Search and cluster protein or nucleotide sequences for homology discovery at large scale. Learn more
Search and cluster protein or nucleotide sequences for homology discovery at large scale. Learn more
Search and cluster protein or nucleotide sequences for homology discovery at large scale. Learn more
Perform multiple sequence alignment using MAFFT (Multiple Alignment using Fast Fourier Transform). Supports multiple algorithms from fast progressive to highly accurate iterative methods.
Align protein, DNA, or RNA sequences with MUSCLE v5.3 using the native PPP or Super5 algorithm, with optional stratified and diversified ensembles.
Calculate batched Levenshtein distances and Needleman–Wunsch or Smith–Waterman sequence scores with StringZilla v5 CPU acceleration.
USAlign (Universal Structure Alignment) aligns protein, RNA, and DNA structures to compute TM-scores and generate superposed structures. Compare 3D structures to assess structural similarity.
Analyze immunoglobulin (antibody) and T cell receptor variable domain sequences. Identifies V/D/J gene segments, delineates CDR regions, and analyzes rearrangement junctions.
Perform multiple sequence alignment on protein or nucleotide sequences using the Clustal Omega algorithm.
Infer approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences.
Sensitive sequence homology search using profile hidden Markov models. More accurate than BLAST for detecting remote homologs, ideal for finding evolutionarily distant protein family members.
Build phylogenetic trees using maximum likelihood with automatic model selection (ModelFinder) and ultrafast bootstrap support.
Number antibody and T cell receptor variable domain sequences using IMGT, Chothia, Kabat, Martin, AHo, or Wolfguy. Reports chain type, the best HMM species match, and optional germline assignments.
Perform multiple sequence alignment using MAFFT (Multiple Alignment using Fast Fourier Transform). Supports multiple algorithms from fast progressive to highly accurate iterative methods.
Align protein, DNA, or RNA sequences with MUSCLE v5.3 using the native PPP or Super5 algorithm, with optional stratified and diversified ensembles.
Calculate batched Levenshtein distances and Needleman–Wunsch or Smith–Waterman sequence scores with StringZilla v5 CPU acceleration.
USAlign (Universal Structure Alignment) aligns protein, RNA, and DNA structures to compute TM-scores and generate superposed structures. Compare 3D structures to assess structural similarity.
Analyze immunoglobulin (antibody) and T cell receptor variable domain sequences. Identifies V/D/J gene segments, delineates CDR regions, and analyzes rearrangement junctions.
Perform multiple sequence alignment on protein or nucleotide sequences using the Clustal Omega algorithm.
Infer approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences.
Sensitive sequence homology search using profile hidden Markov models. More accurate than BLAST for detecting remote homologs, ideal for finding evolutionarily distant protein family members.
Build phylogenetic trees using maximum likelihood with automatic model selection (ModelFinder) and ultrafast bootstrap support.
Number antibody and T cell receptor variable domain sequences using IMGT, Chothia, Kabat, Martin, AHo, or Wolfguy. Reports chain type, the best HMM species match, and optional germline assignments.
Configure inputs to begin
Set options on the left, then click “Run MMseqs2”.
MMseqs2 (Many-against-Many sequence searching) is an open-source sequence search and clustering suite for protein and nucleotide data. Its k-mer prefilter and vectorized alignment stages make large database searches much faster than conventional pairwise tools while retaining sensitive local alignment.
Search mode finds homologs between query and target sets. Cluster mode groups related sequences with a cascaded workflow, while Linclust uses a linear-time prefilter for very large collections. For multiple sequence alignment after collecting homologs, Clustal Omega can align the selected sequences.
ProteinIQ runs MMseqs2 18 online from one FASTA or FASTQ query set. Search mode also takes a FASTA or FASTQ target set and returns the native 12-column m8 file. Cluster and Linclust modes return native membership, representative-sequence, and all-sequence files, together with run diagnostics and reproducibility metadata.
| Input | Description |
|---|---|
Query sequences | One protein, DNA, or RNA sequence set in FASTA or FASTQ format. Clustering requires at least two records. |
Target sequences | One protein, DNA, or RNA sequence set in FASTA or FASTQ format. Required only for search mode. |
Sequence type uses MMseqs2's native auto mode by default. An explicit protein or nucleotide choice maps to the source search or database-type option. Auto mode is safer than inferring the alphabet locally, especially for nucleotide records containing ambiguity symbols such as N.
| Setting | Search | Cluster | Linclust |
|---|---|---|---|
Sensitivity | 5.7 | 4.0 | Determined by Linclust |
Minimum sequence identity | Not used | 0 | 0 |
Minimum coverage | 0 | 0.8 | 0.8 |
Coverage mode | 0 | 0 | 0 |
Maximum hits per query | 300 | Not used | Not used |
E-value threshold | 0.001 | Not used | Not used |
Search and cluster sensitivity can range from 1 to 7.5. Higher values examine more candidate matches and take longer. Linclust does not accept the same sensitivity control.
The Minimum coverage value is interpreted according to Coverage mode:
Modes 3 to 5 are length-ratio filters. They are not bidirectional alignment-coverage calculations.
In standard Cluster mode, Reassign clusters performs the native post-clustering reassignment step. Linclust does not support that option. Memory limit maps to --split-memory-limit; 0 keeps the native all-available-memory behavior, while positive values cap each split in GiB. CPU threads are limited to the four CPUs allocated to the run.
The primary search file is MMseqs2's native, headerless BLAST-TAB output. ProteinIQ does not replace its identity unit or insert a synthetic header.
| Position | Native field | Meaning |
|---|---|---|
| 1 | query | Query identifier |
| 2 | target | Target identifier |
| 3 | fident | Fraction of identical aligned residues, from 0 to 1 |
| 4 | alnlen | Alignment length |
| 5 | mismatch | Mismatch count |
| 6 | gapopen | Gap-opening count |
| 7-8 | qstart, qend | Query alignment coordinates |
| 9-10 | tstart, tend | Target alignment coordinates |
| 11 | evalue | Expected number of chance matches |
| 12 | bits | Bit score |
Lower E-values and higher bit scores indicate stronger evidence for homology. Identity and coverage should be interpreted together: a high identity over a short segment does not establish full-length similarity.
MMseqs2 creates three native files:
| File | Contents |
|---|---|
*_cluster.tsv | Headerless representative-member pairs |
*_rep_seq.fasta | One representative sequence per cluster |
*_all_seqs.fasta | All input sequences grouped by cluster |
The default identity threshold is 0, not 30%. To remove near-duplicates, a threshold such as 0.9 or 0.95 is a common starting point. Lower thresholds can merge more remote relationships, but the biological meaning depends on sequence length, coverage mode, and protein family.
Linclust is preferable when the input contains hundreds of thousands or millions of sequences. Standard cluster mode performs a more sensitive cascaded search and is generally better for smaller sets where cluster quality matters more than linear scaling.