Structure-based Drug Design

I provide below a summary of the presentations and workshops to take place in our eCheminfo Structure-based Drug Design session to be held Monday 16th October 2006 at the joint eCheminfo and InnovationWell Community of Practice meeting at Bryn Mawr College, Philadelphia

Barry Hardy

Structure-based Drug Design
eCheminfo InterAction Meeting Session, Bryn Mawr, Philadelphia, USA
http://www.innovationwell.net/COMTY_drugdesign/
Monday, 16 October 2006
chaired by Frank Hollinger (Locus Pharmaceuticals)

While there are a multitude of approaches being deployed in early stage drug discovery, few have undergone the multitude of changes that structure based drug design has over the past 10-20 years.  Structure based drug design combines science and technology from the fields of computational chemistry, informatics, medicinal chemistry, biology, biochemistry, structural (crystallography and NMR).  Collectively scientists involved in these disciplines form the teams to make a structure based design effort successful.

The convergence of improved structural capabilities (highly efficient protein generation and purification techniques, high throughput crystallography, SAR by NMR, etc.), improved computational algorithms combined with faster, economical compute capability to evaluate ligand/ protein interactions, and the evolution of higher throughput chemistry techniques (e.g. parallel synthesis, focused library design and synthesis, etc.) has resulted in an increase in successful applications of structure based drug design.

It is not uncommon in this generation of drug discovery to start a therapeutic program with a 3D structure (co-crystal or NMR) of your ligand/ protein complex.  This complex structure can significantly enhance a discovery team’s understanding of the ligand’s binding to the protein as well as provide insights in how to enhance those interactions. 

The main objective of the Structure Based Drug Design symposium is to present several case studies of structure based design highlighting some of the diverse approaches deployed to illustrate best practices for the next time they will repeat the process.  We will expect to hear how structural information is capable of identifying hits as well as optimization of those hits to incorporate the necessary drug-like properties for in vivo efficacy.  This session should foster discussion among researchers working in early stage drug discovery through the optimization process about the best practices teams should consider using to achieve success in SBDD focused projects.  This interaction will lead to a better understanding of the current state-of-the-art, improved structure based design approaches and processes and enhanced awareness of how to apply the current set of tools.

Presentations

Structure-Based Design of Estrogen Receptor-beta Selective Compounds

Michael S. Malamas, Wyeth

Michael S. Malamas, Heather A. Harris, James C. Keith, Jr., Robert McDevitt, Iwan Gunawan, Christopher P. Miller, Eric Manas and Richard Mewshaw

The discovery of a second subtype of the estrogen receptor (ER-beta) in 1996 prompted an intense discovery effort within the scientific community to identify ER-beta selective ligands in order to elucidate the receptor’s physiological role in mediating estrogen action. However, until very recently ER-beta’s physiological role has remained unclear. One approach that has not yet been fully exploited to date is the use of highly ER-beta selective ligands to elucidate the functional role of ER-beta. Toward this end, we have designed highly potent and selective agonists for ER-beta and have characterized their activity in several clinically relevant rodent models.

While only two subtle amino acids differences are present in the ligand binding domains of the two ER-alpha and ER-beta isoforms, our structure-based approach enabled us to rapidly advance our initial leads to successful candidates. X-ray crystallography data and molecular modeling tools allowed us to exploit a single amino acid difference between the two ERs (ER-betaIle421 to ER-alphaMet373). Several ligands were found to highly bind to ER-beta with IC50 values of 3-5 nM, which are similar to 17beta-estradiol. However, unlike estradiol, these ligands are >100 fold selective for ER-beta over ER-alpha.

Our ER-beta selective ligands exhibited little or no utility in hormone therapy or as contraceptive agents (e.g., uterotrophy, osteopenia, mammotrophy, thermoregulatory dysfunction, and ovulation) where nonselective estrogens (e.g. 17beta-estradiol) or ER-alpha selective agonists are known to have robust effects. However, we have found that ER-beta selective agonists have a dramatic beneficial effect in animal models of various chronic inflammatory conditions (inflammatory bowel disease, arthritis) and they are currently undergoing clinical trials as novel therapies to treat such conditions. The data suggest that one function of ER-beta may be to modulate the immune response, and that ER-beta selective agonists may offer a novel therapy to treat chronic inflammatory conditions.

