bioexcel webinar series #4: mutation free energy calculations with pmx

bioexcel.eu

Partners Funding

Mutation free energy calculations with pmx

Presenters: Bert de Groot, Vance JaegerHost: Rossen Apostolov

BioExcel Educational Webinar Series #4

10 June, 2016

bioexcel.eu

This webinar is being recorded

bioexcel.eu

BioExcel Overview• Excellence in Biomolecular Software

- Improve the performance, efficiency and scalability of key codes

• Excellence in Usability- Devise efficient workflow environments

with associated data integration

• Excellence in Consultancy and Training - Promote best practices and train end users

bioexcel.eu

Interest Groups

• Integrative Modeling IG• Free Energy Calculations IG• Best practices for performance tuning IG• Hybrid methods for biomolecular systems IG• Biomolecular simulations entry level users IG• Practical applications for industry IG

Support platformshttp://bioexcel.eu/contact

Forums Code Repositories Chat channel Video Channel

http://bioexcel.eu/contact

http://bioexcel.eu/contact

bioexcel.eu

The presenters

Prof. Bert de Groot heads the computational biomolecular dynamics group at the MPIbpc since 2004. He is member of the editorial board of the Biophysical Journal and PLOS computational biology. His research focuses on computational simulations to understand biomolecular function at the atomistic level. He has supervised 14 PhD theses and authored 136 peer reviewed scientific articles (H-index: 49)

Vance earned his PhD in chemical engineering from the University of Washington in 2015. He has since been working as a postdoc in the group of Bert de Groot at the Max Planck Institute for Biophysical Chemistry, where he uses alchemical free energy simulations to study the effects of protein mutations on thermostability and surface entropy. Vance is applying the tools and methods presented here today to better understand the thermodynamics of protein crystallization.

Free energy calculations with pmx

Vytautas GapsysVance JaegerBert de Groot

Max Planck Institute for Biophysical

ChemistryGöttingen, Germany

Free energy calculations with pmx

7

Why free energies?

Why protein mutations?

• proteins are nature’s nanomachines• many diseases are linked to protein mutations• mutations key to protein engineering and design

• affinities are free energies (of binding) -ligand/drug design -protein-protein binding• stabilities are free energies (of folding)

Need for accurate, automated and user friendly framework

Flavors of free energy calculations

Boltzmann‘counting’

Thermodynamic cycle

Thermodynamic Integration

either: averaging at discrete points lambda

or: Slow Growthincrease of lambda at every integration step, one value for each lambda

Alchemical changes

Free energies despite integrating only dH/dl (why?)

Non-equilibrium free energy calculation setup

Alchemical free energy calculations

Mutation free energies: thermostability

Seeliger et al. Biophys. J. (2010)

Gapsys et al. JCC (2015)

https://github.com/dseeliger/pmx/

(also available as virtual machine)

pmx



Amino acids: validation

Prediction of protein thermostability

Mutation free energies: thermostability

Seeliger et al. Biophys. J. (2010)

direct comparison

to ITC :

119 point mutations in

barnase

accuracy assessment;

Gapsys et al. JCC (2015)




Thermodynamic benchmark: mutant thermostability

Gapsys et al. Angewandte Chemie (2016)

Overall performance


Mutation free energies: sources of error


Mutation free energies: sources of error

Similar error due to:• force field• sampling• experimental error

consensusapproachreduces error

General validityNTR1

SNase


pmx in practice

How to Calculate Mutant Folding Free Energies Using pmx

Trp-Cage (pdb: 1L2Y)1. Hybrid Structure and Topology

2. Minimization and Equilibration

3. Alchemical Mutation

4. Analysis of Results

Special Considerations

http://www3.mpibpc.mpg.de/groups/de_groot/cecam2015/peptide_mutation/

Trp-Cage Mini Protein

Trp-Cage (pdb: 1L2Y) Synthetic

20 residues Secondary structure Central TRP residue

How stable would Phe-Cage be?

