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# From Possibility to Precision in Macromolecular Ensemble Prediction

Stephanie Wankowicz, Massimiliano Bonomi

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Stephanie Wankowicz, Massimiliano Bonomi. From Possibility to Precision in Macromolecular Ensemble Prediction. 2025. pasteur-05290832

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# From Possibility to Precision in Macromolecular Ensemble Prediction

Stephanie A. Wankowicz¹*, Massimiliano Bonomi²

1) Molecular Physiology and Biophysics, Biochemistry, Center for Applied AI in Protein Dynamics, Center for Structural Biology, Vanderbilt University, Nashville, TN

2) Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Computational Structural Biology Unit, Paris, France

*Correspondence: stephanie@wankowiczlab.com

## Abstract

Proteins and other macromolecules do not exist in a single state but as dynamic ensembles of interconverting conformations, which are essential for functions such as catalysis, allosteric regulation, and molecular recognition. While AI-based structure predictors like AlphaFold have revolutionized static structure prediction, they are not yet capable of capturing conformational heterogeneity. Progress towards the next generation of AI models capable of ensemble prediction is currently limited by the lack of accurate, high-resolution ground truth ensembles at the scale required for training and validation. No single experimental technique can fully resolve the atomistic complexity of conformational landscapes, and fundamental challenges remain in defining, representing, comparing, and validating structural ensembles. Here, we outline the infrastructure and methodological advances needed to overcome these barriers. We highlight emerging strategies for integrating heterogeneous experimental data into unified ensemble encoding representations and how to leverage these new methodologies to build benchmarks and establish ensemble-specific validation protocols. Finally, we discuss how ensemble predictions will be an interactive cycle of experimental and computational innovation. Establishing this ecosystem will allow structural biology to move beyond static snapshots toward a dynamic understanding of molecular behavior that captures the full complexity of biological systems.
## Main

Biology is fundamentally a study of motion and change, where systems, from the environment to atoms, are never still. While biology relies on these motions to function, we tend to study biology through static representations, which restricts our understanding of many of its fundamental principles¹,². This limitation is evident in macromolecules whose functions arise from conformational ensembles³, yet are often interpreted as a single, static structure⁴,⁵. Macroscopic equilibrium properties like binding and stability emerge from a Boltzmann-weighted ensemble of conformations, where each microstate contributes to the partition function defining the system's thermodynamic behavior³,⁶. However, our structural interpretations of macromolecules and their macroscopic properties mainly stem from atomistic static models, often derived from cryo-electron microscopy (cryo-EM) and macromolecular crystallography, including X-ray, neutron, fiber, and electron diffraction (X-ray crystallography). While nuclear magnetic resonance (NMR) and molecular dynamics (MD) have provided critical insights into ensembles of conformational states, each method has its limitations. NMR is primarily constrained by system size and sensitivity⁷, whereas MD is limited by force field accuracy and timescale accessible⁸. While static models have offered invaluable insights into structure and function, they represent a limited subset of the underlying conformational ensemble that drives biological activity. This overemphasis on static representations is perpetuated in protein structure prediction, limiting these algorithms' ability to predict protein functions⁹⁻¹¹. Being able to computationally model and ultimately predict macromolecular conformational ensembles will have transformational impacts across biology by directly connecting structural models to macroscopic measurements, revealing disease mechanisms, evolutionary processes, and advancing next-generation therapeutics¹²⁻¹⁴.

Recognizing the limitations in the static representations of macromolecules, the structural biology community has begun shifting its focus towards modeling conformational ensembles, details of which are often encoded in the very experimental data used to derive static structures¹⁵⁻²⁰. This paradigm shift is also reshaping structure prediction, with growing numbers of efforts to predict conformational ensembles²¹⁻²⁴. While incredibly exciting and promising, this shift brings new challenges: unlike static structures, where high-resolution models serve as widely accepted ground truths²⁵, conformational ensembles lack a single experimental technique capable of fully capturing the entire landscape at atomistic resolution, leaving the field without a definitive benchmark or gold standard for validation of predictions. As a result, it remains difficult, if not impossible, for computational methods, including MD and structural ensemble prediction algorithms, to quantitatively validate predicted ensembles at atomic resolution across the full range of conformational states. Without a clear standard, the field faces foundational questions: What exactly are we trying to predict, and how will we know when we have succeeded?

