Decrypting the Information Exchange Pathways across the Spliceosome Machinery

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification

As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaboration correlations as the brain region gene community network (BG-CN) and present a new deep learning approach, such as the community graph convolutional neural network (Com-GCN), for investigating the transmission of information within and between communities. The results can be used for diagnosing and extracting causal factors for Alzheimer's disease (AD). First, an affinity aggregation model for BG-CN is developed to describe intercommunity and intracommunity information transmission. Second, we design the Com-GCN architecture with intercommunity convolution and intracommunity convolution operations based on the affinity aggregation model. Through sufficient experimental validation on the AD neuroimaging initiative (ADNI) dataset, the design of Com-GCN matches the physiological mechanism better and improves the interpretability and classification performance. Furthermore, Com-GCN can identify lesioned brain regions and disease-causing genes, which may assist precision medicine and drug design in AD and serve as a valuable reference for other neurological disorders.

Graph theory approaches for molecular dynamics simulations

Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems.

Third Metal Ion Dictates the Catalytic Activity of the Two-Metal-Ion Pre-Ribosomal RNA-Processing Machinery

Nucleic acid processing enzymes use a two-Mg2+-ion motif to promote the formation and cleavage of phosphodiester bonds. Yet, recent evidence demonstrates the presence of spatially conserved second-shell cations surrounding the catalytic architecture of proteinaceous and RNA-dependent enzymes. The RNase mitochondrial RNA processing (MRP) complex, which cleaves the ribosomal RNA (rRNA) precursor at the A3 cleavage site to yield mature 5'-end of 5.8S rRNA, hosts in the catalytic core one atypically-located Mg2+ ion, in addition to the ions forming the canonical catalytic motif. Here, we employ biased quantum classical molecular dynamics simulations of RNase MRP to discover that the third Mg2+ ion inhibits the catalytic process. Instead, its displacement in favour of a second-shell monovalent K+ ion propels phosphodiester bond cleavage by enabling the formation of a specific hydrogen bonding network that mediates the essential proton transfer step. This study points to a direct involvement of a transient K+ ion in the catalytic cleavage of the phosphodiester bond and implicates cation trafficking as a general mechanism in nucleic acid processing enzymes and ribozymes.

Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) lncRNA Conformational Dynamics in Complex with RNA-Binding Protein with Serine-Rich Domain 1 (RNPS1) in the Pan-cancer Splicing and Gene Expression

Alternative splicing events increase the transcriptomic and proteomic complexity in cancers. Overexpression of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a highly conserved lncRNA, is widely known to promote cancer development, one mechanism for which may be through the regulation of alternative splicing and, thereby, gene expression. Its regulatory interactions with proteins have been a subject of much interest, yet little research has been carried out on the mechanisms adopted. It has been observed that MALAT1 binds to RNA-binding protein with serine-rich domain 1 (RNPS1), being colocalized in the nuclear speckles, and together, these two binding partners may regulate alternative splicing. Upregulated RNPS1 is predicted to play a key role in the pan-cancer development. Experimental tertiary structure of full-length MALAT1 is currently lacking despite the availability of the 3D structure of 3' expression and nuclear retention element. We hypothesize that the computationally modeled tertiary structures of the specific binding motifs in the M-region, E-region, and full-length structures of MALAT1 may adopt a modular structure and bind to the RNPS1 loop region of RS/P domain involved in exon skipping, interacting in a manner fully consistent with the biochemical experiments. Extensive observations using the powerful molecular dynamics (MD) simulations of MALAT1 regions bound to RNPS1 suggested that all three regions form interactive, yet stable complexes. The ranking of the MM-GBSA- and MM-PBSA-derived binding free energies between these complexes corroborated well in the MD simulations and experiments. Energy decomposition analyses suggested that arginines in the RNPS1 protein are among the major contributors toward the binding free energies as calculated by MM-GBSA present in the Amber package; while among the nucleotides, the major contributors were nucleotides with G and A nucleobases, with more contributory effect in comparison to arginines, across the bound M-region, E-region, and full-length MALAT1. This suggests that specific purines play a greater role in the complex formation, in a loop-specific manner, and the more proactive approach in complexation tilts toward MALAT1. To the best of our knowledge, our studies are the first studies taking a unique approach, utilizing the binding motifs to deduce a tertiary structure of MALAT1, toward our understanding of the lncRNA-protein interactions, stability, and binding on a structural basis. The therapeutic implications of targeting this complex formation to regulate splicing and hence, oncogenesis, is further envisaged.