1. Hybrid Structure and Topology

NMR Structure Hybrid Structure(TRP/PHE)

Automated method


hybrid.top

hybrid.pdb

Manual or scripted method

i. Pre-process the structure Starting from a structure 1L2Y pdb2gmx Amber99sb force field

> pdb2gmx -f 1l2y_model1.pdb -o pdb2gmx.pdb -ff amber99sbmut -ignh -water none


1. Hybrid Structure and Topology ii. Create a hybrid structure interactively

Use prepared structure from the previous step mutate.py One may also script multiple mutations (-script)

> python mutate.py -f pdb2gmx.pdb -o hybrid.pdb -ff amber99sbmut

Select residue to mutate: 1-ASN-A 2-LEU-A 3-TYR-A 4-ILE-A 5-GLN-A 6-TRP-A 7-LEU-A 8-LYS-A 9-ASP-A 10-GLY-A 11-GLY-A 12-PRO-A 13-SER-A 14-SER-A 15-GLY-A 16-ARG-A 17-PRO-A 18-PRO-A 19-PRO-A 20-SER-AEnter residue number: 6

Select new amino acid for 6-TRP: Three- or one-letter code (or four-letter for ff specific residues): phe

Apply another mutation [y/n]? n

ATOM 93 N W2F A 6 -2.074 -0.459 -1.528 1.00 0.00ATOM 94 H W2F A 6 -2.606 0.336 -1.236 1.00 0.00ATOM 95 CA W2F A 6 -0.716 -0.631 -0.993 1.00 0.00ATOM 96 HA W2F A 6 -0.152 -0.900 -1.774 1.00 0.00ATOM 97 CB W2F A 6 -0.221 0.703 -0.417 1.00 0.00ATOM 98 HB1 W2F A 6 -0.859 1.001 0.293 1.00 0.00ATOM 99 HB2 W2F A 6 -0.199 1.381 -1.152 1.00 0.00ATOM 100 CG W2F A 6 1.148 0.652 0.194 1.00 0.00ATOM 101 CD1 W2F A 6 2.319 0.664 -0.482 1.00 0.00ATOM 102 HD1 W2F A 6 2.411 0.737 -1.475 1.00 0.00ATOM 103 CD2 W2F A 6 1.508 0.564 1.606 1.00 0.00ATOM 104 NE1 W2F A 6 3.371 0.560 0.411 1.00 0.00ATOM 105 HE1 W2F A 6 4.334 0.522 0.144 1.00 0.00ATOM 106 CE2 W2F A 6 2.928 0.515 1.710 1.00 0.00ATOM 107 CE3 W2F A 6 0.779 0.524 2.812 1.00 0.00ATOM 108 HE3 W2F A 6 -0.220 0.561 2.786 1.00 0.00ATOM 109 CZ2 W2F A 6 3.599 0.445 2.938 1.00 0.00ATOM 110 HZ2 W2F A 6 4.598 0.423 2.972 1.00 0.00ATOM 111 CZ3 W2F A 6 1.439 0.433 4.053 1.00 0.00ATOM 112 HZ3 W2F A 6 0.903 0.386 4.896 1.00 0.00ATOM 113 CH2 W2F A 6 2.842 0.407 4.120 1.00 0.00ATOM 114 HH2 W2F A 6 3.301 0.362 5.008 1.00 0.00ATOM 115 C W2F A 6 -0.631 -1.766 0.044 1.00 0.00ATOM 116 O W2F A 6 0.295 -2.579 -0.004 1.00 0.00ATOM 117 DCD1 W2F A 6 1.293 0.567 1.641 1.00 0.00ATOM 118 DHD1 W2F A 6 0.412 0.488 2.264 1.00 0.00ATOM 119 DCD2 W2F A 6 2.334 0.666 -0.562 1.00 0.00ATOM 120 DHD2 W2F A 6 2.263 0.668 -1.640 1.00 0.00ATOM 121 DCE1 W2F A 6 2.565 0.605 2.245 1.00 0.00ATOM 122 DHE1 W2F A 6 2.669 0.576 3.322 1.00 0.00ATOM 123 DCE2 W2F A 6 3.605 0.716 0.042 1.00 0.00ATOM 124 DHE2 W2F A 6 4.496 0.782 -0.585 1.00 0.00ATOM 125 DCZ W2F A 6 3.722 0.714 1.447 1.00 0.00ATOM 126 DHZ W2F A 6 4.696 0.771 1.892 1.00 0.00


