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3Fe4S clusters in real-space refinement
by Rhys Grinter 28 Apr '22

28 Apr '22

Hi PHENIX BB, I'm currently refining a structure with 3Fe4S clusters into some high resolution cryoEM maps (1.57 Ang). However, refinement messes up the Fe4S cluster, by moving the existing Fe and S atoms into the wrong positions in the density. Is anyone aware of this issue and/or knows of a simple way to prevent this from happening? Cheers, Rhys -- Dr Rhys Grinter Lab Head Molecular Physiology of Microbial Pathogens (MP2) Lab Monash University +61 (0)3 9902 9213 +61 (0)403 896 767

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Re: [phenixbb] NaN of AlphaFold
by Randy John Read 26 Apr '22

26 Apr '22

Did you try looking at a surface representation of the structure? The one that Pavel generated from a more simplistic random sequence looked vaguely plausible as a chain trace, but had very large cavities when viewed as accessible surface, e.g. in ChimeraX. Randy > On 24 Apr 2022, at 01:36, James Holton <jmholton(a)lbl.gov> wrote: > > I downloaded all sequences from the PDB to do a frequency analysis, which I think will enable a more sophisticated "random peptide" than giving all 20 amino acids equal likelihood with no neighbor correlations. Turns out the most common heptapeptide in the PDB is: > XXXXXXX > > because, apparently, NCBI lists gaps like this. If I eliminate "X", the most common heptapeptide is: > > SHHHHHH > > Right. His tags. If I eliminate those, the next one is: > GLVPRGS > thrombin cleavage site. Ugh! > > Cleaving off those, the next one is: > AAAAAAA > apparently also used to denote unknown amino acids. > > Ignoring those, the next one is: > YFPEPVT > this is from the human antibody heavy chain. Thousands of those in the PDB. This made me realize that there is going to be a lot of "homology bias". > > I next tried the NCBI refseq_select to try and get something non-redundant, and then I get: > GSGKSTL > lots of ABC transporters. >10k of this sequence in the db. > > On the other hand, of the ~330e6 heptapeptide sequences I'm looking at, 52% of them only appear once. Another 19% of my list of unique sequences occur twice, 9% occur 3x, etc. It is a VERY steep curve. Only 4% of my sample sequences occur more than 10 times, and 0.05% occur more than 100x. I never thought of the PDB this way, but I take this as indicative of how much repetition there is in the sampling. Perhaps I need to brush up on my statistics, but I did not expect this. > > So, I said "heck with it" and simply extracted all heptads that only occur EXACTLY ONCE in the PDB. There are 7.4e6 of them. I chose one at random as the starting 7 residues: FLACISE (from 6ekr). Rather than simply appending random heptads, I took the final ISE and extracted all one-off heptads that begin with ISE. There are 1905 of those. I chose one at random: ISENDGN (from 6ixy). I then extracted heptads beginning with DGN, etc. Final sequence of randomly-assembled yet abbuting heptads from random places in the PDB is then: > > > random1 > FLACISENDGNDGDKSNLAQDTDLALGTWVRDISIALRSGFIAEEYPAWWKESVVPYYNQLNGN > DIEHSHSALLPTGIYFLEIPLVGTRDVALDTEGETTEEAFRHLDGIVGHPIVIGRRAADGGPLVIRGL > NEVLLRLSEVYNAWKLLKVQEILKGLEAYNRAMDYETILREGTWKPAGYTPNALAGGAQCGNAD > IVNERIISDGVEASQLATLERRENRALQTALEIENGELEQKWGRLPSAXPAVERQRYRGPSTATW > EGFSTDGRFPSEVDIFDRETLDGVEGLAKDVEALKGVLMGLFAPYWKWCTHNCIGYAARFVALN > KAIRTALTFARTHSNNETIHNEVVGMIPDIDIAVSDINSQEYTKTRSERQGGVLKEHLNALNAKIEPS > VNLKVSA > > A quick BLASTP shows a surprisingly high homology match: > <o4TgiNP8vaFOcUkl.png> > > I mean... not great, but higher than I'd expect for "completely random" ? > > > The alphafold2 colab result looks like this: > > > <rHffn0Fa0WfwR4Yg.png> > > So still lots of helical content. This shape makes me want to make this sequence and see if it folds. It probably doesn't. But if this thing weren't red I'd wonder. > > Discuss? > > -James > ----- Randy J. Read Department of Haematology, University of Cambridge Cambridge Institute for Medical Research Tel: +44 1223 336500 The Keith Peters Building Fax: +44 1223 336827 Hills Road E-mail: rjr27(a)cam.ac.uk Cambridge CB2 0XY, U.K. www-structmed.cimr.cam.ac.uk

