# Usages ### Generating MSA #### Generate an MSA by *`hhblits`* ```bash # Use HHBLITS to generate MSA tmp_dirt= n_threads=4 # number of threads id=1A2YA # PDB ID and chain of an example hhblits \ -v 0 \ -d //uniclust/UniRef30_2020_03 \ -cpu ${n_threads} \ -n 5 \ -E 0.001 \ -o ${tmp_dirt}/${id}.hhr \ -opsi ${tmp_dirt}/${id}_psiblast.aln \ -i //${id}.fasta ``` #### Generate an MSA by *`jackhmmer`* ```bash jackhmmer \ --notextw \ --cpu ${n_threads} \ -A ${tmp_dirt}/${id}.sto \ --tblout ${tmp_dirt}/${id}_tbl.out \ --domtblout ${tmp_dirt}/${id}_domtbl.out \ -E 0.01 \ --popen 0.25 \ --pextend 0.4 \ ${seq_dirt}/${id}.fasta \ //uniref100.fasta \ # Convert format amo sequence_converter \ -jobname ${id} \ -type 1 \ -msa ${tmp_dirt}/${id}.sto \ -threads ${n_threads} \ -output ${out_dirt} ``` ### Usage of AMO ``` amo: usage command line amo These are common Ler commands used in various situations: get amo and system info. (see also: amo help tutorial) about -about amo usage -usage of amo work on the omics (see also: amo help metaomics) sff2fastq -convert an SFF file from the 454 genome sequencer to FASTQ including the sequences and quality scores base_call -Base calling atgc_stats -Statistics of FASTQ sequence atgc_quality -Quality control atgc_correct -Correct errors in sequencing data atgc_assembly -sequence assembly by de Bruijn graph or ant colony optimization read_filter -Filter reads with low quality kmer_counter -count kmer kmer_generator -generate kmers work on the proteomics (see also: amo help proteomics) sequence_converter -convert the ouput of Jackhmmer to fasta sequence_mix -pairwise-mixture protein sequences (particular for GPCR-G proteins) sequence_trim -trim a multiple protein sequence alignment sequence_stats -statistical analysis on a protein mutiple sequence alignment sequence_coupling -coupled relationship betwen pairwise protein residues and functional sectors sequence_eca -infer coupled blocks of protein residues at conserved sites sequence_energy -calculate energy for a protein sequence or multiple sequences sequence_design -design a protein sequence sequence_ddg -infer protein stability (ddG = dG_wt - dG_mutant) point_mutation -point mutation for a given protein sequence residue_contact -estimate contacts between pairwise residues from protein MSA selectivity_sector -detect selectivity sectors of determinants in GPCR-G proteins work on protein folding pdb_parser -parser a PDB file and write it out in simplified PDB format and/or FASTA sequence file fasta2pdb -convert protien amino acid sequence into its extended structure fold_protein -predict tertiary structure protein_folding -predict folding pathways & tertiary structure calc_contact -calculate residue contacts from a give PDB file work on statistics (see also: amo help statistics) stats_ica -independent component analysis on a given matrix stats_pearson -Pearson correlation stats_tsne -t-distributed stochastic neighbor embedding (t-SNE) method work on optimization tools (see also: amo help optimization) opt_chpso -the convergent heterogeneous particle swarm optimizer opt_jde -the differential evolution optimizer work on sampling (see also: amo help sampling) nested_sampler -nested sampler work on machine learning (see also: amo help learning) senet -sequentially evolving neural network optifel -evolving fuzzy modeling approach phsior -protein torsion angle predictor based on CNN deep network ``` Example pipeline ```bash #!/bin/bash jackhmmer=/usr/local/bin/jackhmmer usrname=`whoami` home_dirt=/home/${usrname} dirt=${home_dirt}/amo-ai #lib_dirt=${dirt}/amo-lib amo=${dirt}/amo-build/tool/amo data_idx=90 database=${home_dirt}/.data/uniref${data_idx}.fasta out_dirt=${dirt}/amo-output #bin_dir=${dirt}/amo-bin #srp_dir=${dirt}/amo-scripts exp_dirt=${dirt}/amo-example seq_dirt=${exp_dirt}/fasta if [ $# -ne 1 ]; then echo "Usage: $0 " echo "where the is the same as that of it FASTA." exit fi protein=$1 job_name=${protein} n_thread=2 start=$(date +%s.