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins

INTRODUCTION

Molecular Dynamics (MD) simulations can support mechanism-based drug design. Indeed, MD simulations by capturing biomolecule motions at finite temperatures can reveal hidden binding sites, accurately predict drug-binding poses, and estimate the thermodynamics and kinetics, crucial information for drug discovery campaigns. Small-Guanosine Triphosphate Phosphohydrolases (GTPases) regulate a cascade of signaling events, that affect most cellular processes. Their deregulation is linked to several diseases, making them appealing drug targets. The broad roles of small-GTPases in cellular processes and the recent approval of a covalent KRas inhibitor as an anticancer agent renewed the interest in targeting small-GTPase with small molecules.

AREA COVERED

This review emphasizes the role of MD simulations in elucidating small-GTPase mechanisms, assessing the impact of cancer-related variants, and discovering novel inhibitors.

EXPERT OPINION

The application of MD simulations to small-GTPases exemplifies the role of MD simulations in the structure-based drug design process for challenging biomolecular targets. Furthermore, AI and machine learning-enhanced MD simulations, coupled with the upcoming power of quantum computing, are promising instruments to target elusive small-GTPases mutations and splice variants. This powerful synergy will aid in developing innovative therapeutic strategies associated to small-GTPases deregulation, which could potentially be used for personalized therapies and in a tissue-agnostic manner to treat tumors with mutations in small-GTPases.

Deciphering Internal Regulatory Patterns within the p53 Core Tetramer: Insights from Community Network Analysis

Gene therapy is one of the most effective strategies for cancer treatment. The p53 protein, commonly known as the "guardian of the genome", plays a critical role in gene activation and tumor suppression. Tetramerization of the p53 core domain is an essential allosteric process that supports its suppression functions. This letter presents a framework to analyze the structure, function, and dynamic connectivity of the p53 tetramer, using community network analysis based on all-atom molecular dynamics simulations. The communities within the p53 monomer exhibit distinct functional roles, while interactions between molecules establish a symmetrical network structure. We identified direct evidence of single, double, and multiple pathway regulations within the p53 tetramer and crucial residue pairs involved in these connections. Our study provides a comprehensive framework to understand the community network of the p53 tetramer, offering new insights into the stable formation of the p53 core tetramer.

Tracing Allostery in the Spliceosome Ski2-like RNA Helicase Brr2

RNA ATPases/helicases remodel substrate RNA-protein complexes in distinct ways. The different RNA ATPases/helicases, taking part in the spliceosome complex, reshape the RNA/RNA-protein contacts to enable premature-mRNA splicing. Among them, the bad response to refrigeration 2 (Brr2) helicase promotes U4/U6 small nuclear (sn)RNA unwinding via ATP-driven translocation of the U4 snRNA strand, thus playing a pivotal role during the activation, catalytic, and disassembly phases of splicing. The plastic Brr2 architecture consists of an enzymatically active N-terminal cassette (N-cassette) and a structurally similar but inactive C-terminal cassette (C-cassette). The C-cassette, along with other allosteric effectors and regulators, tightly and timely controls Brr2's function via an elusive mechanism. Here, microsecond-long molecular dynamics simulations, dynamical network theory, and community network analysis are combined to elucidate how allosteric effectors/regulators modulate the Brr2 function. We unexpectedly reveal that U4 snRNA itself acts as an allosteric regulator, amplifying the cross-talk of distal Brr2 domains and triggering a conformational reorganization of the protein. Our findings offer fundamental understanding into Brr2's mechanism of action and broaden our knowledge on the sophisticated regulatory mechanisms by which spliceosome ATPases/helicases control gene expression. This includes their allosteric regulation exerted by client RNA strands, a mechanism that may be broadly applicable to other RNA-dependent ATPases/helicases.

Assessing the mechanism of fast-cycling cancer-associated mutations of Rac1 small Rho GTPase

Rho-GTPases proteins function as molecular switches alternating from an active to an inactive state upon Guanosine triphosphate (GTP) binding and hydrolysis to Guanosine diphosphate (GDP). Among them, Rac subfamily regulates cell dynamics, being overexpressed in distinct cancer types. Notably, these proteins are object of frequent cancer-associated mutations at Pro29 (P29S, P29L, and P29Q). To assess the impact of these mutations on Rac1 structure and function, we performed extensive all-atom molecular dynamics simulations on wild-type (wt) and oncogenic isoforms of this protein in GDP- and GTP-bound states. Our results unprecedentedly elucidate that P29Q/S-induced structural and dynamical perturbations of Rac1 core domain weaken the binding of the catalytic site Mg2+ ion, and reduce the GDP residence time within protein, enhancing the GDP/GTP exchange rate and Rac1 activity. This broadens our knowledge of the role of cancer-associated mutations on small GTPases mechanism supplying valuable information for future drug discovery efforts targeting specific Rac1 isoforms.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Monovalent metal ion binding promotes the first transesterification reaction in the spliceosome

