Viral DNA Binding to NLRC3, an Inhibitory Nucleic Acid Sensor, Unleashes STING, a Cyclic Dinucleotide Receptor that Activates Type I Interferon.

Immune suppression is a crucial component of immunoregulation and a subgroup of nucleotide-binding domain (NBD), leucine-rich repeat (LRR)-containing proteins (NLRs) attenuate innate immunity. How this inhibitory function is controlled is unknown. A key question is whether microbial ligands can regulate this inhibition. NLRC3 is a negative regulator that attenuates type I interferon (IFN-I) response by sequestering and attenuating stimulator of interferon genes (STING) activation. Here, we report that NLRC3 binds viral DNA and other nucleic acids through its LRR domain. DNA binding to NLRC3 increases its ATPase activity, and ATP-binding by NLRC3 diminishes its interaction with STING, thus licensing an IFN-I response. This work uncovers a mechanism wherein viral nucleic acid binding releases an inhibitory innate receptor from its target.

Allosteric inactivation of an engineered optogenetic GTPase.

Optogenetics is a technique for establishing direct spatiotemporal control over molecular function within living cells using light. Light application induces conformational changes within targeted proteins that produce changes in function. One of the applications of optogenetic tools is an allosteric control of proteins via light-sensing domain (LOV2), which allows direct and robust control of protein function. Computational studies supported by cellular imaging demonstrated that application of light allosterically inhibited signaling proteins Vav2, ITSN, and Rac1, but the structural and dynamic basis of such control has yet to be elucidated by experiment. Here, using NMR spectroscopy, we discover principles of action of allosteric control of cell division control protein 42 (CDC42), a small GTPase involved in cell signaling. Both LOV2 and Cdc42 employ flexibility in their function to switch between "dark"/"lit" or active/inactive states, respectively. By conjoining Cdc42 and phototropin1 LOV2 domains into the bi-switchable fusion Cdc42Lov, application of light-or alternatively, mutation in LOV2 to mimic light absorption-allosterically inhibits Cdc42 downstream signaling. The flow and patterning of allosteric transduction in this flexible system are well suited to observation by NMR. Close monitoring of the structural and dynamic properties of dark versus "lit" states of Cdc42Lov revealed lit-induced allosteric perturbations that extend to Cdc42's downstream effector binding site. Chemical shift perturbations for lit mimic, I539E, have distinct regions of sensitivity, and both the domains are coupled together, leading to bidirectional interdomain signaling. Insights gained from this optoallosteric design will increase our ability to control response sensitivity in future designs.

Differentiable rotamer sampling with molecular force fields.

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

Rational peptide design for targeting cancer cell invasion.

Cell invasion is an important process in cancer progression and recurrence. Invasion and implantation of cancer cells from their original place to other tissues, by disabling vital organs, challenges the treatment of cancer patients. Given the importance of the matter, many molecular treatments have been developed to inhibit cancer cell invasion. Because of their low production cost and ease of production, peptides are valuable therapeutic molecules for inhibiting cancer cell invasion. In recent years, advances in the field of computational biology have facilitated the design of anti-cancer peptides. In our investigation, using computational biology approaches such as evolutionary analysis, residue scanning, protein-peptide interaction analysis, molecular dynamics, and free energy analysis, our team designed a peptide library with about 100 000 candidates based on A6 (acetyl-KPSSPPEE-amino) sequence which is an anti-invasion peptide. During computational studies, two of the designed peptides that give the highest scores and showed the greatest sequence similarity to A6 were entered into the experimental analysis workflow for further analysis. In experimental analysis steps, the anti-metastatic potency and other therapeutic effects of designed peptides were evaluated using MTT assay, RT-qPCR, zymography analysis, and invasion assay. Our study disclosed that the IK1 (acetyl-RPSFPPEE-amino) peptide, like A6, has great potency to inhibit the invasion of cancer cells.

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.

Deciphering the Mechanistic Basis for Perfluoroalkyl-Protein Interactions.

