Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement

Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.

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.

Probing the conformational dynamics of an Ago-RNA complex in water/methanol solution

Argonaute (Ago) proteins mediate target recognition guiding miRNA to bind complementary mRNA primarily in the seed region. However, additional pairing can occur beyond the seed, forming a supplementary duplex that can contribute to the guide-target affinity. In order to shed light on the connection, between protein-RNA interactions and miRNA-mRNA seed and supplementary duplex mobility, we carried out molecular dynamics simulations at the microsecond time-scale using a different approach compared to the ones normally used. Until now, theoretical investigations with classical MD on Ago-RNA complexes have been focused primarily on pure water solvent, which mimics the natural environment of biological molecules. Here, we explored the conformational space of a human Ago2 (hAgo2) bound to the seed + supplementary miRNA-mRNA duplex, using the solvent environment as a molecular probe. MD simulations have been performed in a mixture of water/MeOH at a molar ratio of 70 : 30 as well as in pure water for comparison. Our findings revealed that the mixed solvent promotes protein RNA association, principally enhancing salt-linkages between basic amino acid side-chains and acidic phosphates of the sugar-phosphate backbone. The primary effect registered was the restriction of supplementary duplex flexibility and the stabilization of the miRNA 3' terminus. Interestingly, we observed that the influence of the solvent appears to have almost no impact on the conformation of the seed duplex.

Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate dynamics using a discretized GME. qMSM can be constructed with much shorter MD trajectories than the MSM. However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study biomolecular conformational changes. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of qMSM in complicated biomolecules. We present a new method, the Integrative GME (IGME), in which we analytically solve the GME under the condition when the memory kernels have decayed to zero. Our IGME overcomes the challenges of the qMSM by using the time integrations of memory kernels, thereby avoiding the numerical instability caused by explicit computation of time-dependent memory kernels. Using our solutions of the GME, we have developed a new approach to compute long-time dynamics based on MD simulations in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, FIP35 WW-domain, and Taq RNA polymerase. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate biomolecular conformational changes.

GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly.

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes

Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.

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.

Mechanistic Insights into the Protection Effect of Argonaute-RNA Complex on the HCV Genome

While host miRNA usually plays an antiviral role, the relentless tides of viral evolution have carved out a mechanism to recruit host miRNA as a viral protector. By complementing miR-122 at the 5' end of the genome, the hepatitis C virus (HCV) gene can form a complex with Argonaute 2 (Ago2) protein to protect the 5' end of HCV RNA from exonucleolytic attacks. Experiments showed that the disruption of the stem-loop 1(SL1) structure and the 9th nucleotide (T9) of HCV site 1 RNA could enhance the affinity of the Ago2 protein to the HCV site 1 RNA (target RNA). However, the underlying mechanism of how the conformation and dynamics of the Ago2: miRNA: target RNA complex is affected by the SL1 and T9 remains unclear. To address this, we performed large-scale molecular dynamics simulations on the AGO2-miRNA complex binding with the WT target, T9-abasic target and SL1-disruption target, respectively. The results revealed that the T9 and SL1 structures could induce the departing motion of the PAZ, PIWI and N domains, propping up the mouth of the central groove which accommodates the target RNA, causing the instability of the target RNA and disrupting the Ago2 binding. The coordinated motion among the PAZ, PIWI and N domains were also weakened by the T9 and SL1 structures. Moreover, we proposed a new model wherein the Ago2 protein could adopt a more constraint conformation with the proximity and more correlated motions of the PAZ, N and PIWI domains to protect the target RNA from dissociation. These findings reveal the mechanism of the Ago2-miRNA complex's protective effect on the HCV genome at the atomic level, which will offer guidance for the design of drugs to confront the protection effect and engineering of Ago2 as a gene-regulation tool.

