nf-core/proteinfold
Protein 3D structure prediction pipeline
Introduction
This document describes the output produced by the pipeline.
Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.
Pipeline overview
The pipeline is built using Nextflow and predicts protein structures using the following methods:
- AlphaFold2
- ColabFold - MMseqs2 (API server or local search) followed by ColabFold
- ESMFold
See main README.md for a condensed overview of the steps in the pipeline, and the bioinformatics tools used at each step.
The directories listed below will be created in the output directory after the pipeline has finished. All paths are relative to the top-level results directory.
AlphaFold2
Output files
alphafold2/standard/
oralphafold2/split_msa_prediction/
based on the selected mode. It contains the computed MSAs, unrelaxed structures, relaxed structures, ranked structures, raw model outputs, prediction metadata, and section timings. Specifically,<SEQUENCE NAME>_plddt_mqc.tsv
presents the pLDDT scores per residue for each of the 5 predicted models.top_ranked_structures/<SEQUENCE NAME>.pdb
that is the structure with the highest pLDDT score per input (ranked first)
DBs/
that contains symbolic links to the downloaded database and parameter files
Below you can find an indicative example of the TSV file with the pLDDT scores per residue for each of the 5 predicted models produced by AlphaFold2, which is included in the MultiQC report:
Positions | rank_0 | rank_1 | rank_2 | rank_3 | rank_4 |
---|---|---|---|---|---|
1 | 66.17 | 60.61 | 60.32 | 64.20 | 65.31 |
2 | 78.01 | 74.20 | 73.11 | 77.36 | 78.46 |
3 | 82.16 | 78.16 | 76.70 | 80.20 | 80.68 |
4 | 86.03 | 82.78 | 81.88 | 82.19 | 83.93 |
5 | 88.08 | 84.38 | 84.73 | 85.58 | 87.70 |
6 | 89.37 | 86.06 | 86.31 | 86.84 | 88.52 |
7 | 91.27 | 88.27 | 88.09 | 87.01 | 88.67 |
8 | 91.28 | 89.42 | 90.17 | 87.47 | 90.07 |
9 | 93.10 | 90.09 | 92.86 | 90.70 | 93.41 |
10 | 93.23 | 91.42 | 93.07 | 90.13 | 92.91 |
11 | 94.12 | 92.44 | 93.00 | 89.90 | 92.97 |
12 | 95.15 | 93.63 | 94.25 | 92.66 | 94.38 |
13 | 95.09 | 93.75 | 94.36 | 92.54 | 94.95 |
14 | 94.08 | 92.72 | 93.43 | 90.31 | 93.63 |
15 | 94.34 | 93.77 | 93.31 | 91.72 | 93.57 |
16 | 95.56 | 94.62 | 94.46 | 93.55 | 95.20 |
17 | 95.54 | 94.75 | 94.65 | 93.61 | 95.37 |
18 | 93.91 | 93.89 | 93.30 | 91.33 | 92.95 |
19 | 95.48 | 95.78 | 94.48 | 93.95 | 95.05 |
20 | 95.96 | 95.46 | 95.14 | 94.01 | 95.83 |
21 | 94.06 | 94.06 | 93.13 | 91.69 | 93.54 |
22 | 92.98 | 93.28 | 91.14 | 88.80 | 91.25 |
23 | 95.28 | 95.13 | 93.39 | 91.48 | 93.56 |
24 | 93.41 | 93.38 | 92.32 | 89.85 | 92.40 |
25 | 90.88 | 91.40 | 88.60 | 85.67 | 87.65 |
26 | 89.30 | 88.90 | 84.58 | 83.11 | 84.52 |
27 | 91.96 | 90.95 | 89.04 | 86.42 | 87.77 |
28 | 91.20 | 90.68 | 88.71 | 86.43 | 87.62 |
29 | 88.01 | 87.53 | 85.83 | 83.11 | 84.95 |
30 | 81.29 | 83.72 | 77.75 | 75.76 | 74.84 |
31 | 87.14 | 86.92 | 82.10 | 82.32 | 78.74 |
32 | 92.34 | 90.13 | 89.04 | 88.31 | 86.49 |
33 | 91.70 | 88.94 | 85.52 | 85.94 | 81.75 |
34 | 90.11 | 88.23 | 84.33 | 85.47 | 80.01 |
35 | 93.35 | 91.49 | 90.60 | 89.40 | 87.10 |
36 | 94.15 | 92.47 | 90.17 | 90.48 | 86.77 |
37 | 93.40 | 92.01 | 86.38 | 87.84 | 80.11 |
38 | 92.79 | 89.97 | 89.31 | 88.55 | 85.15 |
39 | 94.66 | 91.29 | 92.74 | 90.67 | 90.30 |
40 | 95.98 | 93.58 | 94.30 | 91.69 | 90.73 |
41 | 94.94 | 92.57 | 88.31 | 88.40 | 80.33 |
42 | 92.89 | 91.03 | 84.03 | 85.31 | 74.66 |
43 | 94.54 | 93.44 | 86.50 | 84.91 | 76.68 |
44 | 96.93 | 95.23 | 92.42 | 91.98 | 86.11 |
45 | 94.40 | 92.27 | 87.40 | 89.02 | 79.44 |
46 | 91.74 | 90.94 | 81.35 | 84.88 | 74.93 |
47 | 96.19 | 94.46 | 90.51 | 89.82 | 84.51 |
48 | 94.84 | 93.04 | 91.02 | 91.57 | 87.72 |
49 | 91.23 | 89.34 | 86.10 | 87.63 | 82.12 |
50 | 91.64 | 89.58 | 84.93 | 85.88 | 79.38 |
ColabFold
Output files
colabfold/webserver/
orcolabfold/local/
