Resistify 🍃

Resistify is a program which rapidly identifies and classifies plant resistance genes from protein sequences. It is designed to be lightweight and easy to use.

Installation

Conda

Resistify is available via the Bioconda channel:

conda create -n resistify resistify
conda activate resistify

Note

If you want to use the GPU-accelerated pipelines, conda may fail to install a GPU-ready version of pytorch. If this occurs, try adding pytorch-gpu to the conda environment.

Singularity

Containers are also available through the biocontainers repository. To use these with singularity, simply run:

singularity exec docker://quay.io/biocontainers/resistify:<tag-goes-here> resistify

Usage

Identifying NLRs

To predict NLRs within a set of protein sequences, simply run:

resistify nlr $PROTEIN_FASTA -o $RESULTS_DIR

and Resistify will identify and classify NLRs, and return some files:

• results.tsv - A table containing the primary results of Resistify.
• motifs.tsv - A table of all the NLR motifs identified for each sequence.
• domains.tsv - A table of all the domains identified for each sequence.
• annotations.tsv - A table of the raw annotations for each sequence.
• nbarc.fa - A fasta file of all the NB-ARC domains identified.
• nlr.fa - A fasta file of all NLRs identified.

By default, Resistify will only return sequences with identifiable NB-ARC domains. If you wish to identify highly fragmented NLRs, you can use the --retain option which will predict and report NLR-associated motifs for all sequences.

If you want to increase the sensitivity of coiled-coil domain annotation, you can use the option --coconat. This will use CoCoNat to predict coiled-coil domains. In practice, I wouldn't expect this mode to pick up on a significant number of missed CC domains, but it can pick up on cryptic CCs that do not have an identifiable EDVID motif.

How does it work?

Resistify carries out an initial search for common NLR domains to quickly filter and annotate the input sequences. Then, Resistify executes a re-implementation of NLRexpress to conduct a fast and accurate search for NLR-associated motifs. If --coconat is used, this will also be executed to scavenge for potentially missed coiled-coil domains. Together, this evidence is used to classify NLRs according to their domain architecture.

Identifying PRRs

Important

This pipeline is currently in development - due to other commitments I can't currently benchmark this properly and make no guarantees to its accuracy yet! Feedback is appreciated.

To predict PRRs within a set of protein sequences, simply run:

resistify prr $PROTEIN_FASTA -o $RESULTS_DIR

and Resistify will identify and classify PRRs, and return some files:

• results.tsv - A table containing the primary results of Resistify.
• motifs.tsv - A table of all the LRR motifs identified for each sequence.
• domains.tsv - A table of all the domains identified for each sequence.
• annotations.tsv - A table of the raw annotations for each sequence.
• prr.fa - A fasta file of all PRRs identified.

Warning

This pipeline is GPU-accelerated and will be slow on CPU only.

How does it work?

First, Resistify searches for domains associated with a recently described classification system for RLP/RLKs. Then, a re-implementation of TMbed is used to predict transmembrane domains - sequences with a single α-helix transmembrane domain and an extracellular domain of at least 50 amino acids are considered as RLPs. Finally, NLRexpress is used to identify LRR domains.

Sequences are classified as being either RLPs or RLKs depending on the presence of an internal kinase domain, and are classified according to their extracellular domain.

Downloading model data

Models are downloaded automatically at runtime. If you're going to run Resistify frequently, for example as part of a pipeline, you might want to pre-download models first. This can be done via:

resistify download

This downloads models to the default Hugging Face / PyTorch Hub cache ($HOME/.cache, or $XDG_CACHE_HOME if set). You can then set $HF_HUB_OFFLINE=1 which will speed up Resistify slightly and prevents too many requests being sent:

HF_HUB_OFFLINE=1 resistify nlr $PROTEIN_FASTA -o $RESULTS_DIR

Results

results.tsv (nlr)

Sequence	Length	LRR_Length	Motifs	Domains	Classification	NBARC_motifs	MADA	CJID
ZAR1	852	306	CNNNNNNNNNLLLLLLLLLLLLL	mCNL	CNL	9	False	False

The main column of interest is "Classification", where we can see that it has been identified as a canonical CNL. The "Motifs" column indicates the series of NLR-associated motifs identified across the sequence - this can be useful if an NLR has an undetermined or unexpected classification. The columns "MADA", and "CJID" correspond to common NLR sequence signatures.

results.tsv (prr)

Sequence	Length	Extracellular_Length	LRR_Length	Type	Classification	Signal_peptide
fls2	1173	806	675	RLK	LRR	True

For PRRs, sequences can be of the type RLP or RLK - both are single pass transmembrane proteins, and RLKs have an internal kinase domain. Classification refers to the domains identified in the external region. If multiple domains are identified, they will each be reported as a semi-colon separated list. If a signal peptide is identified in the sequence, this is reported accordingly.

