Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data

University of Helsinki
Université Paris Diderot
Development of Computational Methods to
Predict Protein Pocket Druggability and
Profile Ligands using Structural Data
Alexandre Borrel
Defence of doctoral dissertation
26 May 2016
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973 1

Université Paris Diderot 26-05-20162
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Outlines
Year 2
Year 1

Background

Structural data
-7.265 20.187 20.701
-4.182 20.865 18.600
Structure of the biological macromolecules (protein) at an atomic level
3D coordinates (x, y, z)
element (oxygen, nitrogen, carbon)
-6.288 20.665 18.600
-4.288 21.665 15.600
-4.188 20.665 18.600
-3.089 20.665 18.600
-6.288 21.685 18.600
-6.288 20.665 18.600

Issues with structural data
110 288 proteins structures (1)
(May 2016)
(1) Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., et al. (2000). Nucleic Acids Res. 28: 235–242.
(2) Fersht, A.R. (2008) Nat. Rev. Mol. Cell Biol. 9: 650–654.
(3) Tari, L.W. (2012). Structure-Based Drug Discovery (Totowa, NJ: Humana Press).
Drug discovery (2-3):
• Rationalize drug discovery
• Open new trails of development
• Reduce the cost and the time
• …

Background

Predict the recognition
Holy grail: predict recognition between a ligand and a target
using only protein and ligand structure.
Computational
methodsTarget structure
Ligand/drug structure

Protein-ligand recognition
“Lock-and-key”, Emil Fischer in 1894
(60 years before the first 3D structure)
Fischer, E. Einfluss. Ber. Dtsch. Chem. Ges. 1894, 27, 2985–2993.
Complementarity of shapes between a ligand (key) and a protein (lock).

Protein-ligand recognition
Koshland, D.E. (1958). Proc. Natl. Acad. Sci. U. S. A. 44: 98–104.
“Induced-fit model” Daniel Koshland, 1958
Proteins and ligands adapt their conformations for the recognition.

Challenges
Many factors influence the protein-ligand recognition such as molecular
interactions, environment (i.e. solvent), …
Water
~4.6 water molecules by binding site (1)
(1) Lu, Y., Wang, R., Yang, C.-Y., and Wang, S. (2007). J. Chem. Inf. Model. 47: 668–675.
H-bond
π-π
hydrophobe
Challenges: model all phenomena which explain the recognition.

Aims of the thesis
Develop computational methods useful for the ligand profiling and contributing in
the improvement of the modeling of the protein-ligand recognition.
Data analysis
Pocket / target space
Medicinal chemistry
Molecular modeling
?

Druggability model
Year 2
Year 1
Recognition
Structural data
Protein
target
http://phdcomics.com/comics.php

Binding sites
A binding site will refer to the atoms of the amino acid at interacting distances (4 to
6 Å) of a bound ligand, and present at the surface of the binding region.
Cavity Channel Protein-protein interphase

Drug-like molecules
Drug-like: compound with acceptable Absorption, Distribution, Metabolism,
and Excretion – toxicity properties to become orally bioavailable drug (1-2).
Rules of five (from 2 200 compounds in the United States Adopted Names
directory) in 1997 (1):
(1) Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. (2001). Adv. Drug Deliv. Rev. 46: 3–26.
(2) Tian, S., Wang, J., Li, Y., Li, D., Xu, L., and Hou, T. (2015). Adv. Drug Deliv. Rev. 86: 2–10.
• Molecular weight ≤ 500 Da
• LogP ≤ 5
• H-bond acceptors ≤ 10
• H-bond donors ≤ 5
Ligand drug-like: Bisindolylmaleimide Inhibitor