## Protein Ensemble Prediction

The groundbreaking advancement in protein structure prediction achieved by AlphaFold2 highlights the transformative potential of curated biological data, rigorous benchmarks, evaluation metrics, and innovative AI approaches¹¹,²⁶. The Protein Data Bank (PDB), which primarily houses static macromolecular structural models, provided an extensive dataset for training predictive models. Simultaneously, the critical assessment of methods of protein structure prediction (CASP) community developed rigorous benchmarks and evaluation metrics for assessing structural predictions²⁷⁻²⁹. Without both of these contributions, it would have been impossible for the AlphaFold breakthrough of static protein structural prediction to occur. To replicate breakthroughs in conformational ensembles prediction, it is imperative to establish equivalent datasets, benchmarks, and evaluation tools, enabling innovations in encoding, architecture, and training. We see four key issues we need to address to achieve AlphaFold-like conformational ensemble predictions:

1) Structural ensembles are defined inconsistently across disciplines.
2) No single experimental technique alone can fully capture structural ensembles with the accuracy and precision needed to reflect their behavior in vivo.

2) Experimental approaches for determining ensembles face major challenges, including ensemble averaging, data sparsity, and intrinsic measurement errors.

4) Standardized representations, comparison metrics, and uncertainty quantification for structural ensembles are still lacking.

Here, we propose actionable strategies to address these challenges and call upon the experimental and computational structural biology communities to collaboratively drive innovations to improve experimental and predictive modeling, moving beyond static snapshots to fully capture macromolecular dynamics.

Defining Macromolecular Structural Ensembles

In statistical thermodynamics, conformational ensembles provide a framework to connect microscopic structural fluctuations to macroscopic properties. Even within its native, folded state, macromolecules exist as an ensemble of hierarchy-related substates, ranging from localized side-chain rotamer flips to loop fluctuations to large domain rearrangements (Figure 1)³⁰,³¹. The conformational ensemble encapsulates a protein's full range of states under a given set of conditions, with each state described by its Boltzmann factor reflecting its thermodynamic probability at equilibrium³,³²⁻³⁵. Thermodynamically, the ensemble of microstates collectively contributes to the protein's partition function ($Z=\sum e^{-\beta E_i}$; Z: partition function, $e^{-\beta E_i}$: Boltzmann probability density of i-th microstate; $E_i$: energy of i-th microstate), which governs macroscopic observable properties. Further, to relate these ensembles to function, we must also understand how the distribution of states' probabilities changes upon perturbation, such as ligand binding, mutation, or changes in environmental factors such as pH, temperature, or crowding³⁶,³⁷.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Conformational ensembles encompass hierarchical heterogeneity spanning from vibrations to small side chain fluctuations to loop movement to large conformational changes. Conformational ensembles are made up of collections of all of this hierarchical heterogeneity.</figcaption></figure>

Although some experimental methods and prediction algorithms often aim to represent conformational ensembles, they typically capture only a limited number of discrete macrostates, groups of microstates sharing similar structural or energetic properties, such as an active kinase. This oversimplification masks the full complexity of the ensemble, hindering our ability to accurately understand how the continuum of microstates contributes to observed macroscopic behavior³⁸. Since every microstate influences the partition function, it is vital to consider the entire ensemble, despite the challenges in modeling or measurement. Improving encoding to capture a more complete representation of all potential states will be key to advancing conformational ensemble predictions.

One striking example of the complexity of determining conformational ensembles comes from the ribosome$^{39,40}$. The ribosome's macro-structural states, such as in the rotated or elongation factor bound states, are thermodynamically stabilized, with each conformation governed by a balance of enthalpic and entropic contributions$^{41-44}$. Even minor fluctuations in the ribosome's protein and RNA components can shift the equilibrium among these states, influencing overall translation speed, fidelity, and responsiveness to external factors$^{39,40}$. Oversimplifying the ribosome's thermodynamic ensemble by considering only macrostates overlooks key details that can influence evolutionary dynamics, lead to mistranslation, and affect drug design strategies.

