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Advanced Research in Macromolecular Crystallography

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 731

Special Issue Editors


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Guest Editor
Department of Physics and Texas Center for Superconductivity, University of Houston, Houston, TX 77204, USA
Interests: direct phasing methods for solving the X-ray phase problem in protein crystallography
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo 315211, China
Interests: iterative phasing methods to solve the phase problem of protein crystallography; deep learning methods to solve the phase problem of X-ray diffraction; deep learning methods to reconstruct protein structures from cryo-EM density maps
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

X-ray crystallography is the most powerful method of obtaining detailed information on the structure of macromolecules. A bottleneck in this procedure lies in the difficulty of constructing a macromolecular image from the X-ray diffraction data; this is known as the phase problem. Traditional methods for solving this problem include anomalous scattering, isomorphous replacement, and molecular replacement. These methods are costly and time-consuming, involve model bias, and are sometimes impossible to implement. While molecular replacement has been greatly enhanced by the advent of AlphaFold3, it can still yield unsatisfactory results when predictions are inaccurate, particularly for structures like long helix bundles.

It would therefore be advantageous to be able to solve the phase problem directly by theoretical means, i.e., by direct phasing. Direct phasing methods rely solely on diffraction amplitude data without requiring prior information, making them especially well-suited for solving unknown structures of novel amino acid sequences. Considerable progress has been made on this issue in the last decade. Researchers have realized that the phase problem is solvable for a significant fraction of crystals with enough constraints, such as high solvent content and the existence of non-crystallographic symmetry. Trial calculations that employ iterative projection algorithms, such as the hybrid input–output algorithm and the difference map algorithm, have demonstrated that a high-resolution structure can be obtained by starting from a random electron density. Ab initio phasing without any model bias is therefore possible for these crystals.

The aim of this Special Issue is to highlight the progress that has been made so far on the direct phasing of macromolecular crystals. We seek contributions from researchers that explore new ground. Through the exchange of ideas, it is hoped that this Special Issue will promote general knowledge and stimulate further growth of this new and important area of research on macromolecular crystallography.

Prof. Dr. Wu-Pei Su
Dr. Hongxing He
Guest Editors

Manuscript Submission Information

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Keywords

  • direct phasing methods
  • X-ray crystallography
  • protein structural determination
  • image reconstruction from Fourier amplitudes
  • iterative projection algorithm

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Published Papers (1 paper)

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Research

23 pages, 6475 KiB  
Article
Genetic Algorithm-Enhanced Direct Method in Protein Crystallography
by Ruijiang Fu, Wu-Pei Su and Hongxing He
Molecules 2025, 30(2), 288; https://doi.org/10.3390/molecules30020288 - 13 Jan 2025
Viewed by 376
Abstract
Direct methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. However, traditional direct methods often converge to local minima during electron density iteration, leading to reconstruction failure. Here, we present an enhanced [...] Read more.
Direct methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. However, traditional direct methods often converge to local minima during electron density iteration, leading to reconstruction failure. Here, we present an enhanced direct method incorporating genetic algorithms for electron density modification in real space. The method features customized selection, crossover, and mutation strategies; premature convergence prevention; and efficient message passing interface (MPI) parallelization. We systematically tested the method on 15 protein structures from different space groups with diffraction resolutions of 1.35∼2.5 Å. The test cases included high-solvent-content structures, high-resolution structures with medium solvent content, and structures with low solvent content and non-crystallographic symmetry (NCS). Results showed that the enhanced method significantly improved success rates from below 30% to nearly 100%, with average phase errors reduced below 40°. The reconstructed electron density maps were of sufficient quality for automated model building. This method provides an effective alternative for solving structures that are difficult to predict accurately by AlphaFold3 or challenging to solve by molecular replacement and experimental phasing methods. The implementation is available on Github. Full article
(This article belongs to the Special Issue Advanced Research in Macromolecular Crystallography)
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