Dear John,
Plants cannot walk away to a more favorable spot. They remain stuck where they 
germinate, e.g. whether the place is sunny, shady, wet, dry, fertile, poor etc. 
So plants compensate by having a lot of genes available to be able to adapt to 
the particular spot where happen to be. And indeed, plants have usually more 
genes then animals!
Best,
Herman

Von: CCP4 bulletin board [mailto:CCP4BB@JISCMAIL.AC.UK] Im Auftrag von John R 
Helliwell
Gesendet: Freitag, 20. September 2019 09:19
An: CCP4BB@JISCMAIL.AC.UK
Betreff: [EXTERNAL] Re: [ccp4bb] AW: [ccp4bb] challenges in structural biology


EXTERNAL : Real sender is owner-ccp...@jiscmail.ac.uk

Dear Martin,
Many thanks for these details of the size of the human genome over the decades 
and also the news of your most interesting upcoming review. I shall read it 
with great interest.
Incidentally is the over 40000 genes for the rice genome number correct? This 
number caught my eye as being interesting how the rice genome is more 
complicated than our genome.
Best wishes,
John
Emeritus Professor John R Helliwell DSc




On 19 Sep 2019, at 08:35, Kollmar, Martin 
<m...@nmr.mpibpc.mpg.de<mailto:m...@nmr.mpibpc.mpg.de>> wrote:
Dear John,
the „100,000 human genes“ is a long-standing myth broad forward by the 
initiators of the U.S. human genome sequencing projects in 1990. This large 
number completely contradicted all genetics and mutation data since the 1940th, 
but the sequencing community (genome, cDNA, EST) didn’t read even the standard 
text books. Thus, the “30,000” genes published with the two human genome papers 
in 2001 are not “surprisingly low” but just in accordance with the predictions 
and the data since the 1940th. The gene number went down to about 23,000 
already in 2004, and the current numbers (depending on database) range around 
20,000 human protein-coding genes. The myth of the large numbers is only 
propagated by those who profit from larger numbers (e.g. bigger grants, papers 
in higher IF journals, big consortia).

I have written a review about the current state (and history) of the human 
protein-coding genes, which will appear online in BioEssays soon and finally in 
the November issue (will be open access). In this review there will be some 
(hopefully) useful plots showing the gene numbers since the 1940th and a 
detailed review of all the numbers and their experimental basis (most were 
actually just extrapolations from small-scale data).

Please excuse this kind of self-advertisement, but it is really more than time 
to move this myth out of science literature and communication.

Best regards,
Martin

Priv. Doz. Dr. Martin Kollmar

Group Systems Biology of Motor Proteins
Department NMR-based Structural Biology
Max-Planck-Institute for Biophysical Chemistry
Am Fassberg 11
37077 Goettingen
Deutschland

www.motorprotein.de<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.motorprotein.de_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=IWuNyBzheAGJ761iddc78L4lz7sB21cKQTrawbV4j0M&e=>
 (Homepage)
www.cymobase.org<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.cymobase.org_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=fMYqMfsorSLNspHq5-Wx_WDkChCYsDr9NEXwrGsKvpo&e=>
 (Database of Cytoskeletal and Motor Proteins)
www.diark.org<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.diark.org_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=QB0-HJUz9wLSC-Xd5_Bj6Gnhx70AaYoxkD8kIu7Ub6A&e=>
 (diArk - a resource for eukaryotic genome research)
www.webscipio.org<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.webscipio.org_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=sy5FDRUX22Q-6XcBo-X_-tgZenKZRLf9N__I8x3pnBo&e=>
 (Scipio - eukaryotic gene identification)

Von: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK<mailto:CCP4BB@JISCMAIL.AC.UK>> 
Im Auftrag von John R Helliwell
Gesendet: Donnerstag, 19. September 2019 08:51
An: CCP4BB@JISCMAIL.AC.UK<mailto:CCP4BB@JISCMAIL.AC.UK>
Betreff: Re: [ccp4bb] challenges in structural biology

Dear James,
Well, 100,000 genes used to be the estimate of the size of the human genome.
(eg see 
https://physicsworld.com/a/protein-crystallography-the-human-genome-in-3-d/<https://urldefense.proofpoint.com/v2/url?u=https-3A__physicsworld.com_a_protein-2Dcrystallography-2Dthe-2Dhuman-2Dgenome-2Din-2D3-2Dd_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=Au_C7wioq-2-qa1kL026q6V6n88YzwYRsi2fGz6xjpg&e=>
 )
It seems it has got easier, albeit still gargantuan, at ~30,000 genes to be 
expressed into proteins.

