Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued e orts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature

Robotics-inspired methods to enhance protein design

The ability to design proteins with specific properties would yield great progress in pharmacology and bio-technologies. Methods to design proteins have been developed since a few decades and some relevant achievements have been made including de novo protein design. Yet, current approaches suffer some serious limitations. By not taking protein’s backbone motions into account, they fail at capturing some of the properties of the candidate design and cannot guarantee that the solution will in fact be stable for the goal conformation. Besides, although multi-states design methods have been proposed, they do not guarantee that a feasible trajectory between those states exists, which means that design problem involving state transitions are out of reach of the current methods. This thesis investigates how robotics-inspired algorithms can be used to efficiently explore the conformational landscape of a protein aiming to enhance protein design methods by introducing additional backbone flexibility. This work also provides first milestones towards protein motion design

Computational study and rational design of pluriZymes

[eng] The increase in production over the last centuries has come at the expense of compromising the environment, urging the need to find solutions. Enzymes are the essential molecules that make life kinetically possible. In industry, enzymes can be a sustainable alternative to using inorganic catalysts. However, their low productivity, poor resistance to industrial conditions, and their cost limit their usage. Thus, enabling the tailoring of biocatalysts at will is crucial to expand their application. The advances in computational power, followed by the repertoire of modeling tools, are helping design the next generation of biocatalysts due to their lower costs and quickness. This thesis aims to develop a novel concept of biocatalysis, which could lower the expression costs of enzymes, named pluriZymes. PluriZymes are proteins with plural catalytic active sites where one (at least) of them is artificially designed. The type of introduced functional site along the thesis has been the hydrolase one due to its simplicity (only 3 catalytic residues needed) and does not need a cofactor. The studied systems were transaminases and esterases since they have several applications in industry, thus, being of broad interest. All computational designs were experimentally validated by our collaborators. The thesis' results include an in-one protease-esterase pluriZyme, a transaminase- esterase pluriZyme with potential applications for the pharmaceutical industry, the rational improvement of substrate promiscuity of hydrolase sites, and a new algorithm to facilitate the design of artificial active sites. Hence, this thesis proves the potential of pluriZymes for the next generation of biocatalysts toward a more sustainable society and the need for computational tools to develop them.[cat] L’augment en la producció dels darrers segles s’ha produït a canvi de comprometre el medi ambient, apressant la necessitat de trobar solucions. Els enzims són les molècules essencials que fan la vida possible cinèticament. En l'àmbit industrial, els enzims poden ser una alternativa sostenible a l’ús de catalitzadors inorgànics. No obstant això, la seva baixa productivitat, la poca resistència a les condicions industrials i el seu cost limiten el seu ús. Així doncs, la capacitat de poder adaptar els biocatalitzadors a voluntat és crucial per ampliar la seva aplicació. Els avenços en els recursos computacionals, seguits pel repertori d’eines de modelatge, estan ajudant a dissenyar la propera generació de biocatalitzadors pels seus baixos costs i la seva rapidesa. Aquesta tesi pretén desenvolupar un nou concepte en biocatàlisi, que podria reduir els costs d’expressió dels enzims, anomenat “pluriZyme”. Els “pluriZymes” són proteïnes amb múltiples llocs actius catalítics on almenys un d’ells està dissenyat artificialment. El tipus de lloc funcional introduït al llarg de la tesi ha estat la hidrolasa per la seva simplicitat (només calen 3 residus catalítics) i no necessita cofactor. Els sistemes estudiats van ser transaminases i esterases, ja que tenen diverses aplicacions a la indústria, per tant, són d'ampli interès. Tots els dissenys computacionals van ser validats experimentalment pels nostres col·laboradors. Els resultats de la tesi inclouen un “pluriZyme” proteasa-esterasa tot en un, un “pluriZyme” transaminasa-esterasa amb aplicacions potencials per a la indústria farmacèutica, la millora racional de la promiscuïtat de substrats de llocs hidrolasa i un nou algorisme per facilitar el disseny de llocs actius artificials. Per tant, aquesta tesi demostra el potencial de pluriZymes per a la propera generació de biocatalitzadors cap a una societat més sostenible i la necessitat d'eines computacionals per desenvolupar-los.[spa] El incremento en la producción de los últimos siglos se ha producido a expensas de comprometer el medioambiente, lo que ha acelerado la necesidad de encontrar soluciones. Las enzimas son moléculas esenciales para que la vida sea cinéticamente posible. En el ámbito industrial, las enzimas pueden ser una alternativa sostenible a los catalizadores inorgánicos. Sin embargo, su baja productividad, poca resistencia a las condiciones industriales y su costo limitan su uso. Por lo tanto, permitir la adaptación de biocatalizadores a voluntad es crucial para expandir su aplicación. Los avances en recursos computacionales, seguidos por el repertorio de herramientas de modelado, están ayudando a diseñar la próxima generación de biocatalizadores debido a su menor costo y rapidez. Esta tesis tiene como objetivo desarrollar un concepto novedoso en el campo de biocatálisis, que podría reducir los costes de expresión de las enzimas, denominado "pluriZymes". Los "pluriZymes" son proteínas con sitios activos catalíticos plurales donde al menos uno de ellos está diseñado artificialmente. El tipo de sitio funcional introducido a lo largo de la tesis ha sido el de hidrolasa debido a su sencillez (solo se necesitan 3 residuos catalíticos) y no necesita cofactor. Los sistemas estudiados fueron transaminasas y esterasas, ya que tienen varias aplicaciones en la industria, por lo tanto, son de amplio interés. Todos los diseños computacionales fueron validados experimentalmente por nuestros colaboradores. Los resultados de la tesis incluyen una proteasa- esterasa "pluriZyme" todo en uno, una transaminasa-esterasa "pluriZyme" con aplicaciones potenciales para la industria farmacéutica, la mejora racional de la promiscuidad de sustratos de sitios hidrolasa y un nuevo algoritmo para facilitar el diseño de sitios activos artificiales. Por lo tanto, esta tesis demuestra el potencial de pluriZymes para la próxima generación de biocatalizadores hacia una sociedad más sostenible y la necesidad de herramientas computacionales para desarrollarlos

