Migrated from GitHub

.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/AF_DATA/pae_tdp.tga filter=lfs diff=lfs merge=lfs -text
+data/AF_DATA/vmdscene.tga filter=lfs diff=lfs merge=lfs -text

data/AF_DATA/cite.bibtex

@@ -0,0 +1,40 @@
+@article{Mirdita2022,
+author= {Mirdita, Milot and Schütze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin },
+doi = {10.1038/s41592-022-01488-1},
+journal = {Nature Methods},
+title = {{ColabFold: Making Protein folding accessible to all}},
+year = {2022},
+comment = {ColabFold including MMseqs2 MSA server}
+}
+@article{Mirdita2019,
+author = {Mirdita, Milot and Steinegger, Martin and S{"{o}}ding, Johannes},
+doi = {10.1093/bioinformatics/bty1057},
+journal = {Bioinformatics},
+number = {16},
+pages = {2856--2858},
+pmid = {30615063},
+title = {{MMseqs2 desktop and local web server app for fast, interactive sequence searches}},
+volume = {35},
+year = {2019},
+comment = {MMseqs2 search server}
+}
+@article{Mirdita2017,
+author = {Mirdita, Milot and von den Driesch, Lars and Galiez, Clovis and Martin, Maria J. and S{"{o}}ding, Johannes and Steinegger, Martin},
+doi = {10.1093/nar/gkw1081},
+journal = {Nucleic Acids Res.},
+number = {D1},
+pages = {D170--D176},
+pmid = {27899574},
+title = {{Uniclust databases of clustered and deeply annotated protein sequences and alignments}},
+volume = {45},
+year = {2017},
+comment = {Uniclust30/UniRef30 database},
+}
+@article{Mitchell2019,
+author = {Mitchell, Alex L and Almeida, Alexandre and Beracochea, Martin and Boland, Miguel and Burgin, Josephine and Cochrane, Guy and Crusoe, Michael R and Kale, Varsha and Potter, Simon C and Richardson, Lorna J and Sakharova, Ekaterina and Scheremetjew, Maxim and Korobeynikov, Anton and Shlemov, Alex and Kunyavskaya, Olga and Lapidus, Alla and Finn, Robert D},
+doi = {10.1093/nar/gkz1035},
+journal = {Nucleic Acids Res.},
+title = {{MGnify: the microbiome analysis resource in 2020}},
+year = {2019},
+comment = {MGnify database}
+}

data/AF_DATA/config.json

@@ -0,0 +1,34 @@
+{
+    "num_queries": 1,
+    "use_templates": false,
+    "num_relax": 0,
+    "msa_mode": "mmseqs2_uniref_env",
+    "model_type": "alphafold2_ptm",
+    "num_models": 1,
+    "num_recycles": 3,
+    "recycle_early_stop_tolerance": null,
+    "num_ensemble": 1,
+    "model_order": [
+        3,
+        4,
+        5,
+        1,
+        2
+    ],
+    "keep_existing_results": false,
+    "rank_by": "plddt",
+    "max_seq": 512,
+    "max_extra_seq": 1024,
+    "pair_mode": "unpaired_paired",
+    "host_url": "https://api.colabfold.com",
+    "stop_at_score": 100.0,
+    "random_seed": 0,
+    "num_seeds": 1,
+    "recompile_padding": 10,
+    "commit": "87a44a0941c0dc4ed4c9499aa6d8bd8dd10c7333",
+    "use_dropout": false,
+    "use_cluster_profile": true,
+    "use_fuse": true,
+    "use_bfloat16": true,
+    "version": "1.5.1"
+}

data/AF_DATA/log.txt

@@ -0,0 +1,31 @@
+2023-07-13 11:33:05,067 Running on GPU
+2023-07-13 11:33:05,212 Found 4 citations for tools or databases
+2023-07-13 11:33:05,212 Query 1/1: tdp43_WtoA_bf4cc (length 414)
+2023-07-13 11:33:05,820 Sleeping for 9s. Reason: PENDING
+2023-07-13 11:33:15,398 Sleeping for 9s. Reason: RUNNING
+2023-07-13 11:33:24,990 Sleeping for 7s. Reason: RUNNING
+2023-07-13 11:33:32,559 Sleeping for 7s. Reason: RUNNING
+2023-07-13 11:33:40,152 Sleeping for 7s. Reason: RUNNING
+2023-07-13 11:33:47,736 Sleeping for 10s. Reason: RUNNING
+2023-07-13 11:33:58,306 Sleeping for 5s. Reason: RUNNING
+2023-07-13 11:34:03,880 Sleeping for 7s. Reason: RUNNING
+2023-07-13 11:34:11,465 Sleeping for 10s. Reason: RUNNING
+2023-07-13 11:34:22,051 Sleeping for 7s. Reason: RUNNING
+2023-07-13 11:34:29,631 Sleeping for 10s. Reason: RUNNING
+2023-07-13 11:34:40,217 Sleeping for 6s. Reason: RUNNING
+2023-07-13 11:34:46,820 Sleeping for 8s. Reason: RUNNING
+2023-07-13 11:34:55,398 Sleeping for 8s. Reason: RUNNING
+2023-07-13 11:35:03,990 Sleeping for 9s. Reason: RUNNING
+2023-07-13 11:35:13,582 Sleeping for 6s. Reason: RUNNING
+2023-07-13 11:35:20,166 Sleeping for 9s. Reason: RUNNING
+2023-07-13 11:35:29,738 Sleeping for 5s. Reason: RUNNING
+2023-07-13 11:35:39,470 Padding length to 414
+2023-07-13 11:35:39,471 Setting max_seq=512, max_extra_seq=1024
+2023-07-13 11:37:19,606 alphafold2_ptm_model_3_seed_000 recycle=0 pLDDT=63.2 pTM=0.29 ipTM=0
+2023-07-13 11:38:05,675 alphafold2_ptm_model_3_seed_000 recycle=1 pLDDT=63.8 pTM=0.287 ipTM=0
+2023-07-13 11:38:51,078 alphafold2_ptm_model_3_seed_000 recycle=2 pLDDT=65.1 pTM=0.311 ipTM=0
+2023-07-13 11:39:36,749 alphafold2_ptm_model_3_seed_000 recycle=3 pLDDT=65.4 pTM=0.326 ipTM=0
+2023-07-13 11:39:36,753 alphafold2_ptm_model_3_seed_000 took 211.3s (3 recycles)
+2023-07-13 11:39:48,337 reranking models by 'plddt' metric
+2023-07-13 11:39:48,339 rank_001_alphafold2_ptm_model_3_seed_000 pLDDT=65.4 pTM=0.326 ipTM=0
+2023-07-13 11:39:51,052 Done

data/AF_DATA/tdp43_WtoA_bf4cc.csv

@@ -0,0 +1,2 @@
+id,sequence
+tdp43_WtoA_bf4cc,MSEYIRVTEDENDEPIEIPSEDDGTVLLSTVTAQFPGACGLRYRNPVSQCMRGVRLVEGILHAPDAGAGNLVYVVNYPKDNKRKMDETDASSAVKVKRAVQKTSDLIVLGLPAKTTEQDLKEYFSTFGEVLMVQVKKDLKTGHSKGFGFVRFTEYETQVKVMSQRHMIDGRACDCKLPNSKQSQDEPLRSRKVFVGRCTEDMTEDELREFFSQYGDVMDVFIPKPFRAFAFVTFADDQIAQSLCGEDLIIKGISVHISNAEPKHNSNRQLERSGRFGGNPGGFGNQGGFGNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSAGMMGMLASQQNQSGPSGNNQNQGNMQREPNQAFGSGNNSYSGSNSGAAIGAGSASNAGSGSGFNGGFGSSMDSKSSGAGM

data/AF_DATA/tdp43_WtoA_bf4cc_distmat/alphafold2_ptm_model_3_seed_000_prob_distributions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4605fe2880dcd582946a7c6b8a89aab4aac3cd0c9ec7715bf5df6cd32659f3a4
+size 21938816

