BSC – 14th, 15th March 2022 SimulationenvironmentforLifeSciences

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www.bsc.es

Simulation environment for Life Sciences

BSC – 14th, 15th March 2022

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Objective

Overview of molecular simulation technologies used in Life Sciences and their specific adaptation to

HPC environment.

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Aknowledgements

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OUTLINE

• Macromolecular dynamics. A key issue to understand molecular recognition

– Why dynamic properties?

– The concept of “ensemble”

• Molecular simulations

– Levels of representation. Atomistic vs. Coarse-grained – Molecular dynamics algorithm(s)

– System preparation (+ Hands on) – Trajectory Analysis (+ Hands on)

• Simulation & HPC

– Algorithmic improvements

– Ensembles and replicated simulations – Simulation Databases

– Data management

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Objectives and outline

14- March

09.00 - 10.30 Welcome & Introduction (JLG) 10.30 - 11.00 Break

11.00 - 11.45 Atomistic MD (JLG)

11.45 - 12.30 Improvements & HPC (JLG) 14.00 - 15.15 Simulation Setup (JLG)

15.15 - 15.30 Software installation (AH) 15.30 - 16.00 Break

16.00 - 18.00 Setup Hands On (AH)

15-March

09.00 - 10.30 Simulation DBs and Data Mgt. (PA) 10.30 - 11.00 Break

11.00 - 11.45 Application Examples (MW) 11.45 - 12.30 CG simulations (PD)

12.30 - 14.00 Break

14.00 - 16.00 Traj. visualization and analysis (AH) 16.00 - 16.30 Break

16.30 - 18.00 Report writing

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Course Materials

• https://inbi-login.bsc.es/www/patc/

• Software to be installed locally:

– Linux O.S, VMD, python, conda – VM available

• Accounts on Minotauro (mt1.bsc.es) to execute simulations

– 64 Bull Blade B505 (62 + 2 login),

• 2 Intel E5649 (6-core/2.53GHz/12MB cache), 24GB RAM, 2 NVIDIA M2090, 1x SSD 250GB

– nct0XXX (XXX = 178 .. 195), pass: 4FSCn5.XXX – Access via ssh (sftp, scp)

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MACROMOLECULAR DYNAMICS A KEY ISSUE FOR MOLECULAR RECOGNITION

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DNA sequence Protein sequence

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ATP (Mg) - ACV

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ATP (Mg) - ACV

Structural rearrangement is necessary

for enzyme (protein) function

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Macromolecules are dynamic entities

• Molecular recognition requires structural

adjustment

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Binding modes

for Rofecoxib and Celecoxib to cyclooxigenase-2

Soliva R. et al. J Med Chem. 2003, 46 (8), pp 1372–1382

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Dynamic properties. Why?

• Docking experiments are very sensitive to receptor structure

Acetylcholinesterase

RMSd docking solutions

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-0,40 0,00 0,40 0,80 1,20

0,15 0,2 0,25 0,3 0,35 0,4 0,45 Comp 1

Comp 2 VDW

+ -

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Component 2

-1 -0.5 0 0.5 1 -1

-0.5 0 0.5 1

-1 -0.5 0 0.5 1

A B

C

E

H F D

G

1dbs 1byi

1phb

1bty 9xia

1xih

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Carlson H.A. & McCammon J.A. (1999) Mol. Pharmacol. 57, 213-218

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The concept of ensemble

• “Ensemble”: set of structures that represents ALL possible microscopic states of the system (or a significant sample of them)

• Thermodynamic properties can be deduced from the average of “ensemble”

properties.

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Experimental ensembles?

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Available structural information is “static”

– X-Ray: Macromolecules must have unique conformations to crystalize. Mobile regions do not have enough electron density to be detected.

• “Experimental” flexibility is given by B-Factors

– NMR: Is “in solution”, however conformation cannot vary too much, otherwise no enough restrains can be derived from experiment. Mobile regions are less defined.

ATOM 2537 CA GLY B 27 54.322 -6.951 4.465 1.00 48.11 C ATOM 2538 C GLY B 27 53.220 -7.408 5.430 1.00 23.01 C ATOM 2539 O GLY B 27 52.901 -6.745 6.433 1.00 17.59 O

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Experimental ensembles

Xray: 1CM8 and other Prot. Kinases NMR: 1A03. Ca²⁺Binding protein

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Theoretical ensembles

• Atomistic Molecular Dynamics is the most used theoretical technique to account for dynamic properties of macromolecules

– Also Monte-Carlo, Normal mode analysis, Discrete dynamics, …

• Analysis of a single system along time is equivalent to the analysis of many copies of the same system (ergodic principle)

• Simple theoretical background.