Cleavage and formation of phosphodiester bonds in nucleic acids is accomplished by large cellular machineries composed of both protein and RNA. Long thought to rely on a two-metal-ion mechanism for catalysis, structure comparisons revealed many contain highly spatially conserved second-shell monovalent cations, whose precise function remains elusive. A recent high-resolution structure of the spliceosome, essential for pre-mRNA splicing in eukaryotes, revealed a potassium ion in the active site. Here, we employ biased quantum mechanics/ molecular mechanics molecular dynamics to elucidate the function of this monovalent ion in splicing. We discover that the K+ ion regulates the kinetics and thermodynamics of the first splicing step by rigidifying the active site and stabilizing the substrate in the pre- and post-catalytic state via formation of key hydrogen bonds. Our work supports a direct role for the K+ ion during catalysis and provides a mechanistic hypothesis likely shared by other nucleic acid processing enzymes.

Connected Component Analysis of Dynamical Perturbation Contact Networks

Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.

Molecular Dynamics Simulations Elucidate the Molecular Basis of Pre-mRNA Translocation by the Prp2 Spliceosomal Helicase

The spliceosome machinery catalyzes precursor-messenger RNA (pre-mRNA) splicing by undergoing at each splicing cycle assembly, activation, catalysis, and disassembly processes, thanks to the concerted action of specific RNA-dependent ATPases/helicases. Prp2, a member of the DExH-box ATPase/helicase family, harnesses the energy of ATP hydrolysis to translocate a single pre-mRNA strand in the 5' to 3' direction, thus promoting spliceosome remodeling to its catalytic-competent state. Here, we established the functional coupling between ATPase and helicase activities of Prp2. Namely, extensive multi-μs molecular dynamics simulations allowed us to unlock how, after pre-mRNA selection, ATP binding, hydrolysis, and dissociation induce a functional typewriter-like rotation of the Prp2 C-terminal domain. This movement, endorsed by an iterative swing of interactions established between specific Prp2 residues with the nucleobases at 5'- and 3'-ends of pre-mRNA, promotes pre-mRNA translocation. Notably, some of these Prp2 residues are conserved in the DExH-box family, suggesting that the translocation mechanism elucidated here may be applicable to all DExH-box helicases.

RNA-protein complexes and force field polarizability

Molecular dynamic (MD) simulations offer a way to study biomolecular interactions and their dynamics at the atomistic level. There are only a few studies of RNA-protein complexes in MD simulations, and here we wanted to study how force fields differ when simulating RNA-protein complexes: 1) argonaute 2 with bound guide RNA and a target RNA, 2) CasPhi-2 bound to CRISPR RNA and 3) Retinoic acid-inducible gene I C268F variant in complex with double-stranded RNA. We tested three non-polarizable force fields: Amber protein force fields ff14SB and ff19SB with RNA force field OL3, and the all-atom OPLS4 force field. Due to the highly charged and polar nature of RNA, we also tested the polarizable AMOEBA force field and the ff19SB and OL3 force fields with a polarizable water model O3P. Our results show that the non-polarizable force fields lead to compact and stable complexes. The polarizability in the force field or in the water model allows significantly more movement from the complex, but in some cases, this results in the disintegration of the complex structure, especially if the protein contains longer loop regions. Thus, one should be cautious when running long-scale simulations with polarizability. As a conclusion, all the tested force fields can be used to simulate RNA-protein complexes and the choice of the optimal force field depends on the studied system and research question.

Disease mutations and phosphorylation alter the allosteric pathways involved in autoinhibition of protein phosphatase 2A

Mutations in protein phosphatase 2A (PP2A) are connected to intellectual disability and cancer. It has been hypothesized that these mutations might disrupt the autoinhibition and phosphorylation-induced activation of PP2A. Since they are located far from both the active and substrate binding sites, it is unclear how they exert their effect. We performed allosteric pathway analysis based on molecular dynamics simulations and combined it with biochemical experiments to investigate the autoinhibition of PP2A. In the wild type (WT), the C-arm of the regulatory subunit B56δ obstructs the active and substrate binding sites exerting a dual autoinhibition effect. We find that the disease mutant, E198K, severely weakens the allosteric pathways that stabilize the C-arm in the WT. Instead, the strongest allosteric pathways in E198K take a different route that promotes exposure of the substrate binding site. To facilitate the allosteric pathway analysis, we introduce a path clustering algorithm for lumping pathways into channels. We reveal remarkable similarities between the allosteric channels of E198K and those in phosphorylation-activated WT, suggesting that the autoinhibition can be alleviated through a conserved mechanism. In contrast, we find that another disease mutant, E200K, which is in spatial proximity of E198, does not repartition the allosteric pathways leading to the substrate binding site; however, it may still induce exposure of the active site. This finding agrees with our biochemical data, allowing us to predict the activity of PP2A with the phosphorylated B56δ and provide insight into how disease mutations in spatial proximity alter the enzymatic activity in surprisingly different mechanisms.

