Hi Ricardo,

If you are using phenix.auto_sharpen with just a map and resolution as inputs, then it will (by default) apply an overall sharpening B-factor up to the resolution limit you supply, and then a blurring B-factor beyond that.  This means that any filtering applied to the input map will make a difference.  I haven't tested systematically, but my guess is that the better the map you start with with the better the map you'll get. So I would recommend starting with the weighted map as in Rosenthal and Henderson.  In this case phenix.auto_sharpen will basically be identifying the overall sharpening that optimizes the clarity and connectivity of the map.

If you sharpen using a model, then a different sharpening is applied at each resolution (basically as in Rosenthal and Henderson, but based on the match between model and map, not between two half-maps), so the starting map should make only a small difference.

All the best,

Tom T

On Wed, Jan 24, 2018 at 6:00 AM, Ricardo Righetto <ricardorighetto@gmail.com> wrote:

Hi,

So far I have liked very much the results of phenix.auto_sharpen, but one thing is not yet clear to me: how does it expect the input map to be filtered?

Would be something like:

a) Unsharpened, unfiltered map (e.g. the simple average of the half-maps)

b) Unsharpened, SSNR-weighted (Cref) (Rosenthal & Henderson, JMB 2003)

c) Doesn't matter / other?

Thanks in advance!

Best wishes,

--
Ricardo Diogo Righetto

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Thomas C Terwilliger

Laboratory Fellow, Los Alamos National Laboratory

Senior Scientist, New Mexico Consortium

100 Entrada Dr, Los Alamos, NM 87544

Email: tterwilliger@newmexicoconsortium.org

Tel: 505-431-0033