Drug-like molecules
“Rules of five” are important to
prioritize/rationalize the chemical space for
virtual screening on the first drug discovery
step (12 billion accessible molecules) (1-2)
(1) Hann, M.M., and Oprea, T.I. (2004). Curr. Opin. Chem. Biol. 8: 255–263.
(2) Ursu, O., Rayan, A., Goldblum, A., and Oprea, T.I. (2011). Rev. Comput. Mol. Sci. 1: 760–781.
(3) Perola, E., Herman, L., and Weiss, J. (2012). J. Chem. Inf. Model. 52: 1027–1038.
(4) Hopkins, A.A.L., and Groom, C.R.C. (2002). The druggable genome. Nat. Rev. Drug Discov. 1: 727–730.
Druggability: “…defined as the ability of a target to bind a
drug-like molecule with a therapeutically useful level of
affinity.” (3-4)

Protein druggability
Similarly to the rules of five to rationalize the ligand space, druggability models
are developed to rationalize the target space
Statistical model
Druggable ?

1. Pocket estimation
Prediction of
druggability (from
properties of the
know druggable
pockets)
2. Model pockets 3. Statistical model
A E
TR
Protein druggability
Similarly to the rules of five to rationalize the ligand space, druggability models
are developed to rationalize the target space

Challenges
Pocket estimation
Availability
« ...different pocket detection methods can assign different sizes and/or numbers
of pockets for the same structure. »
(1) Gao, M., & Skolnick, J. (2013). Bioinformatics (Oxford, England), 29(5), 597–604
Hajduk’s model
SCREEN
MAPPOD
SiteMap
DLID
Huang’s model
Huang’s model
Fpocket
DrugPred
DoGSite-Scorer
CAVITY-Score
DrugFEATURE
FTMap
Druggability models are depending on a pocket estimation method, which limit their
availability for pocket differently estimated using visual expertize for example.

Pockets estimated on a same binding site
have a weak average overlap (%)
• Prox - Fpocket = 30 % (±14 %)
• Prox - DoGSite = 28 % (± 14 %)
• Fpocket- DoGSite = 30 % (± 16 %)
Step 1: Pocket estimation
Develop a druggability model which considers several pocket estimations
We used three pocket estimation methods:
• Ligand proximity (Prox)
• Geometric approach (Fpocket) (1)
• Energetic approach (DoGSite) (2)
(1) Guilloux, V. Le, Schmidtke, P., and Tuffery, P. (2009). BMC Bioinformatics 10: 168.
(2) Volkamer, A., Griewel, A., Grombacher, T., and Rarey, M. (2010). J. Chem. Inf. Model. 50: 2041–2052.

Step 2: Pocket modeling
Pocket are modeled using a set of 52 descriptors implemented
Composition (1-2)
(atomic and residues) Hydrophobicity (2-4) Geometry (5)
(1) Milletti, F., and Vulpetti, A. (2010). J. Chem. Inf. Model. 50: 1418–1431.
(2) Kyte, J., and Doolittle, R.F. (1982).J. Mol. Biol. 157: 105–132.
(3) Eyrisch, S., and Helms, V. (2007). J. Med. Chem. 50: 3457–3464.
(4) Hubbard, SJ and Thornton, J. (1992). NACCESS version 2.1.1.
(5) Petitjean, M. (1992). J. Chem. Inf. Model. 32: 331–337.
G A
DY Aromatic
Polar
Charged

Principal component analysis for pocket sets estimated differently (Prox, Fpocket
and DoGSite) using a unique dataset of 111 binding sites (NRDLD) (1).
Step 3: Pocket spaces
(1) Krasowski, A., Muthas, D., Sarkar, A., Schmitt, S., and Brenk, R. (2011). J. Chem. Inf. Model. 51: 2829–2842.

