Drug repurposing for identification of potential spike inhibitors for SARS-CoV-2 using molecular docking and molecular dynamics simulations.

For the last two years, the COVID-19 pandemic has continued to bring consternation on most of the world. According to recent WHO estimates, there have been more than 5.6 million deaths worldwide. The virus continues to evolve all over the world, thus requiring both vigilance and the necessity to find and develop a variety of therapeutic treatments, including the identification of specific antiviral drugs. Multiple studies have confirmed that SARS-CoV-2 utilizes its membrane-bound spike protein to recognize human angiotensin-converting enzyme 2 (ACE2). Thus, preventing spike-ACE2 interactions is a potentially viable strategy for COVID-19 treatment as it would block the virus from binding and entering into a host cell. This work aims to identify potential drugs using an in silico approach. Molecular docking was carried out on both approved drugs and substances previously tested in vivo. This step was followed by a more detailed analysis of selected ligands by molecular dynamics simulations to identify the best molecules that thwart the ability of the virus to interact with the ACE2 receptor. Because the SARS-CoV-2 virus evolves rapidly due to a plethora of immunocompromised hosts, the compounds were tested against five different known lineages. As a result, we could identify substances that work well on individual lineages and those showing broader efficacy. The most promising candidates among the currently used drugs were zafirlukast and simeprevir with an average binding affinity of -22 kcal/mol for spike proteins originating from various lineages. The first compound is a leukotriene receptor antagonist that is used to treat asthma, while the latter is a protease inhibitor used for hepatitis C treatment. From among the in vivo tested substances that concurrently exhibit promising free energy of binding and ADME parameters (indicating a possible oral administration) we selected the compound BDBM50136234. In conclusion, these molecules are worth exploring further by in vitro and in vivo studies against SARS-CoV-2.

Developing an Updated Strategy for Estimating the Free-Energy Parameters in RNA Duplexes.

For the last 20 years, it has been common lore that the free energy of RNA duplexes formed from canonical Watson-Crick base pairs (bps) can be largely approximated with dinucleotide bp parameters and a few simple corrective constants that are duplex independent. Additionally, the standard benchmark set of duplexes used to generate the parameters were GC-rich in the shorter duplexes and AU-rich in the longer duplexes, and the length of the majority of the duplexes ranged between 6 and 8 bps. We were curious if other models would generate similar results and whether adding longer duplexes of 17 bps would affect the conclusions. We developed a gradient-descent fitting program for obtaining free-energy parameters-the changes in Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS), and the melting temperature (Tm)-directly from the experimental melting curves. Using gradient descent and a genetic algorithm, the duplex melting results were combined with the standard benchmark data to obtain bp parameters. Both the standard (Turner) model and a new model that includes length-dependent terms were tested. Both models could fit the standard benchmark data; however, the new model could handle longer sequences better. We developed an updated strategy for fitting the duplex melting data.

Free energy-based model of CTCF-mediated chromatin looping in the human genome.

In recent years, high-throughput techniques have revealed considerable structural organization of the human genome with diverse regions of the chromatin interacting with each other in the form of loops. Some of these loops are quite complex and may encompass regions comprised of many interacting chain segments around a central locus. Popular techniques for extracting this information are chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture (Hi-C). Here, we introduce a physics-based method to predict the three-dimensional structure of chromatin from population-averaged ChIA-PET data. The approach uses experimentally-validated data from human B-lymphoblastoid cells to generate 2D meta-structures of chromatin using a dynamic programming algorithm that explores the chromatin free energy landscape. By generating both optimal and suboptimal meta-structures we can calculate both the free energy and additionally the relative thermodynamic probability. A 3D structure prediction program with applied restraints then can be used to generate the tertiary structures. The main advantage of this approach for population-averaged experimental data is that it provides a way to distinguish between the principal and the spurious contacts. This study also finds that euchromatin appear to have rather precisely regulated 2D meta-structures compared to heterochromatin. The program source-code is available at https://github.com/plewczynski/looper.

One protein to rule them all: The role of CCCTC-binding factor in shaping human genome in health and disease.

