18 research outputs found
Drug design for ever, from hype to hope
In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data
Optimiertes Design kombinatorischer Verbindungsbibliotheken durch Genetische Algorithmen und deren Bewertung anhand wissensbasierter Protein-Ligand Bindungsprofile
In dieser Arbeit sind die zwei neuen Computer-Methoden DrugScore Fingerprint (DrugScoreFP) und GARLig in ihrer Theorie und Funktionsweise vorgestellt und validiert worden.
DrugScoreFP ist ein neuartiger Ansatz zur Bewertung von computergenerierten Bindemodi potentieller Liganden für eine bestimmte Zielstruktur. Das Programm basiert auf der etablierten Bewertungsfunktion DrugScoreCSD und unterscheidet sich darin, dass anhand bereits bekannter Kristallstrukturen für den zu untersuchenden Rezeptor ein Referenzvektor generiert wird, der zu jedem Bindetaschenatom Potentialwerte für alle möglichen Interaktionen enthält. Für jeden neuen, computergenerierten Bindungsmodus eines Liganden lässt sich ein entsprechender Vektor generieren. Dessen Distanz zum Referenzvektor ist ein Maß dafür, wie ähnlich generierte Bindungsmodi zu bereits bekannten sind. Eine experimentelle Validierung der durch DrugScoreFP als ähnlich vorhergesagten Liganden ergab für die in unserem Arbeitskreis untersuchten Proteinstrukturen Trypsin, Thermolysin und tRNA-Guanin Transglykosylase (TGT) sechs Inhibitoren fragmentärer Größe und eine Thermolysin Kristallstruktur in Komplex mit einem der gefundenen Fragmente.
Das in dieser Arbeit entwickelte Programm GARLig ist eine auf einem Genetischen Algorithmus basierende Methode, um chemische Seitenkettenmodifikationen niedermolekularer Verbindungen hinsichtlich eines untersuchten Rezeptors effizient durchzuführen. Zielsetzung ist hier die Zusammenstellung einer Verbindungsbibliothek, welche eine benutzerdefiniert große Untermenge aller möglichen chemischen Modifikationen Ligand-ähnlicher Grundgerüste darstellt. Als zentrales Qualitätskriterium einzelner Vertreter der Verbindungsbibliothek dienen durch Docking erzeugte Ligand-Geometrien und deren Bewertungen durch Protein-Ligand-Bewertungsfunktionen. In mehreren Validierungsszenarien an den Proteinen Trypsin, Thrombin, Faktor Xa, Plasmin und Cathepsin D konnte gezeigt werden, dass eine effiziente Zusammenstellung Rezeptor-spezifischer Substrat- oder Ligand-Bibliotheken lediglich eine Durchsuchung von weniger als 8% der vorgegebenen Suchräume erfordert und GARLig dennoch im Stande ist, bekannte Inhibitoren in der Zielbibliothek anzureichern
Structure-based drug design strategies in medicinal chemistry
A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of\ud
leads, as well as to accelerate the development of high quality drug candidates. Structure-based drug design (SBDD)\ud
methods are becoming increasingly powerful, versatile and more widely used. This review summarizes current\ud
developments in structure-based virtual screening and receptor-based pharmacophores, highlighting achievements as well\ud
as challenges, along with the value of structure-based lead optimization, with emphasis on recent examples of successful\ud
applications for the identification of novel active compounds.CNPqFAPES
Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening
Lead optimization for new antimalarials and Successful lead identification
for metalloproteinases: A Fragment-based approach Using Virtual Screening
Computer-aided drug design is an essential part of the modern medicinal
chemistry, and has led to the acceleration of many projects. The herein
described thesis presents examples for its application in the field of lead
optimization and lead identification for three metalloproteins.
DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent
isoprenoid biosynthesis. Structure-activity relationships for 43 DXR
inhibitors are established, derived from protein-based docking, ligand-based
3D QSAR and a combination of both approaches as realized by AFMoC. As part
of an effort to optimize the properties of the established inhibitor
Fosmidomycin, analogues have been synthesized and tested to gain further
insights into the primary determinants of structural affinity.
Unfortunately, these structures still leave the active Fosmidomycin
conformation and detailed reaction mechanism undetermined. This fact,
together with the small inhibitor data set provides a major challenge for
presently available docking programs and 3D QSAR tools. Using the recently
developed protein tailored scoring protocol AFMoC precise prediction of
binding affinities for related ligands as well as the capability to estimate
the affinities of structurally distinct inhibitors has been achieved.
