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Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities.Author summaryCompared with small-molecule drugs, aptamer drugs are short RNAs/DNAs that can specifically bind to targets with high strength. With the interest of discovering novel aptamer drugs as an alternative to address the long-lasting COVID-19 pandemic, in this research, we developed an artificial intelligence (AI) framework for the in silico design of novel aptamer drugs that can prevent the SARS-CoV-2 virus from entering human cells. Our research is valuable as we explore a novel approach for the treatment of SARS-CoV-2 infection and the AI framework could be applied to address future health crises.

RNA three-dimensional structures provide rich and vital information for understanding their functions. Recent advances in cryogenic electron microscopy (cryo-EM) allow structure determination of RNAs and ribonucleoprotein (RNP) complexes. However, limited global and local resolutions of RNA cryo-EM maps pose great challenges in tracing RNA coordinates. The Rosetta-based "auto-DRRAFTER" method builds RNA models into moderate-resolution RNA cryo-EM density as part of the Ribosolve pipeline. Here, we describe a step-by-step protocol for auto-DRRAFTER using a glycine riboswitch from Fusobacterium nucleatum as an example. Successful implementation of this protocol allows automated RNA modeling into RNA cryo-EM density, accelerating our understanding of RNA structure-function relationships. Input and output files are being made available at https://github.com/auto-DRRAFTER/springer-chapter .

The

RNA-guided systems, such as CRISPR–Cas, combine programmable substrate recognition with enzymatic function, a combination that has been used advantageously to develop powerful molecular technologies1,2. Structural studies of these systems have illuminated how the RNA and protein jointly recognize and cleave their substrates, guiding rational engineering for further technology development3. Recent work identified a new class of RNA-guided systems, termed OMEGA, which include IscB, the likely ancestor of Cas9, and the nickase IsrB, a homologue of IscB lacking the HNH nuclease domain4. IsrB consists of only around 350 amino acids, but its small size is counterbalanced by a relatively large RNA guide (roughly 300-nt ωRNA). Here, we report the cryogenic-electron microscopy structure of Desulfovirgula thermocuniculi IsrB (DtIsrB) in complex with its cognate ωRNA and a target DNA. We find the overall structure of the IsrB protein shares a common scaffold with Cas9. In contrast to Cas9, however, which uses a recognition (REC) lobe to facilitate target selection, IsrB relies on its ωRNA, part of which forms an intricate ternary structure positioned analogously to REC. Structural analyses of IsrB and its ωRNA as well as comparisons to other RNA-guided systems highlight the functional interplay between protein and RNA, advancing our understanding of the biology and evolution of these diverse systems.

The discovery and design of biologically important RNA molecules is outpacing three-dimensional structural characterization. Here, we demonstrate that cryo-electron microscopy can routinely resolve maps of RNA-only systems and that these maps enable subnanometer-resolution coordinate estimation when complemented with multidimensional chemical mapping and Rosetta DRRAFTER computational modeling. This hybrid 'Ribosolve' pipeline detects and falsifies homologies and conformational rearrangements in 11 previously unknown 119- to 338-nucleotide protein-free RNA structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length Vibrio cholerae and Fusobacterium nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and the computer-designed ATP-TTR-3 aptamer with and without AMP. Simulation benchmarks, blind challenges, compensatory mutagenesis, cross-RNA homologies and internal controls demonstrate that Ribosolve can accurately resolve the global architectures of RNA molecules but does not resolve atomic details. These tests offer guidelines for making inferences in future RNA structural studies with similarly accelerated throughput.

Specimens below 50 kDa have generally been considered too small to be analyzed by single-particle cryo-electron microscopy (cryo-EM). The high flexibility of pure RNAs makes it difficult to obtain high-resolution structures by cryo-EM. In bacteria, riboswitches regulate sulfur metabolism through binding to the S-adenosylmethionine (SAM) ligand and offer compelling targets for new antibiotics. SAM-I, SAM-I/IV, and SAM-IV are the three most commonly found SAM riboswitches, but the structure of SAM-IV is still unknown. Here, we report the structures of apo and SAM-bound SAM-IV riboswitches (119-nt, ~40 kDa) to 3.7 Å and 4.1 Å resolution, respectively, using cryo-EM. The structures illustrate homologies in the ligand-binding core but distinct peripheral tertiary contacts in SAM-IV compared to SAM-I and SAM-I/IV. Our results demonstrate the feasibility of resolving small RNAs with enough detail to enable detection of their ligand-binding pockets and suggest that cryo-EM could play a role in structure-assisted drug design for RNA., The conformational heterogeneity of RNA molecules makes their structure determination by X-ray crystallography and NMR challenging. Here the authors show that RNA structures can be solved by cryo-EM and present the structures of a 40 kDa SAM-IV riboswitch in the apo form and bound to its ligand S-adenosylmethionine.