Druggable pockets
Druggable and less druggable pocket
spaces are separated in the projection.
Volume Polarity
Hydrophobicity
Aromaticity

Training phase
Parsimonious linear discriminant analysis
models (internal validation cross
validation 10-folds)
Selection of the models performing on
different pockets sets estimated differently
Consensus model (average of 7 linear
discriminate analysis models)

External validation
+ 10% in accuracy
+ 0.20 in MCC
Matthew’s Coefficient Correlation (MCC)
(11) Desaphy, J., Azdimousa, K., Kellenberger, E., and Rognan, D. (2012). J. Chem. Inf. Model. 52: 2287–2299.
(14) Krasowski, A., Muthas, D., Sarkar, A., Schmitt, S., and Brenk, R. (2011). J. Chem. Inf. Model. 51: 2829–2842.
(10) Halgren, T. a (2009). J. Chem. Inf. Model. 49: 377–389.
(12) Guilloux, V. Le, Schmidtke, P., and Tuffery, P. (2009). BMC Bioinformatics 10: 168.
(15) Volkamer, A., Kuhn, D., Rippmann, F., and Rarey, M. (2012). Bioinformatics 28: 2074–2075.

Output of PockDrug model
Geometry Hydrophobicity Aromaticity
Acetylcholinesterase
complexed with Huprine
0.82 +/- 0.09
Druggable probability
(Average)
Confidence
(Standard deviation)
PockDrug combines three pocket properties
i.e. geometry, hydrophobicity and the
aromaticity

PockDrug model
Druggability model developed, named PockDrug:
• Robust for different pocket estimation methods
• Exhibits better performances that other models in the literature
• Define important global properties for the recognition (hydrophobicity,
aromaticity and geometry)
Borrel, A., Regad, L., Xhaard, H.G., Petitjean, M., and Camproux, A.-C. (2015). PockDrug: a
model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem.
Inf. Model. 55: 882–895.

PockDrug-Server
Druggability model developed, named PockDrug:
• Robust for different pocket estimation methods
• Exhibits better performances that other models in the literature
• Define important global properties for the recognition (hydrophobicity,
aromaticity and geometry)
http://pockdrug.rpbs.univ-paris-diderot.fr/
Borrel, A., Regad, L., Xhaard, H.G., Petitjean, M., and Camproux, A.-C. (2015). PockDrug: a
model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem.
Inf. Model. 55: 882–895.
Hussein, H.A*., Borrel, A.*, Geneix, C., Petitjean, M., Regad, L., and Camproux, A.-C. (2015).
PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.
Nucleic Acids Res. 1–7.

PockDrug-Server

To the ligand profiling
Which profile of ligands?

Local structural replacements
2 year
1 year
Recognition
Structural data
Druggability
Binding site
Ligand

Drug optimization
Hann, M.M. (2011). Medchemcomm 2: 349–355.
Develop series of chemical
modifications to modulate drug
properties.

Bioisosterism
(1) Brown, N. (2014). Mol. Inform. 33: 458–462.
(2) Southall, N.T., and Ajay (2006). J. Med. Chem. 49: 2103–2109
Example of bioisosteres from the
kinase patent space (2)
“Bioisosterism is the concept of similarity between functional groups or scaffolds
in molecules that exhibit the same shape in terms of their potential biological
interactions.”(1)
Which replacements are possible?

Hypothesis: From two superimposed homologue
proteins, chemical groups which occupy the same
space may be bioisosteric replacements.
26-05-201635
Local structure replacements
Local structural replacement (LSR)
Computational methods to extract the local
structural replacements

Study case: phosphate
• Attractive target for therapeutic development (1).
• 30% of the cellular proteins are phosphoproteins
• Phosphate group is charged at biological pH, poorly
permeable (2).
ATP
Phosphate groups
(1) Cohen, P. (2000). Trends Biochem. Sci. 25: 596–601.
(2) Smith, F.W., Mudge, S.R., Rae, A.L., and Glassop, D. (2003). Plant Soil 248: 71–83.

