MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology

Structural basis of transcription: RNA polymerase II substrate binding and metal coordination using a free-electron laser

Catalysis and translocation of multisubunit DNA-directed RNA polymerases underlie all cellular mRNA synthesis. RNA polymerase II (Pol II) synthesizes eukaryotic pre-mRNAs from a DNA template strand buried in its active site. Structural details of catalysis at near-atomic resolution and precise arrangement of key active site components have been elusive. Here, we present the free-electron laser (FEL) structures of a matched ATP-bound Pol II and the hyperactive Rpb1 T834P bridge helix (BH) mutant at the highest resolution to date. The radiation-damage-free FEL structures reveal the full active site interaction network, including the trigger loop (TL) in the closed conformation, bonafide occupancy of both site A and B Mg2+, and, more importantly, a putative third (site C) Mg2+ analogous to that described for some DNA polymerases but not observed previously for cellular RNA polymerases. Molecular dynamics (MD) simulations of the structures indicate that the third Mg2+ is coordinated and stabilized at its observed position. TL residues provide half of the substrate binding pocket while multiple TL/BH interactions induce conformational changes that could allow translocation upon substrate hydrolysis. Consistent with TL/BH communication, a FEL structure and MD simulations of the T834P mutant reveal rearrangement of some active site interactions supporting potential plasticity in active site function and long-distance effects on both the width of the central channel and TL conformation, likely underlying its increased elongation rate at the expense of fidelity.

pyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Feasibility of the inhibitor development for cancer: A systematic approach for drug design

The traditional Chinese medicine (TCM) bupleurum-ginger-licorice formula presents significant anti-cancer effects, but its active ingredients and inhibitory mechanism remain unclear. In this work, the core effective ingredient quercetin and its signal transducer and activator of transcription 3 (Stat3) receptor both were identified by network pharmacology. Quercetin is a low-toxicity, non-carcinogenic flavonoid with antioxidant, anti-inflammatory and anticancer activities, which is widely distributed in edible plants. Stat3 can bind to specific DNA response elements and serves as a transcription factor to promote the translation of some invasion/migration-related target genes, considered as a potential anticancer target. Here, molecular docking and molecular dynamics (MD) simulation both were used to explore molecular recognition of quercetin with Stat3. The results show that quercetin impairs DNA transcription efficiency by hindering Stat3 dimerization, partially destroying DNA conformation. Specifically, when the ligand occupies the SH2 cavity of the enzyme, spatial rejection is not conductive to phosphokinase binding. It indirectly prevents the phosphorylation of Y705 and the formation of Stat3 dimer. When the inhibitor binds to the DT1005 position, it obviously shortens the distance between DNA and DBD, enhances their binding capacity, and thereby reduces the degree of freedom required for transcription. This work not only provides the binding modes between Stat3 and quercetin, but also contributes to the optimization and design of such anti-cancer inhibitors.

Insights into the DNA and RNA Interactions of Human Topoisomerase III Beta Using Molecular Dynamics Simulations

Human topoisomerase III beta (hTOP3B) is the only topoisomerase in the human cell that can act on both DNA and RNA substrates. Recent findings have emphasized the physiological importance of hTOP3B and consolidated it as a valuable drug target for antiviral and anticancer therapeutics. Although type IA topoisomerases of different organisms have been studied over the years, the step-by-step interaction of hTOP3B and nucleic acid substrates is still not well understood. Due to the lack of hTOP3B-RNA structures as well as DNA/RNA covalent complexes, computational investigations have been limited. In our study, we utilized molecular dynamics (MD) simulations to study the interactions between hTOP3B and nucleic acids to get a closer look into the residues that play a role in binding DNA or RNA and facilitate catalysis, along with the differences and similarities when hTOP3B interacts with DNA compared to RNA. For this, we generated multiple models of hTOP3B complexed with DNA and RNA sequences using the hTOP3B crystal structure and 8-mer single-stranded DNA and RNA sequences. These models include both covalent and noncovalent complexes, which are then subjected to MD simulations and analyzed. Our findings highlight the complexes' stability, sequence preference, and interactions of the binding pocket residues with different nucleotides. Our work demonstrates that hTOP3B forms stable complexes with both DNA and RNA and provides a better understanding of the enzyme's interaction with different nucleic acid substrate sequences.

Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity

Ataxia Telangiectasia (AT) is a rare multisystemic neurodegenerative disease caused by biallelic mutations in the ATM gene. Few clinical studies on AT disease have been conducted in Tunisia, however, the mutational landscape is still undefined. Our aim is to determine the clinical and genetic spectrum of AT Tunisian patients and to explore the potential underlying mechanism of variant pathogenicity. Sanger sequencing was performed for nine AT patients. A comprehensive computational analysis was conducted to evaluate the possible pathogenic effect of ATM identified variants. Genetic screening of ATM gene has identified nine different variants from which six have not been previously reported. In silico analysis has predicted a pathogenic effect of identified mutations. This was corroborated by a structural bioinformatics study based on molecular modeling and docking for novel missense mutations. Our findings suggest a profound impact of identified mutations not only on the ATM protein stability, but also on the ATM-ligand interactions. Our study characterizes the mutational landscape of AT Tunisian patients which will allow to set up genetic counseling and prenatal diagnosis for families at risk and expand the spectrum of ATM variants worldwide. Furthermore, understanding the mechanism that underpin variant pathogenicity could provide further insights into disease pathogenesis.

Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment

This roadmap reviews the new, highly interdisciplinary research field studying the behavior of condensed matter systems exposed to radiation. The Review highlights several recent advances in the field and provides a roadmap for the development of the field over the next decade. Condensed matter systems exposed to radiation can be inorganic, organic, or biological, finite or infinite, composed of different molecular species or materials, exist in different phases, and operate under different thermodynamic conditions. Many of the key phenomena related to the behavior of irradiated systems are very similar and can be understood based on the same fundamental theoretical principles and computational approaches. The multiscale nature of such phenomena requires the quantitative description of the radiation-induced effects occurring at different spatial and temporal scales, ranging from the atomic to the macroscopic, and the interlinks between such descriptions. The multiscale nature of the effects and the similarity of their manifestation in systems of different origins necessarily bring together different disciplines, such as physics, chemistry, biology, materials science, nanoscience, and biomedical research, demonstrating the numerous interlinks and commonalities between them. This research field is highly relevant to many novel and emerging technologies and medical applications.

Structural and Dynamic Features of Acinetobacter baumannii OXA-66 β-Lactamase Explain Its Stability and Evolution of Novel Variants

OXA-66 is a member of the OXA-51 subfamily of class D β-lactamases native to the Acinetobacter genus that includes Acinetobacter baumannii, one of the ESKAPE pathogens and a major cause of drug-resistant nosocomial infections. Although both wild type OXA-66 and OXA-51 have low catalytic activity, they are ubiquitous in the Acinetobacter genomes. OXA-51 is also remarkably thermostable. In addition, newly emerging, single and double amino acid variants show increased activity against carbapenems, indicating that the OXA-51 subfamily is growing and gaining clinical significance. In this study, we used molecular dynamics simulations, X-ray crystallography, and thermal denaturation data to examine and compare the dynamics of OXA-66 wt and its gain-of-function variants: I129L (OXA-83), L167V (OXA-82), P130Q (OXA-109), P130A, and W222L (OXA-234). Our data indicate that OXA-66 wt also has a high melting temperature, and its remarkable stability is due to an extensive and rigid hydrophobic bridge formed by a number of residues around the active site and harbored by the three loops, P, Ω, and β5-β6. Compared to the WT enzyme, the mutants exhibit higher flexibility only in the loop regions, and are more stable than other robust carbapenemases, such as OXA-23 and OXA-24/40. All the mutants show increased rotational flexibility of residues I129 and W222, which allows carbapenems to bind. Overall, our data support the hypothesis that structural features in OXA-51 and OXA-66 promote evolution of multiple highly stable variants with increased clinical relevance in A. baumannii.