From quantum-derived principles underlying cysteine reactivity to combating the COVID-19 pandemic

The COVID‐19 pandemic poses a challenge in coming up with quick and effective means to counter its cause, the SARS‐CoV‐2. Here, we show how the key factors governing cysteine reactivity in proteins derived from combined quantum mechanical/continuum calculations led to a novel multi‐targeting strategy against SARS‐CoV‐2, in contrast to developing potent drugs/vaccines against a single viral target such as the spike protein. Specifically, they led to the discovery of reactive cysteines in evolutionary conserved Zn²⁺‐sites in several SARS‐CoV‐2 proteins that are crucial for viral polypeptide proteolysis as well as viral RNA synthesis, proofreading, and modification. These conserved, reactive cysteines, both free and Zn²⁺‐bound, can be targeted using the same Zn‐ejector drug (disulfiram/ebselen), which enables the use of broad‐spectrum anti‐virals that would otherwise be removed by the virus's proofreading mechanism. Our strategy of targeting multiple, conserved viral proteins that operate at different stages of the virus life cycle using a Zn‐ejector drug combined with other broad‐spectrum anti‐viral drug(s) could enhance the barrier to drug resistance and antiviral effects, as compared to each drug alone. Since these functionally important nonstructural proteins containing reactive cysteines are highly conserved among coronaviruses, our proposed strategy has the potential to tackle future coronaviruses.

This article is categorized under:

Structure and Mechanism > Reaction Mechanisms and Catalysis

Structure and Mechanism > Computational Biochemistry and Biophysics

Electronic Structure Theory > Density Functional Theory

Elucidation of the conformational dynamics and assembly of Argonaute−RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Argonaute (AGO) proteins, the core of RNA-induced silencing complex, are guided by microRNAs (miRNAs) to recognize target RNA for repression. The miRNA–target RNA recognition forms initially through pairing at the seed region while the additional supplementary pairing can enhance target recognition and compensate for seed mismatch. The extension of miRNA lengths can strengthen the target affinity when pairing both in the seed and supplementary regions. However, the mechanism underlying the effect of the supplementary pairing on the conformational dynamics and the assembly of AGO–RNA complex remains poorly understood. To address this, we performed large-scale molecular dynamics simulations of AGO–RNA complexes with different pairing patterns and miRNA lengths. The results reveal that the additional supplementary pairing can not only strengthen the interaction between miRNA and target RNA, but also induce the increased plasticity of the PAZ domain and enhance the domain connectivity among the PAZ, PIWI, N domains of the AGO protein. The strong community network between these domains tightens the mouth of the supplementary chamber of AGO protein, which prevents the escape of target RNA from the complex and shields it from solvent water attack. Importantly, the inner stronger matching pairs between the miRNA and target RNA can compensate for weaker mismatches at the edge of supplementary region. These findings provide guidance for the design of miRNA mimics and anti-miRNAs for both clinical and experimental use and open the way for further engineering of AGO proteins as a new tool in the field of gene regulation.

Critical role of backbone coordination in the mRNA recognition by RNA induced silencing complex

Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems.

Binding-induced functional-domain motions in the Argonaute characterized by adaptive advanced sampling

Argonaute proteins in combination with short microRNA (miRNAs) can target mRNA molecules for translation inhibition or degradation and play a key role in many regulatory processes. The miRNAs act as guide RNAs that associate with Argonaute and the complementary mRNA target region. The complex formation results in activation of Argonaute and specific cleavage of the target mRNA. Both the binding and activation processes involve essential domain rearrangements of functional importance. For the Thermus Thermophilus Argonaute (TtAgo) system guide-bound (binary) and guide/target-bound (ternary) complexes are known but how the binding of guide and target mediate domain movements is still not understood. We have studied the Argonaute domain motion in apo and guide/target bound states using Molecular Dynamics simulations and a Hamiltonian replica exchange (H-REMD) method that employs a specific biasing potential to accelerate domain motions. The H-REMD technique indicates sampling of a much broader distribution of domain arrangements both in the apo as well as binary and ternary complexes compared to regular MD simulations. In the apo state domain arrangements corresponding to more compact (closed) states are mainly sampled which undergo an opening upon guide and guide/target binding. Whereas only limited overlap in domain geometry between apo and bound states was found, a larger similarity in the domain distribution is observed for the simulations of binary and ternary complexes. Comparative simulations on ternary complexes with 15 or 16 base pairs (bp) formed between guide and target strands (instead of 14) resulted in dissociation of the 3’-guide strand from the PAZ domain and domain rearrangement. This agrees with the experimental observation that guide-target pairing beyond 14 bps is required for activation and gives a mechanistic explanation for the experimentally observed activation process.