%N) if [ ! -d ${out_dirt}/${job_name} ] then mkdir -p ${out_dirt}/${job_name} fi fasta=${seq_dirt}/${protein}.fasta fasta_wt=${out_dirt}/${job_name}/${protein}_WT.fasta # date +"%T-%m-%d-%y" printf "## AMO-AI is working on protein %s \n" "${protein}" # Pre-process printf "## L1. Obtaining FASTA of %s ...\n" "${protein}" n=`cat ${fasta} | wc | awk '{print $1}'` echo "`head -1 ${fasta}`" > ${fasta_wt} for i in `seq 2 ${n}` do cat ${fasta} | head -${i} | tail -1 | while read line; do printf "%s" "$line"; done >> ${fasta_wt} done printf "## Protein name: %s\n" "${protein}" printf "## Residue number: %s\n" "`tail -1 ${fasta_wt} | wc -c`" # echo "## `tail -1 ${fasta_wt}`" printf "## L2. Searhing protein (%s) against UniRef%s database ...\n" "${protein}" "${data_idx}" flag_jackhmmer=0 if [ ! -f ${out_dirt}/${job_name}/${protein}.sto ]; then flag_jackhmmer=1 elif [[ -f ${out_dirt}/${job_name}/${protein}.sto && `ls -s ${out_dirt}/${job_name}/${protein}.sto | awk '{print $1}'` -eq 0 ]]; then flag_jackhmmer=1 fi if [ ${flag_jackhmmer} -eq 1 ]; then start=`date +%s` $jackhmmer --notextw \ --cpu ${n_thread} \ -A ${out_dirt}/${job_name}/${protein}.sto \ --tblout ${out_dirt}/${job_name}/${protein}_tbl.out \ --domtblout ${out_dirt}/${job_name}/${protein}_domtbl.out \ -E 0.01 \ --popen 0.25 \ --pextend 0.4 \ ${fasta_wt} \ ${database} \ >/dev/null #--mxfile ${lib_dirt}/common/BLOSUM62.mat \ # -T 0.4 \ bit scorce end=`date +%s` run_time=$((end-start)) printf "## Running time: %f" ${run_time} fi printf "## L3. Converting from the file (*.sto) of %s generated by jackhmmer to *.a2m ...\n" "${protein}" ${amo} sequence_converter \ -jobname ${job_name} \ -msa ${out_dirt}/${job_name}/${protein}.sto \ -threads ${n_thread} \ -output ${out_dirt}/ printf "## L4. Trimming the obtained MSA of %s ...\n" "${protein}" ${amo} sequence_trim \ -jobname ${job_name} \ -msa ${out_dirt}/${job_name}/${job_name}_msa.a2m \ -gap_frac 0.15 \ -threads ${n_thread} \ -output ${out_dirt}/ printf "## L4. Compute statistics on the aligned sequences of protein %s ...\n" "${protein}" ${amo} sequence_stats \ -jobname ${job_name} \ -msa ${out_dirt}/${job_name}/${job_name}_msa_trimmed.aln \ -threads ${n_thread} \ -max_iter 1000 \ -output ${out_dirt}/ printf "## L5. Compute coupling on the aligned sequences of protein %s ...\n" "${protein}" ${amo} sequence_coupling \ -jobname ${job_name} \ -msa ${out_dirt}/${job_name}/${job_name}_msa.a2m \ -threads ${n_thread} \ -max_iter 10 \ -output ${out_dirt}/ printf "## L6. Point_mutation for protein %s\n" "${protein}" ${amo} point_mutation \ -jobname ${job_name} \ -threads ${n_thread} \ -fastx ${fasta_wt} \ -mat ${out_dirt}/${job_name}/${job_name}_sequence_potential.csv \ -output ${out_dirt}/ printf "## L7. Design sequence for protein %s\n" "${protein}" ${amo} sequence_design \ -jobname ${job_name} \ -threads ${n_thread} \ -fastx ${fasta_wt} \ -mat ${out_dirt}/${job_name}/${job_name}_sequence_potential.csv \ -max_iter 100000 \ -output ${out_dirt}/ printf "## L8. Compute sequence energy for protein %s\n" "${protein}" ${amo} sequence_energy \ -jobname ${job_name} \ -threads ${n_thread} \ -msa ${out_dirt}/${job_name}/${job_name}_msa_trimmed.aln \ -mat ${out_dirt}/${job_name}/${job_name}_sequence_potential.csv \ -output ${out_dirt}/ end=$(date +%s.%N) runtime=$(python -c "print(${end} - ${start})") printf "## L9. Completed in %f\n\n" "${runtime}" #date +"%T-%m-%d-%y" ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://amo-ai.gitbook.io/amo/usage.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.