<div dir="ltr"><div>[I forgot to copy my reply to the bulletin board, so here it is, reproduced for the record.]</div><div><br></div><div>The official publication for CaBLAM is the 2017 Molprobity paper in Protein Science, here: <a class="gmail-m_502587855306421605epub-doi" href="https://doi.org/10.1002/pro.3330" target="_blank">https://doi.org/10.1002/pro.3330</a> Further technical documentation on CaBLAM can be found in Phenix's Computational Crystallography Newsletter, here: <a href="https://www.phenix-online.org/newsletter/CCN_2018_07.pdf#page=7" target="_blank">https://www.phenix-online.org/newsletter/CCN_2018_07.pdf#page=7</a> I recommend the newsletter article as a fast read.<br></div><div><br></div><div>To
identify outliers, CaBLAM looks at a structure's CA trace, which is
generally well-modeled. For each residue, it compares the local peptide
plane orientations of the model to the observed distribution of peptide
plane orientations for high quality residues with matching CA trace
geometry. The CaBLAM score is a percentile score that rates how well
the model matches with the expected distribution. The lower the score,
the rarer the observed conformation is in our database of quality
structures. A conformation falling in the bottom 5% of observed
behavior is potentially suspicious ("Disfavored") and a conformation
falling in the bottom 1% is considered an outlier.</div><div><br></div><div>This
percentile-based scoring is fundamentally the same scoring used in
MolProbity's description of Ramachandran and rotamer outliers, though of
course CaBLAM puts its cutoffs in different places.</div><div><br></div><div>As
a matter of interpretation, loop/coil regions tend to be highly
varied. CaBLAM "Disfavored" conformations in loops can largely be
ignored. However, disfavored conformations in regions expected to by
highly regular (repeating secondary structure) should be taken
seriously. CaBLAM outliers should be inspected wherever they occur.</div><div><br></div><div>The
CA geometry score looks at just the CA trace, and takes the CA virtual
angle into account (defined by CAi-1, CAi, CAi+1). Outliers in this
space reflect some sort of severe problem with CA geometry, often
involving an over-extended or over-compressed CA virtual angle.</div><div><br></div><div>The
secondary structure scores are based on how well a residue's local CA
trace matches the expected CA trace of each major secondary structure
type, alpha, 3-10, and beta. You can see the contours used for this
assessment in Figure 3 of the newsletter article. Each residue receives
individual secondary structure scores. Then regions of residues that
all pass a scoring threshold are assembled into probable secondary
structure elements. This is where the "try beta sheet" recommendations
come from. That recommendation indicates that the residue in question <i>and its neighboring residues</i> all have CA traces that look like beta sheet.<br></div><div><br></div><div>I
wish I had a simple recommendation for you, but fixing CaBLAM outliers
systematically has proven to be a challenge. Take a look at your
structure and see if you believe that the outlier residues really are
intended to be part of beta sheets. If so, beta sheets have distinctive
hydrogen bonding patterns that tend to be disrupted by the kind of
problems that CaBLAM identifies. Ideally, you will be able to use
Coot's tools to restore the proper hydrogen bonding. Then, applying
hydrogen bonding restraints during refinement may help keep your work in
place.</div><div><br></div><div>If you have large regions of outliers,
it may instead be more practical to strip out the existing model and
replace it with idealized beta sheet structure, then rerefine. Again,
hydrogen bonding restraints may be helpful.</div><div><br></div><div>As a
general rule, CaBLAM outliers usually indicate a problem with the
orientations for one or more peptide planes. Look for a way to reorient
the peptide either to remove clashes or establish hydrogen bonds. Make
sure you build good regular secondary structure, don't sweat about the
loops too much, and trust your judgement and experience to identify the
real and justified outliers.<br></div><div><br></div><div>We of the
Richardson Lab generally dislike torsion-based Ramachandran
restraints/secondary structure restraints. It's very easy to
accidentally generate a model that looks better than it really is using
these methods. However, we recognize that these are powerful tools for a
difficult problem and you may get good out of careful use.<br></div><div><br></div><div>Hope that helps, and good luck</div><div class="gmail-yj6qo gmail-ajU"><div id="gmail-:1ei" class="gmail-ajR" tabindex="0"><img class="gmail-ajT" src="https://ssl.gstatic.com/ui/v1/icons/mail/images/cleardot.gif"></div></div><span class="gmail-HOEnZb gmail-adL"><font color="#888888"><div>-Christopher Williams</div><div>---Richardson Lab, Duke University</div></font></span></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Jun 26, 2019 at 2:21 PM Ahmad Khalifa <<a href="mailto:underoath006@gmail.com">underoath006@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hello,<div><br></div><div>I would appreciate some insights on how to interpret the validation results under the CaBLAM tab and how to use it to fix my structure in Coot? </div><div><br></div><div>What are the CaBLAM score, the Ca geometry score, the helix score and the 3-10 score? </div><div><br></div><div>How to use the information under the secondary structure tab? In my protein for example, I see a lot of "try beta sheets", should I try to refine that segment using beta sheets restraints? </div><div><br></div><div>Best regards. </div></div>
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