Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

CHIP (C-terminus of Hsc70-interacting protein) and its worm ortholog CHN-1 are E3 ubiquitin ligases that link the chaperone system with the ubiquitin-proteasome system (UPS). CHN-1 can cooperate with UFD-2, another E3 ligase, to accelerate ubiquitin chain formation; however, the basis for the high processivity of this E3s set has remained obscure. Here, we studied the molecular mechanism and function of the CHN-1-UFD-2 complex in Caenorhabditis elegans. Our data show that UFD-2 binding promotes the cooperation between CHN-1 and ubiquitin-conjugating E2 enzymes by stabilizing the CHN-1 U-box dimer. However, HSP70/HSP-1 chaperone outcompetes UFD-2 for CHN-1 binding, thereby promoting a shift to the autoinhibited CHN-1 state by acting on a conserved residue in its U-box domain. The interaction with UFD-2 enables CHN-1 to efficiently ubiquitylate and regulate S-adenosylhomocysteinase (AHCY-1), a key enzyme in the S-adenosylmethionine (SAM) regeneration cycle, which is essential for SAM-dependent methylation. Our results define the molecular mechanism underlying the synergistic cooperation of CHN-1 and UFD-2 in substrate ubiquitylation.

BioShell is an open-source package for processing biological data, particularly focused on structural applications. The package provides parsers, data structures and algorithms for handling and analyzing macromolecular sequences, structures and sequence profiles. The most frequently used routines are accessible by a set of easy-to-use command line utilities for a Linux environment. The full functionality of the package assumes knowledge of C++ or Python to assemble an application using this software library. Since the last publication that announced the version 2.0, the package has been greatly expanded and rewritten in C++ standard 11 (C++11) to improve its modularity and efficiency. A new testing platform has been implemented to continuously test the correctness and integrity of the package. More than two hundred test programs have been published to provide simple examples that can be used as templates. This makes BioShell an easy to use library that greatly speeds up development of bioinformatics applications and web services without compromising computational efficiency.

The ubiquitin-proteasome system (UPS) removes damaged and unwanted proteins by attaching ubiquitin to lysines in a process termed ubiquitination. Little is known how functional components of the UPS, often exposed to erroneous labeling by ubiquitin during functioning, avoid premature proteolysis. An extensive lysine-less region (lysine desert) in the yeast E3 ligase Slx5 was shown to counteract its ubiquitin-dependent turnover. We conducted bioinformatic screens among prokaryotes and eukaryotes to describe the scope and conservation of this phenomenon. We found that lysine deserts are widespread among bacteria using pupylation-dependent proteasomal degradation, an analog of the UPS. In eukaryotes, lysine deserts appear with increasing organismal complexity, and the most evolutionarily conserved are enriched in the UPS members. Using VHL and SOCS1 E3 ligases, which elongate their lysine desert in the course of evolution, we established that they are non-lysine ubiquitinated, which does not influence their stability, and can be subject to proteasome turnover irrespective of ubiquitination. Our data suggest that a combination of non-lysine ubiquitination and ubiquitin-independent degradation may control the function and fate of the lysine-deficient proteome, as the presence of lysine deserts does not correlate with the half-life.

The E3 ubiquitin ligases CHIP/CHN-1 and UFD-2 team up to accelerate ubiquitin chain formation. However, it remained largely unclear how the high processivity of this E3 set is achieved. Here we studied the molecular mechanism and function of the CHN-1/UFD-2 complex in Caenorhabditis elegans. Our data show that UFD-2 binding promotes the cooperation between CHN-1 and ubiquitin-conjugating E2 enzymes by stabilizing the CHN-1 U-box dimer. The HSP-1 chaperone outcompetes UFD-2 for CHN-1 binding and promotes the auto-inhibited CHN-1 state by acting on the conserved position of the U-box domain. The interaction with UFD-2 enables CHN-1 to efficiently ubiquitinate S-Adenosylhomocysteinase (AHCY-1), an enzyme crucial for lipid metabolism. Our results define the molecular mechanism underlying the synergistic cooperation of CHN-1 and UFD-2 in substrate ubiquitylation.HIGHLIGHTSE3 ligase UFD-2 stimulates ubiquitylation activity of CHIP/CHN-1UFD-2 binding promotes dimerization of CHIP/CHN-1 U-box domains and utilization of E2 enzymesHSP70/HSP-1 by latching the U-box and TPR domains stabilizes the autoinhibitory state of CHIP/CHN-1, limiting interactions with E2s and UFD-2Assembly with UFD-2 enables CHIP/CHN-1 to regulate lipid metabolism by ubiquitylation of S-Adenosylhomocysteinase

The ubiquitin-proteasome system is a proteolytic pathway that removes damaged and unwanted proteins. Their selective turnover is initiated by ubiquitin (Ub) attachment, mainly by Ub ligases that recognize substrates through their short linear motifs termed degrons. A degradation-targeting degron comprises a nearby Ub-modified residue and an intrinsically disordered region (IDR) involved in interaction with the proteasome. Degron-signaling has been studied over the last decades, yet there are no resources for systematic screening of degron sites to facilitate studies on their biological significance, such as targeted protein degradation approaches. To bridge this gap, we developed DEGRONOPEDIA, a web server that allows exploration of degron motifs in the proteomes of seven model organisms and maps these data to Lys, Cys, Thr, and Ser residues that can undergo ubiquitination and to IDRs proximal to them, both in sequence and structure. The server also reports the post-translational modifications and pathogenic mutations within the degron and its flanking regions, as these can modulate the degron’s accessibility. Degrons often occur at the amino or carboxyl end of a protein substrate, acting as initiators of the N-/C-degron pathway, respectively. Therefore, since they may appear following the protease cleavage, DEGRONOPEDIA simulate sequence nicking based on experimental data and theoretical predictions and screen for emerging degron motifs. Moreover, we implemented machine learning to predict the stability of the N-/C-termini, facilitating the identification of substrates of the N-/C-degron pathways. We are confident that our tool will stimulate research on degron-signaling providing output information in a ready-to-validate context. DEGRONOPEDIA can be freely accessed at degronopedia.com.

Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt—a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)—the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.