Post-transcriptional gene silencing is an important process to regulate protein synthesis in eukaryotes and prokaryotes. The Argonaute proteins as part of the RNA-induced-silencing-complex (RISC) form a central element of the process by silencing of a target messenger RNA (mRNA) via degradation or repression of translation. The Argonaute protein binds initially a short RNA that acts as a guide to promote binding of a complementary target mRNA. The complex formation can lead to activation of Argonaute and specific cleavage of the target mRNA. The whole process involves domain rearrangements that are not fully understood. We applied an advanced Molecular Dynamics sampling technique to specifically accelerate domain motions of the Thermus Thermophilus Argonaute (TtAgo) system in apo, guide bound and guide/target bound states. The simulations indicate only limited overlap of domain arrangements in apo and bound states and identified domain opening motions necessary for guide and target binding. The study also offers an explanation why a minimum of 15 or 16 base pairs between guide and target strands are necessary for Argonaute activation.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

A comprehensive mechanism for 5-carboxylcytosine-induced transcriptional pausing revealed by Markov state models

RNA polymerase II (Pol II) surveils the genome, pausing as it encounters DNA lesions and base modifications and initiating signals for DNA repair among other important regulatory events. Recent work suggests that Pol II pauses at 5-carboxycytosine (5caC), an epigenetic modification of cytosine, because of a specific hydrogen bond between the carboxyl group of 5caC and a specific residue in fork loop 3 of Pol II. This hydrogen bond compromises productive NTP binding and slows down elongation. Apart from this specific interaction, the carboxyl group of 5caC can potentially interact with numerous charged residues in the cleft of Pol II. However, it is not clear how other interactions between Pol II and 5caC contribute to pausing. In this study, we use Markov state models (a type of kinetic network models) built from extensive molecular dynamics simulations to comprehensively study the impact of 5caC on Pol II translocation. We describe two translocation intermediates with specific interactions that prevent the template base from loading into the Pol II active site. In addition to the previously observed state with 5caC constrained by fork loop 3, we discovered a new intermediate state with a hydrogen bond between 5caC and fork loop 2. Surprisingly, we find that 5caC may curb translocation by suppressing kinking of the helix bordering the active site (the bridge helix) because its high flexibility is critical to translocation. Our work provides new insights into how epigenetic modifications of genomic DNA can modulate Pol II translocation, inducing pauses in transcription.

Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model

To initiate transcription, the holoenzyme (RNA polymerase [RNAP] in complex with σ factor) loads the promoter DNA via the flexible loading gate created by the clamp and β-lobe, yet their roles in DNA loading have not been characterized. We used a quasi-Markov State Model (qMSM) built from extensive molecular dynamics simulations to elucidate the dynamics of Thermus aquaticus holoenzyme's gate opening. We showed that during gate opening, β-lobe oscillates four orders of magnitude faster than the clamp, whose opening depends on the Switch 2's structure. Myxopyronin, an antibiotic that binds to Switch 2, was shown to undergo a conformational selection mechanism to inhibit clamp opening. Importantly, we reveal a critical but undiscovered role of β-lobe, whose opening is sufficient for DNA loading even when the clamp is partially closed. These findings open the opportunity for the development of antibiotics targeting β-lobe of RNAP. Finally, we have shown that our qMSMs, which encode non-Markovian dynamics based on the generalized master equation formalism, hold great potential to be widely applied to study biomolecular dynamics.

