Matthew:
One more thing to think about:
Maybe you do not really need to do all the residues on one run. For
example, a solvent exposed Aspartic acid y VERY unlikely to be
protonated at pH 10, and a lysine is very unlikely to be unprotonated at
pH 2.
So, what we have done in the past is to do 'shorter' simulations with
all residues and extreme pHs, and then trim the set and only looking at
tings we expect to really change protonation states at the pHs we want
to look at.
We have also looked at clusters of residues, where we keep spatially
close residues titrating, but keep thing far away as fixed.
This requires knowing your system, trying many things, but in the end,
it might be worth it.
Adrian
On 6/24/21 2:47 PM, Matthew Guberman-Pfeffer wrote:
> [External Email]
>
> Hello, Vinicius,
>
> Yes, that makes complete sense. Thank you! I alway learn something from our exchanges.
>
> Best,
> Matthew
>
>> On Jun 24, 2021, at 2:41 PM, Cruzeiro, Vinicius <vcruzeiro.UCSD.EDU> wrote:
>>
>> *Message sent from a system outside of UConn.*
>>
>>
>> Hello Matthew,
>>
>> I am going to assume you are inquiring about the case where the MD is done in an explicit solvent representation. In this case, the solvent relaxation is generally the costliest step in CpHMD in comparison to regular MD. Before solvent relaxation is executed, one protonation state change attempt is performed for each titratable residue (as a reminder, the solvent is stripped and the Delta G necessary for the Monte Carlo step is computed with an implicit solvent representation). So, when you have more titratable residues, this is going to increase the amount of GB calculations to be performed, but the solvent relaxation cost is unaffected (in principle!). So, in principle, the computational cost should not change dramatically when you consider more titratable residues. One exception to this that I can think is the following: since solvent relaxation is executed only if at least one protonation state change is accepted, when you have more titratable residues you might add residues that are active at a given pH value which might lead to more solvent relaxation steps; effectively, this might make the simulation slower at pH values that previously did not have many solvent relaxation steps. Makes sense?
>>
>> I hope this helps,
>> Best,
>>
>> Vinícius Wilian D. Cruzeiro, PhD
>> Postdoctoral Researcher
>> San Diego Supercomputer Center
>> Department of Chemistry and Biochemistry
>> University of California, San Diego
>> Voice: +1(858)246-5584
>> Twitter: .vwcruzeiro
>> ________________________________
>> From: Matthew Guberman-Pfeffer <matthew.guberman-pfeffer.uconn.edu>
>> Sent: Thursday, June 24, 2021 11:22 AM
>> To: AMBER Mailing List <amber.ambermd.org>
>> Subject: Re: [AMBER] Constant pH for large number of titratable residues
>>
>> Hello Vinicius!
>>
>> Thanks for your quick, and as usual, very helpful reply. Just as a curious follow-up, is there a known scaling relationship for how slowly the predicted pKas converge as a function of the number of titratable groups? Part of the concern behind my question was that having ~80 titratable groups wouldn’t be tractable. If my instinct was wrong on that point, that’s great.
>>
>> Best,
>> Matthew
>>
>>> On Jun 24, 2021, at 2:03 PM, Cruzeiro, Vinicius <vcruzeiro.UCSD.EDU> wrote:
>>>
>>> *Message sent from a system outside of UConn.*
>>>
>>>
>>> Hello Matthew,
>>>
>>> At the moment, you can control the maximum number of titratable residues inside the source code, at $AMBERHOME/src/pmemd/src/constantph.F90 for pmemd or $AMBERHOME/AmberTools/src/sander/constantph.F90 for Sander. You need to modify the following lines:
>>>
>>> ! Limits on the sizes of the data structures
>>> #define MAX_TITR_RES 50
>>> #define MAX_TITR_STATES 200
>>> #define MAX_ATOM_CHRG 1000
>>> #define MAX_H_COUNT 4
>>>
>>> As you can see, the default limit is 50 titratable residues. Once you change the limits above, you need to recompile pmemd and/or sander before running your simulation.
>>>
>>> However, we have recently implemented a new namelist in the CPIN file (called CNSTPHE_LIMITS) that specifies these limits. Parmed from AmberTools21 now prints these limits automatically. Sander from AmberTools21 is already capable of reading it, and pmemd from Amber22 will be able as well. Thus, with this change, there is no need any more to recompile the code when you have many titratable residues; from now on, Amber should not complain when users simulate many titratable residues or states, etc 😉
>>>
>>> I hope this helps,
>>> All the best,
>>>
>>> Vinícius Wilian D. Cruzeiro, PhD
>>> Postdoctoral Researcher
>>> San Diego Supercomputer Center
>>> Department of Chemistry and Biochemistry
>>> University of California, San Diego
>>> Voice: +1(858)246-5584
>>> Twitter: .vwcruzeiro
>>> ________________________________
>>> From: Matthew Guberman-Pfeffer <matthew.guberman-pfeffer.uconn.edu>
>>> Sent: Thursday, June 24, 2021 9:35 AM
>>> To: amber.ambermd.org <amber.ambermd.org>
>>> Subject: [AMBER] Constant pH for large number of titratable residues
>>>
>>> Dear Amber Community,
>>>
>>> What is the practical limit on the number of residues I should designate as titratable for a constant pH simulation? I want to estimate pKas, as well as study conformational effects at pH extremes (2 versus 10), but my system has 62 acidic and 67 basic residues.
>>>
>>> Best regards,
>>> Matthew
>>>
>>>
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>>> AMBER mailing list
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--
Dr. Adrian E. Roitberg
V.T. and Louise Jackson Professor in Chemistry
Department of Chemistry
University of Florida
roitberg.ufl.edu
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Received on Mon Jun 28 2021 - 07:30:02 PDT