Although rarely used in nature, fluorine has emerged as an important elemental ingredient in the design of proteins with altered folding, stability, oligomerization propensities, and bioactivity. Adding to the molecular modification toolbox, here we report the ability of privileged perfluorinated amphiphiles to noncovalently decorate proteins to alter their conformational plasticity and potentiate their dispersion into fluorous phases. Employing a complementary suite of biophysical, in-silico and in-vitro approaches, we establish structure-activity relationships defining these phenomena and investigate their impact on protein structural dynamics and intracellular trafficking. Notably, we show that the lead compound, perfluorononanoic acid, is 106 times more potent in inducing non-native protein secondary structure in select proteins than is the well-known helix inducer trifluoroethanol, and also significantly enhances the cellular uptake of complexed proteins. These findings could advance the rational design of fluorinated proteins, inform on potential modes of toxicity for perfluoroalkyl substances, and guide the development of fluorine-modified biologics with desirable functional properties for drug discovery and delivery applications.

Rational design and implementation of a chemically inducible heterotrimerization system.

Chemically inducible dimerization (CID) uses a small molecule to induce binding of two different proteins. CID tools such as the FK506-binding protein-FKBP-rapamycin-binding- (FKBP-FRB)-rapamycin system have been widely used to probe molecular events inside and outside cells. While various CID tools are available, chemically inducible trimerization (CIT) does not exist, due to inherent challenges in designing a chemical that simultaneously binds three proteins with high affinity and specificity. Here, we developed CIT by rationally splitting FRB and FKBP. Cellular and structural datasets showed efficient trimerization of split pairs of FRB or FKBP with full-length FKBP or FRB, respectively, by rapamycin. CIT rapidly induced tri-organellar junctions and perturbed intended membrane lipids exclusively at select membrane contact sites. By conferring one additional condition to what is achievable with CID, CIT expands the types of manipulation in single live cells to address cell biology questions otherwise intractable and engineer cell functions for future synthetic biology applications.

Break to Build: Isothermal Assembly of Nucleic Acid Nanoparticles (NANPs) via Enzymatic Degradation.

The intrinsic properties of RNA and DNA biopolymers emphasized by engineered nucleic acid nanoparticles (NANPs) offer accelerated development of next-generation therapies. The rational design of NANPs facilitates programmable architectures intended for regulated molecular and cellular interactions. The conventional bottom-up assembly of NANPs relies on the thermal annealing of individual strands. Here, we introduce a concept of nuclease-driven production of NANPs where selective digestion of functionally inert structures leads to isothermal self-assembly of liberated constituents. The working principles, morphological changes, assembly kinetics, and the retention of structural integrity for system components subjected to anhydrous processing and storage are assessed. We show that the assembly of precursors into a single structure improves stoichiometry and enhances the functionality of nuclease-driven products. Furthermore, the experiments with immune reporting cell lines show that the developed protocols retain the immunostimulatory functionality of tested NANPs. The presented approach enables exploitation of the advantages of conditionally produced NANPs and demonstrates that NANPs' stability, immunorecognition, and assembly can be regulated to allow for a more robust functional system.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis.

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aß, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Locking and Unlocking Thrombin Function Using Immunoquiescent Nucleic Acid Nanoparticles with Regulated Retention In Vivo.

The unbalanced coagulation of blood is a life-threatening event that requires accurate and timely treatment. We introduce a user-friendly biomolecular platform based on modular RNA-DNA anticoagulant fibers programmed for reversible extracellular communication with thrombin and subsequent control of anticoagulation via a "kill-switch" mechanism that restores hemostasis. To demonstrate the potential of this reconfigurable technology, we designed and tested a set of anticoagulant fibers that carry different thrombin-binding aptamers. All fibers are immunoquiescent, as confirmed in freshly collected human peripheral blood mononuclear cells. To assess interindividual variability, the anticoagulation is confirmed in the blood of human donors from the U.S. and Brazil. The anticoagulant fibers reveal superior anticoagulant activity and prolonged renal clearance in vivo in comparison to free aptamers. Finally, we confirm the efficacy of the "kill-switch" mechanism in vivo in murine and porcine models.

Toxic SOD1 trimers are off-pathway in the formation of amyloid-like fibrils in ALS.

Accumulation of insoluble amyloid fibrils is widely studied as a critical factor in the pathology of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease. Misfolded Cu, Zn superoxide dismutase (SOD1) was the first protein linked to ALS, and non-native SOD1 trimeric oligomers were recently linked to cytotoxicity, while larger oligomers were protective to cells. The balance between trimers and larger aggregates in the process of SOD1 aggregation is, thus, a critical determinant of potential therapeutic approaches to treat ALS. However, it is unknown whether these trimeric oligomers are a necessary intermediate for larger aggregate formation or a distinct off-pathway species competing with fibril formation. Depending on the on- or off-pathway scenario of trimer formation, we expect drastically different therapeutic approaches. Here, we show that the toxic SOD1 trimer is an off-pathway intermediate competing with protective fibril formation. We design mutant SOD1 constructs that remain in a trimeric state (super-stable trimers) and show that stabilizing the trimeric SOD1 prevents formation of fibrils in vitro and in a motor neuron-like cell model (NSC-34). Using size exclusion chromatography, we track the aggregation kinetics of purified SOD1 and show direct competition of trimeric SOD1 with larger oligomer and fibril formation. Finally, we show the trimer is structurally independent of both larger soluble oligomers and insoluble fibrils using circular dichroism spectroscopy and limited proteolysis.