Molecular Dynamics Study on the Binding of an Anticancer DNA G-Quadruplex Stabilizer, CX-5461, to Human Telomeric, c-KIT1, and c-Myc G-Quadruplexes and a DNA Duplex

DNA G-quadruplex (G4) stabilizer, CX-5461, is in phase I/II clinical trials for advanced cancers with BRCA1/2 deficiencies. A FRET-melting temperature increase assay measured the stabilizing effects of CX-5461 to a DNA duplex (∼10 K), and three G4 forming sequences negatively implicated in the cancers upon its binding: human telomeric (∼30 K), c-KIT1 (∼27 K), and c-Myc (∼25 K). Without experimentally solved structures of these CX-5461-G4 complexes, CX-5461's interactions remain elusive. In this study, we performed a total of 73.5 μs free ligand molecular dynamics binding simulations of CX-5461 to the DNA duplex and three G4s. Three binding modes (top, bottom, and side) were identified for each system and their thermodynamic, kinetic, and structural nature were deciphered. The molecular mechanics/Poisson Boltzmann surface area binding energies of CX-5461 were calculated for the human telomeric (-28.6 kcal/mol), c-KIT1 (-23.9 kcal/mol), c-Myc (-22.0 kcal/mol) G4s, and DNA duplex (-15.0 kcal/mol) systems. These energetic differences coupled with structural differences at the 3' site explained the different melting temperatures between the G4s, while CX-5461's lack of intercalation to the duplex explained the difference between the G4s and duplex. Based on the interaction insight, CX-5461 derivatives were designed and docked, showing higher selectivity to the G4s over the duplex.

Inhibition of striatal-enriched protein tyrosine phosphatase by targeting computationally revealed cryptic pockets

Cryptic pockets, which are not apparent in crystallographic structures, provide promising alternatives to traditional binding sites for drug development. However, identifying cryptic pockets is extremely challenging and the therapeutic potential of cryptic pockets remains unclear. Here, we reported the discovery of novel inhibitors for striatal-enriched protein tyrosine phosphatase (STEP), a potential drug target for multiple neuropsychiatric disorders, based on cryptic pocket detection. By combining the use of molecular dynamics simulations and fragment-centric topographical mapping, we identified transiently open cryptic pockets and identified 12 new STEP inhibition scaffolds through structure-based virtual screening. Site-directed mutagenesis verified the binding of ST3 with the predicted cryptic pockets. Moreover, the most potent and selective inhibitors could modulate the phosphorylation of both ERK1/2 and Pyk2 in PC12 cells.

Mechanistic Model for the Hsp90-Driven Opening of Human Argonaute

The assembly of RNA-induced silencing complex (RISC) is a key process in small RNA-mediated gene silencing. Loading of small RNAs into Argonaute (Ago), the key player protein in the process, has been shown to depend on the Hsp90 chaperone machinery. Experimental single-molecule data indicate that ATP binding to the chaperone facilitates the conformational changes leading to the open state of Ago essential to form a complex with small-RNA duplexes. Yet, no atomic-level description of the dynamic mechanisms and protein-protein interactions underpinning Hsp90-mediated Ago conformational activation is available. Here we investigate the functionally oriented structural and dynamic features of Hsp90-human Ago (hAgo2) complexes in different ligand states by integrating protein-protein docking techniques, all-atom MD simulations, and novel methods of analysis of protein internal dynamics and energetics. On this basis, we develop a structural-dynamic model of the mechanisms underlying the chaperone-assisted human RISC assembly. Our approach unveils the large conformational variability displayed by hAgo2 in the unbound vs the Hsp90-bound states. In this context, several hAgo2 states are found to coexist in isolation, while Hsp90 selects and stabilizes the active form. Hsp90 binding modulates the conformational plasticity of hAgo2 (favoring its opening) by modifying the patterns of hAgo2 intramolecular interactions. Finally, we identify a series of experimentally verifiable key sites that can be mutated to modulate Hsp90-mediated hAgo2 conformational response and ability to bind RNA.