Structural Insights into Phosphorylation-Mediated Polymerase Function Loss for DNA Polymerase β Bound to Gapped DNA

DNA polymerase β is a member of the X-family of DNA polymerases, playing a critical role in the base excision repair (BER) pathway in mammalian cells by implementing the nucleotide gap-filling step. In vitro phosphorylation of DNA polymerase β with PKC on S44 causes loss in the enzyme's DNA polymerase activity but not single-strand DNA binding. Although these studies have shown that single-stranded DNA binding is not affected by phosphorylation, the structural basis behind the mechanism underlying phosphorylation-induced activity loss remains poorly understood. Previous modeling studies suggested phosphorylation of S44 was sufficient to induce structural changes that impact the enzyme's polymerase function. However, the S44 phosphorylated-enzyme/DNA complex has not been modeled so far. To address this knowledge gap, we conducted atomistic molecular dynamics simulations of pol β complexed with gapped DNA. Our simulations, which used explicit solvent and lasted for microseconds, revealed that phosphorylation at the S44 site, in the presence of Mg ions, induced significant conformational changes in the enzyme. Specifically, these changes led to the transformation of the enzyme from a closed to an open structure. Additionally, our simulations identified phosphorylation-induced allosteric coupling between the inter-domain region, suggesting the existence of a putative allosteric site. Taken together, our results provide a mechanistic understanding of the conformational transition observed due to phosphorylation in DNA polymerase β interactions with gapped DNA. Our simulations shed light on the mechanisms of phosphorylation-induced activity loss in DNA polymerase β and reveal potential targets for the development of novel therapeutics aimed at mitigating the effects of this post-translational modification.

Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase

Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.

Machines on Genes through the Computational Microscope

Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.

All-Atom Simulations Elucidate the Impact of U2AF2 Cancer-Associated Mutations on Pre-mRNA Recognition

The U2AF2 splicing factor, made of two tandem RNA recognition motifs (RRMs) joined by a flexible linker, selects the intronic polypyrimidine sequence of premature mRNA, thus ensuring splicing fidelity. Increasing evidence links mutations of key splicing factors, including U2AF2, to a variety of cancers. Nevertheless, the impact of U2AF2 cancer-associated mutations on polypyrimidine recognition remains unclear. Here, we combined extensive (18 μs-long) all-atom molecular dynamics simulations and dynamical network theory analysis (NWA) of U2AF2, in its wild-type form and in the presence of the six most frequent cancer-associated mutations, bound to a poly-U strand. Our results reveal that the selected mutations affect the pre-mRNA binding at two hot spot regions, irrespectively of where these mutants are placed on the distinct U2AF2 domains. Complementarily, NWA traced the existence of cross-communication pathways, connecting each mutation site to these recognition hot spots, whose strength is altered by the mutations. Our outcomes suggest the existence of a structural/dynamical interplay of the two U2AF2's RRMs underlying the recognition of the polypyrimidine tract and reveal that the cancer-associated mutations affect the polypyrimidine selection by altering the RRMs' cooperativity. This mechanism may be shared by other RNA binding proteins hallmarked, like U2AF2, by multidomain architecture and high plasticity.

Structural Analysis of the Spike Protein of SARS-CoV-2 Variants and Other Betacoronaviruses Using Molecular Dynamics

A structural analysis over various spike proteins from three highly pathogenic Betacoronavirus was done to understand their structural differences. The proteins were modeled using crystal structures from SARS-CoV, MERS-CoV, and other Betacoronavirus that infect bats and pangolins. The group was split in two sets; the first set corresponds to the non-mutated spike proteins, while the second set corresponds to mutated spike variants alpha, beta, gamma, delta, omicron and mu; five of them classified as variants of concern and the last one as variant of interest. A conformational space exploration was carried out for every protein by using molecular dynamic simulations. Root mean square fluctuations, principal component and cross-correlation analysis were carried out over the dynamics to analyze the flexibility and rigidity of every protein in comparison to the wild type Spike protein from the SARS-CoV-2. The obtained results indicate that the proteins, which are not spread among humans, have smooth movements compared to those of SARS-CoV-2 and its variants. In addition, a relationship between the speed of the virulence and the movement of the protein can explain the behavior of delta and omicron variants.

