Knowledge of protein–ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.

Large biomolecules—proteins and nucleic acids—are composed of building blocks which define their identity, properties and binding capabilities. In order to shed light on the energetic side of interactions of amino acids between themselves and with deoxyribonucleotides, we present the Amino Acid Interaction web server (http://bioinfo.uochb.cas.cz/INTAA/). INTAA offers the calculation of the residue Interaction Energy Matrix for any protein structure (deposited in Protein Data Bank or submitted by the user) and a comprehensive analysis of the interfaces in protein–DNA complexes. The Interaction Energy Matrix web application aims to identify key residues within protein structures which contribute significantly to the stability of the protein. The application provides an interactive user interface enhanced by 3D structure viewer for efficient visualization of pairwise and net interaction energies of individual amino acids, side chains and backbones. The protein–DNA interaction analysis part of the web server allows the user to view the relative abundance of various configurations of amino acid–deoxyribonucleotide pairs found at the protein–DNA interface and the interaction energies corresponding to these configurations calculated using a molecular mechanical force field. The effects of the sugar-phosphate moiety and of the dielectric properties of the solvent on the interaction energies can be studied for the various configurations.

The Protein Data Bank in Europe – Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.

The growing number of high-quality experimental (X-ray, NMR) structures of protein–DNA complexes has sufficient enough information to assess whether universal rules governing the DNA sequence recognition process apply. While previous studies have investigated the relative abundance of various modes of amino acid–base contacts (van der Waals contacts, hydrogen bonds), relatively little is known about the energetics of these noncovalent interactions. In the present study, we have performed the first large-scale quantitative assessment of binding preferences in protein–DNA complexes by calculating the interaction energies in all 80 possible amino acid–DNA base combinations. We found that several mutual amino acid–base orientations featuring bidentate hydrogen bonds capable of unambiguous one-to-one recognition correspond to unique minima in the potential energy space of the amino acid–base pairs. A clustering algorithm revealed that these contacts form a spatially well-defined group offering relatively little conformational freedom. Various molecular mechanics force field and DFT-D ab initio calculations were performed, yielding similar results.

Decades of intensive experimental studies of the recognition of DNA sequences by proteins have provided us with a view of a diverse and complicated world in which few to no features are shared between individual DNA-binding protein families. The originally conceived direct readout of DNA residue sequences by amino acid side chains offers very limited capacity for sequence recognition, while the effects of the dynamic properties of the interacting partners remain difficult to quantify and almost impossible to generalise. In this work we investigated the energetic characteristics of all DNA residue—amino acid side chain combinations in the conformations found at the interaction interface in a very large set of protein—DNA complexes by the means of empirical potential-based calculations. General specificity-defining criteria were derived and utilised to look beyond the binding motifs considered in previous studies. Linking energetic favourability to the observed geometrical preferences, our approach reveals several additional amino acid motifs which can distinguish between individual DNA bases. Our results remained valid in environments with various dielectric properties.

Our study presents a novel germline c.1715G>T (p.G572V) mutation in the gene encoding Toll-like receptor 8 (TLR8) causing an autoimmune and autoinflammatory disorder in a family with monozygotic male twins, who suffer from severe autoimmune hemolytic anemia worsening with infections, and autoinflammation presenting as fevers, enteritis, arthritis, and CNS vasculitis. The pathogenicity of the mutation was confirmed by in vitro assays on transfected cell lines and primary cells. The p.G572V mutation causes impaired stability of the TLR8 protein, cross-reactivity to TLR7 ligands and reduced ability of TLR8 to attenuate TLR7 signaling. This imbalance toward TLR7-dependent signaling leads to increased pro-inflammatory responses, such as nuclear factor-κB (NF-κB) activation and production of pro-inflammatory cytokines IL-1β, IL-6, and TNFα. This unique TLR8 mutation with partial TLR8 protein loss and hyperinflammatory phenotype mediated by TLR7 ligands represents a novel Michael Svaton and Veronika Kanderova contributed equally to this work.

Representative pairs of amino acid side chains and nucleic acid bases extracted from available high-quality structures of protein-DNA complexes were analyzed using a range of computational methods. CCSD(T)/CBS interaction energies were calculated for the chosen 272 pairs. These reference interaction energies were used to test the MP2.5/CBS, MP2.X/CBS, MP2-F12, DFT-D3, PM6, and Amber force field methods. Method MP2.5 provided excellent agreement with reference data (root-mean-square error (RMSE) of 0.11 kcal/mol), which is more than 1 order of magnitude faster than the CCSD(T) method. When MP2-F12 and MP2.5 were combined, the results were within reasonable accuracy (0.20 kcal/mol), with a computational savings of almost 2 orders of magnitude. Therefore, this method is a promising tool for accurate calculations of interaction energies in protein-DNA motifs of up to ∼100 atoms, for which CCSD(T)/CBS benchmark calculations are not feasible. B3-LYP-D3 calculated with def2-TZVPP and def2-QZVP basis sets yielded sufficiently good results with a reasonably small RMSE. This method provided better results for neutral systems, whereas positively charged species exhibited the worst agreement with the benchmark data. The Amber force field yielded unbalanced results-performing well for systems containing nonpolar amino acids but severely underestimating interaction energies for charged complexes. The semiempirical PM6 method with corrections for hydrogen bonding and dispersion energy (PM6-D3H4) exhibited considerably smaller error than the Amber force field, which makes it an effective tool for modeling extended protein-ligand complexes (of up to 10,000 atoms).

We systematically investigate the applicability of a molecular dynamics-based setup for the calculations of standard binding free energies of biologically relevant protein−DNA complexes. The free energies are extracted from a potential of mean force calculated using umbrella sampling simulations. Two protein−DNA systems derived from a homeodomain transcription factor complex are studied in order to investigate the binding of both disordered and globular proteins. Free energies and trajectories obtained using two modern molecular mechanical force fields are compared to each other and to experimental data. The temperature dependence of the calculated standard binding free energies is investigated by performing all simulations over a range of temperatures. We show that the values of standard binding free energies obtained from these simulations are overestimated compared to experimental results. Significant differences are observed between the two protein−DNA systems and between the two force fields, which are explained by different propensities to form inter-and intramolecular contacts. The number of protein−DNA contacts increases with increasing temperature, in agreement with the experimentally known temperature dependence of enthalpies of binding. However, conclusions about the temperature dependence of the standard binding free energies cannot be made with confidence, as the differences among the values are on the order of statistical uncertainty.