The current role and evolution of X-ray crystallography in drug discovery and development

Bijak, Vanessa; Szczygiel, Michal; Lenkiewicz, Joanna; Gucwa, Michal; Cooper, David R.; Murzyn, Krzysztof; Minor, Wladek

doi:10.1080/17460441.2023.2246881

Cited by 10 publications

(2 citation statements)

References 65 publications

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“…While numerous methods have been developed that have some efficacy in improving protein crystallization properties (Anstrom et al, 2005; Cieslik & Derewenda, 2009; Cooper et al, 2007; Czepas et al, 2004; Derewenda, 2004a; Derewenda, 2004b; Derewenda & Godzik, 2017; Derewenda & Vekilov, 2006; Janda et al, 2004; Longenecker et al, 2001; Mateja et al, 2002; Qiu & Janson, 2004), none work with sufficiently high efficiency to have been applied with significant frequency by practicing crystallographers. While the development of AlphaFold has provided a de facto solution to the protein‐folding problem for many sequence families (Jumper et al, 2021; Jumper & Hassabis, 2022; Senior et al, 2020), its occasional failure, limited stereochemical accuracy, and inability to date to model ligand complexes means protein crystallography remains widely practiced (Chowdhury et al, 2022; Hendrickson, 2023; Oeffner et al, 2022; Terwilliger et al, 2022; Terwilliger et al, 2023), especially for structure‐based drug discovery projects (Bijak et al, 2023). We therefore set out to develop more efficient methods for rational engineering of protein surface properties to improve crystallization propensity.…”

Section: Introductionmentioning

confidence: 99%

Systematic enhancement of protein crystallization efficiency by bulk lysine‐to‐arginine (KR) substitution

Banayan,

Loughlin,

Singh

et al. 2024

Protein Science

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Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x‐ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal‐packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine‐to‐arginine (KR) substitutions should improve crystallization. To test this hypothesis, we developed software that ranks lysine sites in a target protein based on the redundancy‐corrected KR substitution frequency in homologs. This software can be run interactively on the worldwide web at https://www.pxengineering.org/. We demonstrate that three unrelated single‐domain proteins can tolerate 5–11 KR substitutions with at most minor destabilization, and, for two of these three proteins, the construct with the largest number of KR substitutions exhibits significantly enhanced crystallization propensity. This approach rapidly produced a 1.9 Å crystal structure of a human protein domain refractory to crystallization with its native sequence. Structures from Bulk KR‐substituted domains show the engineered arginine residues frequently make hydrogen‐bonds across crystal‐packing interfaces. We thus demonstrate that Bulk KR substitution represents a rational and efficient method for probabilistic engineering of protein surface properties to improve crystallization.

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Section: Introductionmentioning

confidence: 99%

Systematic enhancement of protein crystallization efficiency by bulk lysine‐to‐arginine (KR) substitution

Banayan,

Loughlin,

Singh

et al. 2024

Protein Science

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“…Protein structures can be determined experimentally by X-ray crystallography [21], nuclear magnetic resonance (NMR) spectroscopy [22], and cryo-electron microscopy [23]. The explosion of the human genome project creates a gap between the number of identified protein sequences and experimentally resolved protein structures but induce an eagerness to utilize computational modeling (template-based modeling and template-free modeling) strategies for protein structure prediction [24].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2

Ng,

Ji,

Liu

et al. 2023

Biomolecules

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Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.

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