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Post-transcriptional RNA modifications (PTxMs) present in small RNA species, specifically circulating extracellular RNAs, were recently identified as clinically relevant readouts, often more indicative of disease severity than the classical up and down changes in their copy number alone. While identification of PTxMs requires multiple and complex sample preparation steps, microgram-range amounts of RNA, followed by expensive and protracted bioinformatics analyses, the clinically relevant information is usually a yes/no for a particular genetic variant(s), and an up/down answer for relevant biomarkers. We have previously shown that molecular beacons (MBs) can identify specific nucleic acid sequences with picomolar sensitivity and single nucleotide specificity by exploiting the target-dependent change in their electrophoretic mobility profile. We now present a method for direct identification of miRNAs and isomiRs in cells and extracellular vesicles using gel electrophoresis, without the need for RNA isolation and purification. The detection is based on discreet changes in the hydrodynamic surface profile, the overall size, charge and charge distribution of the MB-target hybrid. Furthermore, using an RNA tertiary structure prediction algorithm (iFoldRNA) and a custom molecular dynamics simulation (DMD), we designed modified MBs specific for m6A-modified nucleotides in target RNA sequences. The sample preparation method coupled to the software package affords the design of specific MBs and sensitive, multiplex-type detection of targets in a wide variety of biofluids and cells, in a simple mix and read approach.

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.

The ability of cells and tissues to differentially resist or adapt to mechanical forces applied in distinct directions is mediated by the ability of load-bearing proteins to preferentially maintain physical linkages in certain directions. However, the molecular basis and biological consequences of directional force-sensitive binding are unclear. Vinculin (Vcn) is a load-bearing linker protein that exhibits directional catch bonding due to interactions between the Vcn tail domain (Vt) and filamentous (F)-actin. We developed a computational approach to predict Vcn residues involved in directional catch bonding and produced a set of associated Vcn variants with unaltered Vt structure, actin binding, or phospholipid interactions. Incorporation of these variants into Vcn biosensors did not perturb Vcn conformation, but reduced Vcn loading consistent with loss of directional catch bonding. Expression of Vcn variants perturbed the coalignment of FAs and F-actin and directed cell migration, establishing key cellular functions for Vcn directional catch bonding.

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 lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by base pair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network which 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 nucleotides 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 accurately predicts all-atom RNA models solely from the nucleotide sequence.

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.

The pH dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. Computational methods, particularly constant pH molecular dynamics (CpHMD), are the best situated tools for this. However, these often struggle with affordable sampling of sufficiently long timescales, accuracy of pKa prediction, and verification of the structures they generate. We introduce Titr-DMD, an affordable CpHMD method with a protonation state sampler that can be systematically improved, to circumvent these issues. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective method to study pH coupled protein dynamics.

Introduction: The primary driver of castration-resistant prostate cancer (CRPC) is the Androgen receptor (AR). Drugs such as Abiraterone or Enzalutamide that target the Ligand Binding Domain (LBD) of AR are largely ineffective on AR splice variants (AR-SVs) that lack LBD and retain the N-terminal Domain (NTD) of AR. AR continues to function through other stimuli (ex: growth factors), leading to disease relapse and progression. Moreover, AR-SVs are known to heterodimerize with AR full length (AR-FL). Hence, treatment strategies that target both AR-FL and AR-NTD are needed for the complete remission of CRPC. Through structure-activity relationship (SAR) studies based on naturally occurring Urolithin A (UroA) structure, we have developed a novel small molecule UroBoc that directly binds to the NTD and inhibits CRPC growth. Methods: To analyze the binding of UroBoc to AR-NTD, we performed differential scanning fluorimetry and measured saturation transfer difference (STD) in NMR spectroscopic studies. In addition, we investigated the effect of UroBoc on CRPC by western blotting, immunofluorescence, Immunoprecipitation, and performed Xenograft studies in castrated/non-castrated mice models. Results: UroBoc treatment shifted the melt temperature (ΔTm) of purified NTD by 12-15 °C (median of 13.5 °C) in differential scanning fluorimetry. Moreover, STD effects observed were 30-18%, indicating a significant binding to NTD in our NMR studies. UroBoc inhibits the growth of AR+ CRPC, including enzalutamide resistant cells, at 800nM concentration, which is 40 times lower than that of the parent compound, UroA. At this concentration, UroBoc is non-toxic to normal prostate epithelial cells or other cancer cells. Interestingly, UroBoc also inhibited AR, AR-SV and PSA expressions in AR-FL (LNCaP, C42B) and AR-V7 (22-RV1) CRPC cell lines. Molecular studies suggested that UroBoc selectively promotes ubiquitination and subsequent degradation of AR and AR-V7 by proteasome activation. Remarkably, UroBoc failed to inhibit AKT, mTOR, PTEN and hormonal receptors such as estrogen and progesterone receptors suggesting AR could be a direct target for UroBoc. Further, in vivo studies revealed that oral administration of UroBoc inhibits tumor growth in CRPC in both castrated and non-castrated mice models. Conclusion: Our results suggest that the novel UroBoc selectively degrades AR, AR-SV and overcomes enzalutamide resistance in preclinical CRPC models. We believe performing PK/PD studies will confirm the translation potential of UroBoc as a therapy for CRPC. Citation Format: Ashish Tyagi, Balaji Chandrasekaran, Uttara Saran, Murali K. Ankem, Nikolay Dokholyan, Jyh M. Lin, Arun K. Sharma, Chendil Damodaran. Selective ubiquitination of androgen receptor and its splice variants inhibits castration resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 425.