Resulting hybrid structure

iii. Prepare a topology file Use hybrid.pdb output from mutate.py pdb2gmx Make sure to set the GMXLIB environment variable

GMXLIB=~/Software/pmx/data/mutff45/

> pdb2gmx -f hybrid.pdb -p topol.top -ff amber99sbmut -water none


Resulting topol.top without the B state

; residue 6 W2F rtp W2F q 0.0 93 N 6 W2F N 93 -0.4157 14.01 ; qtot 0.5843 94 H 6 W2F H 94 0.2719 1.008 ; qtot 0.8562 95 CT 6 W2F CA 95 -0.0275 12.01 ; qtot 0.8287 96 H1 6 W2F HA 96 0.1123 1.008 ; qtot 0.941 97 CT 6 W2F CB 97 -0.005 12.01 ; qtot 0.936 98 HC 6 W2F HB1 98 0.0339 1.008 ; qtot 0.9699 99 HC 6 W2F HB2 99 0.0339 1.008 ; qtot 1.004 100 C* 6 W2F CG 100 -0.1415 12.01 ; qtot 0.8623 101 CW 6 W2F CD1 101 -0.1638 12.01 ; qtot 0.6985 102 H4 6 W2F HD1 102 0.2062 1.008 ; qtot 0.9047 103 CB 6 W2F CD2 103 0.1243 12.01 ; qtot 1.029 104 NA 6 W2F NE1 104 -0.3418 14.01 ; qtot 0.6872 105 H 6 W2F HE1 105 0.3412 1.008 ; qtot 1.028 106 CN 6 W2F CE2 106 0.138 12.01 ; qtot 1.166 107 CA 6 W2F CE3 107 -0.2387 12.01 ; qtot 0.9277 108 HA 6 W2F HE3 108 0.17 1.008 ; qtot 1.098 109 CA 6 W2F CZ2 109 -0.2601 12.01 ; qtot 0.8376 110 HA 6 W2F HZ2 110 0.1572 1.008 ; qtot 0.9948 111 CA 6 W2F CZ3 111 -0.1972 12.01 ; qtot 0.7976 112 HA 6 W2F HZ3 112 0.1447 1.008 ; qtot 0.9423 113 CA 6 W2F CH2 113 -0.1134 12.01 ; qtot 0.8289 114 HA 6 W2F HH2 114 0.1417 1.008 ; qtot 0.9706 115 C 6 W2F C 115 0.5973 12.01 ; qtot 1.568 116 O 6 W2F O 116 -0.5679 16 ; qtot 1 117 DUM_CA 6 W2F DCD1 117 0 12.01 ; qtot 1 118 DUM_HA 6 W2F DHD1 118 0 1.008 ; qtot 1 119 DUM_CA 6 W2F DCD2 119 0 12.01 ; qtot 1 120 DUM_HA 6 W2F DHD2 120 0 1.008 ; qtot 1 121 DUM_CA 6 W2F DCE1 121 0 12.01 ; qtot 1 122 DUM_HA 6 W2F DHE1 122 0 1.008 ; qtot 1 123 DUM_CA 6 W2F DCE2 123 0 12.01 ; qtot 1 124 DUM_HA 6 W2F DHE2 124 0 1.008 ; qtot 1 125 DUM_CA 6 W2F DCZ 125 0 12.01 ; qtot 1 126 DUM_HA 6 W2F DHZ 126 0 1.008 ; qtot 1


iv. Making a hybrid topology with a B state Use topol.top from the previous step generate_hybrid_topology.py Adds B-state to the hybrid topology