Conformational Dynamics in Corynebacterium glutamicum Diaminopimelate Epimerase: Insights from Ligand Parameterization, Atomistic Simulation, and Markov State Modeling

The microbial enzyme diaminopimelate epimerase (DapF), a vital enzyme in the lysine biosynthetic pathway, catalyzes the conversion of L, L-diaminopimelate (L, L-DAP) to D, L-diaminopimelate (D, L-DAP) using a catalytic cysteine dyad with one cysteine in thiol state and another in thiolate. Under oxidizing conditions, the catalytic cysteines of apo DapF form a disulfide bond that alters the structure and function of DapF. Given its potential as a target for antimicrobial resistance treatments, understanding DapF's functional dynamics is imperative. In the present work, we employ microsecond-scale all-atom molecular dynamics simulations of product-bound DapF and apo-DapF under oxidized and reduced conditions. We employ a polarized charge model for the ligand and the active site residues, which was necessary to preserve the electrostatic environment in the active site and retain the ligand in the active site. The product-bound DapF and apo-DapF in oxidized and reduced conditions exhibit a closed, semi-open, and open conformation, respectively, as identified using the internal coordinates of the dimeric enzyme and the principal component analysis. The conformational switch is guided by the dynamic catalytic (DC) loop, loop II, and loop III movements in the active site. The time scale of the close-to-open conformational transition is estimated to be 0.8 μs through Markov state modeling (MSM) and transition path theory (TPT). The present study explains the role of various active site residues and loops in ligand binding and protein dynamics in the DapF enzyme under different redox conditions. Such information will be helpful in future inhibitor design studies targeting the DapF enzyme.

Four-dimensional imaging for cryo-electron microscopy experiments using molecular simulations and manifold learning

Elucidating protein conformational changes is essential because conformational changes are closely related to the functions of proteins. Cryo-electron microscopy (cryo-EM) experiment can be used to reconstruct protein conformational changes via a method that involves using the experimental data (two-dimensional protein images). In this study, a reconstruction method, referred to as the "four-dimensional imaging," was proposed. In our four-dimensional imaging technique, the protein conformational change was obtained using the two-dimensional protein images (the three-dimensional electron density maps used in previously proposed techniques were not used). The protein conformation for each two-dimensional protein image was obtained using our original protocol with molecular dynamics simulations. Using a manifold-learning technique and two-dimensional protein images, the protein conformations were arranged according to the conformational change of the protein. By arranging the protein conformations according to the arrangement of the protein images, four-dimensional imaging is constructed. A simulation for a cryo-EM experiment demonstrated the validity of our four-dimensional imaging technique.

Molecular recognition of ITIM/ITSM domains with SHP2 and their allosteric effect

Src homology 2-domain-containing tyrosine phosphatase 2 (SHP2) is a non-receptor protein tyrosine phosphatase that is widely expressed in a variety of cells and regulates the immune response of T cells through the PD-1 pathway. However, the activation mechanism and allosteric effects of SHP2 remain unclear, hindering the development of small molecule inhibitors. For the first time, in this study, the complex structure formed by the intact PD-1 tail and SHP2 was modeled. The molecular recognition and conformational changes of inactive/active SHP2 versus ITIM/ITSM were compared based on prolonged MD simulations. The relative flexibility of the two SH2 domains during MD simulations contributes to the recruitment of ITIM/ITSM and supports the subsequent conformational change of SHP2. The binding free energy calculation shows that inactive SHP2 has a higher affinity for ITIM/ITSM than active SHP2, mainly because the former's N-SH2 refers to the α-state. In addition, a significant decrease in the contribution to the binding energy of certain residues (e.g., R32, S34, K35, T42, and K55) of conformationally transformed SHP2 contributes to the above result. These detailed changes during conformational transition will provide theoretical guidance for the molecular design of subsequent novel anticancer drugs.