The eukaryotic genome, constituting several billion base pairs, must be contracted to fit within the volume of a nucleus where the diameter is on the scale of µm. The 3D structure and packing of such a long sequence cannot be left to pure chance, as DNA must be efficiently used for its primary roles as a matrix for transcription and replication. In recent years, methods like chromatin conformation capture (including 3C, 4C, Hi-C, ChIA-PET and Multi-ChIA) and optical microscopy have advanced substantially and have shed new light on how eukaryotic genomes are hierarchically organized; first into 10-nm fiber, next into DNA loops, topologically associated domains and finally into interphase or mitotic chromosomes. This knowledge has allowed us to revise our understanding regarding the mechanisms governing the process of DNA organization. Mounting experimental evidence suggests that the key element in the formation of loops is the binding of the CCCTC-binding factor (CTCF) to DNA; a protein that can be referred to as the chief organizer of the genome. However, CTCF does not work alone but in cooperation with other proteins, such as cohesin or Yin Yang 1 (YY1). In this short review, we briefly describe our current understanding of the structure of eukaryotic genomes, how they are established and how the formation of DNA loops can influence gene expression. We discuss the recent discoveries describing the 3D structure of the CTCF-DNA complex and the role of CTCF in establishing genome structure. Finally, we briefly explain how various genetic disorders might arise as a consequence of mutations in the CTCF target sequence or alteration of genomic imprinting.

The structural variability of the influenza A hemagglutinin receptor-binding site.

Hemagglutinin (HA) is a transmembrane protein of the influenza A virus and a key component in its life cycle. The protein allows the virus to enter a host cell by recognizing specific glycans attached to transmembrane proteins of the host, which leads to viral endocytosis. In recent years, significant progress has been made in understanding the structural relationship between changes in the HA receptor-binding site (RBS) and the sialylated glycans that bind them. Several mutations were identified in the HA RBS that allows the virus to change host tropism. Their impact on binding the analogs of human and avian receptors was determined with X-ray crystallography. In this article, we provide a short overview of the HA protein structure and briefly discuss the adaptive mutations introduced to different HA subtypes.

RNA structure interactions and ribonucleoprotein processes of the influenza A virus.

In one more years, we will 'celebrate' an exact centenary of the Spanish flu pandemic. With the rapid evolution of the influenza virus, the possibility of novel pandemic remains ever a concern. This review covers our current knowledge of the influenza A virus: on the role of RNA in translation, replication, what is known of the expressed proteins and the protein products generated from alternative splicing, and on the role of base pairing in RNA structure. We highlight the main events associated with viral entry into the cell, the transcription and replication process, an export of the viral genetic material from the nucleus and the final release of the virus. We discuss the observed potential roles of RNA secondary structure (the RNA base-pairing arrangement) and RNA/RNA interactions in this scheme.

SupeRNAlign: a new tool for flexible superposition of homologous RNA structures and inference of accurate structure-based sequence alignments.

RNA has been found to play an ever-increasing role in a variety of biological processes. The function of most non-coding RNA molecules depends on their structure. Comparing and classifying macromolecular 3D structures is of crucial importance for structure-based function inference and it is used in the characterization of functional motifs and in structure prediction by comparative modeling. However, compared to the numerous methods for protein structure superposition, there are few tools dedicated to the superimposing of RNA 3D structures. Here, we present SupeRNAlign (v1.3.1), a new method for flexible superposition of RNA 3D structures, and SupeRNAlign-Coffee-a workflow that combines SupeRNAlign with T-Coffee for inferring structure-based sequence alignments. The methods have been benchmarked with eight other methods for RNA structural superposition and alignment. The benchmark included 151 structures from 32 RNA families (with a total of 1734 pairwise superpositions). The accuracy of superpositions was assessed by comparing structure-based sequence alignments to the reference alignments from the Rfam database. SupeRNAlign and SupeRNAlign-Coffee achieved significantly higher scores than most of the benchmarked methods: SupeRNAlign generated the most accurate sequence alignments among the structure superposition methods, and SupeRNAlign-Coffee performed best among the sequence alignment methods.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Electron Transport in a Dioxygenase-Ferredoxin Complex: Long Range Charge Coupling between the Rieske and Non-Heme Iron Center.