data/AF_DATA/tdp43_WtoA_bf4cc_env/msa.sh

@@ -0,0 +1,84 @@
+#!/bin/bash -e
+MMSEQS="$1"
+QUERY="$2"
+BASE="$4"
+DB1="$5"
+DB2="$6"
+DB3="$7"
+USE_ENV="$8"
+USE_TEMPLATES="$9"
+FILTER="${10}"
+TAXONOMY="${11}"
+M8OUT="${12}"
+EXPAND_EVAL=inf
+ALIGN_EVAL=10
+DIFF=3000
+QSC=-20.0
+MAX_ACCEPT=1000000
+if [ "${FILTER}" = "1" ]; then
+# 0.1 was not used in benchmarks due to POSIX shell bug in line above
+#  EXPAND_EVAL=0.1
+  ALIGN_EVAL=10
+  QSC=0.8
+  MAX_ACCEPT=100000
+fi
+export MMSEQS_CALL_DEPTH=1
+SEARCH_PARAM="--num-iterations 3 --db-load-mode 2 -a --k-score 'seq:96,prof:80' -e 0.1 --max-seqs 10000"
+FILTER_PARAM="--filter-min-enable 1000 --diff ${DIFF} --qid 0.0,0.2,0.4,0.6,0.8,1.0 --qsc 0 --max-seq-id 0.95"
+EXPAND_PARAM="--expansion-mode 0 -e ${EXPAND_EVAL} --expand-filter-clusters ${FILTER} --max-seq-id 0.95"
+mkdir -p "${BASE}"
+"${MMSEQS}" createdb "${QUERY}" "${BASE}/qdb"
+"${MMSEQS}" search "${BASE}/qdb" "${DB1}" "${BASE}/res" "${BASE}/tmp1" $SEARCH_PARAM
+"${MMSEQS}" mvdb "${BASE}/tmp1/latest/profile_1" "${BASE}/prof_res"
+"${MMSEQS}" lndb "${BASE}/qdb_h" "${BASE}/prof_res_h"
+
+"${MMSEQS}" expandaln "${BASE}/qdb" "${DB1}.idx" "${BASE}/res" "${DB1}.idx" "${BASE}/res_exp" --db-load-mode 2 ${EXPAND_PARAM}
+"${MMSEQS}" align "${BASE}/prof_res" "${DB1}.idx" "${BASE}/res_exp" "${BASE}/res_exp_realign" --db-load-mode 2 -e ${ALIGN_EVAL} --max-accept ${MAX_ACCEPT} --alt-ali 10 -a
+"${MMSEQS}" filterresult "${BASE}/qdb" "${DB1}.idx" "${BASE}/res_exp_realign" "${BASE}/res_exp_realign_filter" --db-load-mode 2 --qid 0 --qsc $QSC --diff 0 --max-seq-id 1.0 --filter-min-enable 100
+if [ "${M8OUT}" = "1" ]; then
+  "${MMSEQS}" filterresult "${BASE}/qdb" "${DB1}.idx" "${BASE}/res_exp_realign_filter" "${BASE}/res_exp_realign_filter_filter" --db-load-mode 2 ${FILTER_PARAM}
+  "${MMSEQS}" convertalis "${BASE}/qdb" "${DB1}.idx" "${BASE}/res_exp_realign_filter_filter" "${BASE}/uniref.m8" --db-load-mode 2 --format-output query,target,fident,alnlen,mismatch,gapopen,qstart,qend,tstart,tend,evalue,bits,tseq
+  "${MMSEQS}" rmdb "${BASE}/res_exp_realign_filter_filter"
+else
+  "${MMSEQS}" result2msa "${BASE}/qdb" "${DB1}.idx" "${BASE}/res_exp_realign_filter" "${BASE}/uniref.a3m" --msa-format-mode 6 --db-load-mode 2 --filter-msa ${FILTER} ${FILTER_PARAM}
+fi
+"${MMSEQS}" rmdb "${BASE}/res_exp_realign"
+"${MMSEQS}" rmdb "${BASE}/res_exp"
+"${MMSEQS}" rmdb "${BASE}/res"
+if [ "${TAXONOMY}" = "1" ] && [ -e "${DB1}_taxonomy" ]; then
+  "${MMSEQS}" convertalis "${BASE}/qdb" "${DB1}.idx" "${BASE}/res_exp_realign_filter" "${BASE}/res_exp_realign_tax" --db-output 1 --format-output "taxid,target,taxlineage" --db-load-mode 2
+  awk 'BEGIN { printf("%c%c%c%c",8,0,0,0); exit; }' > "${BASE}/res_exp_realign_tax.dbtype"
+  MMSEQS_FORCE_MERGE=1 "${MMSEQS}" filtertaxdb "${DB1}" "${BASE}/res_exp_realign_tax" "${BASE}/res_exp_realign_tax_filt" --taxon-list '!12908&&!28384'
+  tr -d '\000' < "${BASE}/res_exp_realign_tax_filt" | sort -u > "${BASE}/uniref_tax.tsv"
+fi
+"${MMSEQS}" rmdb "${BASE}/res_exp_realign_filter"
+
+if [ "${USE_TEMPLATES}" = "1" ]; then
+  "${MMSEQS}" search "${BASE}/prof_res" "${DB2}" "${BASE}/res_pdb" "${BASE}/tmp2" --db-load-mode 2 -s 7.5 -a -e 0.1
+  "${MMSEQS}" convertalis "${BASE}/prof_res" "${DB2}.idx" "${BASE}/res_pdb" "${BASE}/pdb70.m8" --format-output query,target,fident,alnlen,mismatch,gapopen,qstart,qend,tstart,tend,evalue,bits,cigar --db-load-mode 2
+  "${MMSEQS}" rmdb "${BASE}/res_pdb"
+fi
+
+if [ "${USE_ENV}" = "1" ]; then
+  "${MMSEQS}" search "${BASE}/prof_res" "${DB3}" "${BASE}/res_env" "${BASE}/tmp3" $SEARCH_PARAM
+  "${MMSEQS}" expandaln "${BASE}/prof_res" "${DB3}.idx" "${BASE}/res_env" "${DB3}.idx" "${BASE}/res_env_exp" -e ${EXPAND_EVAL} --expansion-mode 0 --db-load-mode 2
+  "${MMSEQS}" align "${BASE}/tmp3/latest/profile_1" "${DB3}.idx" "${BASE}/res_env_exp" "${BASE}/res_env_exp_realign" --db-load-mode 2 -e ${ALIGN_EVAL} --max-accept ${MAX_ACCEPT} --alt-ali 10 -a
+  "${MMSEQS}" filterresult "${BASE}/qdb" "${DB3}.idx" "${BASE}/res_env_exp_realign" "${BASE}/res_env_exp_realign_filter" --db-load-mode 2 --qid 0 --qsc $QSC --diff 0 --max-seq-id 1.0 --filter-min-enable 100
+  if [ "${M8OUT}" = "1" ]; then
+    "${MMSEQS}" filterresult "${BASE}/qdb" "${DB3}.idx" "${BASE}/res_env_exp_realign_filter" "${BASE}/res_env_exp_realign_filter_filter" --db-load-mode 2 ${FILTER_PARAM}
+    "${MMSEQS}" convertalis "${BASE}/qdb" "${DB3}.idx" "${BASE}/res_env_exp_realign_filter_filter" "${BASE}/bfd.mgnify30.metaeuk30.smag30.m8" --db-load-mode 2 --format-output query,target,fident,alnlen,mismatch,gapopen,qstart,qend,tstart,tend,evalue,bits,tseq
+    "${MMSEQS}" rmdb "${BASE}/res_env_exp_realign_filter_filter"
+  else
+	"${MMSEQS}" result2msa "${BASE}/qdb" "${DB3}.idx" "${BASE}/res_env_exp_realign_filter" "${BASE}/bfd.mgnify30.metaeuk30.smag30.a3m" --msa-format-mode 6 --db-load-mode 2 --filter-msa ${FILTER} ${FILTER_PARAM}
+  fi
+  "${MMSEQS}" rmdb "${BASE}/res_env_exp_realign_filter"
+  "${MMSEQS}" rmdb "${BASE}/res_env_exp_realign"
+  "${MMSEQS}" rmdb "${BASE}/res_env_exp"
+  "${MMSEQS}" rmdb "${BASE}/res_env"
+fi
+
+"${MMSEQS}" rmdb "${BASE}/qdb"
+"${MMSEQS}" rmdb "${BASE}/qdb_h"
+"${MMSEQS}" rmdb "${BASE}/res"
+rm -f -- "${BASE}/prof_res"*
+rm -rf -- "${BASE}/tmp1" "${BASE}/tmp2" "${BASE}/tmp3"

data/AF_DATA/tdp43_WtoA_bf4cc_env/out.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1950695442b9666f9664d36f03a3019ca0e5cff6b87bac443d7305697c76fc8b
+size 1795213

data/AF_DATA/tdp43_WtoA_bf4cc_env/pdb70.m8

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data/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+    (at your option) any later version.
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
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+    <program>  Copyright (C) <year>  <name of author>
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+parts of the General Public License.  Of course, your program's commands
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+
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+if any, to sign a "copyright disclaimer" for the program, if necessary.
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+<https://www.gnu.org/licenses/>.
+
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+may consider it more useful to permit linking proprietary applications with
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

data/environment.yml

@@ -0,0 +1,12 @@
+name: AlphaFold-IDP
+dependencies:
+  - python=3.8
+  - numpy
+  - pandas
+  - scipy
+  - matplotlib
+  - notebook
+  - conda-forge::mdtraj
+  - plumed
+  - gromacs
+

data/requirements.txt

@@ -0,0 +1,8 @@
+numpy
+pandas
+scipy
+sklearn
+mdtraj 
+matplolib
+plumed
+gromacs

data/scripts_prep/backmap.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+
+
+#Make the plumed file
+import mdtraj as md
+import os
+import sys
+name='input_af'
+pdb=name+'.pdb'
+########### Conactenate ########
+n_rep=int(sys.argv[1])
+trajs = list()
+skip=2
+eq=10
+
+
+#############
+xtc='r0/conf-protein.xtc'
+topology = md.load(pdb).topology
+traj = md.load(xtc, top=pdb)
+
+table, bonds = topology.to_dataframe()
+print('CALVADOS topology',table.head())
+
+#########
+fasta_file = open('sequence.dat',mode='r')
+fasta = fasta_file.read().replace("\n","")
+fasta_file.close()
+print(fasta[0])
+
+aminoacids=['ALA','ARG','ASN','ASP','CYS','GLU','GLN','GLY','HIS','ILE','LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL']
+aminos_sin=['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V']
+
+print(aminoacids[aminos_sin.index(fasta[0])])
+print(aminoacids[aminos_sin.index(fasta[-1])])
+table.loc[table.index[-1],'resName']=aminoacids[aminos_sin.index(fasta[-1])]
+table.loc[table.index[0],'resName']=aminoacids[aminos_sin.index(fasta[0])]
+
+table.loc[table.index[-1],'name']=aminoacids[aminos_sin.index(fasta[-1])]
+table.loc[table.index[0],'name']=aminoacids[aminos_sin.index(fasta[0])]
+table['serial'] = table['resSeq']
+########
+
+for AA in aminoacids:
+    table['name'] = table['name'].replace([AA], 'CA')
+
+
+print('new topology with CA, cg naming', table.head())
+
+
+topologyCA = md.Topology.from_dataframe(table, bonds)
+
+
+print(topologyCA)
+
+
+traj = md.load_pdb(pdb,top=topologyCA)
+traj.save(name+'cg.pdb')
+
+
+
+
+#Trajectory
+for  j in range(5,5+1):
+   name='input_af'
+   pdb=name+'.pdb'
+   dcd='r0/conf-protein.xtc'
+   traj_name=name+'_'+str(j)+'dcd.pdb'
+   topology = md.load(pdb).topology
+   traj = md.load(dcd, top=pdb)
+   traj.save(traj_name)
+   pdb=traj_name
+   table, bonds = topology.to_dataframe()
+   print('CALVADOS topology',table.head())
+   #########
+   fasta_file = open('sequence.dat',mode='r')
+   fasta = fasta_file.read().replace("\n","")
+   fasta_file.close()
+   print(fasta[0])
+ 
+   aminoacids=['ALA','ARG','ASN','ASP','CYS','GLU','GLN','GLY','HIS','ILE','LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL']
+   aminos_sin=['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V']
+
+   print(aminoacids[aminos_sin.index(fasta[0])])
+   print(aminoacids[aminos_sin.index(fasta[-1])])
+   table.loc[table.index[-1],'resName']=aminoacids[aminos_sin.index(fasta[-1])]
+   table.loc[table.index[0],'resName']=aminoacids[aminos_sin.index(fasta[0])]
+
+   table.loc[table.index[-1],'name']=aminoacids[aminos_sin.index(fasta[-1])]
+   table.loc[table.index[0],'name']=aminoacids[aminos_sin.index(fasta[0])]
+   table['serial'] = table['resSeq']
+   ########
+   for AA in aminoacids:
+      table['name'] = table['name'].replace([AA], 'CA')
+   print('new topology with CA, cg naming', table.head())
+   topologyCA = md.Topology.from_dataframe(table, bonds)
+   traj = md.load_pdb(pdb,top=topologyCA)
+   traj.save(name+'_'+str(j)+'dcdcg.pdb')
+   print('laste'+str(j))
+   for i in range(0,len(traj)):
+      traj[i].save('segment_'+str(j)+'_'+name+'_'+str(i)+'.pdb')