Design and Synthesis of Dual-Target Inhibitors Targeting Androgen Receptors and Glucocorticoid Receptors to Overcome Antiandrogen Resistance in Castration-Resistant Prostate Cancer

Androgen receptor (AR) antagonists play important roles in the treatment of castration-resistant prostate cancer (CRPC). The glucocorticoid receptor (GR) upregulation leads to drug resistance for clinically used antiandrogens. Therefore, blocking AR/GR signaling simultaneously has become an efficient strategy to overcome the drug resistance of CRPC. Our previous work indicated that Z19 could inhibit the activity of both AR and GR. Herein, we optimized the structure of Z19 and identified GA32 as a potent AR/GR dual inhibitor. GA32 efficiently reduced the mRNA and protein levels of AR/GR downstream genes. GA32 efficiently inhibited the proliferation of enzalutamide resistance CRPC both in vitro and in vivo. GA32 could directly bind to AR and GR, and the predicted binding modes for GA32 with AR/GR suggested that GA32 binds to the AR or GR hormone binding pocket. This work provides a potential lead compound with dual AR/GR inhibitory activity to conquer the drug resistance of CRPC.

Experiment and molecular dynamics simulations reveal proanthocyanidin B2 and B3 can inhibit prion aggregation by different mechanisms

Prion diseases are a group of fatal neurodegenerative diseases caused by the misfolding and aggregation of prion protein (PrP), and the inhibition of PrP aggregation is one of the most effective therapeutic strategies. Proanthocyanidin B2 (PB2) and B3 (PB3), the effective natural antioxidants have been evaluated for the inhibition of amyloid-related protein aggregation. Since PrP has similar aggregation mechanism with other amyloid-related proteins, will PB2 and PB3 affect the aggregation of PrP? In this paper, experimental and molecular dynamics (MD) simulation methods were combined to investigate the influence of PB2 and PB3 on PrP aggregation. Thioflavin T assays showed PB2 and PB3 could inhibit PrP aggregation in a concentrate-dependent manner in vitro. To understand the underlying mechanism, we performed 400 ns all-atom MD simulations. The results suggested PB2 could stabilize the α2 C-terminus and the hydrophobic core of protein by stabilizing two important salt bridges R156-E196 and R156-D202, and consequently made global structure of protein more stable. Surprisingly, PB3 could not stabilize PrP, which may inhibit PrP aggregation through a different mechanism. Since dimerization is the first step of aggregation, will PB3 inhibit PrP aggregation by inhibiting the dimerization? To verify our assumption, we then explored the effect of PB3 on protein dimerization by performing 800 ns MD simulations. The results suggested PB3 could reduce the residue contacts and hydrogen bonds between two monomers, preventing dimerization process of PrP. The possible inhibition mechanism of PB2 and PB3 on PrP aggregation could provide useful information for drug development against prion diseases.Communicated by Ramaswamy H. Sarma.

Enhanced Sampling of Buried Charges in Free Energy Calculations Using Replica Exchange with Charge Tempering

Buried ionizable groups in proteins often play important structural and functional roles. However, it is generally challenging to study the detailed molecular mechanisms solely based on experimental measurements. Free energy calculations using atomistic simulations, on the other hand, complement experimental studies and can provide high temporal and spatial resolution information that can lead to mechanistic insights. Nevertheless, it is also well recognized that sufficient sampling of such atomistic simulations can be challenging, considering that structural changes related to the buried charges may be very slow. In the present study, we describe a simple but effective enhanced sampling technique called replica exchange with charge tempering (REChgT) with a novel free energy method, multisite λ dynamics (MSλD), to study two systems containing buried charges, pKa prediction of a small molecule, orotate, in complex with the dihydroorotate dehydrogenase, and relative stability of a Glu-Lys pair buried in the hydrophobic core of two variants of Staphylococcal nuclease. Compared to the original MSλD simulations, the usage of REChgT dramatically increases sampling in both conformational and alchemical spaces, which directly translates into a significant reduction of wall time to converge the free energy calculations. This study highlights the importance of sufficient sampling toward developing improved free energy methods.