motifs.tsv

Sequence	Motif	Position	Probability	Downstream_sequence	Motif_sequence	Upstream_sequence
ZAR1	extEDVID	66	0.85	LVADL	RELVYEAEDILV	DCQLA
ZAR1	VG	160	1.0	YDHTQ	VVGLE	GDKRK
ZAR1	P-loop	189	1.0	IMAFV	GMGGLGKTT	IAQEV
ZAR1	RNBS-A	212	1.0	EIEHR	FERRIWVSVS	QTFTE
ZAR1	Walker-B	260	1.0	QYLLG	KRYLIVMD	DVWDK
ZAR1	RNBS-B	291	1.0	RGQGG	SVIVTTR	SESVA
ZAR1	RNBS-C	318	1.0	HRPEL	LSPDNSWLLF	CNVAF
ZAR1	GLPL	357	1.0	VTKCK	GLPLT	IKAVG
ZAR1	RNBS-D	418	1.0	SHLKS	CILTLSLYP	EDCVI
ZAR1	MHD	487	1.0	IITCK	IHD	MVRDL
ZAR1	LxxLxL	512	0.96	PEGLN	CRHLGI	SGNFD
ZAR1	LxxLxL	532	0.89	KVNHK	LRGVVS	TTKTG
ZAR1	LxxLxL	561	1.0	TDCKY	LRVLDI	SKSIF
ZAR1	LxxLxL	588	1.0	ASLQH	LACLSL	SNTHP
ZAR1	LxxLxL	612	1.0	EDLHN	LQILDA	SYCQN
ZAR1	LxxLxL	636	1.0	VLFKK	LLVLDM	TNCGS
ZAR1	LxxLxL	660	1.0	GSLVK	LEVLLG	FKPAR
ZAR1	LxxLxL	686	1.0	KNLTN	LRKLGL	SLTRG
ZAR1	LxxLxL	713	1.0	INLSK	LMSISI	NCYDS
ZAR1	LxxLxL	741	1.0	TPPHQ	LHELSL	QFYPG
ZAR1	LxxLxL	766	1.0	HKLPM	LRYMSI	CSGNL
ZAR1	LxxLxL	793	1.0	NTHWR	IEGLML	SSLSD
ZAR1	LxxLxL	818	1.0	QSMPY	LRTVTA	NWCPE

Here, the positions, probabilities, and sequence of NLRexpress motif hits are listed. The five amino acids upstream and downstream of the motif site are also provided. In PRR mode, only LRR motifs will be reported.

domains.tsv

Sequence	Domain	Start	End
ZAR1	MADA	1	21
ZAR1	CC	5	128
ZAR1	NBARC	163	337
ZAR1	LRR	512	817

This file contains the coordinates of the domains identified by Resistify.

annotations.tsv

Sequence	Domain	Start	End	Accession	Score	Source
ZAR1	MADA	1	21	CC_motif_1	16.2	hmmer
ZAR1	CC	5	128	PF18052	69.72	hmmer
ZAR1	NBARC	163	337	PF00931	196.02	hmmer
ZAR1	LRR	512	817			resistify
ZAR1	LRR	541	781	PF23598	99.53	hmmer
ZAR1	LRR	676	808	PF25019	31.39	hmmer

This file contains the raw annotations for each sequence, and the method which was used to identify them.

plots.tar.gz

By default, Resistify generates some rudimentary plots for each protein, saved as a plots.tar.gz archive. You can disable these via --no-draw if ya want.

Frequently asked questions

Q: Can Resistify be used to predict resistance genes from genomic data?

A: Unfortunately, Resistify cannot be directly applied to a genome to predict resistance genes, unlike tools such as NLR-Annotator. If gene annotations are unavailable for your genome, my advice would be to use a tool like Helixer or ANNEVO to perform ab initio gene prediction first, then pass these to Resistify. Currently, I find that Helixer tends to identify more NLRs than ANNEVO (in Solanum):

Q: According to the Motif string, some of my genes have NLR motifs in unexpected places - are these significant?

A: False positives do occur for the motif predictions, and unexpected predictions such as a single CC motif in the LRR domain are unlikely to be representative of a true domain annotation. False positives shouldn't interfere with the classification accuracy.

Contributing

Contributions are greatly appreciated! If you experience any issues running Resistify, please get in touch via the Issues page. If you have any suggestions for additional features, get in touch!

Citing

Smith M., Jones J. T., Hein I. (2025) Resistify: A Novel NLR Classifier That Reveals Helitron-Associated NLR Expansion in Solanaceae. Bioinformatics and Biology Insights. 2025;19. doi:10.1177/11779322241308944

You must also cite:

Martin, E. C., Spiridon, L., Goverse, A., & Petrescu, A. J. (2022). NLRexpress—A bundle of machine learning motif predictors—Reveals motif stability underlying plant Nod-like receptors diversity. Frontiers in Plant Science, 13, 975888. https://doi.org/10.3389/fpls.2022.975888

If you use the CoCoNat module, please cite:

Madeo, G., Savojardo, C., Manfredi, M., Martelli, P. L., & Casadio, R. (2023). CoCoNat: a novel method based on deep learning for coiled-coil prediction. Bioinformatics, 39(8), btad495. https://doi.org/10.1093/bioinformatics/btad495

If you use the PRR module, please cite:

Bernhofer, M., & Rost, B. (2022). TMbed: transmembrane proteins predicted through language model embeddings. BMC bioinformatics, 23(1), 326. https://doi.org/10.1186/s12859-022-04873-x