Mapping co-regulatory interactions among ligand-binding sites in ryanodine receptor 1.

Ryanodine receptor 1 (RyR1) is an intracellular calcium ion (Ca2+ ) release channel required for skeletal muscle contraction. Although cryo-electron microscopy identified binding sites of three coactivators Ca2+ , ATP, and caffeine (CFF), the mechanism of co-regulation and synergy of these activators is unknown. Here, we report allosteric connections among the three ligand-binding sites and pore region in (i) Ca2+ bound-closed, (ii) ATP/CFF bound-closed, (iii) Ca2+ /ATP/CFF bound-closed, and (iv) Ca2+ /ATP/CFF bound-open RyR1 states. We identified two dominant networks of interactions that mediate communication between the Ca2+ -binding site and pore region in Ca2+ bound-closed state, which partially overlapped with the pore communications in ATP/CFF bound-closed RyR1 state. In Ca2+ /ATP/CFF bound-closed and -open RyR1 states, co-regulatory interactions were analogous to communications in the Ca2+ bound-closed and ATP/CFF bound-closed states. Both ATP- and CFF-binding sites mediate communication between the Ca2+ -binding site and the pore region in Ca2+ /ATP/CFF bound-open RyR1 structure. We conclude that Ca2+ , ATP, and CFF propagate their effects to the pore region through a network of overlapping interactions that mediate allosteric control and molecular synergy in channel regulation.

Evaluation of pH change effects on the HSA folding and its drug binding characteristics, a computational biology investigation.

The binding of therapeutics to human serum albumin (HSA), which is an abundant protein in plasma poses a major challenge in drug discovery. Although HSA has several binding pockets, the binding site I on D2 and binding site II on D3 are the main binding pockets of HSA. To date, a few experiments have been conducted to examine the effects of the potential of hydrogen (pH) changes on HSA attributes. In the present investigation, the effect of acidic (pH 7.1) and basic states (pH 7.7) on HSA structure and its drug binding potency were examined in comparison with the physiological state (pH 7.4). For this purpose, molecular dynamics (MD), free energy landscape (FEL), principal component analysis (PCA), probability distribution function (PDF), tunnel-cavity investigation, secondary structure analysis, docking study, and free energy investigation were employed to investigate the effect of pH changes on the structural characteristics of HSA at the atomic level. The results obtained from this study revealed the significant effect of pH alterations on the secondary and tertiary structure of HSA. In addition, HSA stability and its drug binding ability can be severely affected following pH changes. Given that pH change frequently occurs in various diseases such as cancer, diabetes, and kidney failure, therefore, pharmaceutical companies should allocate specific consideration to this subject throughout their drug design experiments.

Peptidomimetics in cancer targeting.

The low efficiency of treatment strategies is one of the main obstacles to developing cancer inhibitors. Up to now, various classes of therapeutics have been developed to inhibit cancer progression. Peptides due to their small size and easy production compared to proteins are highly regarded in designing cancer vaccines and oncogenic pathway inhibitors. Although peptides seem to be a suitable therapeutic option, their short lifespan, instability, and low binding affinity for their target have not been widely applicable against malignant tumors. Given the peptides' disadvantages, a new class of agents called peptidomimetic has been introduced. With advances in physical chemistry and biochemistry, as well as increased knowledge about biomolecule structures, it is now possible to chemically modify peptides to develop efficient peptidomimetics. In recent years, numerous studies have been performed to the evaluation of the effectiveness of peptidomimetics in inhibiting metastasis, angiogenesis, and cancerous cell growth. Here, we offer a comprehensive review of designed peptidomimetics to diagnose and treat cancer.