Computational workflow

Computational workflow
15 819 phosphate replacements

Hierarchical organization
• Local structure containing
• 16 Protein family (KS = Kinase)
• 70 clusters (30% of identity sequences)
• LGD (Ligand)
• LSR (Local Structure Replacements)
• BS (Binding site, 4.5 Å)

Hierarchical organization

Phosphate is not replaced by the ligand but by the protein (flexible loop)
PDB code: 3JZI – 1DV2
These observations are not quantitative in terms of affinity
Mechanisms of replacements

Congener series
U-shape replacements, found in different protein families.
Considering the congener series in different families, when the U-shape is
destabilized the binding affinity decreases.

Hydrophobic replacements, favour hydrophobic
contacts in binding site.
Miscellaneous replacements
Positively charged replacement is
surprising considering that the phosphate
groups are negatively charged.

Conclusion (LSR)
• 15 819 phosphate replacements
• Organization based on target and type of replacements
• Discussion of some mechanisms for the recognition
A. Borrel*; Y. Zhang*; L. Ghemtio; L. Regad; G. Boije af Gennäs; A.-C. Camproux; J. Yli-
Kauhaluoma; H. Xhaard. Structural replacements of phosphate groups in the Protein Data Bank
(Manuscript)
Perspective:
• Workflow is fully customizable

Molecular interactions
Year 2
Year 1
Recognition
Structural data
Molecular
interactions
Druggability
Binding sites
Ligand
replacements

H-bond
π-π
“Intramolecular attractions or repulsions between atoms that are not directly
linked to each other, affecting the thermodynamic stability of the chemical species
concerned.” (IUPAC)

Example of H-bond
• Geometry (180°), distance criteria
• Directionality
• Partial charges
Hydrogen bond is a non-bonded interaction where two electronegative atoms or
group of atoms share a hydrogen.

Challenges

Challenges
Distance (Å)

Challenges
However, some type of interactions i.e. salt-bridges combines different energy
types, influencing the geometry and the strength. Also the environment or/and
interaction network influences the binding interaction.

Hypothesis of ionic groups
PDB (~100 000 proteins structures), important diversity of interactions.
Data-mining to investigate/model the neighborhood of these interactions.
Six different ionic groups have characterized, only primary amine is presented.
Qualitative and quantitative description of ionic interactions in the binding
site based on their environments.

Neighborhood
1 632 in protein-ligand interactions
154 979 in intra-protein interactions

Neighborhood
Neighborhood: group of 4 first atoms close to the primary amine
Oxygen in carboxylate (Oox)
Oxygen in water molecules (Ow)
Oxygen in hydroxyl (Oh)
Oxygen carbonyl (Oc)
Nitrogen in amide (Nam)
Nitrogen imidazole (Nim)
Nitrogen in guanidinium (Ngu)
Nitrogen in lysine (NaI)
Carbon sp2 and nitrogen sp2 (aromatic) (Car)
Other carbon or sulfur atom (Xot)
Oh
Oh
Oox
Oox
Ow
Ow
Oh
Oox
Oh
Nim
Oc

Neighborhood (first neighbors)
Combination of the four first neighbors, distance is not considered
Environments including a carboxylate (Oox)
Quantitative analysis (50% of
primary amines are ionized with
a carboxylate)
Preferential environments and also missing and poor represented environments.

Modeling the environment
Neighbors
1 2 3 4
Type
Oox 234 400 320 123
Ow 789 457 690 389
Oh 589 673 590 499
…
Contingency table by position
2D projections
Correspondence analysis: dependency between the neighbor and the atom type.

Two environments are clearly different in terms of neighbors and closest atoms.
+++ Carboxylate (Oox)
++ Hydroxyl (Oh)
+++ water molecules (Ow)
++ Oxygen carbonyl (Oc)
++ Hydroxyl (Oh)

Interaction modeling
• Similar conclusions for intra-protein interactions and protein-ligand
interaction.
• Acidic and basic groups are interacting with a counter ion in 45-54% of cases.
When functional groups of ionizable character are accounted (Oh, Ow) this
number raise to 71%-100% of molecular complexes depending the functional
group at hand.
• Water molecules play a key role in the stabilization of polar groups
especially in absence of salt bridges.