S288T mutation altering MmpL3 periplasmic domain channel and H-bond network: a novel dual drug resistance mechanism

CONTEXT

Mycobacterial membrane proteins Large 3 (MmpL3) is responsible for the transport of mycobacterial acids out of cell membrane to form cell wall, which is essential for the survival of Mycobacterium tuberculosis (Mtb) and has become a potent anti-tuberculosis target. SQ109 is an ethambutol (EMB) analogue, as a novel anti-tuberculosis drug, can effectively inhibit MmpL3, and has completed phase 2b-3 clinical trials. Drug resistance has always been the bottleneck problem in clinical treatment of tuberculosis. The S288T mutant of MmpL3 shows significant resistance to the inhibitor SQ109, while the specific action mechanism remains unclear. The results show that MmpL3 S288T mutation causes local conformational change with little effect on the global structure. With MmpL3 bound by SQ109 inhibitor, the distance between D710 and R715 increases resulting in H-bond destruction, but their interactions and proton transfer function are still restored. In addition, the rotation of Y44 in the S288T mutant leads to an obvious bend in the periplasmic domain channel and an increased number of contact residues, reducing substrate transport efficiency. This work not only provides a possible dual drug resistance mechanism of MmpL3 S288T mutant but also aids the development of novel anti-tuberculosis inhibitors.

METHODS

In this work, molecular dynamics (MD) and quantum mechanics (QM) simulations both were performed to compare inhibitor (i.e., SQ109) recognition, motion characteristics, and H-bond energy change of MmpL3 after S288T mutation. In addition, the WT_SQ109 complex structure was obtained by molecular docking program (Autodock 4.2); Molecular Mechanics/ Poisson Boltzmann Surface Area (MM-PBSA) and Solvated Interaction Energy (SIE) methods were used to calculate the binding free energies (∆Gbind); Geometric criteria were used to analyze the changes of hydrogen bond networks.

Guiding discovery of protein sequence-structure-function modeling

MOTIVATION

Protein engineering techniques are key in designing novel catalysts for a wide range of reactions. Although approaches vary in their exploration of the sequence-structure-function paradigm, they are often hampered by the labor-intensive steps of protein expression and screening. In this work, we describe the development and testing of a high-throughput in silico sequence-structure-function pipeline using AlphaFold2 and fast Fourier transform docking that is benchmarked with enantioselectivity and reactivity predictions for an ancestral sequence library of fungal flavin-dependent monooxygenases.

RESULTS

The predicted enantioselectivities and reactivities correlate well with previously described screens of an experimentally available subset of these proteins and capture known changes in enantioselectivity across the phylogenetic tree representing ancestorial proteins from this family. With this pipeline established as our functional screen, we apply ensemble decision tree models and explainable AI techniques to build sequence-function models and extract critical residues within the binding site and the second-sphere residues around this site. We demonstrate that the top-identified key residues in the control of enantioselectivity and reactivity correspond to experimentally verified residues. The in silico sequence-to-function pipeline serves as an accelerated framework to inform protein engineering efforts from vast informative sequence landscapes contained in protein families, ancestral resurrects, and directed evolution campaigns.

AVAILABILITY

Jupyter notebooks detailing the sequence-structure-function pipeline are available at https://github.com/BrooksResearchGroup-UM/seq_struct_func.

Molecular Modeling and Simulation of the Mycobacterial Cell Envelope: From Individual Components to Cell Envelope Assemblies

Unlike typical Gram-positive bacteria, the cell envelope of mycobacteria is unique and composed of a mycobacterial outer membrane, also known as the mycomembrane, a peptidoglycan layer, and a mycobacterial inner membrane, which is analogous to that of Gram-negative bacteria. Despite its importance, however, our understanding of this complex cell envelope is rudimentary at best. Thus, molecular modeling and simulation of such an envelope can benefit the scientific community by proposing new hypotheses about the biophysical properties of its different layers. In this Perspective, we present recent advances in molecular modeling and simulation of the mycobacterial cell envelope from individual components to cell envelope assemblies. We also show how modeling other types of cell envelopes, such as that of Escherichia coli, may help modeling part of the mycobacterial envelopes. We hope that the studies presented here are just the beginning of the road and more and more new modeling and simulation studies help us to understand crucial questions related to mycobacteria such as antibiotic resistance or bacterial survival in the host.

