[]✉ For<EMAIL_ADDRESS>(JMHO) []FundingEI thanks the Helmholtz
European Partnering program (“Innovative high-performance computing approaches
for molecular neuromedicine”) for funding. UR gratefully acknowledges funding
from the Swiss National Science Foundation via the NCCR MUST and the
individual grants 200020-185092 and 200020-219440, as well as computing time
from the Swiss National Computing Centre CSCS. JMHO gratefully acknowledges
the financial support from VILLUM FONDEN (Grant No. VIL29478). []Competing
interestsThe authors declare no competing interests. Carrasco-Busturia

# Multiscale Biomolecular Simulations in the Exascale Era

David Carrasco-Busturia  Emiliano Ippoliti  Simone Meloni  Ursula
Rothlisberger  Jógvan Magnus Haugaard Olsen  0000-0002-9998-0058

###### Abstract

The complexity of biological systems and processes, spanning molecular to
macroscopic scales, necessitates the use of multiscale simulations to get a
comprehensive understanding. Quantum mechanics/molecular mechanics (QM/MM)
molecular dynamics (MD) simulations are crucial for capturing processes beyond
the reach of classical MD simulations. The advent of exascale computing offers
unprecedented opportunities for scientific exploration, not least within life
sciences, where simulations are essential to unravel intricate molecular
mechanisms underlying biological processes. However, leveraging the immense
computational power of exascale computing requires innovative algorithms and
software designs. In this context, we discuss the current status and future
prospects of multiscale biomolecular simulations on exascale supercomputers
with a focus on QM/MM MD. We highlight our own efforts in developing a
versatile and high-performance multiscale simulation framework with the aim of
efficient utilization of state-of-the-art supercomputers. We showcase its
application in uncovering complex biological mechanisms and its potential for
leveraging exascale computing.

##  1 Introduction

Biological processes extend across wide scales in space and time due to the
hierarchical organization of biological matter [1]. The characteristic
dimensions span from the molecular scale of a few ångströms with ultrafast
electronic processes on the order of atto- and femtoseconds and rapid chemical
reactions that occur within pico- to microseconds, to the macroscopic scale of
cells and organs that are visible to the naked eye and where processes extend
to seconds and even days and years. The complexity in living organisms largely
stems from this hierarchical structure, where a local process may trigger a
cascade of events across multiple spatial and temporal scales. Thus,
multiscale approaches integrating different resolutions and methodologies are
essential for capturing the entire spectrum of biological events [2, 3].

The continuous development of multiscale methods in computational structural
biology is driven by simultaneous advancements in algorithms, software
implementations, and hardware technology that push the boundaries of molecular
simulations in terms of accessible time scales and system sizes, capturing
biological systems from the atomistic to the cellular level. In particular,
quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD)
simulations have become increasingly important in the last decades for
studying processes where a description of electronic degrees of freedom is
paramount and thus beyond the capabilities of classical MD based on standard
analytical force fields. Examples of such events encompass all types of
chemical reactions, including proton-coupled electron transfers [4, 5],
photochemistry [6], and the manifold of chemical transformations observed in
enzymatic reactions [7, 8], especially those involving transition metals [9].
A detailed characterization of these phenomena, including reactants,
transition states, intermediates, and products, as well as the involved
relative free energies, reaction rates, and binding affinities in both
electronic ground and excited states, also has direct implications for drug
design [10]. In QM/MM models of biomolecular systems, a smaller part (the QM
subsystem), such as the active site of an enzyme or a chromophore embedded in
a protein or lipid bilayer, is described at the quantum mechanical level,
while the remainder (the MM subsystem) is modeled using molecular mechanics.
This multiscale strategy balances a detailed and accurate but computationally
costly description of the essential part of a system with a coarser but
computationally expedient approach for the much larger MM part. The dynamic
aspect of QM/MM MD is crucial to accurately capture the behavior and function
of complex proteins [11, 12]. However, QM/MM MD simulations have a
substantially higher computational cost than classical (i.e., MM) MD
simulations, severely limiting the accessible time scales. Typically, for a
density functional theory (DFT)-based QM/MM MD simulation with around 100
atoms in the QM subsystem, accessible time scales are limited to a few hundred
picoseconds [8, 13].