Single-particle cryogenic electron microscopy (cryo-EM) has become a standard technique for determining protein structures at atomic resolution1–3. However, cryo-EM studies of protein-free RNA are in their early days. The Tetrahymena thermophila group I self-splicing intron was the first ribozyme to be discovered and has been a prominent model system for the study of RNA catalysis and structure–function relationships4, but its full structure remains unknown. Here we report cryo-EM structures of the full-length Tetrahymena ribozyme in substrate-free and bound states at a resolution of 3.1 A. Newly resolved peripheral regions form two coaxially stacked helices; these are interconnected by two kissing loop pseudoknots that wrap around the catalytic core and include two previously unforeseen (to our knowledge) tertiary interactions. The global architecture is nearly identical in both states; only the internal guide sequence and guanosine binding site undergo a large conformational change and a localized shift, respectively, upon binding of RNA substrates. These results provide a long-sought structural view of a paradigmatic RNA enzyme and signal a new era for the cryo-EM-based study of structure–function relationships in ribozymes. Cryo-electron microscopy has been used to determine the structure of the Tetrahymena ribozyme (a catalytic RNA) at sufficiently high resolution to model side chains and metal ions.

A hallmark of the initiation step of HIV-1 reverse transcription, in which viral RNA genome is converted into double-stranded DNA, is that it is slow and non-processive. Biochemical studies have identified specific sites along the viral RNA genomic template in which reverse transcriptase (RT) stalls. These stalling points, which occur after the addition of three and five template dNTPs, may serve as checkpoints to regulate the precise timing of HIV-1 reverse transcription following viral entry. Structural studies of reverse transcriptase initiation complexes (RTICs) have revealed unique conformations that may explain the slow rate of incorporation; however, questions remain about the temporal evolution of the complex and features that contribute to strong pausing during initiation. Here we present cryo-electron microscopy and single-molecule characterization of an RTIC after three rounds of dNTP incorporation (+3), the first major pausing point during reverse transcription initiation. Cryo-electron microscopy structures of a +3 extended RTIC reveal conformational heterogeneity within the RTIC core. Three distinct conformations were identified, two of which adopt unique, likely off-pathway, intermediates in the canonical polymerization cycle. Single-molecule Forster resonance energy transfer experiments confirm that the +3 RTIC is more structurally dynamic than earlier-stage RTICs. These alternative conformations were selectively disrupted through structure-guided point mutations to shift single-molecule Forster resonance energy transfer populations back toward the on-pathway conformation. Our results support the hypothesis that conformational heterogeneity within the HIV-1 RTIC during pausing serves as an additional means of regulating HIV-1 replication.

Reverse transcription of the HIV-1 RNA genome into double-stranded DNA is a central step in viral infection1 and a common target of antiretroviral drugs2. The reaction is catalysed by viral reverse transcriptase (RT)3,4 that is packaged in an infectious virion with two copies of viral genomic RNA5 each bound to host lysine 3 transfer RNA (tRNALys3), which acts as a primer for initiation of reverse transcription6,7. Upon viral entry into cells, initiation is slow and non-processive compared to elongation8,9. Despite extensive efforts, the structural basis of RT function during initiation has remained a mystery. Here we use cryo-electron microscopy to determine a three-dimensional structure of an HIV-1 RT initiation complex. In our structure, RT is in an inactive polymerase conformation with open fingers and thumb and with the nucleic acid primer–template complex shifted away from the active site. The primer binding site (PBS) helix formed between tRNALys3 and HIV-1 RNA lies in the cleft of RT and is extended by additional pairing interactions. The 5′ end of the tRNA refolds and stacks on the PBS to create a long helical structure, while the remaining viral RNA forms two helical stems positioned above the RT active site, with a linker that connects these helices to the RNase H region of the PBS. Our results illustrate how RNA structure in the initiation complex alters RT conformation to decrease activity, highlighting a potential target for drug action. A cryo-EM structure of an initiation complex of HIV-1 reverse transcriptase sheds light on the initiation of reverse transcription of viral RNA.

Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code., The RNA sequence and secondary structure regulate RNA editing by ADAR. Here the authors employ a CRISPR/Cas9-mediated saturation mutagenesis and machine learning to predict RNA editing efficiency of specific substrates.

Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule’s sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

The discovery and design of biologically important RNA molecules is dramatically outpacing three-dimensional structural characterization. To address this challenge, we present Ribosolve, a hybrid method integrating moderate-resolution cryo-EM maps, chemical mapping, and Rosetta computational modeling, and demonstrate its application to thirteen previously unknown 119-to 338-nucleotide protein-free RNA-only structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length V. cholerae and F. nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and computer-designed spinach-TTR-3, eterna3D-JR_1, and ATP-TTR-3 with and without AMP. Blind challenges, prospective compensatory mutagenesis, internal controls, and simulation benchmarks validate the Ribosolve models and establish that modeling convergence is quantitatively predictive of model accuracy. These results demonstrate that RNA-only 3D structure determination can be rapid and routine.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at ., This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.

The molecular mechanisms of exon definition and back-splicing are fundamental unanswered questions in pre-mRNA splicing. Here we report cryo-electron microscopy structures of the yeast spliceosomal E complex assembled on introns, providing a view of the earliest event in the splicing cycle that commits pre-mRNAs to splicing. The E complex architecture suggests that the same spliceosome can assemble across an exon, and that it either remodels to span an intron for canonical linear splicing (typically on short exons) or catalyses back-splicing to generate circular RNA (on long exons). The model is supported by our experiments, which show that an E complex assembled on the middle exon of yeast EFM5 or HMRA1 can be chased into circular RNA when the exon is sufficiently long. This simple model unifies intron definition, exon definition, and back-splicing through the same spliceosome in all eukaryotes and should inspire experiments in many other systems to understand the mechanism and regulation of these processes.

Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.Significance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

Additional file 2: The analysis was performed also for various combinations of sequences of homologs (related to Fig. 6). The results of an analysis of core RMSD of all possible combinations of five input sequences of homologs for all 8 RNA families investigated in this work: Adenine riboswitch (Ade), c-di-GMP riboswitch (GMP), TPP riboswitch (TPP), THF riboswitch (THF), tRNA, RNA-Puzzle 13 (RP13), RNA-Puzzle 14 (RP14), RNA-Puzzle 17 (RP17). This analysis was performed with the evox_all_variants.py from the EvoClustRNA package. Each sequence of homologs was ordered from 1 to 3. A mode “h1” means models of the first homolog and the target sequence used for clustering, “h2” means models of the second homolog and the target sequence. “h234” means that models of three homologs were considered during clustering, the second homolog, third and fourth. For each variant 5 top clusters are shown and the first cluster is marked with a black dot. The first panel combines the results for SimRNA and Rosetta, the second panel shows the results for SimRNA and the third only for Rosetta.

Maintenance of telomeres by telomerase permits continuous proliferation of rapidly dividing cells, including the majority of human cancers. Despite its direct biomedical significance, the architecture of the human telomerase complex remains unknown. Generating homogeneous telomerase samples has presented a significant barrier to developing improved structural models. Here we pair single-molecule Förster resonance energy transfer (smFRET) measurements with Rosetta modeling to map the conformations of the essential telomerase RNA core domain within the active ribonucleoprotein. FRET-guided modeling places the essential pseudoknot fold distal to the active site on a protein surface comprising the C-terminal element, a domain that shares structural homology with canonical polymerase thumb domains. An independently solved medium-resolution structure of Tetrahymena telomerase provides a blind test of our modeling methodology and sheds light on the structural homology of this domain across diverse organisms. Our smFRET-Rosetta models reveal nanometer-scale rearrangements within the RNA core domain during catalysis. Taken together, our FRET data and pseudoatomic molecular models permit us to propose a possible mechanism for how RNA core domain rearrangement is coupled to template hybrid elongation.

The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance among their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNA's nearest-neighbor (NN) free-energy parameters, but these methods have not been tested in predictive trials or on nonstandard nucleotides. Here, we present, to our knowledge, the first such tests through a RECCES-Rosetta (reweighting of energy-function collection with conformational ensemble sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values' systematic uncertainties. RECCES-Rosetta recovers the 10 NN parameters for Watson-Crick stacked base pairs and 32 single-nucleotide dangling-end parameters with unprecedented accuracies: rmsd of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For set-aside test sets, RECCES-Rosetta gives rmsd values of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and 0.99 kcal/mol on seven stacked pairs involving nonstandard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol rmsd accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.