Conformational Heterogeneity of RNA Stem-Loop Hairpins Bound to FUS-RNA Recognition Motif with Disordered RGG Tail Revealed by Unbiased Molecular Dynamics Simulations

RNA-protein complexes use diverse binding strategies, ranging from structurally well-defined interfaces to completely disordered regions. Experimental characterization of flexible segments is challenging and can be aided by atomistic molecular dynamics (MD) simulations. Here, we used an extended set of microsecond-scale MD trajectories (400 μs in total) to study two FUS-RNA constructs previously characterized by nuclear magnetic resonance (NMR) spectroscopy. The FUS protein contains a well-structured RNA recognition motif domain followed by a presumably disordered RGG tail that binds RNA stem-loop hairpins. Our simulations not only provide several suggestions complementing the experiments but also reveal major methodological difficulties in studies of such complex RNA-protein interfaces. Despite efforts to stabilize the binding via system-specific force-field adjustments, we have observed progressive distortions of the RNA-protein interface inconsistent with experimental data. We propose that the dynamics is so rich that its converged description is not achievable even upon stabilizing the system. Still, after careful analysis of the trajectories, we have made several suggestions regarding the binding. We identify substates in the RNA loops, which can explain the NMR data. The RGG tail localized in the minor groove remains disordered, sampling countless transient interactions with the RNA. There are long-range couplings among the different elements contributing to the recognition, which can lead to allosteric communication throughout the system. Overall, the RNA-FUS systems form dynamical ensembles that cannot be fully represented by single static structures. Thus, albeit imperfect, MD simulations represent a viable tool to investigate dynamic RNA-protein complexes.

Role of computational and structural biology in the development of small-molecule modulators of the spliceosome

INTRODUCTION

RNA splicing is a pivotal step of eukaryotic gene expression during which the introns are excised from the precursor (pre-)RNA and the exons are joined together to form mature RNA products (i.e a protein-coding mRNA or long non-coding (lnc)RNAs). The spliceosome, a complex ribonucleoprotein machine, performs pre-RNA splicing with extreme precision. Deregulated splicing is linked to cancer, genetic, and neurodegenerative diseases. Hence, the discovery of small-molecules targeting core spliceosome components represents an appealing therapeutic opportunity.

AREA COVERED

Several atomic-level structures of the spliceosome and distinct splicing-modulators bound to its protein/RNA components have been solved. Here, we review recent advances in the discovery of small-molecule splicing-modulators, discuss opportunities and challenges for their therapeutic applicability, and showcase how structural data and/or all-atom simulations can illuminate key facets of their mechanism, thus contributing to future drug-discovery campaigns.

EXPERT OPINION

This review highlights the potential of modulating pre-RNA splicing with small-molecules, and anticipates how the synergy of computer and wet-lab experiments will enrich our understanding of splicing regulation/deregulation mechanisms. This information will aid future structure-based drug-discovery efforts aimed to expand the currently limited portfolio of selective splicing-modulators.

Emerging Methods and Applications to Decrypt Allostery in Proteins and Nucleic Acids

Many large protein-nucleic acid complexes exhibit allosteric regulation. In these systems, the propagation of the allosteric signaling is strongly coupled to conformational dynamics and catalytic function, challenging state-of-the-art analytical methods. Here, we review established and innovative approaches used to elucidate allosteric mechanisms in these complexes. Specifically, we report network models derived from graph theory and centrality analyses in combination with molecular dynamics (MD) simulations, introducing novel schemes that implement the synergistic use of graph theory with enhanced simulations methods and ab-initio MD. Accelerated MD simulations are used to construct "enhanced network models", describing the allosteric response over long timescales and capturing the relation between allostery and conformational changes. "Ab-initio network models" combine graph theory with ab-initio MD and quantum mechanics/molecular mechanics (QM/MM) simulations to describe the allosteric regulation of catalysis by following the step-by-step dynamics of biochemical reactions. This approach characterizes how the allosteric regulation changes from reactants to products and how it affects the transition state, revealing a tense-to-relaxed allosteric regulation along the chemical step. Allosteric models and applications are showcased for three paradigmatic examples of allostery in protein-nucleic acid complexes: (i) the nucleosome core particle, (ii) the CRISPR-Cas9 genome editing system and (iii) the spliceosome. These methods and applications create innovative protocols to determine allosteric mechanisms in protein-nucleic acid complexes that show tremendous promise for medicine and bioengineering.

Network theory reveals principles of spliceosome structure and dynamics

Cryoelectron microscopy has revolutionized spliceosome structural biology, and structures representing much of the splicing process have been determined. Comparison of these structures is challenging due to extreme dynamics of the splicing machinery and the thousands of changing interactions during splicing. We have used network theory to analyze splicing factor interactions by constructing structure-based networks from protein-protein, protein-RNA, and RNA-RNA interactions found in eight different spliceosome structures. Our analyses reveal that connectivity dynamics result in step-specific impacts of factors on network topology. The spliceosome's connectivity is focused on the active site, in part due to contributions from nonglobular proteins. Many essential factors exhibit large shifts in centralities during splicing. Others show transiently high betweenness centralities at certain stages, thereby suggesting mechanisms for regulating splicing by briefly bridging otherwise poorly connected network nodes. These observations provide insights into organizing principles of the spliceosome and provide frameworks for comparative analysis of other macromolecular machines.