Three-Dimensional Structure of RNA Monomeric G-Quadruplex Containing ALS and FTD Related G4C2 Repeat and Its Binding with TMPyP4 Probed by Homology Modeling based on Experimental Constraints and Molecular Dynamics Simulations

The G-quadruplex-forming hexanucleotide repeat expansion (HRE), d(G4C2)_n, within the human C9orf72 gene is the root cause for familial amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD). A recent study has shown that TMPyP4 has good potential to work as a RNA G-quadruplex binder in treating ALS and FTD. Although the high-resolution structure of the monomeric DNA antiparallel G-quadruplex form of the monomeric hexanucleotide repeat was recently solved, the RNA parallel G-quadruplex structure and its complex with TMPyP4 are not available yet. In this study, we first constructed the homology model for the parallel monomeric RNA G-quadruplex of r(G4C2)₃G4 based on experimental constraints and the parallel monomeric G-quadruplex DNA crystal structure. Although the G-tetra core of the homology model was stable observed in 15 μs molecular dynamics (MD) simulations, we observed that the loops adopt additional conformations besides the initial crystal conformation, where TMPyP4 binding was found to reduce the loop fluctuation of the RNA monomeric G-quadruplex. Next, we probed the elusive binding behavior of TMPyP4 to the RNA monomeric G-quadruplex. Encouragingly, the binding modes observed are similar to the modes observed in two experimental complexes of a parallel DNA G-quadruplex with TMPyP4. We also constructed a Markov state model to provide insights into the binding pathways. Together, the findings from our study may assist future development of G-quadruplex-specific ligands in the treatment of neurodegenerative diseases like ALS and FTD.

Why Is a High Temperature Needed by Thermus thermophilus Argonaute During mRNA Silencing: A Theoretical Study

Thermus thermophiles Argonaute (TtAgo) is a complex, which is consisted of 5′-phosphorylated guide DNA and a series of target DNA with catalytic activities at high temperatures. To understand why high temperatures are needed for the catalytic activities, three molecular dynamics simulations and binding free energy calculations at 310, 324, and 338K were performed for the TtAgo-DNA complex to explore the conformational changes between 16-mer guide DNA/15-mer target DNA and TtAgo at different temperatures. The simulation results indicate that a collapse of a small β-strand (residues 507–509) at 310 K caused Glu512 to move away from the catalytic residues Asp546 and Asp478, resulting in a decrease in catalytic activity, which was not observed in the simulations at 324 and 338 K. The nucleic acid binding channel became enlarged at 324 and 338K, thereby facilitating the DNA to slide in. Binding free energy calculations and hydrogen bond occupancy indicated that the interaction between TtAgo and the DNA was more stable at 324K and 338K than at 310 K. The DNA binding pocket residues Lys575 and Asn590 became less solvent accessible at 324 and 338K than at 310 K to influence hydrophilic interaction with DNA. Our simulation studies shed some light on the mechanism of TtAgo and explained why a high temperature was needed by TtAgo during gene editing of CRISPR.

MicroRNA-mediated immune regulation in rheumatic diseases

MicroRNAs (miRNAs) are endogenous small, non-coding RNAs that regulate genome expression at the post-transcriptional level. They are involved in a wide range of physiological processes including the maintenance of immune homeostasis and normal function. Accumulating evidence from animal studies show that alterations in pan or specific miRNA expression would break immunological tolerance, leading to autoimmunity. Differential miRNA expressions have also been documented in patients of many autoimmune disorders. In this review, we highlight the evidence that signifies the critical role of miRNAs in autoimmunity, specifically on their regulatory roles in the pathogenesis of several rheumatic diseases including systemic lupus erythematosus, rheumatoid arthritis and spondyloarthritis. The potential of miRNAs as biomarkers and therapeutic targets is also discussed. Manipulation of dysregulated miRNAs in vivo through miRNA delivery or inhibition offers promise for new therapeutic strategies in treating rheumatic diseases.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Improving the productivity of monodisperse polyhedral cages by the rational design of kinetic self-assembly pathways