Perspectives
Perspectives
• Docking scoring function, i.e. function which considers environment of ligand
decomposition substructure.
• Quantify the preference or missing environments of the interactions
A. Borrel; A.-C. Camproux; H. Xhaard. Interactions of amine, carboxylic acid, imidazole, and
guanidinium groups in proteins and protein-ligand complexes (Manuscript)

Outlines
2 year
1 year
Recognition
Structural data
Druggability
Binding sites
Ligand
replacements
Interaction

And water molecules
2 year
1 year
Recognition
Structural data
Water
molecules
Ligand
replacements
Druggability
Binding sites
Interactions

Water molecules
• Poorly crystallized
• Present only at very high resolution
(< 1.5 Å)
Method to position water molecules

In development
Geometric based approach to position water molecules.
Preliminary results: 80%
of the water molecules are
well repositioned.

Conclusion
2 year
1 year
Recognition
Structural data
Druggability
Binding site
Ligand
replacements
Interaction

Conclusion
Develop computational methods useful for the ligand profiling and contributing in
the improvement of the modeling of the protein-ligand recognition.
Data analysis
Pocket / target space
Medicinal chemistry
Molecular modeling
?

Conclusion
Binding sites and the targets:
druggability model

Conclusion
Ligands: Methods for local
structure replacements
26-05-2016 67
druggability model

Conclusion
Interactions: Methods for
local replacements
druggability model

Conclusion
Interactions: Methods for
local replacements
Environment: positioning of
water molecules
druggability model

H. Abi Hussein
Dr. K. Audouze
I. Allam
Dr. A. Badel
H. Borges
J. Bécot
Dr. D. Flatters
C. Geneix
Dr. M. Kuenemann
Dr. D. Lagorce
L. Legall
Dr. M. Louet
Dr. M. Miteva
Dr. M. Petitjean
I. Rasolohery
Dr. L. Regad
26-05-201670
Acknowledgements
Laboratory MTi
(Paris Diderot)
Division of pharmaceutical
chemistry and technology
(Helsinki)
Members of the jury
Dr. O. Sperandio
Prof. O. Taboureau
I. Toussies
D. Triki
Dr. B. Villoutreix
Dr. B. Zarzycka
D. Brandao
K. Culotta
Dr. L. Ghemtio
L. Kharu
A. Legehar
Dr. A. Magarkar
M. Rinne
M. Stepniewski
V. Subramanian
A. Turku
F. Vedovi
Dr. G. Wissel
Dr. Y. Zhang
Dr. N. Brown
Prof. A.C. Camproux
Prof. C. Etchebest
Prof. B. Offmann
Prof. A. Poso
Dr. H. Xhaard
Prof. J. Yli-Kauhaluoma

Merci
Thank you
Kiitos

Annexes

Classification
Dependency of the protein target = classification of different local structure
replacements.
Meanwell, N.A.N.N. a (2011). J. Med. Chem. 54: 2529–2591.
Angiotensin II receptor antagonist analogs
cPLAA2α inhibitor analogs
+ + + - - -

Congener series
“One of two or more substances related to each other by origin, structure, or
function.” (IUPAC)
Shin, Y., Chen, W., Habel, J., Duckett, D., Ling, Y.Y., Koenig, M., et al. (2009). Bioorganic Med. Chem. Lett. 19: 3344–3347.
A group change and the
affinity (IC50)

Drug discovery
Drug discovery: the process by which new candidate medications are discovered
- Target identification
- Affinity
- Drug candidate
- Lead selection
- Lead optimization
Kerns, E.; Di, L. Drug-like Properties: Concepts, Structure Design and Methods; Kerns, E., Ed.; Elsevier Inc., 2008.
- Manufacturing
- Side effects monitoring
- Formulation
- Phase 1: human safety
- Phase 2: human
efficiency
- Phase 3: large scale
efficiency

