Evidence map›Paper›PMID 41047202›Full record

ArticleMethods in enzymology2025

De novo design of four-helix bundle proteins to bind metalloporphyrin cofactors.

Karen R Coronado, Yixuan Zhu, Samuel I Mann

Abstract read
In one paragraph

Article in Methods in enzymology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Karen R CoronadoDepartment of Chemistry, University of California - Riverside, Riverside, CA, United States.
Yixuan ZhuDepartment of Chemistry, University of California - Riverside, Riverside, CA, United States.
Samuel I MannDepartment of Chemistry, University of California - Riverside, Riverside, CA, United States. Electronic address: samuel.mann@ucr.edu.

Funding

De novo design of functional metallocofactor-binding proteinsR00GM143529 · NIGMS · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI Samuel I Mann · 2024 to 2026
$747k
NIGMS NIH HHS R00 GM143529
6 · The paper itself

Abstract

The versatility of heme proteins in nature stems from the intricate control exerted by their protein scaffolds. De novo protein design offers a powerful means to dissect and recreate these structure-function relationships, enabling construction of novel metalloproteins with tailored functionalities. Here, we describe the computational design and characterization MPP1, a four-helix bundle protein designed to bind an abiological Mn-diphenylporphyrin (MnDPP) cofactor. Using parameterized coiled-coil backbones, flexible backbone sequence design in Rosetta, and structure-guided loop building, MPP1 was designed to accommodate the cofactor with precise positioning of axial ligands and second-shell interactions, as well as purposeful accessibility for oxidants and substrates. The resulting protein was the first crystallographically characterized de novo designed porphyrin-binding protein. MPP1 demonstrated the ability to stabilize a high-valent Mn(V)-oxo species and mediate thioether oxidation. This chapter details the computational strategies, cofactor incorporation, and solution characterization necessary to design and evaluate four-helix bundle proteins capable of binding porphyrin and porphyrin-like cofactors with atomic-level precision. Keywords: de novo design, protein design, bioinorganic chemistry, metalloporphyrins, heme proteins.

Indexed as

HemeproteinsMetalloporphyrinsProtein EngineeringBinding SitesCrystallography, X-RayModels, MolecularOxidation-ReductionProtein BindingHemeproteinsMetalloporphyrinscomputational designDe novo proteinmetalloproteinsporphyrinprotein design

Identifiers

PMID41047202
PMCPMC12497995

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.