Hollow polyhedral cages hold great potential for application in nanotechnological and biomedical fields. Understanding the formation mechanism of these self-assembled structures could provide guidance for the rational design of the desired polyhedral cages. Here, by constructing kinetic network models from extensive coarse-grained molecular dynamics simulations, we elucidated the formation mechanism of the dodecahedral cage, which is formed by the self-assembly of patchy particles. We found that the dodecahedral cage is formed through increasing the aggregate size followed by structure rearrangement. Based on this mechanistic understanding, we improved the productivity of the dodecahedral cage through the rational design of the patch arrangement of patchy particles, which promotes the structural rearrangement process. Our results demonstrate that it should be a feasible strategy to achieve the rational design of the desired nanostructures via the kinetic analysis. We anticipate that this methodology could be extended to other self-assembly systems for the fabrication of functional nanomaterials.

Harnessing complexity in molecular self-assembly using computer simulations

In molecular self-assembly, hundreds of thousands of freely-diffusing molecules associate to form ordered and functional architectures in the absence of an actuator. This intriguing phenomenon plays a critical role in biology and has become a powerful tool for the fabrication of advanced nanomaterials. Due to the limited spatial and temporal resolutions of current experimental techniques, computer simulations offer a complementary strategy to explore self-assembly with atomic resolution. Here, we review recent computational studies focusing on both thermodynamic and kinetic aspects. As we shall see, thermodynamic approaches based on modeling and statistical mechanics offer initial guidelines to design nanostructures with modest computational effort. Computationally more intensive analyses based on molecular dynamics simulations and kinetic network models (KNMs) reach beyond it, opening the door to the rational design of self-assembly pathways. Current limitations of these methodologies are discussed. We anticipate that the synergistic use of thermodynamic and kinetic analyses based on computer simulations will provide an important contribution to the de novo design of self-assembly.

Exploring PAZ/3'-overhang interaction to improve siRNA specificity. A combined experimental and modeling study

The understanding of the dynamical and mechanistic aspects that lie behind siRNA-based gene regulation is a requisite to boost the performance of siRNA therapeutics. A systematic experimental and computational study on the 3'-overhang structural requirements for the design of more specific and potent siRNA molecules was carried out using nucleotide analogues differing in structural parameters, such as sugar constraint, lack of nucleobase, distance between the phosphodiester backbone and nucleobase, enantioselectivity, and steric hindrance. The results established a set of rules governing the siRNA-mediated silencing, indicating that the thermodynamic stability of the 5'-end is a crucial determinant for antisense-mediated silencing but is not sufficient to avoid sense-mediated silencing. Both theoretical and experimental approaches consistently evidence the existence of a direct connection between the PAZ/3'-overhang binding affinity and siRNA's potency and specificity. An overall description of the systems is thus achieved by atomistic simulations and free energy calculations that allow us to propose a robust and self-contained procedure for studying the factors implied in PAZ/3'-overhang siRNA interactions. A higher RNAi activity is associated with a moderate-to-strong PAZ/3'-overhang binding. Contrarily, lower binding energies compromise siRNA potency, increase specificity, and favor siRNA downregulation by Ago2-independent mechanisms. This work provides in-depth details for the design of powerful and safe synthetic nucleotide analogues for substitution at the 3'-overhang, enabling some of the intrinsic siRNA disadvantages to be overcome.

Elucidation of the conformational dynamics of multi-body systems by construction of Markov state models

Constructing Markov State Models (MSMs) based on short molecular dynamics simulations is a powerful computational technique to complement experiments in predicting long-time kinetics of biomolecular processes at atomic resolution. Even though the MSM approach has been widely applied to study one-body processes such as protein folding and enzyme conformational changes, the majority of biological processes, e.g. protein-ligand recognition, signal transduction, and protein aggregation, essentially involve multiple entities. Here we review the attempts at constructing MSMs for multi-body systems, point out the challenges therein and discuss recent algorithmic progresses that alleviate these challenges. In particular, we describe an automatic kinetics based partitioning method that achieves optimal definition of the conformational states in a multi-body system, and discuss a novel maximum-likelihood approach that efficiently estimates the slow uphill kinetics utilizing pre-computed equilibrium populations of all states. We expect that these new algorithms and their combinations may boost investigations of important multi-body biological processes via the efficient construction of MSMs.

