# MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling

Nessim Raouraoua, Claudio Mirabello, Thibaut Véry, Christophe Blanchet, Björn Wallner, Marc Lensink, Guillaume Brysbaert

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Nessim Raouraoua, Claudio Mirabello, Thibaut Véry, Christophe Blanchet, Björn Wallner, et al. MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling. Nature Computational Science, 2024, Nature Computational Science, 4 (11), pp.824-828. 10.1038/s43588-024-00714-4. hal-04981415

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# MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling

Received: 16 September 2024

Accepted: 3 October 2024

Published online: 11 November 2024

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Nessim Raouraoua ⒰<fnref n="1" />, Claudio Mirabello ⒰², Thibaut Véry<fnref n="3" />, Christophe Blanchet⁴, Björn Wallner ⒰⁵, Marc F. Lensink ⒰<fnref n="1" /> & Guillaume Brysbaert ⒰<fnref n="1" />✉

Massive sampling in AlphaFold enables access to increased structural diversity. In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimized and customizable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. MassiveFold is scalable and able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.

The protein structural space has been rendered substantially more accessible with DeepMind's AlphaFold and the AlphaFold Protein Structure Database<fnref n="1" />⁻<fnref n="3" />. AlphaFold was originally trained and parameterized on and for single protein chains only, but it has since been retrained for multimer applications⁴. The recent CASP15-CAPRI round of blind structure prediction has shown widespread use of its inference engine in the modeling of protein assemblies, with notable success⁵,⁶. After demonstrating a proof of concept with the application of massive AlphaFold sampling to protein-peptide interaction⁷, the AFsample tool was successfully applied to the modeling of protein complexes, including difficult-to-model nanobody complexes⁸,⁹, and ranked first in the CASP15-CAPRI assembly modeling category⁵. Very recently, it was shown that the massive sampling approach can also be applied to such specific binding as antigen-antibody interactions¹⁰. For monomeric structures too, increasing the sampling can help in the investigation of conformational variability¹¹. In addition, it has become evident that increasing the number of recycles in the process may also lead to an improvement in the quality of prediction¹², but at the cost of prolonged computing times for every single prediction. As a whole, the application comes with a high cost as it cannot run in parallel and is very greedy in terms of graphics processing unit (GPU)

resources and time, making it impractical to run even for dedicated research teams.

The computing infrastructures that host GPU clusters and provide resources for such high computing demands often carry restrictive job walltimes due to the high demand on these clusters, preventing prolonged AlphaFold calculations. For large assemblies, it may even be that these walltimes prevent the conclusion of a 'standard' AlphaFold-Multimer run of 25 predictions.

In this Brief Communication we present MassiveFold, which combines the framework of AlphaFold<fnref n="1" /> with the enhanced sampling of AFsample⁸ and the added functionality from ColabFold¹². Massive-Fold is a parallelization engine that calls the structure prediction tool, which can be AFmassive, an extended version of AFsample that we developed alongside MassiveFold, or ColabFold, and then performs a post-treatment on the results. Other structure prediction engines can be integrated into MassiveFold in the future, provided they are massive sampling enabled. MassiveFold includes all versions of neural network (NN) models released by AlphaFold so far, contains multiple parameters that lead to an increase in structural diversity (a full list is provided in the Methods), and can be instructed to keep only the results of the most promising predictions. The program can run many

<footnote n="1">¹Université de Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Université de Lille, CNRS, Lille, France. ²Science for Life Laboratory, Department of Physics, Chemistry and Biology, National Bioinformatics Infrastructure Sweden, Linköping University, Linköping, Sweden.</footnote>

<footnote n="3">³Institut du Développement et des Ressources en Informatique Scientifique (IDRIS), CNRS, Université Paris-Saclay, Orsay, France. ⁴IFB-core, Institut Français de Bioinformatique (IFB), CNRS, INSERM, INRAE, CEA, Evry, France. ⁵Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden. ✉e-mail: guillaume.brysbaert@univ-lille.fr</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1 | Computing processes handled automatically by MassiveFold.** The provided inputs are the FASTA sequence(s) and parameter options for AFmassive or ColabFold. MassiveFold then runs the alignments on a CPU, producing multiple sequence alignments (MSAs) and divides the structure predictions for</figcaption></figure>

massive sampling in batches to be run on GPUs. After completion, MassiveFold automatically gathers all predictions, ranks them following the AlphaFold ranking confidence score, the predicted template modeling score (pTM) and interface predicted template modeling score (ipTM), and generates plots.

instances in parallel, down to a single prediction per GPU, therefore making optimal use of the available computing infrastructure and allowing a substantial reduction in time required to obtain prediction results, from several months to hours. MassiveFold is easy to install through a conda environment and is easy to use, running a simple command line with a JavaScript Object Notation (JSON) parameter file.