SummaryHigh-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA/RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

RNA-protein assemblies carry out many critical biological functions including translation, RNA splicing, and telomere extension. Increasingly, cryo-electron microscopy (cryoEM) is used to determine the structures of these complexes, but nearly all maps determined with this method have regions in which the local resolution does not permit manual coordinate tracing. Because RNA coordinates typically cannot be determined by docking crystal structures of separate components and existing structure prediction algorithms cannot yet model RNA-protein complexes, RNA coordinates are frequently omitted from final models despite their biological importance. To address these omissions, we have developed a new framework for De novo Ribonucleoprotein modeling in Real-space through Assembly of Fragments Together with Electron density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of small RNA-protein complexes, as well as for large RNA-protein machines, including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes where the availability of both high and low resolution maps enable rigorous tests. Blind tests on yeast U1 snRNP and spliceosomal P complex maps demonstrate that the method can successfully build RNA coordinates in real-world modeling scenarios. Additionally, to aid in final model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply this method to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating that DRRAFTER can be used to accelerate accurate model building in challenging cases.

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/.

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta’s success is the energy function: amodel parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, beta_nov15. Applying these concepts,we explain how to use Rosetta energies to identify and analyze the features of biomolecular models.Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, carbohydrates, nucleic acids, and other macromolecules.

The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance amongst their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNA’s nearest neighbor (NN) free energy parameters, but these methods have not been tested in predictive trials or on non-standard nucleotides. Here, we present the first such tests through a RECCES-Rosetta (Reweighting of Energy-function Collection with Conformational Ensemble Sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values’ systematic uncertainties. RECCES-Rosetta recovers the ten NN parameters for Watson-Crick stacked base pairs and thirty-two single-nucleotide dangling-end parameters with unprecedented accuracies – root-mean-square deviations (RMSD) of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For set-aside test sets, RECCES-Rosetta gives an RMSD of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and an RMSD of 0.99 kcal/mol on seven stacked pairs involving non-standard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol RMSD accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.SignificanceUnderstanding RNA machines and how their behavior can be modulated by chemical modification is increasingly recognized as an important biological and bioengineering problem, with continuing discoveries of riboswitches, mRNA regulons, CRISPR-guided editing complexes, and RNA enzymes. Computational strategies to understanding RNA energetics are being proposed, but have not yet faced rigorous tests. We describe a modeling strategy called ‘RECCES-Rosetta’ that models the full ensemble of motions of RNA in single-stranded form and in helices, including non-standard nucleotides such as 2,6-diaminopurine, a variant of adenosine. When compared to experiments, including blind tests, the energetic accuracies of RECCES-Rosetta calculations are at levels close to experimental error, suggesting that computation can now be used to predict and design basic RNA energetics.

Sortase enzymes are vitally important for the virulence of gram-positive bacteria as they play a key role in the attachment of surface proteins to the cell wall. These enzymes recognize a specific sorting sequence in proteins destined to be displayed on the surface of the bacteria and catalyze the transpeptidation reaction that links it to a cell wall precursor molecule. Because of their role in establishing pathogenicity, and in light of the recent rise of antibiotic-resistant bacterial strains, sortase enzymes are novel drug targets. Here, we present a study of the prototypical sortase protein Staphylococcus aureus Sortase A (SrtA). Both conventional and accelerated molecular dynamics simulations of S. aureus SrtA in its apo state and when bound to an LPATG sorting signal (SS) were performed. Results support a binding mechanism that may be characterized as conformational selection followed by induced fit. Additionally, the SS was found to adopt multiple metastable states, thus resolving discrepancies between binding conformations in previously reported experimental structures. Finally, correlation analysis reveals that the SS actively affects allosteric pathways throughout the protein that connect the first and the second substrate binding sites, which are proposed to be located on opposing faces of the protein. Overall, these calculations shed new light on the role of dynamics in the binding mechanism and function of sortase enzymes.

Elucidating the detailed process of ligand binding to a receptor is pharmaceutically important for identifying druggable binding sites. With the ability to provide atomistic detail, computational methods are well poised to study these processes. Here, accelerated molecular dynamics (aMD) is proposed to simulate processes of ligand binding to a G-protein-coupled receptor (GPCR), in this case the M3 muscarinic receptor, which is a target for treating many human diseases, including cancer, diabetes and obesity. Long-timescale aMD simulations were performed to observe the binding of three chemically diverse ligand molecules: antagonist tiotropium (TTP), partial agonist arecoline (ARc) and full agonist acetylcholine (ACh). In comparison with earlier microsecond-timescale conventional MD simulations, aMD greatly accelerated the binding of ACh to the receptor orthosteric ligand-binding site and the binding of TTP to an extracellular vestibule. Further aMD simulations also captured binding of ARc to the receptor orthosteric site. Additionally, all three ligands were observed to bind in the extracellular vestibule during their binding pathways, suggesting that it is a metastable binding site. This study demonstrates the applicability of aMD to protein–ligand binding, especially the drug recognition of GPCRs.

