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.
v2.0.0 has introduced a full reimplementation of NLRexpress that uses ESM2 8M embeddings instead of jackhmmer.
It is ~60x faster, uses ~10x less memory, and should be slightly more accurate - full benchmarks are available below.
As part of this, I have (for now) removed the TIR motif predictors. I'll aim to reinclude these, but they were never critical for classification as they are so conserved. If you really need these, let me know and I'll add them back in...
By default, Resistify now creates individual SVG plots of sequences in the results folder.
I decided to replace the matplotlib method because it was a bit clunky, and SVG should be sufficient for most purposes.
I pulled additional NLR-associated HMMs from Pfam-A into the Resistify database (e.g., more LRR HMMs), so you'll see more hits for these in annotations.tsv.
This shouldn't affect classifications, but expect domain boundaries to change slightly.
Result formats have changed slightly, refer to the latest README.md (here!) for up to date outputs.
There might be some bugs! Please raise an issue if you spot a missing feature, or any unexpected problems.
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.
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
To predict NLRs within a set of protein sequences, simply run:
resistify nlr <input.fa> -o $RESULTS_DIR
and Resistify will identify and classify NLRs, and return some files:
results.tsv- A table containing the primary results ofResistify.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.
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.
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 <input.fa> -o $RESULTS_DIR
and Resistify will identify and classify PRRs, and return some files:
results.tsv- A table containing the primary results ofResistify.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.
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.
| 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.
| 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.
| 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.
| 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.
| 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.
By default, Resistify generates some rudimentary plots for each protein.
You can disable these via --no-draw if ya want.
Below is the prediction accuracy of the current ESM2 8M NLRexpress models.
| Motif | Precision | Recall | F1 |
|---|---|---|---|
| extEDVID | 0.94 | 0.96 | 0.95 |
| P-loop | 1.00 | 1.00 | 1.00 |
| GLPL | 1.00 | 1.00 | 1.00 |
| MHD | 0.98 | 0.99 | 0.98 |
| Walker-B | 0.99 | 0.99 | 0.99 |
| RNBS-A | 0.99 | 0.93 | 0.96 |
| RNBS-B | 0.98 | 0.98 | 0.98 |
| RNBS-C | 1.00 | 1.00 | 1.00 |
| RNBS-D | 1.00 | 1.00 | 1.00 |
| VG | 0.96 | 0.94 | 0.95 |
| LxxLxL | 0.93 | 0.91 | 0.92 |
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.
The following are some quick benchmarks of the various resistify pipelines against the DM potato genome annotation, which contains 44,851 protein sequences.
Benchmarking was conducted on an HPC node called "buckbeak" with 16 threads and 1 A100 GPU made available.
CPU-only runtimes will be longer when --coconat is enabled, or on the PRR pipeline.
| Pipeline | CPU time | Real time | MaxRSS |
|---|---|---|---|
nlr |
339.1s | 31.2s | 1744 MB |
nlr --retain |
15709.4s | 1072.7s | 2246 MB |
nlr --coconat |
388.9s | 75.9s | 8339 MB |
prr |
5827.7s | 1401.4s | 4101 MB |
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!
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