The advent of exascale computing marks a pivotal moment for all simulation-
based scientific fields [14, 15]. This remarkable technological achievement
was accomplished by connecting thousands of computing nodes through high-speed
network interconnects. Each node combines traditional general-purpose central
processing units (CPUs) with powerful graphics processing units (GPUs).
However, exascale supercomputer architectures also introduce new challenges
since programming software applications for these heterogeneous machines
requires a judicious decomposition of the computational work to exploit each
component of the machines optimally [16]. Therefore, to fully exploit the
computational power of present and future heterogeneous HPC architectures, a
high degree of concurrent parallelism is needed, where different parts of the
supercomputer work on different subdomains of the computational model, each
described within a different theoretical method. Together, exascale computing
and the development of novel computational methods and software can enable
longer and more accurate simulations on larger and more complex systems. This
opens up new opportunities for discovery and innovation in the life and health
sciences, potentially revolutionizing areas such as drug design and
bioengineering.

The heterogeneous CPU/GPU technology highlighted above is taken to the next
level with modular supercomputer architectures that integrate a variety of
hardware technologies into interconnected partitions [17, 18]. A prime example
of this is the LUMI supercomputer [19], along with the upcoming exascale
JUPITER supercomputer [20], both procured by the European HPC Joint
Undertaking (EuroHPC JU). Currently, LUMI consists of two primary _number-
crunching_ partitions: a general-purpose CPU partition and a high-performance
GPU-accelerated partition. Additionally, it features an interactive data
analytics partition and an accelerated storage partition. Looking ahead, the
integration of prospective technologies like quantum and neuromorphic
computing into traditional HPC infrastructure is expected [17, 18]. Indeed,
the procurement of a quantum computing partition for LUMI is already underway
[21], and the Jülich Supercomputing Center in Germany is already experimenting
with this kind of integration [22]. Modular supercomputer architectures offer
substantial benefits, particularly in allowing calculations to run on the most
suitable hardware for the specific problem. Moreover, existing software
packages that have yet to be optimized for new hardware remain useful for
solving important scientific problems. Fully exploiting the capabilities of
complex modular supercomputers for exceptionally challenging scientific
problems, which require the full computing power of the machine, necessitates
a new computational paradigm. Here, we highlight an approach recently
introduced in the field of multiscale biomolecular simulations.

Multiscale methods, especially the QM/MM approach, have proven indispensable
for exploring complex biological phenomena. Interestingly, QM/MM is not only a
robust technique in itself but also offers a route to overcome some of the
existing limitations in the development of software for atomistic simulations
on modern hybrid computer architectures [14]. Beyond QM/MM, multiscale methods
can also be extended to incorporate multiple layers ranging from coarse-
grained (CG) to continuum models of matter where parts of a system, e.g.,
membrane regions that are sufficiently distant from an embedded protein of
interest, are treated by techniques usually employed for meso- and macroscopic
systems [23, 24, 25, 26, 27].

To reach the full potential offered by exascale supercomputers, it is
imperative that multiscale interfaces scale efficiently, fully leveraging the
extensive network of CPUs and GPUs. That is one of the main objectives of
MiMiC (multiscale modeling in computational chemistry), a high-performance and
versatile framework for multiscale simulations [28]. The _program-agnostic_
MiMiC framework is designed to combine virtually any QM and MM (or other)
program without compromising the computational efficiency and scalability of
the simulation. Indeed, MiMiC, although still in its nascent stages, has
demonstrated its ability to efficiently scale QM/MM MD simulations across
thousands of CPU-based computing nodes [29, 13]. Here we will showcase the
MiMiC framework for biomolecular simulations, illustrating its potentiality in
leveraging state-of-the-art supercomputing resources and its capability to
address complex biochemical and biophysical problems.

##  2 Current Status of Multiscale Biomolecular Simulation Software

Multiscale QM/MM capabilities are often implemented by either extending
dedicated QM or MM programs with the functionalities of the other [30, 31, 32,
33, 34, 35, 36, 37] or by creating ad hoc interfaces between stand-alone QM
and MM programs [38, 39, 40, 41]. Additionally, some programs offer flexible
interfaces that facilitate easy coupling with other programs [42, 43, 44].
Beyond these, there are integrative frameworks that do not contain inherent
QM, MM, or similar functionalities, but instead depend entirely on other
programs for these capabilities [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 59].

The interface between QM and MM components can be classified as tightly or
loosely coupled, reflecting the degree of interdependence between the
components. Loose coupling is preferred for its flexibility and ease of
maintenance, enabling straightforward integration of independent programs.
This approach supports the quick incorporation of new functionalities and
advancements within individual programs without necessitating alterations to
other coupled components. Crucially, loose coupling permits independent
optimization of each program for peak performance. However, this flexibility
can come at the cost of computational efficiency due to slower inter-program
communication, particularly when compared to the tight coupling approach that
allows for direct in-memory data sharing.

