Bioorganic Medicinal Chemistry Letters 14 2004 1447 1454
Bioorganic & Medicinal Chemistry Letters 14 (2004) 1447 -1454 HIV-1 integrase pharmacophore model derived from diverse classes of inhibitors Gabriela Iurcu Mustata, a Alessandro Brigob and James M. Briggsa, * -Presentation by Kavitha Bharatham
Introduction to HIV-1 Integrase Life cycle of HIV-1 A movie 2, 3
Structure of HIV-1 Integrase Catalytic core (A), N-terminus (B), C-terminus (C)
The Integration of HIV-1 DNA Integration 4 occurs in three consecutive steps 3' processing Removal of two nucleotide at 3 ends Exposure of CA dinucleotide Strand transfer Joining of the previously processed 3' ends to the 5' ends of strands of target DNA at the site of integration Disintegration Repair of the small gaps in the target sequence flanking the viral genome and joining of the 5`-ends of viral DNA to host DNA
Introduction to Pharmacophore • One of the most analogue based drug design techniques is the generation of Pharmacophore models 아날로그에 기초를 둔 약 디자인 기술의 하나는 Pharmacophore 모형 의 형성이다 • A Pharmacophore is a simplified 3 -D description of the key structural features of a set of known compounds. Pharmacophore는 한 세트이 알려진 성분들의 중요한 구조상 특징의 간단한 3 -D 묘사이다 • The structure of a chemical influences its properties and biological activity. “Similar compounds behave similarly” 화학제품의 구조는 그것의 특징 및 생물학적 활동을 좌우한다 “동이한 화합물은 동이하게 행동한다” • Catalyst 1 generates a pharmacophore hypothesis that represents the structure-activity relationships from a set of compounds. Catalyst 1은 화합물의 세트에서 구조 활동 관계를 대표하는 pharmacophore 가설을 생성한다.
Pharmacophore Generation Methodology 431 molecules with HIV-1 integrase activity data Selected based on criteria 26 structures as training set were sketched Conformational search High active IC 50<=10 u. M Moderately active 100 u. M>IC 50>10 u. M) Pharmacophore Generation Inactive IC 50>=100 u. M
Selection of Training Set • Set of molecules taken to generate a pharmacophore is a Training set • Molecules must have same assay Ex: 50% inhibitory activity against HIV-1 integrase • Training set must covers 4 orders of magnitude of biological activity data (IC 50 ranges from 0. 04 -1000). Ex: 0. 01 -0. 1; 0. 1 -0, 0 -10, 10 -100 represents each magnitude • Each order of magnitude is represented by at least three compounds • If two compounds had similar structures, they had to differ in activity by one order of magnitude to be included in our dataset. • If two compounds were found to have similar activities, they had to be structurally distinct in order to be included.
Training Set Molecules
Pharmacophore Generation After Conformational search (maximum 256), pharmacophore is generated based on the following phases Constructive Phase: • Identifies most active compounds • Identifies hypothesis that are common among active compounds Subtractive phase: • Identifies most inactive compounds • Removes hypothesis that are common among inactive compounds Optimization Phase • Attempts to improve the initial hypothesis
Pharmacophore Hypothesis Generation 높게 활동 Hydrogen bond Donor (HD) Hydrogen bond Acceptor (HA) Hydrophobic (HY) Negative Charge (NC)
Pharmacophore Hypothesis HY 12. 05 Å 10. 29 Å 6. 98 Å 5. 32 Å HA 2 3. 6 Å HA 1 3. 0 Å HD
Criteria for a good hypothesis The Hypo. Gen module in catalyst performs • A fixed cost calculation, which represents the simple model that fits all data perfectly. Perfect Pharmacophore Fixed cost is Minimum Ex: 72 bits • A null cost calculation, which presumes that there Very Bad is no relationship in the dataset and that the Pharmacophore experimental activities are normally distributed Null cost is Maximum around their average value. Ex: 149 bits A meaningful pharmacophore hypothesis may result when the difference between these two values is large [Very Bad Pharmacophore (null cost)]-[Perfect Pharmacophore (Fixed cost)] > 40 bits 5 149 -72 = 77 bits Good hypothesis The total cost of any pharmacophore hypothesis should be close to the fixed cost to provide any useful models.
Results and Discussion Ten pharmacophore hypothesis are generated Null Cost = 182 bits Fixed Cost = 106 bits Null Cost – Fixed Cost = 72 bits Our Hypothesis is good! Total cost = cost taken for generated hypothesis Total Cost must be near Fixed cost
Results and Discussion
Validation of the pharmacophore model • • Molecules used for validation are called Test Set 14 highly active molecules were selected They were built, minimized and conformers were generated They were mapped onto the pharmacophore hypothesis and activities were predicted
Validation of the pharmacophore model
Conclusions • Our pharmacophore was able to accurately predict known inhibitors, including the recently published azido-containing HIV-1 IN inhibitors 6 • The mapping information based on the pharmacophore model we developed is now being taken advantage of in the identification of novel lead compounds with improved inhibitory activity through 3 -D database searches. • These hypotheses thereby save valuable time in the laboratory
References 1) 2) 3) 4) 5) CATALYST 4. 6; Accelrys, Inc. , San Diego, CA, 2001, http: //www. accelrys. com Asante-Appiah, E. ; Skalka, A. M. Antiviral Res. 1997, 36, 139. Hindmarsh, P. ; Leis, J. Microbiol. Mol. Biol. Rev. 1999, 63, 836. Brown, P. O. ; Co. n, J. M. ; Hughes, S. H. ; Varmus, H. E. Retroviruses; Cold Spring Harbor Lab. Press: Plainview, New York, 1997; 161. Guner, O. F. In Pharmacophore Perception, Development, and Use in Drug Design; Ed. ; International University Line: La Jolla, California, 2000, pp 173. 188
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