>python generate_hybrid_topology.py -p topol.top -o hybrid.top -ff amber99sbmut


Resulting hybrid topology with the B state

95 CT 6 W2F CA 95 -0.027500 12.0100 CT -0.002400 12.0100 96 H1 6 W2F HA 96 0.112300 1.0080 H1 0.097800 1.0080 97 CT 6 W2F CB 97 -0.005000 12.0100 CT -0.034300 12.0100 98 HC 6 W2F HB1 98 0.033900 1.0080 HC 0.029500 1.0080 99 HC 6 W2F HB2 99 0.033900 1.0080 HC 0.029500 1.0080 100 C* 6 W2F CG 100 -0.141500 12.0100 CA 0.011800 12.0100 101 CW 6 W2F CD1 101 -0.163800 12.0100 DUM_CW 0.000000 1.0000 102 H4 6 W2F HD1 102 0.206200 1.0080 DUM_H4 0.000000 1.0000 103 CB 6 W2F CD2 103 0.124300 12.0100 DUM_CB 0.000000 1.0000 104 NA 6 W2F NE1 104 -0.341800 14.0100 DUM_NA 0.000000 1.0000 105 H 6 W2F HE1 105 0.341200 1.0080 DUM_H 0.000000 1.0000 106 CN 6 W2F CE2 106 0.138000 12.0100 DUM_CN 0.000000 1.0000 107 CA 6 W2F CE3 107 -0.238700 12.0100 DUM_CA 0.000000 1.0000 108 HA 6 W2F HE3 108 0.170000 1.0080 DUM_HA 0.000000 1.0000 109 CA 6 W2F CZ2 109 -0.260100 12.0100 DUM_CA 0.000000 1.0000 110 HA 6 W2F HZ2 110 0.157200 1.0080 DUM_HA 0.000000 1.0000 111 CA 6 W2F CZ3 111 -0.197200 12.0100 DUM_CA 0.000000 1.0000 112 HA 6 W2F HZ3 112 0.144700 1.0080 DUM_HA 0.000000 1.0000 113 CA 6 W2F CH2 113 -0.113400 12.0100 DUM_CA 0.000000 1.0000 114 HA 6 W2F HH2 114 0.141700 1.0080 DUM_HA 0.000000 1.0000 115 C 6 W2F C 115 0.597300 12.0100 116 O 6 W2F O 116 -0.567900 16.0000 117 DUM_CA 6 W2F DCD1 117 0.000000 1.0000 CA -0.125600 12.0100 118 DUM_HA 6 W2F DHD1 118 0.000000 1.0000 HA 0.133000 1.0080 119 DUM_CA 6 W2F DCD2 119 0.000000 1.0000 CA -0.125600 12.0100 120 DUM_HA 6 W2F DHD2 120 0.000000 1.0000 HA 0.133000 1.0080 121 DUM_CA 6 W2F DCE1 121 0.000000 1.0000 CA -0.170400 12.0100 122 DUM_HA 6 W2F DHE1 122 0.000000 1.0000 HA 0.143000 1.0080 123 DUM_CA 6 W2F DCE2 123 0.000000 1.0000 CA -0.170400 12.0100 124 DUM_HA 6 W2F DHE2 124 0.000000 1.0000 HA 0.143000 1.0080 125 DUM_CA 6 W2F DCZ 125 0.000000 1.0000 CA -0.107200 12.0100 126 DUM_HA 6 W2F DHZ 126 0.000000 1.0000 HA 0.129700 1.0080


2. Minimization and Equilibration i. Edit the box shape and dimensions A cubic box with 1.2 nm of buffer on each side

ii. Solvate the box

iii. Add ions Generate a .tpr with em.mdp

Neutralize the box

iv. Run minimization

6W2F

mini equil production

forward back forwardback

1 2 ...1 2 ...

> editconf -f hybrid.pdb -o edit.pdb -bt cubic -d 1.2

> genbox -p hybrid.top -cp edit.pdb -o box.pdb -cs spc216.gro

> grompp -f e.mdp -c box.pdb -p hybrid.top

> genion -conc 0.150 -neutral -p hybrid.top -o conf.gro

> mdrun


2. Minimization and Equilibration v. Set the lambda parameters in the eq.mdp file (state A=0 and B=1)

vi. Generate two .tpr run files in separate folders Go to /equil/forward

Do the same for /equil/back with eqB.mdp

vii. Run equilibration

viii. Extract frames from the trajectory


free-energy = yesinit-lambda = 0delta-lambda = 0

> cp ../mini/hybrid.top topol.top> cp ../mini/confout.gro conf.gro> grompp -f eqA.mdp

> mdrun

> trjconv -f traj.trr -o conf.gro -pbc whole -s topol.tpr -sep -b 5000 -skip 50

3. Alchemical Mutation Commonly adjusted parameters:

Transition time (dhdl.mdp)