There are three communication mechanisms for exchanging data between programs,
namely the file-, library-, and network-based approaches. Most commonly
utilized for general interfaces and integrative frameworks is the file-based
approach, which does not require modifications to the source code of the
interfaced programs, making it the simplest to implement. Here, the main
driver program generates input files for the interfaced programs, executes
them, and subsequently reads their output files. However, it is notably the
least efficient communication mechanism, primarily due to slow disk write/read
operations and overhead associated with executing and terminating the
interfaced programs. Both drawbacks are avoided in the library-based approach,
where the interfaced programs are converted into a library that is linked to
the main program, thus enabling efficient in-memory data exchange. The
disadvantages are the rather intrusive modifications needed in the source
codes of the interfaced programs, and the risk of ending up with a tight
coupling focused on a few specific programs. Moreover, a critical aspect of
multiscale implementations is parallel scalability and efficiency. The optimal
parallel algorithms for QM and MM components, or even among different QM
methods, may differ significantly and may not seamlessly integrate within the
more tightly coupled environments.

The network-based approach, on the other hand, offers a solution that
potentially circumvents the limitations inherent in both file- and library-
based methods. It achieves a balance between flexibility and communication
efficiency by enabling data exchange over a network. This facilitates seamless
communication between programs running on different processors (CPUs and
GPUs), across computing nodes, or even among supercomputer partitions. Such an
approach retains the benefits of loose coupling, i.e., ease of integration and
maintenance, while surpassing file-based approaches in performance through the
use of fast network protocols and remote direct memory access (RDMA)
technologies. Crucially, it can be implemented so as not to disrupt the normal
execution of interfaced programs, thus preserving their performance. However,
the network-based approach requires a more sophisticated design and management
strategy to reduce latency and enhance throughput. Despite these challenges,
with proper implementation, the network-based approach substantially improves
the efficiency and scalability of multiscale simulations by facilitating high-
speed communication between diverse computational models.

##  3 Toward Multiscale Biomolecular Simulations on Exascale Architectures

For multiscale simulations to run efficiently on state-of-the-art
supercomputers, first and foremost, it is imperative that the programs
dedicated to a specific methodology, such as QM and MM, are able to take
advantage of the strengths of both CPUs and GPUs. Moreover, these programs
must be capable of using the vast parallel processing capabilities of exascale
architectures. Considerable efforts are underway to push QM- and MM-based
programs towards exascale [60, 61, 62, 63, 64, 65, 66, 67] with support from
EuroHPC JU through its HPC Centres of Excellence [68] and the Exascale
Computing Project (ECP) led by the US Department of Energy [69].

Figure 1: Illustration of the strategy used by the MiMiC framework

For a multiscale simulation framework to fully exploit the capabilities of
these QM and MM programs, it must seamlessly integrate with their optimal
parallelization strategies without introducing inefficiencies. This entails
enabling the concurrent execution of interfaced programs, eliminating the need
for their repeated startup and shutdown, and reducing communication overhead
to a minimum. Crucially, the calculation of subsystem interactions, such as
those between QM and MM particles, needs to be highly parallel and efficient
to prevent it from becoming a computational bottleneck. Implementing effective
and automatic load balancing is also critical to ensure that computational
resources are utilized optimally, thereby maximizing the throughput of
individual simulations.

The versatile and high-performance MiMiC framework for multiscale simulations
was designed with the aim of addressing the criteria outlined above [28]. The
strategy used by the MiMiC framework is illustrated in Figure 1. On one side,
MiMiC connects to a simulation driver that manages the overall simulation
process, including the integration of the equations of motion and the
maintenance of temperature and pressure. On the other side, it interfaces
multiple external programs using a client-server approach combined with a
multiple-program multiple-data (MPMD) model. Each external program is tasked
with calculations belonging to a given subsystem, while MiMiC calculates
subsystem interactions. Importantly, the external programs run concurrently on
separate computational resources using their own optimal parallelization
capabilities. Communication between MiMiC and the external programs is
facilitated by the MiMiC Communication Library (MCL), a dedicated lightweight
library that ensures efficient network-based communication and simplifies the
interfacing with external programs. An illustration of a MiMiC-based
simulation workflow is shown in Figure 2. The scope of the MiMiC framework is
broad, extending beyond QM/MM. This includes QM/QM, which integrates different
levels of QM theory, QM/MM/CG, and even models that incorporate machine-
learning (ML) techniques like ML/MM or QM/ML.

