Subject: Summary:Monte-Carlo

From: Jean-Luc Verschelde <[email-masked]>
Date: Fri, 24 Mar 1995 09:19:24 +0100 (MET)

>From [email-masked] Mar 24 09:07:52 1995
Date: Sat, 25 Feb 95 14:45:58 -0800
From: "Arne Eofsson ([email-masked])" <[email-masked]>
Subject: Re: CCL:Monte carlo 

The latest advances are a set of Genetic Algorithms
see dandekar & Argos, Legrand & Merz, Bowie & Eisenber & S. Sun
(all have a set of papers lately)
for simplified models see work by Dill and by Shahknowitz

for lattice models see work by Skolnick

for my latest paper (submitted to Proteins (with a lot of references))
see http://www.mbi-doe.ucla.edu/arne/grail.ps

good luck arne

>From [email-masked] Mar 24 09:08:04 1995
Date: Sun, 26 Feb 1995 14:44:26 +0100 (WET)
From: "Konrad, IRBM Chemistry, +39-6-91093606" <[email-masked]>
Subject: RE: CCL:Monte carlo references

Jean-Luc:

   The following reference would seem relevant to faster/smarter sampling
of protein conformational space:

Guarnieri, F.; Still, W. C. A rapidly convergent simulation method:  mixed
Monte Carlo/stochastic dynamics.  J. Comput. Chem.   1994, 15, 1302-1309. 

   For recent refs on low resolution (lattice protein model) sampling of
conformational states:

Sali, A.; Shakhnovich, E.; Karplus, M. How does a protein fold?  Nature   1994,
369, 248-251. 

Sali, A.; Shakhnovich, E.; Karplus, M. Kinetics of protein folding. A lattice
model study of the requirements for folding to the native state.  J. Mol. Biol.
1994, 235, 1614-1636. 

   Ciao,

Konrad

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>From [email-masked] Mar 24 09:08:20 1995
Date: Mon, 27 Feb 1995 08:59:47 +0100
From: Barry Hardy <[email-masked]>
Subject: Re: CCL:Monte carlo

Jean-Luc, 
    I have a couple of recent papers on efficient Monter Carlo
sampling which you may find interesting. Try:
1) http://alcyone.pcl.ox.ac.uk/people/barry_eccc_final/paper_text.html 
2) B.J. Hardy and R.W. Pastor, J. Comp. Chem., 15, 208 (1994) 
(This paper also references papers on several other MC methods).
    Drop me an email if you want to discuss any of this.
All the best, Barry

Barry J. Hardy
An Oxford_Attraction: http://alcyone.pcl.ox.ac.uk/people/barry.html
>From [email-masked] Mar 24 09:08:45 1995
Date: Mon, 27 Feb 95 09:31:00 -0800
From: Max Vasquez <[email-masked]>
To: [email-masked]
Subject: Monte Carlo of peptides (proteins)

I have recently written a review of energy calculations on peptides and
proteins (Chemical Reviews 94, 2183 [1994]) that includes sections on
simulated annealing, Monte Carlo-Minimization, and other stochastic
methods.  I'd be happy to send you a reprint if you are interested.

Some of the most intriguing methods belong to the class of "multicanonical"
algorithms (and the equivalent "entropy-sampling" procedures).  These have
been used to treat full-atom models of small peptides (Hansmann & Okamoto
J. Comp. Chem. 14, 1333 [1993]) as well as lattice models of proteins (Hao
& Scheraga J. Phys. Chem. 98, 4940 [1994]).

The conventional "wisdom" has been that MC is not competetive with MD for
larger polypeptides and proteins (Northrup & McCammon Biopolymers 19, 1001
[1980]); some new methods may be starting to change this perception,
particularly Bouzida, Kumar & Swendsen Physical Review A 45, 8894 [1992] as
well as work by Bouzida et al in Protein Science 3, 911 [1994].

One should also distinguish between MC methods used for conformational
search and methods used to generate ensemble averages distributed according
to a Boltzmann distribution at a given temperature.  Many MC methods in the
literature of peptides and protein are not "theoretically guaranteed" to
preserve proper sampling, although they may still be excellent "search
tools."  This issue is discussed a bit in the review mentioned above, as
well as in Vasquez et al. J. Phys. Chem. 98, 9380 [1994] and in Gibrat et
al Immunomethods 1, 107 [1992]. The first paper deals with small *isolated*
peptides while the second deals with loops of peptide *segments* that are
part of a full protein.

Hope this helps; best wishes,

Max

************************************************
Max Vasquez, PhD
Protein Design Labs, Inc.
2375 Garcia Ave.
Mountain View, CA 94043
(415) 903-3744
(415) 903-3730 (FAX)
Internet:  [email-masked]
*************************************************

>From [email-masked] Mar 24 09:09:45 1995
Date: Fri, 3 Mar 95 11:36:26 +0530
From: [email-masked]
To: [email-masked]
Subject: RE: CCL:Monte carlo

==>Hi all,
==>
==>	I want to use advanced Monte Carlo to explore protein spaces.
==>	Can someone give me references of the newest methods to 
==>	sample more efficiently or smarter.
==>
==>	Thanks,
==>
==>			Jean-Luc
==>

I guess BOSS from W.L. Jorgensen lab will solve your problem. he can be reached
at 

Dept. of chemistry, 
Yale university
New Haven, Connecticut 06511
USA
email: [email-masked]
If you get anything 'smarter' pls. let me know as well.

Mrigank
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