Enhanced specificity mutations perturb allosteric signaling in CRISPR-Cas9

CRISPR-Cas9 (clustered regularly interspaced short palindromic repeat and associated Cas9 protein) is a molecular tool with transformative genome editing capabilities. At the molecular level, an intricate allosteric signaling is critical for DNA cleavage, but its role in the specificity enhancement of the Cas9 endonuclease is poorly understood. Here, multi-microsecond molecular dynamics is combined with solution NMR and graph theory-derived models to probe the allosteric role of key specificity-enhancing mutations. We show that mutations responsible for increasing the specificity of Cas9 alter the allosteric structure of the catalytic HNH domain, impacting the signal transmission from the DNA recognition region to the catalytic sites for cleavage. Specifically, the K855A mutation strongly disrupts the allosteric connectivity of the HNH domain, exerting the highest perturbation on the signaling transfer, while K810A and K848A result in more moderate effects on the allosteric communication. This differential perturbation of the allosteric signal correlates to the order of specificity enhancement (K855A > K848A ~ K810A) observed in biochemical studies, with the mutation achieving the highest specificity most strongly perturbing the signaling transfer. These findings suggest that alterations of the allosteric communication from DNA recognition to cleavage are critical to increasing the specificity of Cas9 and that allosteric hotspots can be targeted through mutational studies for improving the system's function.

Molecular Basis of SARS-CoV-2 Nsp1-Induced Immune Translational Shutdown as Revealed by All-Atom Simulations

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic represents the most severe global health crisis in modern human history. One of the major SARS-CoV-2 virulence factors is nonstructural protein 1 (Nsp1), which, outcompeting with the binding of host mRNA to the human ribosome, triggers a translation shutdown of the host immune system. Here, microsecond-long all-atom simulations of the C-terminal portion of the SARS-CoV-2/SARS-CoV Nsp1 in complex with the 40S ribosome disclose that SARS-CoV-2 Nsp1 has evolved from its SARS-CoV ortholog to more effectively hijack the ribosome by undergoing a critical switch of Q/E158 and E/Q159 residues that perfects Nsp1's interactions with the ribosome. Our outcomes offer a basis for understanding the sophisticated mechanisms underlying SARS-CoV-2 diversion and exploitation of human cell components to its deadly purposes.

Distant residues modulate conformational opening in SARS-CoV-2 spike protein

The novel coronavirus (SARS-CoV-2) pandemic resulted in the largest public health crisis in recent times. Significant drug design effort against SARS-CoV-2 is focused on the receptor-binding domain (RBD) of the spike protein, although this region is highly prone to mutations causing therapeutic resistance. We applied deep data analysis methods on all-atom molecular dynamics simulations to identify key non-RBD residues that play a crucial role in spike−receptor binding and infection. Because the non-RBD residues are typically conserved across multiple coronaviruses, they can be targeted by broad-spectrum antibodies and drugs to treat infections from new strains that might appear during future epidemics.

Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the attachment of the receptor-binding domain (RBD) of its spike proteins to the ACE2 receptors on the peripheral membrane of host cells. Binding is initiated by a down-to-up conformational change in the spike protein, the change that presents the RBD to the receptor. To date, computational and experimental studies that search for therapeutics have concentrated, for good reason, on the RBD. However, the RBD region is highly prone to mutations, and is therefore a hotspot for drug resistance. In contrast, we here focus on the correlations between the RBD and residues distant to it in the spike protein. This allows for a deeper understanding of the underlying molecular recognition events and prediction of the highest-effect key mutations in distant, allosteric sites, with implications for therapeutics. Also, these sites can appear in emerging mutants with possibly higher transmissibility and virulence, and preidentifying them can give clues for designing pan-coronavirus vaccines against future outbreaks. Our model, based on time-lagged independent component analysis (tICA) and protein graph connectivity network, is able to identify multiple residues that exhibit long-distance coupling with the RBD opening. Residues involved in the most ubiquitous D614G mutation and the A570D mutation of the highly contagious UK SARS-CoV-2 variant are predicted ab initio from our model. Conversely, broad-spectrum therapeutics like drugs and monoclonal antibodies can target these key distant-but-conserved regions of the spike protein.