data/scripts_prep/dcd2xtc.py

@@ -0,0 +1,23 @@
+#Make the plumed file
+import mdtraj as md
+import os
+import sys
+name='input_af'
+pdb=name+'.pdb'
+########### Conactenate ########
+n_rep=int(sys.argv[1])
+trajs = list()
+skip=1
+eq=1
+os.system('rm input_af_*rebuilt.xtc input_af_bm_em.pdb input_af_bm_em_*.pdb *rebuilt.pdb segment*pdb *dcd')
+for i in range(0,n_rep):
+   try:
+      print(f'loading {i}')
+      traj = md.load_dcd(f'../output_{i}.dcd', top='input_af.pdb')
+
+      traj.save('output_'+str(i)+'.xtc')
+   except KeyboardInterrupt:
+       raise
+   except Exception as e:
+       raise
+

data/scripts_prep/fes2.py

@@ -0,0 +1,54 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import itertools
+import os
+import pandas as pd
+import argparse, sys
+parser = argparse.ArgumentParser()
+parser.add_argument("--CV_col", help="CV column in the COLVAR file (starting at 0)",required=True,type=str)
+parser.add_argument("--CV_name", help="CVname",required=True,type=str)
+
+args = parser.parse_args()
+
+CVcol =args.CV_col
+CVname =args.CV_name
+print(int(CVcol))
+eq_steps=1
+
+time=np.loadtxt("FULLBIAS")[:, 0]
+bias=np.loadtxt("FULLBIAS")[:, 1]
+CV=np.loadtxt("COLVAR")[:, int(CVcol)]
+KBT = 2.49
+weights = np.exp(bias/ KBT)
+weights /= weights.sum()
+
+#value, bins=np.histogram(CVname[eq_steps:-1], bins=30, weights=weights[eq_steps:-1], density=True)
+value1, bins1=np.histogram(CV[eq_steps:int(len(CV)/5)], bins=30, weights=weights[eq_steps:int(len(CV)/5)], density=True)
+value2, bins2=np.histogram(CV[eq_steps:int(2*len(CV)/5)], bins=30, weights=weights[eq_steps:int(2*len(CV)/5)], density=True)
+value3, bins3=np.histogram(CV[eq_steps:int(3*len(CV)/5)], bins=30, weights=weights[eq_steps:int(3*len(CV)/5)], density=True)
+value4, bins4=np.histogram(CV[eq_steps:int(4*len(CV)/5)], bins=30, weights=weights[eq_steps:int(4*len(CV)/5)], density=True)
+value5, bins5=np.histogram(CV[eq_steps:int(5*len(CV)/5)], bins=30, weights=weights[eq_steps:int(5*len(CV)/5)], density=True)
+
+logvalue1=-np.log(value1)
+logvalue1=logvalue1-min(logvalue1)
+logvalue2=-np.log(value2)
+logvalue2=logvalue2-min(logvalue2)
+logvalue3=-np.log(value3)
+logvalue3=logvalue3-min(logvalue3)
+logvalue4=-np.log(value4)
+logvalue4=logvalue4-min(logvalue4)
+
+logvalue5=-np.log(value5)
+logvalue5=logvalue5-min(logvalue5)
+plt.tick_params(labelsize=24)
+plt.plot(bins1[:-1],logvalue1,linewidth=3,color='red')
+plt.plot(bins2[:-1],logvalue2,linewidth=3,color='green')
+plt.plot(bins3[:-1],logvalue3,linewidth=3,color='blue')
+plt.plot(bins4[:-1],logvalue4,linewidth=3,color='pink')
+plt.plot(bins5[:-1],logvalue5,linewidth=3,color='yellow')
+plt.legend( ['t1', 't2','t3','t4','t5'],loc='lower right',ncol=2,prop={'size': 11})
+figure = plt.gcf()
+figure.set_size_inches(6, 7)
+plt.xlabel(str(CVname),fontsize=24 )
+plt.ylabel('F (kJ/mol)',fontsize=24 )
+plt.savefig('FES'+str(CVname)+'.png',dpi=400,transparent=True, bbox_inches='tight')

data/scripts_prep/gen_xml_and_constraints.py

@@ -0,0 +1,124 @@
+#python gen_xml_and_constraints.py sequence.dat 7.4 298 0.2
+import pandas as pd
+import numpy as np
+import pickle
+import mdtraj as md
+import sys
+
+def adjust_terminals_HIS(r,pH,fasta):
+    r.loc['H','q'] = 1. / ( 1 + 10**(pH-6) )
+    r.loc['X'] = r.loc[fasta[0]]
+    r.loc['X','q'] = r.loc[fasta[0],'q'] + 1.
+    r.loc['X','MW'] = r.loc[fasta[0],'MW'] + 2.
+    r.loc['X','three'] = 'X'
+    
+    r.loc['Z'] = r.loc[fasta[-1]]
+    r.loc['Z','q'] = r.loc[fasta[-1],'q'] - 1.
+    r.loc['Z','MW'] = r.loc[fasta[-1],'MW'] + 16.
+    r.loc['Z','three'] = 'Z'
+    
+    return r.set_index('three')
+	
+def calculate_yukawa_params(temp,ionic):
+    RT = 8.3145*temp*1e-3
+    fepsw = lambda T : 5321/T+233.76-0.9297*T+0.1417*1e-2*T*T-0.8292*1e-6*T**3
+    epsw = fepsw(temp)
+    lB = 1.6021766**2/(4*np.pi*8.854188*epsw)*6.022*1000/RT
+    yukawa_kappa = np.sqrt(8*np.pi*lB*ionic*6.022/10)
+    return yukawa_kappa, RT, lB
+	
+def generate_atomtypes_block(res):
+    tag = 'AtomTypes'
+    atomtypes = ""
+    for i,r in res.iterrows():
+        atomtype = '<Type name="{}" class="C" element="C-{}" mass="{}"/>\n'.format(i,i,str(r.MW))
+        atomtypes += "  " + atomtype
+    out = " <{0}>\n{1} </{0}>\n\n".format(tag,atomtypes)
+    return out
+	
+def generate_residues_block(res):
+    tag = 'Residues'
+    residues = ""
+    for i,r in res.iterrows():
+        atom = '   <Atom name="{0}" type="{0}" charge="{1}"/>'.format(i,r.q)
+        bond = '   <ExternalBond atomName="{}"/>'.format(i)
+        if i not in ['X','Z']:
+            bond += '\n   <ExternalBond atomName="{}"/>'.format(i)
+        residue = '<Residue name="{}">\n{}\n{}\n  </Residue>\n'.format(i,atom,bond)
+        residues += "  " + residue
+    out = " <{0}>\n{1} </{0}>\n\n".format(tag,residues)
+    return out
+	
+def generate_harmonicBond_block(length,k):
+    tag = "HarmonicBondForce"
+    bond = '  <Bond class1="C" class2="C" length="{}" k="{}"/>'.format(length,k)
+    out = " <{0}>\n{1}\n </{0}>\n\n".format(tag,bond)
+    return out
+	
+def generate_yukawa_block(res,temp,ionic,r_cut):
+    kappa, RT, lB = calculate_yukawa_params(temp,ionic)
+    energy_eq = "RT*lB*epsilon1*epsilon2*(exp(-r*kappa)/r-exp(-r_cut*kappa)/r_cut)"
+    tag = "CustomNonbondedForce"
+    outer = ' <{} energy="{}" bondCutoff="0">\n'.format(tag,energy_eq)
+    inner = '  <GlobalParameter name="RT" defaultValue="{}"/>\n'.format(RT)
+    inner += '  <GlobalParameter name="lB" defaultValue="{}"/>\n'.format(lB)
+    inner += '  <GlobalParameter name="kappa" defaultValue="{}"/>\n'.format(kappa)
+    inner += '  <GlobalParameter name="r_cut" defaultValue="{}"/>\n'.format(r_cut)
+    inner += '  <PerParticleParameter name="epsilon"/>\n'
+    for i,r in res.iterrows():
+        inner += '  <Atom type="{}" epsilon="{}"/>\n'.format(i,r.q)
+    out = outer+inner+" </{}>\n\n".format(tag)
+    return out
+
+def generate_ashbaugh_block(res,rcut):
+    lj_eps = 4.184*.2
+    threshold= 2**(1/6)
+    energy_eq = "c1*vlj+c2-shift;vlj=4*epsilon*((sigma/r)^12-(sigma/r)^6);c1=select(delta(d),lambda,s1);c2=select(delta(d),0,s2);s1=select(step(d),lambda,1);s2=select(step(d),0,(1-lambda)*epsilon);d=r-threshold*sigma;shift=lambda*vlj_shift;vlj_shift=4*epsilon*((sigma/rcut)^12-(sigma/rcut)^6);lambda=0.5*(lambda1+lambda2);sigma=0.5*(sigma1+sigma2)"
+    tag = "CustomNonbondedForce"
+    outer = '  <{} energy="{}" bondCutoff="0">\n'.format(tag,energy_eq)
+    inner = '  <GlobalParameter name="epsilon" defaultValue="{}"/>\n'.format(lj_eps)
+    inner += '  <GlobalParameter name="threshold" defaultValue="{}"/>\n'.format(threshold)
+    inner += '  <GlobalParameter name="rcut" defaultValue="{}"/>\n'.format(rcut)
+    inner += '  <PerParticleParameter name="sigma"/>\n'
+    inner += '  <PerParticleParameter name="lambda"/>\n'
+    for i,r in res.iterrows():
+        inner += '  <Atom type="{}" sigma="{}" lambda="{}"/>\n'.format(i,r.sigmas,r.lambdas)
+    out = outer+inner+" </{}>\n\n".format(tag)
+    return out
+
+def generate_forcefield_xml(fasta, residues, xml_name, pH, temp, ionic):
+    residues = adjust_terminals_HIS(residues,pH,fasta)
+    atomTypes_block = generate_atomtypes_block(residues)
+    residues_block = generate_residues_block(residues)
+    harmonic_bond_block = generate_harmonicBond_block(0.38,8033.0)
+    yukawa_block = generate_yukawa_block(residues,temp,ionic,4)
+    ashbaugh_block = generate_ashbaugh_block(residues,2)
+    xml_output="<ForceField>\n\n{}{}{}{}{}</ForceField>".format(atomTypes_block,residues_block,harmonic_bond_block,yukawa_block,ashbaugh_block)
+    with open(xml_name, "w") as f:
+        f.write(xml_output)
+		
+def create_exclusions_file(n_res):
+    excl = []
+    for i in range(n_res-1):
+        excl.append([i,i+1])
+    with open('r1_excl.pkl', 'wb') as fp:
+        pickle.dump(excl, fp)
+
+if len(sys.argv) == 5:
+    pH = float(sys.argv[2])
+    #print(pH)
+    temp = float(sys.argv[3])
+    ionic = float(sys.argv[4])
+else:
+    pH = 7.4
+    temp = 298
+    ionic = 0.2
+
+fasta_file = open(sys.argv[1],mode='r')
+fasta = fasta_file.read().replace("\n","")
+fasta_file.close()
+
+residues = pd.read_csv('residues.csv').set_index('one',drop=False)
+
+generate_forcefield_xml(fasta, residues, "forcefield.xml", pH, temp, ionic)
+create_exclusions_file(len(fasta))

