[phenixbb] [EXTERNAL] Re: Improving Ramachandran plot

Reza Khayat rkhayat at ccny.cuny.edu
Wed Sep 9 06:21:24 PDT 2020

Hi Oleg,

Thanks for the response.

1. Yes, I am using the command line version

2. The run.eff file that I had included in my e-mail includes the lines you sent. I would appreciate the help in converting the old-style into the new style. ?

Best wishes,

Reza Khayat, PhD
Associate Professor
City College of New York
Department of Chemistry and Biochemistry
New York, NY 10031
From: Oleg Sobolev <osobolev at lbl.gov>
Sent: Tuesday, September 8, 2020 7:43 PM
To: Reza Khayat
Cc: PHENIX user mailing list
Subject: [EXTERNAL] Re: [phenixbb] Improving Ramachandran plot

Hi Reza,

I assume that you run Phenix via command line. We have a new enhanced "ramachandran_plot_restraints" scope to define these (similar) parameters. The previous one is there (for a short while) for backward compatibility (so users can restore old GUI jobs). The new scope looks like:

    ramachandran_plot_restraints {
      enabled = True
      favored = *oldfield emsley emsley8k
      allowed = *oldfield emsley emsley8k
      outlier = *oldfield emsley emsley8k
      selection = None
      inject_emsley8k_into_oldfield_favored = True
      oldfield {
        weight = 0.
        weight_scale = 0.01
        distance_weight_min = 2.0
        distance_weight_max = 10.0
        plot_cutoff = 0.027
      emsley {
        weight = 1.0
        scale_allowed = 1.0
      emsley8k {
        weight_favored = 5.0
        weight_allowed = 10.0
        weight_outlier = 10.0
So please use this one. Notable change is that now you can restrain favored/allowed/outliers separately (or not restrain one or another).
Please let me know if you need to know exactly how to convert old-style parameters into new ones.

In case you are going to perform a similar study as you are mentioning, I would like to suggest adding the Rama-Z metric to the set of validation metrics. It was included particularly to judge the distribution of residues on Ramachandran plot: https://doi.org/10.1016/j.str.2020.08.005<https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1016_j.str.2020.08.005&d=DwMFaQ&c=4NmamNZG3KTnUCoC6InoLJ6KV1tbVKrkZXHRwtIMGmo&r=1DzJFW0v6TgEhkW1gy_-ke-RbtvS1fzEbD5_hcb9Up0&m=CC98DLroSZABXnlwA93ckm5Bfu3Zx893dguNRHGVqJE&s=ptg6ZgZREaTRrW9ujd0LnZ-8kkjQnOgvn0w4NVpj8jI&e=>

Let me know if you have any questions.

Best regards,
Oleg Sobolev.

On Sun, Sep 6, 2020 at 9:12 PM Reza Khayat <rkhayat at ccny.cuny.edu<mailto:rkhayat at ccny.cuny.edu>> wrote:


I'm trying to follow the protocol described in White et al., Structural principles of SNARE complex recognition by the AAA+ protein NSF e-life 2018


Here is the pertinent paragraph:

"To improve the Ramachandran statistics and geometry of the models, a two-parameter grid search of real space refinements was performed in which 3-5 macrocycles of global minimization and local grid search were performed in the presence of secondary structure restraints. First, a grid refinement search was performed for both target functions with around 1000 refinements each. For the emsley target function, rama_weight and scale_allowed were varied from 0.01 to 300; for the oldfield target function, a grid of refinements was performed over plot_cutoff values from 0.01 to 1.0 and weight_scale values from 0.1 to 300. Results were judged empirically and based primarily on a balance between CCmask and a minimal fraction of residues flagged by the program CaBLAM (Richardson et al., 2018<https://urldefense.proofpoint.com/v2/url?u=https-3A__elifesciences.org_articles_38888-23bib37&d=DwMFaQ&c=4NmamNZG3KTnUCoC6InoLJ6KV1tbVKrkZXHRwtIMGmo&r=1DzJFW0v6TgEhkW1gy_-ke-RbtvS1fzEbD5_hcb9Up0&m=CC98DLroSZABXnlwA93ckm5Bfu3Zx893dguNRHGVqJE&s=A-IYsxXhwe3dvl7oZ8JRr0rxyZ4W29522VR7YCPRmKg&e=>) because focusing on the fraction of residues with favored CCmask and Ramachandran statistics alone often resulted in unrealistic models with serious problems (Figure 12<https://urldefense.proofpoint.com/v2/url?u=https-3A__elifesciences.org_articles_38888-23fig12&d=DwMFaQ&c=4NmamNZG3KTnUCoC6InoLJ6KV1tbVKrkZXHRwtIMGmo&r=1DzJFW0v6TgEhkW1gy_-ke-RbtvS1fzEbD5_hcb9Up0&m=CC98DLroSZABXnlwA93ckm5Bfu3Zx893dguNRHGVqJE&s=e7ESAeeMjGInzQkzBfSgzzZPRTccskQn25ODvsw4nlQ&e=>, Table 2<https://urldefense.proofpoint.com/v2/url?u=https-3A__elifesciences.org_articles_38888-23table2&d=DwMFaQ&c=4NmamNZG3KTnUCoC6InoLJ6KV1tbVKrkZXHRwtIMGmo&r=1DzJFW0v6TgEhkW1gy_-ke-RbtvS1fzEbD5_hcb9Up0&m=CC98DLroSZABXnlwA93ckm5Bfu3Zx893dguNRHGVqJE&s=lgVCnWzCNio89ZUuhejZ7-iD0MwdtumGbIm5-yMuYLw&e=>)."

There are no differences in the refined structures regardless of what parameters I change. Attached is the input file I use for phenix.real_space_refine (1.18.2?). Altering the oldfield weight_scale (0.1 to 300) and plot_cutoff (0.1 to 0.5) makes no difference. Any help is highly appreciated.

Best wishes,

Reza Khayat, PhD
Associate Professor
City College of New York
Department of Chemistry and Biochemistry
New York, NY 10031
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