Harnessing the power of Structure Based Drug Design using a Fragment Based Approach

Frank Hollinger, Locus Pharmaceuticals

Prospective structure based drug design is one of the more challenging aspects of drug discovery. A powerful design process has been developed which allows the identification and optimization of novel, diverse and potent ligands with druggable properties. This design process has been made possible by harnessing the power of fragment centered structure based drug design.

The development of a novel grand canonical Monte Carlo (GCMC) simulation paradigm permits the generation of binding free energies for fragments to a protein. The output of such a simulation provides all the information necessary to accurately identify high affinity interaction sites, binding sites and druggable binding sites on a protein surface. This computational approach also enables the design team to understand the role of water (just another fragment in our paradigm) and to take full advantage of that information. Using novel analysis tools we are able to combine fragments into synthetically accessible drug-like molecules for evaluation against a desired protein target. A case study will be presented describing the design of novel, potent and selective allosteric inhibitors for p38-alpha. Experimental data will be presented which validates our process of using fragment based ligand design approaches to identify leads and optimize their properties.

Structural Interactions of CCR5 with HIV-1 entry inhibitors

Debananda Das, National Cancer Institute

Debananda Das, Kenji Maeda, Philip Yin, Kiyoto Tsuchiya, Hiroaki Mitsuya

Affiliation: HIV & AIDS Malignancy Branch, National Cancer Institute, National Institute of Health; 10 Center Drive, Room 10S255 - MSC 1868; Bethesda, MD 20892-1868

Effective treatment of HIV continues to be a daunting challenge due to the emergence of drug resistant mutations in the target viral enzymes. CCR5 is a novel cellular target for the intervention of HIV replication. However, an X-ray or NMR structure of CCR5, a GPCR, does not exist. By combining the results of site directed mutagenesis experiments, homology modeling, and docking that accounted for the flexibility of the receptor side chains, we characterized the structural and molecular interactions of CCR5 with multiple CCR5 inhibitors active against R5 HIV-1 including a potent in vitro and in vivo CCR5 inhibitor aplaviroc. The quality of the structural model was evaluated by carrying out new saturation binding experiments by mutating CCR5 residues predicted to be important by the model. The structural model enabled us to precisely define the binding site of CCR5 inhibitors within CCR5 and elucidated the key binding site interactions responsible for the anti-viral activity of the inhibitors. We will discuss structure based drug design strategies that target specific residues of CCR5 to minimize toxic side effects.

Main points to be covered:
* Combination of site directed mutagenesis and molecular modeling can be a powerful tool for structure based drug design
* Iterative structure refinement that accounts for the flexibility of the receptor is important for generating a robust homology model.

Small Molecule Inhibitors of Protein-Protein Interactions

Max Cummings, Johnson & Johnson PR&D

Normal cell function is dependent upon many protein-protein interactions. Specific inhibitors of protein-protein interactions can serve as useful tools in the study of biochemical pathways, and may ultimately lead to the development of new drugs. Protein-protein interactions are perceived as a potential source of many new targets for drug action, but at the same time are thought to be particularly challenging targets for drug discovery. They are commonly characterized as a distinct target class with respect to small molecule drug discovery. Structures related to the HDM2-p53 protein-protein interaction will serve as an introduction, followed by a more general discussion of selected structural aspects of protein-protein and protein-small molecule binding interactions.

Using ab initio calculations as routine tools to help design CDK2 inhibitors

Jose Duca, Schering-Plough

Ab initio methods can be used systematically in drug design to provide insights when experimental data is not easily obtainable. In this talk we present three examples of Structure Based Drug Design where ab initio tools played a prominent role. In the first study ab initio methods were utilized to compute pKa values using a model of the catalytic site of TACE and to predict a proton transfer concomitant with binding. Second, the use of ab initio calculations to compute pKa values and tautomer properties of a series of substituted pyrazolopyridines (CDK2 inhibitors) is presented. Finally, a series of ab initio free energy calculations is used to identify determinants of binding affinity for some recently published pyrazolopyridine inhibitors of CDK2.