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Computational Design for Metal Organic Responsive Frameworks (MORFs)

Metal organic Frameworks (MOFs) that experience stimuli induced structural transformation could enable a whole new class of materials with remarkable properties. Photoactuating moieties in the structure could effect changes in the pore space or macroscale shape change enabling light driven gas separation and actuators. Here, we present a novel approach based on using graph grammars that makes the problem of discovering MOFs for specific applications amenable to powerful artificial intelligence tree search algorithms. We develop methods for rapidly evaluating MOFs for structural transitions. We will show how this approach may be used to computationally search for candidate MOFs that undergo reversible stimuli induced structural transformation

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Protein design, modeling, and the evolution of proteins

Proteins are crucial players in the functional processes that allow for cellular life. Changes in the sequences of proteins have consequences for how these proteins function. Therefore, the study of how proteins change over time has been a central question in the field of evolutionary biology. As our understanding of how proteins function and change increases, we are not only able to test our hypotheses but we are also able to design and model new proteins, which is the ultimate test of our knowledge of how proteins function. Using the information from our protein modeling attempts, we can learn more about how natural proteins function and change over time. In this dissertation, I used protein modeling techniques to understand protein evolution. In Chapter 2, I assessed how closely designed proteins recapitulate observed patterns in natural proteins. I have found that designing proteins with a flexible-backbone protocol results in site variability that more closely mimics what is seen in natural proteins. In addition, I have also found that, in designed proteins, hydrophobic residues are often underrepresented in the core of the protein. These results suggest that our scoring functions and/or backbone sampling methods could be further improved. In Chapter 3, I used protein design to predict site-wise evolutionary rates in proteins. I found that protein design is a poor predictor of evolutionary rate, explaining only approximately 7% of the variation in rate across sites in enzymes. In Chapter 4, I used protein design and homology modeling to predict tolerance to deletions in enhanced green fluorescent protein. I also compared these predictions to predictions made using other structural properties including solvent accessibility, local packing density and secondary structure. I found that when combining computational scores from modeled structures along with other structural properties (i.e., local packing density, solvent accessibility and secondary structure) as predictors, I was largely able predict whether or not a given deletion would result in a functional protein product. Finally, in Chapter 5, I developed a computational pipeline to assess binding affinity in protein-protein interactions. I used this pipeline to recapitulate patterns of Machupo virus entry across various species. Taken together, the work presented in this dissertation has given us insight into which structural constraints affect protein evolution.Ecology, Evolution and Behavio

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds

The Moving Page

This paper investigates transitional states of spaces between images, moving images, and the use of sketchbook/page works through a questioning and auto-ethnographic approach to research and practice. Viewing illustration as a refexive space, the investigations demonstrate exchangesbetween authorship, interaction, narrative, time, and space. Valuing the ‘in-between’ states that exist between the unfnished and fnished, the research questions notions of in-fux, moving, nebulous states. Through alternative publishing forms, the research concerns dissemination through emerging digital platforms