Recent breakthroughs in the determination of the crystal structures of G protein-coupled receptors (GPCRs) have provided new opportunities for structure-based drug design strategies targeting this protein family. With the aim of evaluating the current status of GPCR structure prediction and ligand docking, a community-wide, blind prediction assessment - GPCR Dock 2008 - was conducted in coordination with the publication of the crystal structure of the human adenosine A(2A) receptor bound to the ligand ZM241385. Twenty-nine groups submitted 206 structural models before the release of the experimental structure, which were evaluated for the accuracy of the ligand binding mode and the overall receptor model compared with the crystal structure. This analysis highlights important aspects for success and future development, such as accurate modelling of structurally divergent regions and use of additional biochemical insight such as disulphide bridges in the extracellular loops.

Introduction Dynamics: A Key to Protein Function Monika Fuxreiter Enzymatic Catalysis: Multiscale QM/MM Calculations Adaptive and Accurate Force-Based QM/MM Calculations Noam Bernstein, Ivan Solt, Letif Mones, Csilla Varnai, Steven A. Winfield, and Gabor Csanyi Conformational and Chemical Landscapes of Enzyme Catalysis Alexandra T.P. Carvalho, Fernanda Duarte, Konstantinos Vavitsas, and Shina Caroline Lynn Kamerlin Interplay between Enzyme Function and Protein Dynamics: A Multiscale Approach to the Study of the NAG Kinase Family and Two Class II Aldolases Enrique Marcos, Melchor Sanchez-Martinez, and Ramon Crehuet Protein Motions: Flexibility Analysis Simplified Flexibility Analysis of Proteins Yves-Henri Sanejouand Approaches to Intrinsically Disordered Proteins ABSINTH Implicit Solvation Model and Force Field Paradigm for Use in Simulations of Intrinsically Disordered Proteins Anuradha Mittal, Rahul K. Das, Andreas Vitalis, and Rohit V. Pappu Intrinsically Disordered Protein: A Thermodynamic Perspective Jing Li, James O. Wrabl, and Vincent J. Hilser Long Molecular Dynamics Simulations of Intrinsically Disordered Proteins Reveal Preformed Structural Elements for Target Binding Elio Cino, Mikko Karttunen, and Wing-Yiu Choy Multiscale Simulations of Large Conformational Changes of Disordered and Ordered Proteins Induced by Their Partners Yong Wang, Xiakun Chu, and Jin Wang Coarse-Grained Simulation of Intrinsically Disordered Proteins David de Sancho, Christopher M. Baker, and Robert B. Best Natural and Directed Evolution of Intrinsically Disordered Proteins Tali H. Reingewertz and Eric J. Sundberg Large-Scale Dynamics Discrete Molecular Dynamics: Foundations and Biomolecular Applications Pedro Sfriso, Agusti Emperador, Josep Lluis Gelpi, and Modesto Orozco Ensemble Methods Use of Ensemble Methods to Describe Biomolecular Dynamics by Small Angle X-Ray Scattering Giancarlo Tria, Dmitri I. Svergun, and Pau Bernado Bridging Experiments and Simulations: Structure Calculations with a Dynamical Touch Florian Heinkel, Alexander Cumberworth, and Jorg Gsponer Index