• Fast to calculate even for big systems

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Theoretical ensembles (MD and others)

Integration step 1 fts (10^-15 seg) → 1 mseg = 1 Eur Billion integration steps Equivalent to follow evolution Neanderthal→ H sapiens with photos every sec

𝑓

_𝑖

= − 𝜕𝐸

_𝑖

𝜕𝑟

_𝑖

𝑎

_𝑖

= 𝑓

_𝑖

ൗ

𝑚

_𝑖

𝑣

_𝑖

= න 𝑎

_𝑖

𝑑𝑡

𝑟

_𝑖

= න 𝑣

_𝑖

𝑑𝑡

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Current limits

• Standard simulations are in ns-ms timescale with 100,000 to 500,000 atoms

• World Records:

– HIV Capsid 64 M atom 1.2 ms – BPTI 1 ms free simulation

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Applications

• Structure optimization

– Refinement of XRay and NMR data.

• Conformational transitions and folding

• Flexibility studies

• Dynamics and function

– Allostery, induced fit

• Generation of theoretical ensembles

– Statistical thermodynamics

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Predicted vs X-Ray Structures

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0,00 1,00 2,00 3,00 4,00 5,00 6,00

0 200 400 600 800 1000

time (ps)

RMSd (A)

Protein

Loop 214-232

Conformational changes

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TK - ATP (Mg) - ACV

Wat

0,0 1,0 2,0 3,0 4,0

0 500 1000 1500 2000

Time (ps)

RMSd (A)

ACV Mg

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0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

0 500 1000 1500 2000

Time (ps)

Dist (A)

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

0 500 1000 1500 2000

Time (ps)

Dist (A)

Some phenomena can only be

understood

^from

dynamics

^!

HBPG Aciclovir

Thymidine kinase catalysis

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Statistics on MD ensembles

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

0 500 1000 1500 2000

Time (ps)

Dist (A)

State A State C

A B B

A N

RT N G = − ln

 _→

Statistics replaces rational thinking!!

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How does the enzyme control ligand access to the heme site?

Long branch (20Å)

B: Ile19, Ala24, Ile25, Val28, Val29, Phe32 E: Phe62, Ala63, Leu66

E-helix B-helix

H-helix G-helix

Short branch (10Å)

0.1 ms MD

AMBER 99 force field

Octahedral box (8500 TIP3P waters)

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CMIP energy isocontour

Closed State Axis of the tunnel

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Phe62

CMIP energy isocontour

Open State Axis of the tunnel

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closed

Phe62

open

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Free energy profile for NO diffusion along the main channel (steered MD simulations)

Heme

Free energy (kcal/mol)

Distance Fe-N(NO) (Å) Closed state

Open state

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Molecular dynamics and HPC

• Present biology requires “all” to go High-Throughput

– Genome/Proteome-wide studies

– Plethora of genomic information available – Towards a “Dynamic” PDB

• Biological scales are already there…

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Molecular dynamics and HPC. The bad news…

• MD algorithms are extremely simple. Several ways to paralelise, but trivial ones do not scale beyond 8-16 threads

• Present supercomputers have several thousands of cores available

– first computer (without GPUs) on the Top500 list holds 7,630,848 cores (Fugaku, Japan), second 10,649,600 cores (Sunway TaihuLight, China).

– with GPUs TOP1 2,414,592 cores (Summit, USA)

• (www.top500.org).

– Applications to get CPU time require to justify a fair use of a large amount of cores.

– No present strategy for MD optimization can scale in practical use to thousands of cores!!

• Rough rule: not less then 100 atoms per core.

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Strategies

Increase granularity

• Coarse-grained simulations

Improve parallelization strategies

• Domain decomposition

• Accelerators (GPU’s )

Replicated simulations

• Reduce process intercommunication.

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Massively parallel execution with biobb and PyCOMPSs

• Pyruvate Kinase

~400.000 atoms MD

• 200 annotated mutations from Uniprot

• ~40,000 cores in BSC Marenostrum