To enable full access to the diversity parameters, MassiveFold integrates an optimized parallelization that consists of three parts (Fig. 1): (1) alignments computation on a central processing unit (CPU), (2) a structure inference split into many batches on GPUs and (3) a final post-processing step on a CPU that gathers the results, ranks all the predictions, and generates plots (details are provided in the Methods).

The post-processing of MassiveFold assembles all prediction results and produces several plots. These include the well-known predicted local distance difference test (pLDDT) and predicted aligned error (PAE) plots following AlphaFold and/or ColabFold coloring schemes (Supplementary Fig. 2a,b), but also the ColabFold alignment depths plots (Supplementary Fig. 2b), even if ColabFold was not selected as the inference engine. In addition, MassiveFold plots the distribution of confidence scores per AlphaFold NN version (Fig. 2a), per individual NN model (Fig. 2d-f) or all together (Supplementary Fig. 3). Because MassiveFold can be run with different parameter sets, a plot to compare the distribution of confidence scores between these sets can also be generated (Fig. 2b). The final plot shows the evolution of the AlphaFold confidence score over the recycling and the distance between consecutive structures, which is to be compared to the early-stop-tolerance parameter (Fig. 2c).

The score distribution and recycle plots show the prediction behavior, as they highlight diversity in the AlphaFold confidence score as a function of the applied NN model. Figure 2d shows the diversity in the predictions for a default run of MassiveFold for CASP15 target H1140 (ref. 6). Here, 75 structures were generated (five per NN model) with a highest score not exceeding 0.6. Extending the calculation to 1,005 structures (67 per NN model, no other changes in parameters) already produces a few outliers with confidence scores above 0.8 (Fig. 2e). This distribution can even be improved by activating the dropout and excluding templates (Fig. 2f), demonstrating the added value of the massive sampling strategy. The figures also show that in this instance, the v1 NN model was the only model that produced high-confidence structures, and the computing time could have been reduced by only extending the sampling of the five first NN models (all v1).

An additional approach to massive sampling is through the recycling parameters, which play a non-negligible role in diversity generation. Figure 2c shows the recycling behavior of a structure prediction for CASP target H1140, using AFmassive with dropout and without templates and an early-stop tolerance of 0.1, while allowing up to 1,000 recycling steps. The figure shows low confidence scores for the first 160 recycling steps, which then suddenly jump to 0.846 and 0.908. With an early-stop tolerance of 0.5, only four of the ten best predictions show this jump, as opposed to all ten for an early-stop tolerance of 0.1 (Supplementary Table 1). One therefore has to consider extensive recycling as a viable alternate complementary to massive sampling. Splitting the computing with MassiveFold allows easy access to such an exploration.

MassiveFold can use either AFmassive or ColabFold as inference engines. In both cases, however, outliers with high confidence scores will only be generated by using a massive sampling strategy with diversity parameters activated (Fig. 2 and Supplementary Fig. 4).

Recently, DeepMind published AlphaFold³, an all-in-one tool for structure predictions of biomolecular interactions, for which the authors claim it improves protein-protein complex predictions compared to their latest release, AlphaFold2.3, in particular for antibody-antigen predictions. To show the added value of using massive sampling via MassiveFold, we computed predictions with AlphaFold3 for the six CASP15 targets highlighted in Wallner's massive sampling manuscript⁹ and for two additional CASP15 antibody-antigen targets for which massive sampling produced better models that were not recognized as such⁹. Supplementary Table 2 shows that AlphaFold3 only marginally outperforms massive sampling for three of the eight targets, whereas MassiveFold produces good models for seven of them. For the remaining target (H1167) neither approach produces acceptable models. However, the main advantage of AlphaFold3 is that it produces a more reliable score than AlphaFold2 for antibody-antigen targets, which fails to score these predictions efficiently, as demonstrated in refs. 9,14. Depending on the target, either MassiveFold or AlphaFold3 may produce the best models, highlighting the benefit of having AlphaFold3 integrated into MassiveFold, which we intend to do, should the code be released by the authors.

