Identification of Hot Spot Residues & Characterizationof Protein-Protein Interfaces with Co-Solvent

Molecular Dynamics

Presented by Ala’a SiamSupervised by Professor Xavier Barril

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Strengths

• Expanding the druggable genome– 130,000 – 650,000 interactome. – Targets previously deemed undruggable.– Makes applied use of the boom of discoveries

in genomics and systems biology.

• More specific targeting– Useful in such complex disorders as cancer

and inflammation.

• Supporting stratified medicine– Allows targeting of mutations to specific to

specific patient groups.

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Weaknesses

• Interface characteristics– 1500 – 3000 A2.

• Poor absorption.– Flat.

• Poor binding.– Hydrophobic.

• Poor solubility.

• Lead generation– Interfaces did not evolve to bind small ligands.– Counter proteins cannot serve as lead because residues are not necessarily contiguous.

• Complex validation– Several biological experiments.

PPI SWOT Analysis - 1

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UCL SCHOOL OF PHARMACY PPI SWOT Analysis - 2

Opportunities

• Hot spots– Few residues contribute to most of the binding energy.– Small surface area.

• Dynamic flexibility– Binding pockets not found in free proteins.– They form either on complex formation or due to

dynamic configuration changes.

• Screening techniques– Rapidly developed for applications in PPI.

• Commercial outlook– Druggabiltiy has been validated by a series of clinical

candidates.

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Threats

• Hot spots– Complex to identify.

• Dynamic flexibility– Difficult & costly to predict.– Limits the use of several established approaches.

• Screening techniques– Most of the most commonly used screening

collections are not suitable for PPI studies.

• Commercial outlook– No PPI drugs are on the market yet.

PPI Drug Discovery

Strategies

• Screening– High throughput screening– Fragment screening– Virtual screening

• Design– Anchor based design– Secondary structure mimetics

• Synthesis– Diversity oriented synthesis– Biology oriented synthesis– Multicomponent reactions

Hot Spot Residues

• Definition– A residue that, when mutated to alanine, give

rise to a distinct drop in the binding energy.– “Distinct” is ambiguous, but most recent

publications use a value of 2 kcal/mol.

• Experimental Determination– Site-directed alanine scanning mutagenesis.

• Computational Challenges– No general patterns of hydrophobicity, shape,

or charge.– Surface screening almost futile.– Virtual learning sets difficult to implement.

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Molecular Dynamics

Molecular Dynamics

• Reliable ΔGbind prediction– Implicit solvation– Limited conformational sampling.– Linear response theory– Empirical relationships– Relative binding energy

• Benefits– Bypasses crude approximations– Retrieves binding mode, affinity, & kinetics correctly– Molecular force fields model protein dynamics accurately– No training set or surface screening

• Shortcomings– Computationally expensive– User training, simulation & analysis time– Timescale– Molecular mechanics still limit ligand modeling– Condition dependent

MDmix

• Theory– Small organic probes cluster at protein’s binding

site.– Clustering concentration measures druggability.

• Benefits– Qualitative & quantitative– Verified for small molecule binding– Short timescale– Probe selection & concentration– User training, simulation, and analysis time

optimised by pyMDmix– Condition parameters optimised

• Shortcomings– Not verified for PPI

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• Assess the ability of Mdmix to quantify the contribution of individual residues to the binding free energy of protein-protein complexes.

• Compare the performance of this method with existing methods.

• Extract conclusions, offer recommendations, and suggest next steps.

My Project: Goals

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My Project: Work

PlanSimulation Phase

• Database Assembly.– 10 complexes.– Structures deposited in the PDB.– Examples of interfaces with small-molecule

inhibitors.

• Protein Preparation.– Adding missing atoms.– Assigning protonation states.– Reviewing force-field parameters.

• Simulation Running.– pyMDmix.– Conditions will be those described in the

literature as optimal.

Analysis Phase

• Energy Distribution.– Direct: Assigning ΔGbind to simulated protein.– Reciprocal: Assigning ΔGbind to partner protein.

• Experimental Comparison.– Comparing per-residue ΔGbind with experimental

ΔΔGbind derived from scanning experiments.– Quantitative: Magnitude correlation.– Qualitative: Hot spot prediction

• Performance Comparison with Other Techniques.– Empirical.– Computational.

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• Two databases of empirical results of alanine scanning mutagenesis applied to PPI.– BID: Binding Interface Database.– ASEdb: Alanine Scanning Energetics database.

Database Assembly

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Database Assembly

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Condition Selection

• Protein rigidity– Unbiased simulations better reproduce experimental binding. – Rigidity favors binding.– Logarithmic relationship between flexibility and ΔGbind.– Rigidity alters the ligand exchange pathway.– Rigidity facilitates convergence of ΔGbind.

– Rigidity facilitates transferability of ΔGbind.

• Mobility– Unrestrained mobility hampers convergence, transferability, and

interpretability of results.