Interactions between RNA and proteins are pervasive in biology, shaping processes such as mRNA translation, localization, and alternative splicing. Developing a predictive understanding of the energetics of these systems would allow us to model biologically relevant mutations of these interactions and ultimately design novel interactions. Despite recent advances in high throughput experimental technologies that measure the energetics of these systems, quantitative computational prediction of relative RNA/protein binding affinities has remained a challenge. This is partly due to the observation that computational binding affinity prediction methods typically break down when the molecules are highly flexible or undergo significant conformational changes, situations that often arise in RNA/protein binding. Here, we present a novel framework within Rosetta for predicting RNA/protein relative binding affinities that begins to address this issue. Specifically, we show that the nearest neighbor energies, which are typically used for RNA secondary structure prediction, can be used to approximate the unbound free energy of the RNA, thus eliminating the need to explicitly account for the flexibility of the unbound RNA or conformational changes of the RNA upon binding. Using this method of calculating the unbound RNA free energy significantly improves the prediction accuracy over a more typical 3D structure-based approach. We optimized this method using a subset of published MS2 coat protein affinities and ultimately made predictions for the system with 1.11-1.28 kcal/mol root mean square (RMS) error. Additionally, we show that this method is able to predict relative binding affinities for four diverse RNA/protein systems with 1.48 kcal/mol RMS error. Finally, to more rigorously assess this method, we independently measured and made blind predictions for PUF3 and PUM2 binding affinities with RMS errors of 1-2 kcal/mol, which is comparable to the accuracy achieved by prediction methods for other types of systems.

Sortase enzymes are vitally important for the virulence of gram-positive bacteria as they play a key role in the attachment of surface proteins to the cell wall. These enzymes recognize a specific sorting sequence in proteins destined to be displayed on the surface of the bacteria and catalyze the transpeptidation reaction that links it to a cell wall precursor molecule. Because of their role in establishing pathogenicity, and in light of the recent rise of antibiotic-resistant bacterial strains, sortase enzymes are novel drug targets. Here, we present a study of the prototypical sortase protein Staphylococcus aureus Sortase A (SrtA). Both conventional and accelerated molecular dynamics simulations of S. aureus SrtA in its apo state and when bound to an LPATG sorting signal (SS) were performed. Results support a binding mechanism that may be characterized as conformational selection followed by induced fit. Additionally, the SS was found to adopt multiple metastable states, thus resolving discrepancies between binding conformations in previously reported experimental structures. Finally, correlation analysis reveals that the SS actively affects allosteric pathways throughout the protein that connect the first and the second substrate binding sites, which are proposed to be located on opposing faces of the protein. Overall, these calculations shed new light on the role of dynamics in the binding mechanism and function of sortase enzymes.

The targeting of surface proteins to the cell wall, necessary for the full virulence of Gram positive bacteria, can be traced back to the actions of sortase enzymes. These enzymes recognize a specific sorting sequence in proteins destined to be displayed on the surface of the bacteria, and catalyze the transpeptidation reaction that results in the attachment of the protein to a cell wall precursor molecule. With the rise of antibiotic resistant strains of bacteria, sortase enzymes are promising new drug targets. Specifically, in light of the growing emergence of methicillin resistant Staphylococcus aureus (MRSA), we are looking at Staphylococcus aureus Sortase A (SrtA). SrtA cleaves proteins at the LPXTG sorting signal and attaches them to lipid II. Here, we have used both conventional and accelerated molecular dynamics simulations to simulate the enzyme in its apo and holo (bound to the LPATG sorting signal) states. Results reveal the importance of loop motions of which are situated proximal to the active site, specifically the β6/β7 and β7/β8 loops, and suggest dual functionality of the catalytic arginine residue. Additionally, in a subset of the holo simulations we observe movement of the sorting signal away from the active site to distinct metastable states and find that motions of the enzyme in the accelerated molecular dynamics simulations suggest an induced fit binding mechanism. These results improve our understanding of the functioning of SrtA and will ultimately aid in the development of new drugs to combat MRSA infections.