High Strain Energies of Bound Ligands

Paul Labute, Chemical Computing Group

The prediction of the bioactive bound conformation of a candidate ligand is important for computational methodologies such as pharmacophore search and docking. The strain energy of a conformation (relative to the global minimum energy) is often used as a criterion for rejection of a conformation from consideration. Recent molecular mechanics studies using ligand-receptor complexes from the PDB have suggested that high strain energies (> 10 kcal/mol) are not only possible but routinely observed. We present the results of computational experiments that attempt to explain these observations and determine their validity.

WORKSHOPS

Quantum Biochemistry Workflows

Lance Westerhoff, QuantumBio

QuantumBio, Inc. in conjunction with Discovery Machine, Inc. has been working to apply computer learning and knowledge management to fully automate the multi-step processes required to characterize biomolecular interactions at a quantum mechanical level within in silico drug discovery workflows. Such workflows involve database searches, structure preparation, molecular mechanics-based cleanup, and finally quantum mechanical treatment in order to fully characterize these interactions. Each of these steps can include any number of subtasks. During the in silico drug discovery process, these complicated workflows are coupled with simulations that involve the characterization of hundreds if not thousands of biomolecular structures at a time. In addition to simulation parameters themselves, quantum mechanics methodologies are notoriously sensitive to structural defects in which the convergence of the calculation will be adversely affected. This leads to longer calculation times and other problems. Therefore, these simulations often require that the user understand not only the chemistry of the structure, but also the theory involved in the computational methodologies so that problem structures can be filtered early in the process.

With this in mind, an intelligent and adaptive system for quantum mechanics-based, in silico drug discovery has been developed to encapsulate these workflows to describe the types and strengths of enzyme-inhibitor interactions that play an important roll in drug discovery efforts. This system is built on the synergy between QuantumBio’s CHEMIX molecular modeling user interface and Discovery Machine’s workflow management solution. The goal of this workshop is to introduce the community to an early version of this system in order to demonstrate its usefulness, and to gain feedback for continued development.

Fragment- and Structure-Based Drug Design

Zenon Konteatis, Jennifer L. Ludington & Frank Hollinger, Locus Pharmaceuticals

Structure-based drug design (SBDD) has evolved over the years and has seen many technological advances which have enabled significant discovery team successes. One of the more recent advances to SBDD has been the development of fragment based techniques.

This workshop will focus on a novel fragment based design process which uses a novel computational approach to calculate predicted binding free energies for a collection of fragments binding to a protein. The design process employed uses highly evolved analysis software to assemble potent, synthetically accessible lead molecules. This process has the natural ability to provide insights into how to further optimize the affinity and physical properties of the designed molecules.

The workshop will consist of two parts, the first part will present the steps in the fragment focused design process using real world examples. The second part will demonstrate the computational software designed specifically to analyze the fragment binding free energy information to yield novel, selective and diverse (physical property) molecules.

A key objective of the Workshop is to foster discussion around fragment focused structure based drug design approaches and illustrate a best practices example which has led to potent, selective, efficacious molecules.

Participants will have ample opportunity to discuss their perspectives and criticisms of the methods studied and should take-away key nuggets of understanding from this intensive session. Participants should return to their labs with new ideas, best practices and software experiences to maximize productivity in their own drug discovery research activities.

Advanced Techniques in Pharmacophore Perception and Successful Applications in Drug Design

Osman F. Güner, Turquoise Consulting

Pharmacophore perception and use of pharmacophores in drug discovery and design has evolved from a specialist activity in early 90s into an essential aspect of modern computer-aided drug design. The significance of pharmacophore technology has been continuously increasing together with the increasing availability of protein targets. In this workshop we will briefly cover the historical evolution of the pharmacophore concept and its successful use and application in drug discovery.

The bulk of the lecture focuses on various techniques for pharmacophore modeling and database searching. Methods for perceiving a pharmacophore are presented, starting from the simplest method of visual pattern recognition. The significance of training set selection is covered with an example for PDE IV inhibitors. Techniques for pharmacophore model refinement are then presented for 5-HT3 inhibitors, and finally predictive model development is covered with two examples: FTP inhibitors and antimalarial agents.