Drug discovery
Drug discovery: the process by which new candidate medications are discovered
Long process ~20 years for a new drug
Only 55 drugs approved by the Food and Drug Administration in 2015
Costly process $51.2 billion invested in 2014 in Biopharmaceutical Research Industry
Mullard, A. 2015 FDA Drug Approvals. Nat. Publ. Gr. 2016, 15, 73–76
(PhRMA. Profile Biopharmaceutical Research Industry. Pharm. Res. Manuf. Am. 2015, 76..

?
Predict the recognition
• Profile a ligand for a target (drug design)
• Prioritize a research approach
• Estimated side effects
• Toxicity
• …
However, the protein-ligand recognition is a complex process which includes
many factors difficult to model.

Structure data, origins
In 1958 John Kendrew and Max Perutz published the first high-
resolution crystalized protein, sharing the Nobel prize in 1962.
It was the first time where protein structure (protein =
fundamental element for the biologic processes) was approached
with an atomic level.
Fersht, A.R. (2008). From the first protein structures to our current knowledge of protein folding: delights and scepticisms. Nat. Rev. Mol. Cell Biol. 9: 650–654.
Kendrew, J.C., Bodo, G., Dintzis, H.M., Parrish, R.G., Wyckoff, H., and Phillips, D.C. (1958). A three-dimensional model of the myoglobin molecule obtained by
x-ray analysis. Nature 181: 662–666.
Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, H., Will, G., and North, A.C. (1960). Structure of haemoglobin: a three-dimensional Fourier synthesis at
5.5-A. resolution, obtained by X-ray analysis. Nature 185: 416–422.

Structure-based (and ligand-based) methods based on 3D structures.
Protein crystallized
X-rays are diffracted by each atoms
presented in the crystal structure
Structure data, today

Importance
Estimates suggest that around 10-15% of
human genome may be druggable (with
small molecule approach) and 600-1500
potential targets
Druggability is important to:
- prioritize potential targets
- avert targets that are unlikely to bind
small molecules with high affinity
(optimize experimental screenings)
- Rational the target space
Human genome ~30,000
Druggable
Genome
~3,000
Diseases
modifying
Genes
~3,000
Drug targets ~ 600-1,500

Dataset
Non redundant dataset: NRDLD (Non Redundant set of Druggable and Less
Druggable binding sites)
Adapted from Krasowski, A. et al. (2011). J.
Med. Chem Inf, 51(11), 2829–42
Experimental:
- HTS
- NMR screening
Database
screening
71 druggable binding sites
44 less druggable binding sites
Widely Characterized Apo protein set included in “Druggable
Cavity Directory” (139 proteins)

2. Compute Linear Discriminant Analysis
(LDA) models with n descriptors
1. Define training and test set
by pocket estimation methods
Learning phase

3. select best models with minimal number
of descriptors
Objective:
- parsimonious model
- Considering several pocket sets
Matthew's Coefficient Correlation
Consensus PockDrug
Learning phase

PockDrug quality
Consensus PockDrug
prox-
test
DoGSite-
test
fpocket-
test
Acc 95 % 87 % 87 %
MCC 0.89 0.73 0.71
Robust on estimations
Good performances for different
pocket sets
fpocket-
score
DoGSite-
Scorer
Acc 76 % 76 %
MCC 0.51 0.54
Better that other models
fpocket-
apo
DoGSite-
apo
Acc 91 % 94 %
MCC 0.45 0.53
Apo pockets

External validation
PockDrug model was validated on different pocket test sets and was compared of other
druggability models available in the literature
Robust performances on
different pocket test set.