Farnesyltransferase is a zinc-metallo enzyme that catalyzes the
posttranslational modification of numerous proteins involved in
intracellular signal transduction. The development of farnesyltransferase
inhibitors is directed towards the so-called non-thiol inhibitors because of
adverse drug effects connected to free thiols. A first step on the way to
non-thiol farnesyltransferase inhibitors was the development of an
CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its
basis bisubstrate analogues were developed as one class of non-thiol
farnesyltransferase inhibitors. In further studies two aryl binding and two
distinct specificity sites were postulated. Flexible docking of model
compounds was applied to investigate the sub-pockets and design highly
active non-thiol farnesyltransferase inhibitor. In addition to affinity,
special attention was paid towards in vivo activity and species specificity.
The second part of this thesis describes a possible strategy for
computer-aided lead discovery. Assembling a complex ligand from simple
fragments has recently been introduced as an alternative to traditional HTS.
While frequently applied experimentally, only a few examples are known for
computational fragment-based approaches. Mostly, computational tools are
applied to compile the libraries and to finally assess the assembled
ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a
computational fragment-based screening protocol has been established.
Starting with a data set of commercially available chemical compounds, a
fragment library has been compiled considering (1) fragment likeness and (2)
similarity to known drugs. The library is screened for target specificity,
resulting in 112 fragments to target the zinc binding area and 75 fragments
targeting the hydrophobic specificity pocket of the enzyme. After analyzing
the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking
experiments were performed for reference fragment to derive a general
applicable crystallization protocol for TLN and subsequently for new
protein-fragment complex structures. 3-Methylsaspirin could be determined to
bind to TLN. Additional studies addressed a retrospective performance
analysis of the applied scoring functions and modification on the screening
hit. Curios about the differences of aspirin and 3-methylaspirin,
3-chloroaspirin has been synthesized and affinities could be determined to
be 2.42 mM; 1.73 mM und 522 μM respectively.
The results of the thesis show, that computer aided drug design approaches
could successfully support projects in lead optimization and lead
identification.
fragments in general, the fragments derived from the screening are docke
DEVELOPMENT OF HINT BASED COMPUTATIONAL TOOLS FOR DRUG DESIGN: APPLICATIONS IN THE DESIGN AND DEVELOPMENT OF NOVEL ANTI-CANCER AGENTS
The overall aim of the research is to develop a computational platform based on HINT paradigm for manipulating, predicting and analyzing biomacromolecular-ligand structure. A second synergistic goal is to apply the above methodology to design novel and potent anti-cancer agents. The crucial role of the microtubule in cell division has identified tubulin as an interesting target for the development of therapeutics for cancer. Pyrrole-containing molecules derived from nature have proven to be particularly useful as lead compounds for drug development. We have designed and developed a series of substituted pyrroles that inhibit growth and promote death of breast tumor cells at nM and μM concentrations in human breast tumor cell lines. In another project, stilbene analogs were designed and developed as microtubule depolymerizing agents that showed anti-leukemic activity. A molecular modeling study was carried out to accurately represent the complex structure and the binding mode of a new class of tubulin inhibitors that bind at the αβ-tubulin colchicine site. These studies coupled with HINT interaction analyses were able to describe the complex structure and the binding modes of inhibitors. Qualitative analyses of the results showed general agreement with the experimental in vitro biological activity for these derivatives. Consequently, we have been designing new analogs that can be synthesized and tested; we believe that these molecules will be highly selective against cancer cells with minimal toxicity to the host tissue. Another goal of our research is to develop computational tools for drug design. The development and implementation of a novel cavity detection algorithm is also reported and discussed. The algorithm named VICE (Vectorial Identification of Cavity Extents) utilizes HINT toolkit functions to identify and delineate a binding pocket in a protein. The program is based on geometric criteria and applies simple integer grid maps to delineate binding sites. The algorithm was extensively tested on a diverse set of proteins and detects binding pockets of different shapes and sizes. The study also implemented the computational titration algorithm to understand the complexity of ligand binding and protonation state in the active site of HIV-1 protease. The Computational titration algorithm is a powerful tool for understanding ligand binding in a complex biochemical environment and allows generating hypothesis on the best model for binding
Fragment Based Protein Active Site Analysis Using Markov Random Field Combinations of Stereochemical Feature-Based Classifications
Recent improvements in structural genomics efforts have greatly increased the
number of hypothetical proteins in the Protein Data Bank. Several computational
methodologies have been developed to determine the function of these proteins but
none of these methods have been able to account successfully for the diversity in
the sequence and structural conformations observed in proteins that have the same
function. An additional complication is the
flexibility in both the protein active site
and the ligand.