Meanwhile funding agencies also look out for Big Ideas:-
https://epsrc.ukri.org/research/ourportfolio/epsrcbigideas/?utm_source=Twitter&utm_medium=social&utm_campaign=SocialSignIn<https://urldefense.proofpoint.com/v2/url?u=https-3A__epsrc.ukri.org_research_ourportfolio_epsrcbigideas_-3Futm-5Fsource-3DTwitter-26utm-5Fmedium-3Dsocial-26utm-5Fcampaign-3DSocialSignIn&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=TMvBW-JMC-G_OW0woSqqukhb03utq-y672taYl38T9Y&e=>
and even helpfully spell out the difference between a Big Idea and a Grand 
Challenge!
Maybe an “Open Door for funding” for us all?

Today also the repertoire of methods capable of resolving in 3D protein 
structures has expanded further with the splendid development of cryoEM.

To define challenges in terms of projects, as Max Perutz taught us 
(“Haemoglobin the Molecular Lung”) avoids methods looking for problems.

Also a final thought, how we organise ourselves in different areas of the World 
varies according to our cultural traditions. So the Big Project is neutral to 
politics and can accommodate all contributions however so arrived at.

“What shall we do with it?”
As Darwin taught us, first make your Collection......

Greetings!
John

Emeritus Professor John R Helliwell DSc
https://www.crcpress.com/The-Whats-of-a-Scientific-Life/Helliwell/p/book/9780367233020<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.crcpress.com_The-2DWhats-2Dof-2Da-2DScientific-2DLife_Helliwell_p_book_9780367233020&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=xJvbX5e1byn0_V91NpWaz5pd5GEYg9BKtNQKHCkMeKM&e=>



On 18 Sep 2019, at 22:15, James Holton 
<jmhol...@lbl.gov<mailto:jmhol...@lbl.gov>> wrote:
Thank you John, an excellent choice as always.  Here is your trillion dollars!  
Now, what are you going to do with it?

Do you think simply scaling up current technology could reach this goal?  More 
screens, more combinations, more compute cycles?  Remember, if you want the 
"genome/proteome" you need all of it, including all those super-cool human 
membrane proteins we gave up on because they were too hard.

I think we all have at least one of those projects in our past.  What was the 
show-stopper in the end?  Did they just not grow crystals? Poor diffraction? 
Weird diffraction? Twinned? Won't phase? Won't refine to a decent R factor? 
Annoying reviewer? Did you try cryoEM? NMR? and did they not work either?

I think a key question for all of us is: what new capability would make you 
decide to go back and pick up your old favorite project again?  Without your 
structure, the genome is incomplete.

-James Holton
MAD Scientist
On 9/16/2019 12:24 AM, John R Helliwell wrote:
Dear James,
Here you go, a “grand challenge” suggestion to consider for funding from the 
“James Holton Foundation for structural biology research”:-
“The human genome/proteome in 3-D”
Greetings,
John
Emeritus Professor John R Helliwell DSc




On 14 Sep 2019, at 02:39, James Holton 
<jmhol...@lbl.gov<mailto:jmhol...@lbl.gov>> wrote:

I would like to thank everyone who took the time to respond to my question that 
started this thread.  It is really good for me to get a sense of the community 
perspective.  Some debates were predictable, others not.  Many ideas I agree 
with, some not so much.  All were thought-provoking. I think this is going to 
be a really good GRC!

Something I did not expect to distill from all the responses is that the 
dominant challenge in structural biology is financial. The most common strategy 
suggested for addressing this challenge was torpedoing other scientists in 
similar fields, perhaps expecting to benefit from the flotsam.  Historically, 
this strategy is often counterproductive and at best inefficient. The good news 
is there is a lot of room for improvement. In reality, we are all on the same 
ship, and the people in our funding agencies fighting to get us what we need 
can be much more effective when armed with positive ideas and clear plans.  
That is a better strategy for overcoming this challenge.

