slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins PowerPoint Presentation
Download Presentation
Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins

Loading in 2 Seconds...

play fullscreen
1 / 34

Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins - PowerPoint PPT Presentation


  • 180 Views
  • Uploaded on

Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins. Melissa R. Landon 1 , Jessamin Yu 1 , Spencer C. Thiel 2 , David R. Lancia 2 , Jr., Sandor Vajda 1,3 1 Bioinformatics Graduate Program, Boston University, Boston MA

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins' - shay


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins

Melissa R. Landon1, Jessamin Yu1, Spencer C. Thiel2, David R. Lancia2, Jr., Sandor Vajda1,3

1Bioinformatics Graduate Program, Boston University, Boston MA

2SolMap Pharmaceuticals, Cambridge MA

3Department of Biomedical Engineering, Boston University, Boston MA

terms
Terms
  • Druggability: the ability of a protein’s binding pocket(s) to bind lead-like molecules with high affinity
  • Hot Spots: specific residues within a binding pocket for which ligands display high affinity
protein mapping and druggability
Protein mapping and druggability
  • Observation based on SAR by NMR:
  • Druggable sites bind a variety of small molecules
  • Binding of probes is restricted to ligand binding sites
  • “Hit rate” in mapping is a predictor of druggability
    • Hajduk PJ, Huth JR, Fesik SW: Druggability indices for protein targets derived from
  • NMR-based screening data.J Med Chem (2005) 48(7):2518-2525.
  • Hajduk PJ, Huth JR, Tse C: Predicting protein druggability. Drug Discov Today
  • (2005) 10(23-24):1675-1682.
cs map introduction
CS-Map: Introduction

CS-Map is based on an experimental

method for ligand binding site identification

by the co-crystallization of a protein in

multiple organic solvents

C. Mattos and D. Ringe. Nature Biotech. 14: 595-599 (1996)

step 1a probe placement
Step 1A: Probe Placement

222 initial probe positions

steps 1b 2 rigid body search and minimization
Steps 1B-2: Rigid Body Search and Minimization
  • Simplex search
  • Free energy-based score
  • Second minimization in CHARMM includes Van der Waal term
step 3 clustering of bound probes
Step 3: Clustering of Bound Probes
  • Interaction-based clustering
step 4 creation of consensus sites
Step 4: Creation of Consensus Sites
  • 5-10 lowest free energy clusters for each probe used
example 1 mapping of lysozyme
Example 1: Mapping of lysozyme

Binding of solvents to lysozyme (Liepinsh & Otting, 1997)

NMR data on the binding of methanol, isopropanol, acetone, acetonitrile, t-butanol, urea, DMSO, and methylene chloride

Based on observed NOEs:

  • All ligands bind at site C

9 NOEs: N59 NH, W63 CdH, W63 NeH, I98 CgH, I98 CdH,

A107 CdH, W108 CdH, W108 CzH, W108 NeH

  • In addition to site C, methanol and methylene chloride bind to an internal site
  • A few week NOEs for isopropanol and acetone show binding at the rim of site C
slide10

Dennis, S., Kortvelyesi T., and Vajda. S.

Computational mapping identifies the binding sites of

organic solvents on proteins.

Proc. Natl. Acad. Sci. USA., 99: 4290-4295, 2002.

Kortvelyesi, T., Dennis, S., Silberstein, M.,

Brown III, L., and Vajda, S. Algorithms for

computational solvent mapping of proteins.

Proteins. 51: 340-351, 2003.

lowest free energy clusters for eight ligands
Lowest free energy clusters for eight ligands

Methanol

Isopropanol

Acetone

Tert-butanol

Urea

DMSO

Acetonitrile

Methylene chloride

W108

A107

I98

N59

W63

subclusters of methanol and isopropanol
Subclusters of methanol and isopropanol

A107

W108

W108

isopropanol

A107

methanol

Q57

Q57

I98

I98

N59

W63

W63

N59

conclusions i the nature of binding sites
Conclusions I: The nature of binding sites
  • Each ligand binds in several rotational states.
  • The van der Waals energy is low in each rotational state: a well defined pocket that can burry the ligands and exclude water
  • The site includes a hydrophobic patch created by hydrophobic side chains
  • The site also includes several hydrogen bond donor or acceptor groups:
  • (for lysozyme N59 NH, W62 NeH, W63 NeH, A107 O, and Q57 O)
example 2 thermolysin
Example 2: Thermolysin

Experimental mapping

English, et al. Proteins37,

628-640 (1999) Protein Eng.14,

47-59 (2001).

Probes:

Isopropanol (IPA)

Acetone (ACN)

Acetonitrile (CCN)

Phenol (IPH)

All in the S’1 pocket

thermolysin computational mapping
Thermolysin – Computational Mapping

Consensus sites 1 and 2

Obtained by the CS-Map

algorithm

Dennis, S., Kortvelyesi T., and Vajda. S. Computational mapping identifies the

binding sites of organic solvents on

proteins. Proc. Natl. Acad. Sci. USA.,

99: 4290-4295, 2002.