Dioxygenase (dOx) utilizes stereospecific oxidation on aromatic molecules; consequently, dOx has potential applications in bioremediation and stereospecific oxidation synthesis. The reactive components of dOx comprise a Rieske structure Cys2[2Fe-2S]His2 and a non-heme reactive oxygen center (ROC). Between the Rieske structure and the ROC, a universally conserved Asp residue appears to bridge the two structures forming a Rieske-Asp-ROC triad, where the Asp is known to be essential for electron transfer processes. The Rieske and ROC share hydrogen bonds with Asp through their His ligands; suggesting an ideal network for electron transfer via the carboxyl side chain of Asp. Associated with the dOx is an itinerant charge carrying protein Ferredoxin (Fdx). Depending on the specific cognate, Fdx may also possess either the Rieske structure or a related structure known as 4-Cys-[2Fe-2S] (4-Cys). In this study, we extensively explore, at different levels of theory, the behavior of the individual components (Rieske and ROC) and their interaction together via the Asp using a variety of density function methods, basis sets, and a method known as Generalized Ionic Fragment Approach (GIFA) that permits setting up spin configurations manually. We also report results on the 4-Cys structure for comparison. The individual optimized structures are compared with observed spectroscopic data from the Rieske, 4-Cys and ROC structures (where information is available). The separate pieces are then combined together into a large Rieske-Asp-ROC (donor/bridge/acceptor) complex to estimate the overall coupling between individual components, based on changes to the partial charges. The results suggest that the partial charges are significantly altered when Asp bridges the Rieske and the ROC; hence, long range coupling through hydrogen bonding effects via the intercalated Asp bridge can drastically affect the partial charge distributions compared to the individual isolated structures. The results are consistent with a proton coupled electron transfer mechanism.

Coarse-grained modeling of RNA 3D structure.

Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.

Modeling of Protein-RNA Complex Structures Using Computational Docking Methods.

A significant part of biology involves the formation of RNA-protein complexes. X-ray crystallography has added a few solved RNA-protein complexes to the repertoire; however, it remains challenging to capture these complexes and often only the unbound structures are available. This has inspired a growing interest in finding ways to predict these RNA-protein complexes. In this study, we show ways to approach this problem by computational docking methods, either with a fully automated NPDock server or with a workflow of methods for generation of many alternative structures followed by selection of the most likely solution. We show that by introducing experimental information, the structure of the bound complex is rendered far more likely to be within reach. This study is meant to help the user of docking software understand how to grapple with a typical realistic problem in RNA-protein docking, understand what to expect in the way of difficulties, and recognize the current limitations.

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction.

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures.

Computational modeling of RNA 3D structures and interactions.

RNA molecules have key functions in cellular processes beyond being carriers of protein-coding information. These functions are often dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is difficult, which has prompted the development of computational methods for structure prediction from sequence. Recent progress in 3D structure modeling of RNA and emerging approaches for predicting RNA interactions with ions, ligands and proteins have been stimulated by successes in protein 3D structure modeling.

Prediction of RNA pseudoknots using heuristic modeling with mapping and sequential folding.

Predicting RNA secondary structure is often the first step to determining the structure of RNA. Prediction approaches have historically avoided searching for pseudoknots because of the extreme combinatorial and time complexity of the problem. Yet neglecting pseudoknots limits the utility of such approaches. Here, an algorithm utilizing structure mapping and thermodynamics is introduced for RNA pseudoknot prediction that finds the minimum free energy and identifies information about the flexibility of the RNA. The heuristic approach takes advantage of the 5' to 3' folding direction of many biological RNA molecules and is consistent with the hierarchical folding hypothesis and the contact order model. Mapping methods are used to build and analyze the folded structure for pseudoknots and to add important 3D structural considerations. The program can predict some well known pseudoknot structures correctly. The results of this study suggest that many functional RNA sequences are optimized for proper folding. They also suggest directions we can proceed in the future to achieve even better results.