data/scripts_prep/keepH.sh

@@ -0,0 +1,20 @@
+for k in $(echo 5);do
+   rm seg_list.dat
+   rm list
+   for f in $(ls segment_"$k"_input_af_*sys.pdb);do
+      grep ATOM $f |tail -n 1|awk '{print $2}' >>list
+   done
+   atom=$(sort -n -k 1 list|uniq -c |sort -r -n -k 1 |awk '{print $2}' |head -n 1)
+   for i in $(ls segment_"$k"_input_af_*sys.pdb);do
+      atoms=$(grep ATOM $i |tail -n 1|awk '{print $2}')
+      echo $atoms
+      if [ $atoms -eq $atom ]; then
+         echo "$i " >> seg_list.dat
+      fi
+   done
+
+   xtcs=$(cat seg_list.dat |sed 's/_sys.pdb/.rebuilt.xtc/'g|xargs -n 100000000 )
+   echo $xtcs
+   gmx trjcat -f $xtcs  -cat -o segment_"$k"_input_af_rebuilt.xtc
+
+done

data/scripts_prep/make_plumed_analysis.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import Bio.PDB
+import sys
+import glob
+import numpy as np
+import pandas as pd
+import ast
+
+
+df = pd.read_csv('ordered_domains.csv')
+kkeys=[]
+lis=[]
+# Convert the string column back to a list column
+for i in df.keys():
+    hi = df[i].apply(ast.literal_eval)
+    kkeys.append(i)
+    lis.append(hi[0])
+
+print(lis)
+my_df  = pd.DataFrame(columns = kkeys)
+my_df.loc[len(my_df)] = lis
+print(my_df)
+ordered_domains=my_df.to_dict('list')
+
+
+df = pd.read_csv('disordered_domains.csv')
+kkeys=[]
+lis=[]
+# Convert the string column back to a list column
+for i in df.keys():
+    hi = df[i].apply(ast.literal_eval)
+    kkeys.append(i)
+    lis.append(hi[0])
+
+print(lis)
+my_df  = pd.DataFrame(columns = kkeys)
+my_df.loc[len(my_df)] = lis
+print(my_df)
+disordered_domains=my_df.to_dict('list')
+
+
+fasta_file = open(sys.argv[1],mode='r')
+sequence = fasta_file.read().replace("\n","")
+fasta_file.close()
+
+print('key dis',ordered_domains)
+print('key dis',disordered_domains)
+
+
+
+
+f=open("plumed_analysis.dat","w")
+r=open("reconstruct.dat","w")
+f.write("RESTART\n")
+f.write("MOLINFO MOLTYPE=protein STRUCTURE=input_af.pdb\n")
+r.write("MOLINFO MOLTYPE=protein STRUCTURE=input_af.pdb\n")
+f.write("WHOLEMOLECULES ENTITY0=1-"+str(len(sequence))+"\n\n")
+r.write("WHOLEMOLECULES ENTITY0=1-"+str(len(sequence))+"\n\n")
+r.write("DUMPATOMS STRIDE=1 FILE=conf_0_recon.gro ATOMS=1-"+str(len(sequence))+"\n")
+
+r.close()
+
+cvnames=[]
+#CV definition 
+f.write("Rg: GYRATION TYPE=RADIUS ATOMS=1-"+str(len(sequence))+"\n")
+cvnames.append("Rg")
+indexx=1
+for i in ordered_domains.keys():
+    f.write("RMSD"+str(indexx)+": RMSD REFERENCE=struct"+str(indexx)+".pdb TYPE=OPTIMAL\n")
+    cvnames.append("RMSD"+str(indexx))
+    indexx+=1
+indexx=1
+
+
+indexx=1
+
+f.write("# PBMetaD\n")
+f.write("PBMETAD ...\n")
+f.write("    LABEL=pb\n")
+f.write("    ARG=__FILL__\n")
+f.write("    SIGMA=__FILL__\n")
+f.write("    SIGMA_MIN=__FILL__\n")
+f.write("    SIGMA_MAX=__FILL__\n")
+f.write("    ADAPTIVE=DIFF\n")
+f.write("    HEIGHT=__FILL__\n")
+f.write("    PACE=20000000\n")
+f.write("    BIASFACTOR=__FILL__\n")
+f.write("    GRID_MIN=__FILL__\n")
+f.write("    GRID_MAX=__FILL__\n")
+f.write("    GRID_WSTRIDE=__FILL__\n")
+f.write("    WALKERS_MPI\n")
+f.write("    TEMP=__FILL__\n")
+f.write("... PBMETAD\n")
+
+
+cv=','.join(cvnames)
+f.write("PRINT ARG=pb.bias FILE=FULLBIAS STRIDE=1\n")
+f.write("PRINT FILE=COLVAR ARG=__FILL__ STRIDE=1\n")
+f.write("ENDPLUMED\n")
+
+f.close()
+