## How do we generate ground truth ensembles?

The ribosome example above underscores the potential of integrating experimental methods to unravel how conformational ensembles govern biological function. The link between ribosomal ensembles and their properties and functions was derived from many techniques and extensive datasets, emphasizing that no single
experimental or computational method can fully capture a structural ensemble, compelling us to move beyond the
one technique, one structure, one solution mindset⁴⁵. Structural modeling algorithms must rethink how to integrate
datasets, including the statistical ensemble modeling of multiple cryo-EM or X-ray datasets, alongside integrative
approaches such as combining cryo-EM with NMR, Small-angle X-ray scattering (SAXS), or Förster resonance
energy transfer (FRET) data. Constructing a conformational ensemble requires two key elements: identifying the
conformation of each state at an atomistic level of uncertainty, and quantifying its contribution to the partition
function. To get there, the structural biology community needs to continue pushing on three fronts.

First, we need to expand the algorithms pioneered by the integrative structural biology community to integrate
statistical analyses on multiple pieces of data from the same data types⁴⁶⁻⁴⁸ **(Figure 2A)**. Different experimental
structural biology techniques provide unique information critical to capture a macromolecular conformational
ensemble, but each has different limitations. cryo-EM and X-ray crystallography can reveal high-resolution atomic
structures, but are restricted due to their frozen state and crystalline environment. In contrast, NMR provides
structural information averaged over multiple conformational states that interconvert more rapidly than the
timescale of the NMR measurement. As a result, separating this averaged data into distinct conformational states
and determining their relative populations remains a major challenge⁴⁹. Spectroscopy approaches like FRET or
double electron electron resonance (DEER) help detect rare states, though the sparsity of the data prevents the
ability to resolve detailed macrostates fully⁵⁰. Combining these concepts can include cryo-EM and X-ray, which
could give us atomistic information on multiplicity of states, with local dynamics supported by NMR, and large
macromolecular motions can be provided by spectroscopy techniques, SAXS, or Atomic Force Microscopy
(AFM)⁵¹⁻⁵³. Equally important is the use of a rigorous statistical framework to extract maximal information on
conformational ensembles from any one technique, for example, by mining the wealth of structural models in the
PDB, analyzing multi-temperature or fragment-screening X-ray datasets, or integrating heterogeneous particle
populations in cryo-EM, to reveal rare or otherwise hidden states²⁰,⁵⁴⁻⁵⁷ **(Figure 2B)**. All of this information can be
greatly supported by incorporating computational approaches, such as MD or structure predictions, as discussed
below.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Creating gold standard conformational ensemble datasets will require using statistical methods, using multiple datasets from the same technique, and integrative methods, using multiple different experimental techniques.</figcaption></figure>

Second, we must improve our ability to harness currently underutilized raw experimental data. Most experimental observables reflect time and ensemble averages over millions of conformations; thus, only conformational ensemble models accurately represent the experimental measurements⁵. However, due to limited data and ensemble averaging, most structural modeling algorithms only tend to model the most likely conformation, discarding the rich heterogeneity in the raw experimental data that reflects a broader conformational ensemble⁵⁸,⁵⁹. While new algorithms have pushed the field beyond a singular state, they still likely only capture a fraction of the accessible states¹⁶,⁶⁰,⁶¹. Exciting new progress in determining conformational ensembles directly from cryo-EM particle stacks and using the often discarded pieces of X-ray crystallography diffuse scattering can bring even more information about conformational ensembles in structural biology data into light⁶²⁻⁶⁴. Recent findings suggest that failing to utilize particle stacks from experimental data explicitly leads to inaccuracies in ensemble modeling⁶⁵, emphasizing the need for integrating particle stacks directly. Developing computational frameworks incorporating these "hidden" signals will provide a more comprehensive picture of macromolecular ensembles.

Third, we need reliable methods to assign conformations' statistical weights in agreement with all available experimental and theoretical data. For example, in cryo-EM, a key challenge is accurately fitting atomic models into continuously varying density maps while assigning appropriate Boltzmann probabilities that reflect the underlying energetic landscape¹⁵. MD is often used to bridge these gaps⁶⁵. Still, current biomolecular force fields have notable limitations, and the sampling problem presents an immense computational burden, particularly for
large conformational changes, such as a domain movement⁶⁶⁻⁶⁹. Additionally, there can be differences in compositional heterogeneity, which must be considered when weighting different states from MD or other approaches. Enhanced sampling techniques such as metadynamics or replica exchange could help fill missing conformational states, primarily if experimental data can anchor metastable states¹⁸,⁷⁰. Yet scaling these approaches to obtain a complete structural ensemble across many biological systems remains a formidable challenge, requiring advances in high-throughput computation and potentially using neural network-based force fields⁷¹.

Finally, we must continue advancing hardware and software technologies to enhance our capacity for collecting and analyzing high-quality structural data at scale. The challenge is twofold: efficiently generating the vast datasets needed to populate conformational ensembles using techniques we know can obtain these conformational ensembles, and designing the computational pipelines to integrate and analyze them in real-time. Examples include real-time cryo-EM data analysis and ongoing efforts to automate and streamline high-throughput X-ray data processing⁷²⁻⁷⁵. Innovations such as robotics for crystal harvesting, machine learning-assisted data evaluation, and real-time quality metrics offer promising steps forward. Integrating these noisy datasets with cryo-electron tomography (cryo-ET), spectroscopy, or non-structural data, such as protein-protein interaction networks or genomic data, could improve ensemble determination by uncovering hidden states⁷⁶,⁷⁷.

## Experimental Challenges of Averaging, Sparsity, Errors, and Encoding

While collecting, leveraging, and modeling vast amounts of structural data at high-throughput scales holds great promise for uncovering conformational ensembles, accurately defining these ensembles remains challenging due to the ill-posed nature of the underlying inverse problem. Structural biology data typically represent ensemble-averaged measurements, resulting in an underdetermined scenario where multiple conformational ensemble models can equally fit the observed data⁷⁸(Figure 3). Furthermore, experimental noise inherently increases uncertainty, reducing the precision and reliability of the reconstructed ensembles. There is a growing field of methods based on Bayesian inference to account for sparsity, data averaging, and errors, which opens the possibility of data integration from multiple experimental approaches⁶⁹,⁷⁹⁻⁸³. Overcoming data sparsity and noise problems will likely involve integrating raw experimental data from multiple complementary techniques. Such integrative approaches leverage the unique strengths of different modalities, such as NMR or diffuse scattering, to identify correlated motion in X-ray or cryo-EM data. However, this poses significant challenges due to differences in data representations and experimental noise. Methods like variational autoencoders have demonstrated potential for capturing continuous conformational states from cryo-EM data. Yet, their effectiveness can be limited by noise generated from different datasets, hindering generalizability across datasets¹⁵,⁸⁴. Advancing methods for calibrating and integrating multimodal data while effectively mitigating noise will be essential for accurate and reliable structural ensemble determination. Ultimately, integrative structural biology's full potential hinges on developing sophisticated computational methods that accurately reweight conformational states based on diverse layers of experimental evidence.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Modeling conformational ensemble from a single structural modality is an ill-posed problem. For example, say you have eight different ‘true’ input conformations that comprise the experimental data (input). All of this data gets collapsed into ensemble-averaged data. While the model captures much of the structural ensemble, it overpopulates some conformations (i.e. green) and misses others (i.e. grey). However, it collectively explains much of the underlying experimental data.</figcaption></figure>

Beyond developing algorithms to disentangle the ensemble-averaged data, the ways that we often encode ensemble models usually lead to some downside in disentangling the averaged data we collect. Traditional encoding in structure prediction uses static PDBx/mmCIF models, which, while highly effective in encoding fixed atomic coordinates, fail to represent conformational states adequately⁸⁵. The current mechanisms to encode different conformational states within the format, including alternative locations (altlocs), B-factors, and multimodel encoding, are insufficient for fully capturing ensemble behavior⁵⁹,⁸⁶. Altlocs typically underestimate conformational diversity, B-factors or atomic displacement factors mix genuine structural fluctuations with experimental noise or lattice movements⁵⁹, and multimodel encoding lacks crucial weighting information needed to represent conformational probabilities accurately⁶¹.