To this end, my first GRC session title is going to be:

"If I had a trillion dollars for structural biology"

I think we can all agree that science in general is vastly under-funded 
relative to the impact it has on the human condition.  For example, I estimate 
the value of a general cure for cancer to be at least a trillion dollars.  This 
is based on the lives claimed every year, multiplied by how much one person 
would gladly pay after being diagnosed (amortized over the rest of their much 
longer life). This is only ~1% of the Gross World Product, a real bargain if we 
can come up with a plan that will actually work.

Now, obviously not all cancer research is structural biology, but not all 
structural biology is cancer research either. Let us suppose for a moment that 
you (yes, I'm talking to YOU), were given a trillion-dollar budget to do your 
science.  After buying all the tools and hiring all the people you wanted: 
would that solve all of your problems?  I expect not. The intellectual and 
technical challenges that remain are what I believe science is really all 
about, and the 2020 Diffraction Methods GRC will focus on the ones facing 
structural biology.

My goals here are twofold:
1) I believe it would be healthy for this field if we all spent a little time 
"thinking big"
2) I want to remove financial anxiety from the discussion, both here and at the 
GRC.

I ask for one restraint: please confine the discussion to structural biology.  
I understand it is difficult to think about the trillion-dollar level without 
involving politics, but the CCP4 Bulletin Board is not a political discussion 
forum, and neither is the GRC. Assume all the other worthy causes in the world 
are given their own ample budgets. This trillion is yours, and you have to 
spend it on structural biology.  If you can't think of anything, think harder.

To get you started, a few things that could be done for under a trillion 
dollars:
1) re-do all the protein crystallization in the PDB, 500 times (saving all 
information)
2) buy Google and Facebook, get their AI teams to do machine learning and 
structure prediction for us
3) hire every "biological scientist" in the world, and give each $1M to work on 
your projects
4) re-do the NASA Apollo program three times
5) build 1000 XFELs and 100,000 Titan microscopes (yes, that's "and")
6) solve the phase problem by brute force.  (zettaflops-scale computing at 
$0.03/gflop)
7) build half a dozen terapixel detectors (ask Colin Nave what those can do)
8) fund every NIH grant submitted in the last 5 years. Not just the awarded 
ones, all of them.
9) X-prize style competitions for landmark achievements, such as predicting 
crystallization outcomes, or finding a universal way to stop protein from 
denaturing on the air-water interface.

This is not a to-do list, but rather an attempt to convey the scale of what can 
be done.  Oh, and you have a month or so to think about it. The meeting is July 
26-31 2020, but my speaker list is due Oct 15.

Now, of course, at the GRC I will not actually have billion-dollar prizes to 
pass around, but I do want to set our sights on those lofty goals, and then 
work on the bridge we will need to get there.

So, when I say "challenge" I mean more than something we all agree is hard.  
Those would make for very short talks.  I am after something more like a 
benchmark.  Useful challenges should have certain properties.  They should be:
a) possible, because something that doesn't work no matter what you do is no 
fun.
b) hard, because something that is too easy is also not very interesting
c) realistic, as in relevant to a real-world problem we all agree is important
d) accessible, as in reasonable download sizes and/or affordable reagents
e) fast, because it if takes forever to try it nobody will have time to 
participate
f) measurable, as in having a clear and broadly acceptable "score"
g) adjustable, as in the level of "difficulty" can be selected continuously 
between "easy" and "impossible".

This last one is important because it is at the transition point between 
success and failure that teaches us the most about what can be improved.

Some challenges that already exist are:
anomalous phasing from weak signals
    
https://bl831.als.lbl.gov/~jamesh/challenge/anom/<https://urldefense.proofpoint.com/v2/url?u=https-3A__bl831.als.lbl.gov_-7Ejamesh_challenge_anom_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=0IJQWNeTwpNxj1WNhXurnD-UrpV5RQ1cHpgke0pHcsw&e=>
anomalous phasing from twinned data
    
https://bl831.als.lbl.gov/~jamesh/challenge/twin/<https://urldefense.proofpoint.com/v2/url?u=https-3A__bl831.als.lbl.gov_-7Ejamesh_challenge_twin_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=dDs_r6utWD9Sh6VhJST1OsWgNZVKRubPS2-H6PH_Ed0&e=>
merging highly incomplete data with an indexing ambiguity
    