based on the selected mode. It contains the computed MSAs, unrelaxed structures, relaxed structures, ranked structures, raw model outputs, prediction metadata, and section timings. Specifically,<SEQUENCE NAME>_plddt_mqc.tsv
presents the pLDDT scores per residue for each of the 5 predicted models.top_ranked_structures/<SEQUENCE NAME>.pdb
that is the structure with the highest pLDDT score per input (ranked first)
DBs/
that contains symbolic links to the downloaded database and parameter files
Below you can find some indicative examples of the output images produced by ColabFold, which are included in the MultiQC report:
Sequence coverage
predicted Local Distance Difference Test (pLDDT)
Predicted Aligned Error (PAE)
ESMFold
Output files
esmfold/default
contains the predicted structures. Specifically,<SEQUENCE NAME>_plddt_mqc.tsv
presents the pLDDT scores per residue for each of the predicted models.top_ranked_structures/<SEQUENCE NAME>.pdb
that is the structure with the highest pLDDT score per input (ranked first)
DBs/
that contains symbolic links to the downloaded database and parameter files
Below you can find an indicative example of the TSV file with the pLDDT scores per atom for predicted model produced by ESMFold, which is included in the MultiQC report:
Atom_serial_number | Atom_name | Residue_name | Residue_sequence_number | pLDDT |
---|---|---|---|---|
1 | N | VAL | 1 | 44.77 |
2 | CA | VAL | 1 | 47.23 |
3 | C | VAL | 1 | 46.66 |
4 | CB | VAL | 1 | 41.88 |
5 | O | VAL | 1 | 45.75 |
6 | CG1 | VAL | 1 | 39.15 |
7 | CG2 | VAL | 1 | 39.59 |
8 | N | THR | 2 | 49.89 |
9 | CA | THR | 2 | 51.41 |
10 | C | THR | 2 | 50.21 |
11 | CB | THR | 2 | 43.84 |
12 | O | THR | 2 | 47.36 |
13 | CG2 | THR | 2 | 35.32 |
14 | OG1 | THR | 2 | 40.12 |
15 | N | VAL | 3 | 51.40 |
16 | CA | VAL | 3 | 54.38 |
17 | C | VAL | 3 | 52.10 |
18 | CB | VAL | 3 | 48.50 |
19 | O | VAL | 3 | 52.58 |
20 | CG1 | VAL | 3 | 38.75 |
21 | CG2 | VAL | 3 | 39.26 |
22 | N | ASP | 4 | 52.00 |
23 | CA | ASP | 4 | 53.92 |
24 | C | ASP | 4 | 52.33 |
25 | CB | ASP | 4 | 46.82 |
26 | O | ASP | 4 | 51.28 |
27 | CG | ASP | 4 | 42.89 |
28 | OD1 | ASP | 4 | 45.89 |
29 | OD2 | ASP | 4 | 53.61 |
30 | N | ASP | 5 | 56.10 |
31 | CA | ASP | 5 | 56.97 |
32 | C | ASP | 5 | 55.75 |
33 | CB | ASP | 5 | 50.34 |
34 | O | ASP | 5 | 54.18 |
35 | CG | ASP | 5 | 45.82 |
36 | OD1 | ASP | 5 | 50.03 |
37 | OD2 | ASP | 5 | 58.01 |
38 | N | LEU | 6 | 56.50 |
39 | CA | LEU | 6 | 58.34 |
40 | C | LEU | 6 | 55.81 |
41 | CB | LEU | 6 | 52.46 |
42 | O | LEU | 6 | 54.42 |
43 | CG | LEU | 6 | 49.17 |
44 | CD1 | LEU | 6 | 44.31 |
45 | CD2 | LEU | 6 | 47.07 |
46 | N | VAL | 7 | 57.23 |
47 | CA | VAL | 7 | 57.68 |
48 | C | VAL | 7 | 57.39 |
49 | CB | VAL | 7 | 52.74 |
50 | O | VAL | 7 | 56.46 |
MultiQC report
Output files
multiqc
<MODE>_multiqc_report.html
: A standalone HTML file that can be viewed in your web browser.<MODE>_multiqc_data/
: Directory containing parsed statistics from the different tools used in the pipeline.<MODE>_multiqc_plots/
: Directory containing static images from the report in various formats.
MultiQC is a visualisation tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available within the report data directory.
Results generated by MultiQC collate pipeline QC from AlphaFold2 or ColabFold.
The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.
Pipeline information
Output files
pipeline_info/
- Reports generated by Nextflow:
execution_report.html
,execution_timeline.html
,execution_trace.txt
andpipeline_dag.dot
/pipeline_dag.svg
. - Reports generated by the pipeline:
pipeline_report.html
,pipeline_report.txt
andsoftware_versions.yml
. Thepipeline_report*
files will only be present if the--email
/--email_on_fail
parameter’s are used when running the pipeline. - Reformatted samplesheet files used as input to the pipeline:
samplesheet.valid.csv
. - Parameters used by the pipeline run:
params.json
.
- Reports generated by Nextflow:
Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.