data/scripts_prep/make_plumed_distmat.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import Bio.PDB
+import sys
+import glob
+import numpy as np
+import pandas as pd
+import ast
+skip_ev=3
+fasta_file = open(sys.argv[1],mode='r')
+PAE_cut=float(sys.argv[2])
+Pr_cut=float(sys.argv[3])
+print(PAE_cut,Pr_cut)
+sequence = fasta_file.read().replace("\n","")
+fasta_file.close()
+
+df = pd.read_csv('ordered_domains.csv')
+kkeys=[]
+lis=[]
+# Convert the string column back to a list column
+for i in df.keys():
+    hi = df[i].apply(ast.literal_eval)
+    kkeys.append(i)
+    lis.append(hi[0])
+
+print(lis)
+my_df  = pd.DataFrame(columns = kkeys)
+my_df.loc[len(my_df)] = lis
+print(my_df)
+ordered_domains=my_df.to_dict('list')
+
+
+df = pd.read_csv('disordered_domains.csv')
+kkeys=[]
+lis=[]
+# Convert the string column back to a list column
+for i in df.keys():
+    hi = df[i].apply(ast.literal_eval)
+    kkeys.append(i)
+    lis.append(hi[0])
+
+print(lis)
+my_df  = pd.DataFrame(columns = kkeys)
+my_df.loc[len(my_df)] = lis
+print(my_df)
+disordered_domains=my_df.to_dict('list')
+
+
+#"MSEYIRVTEDENDEPIEIPSEDDGTVLLSTVTAQFPGACGLRYRNPVSQCMRGVRLVEGILHAPDAGAGNLVYVVNYPKDNKRKMDETDASSAVKVKRAVQKTSDLIVLGLPAKTTEQDLKEYFSTFGEVLMVQVKKDLKTGHSKGFGFVRFTEYETQVKVMSQRHMIDGRACDCKLPNSKQSQDEPLRSRKVFVGRCTEDMTEDELREFFSQYGDVMDVFIPKPFRAFAFVTFADDQIAQSLCGEDLIIKGISVHISNAEPKHNSNRQLERSGRFGGNPGGFGNQGGFGNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSAGMMGMLASQQNQSGPSGNNQNQGNMQREPNQAFGSGNNSYSGSNSGAAIGAGSASNAGSGSGFNGGFGSSMDSKSSGAGM"
+file=open('resid_sel_all.dat', 'w')
+#for i in range(1,len(sequence)+1,skip_ev):
+for i in range(1,len(sequence)+1):
+ #   for j in range(i+skip_ev,len(sequence),skip_ev):
+    for j in range(i+skip_ev,len(sequence)):
+        print(i,j)
+        file.write(str(i)+" "+str(j)+"\n")
+file.close()
+
+
+# ### Read the AF csv contact map distances
+
+# In[2]:
+
+
+# relative path to search all text files
+files = glob.glob("mean_af.csv")
+
+#The contact map is in distance (Angstong)
+print(files[0])
+AF_contacts_file=files[0]
+AF_resid_sel_file="resid_sel_all.dat"
+
+AF_contacts=pd.read_csv(AF_contacts_file)
+AF_contacts
+
+exp=[]
+labels=[]
+lista=[]
+residue_pairs=np.loadtxt(AF_resid_sel_file,usecols=(0,1))
+
+####### Select residues that have a prob at p_d_i<0.03 at the last value
+
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+js = glob.glob("*json")
+
+
+f=open(js[0])
+data=json.load(f)
+l=0
+PAE_array = np.empty(shape=(len(sequence),len(sequence)))
+PAE_array.fill('NaN')
+
+for i in data["predicted_aligned_error"]:
+   print(i,len(i))
+   for j in range(0,len(i)):
+      #if (i[j]<5):
+      PAE_array[l][j]=i[j]
+      #print(i[j])
+   print('l',l)
+   l+=1
+
+plt.matshow(PAE_array,cmap='jet',interpolation='none',vmin=0, vmax=40)
+plt.colorbar()
+plt.savefig('pae_m.png')
+plt.close()
+
+f.close()
+
+
+pddf_pairs=np.load('prob.npy')
+for i in range(len(residue_pairs)):
+
+    r1=int(residue_pairs[i][0])
+    r2=str(int(residue_pairs[i][1]))
+    #This is to accoun that the trajectory starts residues from 1 while the AF contact map from 0.
+    r1_m1=int(residue_pairs[i][0]-1)
+    
+    r1_label=str(r1)
+    r2_label=r2
+    ind1=int(r1_label)-1
+    ind2=int(r2_label)-1
+
+    if ( (PAE_array[ind1][ind2]+PAE_array[ind2][ind1])/2.0 <PAE_cut and (PAE_array[ind1][ind2]+PAE_array[ind2][ind1])/2.0 > 0 and pddf_pairs[ind1][ind2+1][-1]<Pr_cut):
+        exp.append(AF_contacts.loc[r1_m1].at[r2])
+        lista.append([int(r1_label),int(r2_label)])
+
+########################
+exp = np.array(exp)
+labels=np.array(labels)
+
+print(exp)
+print (labels)
+#len(exp)
+print(len(lista))
+print(lista)
+with open('AF_contacts_constr.txt', 'w') as f:
+    for line in range(len(exp)):
+        f.write(str(exp[line])+"\n")
+        
+
+
+# ### AA af -> CG af
+
+# In[ ]:
+
+files = glob.glob("pdb_af.pdb")
+print(files)
+AF_pdb=files[0]
+#"TDP_43_WtoA_7643e_unrelaxed_rank_001_alphafold2_ptm_model_3_seed_000.pdb"
+name='input_af'
+
+import mdtraj as md
+traj=md.load(AF_pdb,top=AF_pdb)
+selection_CA=traj.topology.select('name CA')
+
+traj_CA=traj.atom_slice(selection_CA)
+
+traj_CA.save(name+'.pdb')
+
+
+topology = md.load(name+'.pdb').topology
+
+table, bonds = topology.to_dataframe()
+
+table['name'] = table['resName']
+table['serial'] = table['resSeq']
+table.loc[table.index[-1],'resName']='Z'
+table.loc[table.index[0],'resName']='X'
+table['name'] = table['resName']
+
+print(table['resName'])
+
+topologyCA = md.Topology.from_dataframe(table, bonds)
+print(topologyCA)
+traj = md.load_pdb(name+'.pdb',top=topologyCA)
+traj.save(name+'.pdb')
+
+
+# In[ ]:
+######################
+p = Bio.PDB.PDBParser()
+#path = '/Volumes/BROTZAKIS/PROJECTS_LOCAL/AF_MI/Rela/prep' # your file path here
+structure = p.get_structure('protein', AF_pdb)
+plddt = [a.get_bfactor() for a in structure.get_atoms()]
+aa=[a.get_name() for a in structure.get_atoms()]
+res_ca=[]
+for i in range(0,len(aa)):
+        if (aa[i] == "CA"):
+                res_ca.append(plddt[i])
+plddt_cut=80
+res_ca2=[n > plddt_cut for n in res_ca]
+print(res_ca2)
+
+
+
+print('key dis',ordered_domains)
+print('key dis',disordered_domains)
+######################
+# In[ ]:
+
+
+
+indexx=1
+for i in ordered_domains.keys():
+    print(ordered_domains[i],str(ordered_domains[i][0][0]),str(ordered_domains[i][0][1]))
+    selection=traj.topology.select('resid '+str(ordered_domains[i][0][0])+' to '+str(ordered_domains[i][0][1]))
+    print(selection)
+    sel=traj.atom_slice(selection)
+    sel.save('struct'+str(indexx)+'.pdb')
+    indexx+=1
+
+
+# In[7]:
+
+
+f=open("plumed.dat","w")
+
+f.write("MOLINFO MOLTYPE=protein STRUCTURE=input_af.pdb\n")
+f.write("WHOLEMOLECULES ENTITY0=1-"+str(len(sequence))+"\n\n")
+    
+#Forward model
+ordered_lista=[]
+f.write("distance_rest_domains:  CONTACTMAP ...\n")
+for i in range(0,len(lista)):
+    for k in ordered_domains.keys():
+        if ( lista[i][0] in [*range(ordered_domains[k][0][0],ordered_domains[k][0][1])] and lista[i][1] in [*range(ordered_domains[k][0][0],ordered_domains[k][0][1])]):
+           ordered_lista.append(i)
+ord_index=0
+##########Make restrained contact map######
+restr_array = np.empty(shape=(len(sequence),len(sequence)))
+restr_array.fill('NaN')
+for i in range(0,len(lista)):
+    if (i not in ordered_lista):
+        restr_array[lista[i][0]-1][lista[i][1]-1]=25
+
+plt.matshow(restr_array,cmap='viridis_r',interpolation='none',vmin=0, vmax=25)
+plt.colorbar()
+plt.savefig('restr_distmap.png')
+plt.close()
+
+
+
+##################
+for i in range(0,len(lista)):
+    if (i not in ordered_lista):
+        ord_index+=1
+        f.write("ATOMS"+str(ord_index)+"="+str(lista[i][0])+","+str(lista[i][1])+"\n")
+f.write("SWITCH={CUSTOM FUNC=x R_0=1}\n")
+f.write("...\n\n")
+
+
+#AF exp distance map definition. It has to be 1 to 1
+f.write("af_dist_rest: CONSTANT VALUES=")
+for i in range (0,len(exp)-1):
+   if (i not in ordered_lista):
+       f.write(str(exp[i]*0.1)+",")
+       #f.write(str(exp[i])+",")
+
+#This is converting to nm
+if (len(exp)-1 not in ordered_lista):
+   f.write(str(exp[len(exp)-1]*0.1)+" NODERIV\n\n")
+f.write("af_dist2: CONSTANT VALUES=0 NODERIV\n")
+
+#CV definition 
+f.write("Rg: GYRATION TYPE=RADIUS ATOMS=1-"+str(len(sequence))+"\n")
+
+indexx=1
+cvnames=[]
+for i in ordered_domains.keys():
+    f.write("RMSD"+str(indexx)+": RMSD REFERENCE=struct"+str(indexx)+".pdb TYPE=OPTIMAL\n")
+    cvnames.append("RMSD"+str(indexx))
+    indexx+=1
+
+indexx=1
+RMSDs=[]
+AT=[]
+KAPPAS=[]
+EXP=[]
+EPS=[]
+OFFSET=[]
+for i in ordered_domains.keys():
+    RMSDs.append("RMSD"+str(indexx))
+    KAPPAS.append("100000")
+    AT.append("0.02")
+    EXP.append("2")
+    EPS.append("1")
+    OFFSET.append("0")
+    indexx+=1
+
+f.write("uwall: UPPER_WALLS ARG="+','.join(RMSDs)+" AT="+','.join(AT)+" KAPPA="+','.join(KAPPAS)+" EXP="+','.join(EXP)+" EPS="+','.join(EPS)+" OFFSET="+','.join(OFFSET)+"\n")
+
+
+f.write("PRINT FILE=DISTANCE_MAP_REST ARG=distance_rest_domains.* STRIDE=200\n")
+f.write("PRINT FILE=COLVAR ARG=__FILL__ STRIDE=200\n")
+f.write("PBMETAD ...\n")
+f.write("    LABEL=pb\n")
+f.write("    ARG=__FILL__\n")
+f.write("    SIGMA=__FILL__ \n")
+f.write("    SIGMA_MIN=__FILL__ \n")
+f.write("    SIGMA_MAX=__FILL__ \n")
+f.write("    ADAPTIVE=DIFF\n")
+f.write("    HEIGHT=__FILL__ \n")
+f.write("    PACE=200\n")
+f.write("    BIASFACTOR=__FILL__ \n")
+f.write("    GRID_MIN=__FILL__ \n")
+f.write("    GRID_MAX=__FILL__ \n")
+f.write("    GRID_WSTRIDE=__FILL__ \n")
+f.write("    WALKERS_MPI\n")
+f.write("    TEMP=__FILL__ \n")
+f.write("... PBMETAD\n")
+f.write("METAINFERENCE ...\n")
+f.write("    ARG=(distance_rest_domains.*),pb.bias REWEIGHT\n")
+f.write("    PARARG=(af_dist_rest.*)\n")
+f.write("    SIGMA_MEAN0=1\n")
+f.write("    NOISETYPE=MGAUSS  OPTSIGMAMEAN=SEM AVERAGING=200\n")
+f.write("    SIGMA0=10.0 SIGMA_MIN=0.0001 SIGMA_MAX=10.0 DSIGMA=0.1\n")
+f.write("    MC_STEPS=10\n")
+f.write("    MC_CHUNKSIZE=23\n")
+f.write("    WRITE_STRIDE=10000\n")
+f.write("    TEMP=__FILL__ \n")
+f.write("    LABEL=af_mi_rest_domains\n")
+f.write("... METAINFERENCE\n")
+f.write("FLUSH STRIDE=200\n")
+f.write("PRINT FILE=ENERGY ARG=pb.bias STRIDE=200\n")
+f.write("PRINT ARG=af_mi_rest_domains.*   STRIDE=200 FILE=BAYES_rest_domains\n")
+f.write("ENDPLUMED\n")
+
+f.close()
+