Section I Introduction: Principles and Fundamentals of RNA Nanotechnology The Emerging Field of RNA Nanotechnology Peixuan Guo Uniqueness, Advantages, Challenges, Solutions, and Perspectives in Therapeutics Applying RNA Nanotechnology Peixuan Guo, Farzin Haque, Brent Hallahan, Randall Reif, Hui Li, and Shaoying Wang Section II RNA Folding, Structure, and Motifs in RNA Nanoparticle Assembly Kink Turn Structural Motif in RNA Peter Daldrop, Lin Huang, Kersten T. Schroeder, Jia Wang, and David M. J. Lilley RNA Nanotechnology: Learning from Biologically Active RNA Nanomachines Neocles B. Leontis and Emil F. Khisamutdinov Natural Selection and Structural Polymorphism of RNA 3D Structures Involving GNRA Loops and Their Receptor Motifs Takahiro Tanaka, Hiroyuki Furuta, and Yoshiya Ikawa RNA Junction Motifs as Scaffolds for Construction of Multifunctional RNA Nanoparticles Farzin Haque and Peixuan Guo Section III RNA Computation and Structure Prediction for RNA Nanoparticle Construction Role of Dynamics in RNA Nanostructure Design Wojciech Kasprzak and Bruce A. Shapiro RNA Three-Dimensional Structure Determination Using Experimental Constraints Feng Ding and Nikolay V. Dokholyan Section IV RNA Chemistry for Nanoparticle Synthesis, Conjugation, and Labeling Nucleotide Chemistry for RNA Nanoparticle Labeling, Conjugation, and Synthesis Brian M. Laing and Donald E. Bergstrom RNA Conjugations and Ligations for RNA Nanotechnology Eduardo Paredes and Subha R. Das Atom-Specific Mutagenesis of RNAs for Structure, Function, and Therapeutics Studies Huiyan Sun and Zhen Huang Section V Single-Molecule and Biophysical Techniques in RNA Nanostructure Analysis Atomic Force Microscopy of RNA: Imaging and Beyond Peter M. Schon, Luda S. Shlyakhtenko, and Yuri L. Lyubchenko Single-Molecule Approach to Study RNA Nanoparticles Hui Zhang, Chris Richards, Zhengyi Zhao, and Peixuan Guo Section VI Methods for the Assembly of RNA Nanoparticles Fabrication Methods for RNA Nanoparticle Assembly Based on Bacteriophage phi29 Packaging RNA Structural Features Yi Shu, Bahar Seremi, and Peixuan Guo Synthetic RNA-Protein Nanostructures and Their Potential Applications Hirohisa Ohno, Eriko Osada, Tan Inoue, and Hirohide Saito DNA Nanotechnology as Reference for RNA Nanotechnology Zhen-Gang Wang and Baoquan Ding Section VII RNA Nanoparticles for Therapy of Cancer, Viral Infections, and Genetic Diseases Thermodynamically Stable RNA Three-Way Junction for Constructing Multifunctional Nanoparticles for Delivery of Therapeutics Dan Shu, Yi Shu, Farzin Haque, Sherine Abdelmawla, and Peixuan Guo Design and Construction of RNA Nanoparticles Targeting Prostate Cancer Randall Reif and Peixuan Guo Conjugation of RNA Aptamer to RNA Nanoparticles for Targeted Drug Delivery Katherine Germer, Fengmei Pi, Peixuan Guo, and Xiaoting Zhang Extracellular Membrane Vesicles and Extracellular Membrane Vesicle-Based Therapeutics for Brain Diseases Jayden A. Smith, Clara Alfaro-Cervello, Chiara Cossetti, Nunzio Iraci, Matilde Stefanini, and Stefano Pluchino Pharmacokinetics and Pharmacodynamics of RNA Nanoparticles Markos Leggas Section VIII RNA Nanotechnology for Diagnostic Applications RNA Nanotechnology in Sensing, Detection, and Disease Diagnosis Ping Lei and Guanxin Shen Potentials of RNA Aptamers for Viral Detection and Treatment Longxin Chen, Runting Li, and Runlin Z. Ma Section IX Application of RNA Aptamers in RNA Nanotechnology and Therapeutics Application of RNA Aptamers in Nanotechnology and Therapeutics Hua Shi Aptamers Targeting a Subunit or a Conformation of Glutamate Ion Channel Receptors Zhen Huang, William Jaremko, Chi-yen Lin, and Li Niu Section X Application of miRNAs in RNA Nanotechnology and Therapeutics Application of MicroRNAs in RNA Nanotechnology and Antiviral Therapeutics Ye Qiu, Xin Ye, Maged Hemida, Mary Zhang, Paul Hanson, and Decheng Yang MicroRNAs: Biology and Role in RNA Nanotechnology Bin Guo and Daniel W. Binzel Section XI Application of siRNAs in RNA Nanotechnology and Therapeutics Current Advances in Self-Assembly RNAi Nanoparticles Ka-To Shum, Jiehua Zhou, and John J. Rossi Self-Assembly of siRNA Containing Nanoparticles Mengyao Zheng, Thomas Kissel, and Olivia M. Merkel Index

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe. © 2011 Elsevier Ltd. All rights reserved.

International audience; 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 (Aβ, 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 over many years.