Number of transitions Analysis methods

To run a single transition:

> cp ../../equil/forward/topol.top .> grommp -f dhdl.mdp -c conf1.gro> mdrun

nstcalcenergy = 1nstdhdl = 1nsteps = 25000init-lambda = 0delta-lambda = 4e-5


3. Alchemical MutationWhat one should expect for an outcome (dhdl.xvg):

Simulation Time (ps)

4. Analysis of ResultsCalculate work distributions and their overlap (cgi.png):> python analyze_crooks.py -pa forward/*/dhdl.xvg -pb back/*/dhdl.xvg -T 300 -nbins 25 -jarz

4. Analysis of Results -------------------------------------------------------- ANALYSIS: Crooks-Gaussian Intersection --------------------------------------------------------

Forward : mean = 10.589 std = 6.319 Backward : mean = -30.241 std = 9.552 Running KS-test .... Forward : gaussian quality = 0.98 ---> KS-Test Ok Backward : gaussian quality = 0.92 ---> KS-Test Ok Calculating Intersection... RESULT: dG ( CGI ) = -6.2740 kJ/mol RESULT: error_dG ( CGI ) = 0.9935 kJ/mol

-------------------------------------------------------- ANALYSIS: Bennett Acceptance Ratio --------------------------------------------------------... -------------------------------------------------------- ANALYSIS: Jarzynski estimator --------------------------------------------------------...

Other outputs (results.dat):

M. Goette and H. Grubmuller. Journal of Computational Chemistry 30 (3), 447-456, (2009)

4. Analysis of ResultsIndicators that your simulations are well converged (W_over_t.png)

20ps 50ps

100ps 200ps

20 transitions 50 transitions

100 transitions

4. Analysis of ResultsComplete the thermodynamic cycle:

Unfolded WT

Folded WT

Unfolded mutant

Folded mutant

ΔΔG = ΔG3-ΔG2 = ΔG1–ΔG4

ΔG3

ΔG1

ΔG2

ΔG4

ΔG4 = -6.27 +/- 0.99 kJ/molΔG1 = 8.03 +/- 0.41 kJ/mol

ΔΔG = 14.30 +/- 1.40 kJ/molΔΔGexp = 12.50 +/- 0.6 kJ/mol

Therefore, Phe-Cage is not a viable alternative to Trp-Cage.

(GFG)(GWG)

B. Barua et al. Protein Engineering Design & Selection, 21, 2008, 171-185 (2008).

Special Considerations1. Proline mutations are not possible in pmx.

2. Charged mutations using PME require box neutrality.- Mutate the protein and the GXG in the same box.

3. Terminal modifications are not supported. V. Gapsys et al. J. Comput. Chem. 36:348-354 (2015).

Thanks:

CamiloAponte(Heidel-berg)

Colin Smith Dirk Matthes

Daniel Seeliger(Boehringer)

Julian Brennecke

Sören Wacker(Calgary)

Ulrich Zachariae(Dundee)

Jan Peters

David Köpfer

RodolfoBriones

Water channels: Affinity prediction:

Molecular recognition:

Ion channels:

Collective dynamics:

Peter PohlClaudia SteinemUlf DiederichsenMarina BenattiTim SaldittTom Walz

Joan CerdaSabine FlitschEric BeitzPeter JohnsonMichael Rützler

Thomas BaukrowitzKornelius ZethXentionClaudia SteinemMarkus Zweckstetter

Oliver LangeChristian GriesingerAdam Lange

Axel MunkRobert TampeChristian Griesinger

Hadas LeonovServaas

Michielssens

Vytautas GapsysJochen Hub

(uni Göttingen)

Shreyas Kaptan

Chen Song (Oxford)

Han Sun

Positions available

Wojciech Kopec

Vance Jaeger

bioexcel.eu

Audience Q&A session

Please use the Questions function in GoToWebinar application

bioexcel.eu

www.bioexcel.eu/contact

NEXT WEBINAR

“QM/MM approaches in CPMD”with Emiliano Ippoliti

30 June 201616:00-17:00 CEST

http://www.bioexcel.eu/contact

bioexcel webinar series #4: mutation free energy calculations with pmx

Science