data/scripts_prep/plumed_TDP-43.dat

@@ -0,0 +1,300 @@
+MOLINFO MOLTYPE=protein STRUCTURE=input_af.pdb
+WHOLEMOLECULES ENTITY0=1-414
+
+distance_rest_domains:  CONTACTMAP ...
+ATOMS1=1,4
+ATOMS2=2,5
+ATOMS3=2,20
+ATOMS4=28,79
+ATOMS5=31,79
+ATOMS6=35,79
+ATOMS7=37,79
+ATOMS8=38,79
+ATOMS9=39,79
+ATOMS10=40,79
+ATOMS11=41,79
+ATOMS12=42,79
+ATOMS13=74,79
+ATOMS14=75,79
+ATOMS15=76,79
+ATOMS16=102,105
+ATOMS17=102,106
+ATOMS18=102,150
+ATOMS19=102,151
+ATOMS20=102,152
+ATOMS21=102,155
+ATOMS22=102,158
+ATOMS23=102,159
+ATOMS24=102,161
+ATOMS25=103,106
+ATOMS26=103,107
+ATOMS27=103,108
+ATOMS28=103,120
+ATOMS29=103,121
+ATOMS30=103,124
+ATOMS31=103,130
+ATOMS32=103,131
+ATOMS33=103,132
+ATOMS34=103,133
+ATOMS35=103,134
+ATOMS36=103,135
+ATOMS37=103,147
+ATOMS38=103,148
+ATOMS39=103,149
+ATOMS40=103,150
+ATOMS41=103,151
+ATOMS42=103,152
+ATOMS43=103,153
+ATOMS44=103,154
+ATOMS45=103,155
+ATOMS46=103,156
+ATOMS47=103,157
+ATOMS48=103,158
+ATOMS49=103,159
+ATOMS50=103,160
+ATOMS51=103,161
+ATOMS52=103,162
+ATOMS53=103,175
+ATOMS54=103,176
+ATOMS55=103,177
+ATOMS56=103,178
+ATOMS57=104,178
+ATOMS58=105,178
+ATOMS59=105,179
+ATOMS60=106,178
+ATOMS61=106,179
+ATOMS62=107,178
+ATOMS63=107,179
+ATOMS64=108,178
+ATOMS65=108,179
+ATOMS66=109,178
+ATOMS67=109,179
+ATOMS68=110,178
+ATOMS69=111,178
+ATOMS70=120,178
+ATOMS71=124,178
+ATOMS72=131,178
+ATOMS73=132,178
+ATOMS74=133,178
+ATOMS75=134,178
+ATOMS76=135,178
+ATOMS77=136,178
+ATOMS78=137,178
+ATOMS79=145,178
+ATOMS80=146,178
+ATOMS81=147,178
+ATOMS82=147,179
+ATOMS83=148,178
+ATOMS84=148,179
+ATOMS85=149,178
+ATOMS86=149,179
+ATOMS87=150,178
+ATOMS88=150,179
+ATOMS89=151,178
+ATOMS90=151,179
+ATOMS91=152,178
+ATOMS92=155,178
+ATOMS93=158,178
+ATOMS94=159,178
+ATOMS95=159,179
+ATOMS96=161,178
+ATOMS97=161,179
+ATOMS98=162,178
+ATOMS99=162,179
+ATOMS100=163,178
+ATOMS101=164,178
+ATOMS102=165,178
+ATOMS103=166,178
+ATOMS104=173,178
+ATOMS105=174,178
+ATOMS106=174,179
+ATOMS107=175,178
+ATOMS108=175,179
+ATOMS109=176,179
+ATOMS110=189,192
+ATOMS111=189,233
+ATOMS112=189,240
+ATOMS113=190,193
+ATOMS114=190,194
+ATOMS115=190,195
+ATOMS116=190,209
+ATOMS117=190,210
+ATOMS118=190,211
+ATOMS119=190,212
+ATOMS120=190,213
+ATOMS121=190,214
+ATOMS122=190,216
+ATOMS123=190,217
+ATOMS124=190,218
+ATOMS125=190,219
+ATOMS126=190,220
+ATOMS127=190,230
+ATOMS128=190,231
+ATOMS129=190,232
+ATOMS130=190,233
+ATOMS131=190,234
+ATOMS132=190,235
+ATOMS133=190,236
+ATOMS134=190,237
+ATOMS135=190,238
+ATOMS136=190,239
+ATOMS137=190,240
+ATOMS138=190,241
+ATOMS139=190,242
+ATOMS140=190,243
+ATOMS141=190,244
+ATOMS142=190,257
+ATOMS143=190,258
+ATOMS144=191,260
+ATOMS145=192,260
+ATOMS146=192,261
+ATOMS147=193,260
+ATOMS148=193,261
+ATOMS149=193,262
+ATOMS150=194,260
+ATOMS151=194,261
+ATOMS152=194,262
+ATOMS153=195,260
+ATOMS154=195,261
+ATOMS155=196,260
+ATOMS156=197,260
+ATOMS157=211,260
+ATOMS158=219,260
+ATOMS159=221,260
+ATOMS160=222,260
+ATOMS161=223,260
+ATOMS162=229,260
+ATOMS163=229,261
+ATOMS164=230,260
+ATOMS165=230,261
+ATOMS166=231,260
+ATOMS167=231,261
+ATOMS168=232,260
+ATOMS169=232,261
+ATOMS170=233,260
+ATOMS171=234,260
+ATOMS172=234,261
+ATOMS173=236,260
+ATOMS174=237,260
+ATOMS175=238,260
+ATOMS176=239,260
+ATOMS177=240,260
+ATOMS178=241,260
+ATOMS179=241,261
+ATOMS180=242,260
+ATOMS181=243,260
+ATOMS182=243,261
+ATOMS183=244,260
+ATOMS184=245,260
+ATOMS185=246,260
+ATOMS186=247,260
+ATOMS187=248,260
+ATOMS188=255,260
+ATOMS189=256,260
+ATOMS190=256,261
+ATOMS191=257,260
+ATOMS192=257,261
+ATOMS193=258,261
+ATOMS194=258,262
+ATOMS195=259,262
+ATOMS196=298,301
+ATOMS197=300,303
+ATOMS198=304,307
+ATOMS199=318,321
+ATOMS200=321,324
+ATOMS201=321,325
+ATOMS202=321,326
+ATOMS203=322,325
+ATOMS204=322,326
+ATOMS205=323,326
+ATOMS206=323,327
+ATOMS207=324,327
+ATOMS208=325,328
+ATOMS209=326,329
+ATOMS210=348,351
+ATOMS211=350,353
+ATOMS212=351,354
+ATOMS213=352,355
+ATOMS214=357,360
+ATOMS215=360,363
+ATOMS216=371,374
+ATOMS217=376,379
+ATOMS218=386,389
+ATOMS219=392,395
+ATOMS220=394,397
+ATOMS221=396,399
+ATOMS222=399,402
+ATOMS223=400,403
+ATOMS224=402,405
+ATOMS225=402,406
+ATOMS226=407,411
+ATOMS227=408,411
+SWITCH={CUSTOM FUNC=x R_0=1}
+...
+
+af_dist_rest: CONSTANT VALUES=1.0032847926020623,1.0311901761218905,0.7455537494271994,1.7088723132386805,1.713635583035648,1.5095473634079102,1.1722138326615095,1.2549746362492442,0.9444370301440359,1.2123430594801905,1.5226123476400972,1.3369803665205837,1.4350552991032601,1.271596952714026,0.8981292195618154,1.084396699629724,1.2897209025919436,1.5121250515803697,1.0559752460569143,1.0836912801489234,0.707867799885571,0.9799594385549426,1.2259459158405663,1.5500808618962765,0.9052655544131994,1.3762398231774569,1.5152623694390062,1.68192987870425,1.6158358810469509,1.3273277390748264,1.1055688032880424,0.7953455133363603,1.0522321155294776,1.3843332368880512,1.5040866650640965,1.9200282409787175,1.9061048422008757,1.6269824001938105,1.2354281768202782,1.0676583984866739,0.6573523612692953,0.6149551127105952,0.7450687747448683,0.7618590366095304,0.5613296514376999,1.0004315715283156,0.8841317959129811,0.5908093221485615,0.9980626434087752,1.263882938399911,1.1281702172011137,1.1415027465671301,1.3193011650815607,1.4435925820842384,1.1070262966677549,1.31037020906806,0.8523610839620233,0.6740215603262186,1.1396584818139672,0.7467208079993726,1.1898928672075273,0.4552792670205237,0.8395836766809226,1.017876618169248,1.3974865483120085,1.0051763331517576,1.1795675404369834,1.4049736192449929,1.459107712842524,1.5650913815945389,1.59471937045455,1.5574198851361871,1.3527641544118525,1.4486837401986123,1.1706370314583183,1.4376086220145226,1.1653784492984414,1.7256552413105966,1.4055226838216186,1.427379228360951,0.9732663879171014,1.2228183863684539,0.9791120953857901,1.3816555082798005,0.7472525445744396,1.2260105818510056,1.0942220810800791,1.5762166528031232,1.1099413389340045,1.5934589028358461,1.434581000171602,1.630307266302407,1.2566113555803897,1.5485478518530726,1.81100521478802,1.3977122131735087,1.7663343697786331,1.162875016592443,1.430508084408939,1.6525550663471222,1.7831156572327018,1.785999870300293,1.6451806398108602,1.4654783833771945,1.2249537132680417,1.3939366523176433,1.0378110969439147,1.3681153113022448,0.9096965923905374,1.0000388860702516,1.2344506287947297,0.8380141992121936,0.8398237504065037,1.0381346080452205,1.4053251046687365,2.167353528179228,1.9828473469242454,1.528205112926662,1.8880876734852792,2.016520819067955,1.5591710267588497,1.5430319080129267,1.4479076098650694,1.236793963611126,1.3141041703522205,1.5537533987313508,1.5692297449335457,1.121472422592342,1.1581335106864572,0.91726502366364,0.9532682375982404,1.2505696462467313,1.1205617936328052,0.6651569686830044,1.0967526400461791,1.140981236472726,0.6660797173157335,0.8100334141403437,1.2571399945765735,1.1800085892900825,0.9563456442207099,1.2749242424964906,1.1340990519151093,0.9276029605418444,0.5047679111361504,0.9407211143523457,0.6309511506929995,1.074688763730228,1.2769522689282895,0.5026340553537011,0.7528656773269176,1.0695354964584112,0.9779949204996229,1.300148520246148,1.1204699540510774,1.5327852481976152,1.4124552657827738,1.2289950992912055,1.0769147641956807,1.4084996918216348,1.2656738823279738,1.0495817463845016,1.1779037110507489,1.095422700792551,1.3921284958720208,0.6597456347197295,1.0181769583374263,0.9364564327523113,1.3884215405210854,0.9055566122755409,1.1202643141150477,1.596531630679965,1.518569000810385,1.201885962113738,1.5418776268139482,1.3921274995431305,0.8939561096951366,1.1407407995313406,1.4240611214190722,1.4680646168068052,1.164111346192658,1.5423512058332562,0.9587342431768777,1.234714117459953,1.4588959828019143,1.6841880340129138,1.6086150078102948,1.4471713358536364,1.2795427026227117,1.444847053103149,1.002940315194428,1.3102364122867585,0.9022639036178589,1.2806951073929669,0.989768156595528,0.9441032137721775,0.9244450947269796,0.9194099459797145,0.9374097302556038,0.7403792202472688,0.8557088484987617,1.1484702169895173,0.7273479776456953,0.8502402873709798,0.6981262018904091,0.8579837949946523,0.7507739521563054,0.7508660649880767,0.7793322708457708,0.9225482998415828,0.9773600473999976,0.9583356784656645,0.9616499662399293,0.9474486894905567,1.0015693807974457,0.9874830450862646,0.9377102639526129,0.9753739142790437,0.9764441156759859,0.973571441695094,0.950638056918979,0.9416109262034296,0.9724794756621122,0.966675561480224,1.2231912130489944,1.235738294199109,0.9654571769759062 NODERIV
+
+af_dist2: CONSTANT VALUES=0 NODERIV
+Rg: GYRATION TYPE=RADIUS ATOMS=1-414
+RMSD1: RMSD REFERENCE=struct1.pdb TYPE=OPTIMAL
+RMSD2: RMSD REFERENCE=struct2.pdb TYPE=OPTIMAL
+RMSD3: RMSD REFERENCE=struct3.pdb TYPE=OPTIMAL
+
+uwall: UPPER_WALLS ARG=RMSD1,RMSD2,RMSD3 AT=0.02,0.02,0.02 KAPPA=100000,100000,100000 EXP=2,2,2 EPS=1,1,1 OFFSET=0,0,0
+Rg1: GYRATION TYPE=RADIUS ATOMS=1-2
+Rg2: GYRATION TYPE=RADIUS ATOMS=80-103
+Rg3: GYRATION TYPE=RADIUS ATOMS=179-190
+Rg4: GYRATION TYPE=RADIUS ATOMS=261-414
+PRINT FILE=COLVAR ARG=Rg,RMSD1,RMSD2,RMSD3,Rg1,Rg2,Rg3,Rg4 STRIDE=200
+PRINT FILE=DISTANCE_MAP_REST ARG=distance_rest_domains.* STRIDE=200
+
+t1: CENTER ATOMS=3-40
+t2: CENTER ATOMS=41-79
+t3: CENTER ATOMS=104-140
+t4: CENTER ATOMS=141-178
+
+torsion1: TORSION ATOMS=t1,t2,t3,t4
+
+tt1: CENTER ATOMS=104-140
+tt2: CENTER ATOMS=141-178
+tt3: CENTER ATOMS=191-225
+tt4: CENTER ATOMS=226-260
+
+torsion2: TORSION ATOMS=tt1,tt2,tt3,tt4
+
+
+# PBMetaD
+PBMETAD ...
+    LABEL=pb
+    ARG=torsion1,torsion2
+    SIGMA=1000
+    SIGMA_MIN=0.05,0.05
+    SIGMA_MAX=0.1,0.1
+    ADAPTIVE=DIFF
+    HEIGHT=0.5
+    PACE=200
+    BIASFACTOR=35
+    GRID_MIN=-pi,-pi
+    GRID_MAX=pi,pi
+    GRID_WSTRIDE=5000
+    WALKERS_MPI
+    TEMP=298
+... PBMETAD
+
+METAINFERENCE ...
+    ARG=(distance_rest_domains.*),pb.bias REWEIGHT
+    PARARG=(af_dist_rest.*)
+    SIGMA_MEAN0=1
+    NOISETYPE=MGAUSS  OPTSIGMAMEAN=SEM AVERAGING=200
+    SIGMA0=10.0 SIGMA_MIN=0.0001 SIGMA_MAX=10.0 DSIGMA=0.1
+    MC_STEPS=10
+    MC_CHUNKSIZE=20
+    WRITE_STRIDE=10000
+    TEMP=298
+    LABEL=af_mi_rest_domains
+... METAINFERENCE
+
+FLUSH STRIDE=200
+PRINT FILE=ENERGY ARG=pb.bias STRIDE=200
+PRINT ARG=af_mi_rest_domains.*   STRIDE=200 FILE=BAYES_rest_domains
+ENDPLUMED

