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Hi Tim,<br>
<br>
<blockquote
cite="mid:CAFLx2u7O=Nfd-kUk5bJpOPVJtHpGmHdd9XLb9MdB3A8Bg5CS9Q@mail.gmail.com"
type="cite">
<div class="gmail_quote">
<div>The method of using the ratio of gradients doesn't make
sense in a maximum likelihood context, <br>
</div>
</div>
</blockquote>
<br>
assuming that by "a maximum likelihood context" you mean refinement
using a maximum-likelihood (ML) criterion as X-ray term (or, more
generally, I would call it experimental data term, as it can be
neutron too, for instance), I find the whole statement above as a
little bit strange since it mixes different and absolutely not
related things: type of crystallographic data term and a method of
relative scale (weight) determination between it and the other term
(restraints). <br>
<br>
I don't see how the choice of crystallographic data term (LS, ML,
real-space or any other) is related to the method of this scale
determination.<br>
<br>
The only difference between LS and ML targets is that the latter
accounts for model completeness and errors in a statistical manner.
The differences between LS and ML are completely irrelevant to the
choice of weight between crystallographic and restraints terms. In
fact, the ML target can even be approximated with LS (<span
style="color: rgb(0, 0, 0); font-family:
verdana,helvetica,arial,sans-serif; font-size: 14px; font-style:
normal; font-variant: normal; font-weight: normal; letter-spacing:
normal; line-height: normal; orphans: 2; text-align: justify;
text-indent: -24px; text-transform: none; white-space: normal;
widows: 2; word-spacing: 0px; background-color: rgb(255, 255,
255);">J. Appl.<span class="Apple-converted-space"> </span><span
class="SpellE">Cryst</span>.</span><span
class="Apple-style-span" style="color: rgb(0, 0, 0); font-family:
verdana,helvetica,arial,sans-serif; font-size: 14px; font-style:
normal; font-variant: normal; font-weight: normal; letter-spacing:
normal; line-height: normal; orphans: 2; text-align: justify;
text-indent: -24px; text-transform: none; white-space: normal;
widows: 2; word-spacing: 0px; background-color: rgb(255, 255,
255); display: inline ! important; float: none;"><span
class="Apple-converted-space"> </span>(2003).<span
class="Apple-converted-space"> </span></span><span style="color:
rgb(0, 0, 0); font-family: verdana,helvetica,arial,sans-serif;
font-size: 14px; font-style: normal; font-variant: normal;
font-weight: normal; letter-spacing: normal; line-height: normal;
orphans: 2; text-align: justify; text-indent: -24px;
text-transform: none; white-space: normal; widows: 2;
word-spacing: 0px; background-color: rgb(255, 255, 255);">36</span><span
class="Apple-style-span" style="color: rgb(0, 0, 0); font-family:
verdana,helvetica,arial,sans-serif; font-size: 14px; font-style:
normal; font-variant: normal; font-weight: normal; letter-spacing:
normal; line-height: normal; orphans: 2; text-align: justify;
text-indent: -24px; text-transform: none; white-space: normal;
widows: 2; word-spacing: 0px; background-color: rgb(255, 255,
255); display: inline ! important; float: none;">, 158-159</span>)
without any noticeable loss. ML target itself can be formulated in a
few different ways and that alone can result in optimal weight
values different by order of magnitude, while showing no difference
in refinement results (since it is a matter of relative scale
between two functions, that can be totally arbitrary).<br>
<br>
The ratio of gradients norms gives a good estimate for the optimal
weight. In fact, if you look in the math, for two-atoms system it
should be multiplied by cos(angle_between_gradient_vectors), which
for a many-atom structure averages out to be approximately ~0.5
(this is what is used in CNS by default), if I remember all this
correctly. <br>
<br>
If the data and restraints terms are normalized (doesn't matter how)
then the weight value becomes predictable. For example, the optimal
weight between ML and stereochemistry restraints in phenix.refine
ranges between 1 and 10, most of the time being ~5, and the ratio of
gradients norms predicts this very well.<br>
<br>
Furthermore, you can always normalize any crystallographic data term
such that the optimal weight will be around 1. <br>
<br>
<blockquote
cite="mid:CAFLx2u7O=Nfd-kUk5bJpOPVJtHpGmHdd9XLb9MdB3A8Bg5CS9Q@mail.gmail.com"
type="cite">
<div class="gmail_quote">
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt
0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
<div bgcolor="#ffffff"> phenix.refine uses repulsion term
only. Although one can imagine reasons why attraction terms
may be helpful, in reality they may be counterproductive if
the model geometry quality is not great since attractive
terms may lock wrong conformations and not let them move
towards correct positions dictated by the electron density.
<br>
<span><font color="#888888"> <br>
</font></span></div>
</blockquote>
<div><br>
Refinement using a force field without electrostatics versus
with electrostatics was recently investigated (<a
moz-do-not-send="true"
href="http://dx.doi.org/10.1021/ct100506d" target="_blank">http://dx.doi.org/10.1021/ct100506d</a>),
and found to favor its inclusion across a range of
models/resolutions. </div>
</div>
</blockquote>
<br>
I had a look at this and more recent papers. I apologize in advance
if I missed it, but I couldn't find an example showing how the
proposed methodology performs for poor models. I mean real working
models (incomplete with errors, like the one you get right out of MR
solution). The tests shown in (<i style="color: rgb(0, 0, 0);
font-family: Times; font-variant: normal; font-weight: normal;
letter-spacing: normal; line-height: normal; orphans: 2;
text-indent: 0px; text-transform: none; white-space: normal;
widows: 2; word-spacing: 0px; font-size: medium;">Acta Cryst.</i><span
class="Apple-style-span" style="color: rgb(0, 0, 0); font-family:
Times; font-style: normal; font-variant: normal; font-weight:
normal; letter-spacing: normal; line-height: normal; orphans: 2;
text-indent: 0px; text-transform: none; white-space: normal;
widows: 2; word-spacing: 0px; font-size: medium; display: inline !
important; float: none;"><span class="Apple-converted-space"> </span>(2011).
D</span><b style="color: rgb(0, 0, 0); font-family: Times;
font-style: normal; font-variant: normal; letter-spacing: normal;
line-height: normal; orphans: 2; text-indent: 0px; text-transform:
none; white-space: normal; widows: 2; word-spacing: 0px;
font-size: medium;">67</b><span class="Apple-style-span"
style="color: rgb(0, 0, 0); font-family: Times; font-style:
normal; font-variant: normal; font-weight: normal; letter-spacing:
normal; line-height: normal; orphans: 2; text-indent: 0px;
text-transform: none; white-space: normal; widows: 2;
word-spacing: 0px; font-size: medium; display: inline ! important;
float: none;">, 957-965</span>) are all performed using models
from PDB, which are supposedly good already. Sure these models may
have small "cosmetic" problems, but as Joosten et al demonstrated
there is always room for improvement of PDB deposited models. This
is partly because the methodology and tools keep improving. So
re-refinement of PDB deposited models using newer tools is very
likely to yield better models, as you confirmed it once again in
your paper. What would be really interesting to see is how your new
methodology performs in real-life routine cases, where a structure
is far away from the good final one.<br>
<br>
All the best!<br>
Pavel<br>
<br>
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