Investigating the Molecular Mechanism of H3B-8800: A Splicing Modulator Inducing Preferential Lethality in Spliceosome-Mutant Cancers

The SF3B1 protein, part of the SF3b complex, recognizes the intron branch point sequence of precursor messenger RNA (pre-mRNA), thus contributing to splicing fidelity. SF3B1 is frequently mutated in cancer and is the target of distinct families of splicing modulators (SMs). Among these, H3B-8800 is of particular interest, as it induces preferential lethality in cancer cells bearing the frequent and highly pathogenic K700E SF3B1 mutation. Despite the potential of H3B-8800 to treat myeloid leukemia and other cancer types hallmarked by SF3B1 mutations, the molecular mechanism underlying its preferential lethality towards spliceosome-mutant cancer cells remains elusive. Here, microsecond-long all-atom simulations addressed the binding/dissociation mechanism of H3B-8800 to wild type and K700E SF3B1-containing SF3b (^K700ESB3b) complexes at the atomic level, unlocking that the K700E mutation little affects the thermodynamics and kinetic traits of H3B-8800 binding. This supports the hypothesis that the selectivity of H3B-8800 towards mutant cancer cells is unrelated to its preferential targeting of ^K700ESB3b. Nevertheless, this set of simulations discloses that the K700E mutation and H3B-8800 binding affect the overall SF3b internal motion, which in turn may influence the way SF3b interacts with other spliceosome components. Finally, we unveil the existence of a putative druggable SF3b pocket in the vicinity of K700E that could be harnessed in future rational drug-discovery efforts to specifically target mutant SF3b.

Allosteric Cross-Talk among Spike's Receptor-Binding Domain Mutations of the SARS-CoV-2 South African Variant Triggers an Effective Hijacking of Human Cell Receptor

The rapid and relentless emergence of novel highly transmissible SARS-CoV-2 variants, possibly decreasing vaccine efficacy, currently represents a formidable medical and societal challenge. These variants frequently hold mutations on the Spike protein's receptor-binding domain (RBD), which, binding to the angiotensin-converting enzyme 2 (ACE2) receptor, mediates viral entry into host cells. Here, all-atom molecular dynamics simulations and dynamical network theory of the wild-type and mutant RBD/ACE2 adducts disclose that while the N501Y mutation (UK variant) enhances the Spike's binding affinity toward ACE2, the concomitant N501Y, E484K, and K417N mutations (South African variant) aptly adapt to increase SARS-CoV-2 propagation via a two-pronged strategy: (i) effectively grasping ACE2 through an allosteric signaling between pivotal RBD structural elements and (ii) impairing the binding of antibodies elicited by infected or vaccinated patients. This information unlocks the molecular terms and evolutionary strategies underlying the increased virulence of emerging SARS-CoV-2 variants, setting the basis for developing the next-generation anti-COVID-19 therapeutics.

Deciphering the Molecular Terms of Arp2/3 Allosteric Regulation from All-Atom Simulations and Dynamical Network Theory

The Arp2/3 molecular machine stimulates the generation of branched actin networks at the cytosolic surface of cellular membranes. Arp2/3 is thus pivotal for cell motility and migration, and its aberrant function is implicated in cancer invasion and metastasis. Here, all-atom multi μs-long molecular dynamics simulations and dynamical NetWork Analysis (NWA) unprecedentedly disclose the molecular terms of Arp2/3 regulation (activation/inhibition) by positive/negative allosteric modulators. After identifying the crucial structural elements underlying Arp2/3's conformational transition toward its active actin-polymerization-competent state, we decrypt the activating signaling paths heading from the allosteric effector (ATP) binding sites to these pivotal regions, also elucidating how small-molecule inhibitors scramble this signal-exchange. As a result, while ATP-induced signaling triggers a harmonious conformational transition toward active Arp2/3, the inhibitors disturb these information channels, desynchronizing Arp2/3 functional movements, thus hindering its activation. Our outcomes supply a conceptual basis for devising small-molecule inhibitors to block infiltrative cancer migration.

Computing Metal-Binding Proteins for Therapeutic Benefit

Over one third of biomolecules rely on metal ions to exert their cellular functions. Metal ions can play a structural role by stabilizing the structure of biomolecules, a functional role by promoting a wide variety of biochemical reactions, and a regulatory role by acting as messengers upon binding to proteins regulating cellular metal-homeostasis. These diverse roles in biology ascribe critical implications to metal-binding proteins in the onset of many diseases. Hence, it is of utmost importance to exhaustively unlock the different mechanistic facets of metal-binding proteins and to harness this knowledge to rationally devise novel therapeutic strategies to prevent or cure pathological states associated with metal-dependent cellular dysfunctions. In this compendium, we illustrate how the use of a computational arsenal based on docking, classical, and quantum-classical molecular dynamics simulations can contribute to extricate the minutiae of the catalytic, transport, and inhibition mechanisms of metal-binding proteins at the atomic level. This knowledge represents a fertile ground and an essential prerequisite for selectively targeting metal-binding proteins with small-molecule inhibitors aiming to (i) abrogate deregulated metal-dependent (mis)functions or (ii) leverage metal-dyshomeostasis to selectively trigger harmful cells death.