MassiveFold was designed to facilitate access to diversity parameters and to optimally manage the computing. It takes full advantage of a GPU cluster for the inference step, while using a CPU for the multiple sequence alignment and post-processing, which do not require a GPU. It is also optimized for use on a single GPU machine, because massive sampling jobs can be run in low priority, thus allowing higher-priority
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<figure><img src="image_10.png" /><figcaption>**Fig. 2** | Plots generated by MassiveFold using AFmassive for structure prediction for CASP15 multimeric target H1140. a, Ranking confidence density for each of the three NN versions currently available, running 67 predictions per NN model, with default parameters, that is, without diversity parameters activated. b, Ranking confidence distributions for two sets of parameters, running 67 predictions per NN model. c, Recycle plot of a single prediction, with an early-stop tolerance set to 0.1 and at most 1,000 recycling steps performed. Ranking confidence is shown in red and the distance to the previous structure in blue. As this distance fell below the early-stop tolerance (shown in gray), early</figcaption></figure>

stop was triggered at step 164. d–f, Boxplots of the ranking confidence for each NN model (15 for multimers, five for monomers) sorted by the highest ranking confidence: five predictions per NN model, default parameters (d), 67 predictions per NN model, default parameters (e), 67 predictions per NN model, activating dropout in the EvoFormer and structure modules, and not using templates (f). Each box in cyan extends from the first quartile to the third quartile, with a green line at the median, the whiskers reach out to the furthest data point within 1.5 times the interquartile range from the box, and outliers (green crosses) lie beyond the whiskers.

jobs to insert themselves into the computing queue. MassiveFold is ready for a massive exploration of the AlphaFold protein structure prediction landscape.

## Methods

MassiveFold was developed in bash and Python 3. MassiveFold v1.2.5 integrates the optimized parallelization into CPU and GPU jobs, including post-processing for reranking and plot generation (Fig. 1). The user can select either AFmassive v1.1.5 or ColabFold v1.5.5¹² for structure inference, both of which are included in the MassiveFold distribution.

AFmassive was developed in Python 3. It is an extended version of AFsample⁸ based on AlphaFold v2.3.2. It integrates all versions of the AlphaFold NN models currently available, that is, one for monomers

and three for multimers, and includes additional parameters (listed in Supplementary Notes). These parameters can be set in the AFmassive JSON parameter file (ColabFold JSON parameter file for ColabFold).

## Diversity parameters

The diversity parameters included in MassiveFold are the following: all NN models released by AlphaFold so far (including previous versions, that is, five for monomers and 15 for multimers), the activation of the dropout in the EvoFormer module and the structure module, the use of templates, and the number of recycle steps and the early-stop tolerance threshold, with the recycling stopping if the distance between the current and preceding structure falls below this threshold. In addition, MassiveFold accepts an additional JSON file as input, specifying
individual dropout rates (Supplementary Fig. 1 presents a list of rates), thereby providing the user with additional options to increase structural diversity.

### MassiveFold process

MassiveFold v1.2.5 integrates parallelization based on the Simple Linux Utility for Resource Management (SLURM) workload manager. Input given on the command line includes a FASTA file with protein sequence(s), a JSON parameter file, the inference engine to use (AFmassive or Colab-Fold so far), and the desired number of predictions per NN model, divided into batches of automatically calibrated or manually set size. An example of the JSON parameter file is provided. It contains the parameters for the computing infrastructure and individual runs, including, most importantly, the diversity parameters. The autocalibration adapts the batch size following an initial basic run of MassiveFold (for example, with five predictions per NN model) to keep the process duration under the wall-time. The maximum prediction time will be used in comparison with the specified walltime to automatically calculate the number of batches. The minimum number of batches is the number of NN models used.

Once these parameters are set, the pipeline is as follows (Fig. 1): (1) the multiple sequence alignments running on CPU cores; (2) the structure inference processing each batch of calculations on a single GPU core, ensuring that the number of GPU cores used corresponds to the number of batches to run; (3) the post-processing running on CPU cores, to gather and rank the predictions (following AlphaFold metrics), and to generate plots.

In step (1), the alignments are either performed with JackHMMer and HHblits when using AFmassive, or MMseqs2 when using ColabFold. In step (2), the structure inference is either performed by AFmassive or ColabFold. In step (3), if ColabFold is used, outputs are converted to AlphaFold's output format: structure file names are prefixed by its ranking index, ranking_debug.json file is created and pickle file names are reformatted. AFmassive uses this format natively. In both cases, a 'light' pickle option is available, which substantially reduces the size of the pickle files while keeping the main information. Steps (2) and (3) only start once the previous step is completed. It should be noted that it is possible to use pre-computed alignments by putting them in the output folder. They will be detected and not computed again unless a recalculation is forced.

