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Motivation: Whole-genome sequencing has revolutionized biosciences by providing tools for constructing complete DNA sequences of individuals. With entire genomes at hand, scientists can pinpoint DNA fragments responsible for oncogenesis and predict patient responses to cancer treatments. Machine learning plays a paramount role in this process. However, the sheer volume of whole-genome data makes it difficult to encode the characteristics of genomic variants as features for learning algorithms., Results: In this article, we propose three feature extraction methods that facilitate classifier learning from sets of genomic variants. The core contributions of this work include: (i) strategies for determining features using variant length binning, clustering and density estimation; (ii) a programing library for automating distribution-based feature extraction in machine learning pipelines. The proposed methods have been validated on five real-world datasets using four different classification algorithms and a clustering approach. Experiments on genomes of 219 ovarian, 61 lung and 929 breast cancer patients show that the proposed approaches automatically identify genomic biomarkers associated with cancer subtypes and clinical response to oncological treatment. Finally, we show that the extracted features can be used alongside unsupervised learning methods to analyze genomic samples., Availability and Implementation: The source code of the presented algorithms and reproducible experimental scripts are available on Github at https://github.com/MNMdiagnostics/dbfe., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate ligands in existing models. The server processes PDB/mmCIF and MTZ files and returns a ranking of 10 most likely ligands for each detected electron density blob along with interactive 3D visualizations. Additionally, for each prediction/validation, a plugin script is generated that enables users to conduct a detailed analysis of the server results in Coot. The CheckMyBlob web server is available at https://checkmyblob.bioreproducibility.org., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Stereochemical restraints are commonly used to aid the refinement of macromolecular structures obtained by experimental methods at lower resolution. The standard restraint library for nucleic acids has not been updated for over two decades and needs revision. In this paper, geometrical restraints for nucleic acids sugars are derived using information from high-resolution crystal structures in the Cambridge Structural Database. In contrast to the existing restraints, this work shows that different parts of the sugar moiety form groups of covalent geometry dependent on various chemical and conformational factors, such as the type of ribose or the attached nucleobase, and ring puckering or rotamers of the glycosidic (χ) or side-chain (γ) torsion angles. Moreover, the geometry of the glycosidic link and the endocyclic ribose bond angles are functionally dependent on χ and sugar pucker amplitude (τm), respectively. The proposed restraints have been positively validated against data from the Nucleic Acid Database, compared with an ultrahigh-resolution Z-DNA structure in the Protein Data Bank, and tested by re-refining hundreds of crystal structures in the Protein Data Bank. The conformation-dependent sugar restraints presented in this work are publicly available in REFMAC, PHENIX and SHELXL format through a dedicated RestraintLib web server with an API function., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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.

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., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)