Marcin Kowiel

ACHESYM: an algorithm and server for standardized placement of macromolecular models in the unit cell

Despite the existence of numerous useful conventions in structural crystallography, for example for the choice of the asymmetric part of the unit cell or of reciprocal space, surprisingly no standards are in use for the placement of the molecular model in the unit cell, often leading to inconsistencies or confusion. A conceptual solution for this problem has been proposed for macromolecular crystal structures based on the idea of the anti-Cheshire unit cell. Here, a program and server (called ACHESYM; http://achesym.ibch.poznan.pl) are presented for the practical implementation of this concept. In addition, the first task of ACHESYM is to find an optimal (compact) macromolecular assembly if more than one polymer chain exists. ACHESYM processes PDB (atomic parameters and TLS matrices) and mmCIF (diffraction data) input files to produce a new coordinate set and to reindex the reflections and modify their phases, if necessary.

ANS complex of St John's wort PR-10 protein with 28 copies in the asymmetric unit: a fiendish combination of pseudosymmetry with tetartohedral twinning

Hyp-1, a pathogenesis-related class 10 (PR-10) protein from H. perforatum, was crystallized in complex with the fluorescent probe 8-anilino-1-naphthalene sulfonate (ANS). The asymmetric unit of the tetartohedrally twinned crystal contains 28 copies of the protein arranged in columns with noncrystallographic sevenfold translational symmetry and with additional pseudotetragonal rotational NCS.

2-[7-(3,5-Dibromo-2-hy­droxy­phen­yl)-6-eth­oxy­carbonyl-2-oxo-5H-2,3,6,7-tetra­hydro­thio­pyrano[2,3-d][1,3]thia­zol-6-yl]acetic acid ethanol monosolvate

The title compound, C17H15Br2NO6S2·C2H5OH, is the esterification reaction product of 2-(8,10-dibromo-2,6-dioxo-3,5,5a,11b-tetrahydro-2H,6H-chromeno[4′,3′:4,5]thiopyrano[2,3-d]thiazol-5a-yl)acetic acid. Cleavage of the lactone ring and formation of ethoxycarbonyl and hydroxy groups from its structural elements were observed. On the other hand, the carboxymethyl group was not esterified. The H atom and carboxymethyl group, both at stereogenic centres, show a cis conformation. The six-membered dihydrothiopyran ring adopts a half-chair conformation. All NH and OH groups participate in the three-dimensional hydrogen-bond network, which is additionally strengthened by C—H⋯O and C—H⋯S interactions. Intramolecular O—H⋯Br and C—H⋯O interactions also occur.

Heterocyclic tautomerism: reassignment of two crystal structures of 2-amino-1,3-thiazolidin-4-one derivatives.

The structures of 5-(2-hydroxyethyl)-2-[(pyridin-2-yl)amino]-1,3-thiazolidin-4-one, C10H11N3O2S, (I), and ethyl 4-[(4-oxo-1,3-thiazolidin-2-yl)amino]benzoate, C12H12N2O3S, (II), which are identical to the entries with refcodes GACXOZ [Váňa et al. (2009). J. Heterocycl. Chem. 46, 635-639] and HEGLUC [Behbehani & Ibrahim (2012). Molecules, 17, 6362-6385], respectively, in the Cambridge Structural Database [Allen (2002). Acta Cryst. B58, 380-388], have been redetermined at 130 K. This structural study shows that both investigated compounds exist in their crystal structures as the tautomer with the carbonyl-imine group in the five-membered heterocyclic ring and an exocyclic amine N atom, rather than the previously reported tautomer with a secondary amide group and an exocyclic imine N atom. The physicochemical and spectroscopic data of the two investigated compounds are the same as those of GACXOZ and HEGLUC, respectively. In the thiazolidin-4-one system of (I), the S and chiral C atoms, along with the hydroxyethyl group, are disordered. The thiazolidin-4-one fragment takes up two alternative locations in the crystal structure, which allows the molecule to adopt R and S configurations. The occupancy factors of the disordered atoms are 0.883 (2) (for the R configuration) and 0.117 (2) (for the S configuration). In (I), the main factor that determines the crystal packing is a system of hydrogen bonds, involving both strong N-H...N and O-H...O and weak C-H...O hydrogen bonds, linking the molecules into a three-dimensional hydrogen-bond network. On the other hand, in (II), the molecules are linked via N-H...O hydrogen bonds into chains.