data/scripts_prep/plumed_analysis_TDP-43.dat

@@ -0,0 +1,51 @@
+RESTART
+MOLINFO MOLTYPE=protein STRUCTURE=input_af.pdb
+WHOLEMOLECULES ENTITY0=1-414
+
+Rg: GYRATION TYPE=RADIUS ATOMS=1-414
+Rg1: GYRATION TYPE=RADIUS ATOMS=1-2
+Rg2: GYRATION TYPE=RADIUS ATOMS=80-103
+Rg3: GYRATION TYPE=RADIUS ATOMS=179-190
+Rg4: GYRATION TYPE=RADIUS ATOMS=261-414
+RMSD1: RMSD REFERENCE=struct1.pdb TYPE=OPTIMAL
+RMSD2: RMSD REFERENCE=struct2.pdb TYPE=OPTIMAL
+RMSD3: RMSD REFERENCE=struct3.pdb TYPE=OPTIMAL
+
+t1: CENTER ATOMS=3-40
+t2: CENTER ATOMS=41-79
+t3: CENTER ATOMS=104-140
+t4: CENTER ATOMS=141-178
+
+torsion1: TORSION ATOMS=t1,t2,t3,t4
+
+tt1: CENTER ATOMS=104-140
+tt2: CENTER ATOMS=141-178
+tt3: CENTER ATOMS=191-225
+tt4: CENTER ATOMS=226-260
+
+torsion2: TORSION ATOMS=tt1,tt2,tt3,tt4
+
+
+# PBMetaD
+PBMETAD ...
+    LABEL=pb
+    ARG=torsion1,torsion2
+    SIGMA=1000
+    SIGMA_MIN=0.05,0.05
+    SIGMA_MAX=0.1,0.1
+    ADAPTIVE=DIFF
+    HEIGHT=0.5
+    PACE=200
+    BIASFACTOR=35
+    GRID_MIN=-pi,-pi
+    GRID_MAX=pi,pi
+    GRID_WSTRIDE=20000000
+    WALKERS_MPI
+    TEMP=298
+... PBMETAD
+
+
+PRINT ARG=pb.bias FILE=FULLBIAS STRIDE=1
+PRINT FILE=COLVAR ARG=Rg,Rg1,Rg2,Rg3,Rg4,torsion1,torsion2,RMSD1,RMSD2,RMSD3 STRIDE=1
+ENDPLUMED
+

data/scripts_prep/pulchra.py

@@ -0,0 +1,27 @@
+from mpi4py import MPI
+import sys
+import os
+from glob import glob
+import numpy as np
+
+ntot=60000
+
+k_file = open('k.dat',mode='r')
+k = k_file.read().replace("\n","")
+k_file.close()
+k=int(k)
+pdb_files = glob('./segment_'+str(k)+'_input_af_*.pdb')
+print(pdb_files)
+split=int(np.ceil(len(pdb_files)/5))
+print ('SPLIT='+str(split))
+start=0
+end=int(np.ceil(len(pdb_files)))
+print('end',end)
+for j in range(start,len(pdb_files),2):
+   print('In j loop', j,'lenpdb',len(pdb_files))
+   name='segment_'+str(k)+'_input_af_'+str(j)
+   os.system("pulchra -v {name}.pdb".format(name=name))
+   os.system('echo "6 \n 7" | gmx pdb2gmx -f {name}.rebuilt.pdb -o {name}_sys.pdb -p topol_{name}.top -i {name}.itp'.format(name=name))
+   os.system("rm topol_{name}.top {name}.itp em_{j}.trr em_{j}.tpr em_{j}.log em_{j}.edr conf_box_{j}.gro ".format(j=j,name=name))
+   os.system('echo "0"|gmx trjconv -f  {name}_sys.pdb -s  {name}_sys.pdb -o {name}.rebuilt.xtc'.format(j=j,name=name))
+

data/scripts_prep/pulchra.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+for i in $(echo 5);do
+	echo $i > k.dat
+	 python pulchra.py 
+done

data/scripts_prep/resample.py

@@ -0,0 +1,66 @@
+import itertools
+import os
+from pathlib import Path
+
+import numpy as np
+from matplotlib import rc
+import matplotlib.pyplot as plt
+#from matplotlib.colors import LinearSegmentedColormap
+#from mpl_toolkits.axes_grid1 import make_axes_locatable
+import mdtraj as md
+#import seaborn as sns
+#from scipy.stats import gaussian_kde
+
+#rc("font", **{"family": "sans-serif",
+#colors = sns.husl_palette()
+
+#get_ipython().run_line_magic('matplotlib', 'inline')
+#get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
+
+
+# In[5]:
+
+
+#from typing import Union, List, Callable
+
+
+KBT = 2.494339 # kJ/mol, 300K
+n_replica = 1
+top_file = "input_af.pdb"
+biased_cvs = {}
+time=np.loadtxt("FULLBIAS")[:, 0]
+bias=np.loadtxt("FULLBIAS")[:, 1]
+bias10=bias
+print(len(bias10))
+weights = np.exp(bias10/ KBT)
+weights /= weights.sum()
+nframes = {weights.shape[0]}
+
+
+# ## Clustering Full Protein
+# Gromos clustering with a trajectory subsample, based on CA RMSD. We first evaluate different cutoffs:
+# gmx trjconv -f cat_traj_recon.gro -s structure.pdb -o cat_traj_recon_noh_skip10_noh.gro -skip 10
+#  
+
+# In[15]:
+
+
+#traj = md.load_xtc("conf_0_recon.xtc",top='output.pdb')
+traj = md.load("conf_0_recon.gro",stride=1)
+
+
+# In[25]:
+
+
+N = 1000
+n_rounds = 5
+
+folder = Path(f"./")
+n_frames = traj.n_frames
+inds = np.arange(n_frames)
+
+for i in range(n_rounds):
+    (folder / f"r{i}").mkdir(exist_ok=True)
+    inds_sample = np.random.choice(inds, size=N, replace=False,
+                               p=weights / weights.sum())
+    traj[inds_sample].save_xtc((folder / f"r{i}" / f"conf-protein.xtc").as_posix())

data/scripts_prep/residues.csv

@@ -0,0 +1,21 @@
+three,one,MW,lambdas,sigmas,q,CALVADOS1,CALVADOS2
+ARG,R,156.19,0.7307624767517166,0.6559999999999999,1,0.7249915947715212,0.7307624767517166
+ASP,D,115.09,0.0416040480605567,0.5579999999999999,-1,0.0291821237763497,0.0416040480605567
+ASN,N,114.1,0.4255859009787713,0.568,0,0.4383272997027284,0.4255859009787713
+GLU,E,129.11,0.0006935460962935,0.5920000000000001,-1,0.0061002816086497,0.0006935460962935
+LYS,K,128.17,0.1790211738990582,0.636,1,0.0586171731586979,0.1790211738990582
+HIS,H,137.14,0.4663667290557992,0.608,0,0.4651948082346978,0.4663667290557992
+GLN,Q,128.13,0.3934318551056041,0.602,0,0.3268188050525212,0.3934318551056041
+SER,S,87.08,0.4625416811611541,0.518,0,0.4648570130065605,0.4625416811611541
+CYS,C,103.14,0.5615435099141777,0.5479999999999999,0,0.610362354303913,0.5615435099141777
+GLY,G,57.05,0.7058843733666401,0.45,0,0.7012713677972457,0.7058843733666401
+THR,T,101.11,0.3713162976273964,0.562,0,0.5379777613307019,0.3713162976273964
+ALA,A,71.07,0.2743297969040348,0.504,0,0.0011162643859539,0.2743297969040348
+MET,M,131.2,0.5308481134337497,0.618,0,0.7458993420826714,0.5308481134337497
+TYR,Y,163.18,0.9774611449343455,0.6459999999999999,0,0.9950108229594324,0.9774611449343455
+VAL,V,99.13,0.2083769608174481,0.5860000000000001,0,0.4185006852559869,0.2083769608174481
+TRP,W,186.22,0.9893764740371644,0.6779999999999999,0,0.9844235478393932,0.9893764740371644
+LEU,L,113.16,0.6440005007782226,0.618,0,0.5563020305733198,0.6440005007782226
+ILE,I,113.16,0.5423623610671892,0.618,0,0.6075268330845265,0.5423623610671892
+PRO,P,97.12,0.3593126576364644,0.5559999999999999,0,0.3729641853599348,0.3593126576364644
+PHE,F,147.18,0.8672358982062975,0.636,0,0.9216959832175944,0.8672358982062975

data/scripts_prep/script.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+nrep=$1
+rm COLVAR* HILLS* FULLBIAS
+cp ../HILLS.* .
+cp ../GRID* .
+cp ../BAYES* .
+
+rm segment*pdb segment*xtc
+#Convert dcds to xtc
+python dcd2xtc.py $nrep
+for ((c=0;c<$nrep;c++))
+do
+
+for part in $(ls output_"$c".xtc)
+do
+echo $part
+gmx trjconv -f $part -s ../input_af.pdb -o nosolv_$c.xtc <<EOF
+0
+EOF
+done
+done
+
+#concatenate xtcs to a single xtc
+gmx trjcat -f nosolv_*.xtc -cat -o cat_trjcat.xtc -settime
+
+plumed driver --plumed plumed_analysis.dat --mf_xtc cat_trjcat.xtc
+plumed --no-mpi driver --plumed reconstruct.dat --mf_xtc cat_trjcat.xtc --timestep 1
+
+#Sample the structural ensemble by weights.
+python resample.py
+
+
+#Backmap from coarse-grained to atomistic
+python backmap.py $nrep
+sh pulchra.sh
+sh keepH.sh

data/scripts_prep/simulate.py

@@ -0,0 +1,129 @@
+#python simulate.py 7.4 298
+from openmm.app import *
+from openmm import *
+from openmm.unit import *
+from sys import stdout
+import pandas as pd
+import numpy as np
+import pickle
+import mdtraj as md
+from mpi4py import MPI
+from openmmplumed import PlumedForce
+import time
+comm1 = MPI.COMM_SELF
+comm2 = MPI.COMM_WORLD
+
+from sys import stdout, argv
+platform = Platform.getPlatformByName('CPU')
+#properties={}
+#properties["Threads"]="1"
+
+residues = pd.read_csv("residues.csv").set_index('one')
+fasta_file = open('sequence.dat',mode='r')
+fasta = fasta_file.read().replace("\n","")
+fasta_file.close()
+
+pH = float(argv[1])
+temp = float(argv[2])
+#fasta = """
+#MSEYIRVTEDENDEPIEIPSEDDGTVLLSTVTAQFPGACGLRYRNPVSQCMRGVRLVEGILHAPDAGAGNLVYVVNYPKDNKRKMDETDASSAVKVKRAVQKTSDLIVLGLPAKTTEQDLKEYFSTFGEVLMVQVKKDLKTGHSKGFGFVRFTEYETQVKVMSQRHMIDGRACDCKLPNSKQSQDEPLRSRKVFVGRCTEDMTEDELREFFSQYGDVMDVFIPKPFRAFAFVTFADDQIAQSLCGEDLIIKGISVHISNAEPKHNSNRQLERSGRFGGNPGGFGNQGGFGNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSAGMMGMLASQQNQSGPSGNNQNQGNMQREPNQAFGSGNNSYSGSNSGAAIGAGSASNAGSGSGFNGGFGSSMDSKSSGAGM""".replace('\n', '')
+
+def adjust_terminals_HIS(r,pH,fasta):
+    r.loc['H','q'] = 1. / ( 1 + 10**(pH-6) )
+    r.loc['X'] = r.loc[fasta[0]]
+    r.loc['X','q'] = r.loc[fasta[0],'q'] + 1.
+    r.loc['X','MW'] = r.loc[fasta[0],'MW'] + 2.
+    r.loc['X','three'] = 'X'
+    
+    r.loc['Z'] = r.loc[fasta[-1]]
+    r.loc['Z','q'] = r.loc[fasta[-1],'q'] - 1.
+    r.loc['Z','MW'] = r.loc[fasta[-1],'MW'] + 16.
+    r.loc['Z','three'] = 'Z'
+    
+    return r.set_index('three')
+
+residues = adjust_terminals_HIS(residues,pH,fasta)
+
+
+atomic_number = 117
+for i,r in residues.iterrows():
+    name = 'carbon-{}'.format(i)
+    symbol = 'C-{}'.format(i)
+    new_element = Element(atomic_number,name,symbol,r.MW*dalton)
+    atomic_number += 1
+
+pdb = PDBFile('input_af.pdb')
+
+top = pdb.topology
+
+for chain in top._chains:
+    for residue in chain._residues:
+        for atom in residue._atoms:
+            atom.element = Element._elements_by_symbol['C-{}'.format(residue.name)]
+            
+atoms = list(top.atoms())
+
+for i in range(len(fasta)-1):
+    top.addBond(atoms[i],atoms[i+1])
+    
+forcefield = ForceField("forcefield.xml")
+
+system = forcefield.createSystem(top,nonbondedMethod=CutoffNonPeriodic)
+
+system.removeForce(3)
+
+
+with open('r1_excl.pkl', 'rb') as fp:
+    r1_exclusions = pickle.load(fp)
+    
+forces = system.getForces()
+
+forces[1].setCutoffDistance(4*nanometers)
+for bond in r1_exclusions:
+    forces[1].addExclusion(bond[0],bond[1])
+    
+forces[2].setForceGroup(1)
+forces[2].setCutoffDistance(2*nanometers)
+for bond in r1_exclusions:
+    forces[2].addExclusion(bond[0],bond[1])
+
+#open text file in read mode
+plumed_file = open("plumed.dat", "r")
+script= plumed_file.read()
+plumed_file.close()
+script = "".join(script)
+print(script)
+
+system.addForce(PlumedForce(script, comm1, comm2))
+                        
+N_res = len(fasta)
+N_save = 3000 if N_res < 100 else int(np.ceil(3e-4*N_res**2)*1000)
+N_steps = 1100*N_save
+
+
+DCD_output_file = "output_{}.dcd".format(comm2.Get_rank())
+#DCD_output_file = "output.dcd".format(comm2.Get_rank())
+stats_output_file = "stats_{}.csv".format(comm2.Get_rank())
+#stats_output_file = "stats.csv".format(comm2.Get_rank())
+integrator = LangevinIntegrator(temp*kelvin, 0.01/picosecond, 0.005*picoseconds)
+
+
+#simulation = Simulation(top, system, integrator,platform,properties)
+simulation = Simulation(top, system, integrator,platform)
+    
+simulation.context.setPositions(pdb.positions)
+
+#simulation.minimizeEnergy()
+
+simulation.loadCheckpoint("checkpoint")
+
+#simulation.reporters.append(DCDReporter(DCD_output_file, N_save))
+simulation.reporters.append(DCDReporter(DCD_output_file, 3000))
+
+simulation.reporters.append(StateDataReporter(stats_output_file, N_save, step=True, potentialEnergy=True, temperature=True))
+time1=time.time()
+#simulation.step(N_steps)
+simulation.step(3000000)
+time2=time.time()
+print(time2-time1)
+