Figure 2: Illustration of MiMiC-based simulation workflow

The MiMiC framework has been used to implement a DFT-based electrostatic
embedding QM/MM method, employing the CPMD program [70] as both the MD driver
and QM engine, and GROMACS [61] as the MM engine [28, 29]. In electrostatic
embedding, the (external) electric field from the point charges in the MM
subsystem is included in the Hamiltonian of the QM subsystem, thus directly
polarizing its electronic density. The calculation of electrostatic QM/MM
interactions is based on a dense grid representation of the electronic
density, which is computationally expensive, especially for large systems,
because the number of integrals over the electron density scales linearly with
the number of atoms in the MM subsystem. The MiMiC framework has implemented
an efficient approach that substantially speeds up the calculation essentially
without compromising the accuracy of the forces, thus reducing the
computational cost by about 80 % for small systems (e.g., small solvated
proteins) and up to 99 % for larger systems (e.g., membrane-embedded proteins)
[28]. Furthermore, MiMiC implements a hybrid shared- and distributed-memory
(OpenMP/MPI) parallelization strategy to ensure that these calculations remain
efficient and do not hinder performance, even under highly parallel conditions
[29]. A powerful example of this is shown in Figure 3, where the extreme
scalability of the CPMD program is harnessed to achieve strong scalability
beyond 80,000 CPU cores with a parallel efficiency of 70 % for a single MiMiC-
based QM/MM MD simulation of the human isocitrate dehydrogenase-1 (IDH1) [13].

Figure 3: Parallel scalability and efficiency of MiMiC-based QM/MM MD
simulations of IDH1. The IDH1 system has a total of 130,828 atoms with 142 QM
atoms [13]. The simulations were run on the CPU partition of JUWELS [71]. The
speedup is given in terms of the time per MD step normalized against a
reference run using seven nodes. Adapted from Raghavan et al. [13], licensed
under CC BY 4.0.

Table 1 shows examples of the computational performance and cost of MiMiC-
based QM/MM MD simulations using CPMD and GROMACS. It is clear that the
simulation throughput depends first and foremost on the chosen exchange-
correlation functional and the size of the QM subsystem. This reflects the
fact that the QM calculation is by far the most computationally demanding part
of a QM/MM MD simulation. Hybrid exchange-correlation functionals are
particularly expensive in a plane-wave basis set such as the one employed by
the CPMD program. Still, due to the excellent parallelism in CPMD, the
simulation throughput can be pushed to 4.8 ps/day for the small QM region (46
atoms) and 0.7 ps/day for the larger QM subsystem (142 atoms). Using instead a
non-hybrid functional pushes the performance to 21 and 5.4 ps/day for the
small and large QM subsystems, respectively.

Table 1: Computational performance and cost of MiMiC-based QM/MM MD simulations. All simulations were run with a 0.5 fs timestep on the CPU partition of JUWELS [71] at the scaling limit (parallel efficiency $\geq 70\%$). The systems are p38$\alpha$ mitogen-activated protein kinase (169,550 atoms) and human isocitrate dehydrogenase-1 (130,828 atoms). We refer to the original work for full computational details [13]. System | p38$\alpha$ | IDH1
---|---|---
QM atoms | 46 | 142
XC functional | BLYP | B3LYP | BLYP | B3LYP
Throughput (ps/day) | 21 | 4.8 | 5.4 | 0.7
Cost (node-hours/ps) | 9 | 1280 | 480 | 60480

##  4 Biological Applications

Figure 4: Illustrations of biological systems that have been studied using the
MiMiC framework. A: ClC-ec1 anion/proton antiporter embedded in a solvated
lipid bilayer with a total of 150,925 atoms (19 QM atoms) [72]. Reprinted with
permission from Ref. [72]. Copyright 2020 American Chemical Society. B: CLCF-
eca fluoride/proton antiporter embedded in a solvated lipid bilayer with a
total of around 174,000 atoms (36 QM atoms) [73]. Reprinted with permission
from Ref. [73]. Copyright 2021 American Chemical Society. C: AMPAR cation
channel embedded in a solvated lipid bilayer where the transmembrane domain
was included in the simulations (22 QM atoms) [74]. Figure by Schackert et al.
[74], licensed under CC BY 4.0.

The MiMiC framework has enabled a number of recent computational studies that
demonstrate its utility and efficiency across a broad spectrum of systems with
biological relevance. In this section, we showcase select examples that
highlight the impactful contributions of MiMiC-based QM/MM MD simulations in
advancing our understanding of complex biological processes.