Mechanism of NTP Binding to the Active Site of T7 RNA Polymerase Revealed by Free-Energy Simulation

In genetic transcription, molecular dynamic details and energetics of NTP binding to the active site of RNA polymerase (RNAP) are poorly understood. In this article, we investigated the NTP binding process in T7 RNAP using all-atom MD simulation combined with the umbrella sampling technique. Based on our simulations, a two-step mechanism was proposed to explain NTP binding: first, substrate NTP in aqueous solution, which carries a magnesium ion, diffuses through a secondary channel of RNAP to attain a pore region, where it undergoes conformational changes to give a correct orientation; next, the NTP establishes initial basepairing contacts with the template nucleoside (TN). Our free-energy calculations suggest that both steps are spontaneous. This mechanism can easily explain the problem of NTP binding with different orientations. Moreover, it is found that the nascent NTP:TN basepair is fragile and easily broken by thermal disturbance. Therefore, we speculate that the fingers domain will be triggered to close, so as to create a steady environment for the next chemical step. The observations from the work provide valuable information for comprehensively understanding the mechanism of the basic step in genetic transcription.

The agotrons: Gene regulators or Argonaute protectors?

Over the last decades, it has become evident that highly complex networks of regulators govern post-transcriptional regulation of gene expression. A novel class of Argonaute (Ago)-associated RNA molecules, the agotrons, was recently shown to function in a Drosha- and Dicer-independent manner, hence bypassing the maturation steps required for canonical microRNA (miRNA) biogenesis. Agotrons are found in most mammals and associate with Ago as ∼100 nucleotide (nt) long RNA species. Here, we speculate on the functional and biological relevance of agotrons: (i) agotrons could serve as non-promiscuous miRNA-like regulators with reduced off-targeting or (ii) agotrons could encompass other putative functions, such as protecting Ago proteins from taking up aberrant short RNAs or by rescuing and stabilizing otherwise unloaded Ago-proteins from degradation. Collectively, agotrons have emerged as a novel class of interesting non-coding RNA molecules, but their full functional potential and biological impact still remain to be disclosed.

Biogenesis and Function of Ago-Associated RNAs

Numerous sophisticated high-throughput sequencing technologies have been developed over the past decade, and these have enabled the discovery of a diverse catalog of small non-coding (nc)RNA molecules that function as regulatory entities by associating with Argonaute (Ago) proteins. MicroRNAs (miRNAs) are currently the best-described class of post-transcriptional regulators that follow a specific biogenesis pathway characterized by Drosha/DGCR8 and Dicer processing. However, more exotic miRNA-like species that bypass particular steps of the canonical miRNA biogenesis pathway continue to emerge, with one of the most recent additions being the agotrons, which escape both Drosha/DGCR8- and Dicer-processing. We review here the current knowledge and most recent discoveries relating to alternative functions and biogenesis strategies for Ago-associated RNAs in mammals.

Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches

MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.

Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories

We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.

Modelling proteins' hidden conformations to predict antibiotic resistance

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

Understanding the core of RNA interference: The dynamic aspects of Argonaute-mediated processes

At the core of RNA interference, the Argonaute proteins (Ago) load and utilize small guide nucleic acids to silence mRNAs or cleave foreign nucleic acids in a sequence specific manner. In recent years, based on extensive structural studies of Ago and its interaction with the nucleic acids, considerable progress has been made to reveal the dynamic aspects of various Ago-mediated processes. Here we review these novel insights into the guide-strand loading, duplex unwinding, and effects of seed mismatch, with a focus on two representative Agos, the human Ago 2 (hAgo2) and the bacterial Thermus thermophilus Ago (TtAgo). In particular, comprehensive molecular simulation studies revealed that although sharing similar overall structures, the two Agos have vastly different conformational landscapes and guide-strand loading mechanisms because of the distinct rigidity of their L1-PAZ hinge. Given the central role of the PAZ motions in regulating the exposure of the nucleic acid binding channel, these findings exemplify the importance of protein motions in distinguishing the overlapping, yet distinct, mechanisms of Ago-mediated processes in different organisms.