The encoding of conformational ensembles falls along the spectrum from the maximum parsimony principle, which aims to explain the underlying data using the fewest parameters¹⁶,⁸⁷, or the maximum entropy principle, which seeks to create the least biased model distribution consistent with the underlying data by maximizing the Shannon entropy. Within experimental data modeling, maximum parsimony models, such as multiconformer models, allow for more direct encoding of occupancy or weights of different conformations, but are less expressive with subtle anharmonic motions and are limited in backbone movement. Maximum entropy methods can be advantageous in fitting very low occupancy states and picking up more subtle anharmonic motion⁶⁹,⁸⁸, but the model complexity necessitates additional methods to extract biologically meaningful insights, such as identifying metastable states and assigning population weights. Other ensemble methods aim to explain the data collectively, but require re-weighting to be translated into maximum entropy models⁶¹,⁸⁹. Regardless of the method used, the experimental data can be overfit if uncertainties are neglected or poorly modeled. Determining the best
method for modeling and encoding ensembles, and how to accurately compare different methodologies, remains a fundamental challenge.

There is a possibility of taking the best from both approaches, though more expressive encoding of conformational ensembles in the PDBx/mmCIF format, including hierarchical information to represent relationships among structural states and separating conformational from compositional heterogeneity to improve interpretability<sup>85</sup>. Future work must include integration and encoding across experimental datasets, such as multiple cryo-EM maps. However, while these encoding approaches facilitate mapping data into a unified embedding space, they may also obscure signals due to uncertainties or low signal-to-noise ratios inherent in some techniques. Regardless, robust methodologies to compare and evaluate different conformational ensemble encoding effectiveness on teasing out the inverse problem and translation to biologically meaningful insights are critical. This will also enable new encoding strategies to be created and easily tested.

## Comparing and Evaluating Structural Ensembles

In addition to encoding, we must establish rigorous frameworks for comparing conformational ensembles. While we have methods to compare coarse-grained ensemble-averaged descriptors of discrete-state heterogeneity, such as in SAXS or FRET, we still lack this ability at an atomistic level. For single static structures, evaluating the agreement between structures often involves root-mean-square deviation (RMSD), which measures the average atomic distance between two structures after alignment. Alternatively, specific structural features (e.g., dihedral angles) can be compared to experimental data, which is often represented by a sharply peaked posterior probability around the expected value. However, accurately representing conformational ensembles requires shifting toward probability distribution functions (PDFs), enabling more physically realistic comparisons to experimental data. MD methods leverage symmetric divergence measures, such as Jensen-Shannon or Jeffrey's divergence, for quantitative PDF comparisons<sup>90</sup>. However, because PDFs are calculated from low-dimensional projections, a single PDF can map onto several structurally distinct, and potentially non-physical, ensembles. Furthermore, metrics based solely on PDFs are thus likely to encounter challenges when integrated into loss or optimization functions, reminiscent of initial difficulties faced with RMSD-based evaluations in CASP, leading to the development of alternative metrics, such as the Local Distance Difference Test (LDDT)<sup>29</sup>. More work is required to devise projection strategies that conserve the defining features of high-dimensional conformational spaces while potentially embedding relevant physical properties in their low-dimensional representations<sup>91</sup>.

Alternatively, structural ensembles can be assessed based on their fit to experimental data, which often leverages log-likelihood methods. Several methodologies have been developed to compute forward models from MD simulations, which allow configurations to be constrained by experimental observables. Back-calculating intensities, maps, or raw particle stacks provides an additional means of evaluating ensemble accuracy<sup>92,93</sup>. However, multiple conformational states may satisfy the same experimental data, raising questions about how to determine the most physically meaningful representation. Considerable work is also needed to develop the best methods to compare conformational ensembles to single or multiple pieces of experimental data.

## Integrating AI and MD to model conformational ensembles

While experimental data can collectively provide ground truth data to determine conformational ensembles, modeling these states cannot be done without computational methods, including MD and AI-based methods. While MD inherently attempts to model conformational ensembles, it is limited by the accuracy of the force fields and the timescales accessible in unbiased MD simulations<sup>8</sup>. A new generation of MD force fields, including machine-learned models, enables atomistic simulations at quantum chemistry accuracy and coarse-grained
potentials that retain atomistic predictive power⁹⁵⁻⁹⁷, allowing longer and more accurate simulations. On the other hand, enhanced-sampling strategies can help overcome sampling limitations, but often require methodological expertise for each system, limiting automation and transferability⁹⁴. Recent machine learning approaches have addressed these issues and improved enhanced sampling strategies, helping to accelerate MD exploration of conformational space⁹⁸.