• Optimization– Single structure is a very crude approximation.– Full protein flexibility leads to methodological & practical challenges.

• Soft restraints– Soft restraints allows local conformational sampling with a minimal

impact on ΔGbind.– Enhancing flexibility on certain regions.

Solvent Modeling

• Role of water– Intermediary in conformations.– Environmental factor that alter interaction preferences.– Important for identification of polar hot spots.

• Modeling– Inhomogeneous fluid solvation theory.– Hydration sites & water displaceability.– Explicit solvation

Probes

• Characteristics– Small & diffuse quickly– Distinct polar and non-polar parts– Hydrogen bond accepting & donation– Mirror groups in amino acids– Water miscible

• Examples– Ethanol– Methanol– Isopropyl alcohol– Acetonitrile

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Simulation Conditions

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Parameters

• Protein representation– PDB structure.– Protonated & termini capped using MOE.– Soft harmonic potential of K = 0.01 kcal/(mol A2).

• Solvent composition– 20% organic/water mixture.

• Environment– AMBER’s tLeap.– Pre-equilibrated solvent mixture boxes.– Truncated octahedral spanning 12 A further from the solute in

each direction.– AMBER force field 99SB.

• Density grids– AMBER’s ptraj program.– Grid spacing = 0.5 A.– Tracking of center-of-mass of solvent molecules.

Steps

• Equilibration– NPT ensemble– Protein & solvent minimized for 3000 steps.– Initial velocities assigned to obtain a distribution at 150

K.– Gradual warming up to 300 K in 800 ps.– Further equilibration for 1 ns at 300 K.

• Production– NVT ensemble.– Storing coordinates every picosecond

• Control– Langevin thermostat with with a collision frequency of

4.– Cutoff for van der Waals interactions = 9 A.– Time step equal to 2 ps.

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Simulation Running

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• Empirical– FoldX: Energy function.– Robetta: Empirical formula.– BeAtMuSiC: Statistical potentials.

• Simulations– MMGBSA: Molecular mechanics.– MMPBSA: Molecular mechanics.– Free Energy Perturbation.

Performance Comparison

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• Sheng, C.; Dong, G.; Miao, Z.; Zhang, W.; Wang, W. State-of-the-Art Strategies for Targeting Protein-Protein Interactions by Small-Molecule Inhibitors. Chem. Soc. Rev. 2015, 44, 8238–8259.

• Kastritis, P. L.; Bonvin, A. M. J. J. On the Binding Affinity of Macromolecular Interactions: Daring to Ask Why Proteins Interact. J. R. Soc. Interface 2013, 10, 20120835.

• DeLano, W. L. Unraveling Hot Spots in Binding Interfaces: Progress and Challenges. Curr. Opin. Struct. Biol. 2002, 12, 14–20.• Cukuroglu, E.; Engin, H. B.; Gursoy, A.; Keskin, O. Hot Spots in Protein-Protein Interfaces: Towards Drug Discovery. Prog. Biophys. Mol. Biol. 2014, 116, 165–

173.• Seco, J.; Luque, F. J.; Barril, X. Binding Site Detection and Druggability Index from First Principles. J. Med. Chem. 2009, 52, 2363–2371.• Ghanakota, P.; Carlson, H. A. Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics. J. Med. Chem. 2016, acs.jmedchem.6b00399.• Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28,

235–242.• Alvarez-Garcia, D.; Barril, X. Relationship between Protein Flexibility and Binding: Lessons for Structure-Based Drug Design. J. Chem. Theory Comput. 2014,

10, 2608–2614.• Alvarez-Garcia, D.; Barril, X. Molecular Simulations with Solvent Competition Quantify Water Displaceability and Provide Accurate Interaction Maps of

Protein Binding Sites. J. Med. Chem. 2014, 57, 8530–8539.• Guerois, R.; Nielsen, J. E.; Serrano, L. Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More than 1000 Mutations. J. Mol.

Biol. 2002, 320, 369–387.• Kortemme, T.; Kim, D. E.; Baker, D. Computational Alanine Scanning of Protein-Protein Interfaces. Sci. STKE 2004, 2004, pl2.• Dehouck, Y.; Kwasigroch, J. M.; Rooman, M.; Gilis, D. BeAtMuSiC: Prediction of Changes in Protein-Protein Binding Affinity on Mutations. Nucleic Acids Res.

2013, 41, 333–339.• Beard, H.; Cholleti, A.; Pearlman, D.; Sherman, W.; Loving, K. A. Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid

Mutations in Protein-Protein Complexes. PLoS One 2013, 8, 1–11.• Ramos, R. M.; Moreira, I. S. Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes. J. Chem.

Theory Comput. 2013, 9, 4243–4256.• Boukharta, L.; Gutiérrez-de-Terán, H.; Aqvist, J. Computational Prediction of Alanine Scanning and Ligand Binding Energetics in G-Protein Coupled Receptors.

PLoS Comput. Biol. 2014, 10, e1003585.

Resources & References

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Any Questions?

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