In addition, a gather_runs.py script is provided to let the user collate several runs of MassiveFold. It gathers all the predictions and ranks them. This was used during the CASP16 MassiveFold generation allowing a consolidated ranking over all eight applied run conditions (including ranking_debug.json, pdb and pickle files), for a total of up to 8,040 predictions per CASP16 target.

### Calculation

All inference calculations were performed on V100 or A100 GPUs. The five sets of parameters used for the massive sampling generation of predictions with AFmassive are listed in the Supplementary Notes, as well as the two sets of parameters used for ColabFold.

### Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this Article.

### Data availability

CASP15 target H1140 was used as a case study, and the sequence for this is available at https://predictioncenter.org/casp15/targetlist.cgi and corresponds to PDB structure 9ERT. All sequences in Supplementary Table 2 are also available at https://predictioncenter.org/casp15/targetlist.cgi. Source data are provided with this paper.

### Code availability

The code for MassiveFold is publicly available at https://github.com/GBLille/MassiveFold, and release v1.2.5 is also available in Zenodo

at https://doi.org/10.5281/zenodo.13870060 (ref. 15). The code for AFmassive is publicly available at https://github.com/GBLille/AFmassive and release v1.1.5 is also available in Zenodo at https://doi.org/10.5281/zenodo.13869932 (ref. 16). A conda environment is provided in their respective repository for each installation. The installation of ColabFold was performed with the ColabFold v1.5.5 conda environment accessible at https://anaconda.org/bioconda/colabfold. MassiveFold conda installation includes the installation of AFmassive and ColabFold. MassiveFold is under the CeCILL v2.1 license.

### References

1. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. *Nature* **596**, 583–589 (2021).

2. Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-Round XIV. *Proteins* **89**, 1607–1617 (2021).

3. Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. *Nucleic Acids Res.* **50**, D439–D444 (2022).

4. Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at *bioRxiv* https://doi.org/10.1101/2021.10.04.463034 (2021).

5. Lensink, M. F. et al. Impact of AlphaFold on structure prediction of protein complexes: the CASP15-CAPRI experiment. *Proteins* **91**, 1658–1683 (2023).

6. Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-Round XV. *Proteins* **91**, 1539–1549 (2023).

7. Johansson-Åkhe, I. & Wallner, B. Improving peptide-protein docking with AlphaFold-Multimer using forced sampling. *Front. Bioinform.* **2**, 959160 (2022).

8. Wallner, B. AFsample: improving multimer prediction with AlphaFold using massive sampling. *Bioinform. Oxf. Engl.* **39**, btad573 (2023).

9. Wallner, B. Improved multimer prediction using massive sampling with AlphaFold in CASP15. *Proteins* **91**, 1734–1746 (2023).

10. Yin, R. & Pierce, B. G. Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy. *Protein Sci. Publ. Protein Soc.* **33**, e4865 (2024).

11. Sala, D., Engelberger, F., Mchaourab, H. S. & Meiler, J. Modeling conformational states of proteins with AlphaFold. *Curr. Opin. Struct. Biol.* **81**, 102645 (2023).

12. Mirdita, M. et al. ColabFold: making protein folding accessible to all. *Nat. Methods* **19**, 679–682 (2022).

13. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. *Nature* **630**, 493–500 (2024).

14. Raouraoua, N., Lensink, M. F. & Brysbaert, G. Massive sampling strategy for antibody-antigen targets in CAPRI Round 55 with MassiveFold. Preprint at *TechRxiv* https://doi.org/10.22541/au.172592104.47153431/v2 (2024).