Kortvelyesi, T., Dennis, S., Silberstein, M., Brown III, L., and Vajda, S. Algorithms for computational solvent mapping of proteins.

Proteins. 51: 340-351, 2003.

why does cs map give better results than earlier methods
Why does CS-Map give better results than earlier methods ?
  • Improved sampling of the regions of interest
  • A scoring potential that accounts for desolvation
  • Clusters are ranked, not individual conformations
  • Consensus site: The binding of different solvents reduces
  • the probability of finding false positives
detection of hot spots within druggable binding pockets by cs map
Detection of Hot Spots within Druggable Binding Pockets by CS-Map
  • Purpose of study: To determine the predictive power of CS-Map toward the identification of hot spots within a binding pocket
  • Comparisons are based on known ligand interactions and NMR data
part 1 identification of hot spots in peptide binding pocket of renin
Part 1: Identification of hot spots in peptide binding pocket of Renin
  • Major target for the treatment of hypertension
  • Over 25 years of research into small molecule inhibitors
  • Most inhibitors are peptidomimetics
  • Novartis in Phase III trials of Aliskiren, a novel non-peptidomimetic renin inhibitor

http://www.merck.com/mmhe/sec03/ch022/ch022a.html

part 1 identification of hot spots in peptide binding pocket of renin22
Part 1: Identification of hot spots in peptide binding pocket of Renin
  • First orally available inhibitor, Aliskiren, binds in a different
  • conformation than peptidomimetic inhibitors
  • -Wood, JM. et. al. Biochem. Biohphys. Res. Commun. 308(4): 698-705 (2003
  • Used the GOLD algorithm for docking
    • -Verdonk, M.L, et. al. Proteins. 52:609-623 (2003)
identification of peptide binding pocket by csmap
Identification of Peptide Binding Pocket by CSMap

1RNE

1BIL

1BIM

1HRN

2REN

Top two consensus sites for each structure

are located in the binding pocket

cs map based identification of hot spots in peptide binding pocket of renin

S1

S2

S3SP

S1

S1

S2’

S2

S4

S3

S1’

CS-Map Based Identification of Hot Spots in Peptide Binding Pocket of Renin
  • Atom-Based Interactions calculated using HBPlus
    • I.K. McDonald and J.M.Thornton. J. Mol. Biol. 238:777-793 (1994)
  • Pearson Correlation between Probes & Aliskiren = .73
  • Pearson Correlation between Probes & Peptidomimetic = .17
slide26

Conclusions IV

Conclusions: Part 1

  • Mapping results indicate the druggable pockets in the renin active site
  • Pockets S2 and S4 are not “hot spots” and should not be targeted.
  • The most important pockets are S1 and S3
  • Pockets S1’ and S2’ are of intermediate importance, but contribute to the
  • binding.
  • Some of these regions, primarily S2’, is not utilized by Aliskiren, suggesting
  • that a higher affinity drug may be developed.
ketopantoate reductase
Ketopantoate Reductase

*Figures reproduced from Ciulli, et. al.

  • NMR studies of E.coli Ketopantoate Reductase using NADPH fragments and co-factor analogues revealed two hot spots located on opposite ends of the NADPH binding region
    • -Ciulli, et. al. J.Med. Chem. 2006 Vol. 49
  • Mutational analysis of residues on opposite ends of the binding region, R31 and N98, confirmed these results.
mapping results ketopantoate reductase

N98

R31

Mapping Results: Ketopantoate Reductase

CS-Map Results

(% Interaction/Residue):

Red: 4.15/residue

Green: 4.13/residue

White: 2.52/residue

Blue: 4.63/residue

  • Mapping analysis of three structures, PDB IDs 1YJQ, 1YON, 1KS9, yielded hot spots on either end of the NADPH binding region, in agreement with the experimental study
part 2 hot spot identification for proteins used in nmr druggability study
Part 2: Hot Spot Identification for Proteins used in NMR druggability study

NMR study published by Hajduk, et. al. J. Med Chem. 2005

*Verified by structural and/or NMR data

example from study fk 506 binding protein
Example from Study: FK-506 Binding Protein

Important Target for Immunosuppression

CS-Map Results Correspond to NMR data

Shuker, S.B., et. al. Science 274(5292): 1531-4 (1996)

conclusions part 2
Conclusions: Part 2
  • CS-Map is capable of determining hot spots within binding pockets of druggable proteins, supported both by NMR and structural data
general conclusions and future directions
General Conclusions and Future Directions
  • The computational prediction of residues important for ligand binding is crucial to structure-based drug design efforts, as well as providing further insight into protein-ligand interactions.
  • Future work will focus on the use of CS-Map derived data to predict hot spots on proteins for which no experimental binding data exists, namely to build pharmacophore models of ligand interactions and to predict hydrogen bonding patterns.
many thanks
The Vajda Group:

Melissa Landon

Karl Clodfelter

Jessamin Yu

Spencer Thiel

David Lancia, Jr.

SolMap Pharmaceuticals:

Frank Guarnieri

Patrick Devaney

This work was funded by

National Institutes of Health

SolMap Pharmaceuticals

Many Thanks