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Refining cyclosporin A
by Armando Albert 23 Apr '22

23 Apr '22

Dear All, We are trying to refine a protein complex with cyclosporin which is a non conventional cyclic peptide. I wonder what is the best strategy to build the cif file. Shall we define the cif file for individual non-conventional amino acids and then link them or shall we define the whole structure as an individual ligand? Does any one have the corresponding cif file? Armando

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Validation of structure with modified residue
by Xavier Brazzolotto 22 Apr '22

22 Apr '22

Dear Phenix users, I don’t know if my problem is related to Phenix but for information I’m running Phenix 1.20.1-4487 under MacOS 12.3.1. I’ve finalized a structure where a ligand covalently modified the protein. I’ve generated the modified residue (named SLG for serine modified by ligand). For this I’ve generated the molecules in SMILES and used eLBOW to generate the restraints. Then I’ve modified the cif file defining the molecule as a L-peptide and replacing the atom names of the Serine part (CA, CB, OG, C, O, N, and OXT) In coot (from CCP4 : 0.9.6 EL), I’ve used the modified cif file and it allowed merging of the modified residue into the polypeptide chain as expected and further refinements went without any issue in Phenix (providing the modified cif file of course). Everything seems well interpreted. So far so good. However, now I would like to validate the structure and both Phenix validation tool and the PDB web server do not accept the final cif file. Checking this file I’ve noticed that the protein seems split into 3 pieces (chain A, first residue up to the one before the modified residue; chain B the modified residue by itself described as HETATM and chain C the rest of the polypeptide up to the C-ter). The PDB file presents only one chain A for the whole protein with the modified residue... I don’t know if this is an issue with Phenix generating this final cif file in this specific case or if I need to modify this final file by hand ? Any help is welcome. Thanks Xavier

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ligand at two-fold
by Patrick Loll 15 Apr '22

15 Apr '22

Hello all, I have a dimer in which the two protomers are related by a crystallographic 2-fold. There is a peptide ligand that sits in the interface between the two protomers. This means that the ligand will be present in two orientations, and I’ve set the occupancies to 0.5. However, the ligand is pushed out of density during refinement, presumably because orientation #1 is repelling orientation #2 (to be clear, orientations #1 and #2 are symmetry mates). To deal with this, I include a parameter file in the GUI that contains the following instruction: custom_nonbonded_symmetry_exclusions = "chain X” [where chain X is the peptide ligand] However, the refinement fails, with the following error message: "Unused parameter definitions: Please check the input file(s) for spelling errors and obsolete parameter definitions." This would seem to imply that the custom_nonbonded_symmetry_exclusions command is obsolete, but it’s still found in the list of all available keywords (?). Any help would be welcome. Thanks, Pat --------------------------------------------------------------------------------------- Patrick J. Loll, Ph. D. (he, him, his) Professor of Biochemistry & Molecular Biology Drexel University College of Medicine Room 10-102 New College Building 245 N. 15th St., Mailstop 497 Philadelphia, PA 19102 USA pjl28(a)drexel.edu pjloll(a)gmail.com

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Error from exceeding max reasonable bond distance
by Mullins, Elwood A. 13 Apr '22

13 Apr '22

I’m having an issue with a real-space refinement job crashing after giving the following error: "RuntimeError : Bond distance > max_reasonable_bond_distance: 62.8014 > 50". I found mention of this problem in a post from 2017. I tried the recommended solution (setting the max reasonable bond distance to 999), but that caused the job to hang. I also tried clicking “proceed with excessive length bonds”. That didn’t make a difference; I received the same error message. I ran validation on the model. There are no bond length outliers and only minor other issues. Any help would be greatly appreciated. Thanks, Elwood

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The lowest resolution score X-ray statics resolution bin is lower than overall datasets.
by Takebe Katsuki 12 Apr '22