Adaptation-proof SARS-CoV-2 vaccine design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface spike glycoprotein - a major antibody target - is critical for virus entry via engagement of human angiotensin-converting enzyme 2 (ACE2) receptor. Despite successes with existing vaccines and therapies that primarily target the receptor binding domain (RBD) of the spike protein, the susceptibility of RBD to mutations provides escape routes for the SARS-CoV-2 from neutralizing antibodies. On the other hand, structural conservation in the spike protein can be targeted to reduce escape mutations and achieve broad protection. Here, we designed candidate stable immunogens that mimic surface features of selected conserved regions of spike protein through 'epitope grafting,' in which we present the target epitope topology on diverse heterologous scaffolds that can structurally accommodate the spike epitopes. Structural characterization of the epitope-scaffolds showed stark agreement with our computational models and target epitopes. The sera from mice immunized with engineered designs display epitope-scaffolds and spike binding activity. We also demonstrated the utility of the designed epitope-scaffolds in diagnostic applications. Taken all together, our study provides important methodology for targeting the conserved, non-RBD structural motifs of spike protein for SARS-CoV-2 epitope vaccine design and demonstrates the potential utility of 'epitope grafting' in rational vaccine design.

Anhydrous Nucleic Acid Nanoparticles for Storage and Handling at Broad Range of Temperatures.

Recent advances in nanotechnology now allow for the methodical implementation of therapeutic nucleic acids (TNAs) into modular nucleic acid nanoparticles (NANPs) with tunable physicochemical properties which can match the desired biological effects, provide uniformity, and regulate the delivery of multiple TNAs for combinatorial therapy. Despite the potential of novel NANPs, the maintenance of their structural integrity during storage and shipping remains a vital issue that impedes their broader applications. Cold chain storage is required to maintain the potency of NANPs in the liquid phase, which greatly increases transportation costs. To promote long-term storage and retention of biological activities at higher temperatures (e.g., +50 °C), a panel of representative NANPs is first exposed to three different drying mechanisms-vacuum concentration (SpeedVac), lyophilization (Lyo), and light-assisted drying (LAD)-and then rehydrated and analyzed. While SpeedVac primarily operates using heat, Lyo avoids temperature increases by taking advantage of pressure reduction and LAD involves a near-infrared laser for uniform drying in the presence of trehalose. This work compares and defines refinements crucial in formulating an optimal strategy for producing stable, fully functional NANPs and presents a forward advancement in their development for clinical applications.

RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Various miRNAs compensate the role of miR-122 on HCV replication.

One of the determinants for tissue tropism of hepatitis C virus (HCV) is miR-122, a liver-specific microRNA. Recently, it has been reported that interaction of miR-122 to HCV RNA induces a conformational change of the 5'UTR internal ribosome entry site (IRES) structure to form stem-loop II structure (SLII) and hijack of translating 80S ribosome through the binding of SLIII to 40S subunit, which leads to efficient translation. On the other hand, low levels of HCV-RNA replication have also been detected in some non-hepatic cells; however, the details of extrahepatic replication remain unknown. These observations suggest the possibility that miRNAs other than miR-122 can support efficient replication of HCV-RNA in non-hepatic cells. Here, we identified a number of such miRNAs and show that they could be divided into two groups: those that bind HCV-RNA at two locations (miR-122 binding sites I and II), in a manner similar to miR-122 (miR-122-like), and those that target a single site that bridges sites I and II and masking both G28 and C29 in the 5'UTR (non-miR-122-like). Although the enhancing activity of these non-hepatic miRNAs were lower than those of miR-122, substantial expression was detected in various normal tissues. Furthermore, structural modeling indicated that both miR-122-like and non-miR-122-like miRNAs not only can facilitate the formation of an HCV IRES SLII but also can stabilize IRES 3D structure in order to facilitate binding of SLIII to the ribosome. Together, these results suggest that HCV facilitates miR-122-independent replication in non-hepatic cells through recruitment of miRNAs other than miR-122. And our findings can provide a more detailed mechanism of miR-122-dependent enhancement of HCV-RNA translation by focusing on IRES tertiary structure.

Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction.

Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and they claim to achieve high prediction accuracy in their tests; we show that these approaches do not generalize, that is, they fail to predict interactions between unknown proteins and unknown small molecules. To address these shortcomings, we develop a new compound-protein interaction predictor, Yuel, which predicts compound-protein interactions with a higher generalizability than the existing methods. Upon comprehensive tests on various data sets, we find that out of all the deep-learning approaches surveyed, Yuel manifests the best ability to predict interactions between unknown compounds and unknown proteins.

Predicting Protein-Ligand Docking Structure with Graph Neural Network.

Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.