Senescent cells as promising targets to tackle age-related diseases

As the world's population progressively ages, the burden on the socio-economic and health systems is escalating, demanding sustainable and lasting solutions. Cellular senescence, one of the hallmarks of ageing, is a state of irreversible cell cycle arrest that occurs in response to various genotoxic stressors and is considered an important factor in the development of many age-related diseases and therefore a potential therapeutic target. Here, the role of senescent cells in age-related diseases is discussed, focusing on their formation and main characteristics. The mechanisms leading to senescent cells are presented, including replicative and premature senescence as well as senescence that occurs in various physiological processes, such as wound healing. The second part comprises a comprehensive description of various biomarkers currently used for the detection of senescent cells along with the investigated therapeutic approaches, namely senolytics, senomorphics and the clearance of senescent cells by the immune system. Potential delivery systems suitable for such therapies and model organisms to study senescence are also briefly examined. This in-depth overview of cellular senescence contributes to a deeper understanding of a rapidly evolving area aimed to tackle the age-related diseases in a more mechanistic way, as well as highlights future research opportunities.

Exploiting Folding and Degradation Machineries To Target Undruggable Proteins: What Can a Computational Approach Tell Us?

Advances in genomics and proteomics have unveiled an ever-growing number of key proteins and provided mechanistic insights into the genesis of pathologies. This wealth of data showed that changes in expression levels of specific proteins, mutations, and post-translational modifications can result in (often subtle) perturbations of functional protein-protein interaction networks, which ultimately determine disease phenotypes. Although many such validated pathogenic proteins have emerged as ideal drug targets, there are also several that escape traditional pharmacological regulation; these proteins have thus been labeled "undruggable". The challenges posed by undruggable targets call for new sorts of molecular intervention. One fascinating solution is to perturb a pathogenic protein's expression levels, rather than blocking its activities. In this Concept paper, we shall discuss chemical interventions aimed at recruiting undruggable proteins to the ubiquitin proteasome system, or aimed at disrupting protein-protein interactions in the chaperone-mediated cellular folding machinery: both kinds of intervention lead to a decrease in the amount of active pathogenic protein expressed. Specifically, we shall discuss the role of computational strategies in understanding the molecular determinants characterizing the function of synthetic molecules typically designed for either type of intervention. Finally, we shall provide our perspectives and views on the current limitations and possibilities to expand the scope of rational approaches to the design of chemical regulators of protein levels.

Atomic-Level Mechanism of Pre-mRNA Splicing in Health and Disease

Intron removal from premature-mRNA (pre-mRNA splicing) is an essential part of gene expression and regulation that is required for the production of mature, protein-coding mRNA. The spliceosome (SPL), a majestic machine composed of five small nuclear RNAs and hundreds of proteins, behaves as an eminent transcriptome tailor, efficiently performing splicing as a protein-directed metallo-ribozyme. To select and excise long and diverse intronic sequences with single-nucleotide precision, the SPL undergoes a continuous compositional and conformational remodeling, forming eight distinct complexes throughout each splicing cycle. Splicing fidelity is of paramount importance to preserve the integrity of the proteome. Mutations in splicing factors can severely compromise the accuracy of this machinery, leading to aberrant splicing and altered gene expression. Decades of biochemical and genetic studies have provided insights into the SPL's composition and function, but its complexity and plasticity have prevented an in-depth mechanistic understanding. Single-particle cryogenic electron microscopy techniques have ushered in a new era for comprehending eukaryotic gene regulation, providing several near-atomic resolution structures of the SPL from yeast and humans. Nevertheless, these structures represent isolated snapshots of the splicing process and are insufficient to exhaustively assess the function of each SPL component and to unravel particular facets of the splicing mechanism in a dynamic environment.In this Account, building upon our contributions in this field, we discuss the role of biomolecular simulations in uncovering the mechanistic intricacies of eukaryotic splicing in health and disease. Specifically, we showcase previous applications to illustrate the role of atomic-level simulations in elucidating the function of specific proteins involved in the architectural reorganization of the SPL along the splicing cycle. Moreover, molecular dynamics applications have uniquely contributed to decrypting the channels of communication required for critical functional transitions of the SPL assemblies. They have also shed light on the role of carcinogenic mutations in the faithful selection of key intronic regions and the molecular mechanism of splicing modulators. Additionally, we emphasize the role of quantum-classical molecular dynamics in unraveling the chemical details of pre-mRNA cleavage in the SPL and in its evolutionary ancestors, group II intron ribozymes. We discuss methodological pitfalls of multiscale calculations currently used to dissect the splicing mechanism, presenting future challenges in this field. The results highlight how atomic-level simulations can enrich the interpretation of experimental results. We envision that the synergy between computational and experimental approaches will aid in developing innovative therapeutic strategies and revolutionary gene modulation tools to fight the over 200 human diseases associated with splicing misregulation, including cancer and neurodegeneration.