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[ Folding procedure | Protein grid docking | Docking simple models | Homology modeling faq | Faq chemsuper | Faq cheminformatics ]
In the following script you are going to search for the lowest energy conformation using the Biased Probability Monte Carlo procedure to generate new conformations and full-atom energy plus solvation electrostatics, surface and entropy contributions. Start 3 or more independent simulations and let them run to convergence. Two features are indicative of convergence: the plot of the best energy achieved should be flat for sufficiently long (store the output in f1.ou and run the following macro: plotBestEnergies "f1" 100. "append display"); and the lowest energy conformation in different simulations are close, e.g. # peptide "pep.se" ; runs: "f1" and "f2" build "pep" display read conf "f1" 0 show stack read conf "f2" 0 show stackWatching trajectory files f1.mov and f2.mov may also be useful. (See also How to evaluate helicity of a peptide from the BPMC simulation and How to calculate an ensemble average). Now, the script: # Example folding script. Use as directed. read libraries build "pep16" # your peptide sequence is in pep16.se file. rename a_*. "f2" # specifies current name. # Several runs (f2,f3, etc.) are recommended nvar = Nof( v_//* ) # number of variables nProc=4 # if you are using parallel version. mncallsMC = nvar*50000 # maximal number of energy evaluations mncalls = 170+nvar*3 # maximal n_of minimization calls after # each random change temperature = 600 # optimal temperature for the simulation tolGrad = 0.01 # exit minimization when gradient is < 0.01 mcBell = 1.0 # the default width of the MC probability distributions mnconf = 40 # maximal n_of low-energy conformations saved # in the stack (f2.cnf file) mnvisits = 25 # if stuck for >= 25 times, push it out mnreject = 10 mnhighEnergy = 30 l_bpmc = yes # use biased probability electroMethod = "MIMEL" surfaceMethod = "constant tension" set terms "vw,14,hb,el,to,sf,en" # ECEPP/2 energy + solvation + entropy (see icm.hdt file) fix v_//?vt* # exclude irrelevant virtual variables specifying # absolute molecular position set vrestraint a_/* # load preferred backbone and side-chain angle zones # for the biased probability MC randomize v_//!omg 180.0 # create random starting conformation vicinity = 15.0 compare v_//phi,psi # use these variables to compare structure montecarlo trajectory # run it and record a trajectory file. # watch the movie later by: # read trajectory "f2"; display ribbon # display trajectory "f2" 4. 8. # analyze the best conf. in the stack by: # build "pep16"; read stack; show stack all # load conf 1 quit
This is a so called "local docking procedure" which docks all orientations of the protein ligand to a certain orientation of the protein receptor. The "global docking procedure" is somewhat different. You may follow the menu items in Docking.Protein-protein or run the docking scripts directly. To illustrate the principal commands and functions we will also consider a series of shell commands to perform a docking procedure. We will use the following steps from the shell to dock the proteins chymotrypsin (5cha) and APPI (1aap). The real structure of the complex is known (1ca0), which can help us to test the validity of the method. This procedure has been recently tested in a dataset of 24 known protein-protein complexes ( Fernández-Recio,Totrov,Abagyan, 2002) The procedure includes the following steps:
This procedure is relatively old and was used previously to explicitly dock two proteins starting from simplified objects. The best solutions are refined in all-atom representation. Currently we prefer docking into grid (see above).
Have an alignment and a pdb file with the template handy, say "sx.ali" "x.brk". If you have a homology module key you can use the build model command and refine the model with the refineModel macro. The build model command builds a complete model and searches for matching loops in all pdb files. You can run the build model command from the GUI interface ( menu Homology ) AlignSS is a good shell function to make a sequence-structure alignment. It incorporates solvent accessibility and secondary structure into the alignment procedure. Alternatively, allow the build model command to perform the alignment on the fly. In the absence of the Homology module, use the following macros/scripts:
Sometimes you like to turn a show a_/* command for residue selections into a proper table. To create an ICM table with this one needs to create columns separately and add them as columns to a table. For example if we have a residue selection res with n residues:
read pdb "1crn" align number # to have numbers from 1 to n show surface area mute # compute surface areas res = a_/10:20 # residue range of interest n = Nof(res) # the number of residues. add column t Sarray(n, Name(Obj(res))[1]),Trim(Label(res),all),Area(res),Area(res)/Area(res type) The last column is the relative residue accessibility. The add column command will create a table with four columns, the last being the relative residue accessibility.
#>T t #>-A-----------B-----------C-----------D---------- 1mui T12 85.264893 0.560953 1mui I13 2.073181 0.010687 1mui K14 102.661064 0.479725 1mui I15 2.916692 0.015034 1mui G16 44.870205 0.50416 1mui G17 66.557358 0.747835 1mui Q18 67.372437 0.354592 1mui L19 141.619446 0.71525 1mui K20 49.295151 0.230351 A number of other properties which can be calculated for residue selections can be added to this table, e.g. Then you can also append rows or other tables from different pdbs to the same table tt with another pdb with this:
add t tt # will append rows of tt to column t
Use the chemSuperBG macro that is designed to take one molecule as a template and flexibly overlay in an optimal way other chemicals from a chemical table to the template. Method.The chemical table can contain 0D, 2D or 3D representation of a compound. The compounds will be optimally superimposed to one or several templates. The flex-overlay tool will convert them on the fly to a flexible 3D form, optimize and dock it to the average property representation of the template compounds. From the template 3D seven grids ( m_g1,m_g2,.. ) will be generated for different atom types. These grids will use the Gaussian expansion of the properties and will be averaged for the superimposed molecules. Each ligand will globally optimize both its internal energy and the grid-map fit. The result will be saved as a 3D .sdf file. From ICM command line the syntax is the following: chemSuperBG ms_template(s)> <chem_table r_effort l_Sample_Rings From the operating system you need to run the _chemSuper script with the following arguments: _chemSuper templates.mol chem_table.sdf superimposed_output.sdf [effort=1.] [-r] The -r option means that the rings will be considered as flexible. From GUI: Select rows in your chemical table, click on the mol-column and select Chemistry/Chemical Template Superposition
[ Faq molcart query | Faq mac gui preferences | Faq molcart dump | Diverse subset ]
This script will read each molecule one by one and convert them to 3D connect molcart "myhost"//"user"//"pass"//"dbase" # use Name(molcart connect) to see if you are connected. for i=1,Nof("asgsynth") # name of a vendor table query molcart "select * from asgsynth where molid="+i name="t" if Nof(t)!=1 | Smiles(Parray(t.mol[1] mol))[1] == "" continue read mol input=t.mol[1] convert2Dto3D a_ yes yes no no # or anything else. this is a macro delete a_*. # clean up endfor
Situation: you are stuck with large font size in ICM workspace or other bad GUI preference and can not restore the defaults: Solution:
Read the molcart page for a general set ICM-molcart commands. Follow these steps:
write molcart table="asgsynth" "tmp2.sdf" See: molcart
Use the make tree command. The group command will then select unique molecules and one will be able to add the columns needed in the cntrs table. Example: read table mol "drugs.sdf" name="t" make tree full t matrix column={"mol"} split="cl" K = 100 # select 100 centers I_out = Split( t.cluster K ) # split into 1K clusters I_out = Index( t.cluster center r_out ) # uses r_out from the above # I_out contains K of centroid indexes t1_K = t[I_out] # your subset
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