This document presents an overview of molecular modeling techniques. It discusses the history of molecular modeling and some common computational methods like molecular mechanics, quantum mechanics and molecular dynamics. It also describes different modeling approaches like template modeling techniques such as homology modeling and threading as well as template-free modeling methods including ab initio and knowledge-based modeling. The document concludes that molecular modeling can provide useful insights for research if used carefully while also noting current limitations, especially for modeling larger protein structures.

1. Presented by:
BHARATESHA.S
9th semester
13th october,2015
Guided by:
Dr. Amruthavalli
“To develop a sufficient accurate model of
the system so that physical experiment may
not be necessary”

2. HISTORY ABOUT MOLECULAR MODELING
Plastic Ball and stick model of Proline

3. “ISIS draw
program “
“3D biological
macromolecular
structural data”

4. Spacefill ball & stick
cartoon

5. CONTENTS
• INTRODUCTION
• HISTORY ABOUT MOLECULAR MODELING
• TEMPLATE MODELING
-Homology modeling
-Threading
• TEMPLATE FREE MODELING(ab initio methods)
• CONCLUSION
• REFERENCES

6. INTRODUCTION
• Molecular modeling describes the generation ,manipulation or
representation of 3 –dimensional structure of molecules and
associated physico-chemical properties.
• It involves a range of computerized technique based on theoretical
chemistry methods and experimental data to predict molecular and
biological properties.
• The three most common computational methods are:
- Molecular mechanics
- Quantum mechanics
- Molecular dynamics

7. Homology modeling
Protein Threading

8. Homology Modeling
Based on two major observations:
1)The structure of a protein is determined by its amino acid sequence
2) Structure is much more conserved than sequence
during evolution.
In general, 30% sequence identity is required to generate an useful model.

9. Steps in Homology Modeling
Step 1: Template Recognition and Initial Alignment
In practice, one just feeds the query sequence to one of the countless BLAST
servers on the web, selects a search of the PDB, and obtains a list of hits—the
modeling templates and corresponding alignments
Step 2: Alignment Correction
Sometimes it may be difficult to align two sequences in a region where the
percentage sequence identity is very low.
One can then use other sequences from homologous proteins to find a solution.

10. Known structure FDICRLPGSAEAV
Model FNVCRMP---EAI
Model FNVCR---MPEAI
S
G
P
L
A
E
R
C
I V
C
R
M
P
E
V
C
R M
P
E
 Correct alignment
F-D-
-A-V

11. Step 3: Backbone Generation
• One simply copies the coordinates of those template residues that
show up in the alignment with the model sequence.
• If two aligned residues differ, only the backbone coordinates
(N,Cα,C and O) can be copied.
• If they are the same, one can also include the side chain.
Step 4: Loop Modeling
There are two main approaches to loop modeling:-
1). Knowledge based: one searches the PDB for known loops with
endpoints that match the residues between which the loop has to be
inserted and simply copies the loop conformation.
2). Energy based: as in true ab initio fold prediction, an energy function
is used to judge the quality of a loop

12. Step 5: Side-Chain Modeling
• Comparing the side-chain conformations (rotamers) of residues that are
conserved in structurally similar proteins.
• Similar torsion angle about the Cα −Cβ bond.
• It is therefore possible to simply copy conserved residues entirely from
the template to the model.
Fig 01 :Prefered rotamers of this tyrosin (colored sticks) the real side-chain (cyan)
fits in one of them.

13. Step 6: Model Optimization
Energy = Stretching Energy +Bending Energy +Torsion Energy +Non-Bonded
Interaction Energy

14. Step 7: Model Validation
Model should be evaluated for:
- correctness of the overall fold/structure
- errors over localized regions
- stereochemical parameters: bond lengths, angles, etc

15. Model!
1: Template recognition
and initial alignment
2: Alignment correction
3: Backbone
generation
4: Loop
modeling
5: Sidechain modeling
6: Model
optimization
7: Model
validation
8: Iteration

16. Protein Threading

18. protein A threaded
on template template
protein B threaded
on template

19.  Ab initio methods
• Physics Based
• Knowledge Based

20. ab initio(from the beginning) methods
If the structure homologues(occasionally analogues)
do not exist, or exist but cannot be identified, models
have to be constructed from scratch.
Three key factors of the modeling algorithms are:
• Energy function
• Conformational search
• Model selection
Physics Based
Knowledge Based

21. QUANTUM MECHANICS
• It provides information about the nuclear position and distribution.
• It is based on study of arrangement and interaction of electrons and nuclei of a
molecular system.
• It is based on the wave properties of electrons and material particles.
Total energy= Potential energy + Kinetic energy
• To calculate the value of potential, electron affinities, heat of
formation, dipole moment.
• To find the electron density in a structure.
• To determine the point at which a structure will react with
electrophiles and nucleophiles.
Physics based energy function

22. MOLECULAR MECHANICS
• Calculation of energy of atoms, force on atoms and their resulting motion.
• Used to model the geometry of the molecule, motion of the molecule and to get
the global minimum energy structures.
Methods:
• Force field
• Study of electrostatics
• Molecular dynamics
• Conformation analysis
Software :
• AMBER
• CHARMM
• GROMOS96

23. Knowledge based energy function
• It refers to the empirical energy terms derived from the
statistics of the solved structures in deposited PDB.
Examples: ROSETTA,TASSER
Conformation search methods
• To find the global minimum energy structure for a given
energy function with complicated energy landscape.
Advantage
ab initio modeling can help us to understand the underlying
principles on how proteins fold in nature.
Limitation
100-120 residue protein structures can be determined using ab initio
methods.

24. CONCLUSION
• Visualize the 3D shape of a molecule
• Carryout a complete analysis of all possible conformation and their
relative energies.
• Predict the binding energy for docking a small molecule i.e. a drug
candidate, with a receptor or enzyme target.
• It is necessary to have standard models which are applicable to very
large systems.
• Nevertheless, molecular modeling if used with caution, can provide
very useful information to the chemist and biologist involved in
medicinal research.

25. REFERENCES
• Alan Hinchliffe,(2003)
Molecular modeling for beginners,
John wiley &sons ltd,England-407pp.
• Ramachandran, Gopakumar Deepa,(2008)
Computational chemistry and molecular modeling,
Springer science and business media,398pp.
• Holtje.H.D. Folkers(1996)
Molecular modeling ,Basic principle and applications,
VCH publishers,Newyork-177pp.