diff --git a/docs/source/Inference.md b/docs/source/Inference.md
index 8c179bca..e8c0a469 100644
--- a/docs/source/Inference.md
+++ b/docs/source/Inference.md
@@ -59,7 +59,7 @@ The following command performs a sequence alignment against the OpenProteinSet d
 ```
 python3 run_pretrained_openfold.py \
     $INPUT_FASTA_DIR \
-    $TEMPLATE_MMCIF_DIR 
+    $TEMPLATE_MMCIF_DIR \
     --output_dir $OUTPUT_DIR \
     --config_preset model_1_ptm \
     --uniref90_database_path $BASE_DATA_DIR/uniref90 \
@@ -168,4 +168,4 @@ To minimize memory usage during inference on long sequences, consider the follow
 
 Using the most conservative settings, we were able to run inference on a 4600-residue complex with a single A100. Compared to AlphaFold's own memory offloading mode, ours is considerably faster; the same complex takes the more efficent AlphaFold-Multimer more than double the time. Use the `long_sequence_inference` config option to enable all of these interventions at once. The `run_pretrained_openfold.py` script can enable this config option with the `--long_sequence_inference` command line option
 
-Input FASTA files containing multiple sequences are treated as complexes. In this case, the inference script runs AlphaFold-Gap, a hack proposed [here](https://twitter.com/minkbaek/status/1417538291709071362?lang=en), using the specified stock AlphaFold/OpenFold parameters (NOT AlphaFold-Multimer).
\ No newline at end of file
+Input FASTA files containing multiple sequences are treated as complexes. In this case, the inference script runs AlphaFold-Gap, a hack proposed [here](https://twitter.com/minkbaek/status/1417538291709071362?lang=en), using the specified stock AlphaFold/OpenFold parameters (NOT AlphaFold-Multimer).