets CES2 for inhibitor design with the goal of amelioration of intestinal cytotoxicity associated with irinotecan chemotherapy. In this work we (i) utilized QSAR technology to design and optimize novel sulfonamide CES2 inhibitors; (ii) combined QSAR with in silico design to generate new CE inhibitor scaffolds that maintained the potency of previous CE inhibitor generations, yet had improved water solubility; and ( iii) investigated the contribution of the loop 7 in CEs to sensitizing the enzyme to inhibition by sulfonamides through docking analysis. Our QSAR model, developed using 57 sulfonamide analogs, identified several features of this class of CE inhibitor that confer their potency. Using a QSAR model, constructed using 4 classes of CE inhibitors (benzils, benzoins, isatins, and sulfonamides), as a pocket site to perform in silico design we generated several new scaffolds predicted to have good solubility and potency. This work suggests that the inner loop 7 on CE plays a role in inhibitor selectivity, and interactions with this loop should be considered in the development of selective CE inhibitors. The contributions from this work will be applicable to the design of novel ABH inhibitors, help to increase the likelihood of these drugs entering in clinical use, and ameliorate the dose-limiting side effect associated with irinotecan

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Computational Approaches for Predicting Drug Targets

This thesis reports the development of several computational approaches to predict human disease proteins and to assess their value as drug targets, using in-house domain functional families (CATH FunFams). CATH-FunFams comprise evolutionary related protein domains with high structural and functional similarity. External resources were used to identify proteins associated with disease and their genetic variations. These were then mapped to the CATH-FunFams together with information on drugs bound to any relatives within the FunFam. A number of novel approaches were then used to predict the proteins likely to be driving disease and to assess whether drugs could be repurposed within the FunFams for targeting these putative driver proteins. The first work chapter of this thesis reports the mapping of drugs to CATHFunFams to identify druggable FunFams based on statistical overrepresentation of drug targets within the FunFam. 81 druggable CATH-FunFams were identified and the dispersion of their relatives on a human protein interaction network was analysed to assess their propensity to be associated with side effects. In the second work chapter, putative drug targets for bladder cancer were identified using a novel computational protocol that expands a set of known bladder cancer genes with genes highly expressed in bladder cancer and highly associated with known bladder cancer genes in a human protein interaction network. 35 new bladder cancer targets were identified in druggable FunFams, for some of which FDA approved drugs could be repurposed from other protein domains in the FunFam. In the final work chapter, protein kinases and kinase inhibitors were analysed. These are an important class of human drug targets. A novel classification protocol was applied to give a comprehensive classification of the kinases which was benchmarked and compared with other widely used kinase classifications. Druginformation from ChEMBL was mapped to the Kinase-FunFams and analyses of protein network characteristics of the kinase relatives in each FunFam used to identify those families likely to be associated with side effects

Development and Application of Computational Biology tools for Biomedicine

Biomolecular simulation can be considered as a virtual microscope for molecular biology, allowing to gain insights into the sub-cellular mechanisms of biological relevance at spatial and temporal scales that are difficult to observe experimentally. It provides a powerful tool to link the laws of physics with the complex behavior of biological systems. Dramatic recent advancements in achievable simulation speed and the underlying physical models will increasingly lead to molecular views of large systems. These improvements may largely affect biological sciences. In this thesis, I have applied computational molecular biology approaches to different biological systems using state of the art structural bioinformatics and computational biophysics tools (Chapter 3). My principal focus was on the computational design of molecular imprinted polymers (MIPs), which have recently attracted significant attention as cost effective substitutes for natural antibodies and receptors in chromatography, sensors and assays. I have used molecular modelling in the optimization of polymer compositions to make high affinity synthetic receptors based on Molecular Imprinting. In particular, I developed a new free of charge protocol that can be performed within just few hours that outputs a list of candidate monomers which are capable of strong binding interactions with the template. Furthermore, I have produced a new computational method for the calculation of the ideal monomer: template stoichiometric ratio to be used in the lab for the MIPs synthesis. These protocols have been implemented as a webserver that is available at http://mirate.di.univr.it/. In parallel, I have also investigated the modelling of much more complex MIPs systems by the introduction of some factors e.g. solvent and cross-linker molecules that are also essential in the polymerisation process. A novel algorithm, which mimics a radical polymerization mechanism, has been written for application in the rational design of MIPs (Chapter 4). Moreover, I have been involved in the field of computational molecular biomedicine. Indeed, in Chapters 5 and 6 I describe the work done in collaboration with two labs at the Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona. In Chapter 5, starting from unpublished experimental data I have computationally characterized the interaction of ACOT8 with HIV-1 Nef accessory protein. I have performed a detailed structural and functional characterization of these two proteins in order to infer any possible functional details about their interactions. The bioinformatics predictions were then confirmed by wet-lab experiments. I have also carried out a detailed structural and functional characterization of two pathogenic mutations of AGT-Mi (Chapter 6). In particular, I have used classical molecular dynamics (MD) simulations to study the possible interference with the dimerization process of AGT-Mi exerted by I244T-Mi and F152I-Mi mutants. Those variants are associated with Primary Hyperoxaluria type 1 disease. In Chapter 7, I present the coarse-grained MD simulations of Membrane/Human ileal bile-acid-binding protein Interactions. This study was carried out in collaboration with the NMR group at the University of Verona and it is a part of an extensive research aimed at better understanding of the main biomolecular interactions in crowded cellular environments. MD simulations results were in agreement with experimental findings

Ion channels and the tree of life

The field of comparative neurobiology has deep roots. I will begin by giving an overview of the parts of its history that I feel are most relevant for this dissertation. Within this history lies a wealth of zoological research and penetrating theories that are underutilized by modern evolutionary biologists. The age of whole-genome sequencing provides a perfect opportunity to revisit and perhaps update this corpus to better understand the phylogenetic history of organismal behavior. The first three chapters of my dissertation will be case studies on the evolution of sodium-selective ion channels. Sodium channels are responsible for much of the electrical signaling in animal nervous systems and muscles, but their evolutionary relationships have not yet been explored with the modern tools of phylogenetics and comparative genomics. Chapter 1 will deal with the classic Nav channels which create action potentials in nerves and muscles. There I will show that this gene family pre-dates the nervous system and even animal multicellularity. Chapter two will investigate sodium leak channels, which likley create the leak conductance measured by Hodgkin and Huxley. These channels turn out to be close relatives of fungal calcium channels, a relationship which illuminates the evolution of both groups. Chapter three is on bacterial sodium channels and their use as models for other sodium channel types. The final chapter will turn away from sodium channels in particular and discuss the evolution of animal nervous systems by means of ion channel genomics. In that chapter I will show that the genomic complements of ion channels that animals with nervous systems possess evolved independently to large degree, and that the early evolution of nervous systems also involved periods of gene loss. I will end with a more general discussion of convergent evolution, a key theme of this dissertation, and its effect on comparative analyses in the age of genomics.Ecology, Evolution and Behavio