FASTDock: A Pipeline for Allosteric Drug Discovery

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

Binding of small molecule inhibitors to RNA polymerase-Spt5 complex impacts RNA and DNA stability

Spt5 is an elongation factor that associates with RNA polymerase II (Pol II) during transcription and has important functions in promoter-proximal pausing and elongation processivity. Spt5 was also recognized for its roles in the transcription of expanded-repeat genes that are related to neurodegenerative diseases. Recently, a set of Spt5-Pol II small molecule inhibitors (SPIs) were reported, which selectively inhibit mutant huntingtin gene transcription. Inhibition mechanisms as well as interaction sites of these SPIs with Pol II and Spt5 are not entirely known. In this study, we predicted the binding sites of three selected SPIs at the Pol II-Spt5 interface by docking and molecular dynamics simulations. Two molecules out of three demonstrated strong binding with Spt5 and Pol II, while the other molecule was more loosely bound and sampled multiple binding sites. Strongly bound SPIs indirectly affected RNA and DNA dynamics at the exit site as DNA became more flexible while RNA was stabilized by increased interactions with Spt5. Our results suggest that the transcription inhibition mechanism induced by SPIs can be related to Spt5-nucleic acid interactions, which were altered to some extent with strong binding of SPIs.

Structural impairment of p53 C-terminal due to the effect of phosphorylation and acetylation: a study on the interdependence of PTM

The C-terminal of tumor suppressor protein p53 is intrinsically disordered while unbound. This particular segment often shows structural plasticity when bound to other binding partners. The disordered component undergoes a disordered to ordered transition upon recognition. Post-translational modifications (PTMs), namely phosphorylation and acetylation, significantly alter the structural motifs of the segment. Among the various types of PTMs, phosphorylation, and acetylation of p53 at both N- and C- terminals lead to stabilization and activation. It has been noted experimentally that phosphorylation often regulates (enhances or reduces) the acetylation at specific sites. The phosphorylation of Thr377 and Ser378 reduces the acetylation of Lys373 and Lys382. Mutations of Thr377 and Ser378 to neutral Ala enhance and phospho mimic Asp reduce the acetylation of Lys373 and Lys382. Simulations of several single-point and pair-wise mutated systems have been generated to compare how the presence or absence of phosphorylation favors or disfavors the acetylation by thermodynamic and conformational analysis. We are using implicit solvent replica exchange molecular dynamics simulations to get 200 ns well-converged conformational ensembles of each system. Different sets of systems having both single and double PTMs are simulated. The results admit the appreciable change in the secondary structural level upon specific PTM. Also, the residual structure of the unbound p53 with single-point PTM varies significantly with pair-wise modifications. These observations further shed light on the relationship between the interdependencies of the specific PTM sites and the secondary structural levels.Communicated by Ramaswamy H. Sarma.

Complex Conformational Space of the RNA Polymerase II C-Terminal Domain upon Phosphorylation

Intrinsically disordered proteins (IDPs) have been closely studied during the past decade due to their importance in many biological processes. The disordered nature of this group of proteins makes it difficult to observe its full span of the conformational space using either experimental or computational studies. In this article, we explored the conformational space of the C-terminal domain (CTD) of RNA polymerase II (Pol II), which is also an intrinsically disordered low complexity domain, using enhanced sampling methods. We provided a detailed conformational analysis of model systems of CTD with different lengths; first with the last 44 residues of the human CTD sequence and finally the CTD model with 2-heptapeptide repeating units. We then investigated the effects of phosphorylation on CTD conformations by performing simulations at different phosphorylated states. We obtained broad conformational spaces in nonphosphorylated CTD models, and phosphorylation has complex effects on the conformations of the CTD. These complex effects depend on the length of the CTD, spacing between the multiple phosphorylation sites, ion coordination, and interactions with the nearby residues.