In this dissertation, novel approaches to deal with both the ligand flexibility
and the diversity in stereochemistry have been proposed. The active site analysis
problem is formalized as a classification problem in which, for a given test protein,
the goal is to predict the class of ligand most likely to bind the active site based
on its stereochemical nature and thereby define its function. Traditional methods
that have adapted a similar methodology have struggled to account for the
flexibility
observed in large ligands. Therefore, I propose a novel fragment-based approach to
dealing with larger ligands. The advantage of the fragment-based methodology is
that considering the protein-ligand interactions in a piecewise manner does not affect
the active site patterns, and it also provides for a way to account for the problems
associated with
flexible ligands. I also propose two feature-based methodologies to account for the diversity observed
in sequences and structural conformations among proteins with the same function.
The feature-based methodologies provide detailed descriptions of the active site
stereochemistry and are capable of identifying stereochemical patterns within the
active site despite the diversity.
Finally, I propose a Markov Random Field approach to combine the individual
ligand fragment classifications (based on the stereochemical descriptors) into a single
multi-fragment ligand class. This probabilistic framework combines the information
provided by stereochemical features with the information regarding geometric constraints
between ligand fragments to make a final ligand class prediction.
The feature-based fragment identification methodology had an accuracy of 84%
across a diverse set of ligand fragments and the mrf analysis was able to succesfully
combine the various ligand fragments (identified by feature-based analysis) into one
final ligand based on statistical models of ligand fragment distances. This novel
approach to protein active site analysis was additionally tested on 3 proteins with very
low sequence and structural similarity to other proteins in the PDB (a challenge for
traditional methods) and in each of these cases, this approach successfully identified
the cognate ligand. This approach addresses the two main issues that affect the
accuracy of current automated methodologies in protein function assignment
Prioritizing Small Molecules for Drug Discovery or Chemical Safety Assessments using Ligand- and Structure-based Cheminformatics Approaches
Recent growth in the experimental data describing the effects of chemicals at the molecular, cellular, and organism level has triggered the development of novel computational approaches for the prediction of a chemical's effect on an organism. The studies described in this dissertation research predict chemical activity at three levels of biological complexity: binding of drugs to a single protein target, selective binding to a family of protein targets, and systemic toxicity. Optimizing cheminformatics methods that examine diverse sources of experimental data can lead to novel insight into the therapeutic use and toxicity of chemicals. In the first study, a combinatorial Quantitative Structure-Activity Relationship (QSAR) modeling workflow was successfully applied to the discovery of novel bioactive compound against one specific protein target: histone deacetylase inhibitors (HDACIs). Four candidate molecules were selected from the virtual screening hits to be tested experimentally, and three of them were confirmed active against HDAC. Next, a receptor-based protocol was established and applied to discover target-selective ligands within a family of proteins. This protocol extended the concept of protein/ligand interaction-guided pose selection by employing a binary classifier to discriminate poses of interest from a calibration set. The resulting virtual screening tools were applied for enriching beta2-adrenergic receptor (β2AR) ligands that are selective against other subtypes in the βAR family (i.e. β1AR and β3AR). Moreover, some computational 3D protein structures used in this study have exhibited comparative or even better performance in virtual screening than X-ray crystal structures of β2AR, and therefore computational tools that use these computational structures could complement tools utilizing experimental structures. Finally, a two-step hierarchical QSAR modeling approach was developed to estimate in vivo toxicity effects of small molecules. Besides the chemical structural descriptors, the developed models utilized additional biological information from in vitro bioassays. The derived models were more accurate than traditional QSAR models utilizing chemical descriptors only. Moreover, retrospective analysis of the developed models helped to identify the most informative bioassays, suggesting potential applicability of this methodology in guiding future toxicity experiments. These studies contribute to the development of computational strategies for comprehensive analysis of small molecules' biological properties, and have the potential to be integrated into existing methods for modern rational drug design and discovery.Doctor of Philosoph