Among the pioneering uses of MiMiC-based QM/MM MD simulations were studies of
CLC proteins, a large family of anion channels and transporters. [72] studied
the molecular mechanism of fluoride inhibition of the anion/proton exchanger
ClC-ec1 from E. coli (Fig. 4A) [72]. The use of QM/MM for this study was
mandatory as ion translocation involves proton transfer processes. On the
basis of QM/MM MD and well-tempered metadynamics (wtMTD) simulations at the
B3LYP and BLYP levels of theory, [72] were able to report proton affinities of
the fluoride ion and the gating glutamate residue E148, thus providing
valuable insights into transport inhibition. In a second study, the mechanisms
of proton transfer and release by the fluoride/proton antiporter CLCF-eca were
investigated (Fig. 4B) [73]. Employing the same simulation techniques, it
could be shown that a triad is formed between fluoride, glutamate E318, and
the gating glutamate E118, eventually releasing protons and fluoride as
hydrogen fluoride.

In a recent study, the ligand iperoxo (routinely used in neuroimaging)
targeting the human muscarinic acetylcholine receptor 2 was investigated [75].
The work focuses on the calculation of the drug unbinding rate constant
$k_{\mathrm{off}}$, a very difficult parameter to correctly estimate with
force field-based approaches. In fact, while methods based on modern force
fields are nowadays able to predict accurate binding free energies and
affinities, this is often not the case for rate constants such as
$k_{\mathrm{off}}$, which also require a correct estimation of the free energy
of transition states. The study shows how sensitive this estimation is to
values of the partial charges of the ligand and that while results obtained
with a QM/MM MD simulation are in good agreement with experimental findings,
standard force field-based procedures lead to qualitatively wrong results,
most likely due to the lack of explicit electronic polarization and charge
transfer, which are included in QM/MM.

Another important application of QM/MM MD simulations is in scenarios where
parts of a system cannot be adequately described using simplified analytical
force fields. Notable examples are metal ions, in particular transition metals
or divalent alkaline-earth ions. The absence of accurate parameters for the
latter hinders a thorough understanding of many biological processes related
to, e.g., ion channels, transporters, and pumps. Recently, [74] studied the
mechanism of calcium permeation in
$\alpha$-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors
(AMPARs), which are key to rapid synaptic transmission in the central nervous
system (Fig. 4C) [74]. In that study, the calcium-binding sites within the
channel, initially identified through classical MD, were further confirmed
using QM/MM MD. This step was crucial for validating a newly developed
classical force field specifically designed for calcium ions. Interestingly,
they observed charge transfer between the calcium ion and the water molecules
in the first solvation shell, which underlines the importance of a QM/MM
description for such systems.

##  5 Outlook

The exascale era is poised to radically change life sciences. With the ability
to conduct longer and more accurate simulations on larger and more complex
molecular systems than ever before, we will be able to tackle scientific
questions that are currently beyond our reach, deepening our understanding of
biological processes. This opens up new opportunities for discovery and
innovation, potentially revolutionizing fields such as drug design and
bioengineering. However, fully realizing the potential of exascale computing
is contingent upon overcoming substantial challenges in software design,
algorithm optimization, and the efficient utilization of modular and
heterogeneous HPC architectures.

The versatility of the MiMiC framework makes it ideal for pushing multiscale
biomolecular simulations towards the exascale. It is capable of exploiting the
results of the large-scale initiatives by EuroHPC JU and ECP for individual
domains such as QM and MM. Indeed, work is already underway to couple a
diverse set of QM programs to MiMiC, namely, Quantum ESPRESSO, CP2K, and DFT-
FE, all of which are being developed to exploit state-of-the-art HPC
technology [67]. Additionally, it is likely that MD simulations will benefit
greatly from future ML-based force fields [76, 77], which MiMiC is fully able
to integrate into advanced simulation workflows [78]. For instance, hybrid
ML/MM models facilitate long simulations of biological systems using ML-based
force fields, achieving near QM/MM precision at a substantially reduced
computational cost [79], while ML-enhanced free-energy methods significantly
accelerate QM/MM calculations of ligand binding affinities for drug discovery
[80]. These and other novel methods are expected to aid in accurately
predicting key biophysical properties like drug-protein binding free energies
and complete free-energy profiles. We anticipate that the integration of
MiMiC-based multiscale simulations with ML and enhanced sampling techniques
will further catalyze a _quantum leap_ in the fundamental atomistic
understanding of biological processes.

###  5.1 Acknowledgment

This preprint was created using the LaPreprint template
(https://github.com/roaldarbol/lapreprint) by Mikkel Roald-Arbøl .

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