The second half of the workshop starts with a review of hit list and pharmacophore analysis metrics. It then provides details for various three-dimensional database-searching techniques. Vector-based queries are exemplified with an application to HIV-1 protease inhibitors and endothelin antagonists. Methotrexate bound conformation within DHFR is used to demonstrate the utility of ligand-shape in 3D searching, as well as combined shape and pharmacophore search. This section ends with a discussion of the conformational flexibility issue in 3D databases and provides a comparison of current solutions.

The availability of new targets and protein structures is now generating a renewed interest in structure-based drug design. In a similar fashion, there is now more interest in the development of receptor-based pharmacophores, as opposed to the traditional ways of using a set of known active molecules to derive a putative pharmacophore. Development of pharmacophore models entirely from the receptor active site provides new challenges. Typically, an active site will have more potential binding sites than the ones that are utilized by a given set of active compounds. Hence, automated pharmacophore model generation from receptor active sites becomes a combinatorial problem and the hundreds to thousands of pharmacophore models that are generated in this manner need to be evaluated and scored. We will discuss these problems and propose solutions based on some recent work. The workshop will close with several examples from literature and success stories.

Further reading:
1. Güner OF: The impact of pharmacophore modeling in drug design. IDrugs (2005) 8(7):567-572.
2. Van Drie J: Pharmacophore-based virtual screening: A practical perspective, In Virtual Screening in Drug Discovery. Alvarez J, Shoichet B (Eds), CRC Press, Boca Raton, FL, USA (2005):157-205.
3. Güner OF: History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem (2002) 2(12):1321-1332.
4. Mason JS, Good AC, Martin EJ: 3D pharmacophores in drug discovery. Curr Pharm Design (2001) 7(7):567-597.
5. Güner, OF (ed): Pharmacophore Perception, Development, and Use in Drug Design. IUL Biotechnology Series, La Jolla, California, USA (2000).

Hypothesis generation from docking results using activity measurements, interaction fingerprints, clustering and 2D visualization methods

Alex Clark, Chemical Computing Group

Given the availability of crystallography data for a drug target, it is possible to generate a large number of reasonable docked poses using modern software. This workshop will address the use of protein:ligand interaction fingerprints, combined with activity data, to reduce the noise which is inherent in docking results. A combination of clustering methods and 2D visualization can be used to produce model hypotheses, which can be applied to subsequent screening of compound databases.

InnovationWell eCheminfo cheminformatics chemoinformatics bioinformatics Computational Chemistry Molecular Modelling Molecular Modeling pharmaceutical pharma meeting workshop conference management executive Bryn Mawr Philadelphia Critical Pathbiomarkers metabolomics toxicology personalised medicine KM Life Sciences Pharma Drug DiscoveryResearch and Development Drug Development Healthcare Innovation Knowledge Management events

Press Release:  http://www.prweb.com/releases/2006/9/prweb443727.htm

Comments

[suhas]

article is too good.
We have interuniversity project competition. I am partcipating in that conest, i chosen subject: "drug design and its application". Can you please help me, to provide all necessary information as i dont know exactly how chemoinformatics play an important role.
Thanks.

[Patrick Kenney]

I am looking for a good source on the history of structure-based drug design including computational and visualization advances. Any help would be much appreciated. Thanks.

[Paul]

Hi,

The above statement seems to be contradictory. The situation is very critical and need an experienced campaigner to resolve it.

George

Drug Intervention New Mexico

[S D WARKAD]

HI,
THE ABOVE ARTICLE IS GOOD,BUT I HAVE SOME IDEAS ABOUT DRUG DESIGNING WHICH ARE BASED ON SOME OTHER HYPOTHESIS LIKE
1. ELECTRON IS MADE UP OF 6 PART
2.ATOMS SHOWING THREE MAIN CHARECTERS
OUT OF WHICH EACH CHARECTER SHOWING DIFF. CONDITIONS.
3. ABILITY OF ELECTRON , PROTON, NEUTRON TO PRODUCE POLARITY AT DIFF. CONDITION, DIFF. TIME , DIFF. POSITION.

[כסאות מנהלים סופרדסק]

This procedure should nurture conversation among scientists working in early on medication development through the process about the best methods squads should consider using to succeed in SBDD.