Properties
Combination of 4 pocket properties
Hydrophobicity
Geometry
Aromaticity
Atom type
Hydrophobicity ++++
Geometric +++
Atom type (H-bond donor-acceptor) ++
Aromaticity +

To the ligand profiling
Which profile of ligand?
Druggable pocket

Computational approaches are important to:
• To identify LSR
• Stock in databases
26-05-201688
In silico approaches
3 types of method to identify bioisosteres :
• Rational approaches, based on similar compounds (BIOSTER or SwissBioisostere)
• Literature searching (limited on precise case)
• Chemoinformatics based on a investigation of the chemical space or protein
complexes
Devereux, M., and Popelier, P.L. a (2010). In silico techniques for the identification of bioisosteric replacements for drug design. Curr. Top. Med. Chem. 10:
657–668.
Ujváry, I. (1997). BIOSTER-a database of structurally analogous compounds. Pestic. Sci. 51: 92–95.
Wirth, M., Zoete, V., Michielin, O., and Sauer, W.H.B. (2013). SwissBioisostere: A database of molecular replacements for ligand design. Nucleic Acids Res.
41: 1137–1143.

Chemoinformatic approaches is based on investigation of chemical space or
X-ray complexes.
26-05-201689
Identification of LSR
Fingerprint interaction Similarity of binding sites using pharmacophores
Desaphy, J., and Rognan, D. (2014). Sc-PDB-Frag: A database of protein-ligand interaction patterns for bioisosteric replacements. J. Chem. Inf. Model. 54:
1908–1918.
Wood, D.J., Vlieg, J. De, Wagener, M., and Ritschel, T. (2012). Pharmacophore fingerprint-based approach to binding site subpocket similarity and its
application to bioisostere replacement. J. Chem. Inf. Model. 52: 2031–2043.
Interaction
Pharmacophores

Molecular recognition
Many parameters influence the molecular recognition, such as molecular
interaction, flexibility, solvent exposition.
A same binding site can host different ligand,
modulating molecular interaction or binding site
flexibility.

Mechanisms of replacements
Local substructures replace the metal
The nitrogen replaces the metal and interactions with the protein are conserved.
Phosphate Local replacement Nitrogen replace
the Mg2+
PDB code: 3ULI – 4EOM

Hydrogen bonds Salt bridges π-πHalogen bonds
cation-π anion-π

Publication IV

Polar contacts
• A sphere of 3.0 Å radius from
point charges carry the
majority of the information
about polar contacts.
• 80% of primary amine are
ionized.
Open some perspectives in interaction
modeling where a distance of 4 Å is
usually considered. Most frequent case
the amine is ionized.
Files, S., Sarthi, P., Gupta, S., Nayek, A., Banerjee, S., Seth, P., et al. (2015). SBION2 : Analyses of Salt Bridges from Multiple Structure Files, Version 2.
11: 2–5.

Neighborhood analysis
Oxygen in carboxylate Oxygen in hydroxyl Oxygen in water molecules
Position of each atom type is discussed separately and considering the environment.

2D projection, using a correspondence from the contingency table of all primary amine
considered in the dataset. Two type of interactions are considered include a Oxygen
carboxylate or not (‘)
Consider the fourth neighbors atoms in the both
environment
Distance between the
neighbor position and the
type of atom characterize
the dependence
1 +++ carboxylate
3 +++ hydroxyl

Ionizable interactions
In the type of the very strong molecular interaction coupling electrostatic
interaction carried by the charges and a hydrogen-bond.
Focused on a type of strong protein-ligand interaction, well characterized in the
intra-protein interaction but poorly characterized in the protein-ligand interaction
• Protein stability
• Thermo-resistance
• Molecular mechanisms
(nucleation, enzyme process)

Publication V

LDA
Linear discriminant analysis (LDA) is a generalization of
Fisher's linear discriminant, a method used in statistics,
pattern recognition and machine learning to find a linear
combination of features that characterizes or separates
two or more classes of objects or events. The resulting
combination may be used as a linear classifier, or, more
commonly, for dimensionality reduction before later
classification.

Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data

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