15. Raouraoua, N. et al. GBLille/MassiveFold: v1.2.5. Zenodo https://doi.org/10.5281/zenodo.13870060 (2024).

16. Raouraoua, N. et al. GBLille/AFmassive: v1.1.5. Zenodo https://doi.org/10.5281/zenodo.13869932 (2024).

### Acknowledgements

This work was carried out as part of Work Package 4 of the Mutualised Digital Spaces for FAIR data in Life and Health Science (MUDIS4LS, ANR 21-ESRE-0048) project led by the Institut Français de Bioinformatique (IFB). The IFB is funded by the Programme d'Investissements d'Avenir (PIA), grant Agence Nationale de la Recherche no. ANR-11-INBS-0013. It was initiated at the IDRIS (Institut du Développement et des Ressources en Informatique Scientifique)
Open Hackathon, part of the Open Hackathons program. We acknowledge OpenACC-Standard.org for their support. This work was granted access to the HPC resources of IDRIS (under allocations 2022-AD010713847, 2023-AD010713847R1 and 2024-A0160713847) made by GENCI (Grand Equipement National de Calcul Intensif). C.M. is financially supported by the Knut and Alice Wallenberg (KAW) Foundation as part of the National Bioinformatics Infrastructure Sweden (NBIS) and SciLifeLab's Technology Development Project 'BeyondFold'.

## Author contributions

N.R. conceived the study, designed the algorithms, developed the software and wrote the paper. C.M. provided technical and conceptual guidance, and developed the software. T.V. provided technical guidance. C.B. provided conceptual guidance. B.W. provided conceptual guidance and developed the software. M.F.L. provided conceptual guidance and wrote the paper. G.B. conceived and supervised the study, provided technical and conceptual guidance, developed the software and wrote the paper.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s43588-024-00714-4.

**Correspondence and requests for materials** should be addressed to Guillaume Brysbaert.

**Peer review information** Primary Handling Editor: Fernando Chirigati, in collaboration with the *Nature Computational Science* team.

**Reprints and permissions information** is available at www.nature.com/reprints.

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**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
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## Software and code

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**Data collection**

The code for MassiveFold v1.2.4 is publicly available at https://github.com/GBLille/MassiveFold. The code for AFmassive v1.1.5 (based on AlphaFold v2.3.2) is publicly available at https://github.com/GBLille/AFmassive. A conda environment is provided in their respective repository for each installation. The installation of ColabFold was performed with the ColabFold v1.5.5 conda environment accessible at https://anaconda.org/bioconda/colabfold. MassiveFold conda installation includes the installation of AFmassive and ColabFold. Alignments are performed with HMMER v3.3.2, HH-suite v3.3.0 and Kalign v2.04 for AFmassive and MMseqs2 v15.6f452 for ColabFold. MassiveFold is under the CeCILL v2.1 license.

**Data analysis**

All analyses were performed with Python3. All inference calculations were performed on V100 or A100 GPUs. The five sets of parameters used for the massive sampling generation of predictions with AFmassive are listed in the Supplementary Information, as well as the two sets of parameters used for ColabFold.

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio [guidelines for submitting code & software](#) for further information.
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CASP15 target H1140 was used as a case study, the sequence is available here: https://predictioncenter.org/casp15/targetlist.cgi and corresponds to the PDB structure 9ERT (Markusson, S., Raasakka, A., Opazo, F., Kursula, P., University of Bergen, Norway, data not published - HOLD status on the Protein DataBank, communicated privately). All sequences of Supplementary Table 2 are also available here: https://predictioncenter.org/casp15/targetlist.cgi

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# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Sample size** The massive sampling strategy was shown before as providing better results than other approaches at CASP14. MassiveFold implements an optimized and parallelized massive sampling strategy. Its added value is exemplified with the H1140 CASP15 target case study for which different sets of parameters were run and up to 1005 predictions per set of parameters were inferred. The size of the sample was chosen following previous work by Björn Wallner, who showed the benefits of a massive sampling strategy (https://doi.org/10.1002/prot.26562).

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**Replication** Not applicable. MassiveFold is a computational method. The massive sampling strategy it allows is based on the fact that increasing the size of the sampling and integrating diversity parameters in the process, one has more chances to get higher quality predictions compared to a simple AlphaFold run. Following the parameters we used, the same or very close predictions will be found by any other predictor, but the number of predictions required could be lower or higher to get them.

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# Behavioural & social sciences study design

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Provide details about the data collection procedure, including the instruments or devices used to record the data (e.g. pen and paper, computer, eye tracker, video or audio equipment) whether anyone was present besides the participant(s) and the researcher, and whether the researcher was blind to experimental condition and/or the study hypothesis during data collection.

**Timing**

Indicate the start and stop dates of data collection. If there is a gap between collection periods, state the dates for each sample cohort.

**Data exclusions**

If no data were excluded from the analyses, state so OR if data were excluded, provide the exact number of exclusions and the rationale behind them, indicating whether exclusion criteria were pre-established.