Elucidation of the Dynamics of Transcription Elongation by RNA Polymerase II using Kinetic Network Models

RNA polymerase II (Pol II) is an essential enzyme that catalyzes transcription with high efficiency and fidelity in eukaryotic cells. During transcription elongation, Pol II catalyzes the nucleotide addition cycle (NAC) to synthesize mRNA using DNA as the template. The transitions between the states of the NAC require conformational changes of both the protein and nucleotides. Although X-ray structures are available for most of these states, the dynamics of the transitions between states are largely unknown. Molecular dynamics (MD) simulations can predict structure-based molecular details and shed light on the mechanisms of these dynamic transitions. However, the employment of MD simulations on a macromolecule (tens to hundreds of nanoseconds) such as Pol II is challenging due to the difficulty of reaching biologically relevant timescales (tens of microseconds or even longer). For this challenge to be overcome, kinetic network models (KNMs), such as Markov State Models (MSMs), have become a popular approach to access long-timescale conformational changes using many short MD simulations. We describe here our application of KNMs to characterize the molecular mechanisms of the NAC of Pol II. First, we introduce the general background of MSMs and further explain procedures for the construction and validation of MSMs by providing some technical details. Next, we review our previous studies in which we applied MSMs to investigate the individual steps of the NAC, including translocation and pyrophosphate ion release. In particular, we describe in detail how we prepared the initial conformations of Pol II elongation complex, performed MD simulations, extracted MD conformations to construct MSMs, and further validated them. We also summarize our major findings on molecular mechanisms of Pol II elongation based on these MSMs. In addition, we have included discussions regarding various key points and challenges for applications of MSMs to systems as large as the Pol II elongation complex. Finally, to study the overall NAC, we combine the individual steps of the NAC into a five-state KNM based on a nonbranched Brownian ratchet scheme to explain the single-molecule optical tweezers experimental data. The studies complement experimental observations and provide molecular mechanisms for the transcription elongation cycle. In the long term, incorporation of sequence-dependent kinetic parameters into KNMs has great potential for identifying error-prone sequences and predicting transcription dynamics in genome-wide transcriptomes.

A Flexible Domain-Domain Hinge Promotes an Induced-fit Dominant Mechanism for the Loading of Guide-DNA into Argonaute Protein in Thermus thermophilus

Argonaute proteins (Ago) are core components of the RNA Induced Silencing Complex (RISC) that load and utilize small guide nucleic acids to silence mRNAs or cleave foreign DNAs. Despite the essential role of Ago in gene regulation and defense against virus, the molecular mechanism of guide-strand loading into Ago remains unclear. We explore such a mechanism in the bacterium Thermus thermophilus Ago (TtAgo), via a computational approach combining molecular dynamics, bias-exchange metadynamics, and protein-DNA docking. We show that apo TtAgo adopts multiple closed states that are unable to accommodate guide-DNA. Conformations able to accommodate the guide are beyond the reach of thermal fluctuations from the closed states. These results suggest an induced-fit dominant mechanism for guide-strand loading in TtAgo, drastically different from the two-step mechanism for human Ago 2 (hAgo2) identified in our previous study. Such a difference between TtAgo and hAgo2 is found to mainly originate from the distinct rigidity of their L1-PAZ hinge. Further comparison among known Ago structures from various species indicates that the L1-PAZ hinge may be flexible in general for prokaryotic Ago's but rigid for eukaryotic Ago's.

Elucidating dominant pathways of the nano-particle self-assembly process

Self-assembly processes play a key role in the fabrication of functional nano-structures with wide application in drug delivery and micro-reactors.

PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models

Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. In this work we present the open-source Python package PyEMMA ( http://pyemma.org ) that provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. Plotting functions to produce a manuscript-ready presentation of the results are available. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA.