Computer-aided Drug Design Approaches Research Articles

Computer-Aided Drug Design for AMP-Activated Protein Kinase Activators

AMP-activated protein kinase (AMPK) is an important therapeutic target for the potential treatment of metabolic disorders, cardiovascular disease and cancer. Recently, various classes of compounds that activate AMPK by direct or indirect interactions have been reported. The importance of computer-aided drug design approaches in the search for potent activators of AMPK is now established, including structure-based design, ligand-based design, fragment-based design, as well as structural analysis. This review article highlights the computer-aided drug design approaches utilized to discover of activators targeting AMPK. The principles, advantages or limitation of the different methods are also being discussed together with examples of applications taken from the literatures.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Computational medicinal chemistry: part II

Future Medicinal ChemistryVol. 3, No. 6 Special Focus: Computational Chemistry - ForewordFree AccessComputational medicinal chemistry: part IIGino D’OcaGino D’OcaCommissioning Editor Future Science Group, Unitec House, 2 Albert Place, London, N3 1QB, UK. Search for more papers by this authorEmail the corresponding author at g.doca@future-science.comPublished Online:9 May 2011https://doi.org/10.4155/fmc.11.29AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit We are delighted to introduce the second instalment of this thematic series dedicated to computational medicinal chemistry [1].Chemists often lament the perceived failure of combinatorial chemistry and other technologies in which much hope was once invested, to deliver high-quality candidates for lead optimization [2]. Consequently, in the ‘post-combinatorial era’ attention has increasingly focused on the application of computer-based approaches in drug discovery and design. As the Guest Editor of this thematic series, Gisbert Schneider, observed in the introductory foreword, “there is ample evidence that computational medicinal chemistry has become an indispensable pillar of modern drug research” [1].This issue features a mixture of reviews and original research from renowned experts in the field, covering the broad and ever-expanding range of computational tools available to the pharmaceutical scientist [2–4].In this issueThe design of high-quality screening collections is of crucial importance for early-stage drug discovery. Renner et al. highlight recent efforts to expand access to bioactive chemical space [5]. They draw on observations from high-throughput screening campaigns, recent successes in chemogenomics and diversity-oriented synthesis, as well as nonclassical approaches, such as fragment-based screening and DNA-encoded chemical libraries. Bryce explores current strategies for physics-based scoring of protein–ligand affinity and, in addition, outlines recent developments in force fields and quantum chemical techniques, touching on prospects for the further integration of higher accuracy physics-based approaches at earlier stages in computationally guided drug discovery [6]. The approaches employed in computer-aided drug design (CADD) distil into two major categories: structure-based and ligand- methods. Wilson et al. discuss the methods that have resulted in recent years following efforts to integrate these two methods in order to enhance the reliability and efficiency of CADD approaches by combining information from both the ligand and the protein [7]. Tautermann discusses the use of in silico receptor models in lead optimization in light of the emergence of new crystal structures of G-protein coupled receptors [8]. Tuccinardi considers RNA molecules as therapeutic targets and, in particular, the docking of RNA-binding ligands [9]. He analyzes the development of RNA docking approaches and the reliability of the docking methods, before evaluating the results of docking-based virtual screening studies reported in the literature. The structure-based design of small-molecule inhibitors of protein–ligand and protein–protein interfaces is currently receiving much attention. Koch discusses how similarities in binding sites and protein interfaces, even in unrelated proteins, can be used to predict polypharmacology and inspire novel drug design [10].Bruno et al. investigate the serotonin receptor 5-HT2A, an antipsychotic drug target that forms functional homodimers and heterodimers with the mGluR2 receptor, with a view to building 3D models of the 5-HT2A/mGluR2 heterodimer and of the 5-HT2A/5-HT2A homodimer [11]. Johansson et al. set out to compare several state-of-the-art methods for generating accurate models with methods for generating transparent models, using results from 16 biopharmaceutical classification tasks.We hope that the articles featured in this issue of Future Medicinal Chemistry provide useful insights into the valuable contributions being made by computational approaches to the advancement of pharmaceutical science.AcknowledgementsSpecial thanks go to Gisbert Schneider for serving as Guest Editor for this thematic series.Financial & competing interests disclosureGino D’Oca is an employee of Future Science Group. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Bibliography1 Schneider G. Computational medicinal chemistry. Future Med. Chem.3(4),393–394 (2011).Link, CAS, Google Scholar2 Ojima I. Medicinal chemistry at a crossroads: challenges and new possibilities. Future Med. Chem.1(3),401–403 (2009).Link, CAS, Google Scholar3 Rabal O, Urbano-Cuadrado M, Oyarzabal J. Computational medicinal chemistry in fragment-based drug discovery: what, how and when. Future Med. Chem.3(1),95–134 (2009).Link, Google Scholar4 Skjevik ÅA, Teigen K, Martinez A. Review of computational methods employed in early-stage drug discovery. Future Med. Chem.1(1),49–63 (2009).Link, CAS, Google Scholar5 Renner S, Popov M, Schuffenhauer A et al. Recent trends and observations in the design of high-quality screening collections. Future Med. Chem.3(6),751–766 (2011).Link, CAS, Google Scholar6 Bryce RA. Physics-based scoring of protein–ligand interactions: explicit polarizability, quantum mechanics and free energies. Future Med. Chem.3(6),683–698 (2011).Link, CAS, Google Scholar7 Wilson GL, Lill MA. Integrating structure-based and ligand-based approaches for computational drug design. Future Med. Chem.3(6),735–750 (2011).Link, CAS, Google Scholar8 Tautermann CS. The use of G-protein coupled receptor models in lead optimization. Future Med. Chem.3(6),709–721 (2011).Link, CAS, Google Scholar9 Tuccinardi T. Binding-interaction prediction of RNA-binding ligands. Future Med. Chem.3(6),723–733 (2011).Link, CAS, Google Scholar10 Koch O. Use of secondary structure element information in drug design: polypharmacology and conserved motifs in protein–ligand binding and protein–protein interfaces. Future Med. Chem.3(6),699–708 (2011).Link, CAS, Google Scholar11 Bruno A, Beato C, Costantino G. Molecular dynamics simulations and docking studies on 3D models of the heterodimeric and homodimeric 5-HT2A receptor subtype. Future Med. Chem.3(6),665–681 (2011).Link, CAS, Google Scholar12 Johansson U, Sönströd C, Norinder U, Boström H. Trade-off between accuracy and interpretability for predictive in silico modeling. Future Med. Chem.3(6),647–663 (2011).Link, CAS, Google ScholarFiguresReferencesRelatedDetailsCited ByPushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discoveryJürgen Bajorath11 December 2015 | Future Medicinal Chemistry, Vol. 7, No. 18Subsystem quantum mechanics and in silico medicinal and biological chemistryChérif F Matta & Lou Massa18 November 2011 | Future Medicinal Chemistry, Vol. 3, No. 16Computational medicinal chemistry: part IIIGino D’Oca27 June 2011 | Future Medicinal Chemistry, Vol. 3, No. 8 Vol. 3, No. 6 Follow us on social media for the latest updates Metrics History Published online 9 May 2011 Published in print April 2011 Information© Future Science LtdAcknowledgementsSpecial thanks go to Gisbert Schneider for serving as Guest Editor for this thematic series.Financial & competing interests disclosureGino D’Oca is an employee of Future Science Group. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download

Physics-based scoring of protein–ligand interactions: explicit polarizability, quantum mechanics and free energies

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein-ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein-ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein-ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein-ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries.

Integrating structure-based and ligand-based approaches for computational drug design

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

Recent Development of Anticancer Therapeutics Targeting Akt

The serine/threonine kinase Akt has proven to be a significant signaling target, involved in various biological functions. Because of its cardinal role in numerous cellular responses, Akt has been implicated in many human diseases, particularly cancer. It has been established that Akt is a viable and feasible target for anticancer therapeutics. Analysis of all Akt kinases reveals conserved homology for an N-terminal regulatory domain, which contains a pleckstrin-homology (PH) domain for cellular translocation, a kinase domain with serine/threonine specificity, and a C-terminal extension domain. These well defined regions have been targeted, and various approaches, including in silico methods, have been implemented to develop Akt inhibitors. In spite of unique techniques and a prolific body of knowledge surrounding Akt, no targeted Akt therapeutics have reached the market yet. Here we will highlight successes and challenges to date on the development of anticancer agents modulating the Akt pathway in recent patents as well as discuss the methods employed for this task. Special attention will be given to patents with focus on those discoveries using computer-aided drug design approaches.

Protein-Protein Interaction Inhibition (2P2I): Fewer and Fewer Undruggable Targets

Low molecular weight inhibitors of Protein-Protein Interactions (PPI's) have been identified for a number of different systems indicating the viability of the computer-aided drug design approaches. However, pathways undertaken by researchers in pharmaceutical companies or in academic laboratories are not always clearly defined and the protocols that allow the identification of lead compounds often remain blurry. We will enumerate in this review the main approaches carried out to identify and validate PPI's inhibitors. Emphasis will be placed, in a first part, on issues of particular significance to PPI's such as the problem of identification and validation of interacting sites and on the methods that allow assessing the 3D structure of a targeted complex. On the second part of this review, we will define approaches that allow a rapid identification of hits capable of inhibiting PPI's. We will highlight the problem of the scoring functions and demonstrate that the majority of the functions available to researchers are not especially relevant for PPI's. We will define why consensus scoring can be an alternative and we will propose GFscore, a non linear ranked-by number consensus scoring function as a solution to this problem. We will finally discuss the actual challenges that still remain, particularly the problem of the treatment of the receptor flexibility and of the water molecules at the interface of the Protein-Protein complexes.

Development of Extracellular Signal-Regulated Kinase Inhibitors

Activation of the extracellular signal-regulated kinase (ERK) signaling pathway has been implicated in mediating a diverse array of cellular functions including cell differentiation, proliferation, and inflammatory responses. In this review, we will discuss approaches to identify inhibitors of ERK proteins through targeting ATP-dependent and ATP-independent mechanisms. Given the diversity of ERK substrates and the importance of ERK signaling in normal cell functions, emphasis will be placed on the methods for identifying small molecular weight compounds that are substrate selective through ATP-independent interactions and potentially relevant to inflammatory processes. The approach for selective targeting of ERK substrates takes advantage of the basic understanding of unique ERK docking domains that are thought to interact with specific amino acid sequences on substrate proteins. Computer aided drug design (CADD) can facilitate the high throughput screening of millions of compounds with the potential for selective interactions with ERK docking domains and disruption of substrate interactions. As such, the CADD approach significantly reduces the number of compounds that will be evaluated in subsequent biological assays and greatly increases the hit rate of biologically active compounds. The potentially active compounds are evaluated for ERK protein binding using spectroscopic and structural biology methods. Compounds that show ERK interactions are then tested for their ability to inhibit substrate interactions and phosphorylation as well as ERK-dependent functions in whole organism or cell-based assays. Finally, the relevance of substrate-selective ERK inhibitors in the context of inflammatory disease will be discussed.

Modelling and Informatics in the Analysis of P. falciparum DHFR Enzyme Inhibitors