However, the future is one in which generative AI-driven techniques alone will enable generating structural ensembles with accuracy comparable to atomistic MD and with extended time scales. Boltzmann generators first demonstrate this by using normalizing flows to generate unbiased conformations from the equilibrium state distribution⁹⁹. More recently, generative diffusion models have taken over the static structure prediction space¹⁰⁰,¹⁰¹. Using similar approaches, but often training with static structure, MD simulations, and in some cases, thermodynamic information, models have begun to predict pieces of conformational ensembles²¹,²⁴,¹⁰²⁻¹⁰⁴. Notably, additional models were shown to efficiently determine the structure of intrinsically disordered proteins and regions¹⁰⁵,¹⁰⁶.

These models and methods offer a solid base to build a multimodal, generative AI-driven framework that leverages extensive experimental datasets. We have recently seen the integration of experimental data with AI methods to improve the modeling of static structures, and some proof of principles of detecting conformational ensembles by integrating Boltzmann generator with experimental data¹⁰⁷⁻¹¹². These models demonstrate the potential of integrating experimental data with different generative AI approaches. Because experiments often provide a lot of underexplored data, we expect that incredible advancements will be made in the next few years by combining or developing novel AI methods with experimental data to model low occupancy conformations, predict rare states, and feed these predictions back into iterative experimental validation and refinement pipelines.

## Conclusion

The thermodynamic hypothesis from Christian Anfinsen states that “the totality of interatomic interactions determines the native conformation and hence the amino acid sequence, in a given environment”¹¹³. Conformational ensembles are driven by both the amino acid sequence and the environment, including any perturbations to the macromolecule. Here, we outline key conceptual and methodological foundations for accurately predicting these ensembles. Advancing our ability to predict conformational landscapes will expand our understanding of complex biological mechanisms and enable the development of novel therapeutic strategies. We can design small molecules that stabilize specific conformations by predicting low-population states or engineer antibodies whose binding depends on a precisely defined structural ensemble¹¹⁴,¹¹⁵. Further, successful enzyme design requires accurately predicting conformational ensembles, as catalysis requires enzymes to traverse conformational landscapes.

It is also important to critically evaluate the current limitations of (static) structure prediction methods. Current limitations include achieving sub-angstrom accuracy or the “last Angstrom problem”, and the accurate modeling of non-protein structures, including RNA. Theoretically, both issues may arise from the lack of ensemble representation. In proteins, many atoms undergo vibrations or subtle conformational fluctuations that are functionally relevant and may have evolved, while RNA is known to be inherently flexible¹¹⁶. However, it is also possible that ensemble prediction algorithms will perpetuate or make these problems worse. Further, existing methods still struggle to predict the effects of mutations, post-translational modifications, or other perturbations¹¹⁷,¹¹⁸. In principle, predicting ensembles could bridge this gap by linking perturbations to shifts in state populations, yet proof will require new data and benchmarks. Finally, many ensemble prediction methods are building off the (AI) architecture of static structure prediction models, which may not be optimal to capture
conformational ensembles. Ensemble prediction will likely demand architectures that model distributions, not
points.

Now is the time to build shared infrastructure for collecting, modeling, encoding, and evaluating conformational ensembles. Such a framework would standardize data formats and benchmarks, enabling objective comparisons of emerging algorithms, clarifying whether ensemble methods solve the perturbation problem, and powering an iterative loop of prediction, experimental validation, and refinement. This infrastructure is also necessary for extending conformational ensembles predictions to in-vivo context or obtaining timescale information. As high-throughput biophysical assays and in-vivo structure determination continue to provide new information on how ensembles change across conditions¹¹⁹⁻¹²¹, we need infrastructure in place to incorporate this new data to enable active learning to improve the prediction of conformational ensembles in different environments and with different perturbations. Moreover, a unified ecosystem will pave the way for capturing kinetic properties, extending ensemble modeling from thermodynamic ensembles to temporally resolved protein dynamics. Overall, this shift promises to move structural biology beyond static snapshots toward a dynamic understanding of molecular behavior that captures the full complexity of biological systems.

## Acknowledgements

We thank Jared Sagendorf, Andrej Sali, Arthur Zalevsky, Frank Noe, and James Fraser for helpful feedback on this manuscript. S.A.W. is supported by the American Cancer Society. M. B. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 101086685 – bAles).

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