**Non-participation**

State how many participants dropped out/declined participation and the reason(s) given OR provide response rate OR state that no participants dropped out/declined participation.

**Randomization**

If participants were not allocated into experimental groups, state so OR describe how participants were allocated to groups, and if allocation was not random, describe how covariates were controlled.

## Ecological, evolutionary & environmental sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Study description**

Briefly describe the study. For quantitative data include treatment factors and interactions, design structure (e.g. factorial, nested, hierarchical), nature and number of experimental units and replicates.

**Research sample**

Describe the research sample (e.g. a group of tagged Passer domesticus, all Stenocereus thurberi within Organ Pipe Cactus National Monument), and provide a rationale for the sample choice. When relevant, describe the organism taxa, source, sex, age range and any manipulations. State what population the sample is meant to represent when applicable. For studies involving existing datasets, describe the data and its source.

**Sampling strategy**

Note the sampling procedure. Describe the statistical methods that were used to predetermine sample size OR if no sample-size calculation was performed, describe how sample sizes were chosen and provide a rationale for why these sample sizes are sufficient.

**Data collection**

Describe the data collection procedure, including who recorded the data and how.

**Timing and spatial scale**

Indicate the start and stop dates of data collection, noting the frequency and periodicity of sampling and providing a rationale for these choices. If there is a gap between collection periods, state the dates for each sample cohort. Specify the spatial scale from which the data are taken

**Data exclusions**

If no data were excluded from the analyses, state so OR if data were excluded, describe the exclusions and the rationale behind them, indicating whether exclusion criteria were pre-established.

**Reproducibility**

Describe the measures taken to verify the reproducibility of experimental findings. For each experiment, note whether any attempts to repeat the experiment failed OR state that all attempts to repeat the experiment were successful.

**Randomization**

Describe how samples/organisms/participants were allocated into groups. If allocation was not random, describe how covariates were controlled. If this is not relevant to your study, explain why.

**Blinding**

Describe the extent of blinding used during data acquisition and analysis. If blinding was not possible, describe why OR explain why blinding was not relevant to your study.

Did the study involve field work?

Yes

No

## Field work, collection and transport

**Field conditions**

Describe the study conditions for field work, providing relevant parameters (e.g. temperature, rainfall).

**Location**

State the location of the sampling or experiment, providing relevant parameters (e.g. latitude and longitude, elevation, water depth).

**Access & import/export**

Describe the efforts you have made to access habitats and to collect and import/export your samples in a responsible manner and in
**Access & import/export** compliance with local, national and international laws, noting any permits that were obtained (give the name of the issuing authority, the date of issue, and any identifying information).

**Disturbance** Describe any disturbance caused by the study and how it was minimized.

# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

## Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>Antibodies</td></tr><tr><td>☑</td><td>Eukaryotic cell lines</td></tr><tr><td>☑</td><td>Palaeontology and archaeology</td></tr><tr><td>☑</td><td>Animals and other organisms</td></tr><tr><td>☑</td><td>Clinical data</td></tr><tr><td>☑</td><td>Dual use research of concern</td></tr></tbody></table>

## Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>ChIP-seq</td></tr><tr><td>☑</td><td>Flow cytometry</td></tr><tr><td>☑</td><td>MRI-based neuroimaging</td></tr></tbody></table>

## Antibodies

**Antibodies used** Describe all antibodies used in the study; as applicable, provide supplier name, catalog number, clone name, and lot number.

**Validation** Describe the validation of each primary antibody for the species and application, noting any validation statements on the manufacturer's website, relevant citations, antibody profiles in online databases, or data provided in the manuscript.

## Eukaryotic cell lines

Policy information about **cell lines and Sex and Gender in Research**

**Cell line source(s)** State the source of each cell line used and the sex of all primary cell lines and cells derived from human participants or vertebrate models.

**Authentication** Describe the authentication procedures for each cell line used OR declare that none of the cell lines used were authenticated.

**Mycoplasma contamination** Confirm that all cell lines tested negative for mycoplasma contamination OR describe the results of the testing for mycoplasma contamination OR declare that the cell lines were not tested for mycoplasma contamination.

**Commonly misidentified lines** (See **ICLAC** register) Name any commonly misidentified cell lines used in the study and provide a rationale for their use.

## Palaeontology and Archaeology

**Specimen provenance** Provide provenance information for specimens and describe permits that were obtained for the work (including the name of the issuing authority, the date of issue, and any identifying information). Permits should encompass collection and, where applicable, export.