12 Apr '22

Dear all, I am working on a 20 KD protein and We scaled the data sets 1.6Å. The lowest resolution score X-ray statics resolution bin is lower than overall datasets. Is this seriously problem? and How do I solve the problem? Could you give me some advice on this problem? DATA USED IN REFINEMENT. RESOLUTION RANGE HIGH (ANGSTROMS) : 1.600 RESOLUTION RANGE LOW (ANGSTROMS) : 44.488 　 [cid:[email protected]]

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2022 ACA session on AI predicted Models in Structural Biology/Crystallography
by Aaron Brewster 12 Apr '22

12 Apr '22

Hi all, we'd like to call your attention to the upcoming session at the 2022 ACA on the use of AI predicted Models in Structural Biology/Crystallography. We'd like to invite abstract submissions to this session from people, including early-career scientists, who have done difficult or interesting work using these AI predicted structural models, such as from AlphaFold2 and RoseTTAFold. We are very excited to hear what people are using these tools for and how the future might unfold in this new world of AI-aided structural analysis and design. Remember that the deadline to submit an abstract for talks is April 15, 2022@ 11:59 PM ET. The full abstract for the session is below. Thanks, -Aaron Brewster and John Moult Prediction of 3D protein structure by AlphaFold2 and RoseTTAFold has achieved revolutionary accuracy using new AI methods. These programs can accurately predict the 3D structure of many proteins from 1D amino acid sequences, opening up a vast array of exciting new experiments and challenges for researchers. In this session we will explore current applications of these structures in molecular replacement, cryo-EM fitting and refinement, structure-based drug design, and tomography. We will explore what kinds of new and difficult methods have been enabled and what remains difficult. Other topics may include how error estimates on AI protein models can be incorporated into these methods, progress in modeling of protein complexes, implications of AI structures on protein design, the generation and use of structure ensembles, and new data analysis best practices that need to be developed as the use of computer-generated structures becomes more common. Sponsored by the BioMAC and Best Practices for Data Analysis and Archiving SIGs

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The lowest resolution score X-ray statics resolution bin is lower than overall datasets.
by Takebe Katsuki 11 Apr '22

11 Apr '22

Dear all, I am working on a 20 KD protein and We scaled the data sets 1.6Å. The lowest resolution score X-ray statics resolution bin is lower than overall datasets. Is this seriously problem? and How do I solve the problem? Could you give me some advice on this problem? DATA USED IN REFINEMENT. RESOLUTION RANGE HIGH (ANGSTROMS) : 1.600 RESOLUTION RANGE LOW (ANGSTROMS) : 44.488 　 [cid:[email protected]]

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Fragment-based drug discovery session at ACA 2022
by Tanner, John J. 09 Apr '22

09 Apr '22

As many of you know, the 2022 ACA meeting will be held in Portland, OR on July 29 to August 2. I would like to draw your attention to the session on fragment-based drug discovery and invite you to submit an abstract for an oral presentation. We welcome contributions from students as well as PIs. The Abstract deadline is April 15. https://www.acameeting.com/ Session details: 3.2.2 Fragment Based Drug Discovery 8/1/2022 @ 2:00:00 PM - 5:00:00 PM Sponsoring SIG(s): Industrial Co-Sponsoring SIG(s): BioMac Session Chair(s): Matt Clifton, Jack Tanner Fragment Based Drug Discovery (FBDD) utilizes small molecule compounds to help identify starting points in the drug discovery process. This process allowed for the discovery of lead compounds for difficult protein targets with small/shallow pockets or protein-protein interactions. In contrast to other screening techniques such as High-Throughput Screening (HTS) or DNA-encoded Libraries (DEL), FBDD utilizes small libraries of molecules (< 2000) with molecular weights less than ~200 Da. FBDD has been key in the development of multiple approved drugs and dozens of other drugs in clinical trials. This session will focus on the biophysical and structural techniques used in FBDD to identify initial starting points, optimization by fragment growing/linking, and the route to lead optimization. -- John J. Tanner Professor of Biochemistry and Chemistry Associate Chair of Biochemistry Department of Biochemistry University of Missouri 117 Schweitzer Hall 503 S College Avenue Columbia, MO 65211 Phone: 573-884-1280 Email: tannerjj(a)missouri.edu<mailto:[email protected]> https://cafnrfaculty.missouri.edu/tannerlab/ Lab: Schlundt Annex rooms 3,6,9, 203B, 203C Office: Schlundt Annex 203A

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