Malaria is one of the most prevalent diseases of our planet which claims millions of lives annually. Plasmodium falciparum is the causative agent of majority of the mortality and morbidity associated with malaria particularly in tropical countries. Resistance of the parasite to the currently available chemotherapeutic agents poses a serious threat to human being. Inhibition of P. falciparum dihydrofolate reductase (DHFR) enzyme has been used as one of the strategies in curbing malaria. However, due to mutation in the active-site of the enzyme particularly at 16, 51, 59, 108, and 164 residues, the parasite developed resistance to most of antifolate drugs such as cycloguanil and pyrimethamine. Thus, design of new and potent antimalarial agents which are effective against both wild-type and mutant enzymes is very essential in order to minimize burden of P. falciparum malaria. Computer-aided drug design approaches are playing a crucial role in the design of potential antimalarial drug candidates. In this article, molecular modelling studies based on docking, pharmacophore mapping, QSAR, homology modelling, and quantum chemical studies are reviewed. The importance of these methods in understanding mechanism of drug resistance at a molecular level, and design of antimalarial drug candidates are discussed briefly. The examples mentioned in the review could give insights into the wide range of possibilities of using computer-aided drug design (CADD) methodologies.

Computer Aided Drug Design Approaches to Develop Cyclooxygenase Based Novel Anti-Inflammatory and Anti-Cancer Drugs

Cyclooxygenases (COXs), the enzymes involved in the formation of prostaglandins from polyunsaturated fatty acids such as arachidonic acid, exist in two forms--the constitutive COX-1 that is cytoprotective and responsible for the production of prostaglandins and COX-2 which is induced by cytokines, mitogens and endotoxins in inflammatory cells and responsible for the increased levels of prostaglandins during inflammation. As a result COX-2 has become the natural target for the development of anti-inflammatory and anti-cancer drugs. While the conventional NSAIDs with gastric side effects inhibit both COX-1 and COX-2, the newly developed drugs for inflammation with no gastric side effects selectively block the COX-2 enzyme. NSAIDs, nonselective non-aspirin NSAIDs and COX-2 selective inhibitors, are being widely used for various arthritis and pain syndromes. Selective inhibitors of COX-2, however, convey a small but definite risk of myocardial infarction and stroke; the extent of which varies depending on the COX-2 specificity. In view of the gastric side effects of conventional NSAIDs and the recent market withdrawal of rofecoxib and valdecoxib due to their adverse cardiovascular side effects there is need to develop alternative anti-inflammatory agents with reduced gastric and cardiovascular problems. The present study reviews various Computer Aided Drug Design (CADD) approaches to develop Cyclooxygenase based anti-inflammatory and anti-cancer drugs.

Strategy of computer-aided drug design.

Modern strategies of computer-aided drug design (CADD) are reviewed. The task of CADD in the pipeline of drug discovery is accelerating of finding the new lead compounds and their structure optimization for the following pharmacological tests. The main directions in CADD are based on the availability of the experimentally determined three-dimensional structure of the target macromolecule. If spatial structure is known the methods of structure-based drug design are used. In the opposite case the indirect methods of CADD based on the structures of known ligands (ligand-based drug design) are used. The interrelationship between the main directions of CADD is reviewed. The main CADD approaches of molecule de novo design and database mining are described. They include methods of molecular docking, de novo design, design of pharmacophore and quantity structure-activity relationship models. New ways and perspectives of CADD are discussed.

Inhibition of Cutaneous UV Light–induced Tumor Necrosis Factor-α Protein Production by Allotrap 1258, a Novel Immunomodulatory Peptide¶

Peptides derived from the heavy chain of the HLA Class-I molecules have been shown to modulate immune responses both in vivo and in vitro. Using a computer-aided rational drug design approach, novel immunomodulatory peptides were designed based on peptide 2702.75-85, derived from HLA-B2702. Several peptides were identified which had increased immunomodulatory activity, including peptides RDP1258 and its D-isomer the peptide Allotrap 1258. The present study using Skh/hr hairless mouse skin model evaluated the in vivo effects of Allotrap 1258 on acute UVB-induced skin inflammation. Here we demonstrate that intraperitoneal administration of Allotrap 1258 1 h prior to UV exposure resulted in significantly diminished levels of UV-induced tumor necrosis factor (TNF)-alpha protein production in the epidermis but had no effect on other parameters of the acute UV-induced inflammatory response. By virtue of its ability to suppress TNF-alpha protein production, Allotrap 1258 could prove to be an effective modulator of inflammatory responses.