**Specimen deposition** Indicate where the specimens have been deposited to permit free access by other researchers.

**Dating methods** If new dates are provided, describe how they were obtained (e.g. collection, storage, sample pretreatment and measurement), where they were obtained (i.e. lab name), the calibration program and the protocol for quality assurance OR state that no new dates are provided.

☐ Tick this box to confirm that the raw and calibrated dates are available in the paper or in Supplementary Information.

**Ethics oversight** Identify the organization(s) that approved or provided guidance on the study protocol, OR state that no ethical approval or guidance was required and explain why not.

Note that full information on the approval of the study protocol must also be provided in the manuscript.
# Animals and other research organisms

Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research, and Sex and Gender in Research

Laboratory animals

For laboratory animals, report species, strain and age OR state that the study did not involve laboratory animals.

Wild animals

Provide details on animals observed in or captured in the field; report species and age where possible. Describe how animals were caught and transported and what happened to captive animals after the study (if killed, explain why and describe method; if released, say where and when) OR state that the study did not involve wild animals.

Reporting on sex

Indicate if findings apply to only one sex; describe whether sex was considered in study design, methods used for assigning sex. Provide data disaggregated for sex where this information has been collected in the source data as appropriate; provide overall numbers in this Reporting Summary. Please state if this information has not been collected. Report sex-based analyses where performed, justify reasons for lack of sex-based analysis.

Field-collected samples

For laboratory work with field-collected samples, describe all relevant parameters such as housing, maintenance, temperature, photoperiod and end-of-experiment protocol OR state that the study did not involve samples collected from the field.

Ethics oversight

Identify the organization(s) that approved or provided guidance on the study protocol, OR state that no ethical approval or guidance was required and explain why not.

Note that full information on the approval of the study protocol must also be provided in the manuscript.

# Clinical data

Policy information about clinical studies

All manuscripts should comply with the ICMJE guidelines for publication of clinical research and a completed CONSORT checklist must be included with all submissions.

Clinical trial registration

Provide the trial registration number from ClinicalTrials.gov or an equivalent agency.

Study protocol

Note where the full trial protocol can be accessed OR if not available, explain why.

Data collection

Describe the settings and locales of data collection, noting the time periods of recruitment and data collection.

Outcomes

Describe how you pre-defined primary and secondary outcome measures and how you assessed these measures.

# Dual use research of concern

Policy information about dual use research of concern

## Hazards

Could the accidental, deliberate or reckless misuse of agents or technologies generated in the work, or the application of information presented in the manuscript, pose a threat to:

No

Yes

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

Public health

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

National security

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

Crops and/or livestock

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

Ecosystems

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

Any other significant area
## Experiments of concern

Does the work involve any of these experiments of concern:

No

Yes

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

Demonstrate how to render a vaccine ineffective

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

Confer resistance to therapeutically useful antibiotics or antiviral agents

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

Enhance the virulence of a pathogen or render a nonpathogen virulent

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

Increase transmissibility of a pathogen

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

Alter the host range of a pathogen

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

Enable evasion of diagnostic/detection modalities

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

Enable the weaponization of a biological agent or toxin

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

Any other potentially harmful combination of experiments and agents

## ChIP-seq

### Data deposition

<figure><img src="image_40.png" /></figure>

Confirm that both raw and final processed data have been deposited in a public database such as GEO.

<figure><img src="image_41.png" /></figure>

Confirm that you have deposited or provided access to graph files (e.g. BED files) for the called peaks.

Data access links

May remain private before publication.

For "Initial submission" or "Revised version" documents, provide reviewer access links. For your "Final submission" document, provide a link to the deposited data.

Files in database submission

Provide a list of all files available in the database submission.

Genome browser session
(e.g. UCSC)

Provide a link to an anonymized genome browser session for "Initial submission" and "Revised version" documents only, to enable peer review. Write "no longer applicable" for "Final submission" documents.

### Methodology

Replicates

Describe the experimental replicates, specifying number, type and replicate agreement.

Sequencing depth

Describe the sequencing depth for each experiment, providing the total number of reads, uniquely mapped reads, length of reads and whether they were paired- or single-end.

Antibodies

Describe the antibodies used for the ChIP-seq experiments; as applicable, provide supplier name, catalog number, clone name, and lot number.

Peak calling parameters

Specify the command line program and parameters used for read mapping and peak calling, including the ChIP, control and index files used.