Data mining of iron(II) and iron(III) bond-valence parameters, and their relevance for macromolecular crystallography

Using all available metal-containing organic compound structures in the Cambridge Structural Database, a novel data-driven method to derive bond-valence R 0 parameters was developed. While confirming almost all reference literature values, two distinct populations of FeII—N and FeIII—N bonds are observed, which are interpreted as low-spin and high-spin states of the coordinating iron. Based on the R 0 parameters derived here, guidelines for the modeling of iron–ligand distances in macromolecular structures are suggested.

Protonation and geometry of histidine rings

The presence of H atoms connected to either or both of the two N atoms of the imidazole moiety in a histidine residue affects the geometry of the five-membered ring. Analysis of the imidazole moieties found in histidine residues of atomic resolution protein crystal structures in the Protein Data Bank (PDB), and in small-molecule structures retrieved from the Cambridge Structural Database (CSD), identified characteristic patterns of bond lengths and angles related to the protonation state of the imidazole moiety. Using discriminant analysis, two functions could be defined, corresponding to linear combinations of the four most sensitive stereochemical parameters, two bond lengths (ND1-CE1 and CE1-NE2) and two endocyclic angles (-ND1- and -NE2-), that uniquely identify the protonation states of all imidazole moieties in the CSD and can be used to predict which N atom(s) of the histidine side chains in protein structures are protonated. Updated geometrical restraint target values are proposed for differently protonated histidine side chains for use in macromolecular refinement.

Conformational space and vibrational spectra of 2-[(2,4-dimethoxyphenyl)amino]-1,3-thiazolidin-4-one

In this work we present the results of a study of the X-ray structure of 2-[(2,4-dimethoxyphenyl)amino]-1,3-thiazolidin-4-one. Using the FTIR spectra in solid state and results of ab initio calculations we explain the issue of the tautomerism of this molecule. The compound is shown to exist as the 2-amino tautomer rather 2-imino tautomer. Here we consider eight possible tautomers. On the basis of the vibrational spectra we can eliminate five possible tautomers, as not existing in the solid state. As the most possible tautomeric form we have found keto 2-amino form.

Conformation-dependent restraints for polynucleotides: I. Clustering of the geometry of the phosphodiester group.

The refinement of macromolecular structures is usually aided by prior stereochemical knowledge in the form of geometrical restraints. Such restraints are also used for the flexible sugar-phosphate backbones of nucleic acids. However, recent highly accurate structural studies of DNA suggest that the phosphate bond angles may have inadequate description in the existing stereochemical dictionaries. In this paper, we analyze the bonding deformations of the phosphodiester groups in the Cambridge Structural Database, cluster the studied fragments into six conformation-related categories and propose a revised set of restraints for the O-P-O bond angles and distances. The proposed restraints have been positively validated against data from the Nucleic Acid Database and an ultrahigh-resolution Z-DNA structure in the Protein Data Bank. Additionally, the manual classification of PO4 geometry is compared with geometrical clusters automatically discovered by machine learning methods. The machine learning cluster analysis provides useful insights and a practical example for general applications of clustering algorithms for automatic discovery of hidden patterns of molecular geometry. Finally, we describe the implementation and application of a public-domain web server for automatic generation of the proposed restraints.

Automatic recognition of ligands in electron density by machine learning

Motivation The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure‐guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time‐consuming iterative fitting. Results Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule‐ligand complexes. Availability and implementation Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob. Supplementary information Supplementary data are available at Bioinformatics online.

Normalized structure factor analysis with the CentroMK program

Statistical analysis of the normalized structure factor E is important during space-group determination. Several approaches to solve this problem have been described in the literature. In this paper, the most popular approach, the ideal asymptotic probability density function developed by Wilson, is compared with the more accurate exact probability density functions described by Shmueli and co-workers. Furthermore, a new computer program, CentroMK, for normalized structure factor analysis, is presented. The program is capable of plotting histograms of the normalized structure factors and exact probability density functions. Moreover, the program calculates five estimators helpful during the space-group determination: 〈|E|〉, 〈|E2 − 1|〉, %E > 2, %E < 0.25 and the discrepancy R function. The two approaches and the error rates of the five listed estimators are compared for nearly 30 600 crystal structures obtained from Acta Crystallographica Section E. It is shown that within a space group the means 〈|E|〉 and 〈|E2 − 1|〉 of real crystal structures show high variability. The comparison shows that decisions based on the exact probability density function are more accurate, the computing time is reasonable, and estimators 〈|E|〉, %E < 0.25 and R are the most accurate and should be preferred during space-group determination.