https://bl831.als.lbl.gov/~jamesh/challenge/microfocus/<https://urldefense.proofpoint.com/v2/url?u=https-3A__bl831.als.lbl.gov_-7Ejamesh_challenge_microfocus_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=PcG8DXXeww_NLMoXqNEb_esUbiARpm2gZ6daHSSVqMI&e=>
extracting motions from diffuse scatter data
    
https://bl831.als.lbl.gov/~jamesh/challenge/diffuse/<https://urldefense.proofpoint.com/v2/url?u=https-3A__bl831.als.lbl.gov_-7Ejamesh_challenge_diffuse_&d=DwMFaQ&c=Dbf9zoswcQ-CRvvI7VX5j3HvibIuT3ZiarcKl5qtMPo&r=HK-CY_tL8CLLA93vdywyu3qI70R4H8oHzZyRHMQu1AQ&m=32JRE8HZHPdJxpaoz1sLz-PnTi-D_zZTMfdQs_FdEcI&s=Q6TAvbSKkAAxEcS8GMgtDkfFTUAQdm6oiK_xQGRohOw&e=>
Coming soon:
dial-a-resolution model building challenge
XFEL data processing reference set

-James Holton
MAD Scientist
On 7/25/2019 10:07 AM, Keller, Jacob wrote:
>>It would seem to me that an important issue is also: do get all information 
>>out of our diffraction data? By integrating the Bragg peaks we usually 
>>neglect the diffuse scattering that could potentially contain additional 
>>(dynamic) structural information. This can be cloudy diffuse scattering 
>>hidden in the background but also diffuse streaks that contain information on 
>>packing disorder and reveals intrinsic interactions in the crystal.



Along these lines, and taking a page from you also, how about “crystallographic 
model refinement as image-faking?” Metrics of the goodness of a particular 
refinement could simply be some measure of the correlation between predicted 
vs. measured images. I have seen some of this done with diffuse scattering, but 
why not with the whole thing, including intensity and shape of Bragg peaks, 
solvent rings, etc? Maybe instead of doing the multiple steps of (indexing, 
integration, scaling, solving…) all of this could be refined as one? Processing 
parameters like moscaicity [sic] etc would now be part of the final model…?

JPK




Loes Kroon-Batenburg

On 07/15/19 21:44, Holton, James M wrote:

Hello folks,



I have the distinct honor of chairing the next Gordon Research

Conference on Diffraction Methods in Structural Biology (July 26-31

2020).  This meeting will focus on the biggest challenges currently

faced by structural biologists, and I mean actual real-world

challenges.  As much as possible, these challenges will take the form of

friendly competitions with defined parameters, data, a scoring system,

and "winners", to be established along with other unpublished results

only at the meeting, as is tradition at GRCs.



But what are the principle challenges in biological structure

determination today?  I of course have my own ideas, but I feel like I'm

forgetting something.  Obvious choices are:

1) getting crystals to diffract better

2) building models into low-resolution maps (after failing at #1)

3) telling if a ligand is really there or not

4) the phase problem (dealing with weak signal, twinning and

pseudotranslation)

5) what does "resolution" really mean?

6) why are macromolecular R factors so much higher than small-molecule ones?

7) what is the best way to process serial crystallography data?

8) how should one deal with non-isomorphism in multi-crystal methods?

9) what is the "structure" of something that won't sit still?



What am I missing?  Is industry facing different problems than

academics?  Are there specific challenges facing electron-based

techniques?  If so, could the combined strength of all the world's

methods developers solve them?  I'm interested in hearing the voice of

this community.  On or off-list is fine.



-James Holton

MAD Scientist





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--



__________________________________________



Dr. Loes Kroon-Batenburg

Dept. of Crystal and Structural Chemistry

Bijvoet Center for Biomolecular Research

Utrecht University

Padualaan 8, 3584 CH Utrecht

The Netherlands



E-mail : l.m.j.kroon-batenb...@uu.nl<mailto:l.m.j.kroon-batenb...@uu.nl>

phone  : +31-30-2532865

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