Data quality

Describe the methods used to ensure data quality in full detail, including how many peaks are at FDR 5% and above 5-fold enrichment.

Software

Describe the software used to collect and analyze the ChIP-seq data. For custom code that has been deposited into a community repository, provide accession details.

## Flow Cytometry

### Plots

Confirm that:

<figure><img src="image_42.png" /></figure>

The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).

<figure><img src="image_43.png" /></figure>

The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).

<figure><img src="image_44.png" /></figure>

All plots are contour plots with outliers or pseudocolor plots.

<figure><img src="image_45.png" /></figure>

A numerical value for number of cells or percentage (with statistics) is provided.

### Methodology

Sample preparation

Describe the sample preparation, detailing the biological source of the cells and any tissue processing steps used.

Instrument

Identify the instrument used for data collection, specifying make and model number.
Software

Describe the software used to collect and analyze the flow cytometry data. For custom code that has been deposited into a community repository, provide accession details.

Cell population abundance

Describe the abundance of the relevant cell populations within post-sort fractions, providing details on the purity of the samples and how it was determined.

Gating strategy

Describe the gating strategy used for all relevant experiments, specifying the preliminary FSC/SSC gates of the starting cell population, indicating where boundaries between "positive" and "negative" staining cell populations are defined.

☐ Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.

## Magnetic resonance imaging

### Experimental design

Design type

Indicate task or resting state; event-related or block design.

Design specifications

Specify the number of blocks, trials or experimental units per session and/or subject, and specify the length of each trial or block (if trials are blocked) and interval between trials.

Behavioral performance measures

State number and/or type of variables recorded (e.g. correct button press, response time) and what statistics were used to establish that the subjects were performing the task as expected (e.g. mean, range, and/or standard deviation across subjects).

### Acquisition

Imaging type(s)

Specify: functional, structural, diffusion, perfusion.

Field strength

Specify in Tesla

Sequence & imaging parameters

Specify the pulse sequence type (gradient echo, spin echo, etc.), imaging type (EPI, spiral, etc.), field of view, matrix size, slice thickness, orientation and TE/TR/flip angle.

Area of acquisition

State whether a whole brain scan was used OR define the area of acquisition, describing how the region was determined.

Diffusion MRI

☐ Used

☐ Not used

### Preprocessing

Preprocessing software

Provide detail on software version and revision number and on specific parameters (model/functions, brain extraction, segmentation, smoothing kernel size, etc.).

Normalization

If data were normalized/standardized, describe the approach(es): specify linear or non-linear and define image types used for transformation OR indicate that data were not normalized and explain rationale for lack of normalization.

Normalization template

Describe the template used for normalization/transformation, specifying subject space or group standardized space (e.g. original Talairach, MNI305, ICBM152) OR indicate that the data were not normalized.

Noise and artifact removal

Describe your procedure(s) for artifact and structured noise removal, specifying motion parameters, tissue signals and physiological signals (heart rate, respiration).

Volume censoring

Define your software and/or method and criteria for volume censoring, and state the extent of such censoring.

### Statistical modeling & inference

Model type and settings

Specify type (mass univariate, multivariate, RSA, predictive, etc.) and describe essential details of the model at the first and second levels (e.g. fixed, random or mixed effects; drift or auto-correlation).

Effect(s) tested

Define precise effect in terms of the task or stimulus conditions instead of psychological concepts and indicate whether ANOVA or factorial designs were used.

Specify type of analysis:

<figure><img src="image_46.png" /></figure>

Whole brain

<figure><img src="image_47.png" /></figure>

ROI-based

<figure><img src="image_48.png" /></figure>

Both

Statistic type for inference
(See Eklund et al. 2016)

Specify voxel-wise or cluster-wise and report all relevant parameters for cluster-wise methods.

Correction

Describe the type of correction and how it is obtained for multiple comparisons (e.g. FWE, FDR, permutation or Monte Carlo).
Models & analysis

n/a | Involved in the study

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

Functional and/or effective connectivity

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

Graph analysis

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

Multivariate modeling or predictive analysis

Functional and/or effective connectivity

Report the measures of dependence used and the model details (e.g. Pearson correlation, partial correlation, mutual information).

Graph analysis

Report the dependent variable and connectivity measure, specifying weighted graph or binarized graph, subject- or group-level, and the global and/or node summaries used (e.g. clustering coefficient, efficiency, etc.).

Multivariate modeling and predictive analysis

Specify independent variables, features extraction and dimension reduction, model, training and evaluation metrics.