Structural Modeling of Cytokine-Receptor-JAK2 Signaling Complexes Using AlphaFold Multimer

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). These cell-surface single-pass transmembrane (TM) glycoproteins regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains, and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all of the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated here by using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts, with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices, causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to the TM α-helices of the active TPOR dimer was proposed. The models also help elucidate the molecular basis of oncogenic mutations that may involve a noncanonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.

Structural basis of transcription: RNA Polymerase II substrate binding and metal coordination at 3.0 Å using a free-electron laser

Catalysis and translocation of multi-subunit DNA-directed RNA polymerases underlie all cellular mRNA synthesis. RNA polymerase II (Pol II) synthesizes eukaryotic pre-mRNAs from a DNA template strand buried in its active site. Structural details of catalysis at near atomic resolution and precise arrangement of key active site components have been elusive. Here we present the free electron laser (FEL) structure of a matched ATP-bound Pol II, revealing the full active site interaction network at the highest resolution to date, including the trigger loop (TL) in the closed conformation, bonafide occupancy of both site A and B Mg2+, and a putative third (site C) Mg2+ analogous to that described for some DNA polymerases but not observed previously for cellular RNA polymerases. Molecular dynamics (MD) simulations of the structure indicate that the third Mg2+ is coordinated and stabilized at its observed position. TL residues provide half of the substrate binding pocket while multiple TL/bridge helix (BH) interactions induce conformational changes that could propel translocation upon substrate hydrolysis. Consistent with TL/BH communication, a FEL structure and MD simulations of the hyperactive Rpb1 T834P bridge helix mutant reveals rearrangement of some active site interactions supporting potential plasticity in active site function and long-distance effects on both the width of the central channel and TL conformation, likely underlying its increased elongation rate at the expense of fidelity.

Spike Protein Fragments Promote Alzheimer's Amyloidogenesis

Alzheimer's disease (AD) is a major cause of dementia inducing memory loss, cognitive decline, and mortality among the aging population. While the amyloid aggregation of peptide Aβ has long been implicated in neurodegeneration in AD, primarily through the production of toxic polymorphic aggregates and reactive oxygen species, viral infection has a less explicit role in the etiology of the brain disease. On the other hand, while the COVID-19 pandemic is known to harm human organs and function, its adverse effects on AD pathobiology and other human conditions remain unclear. Here we first identified the amyloidogenic potential of 1058HGVVFLHVTYV1068, a short fragment of the spike protein of SARS-CoV-2 coronavirus. The peptide fragment was found to be toxic and displayed a high binding propensity for the amyloidogenic segments of Aβ, thereby promoting the aggregation and toxicity of the peptide in vitro and in silico, while retarding the hatching and survival of zebrafish embryos upon exposure. Our study implicated SARS-CoV-2 viral infection as a potential contributor to AD pathogenesis, a little explored area in our quest for understanding and overcoming Long Covid.

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Intrinsically disordered proteins (IDPs) play a critical role in many biological processes. Due to the inherent structural flexibility of IDPs, experimental methods present significant challenges for sampling their conformational information at the atomic level. Therefore, molecular dynamics (MD) simulations have emerged as the primary tools for modeling IDPs whose accuracy depend on force field and water model. To enhance the accuracy of physical modeling of IDPs, several force fields have been developed. However, current water models lack precision and underestimate the interaction between water molecules and proteins. Here, we used Monte-Carlo re-weighting method to re-parameterize a three-point water model based on OPC3 for IDPs (named OPC3-B). We benchmarked the performance of OPC3-B compared with nine different water models for 10 IDPs and three ordered proteins. The results indicate that the performance of OPC3-B is better than other water models for both IDPs and ordered proteins. At the same time, OPC3-B possess the power of transferability with other force field to simulate IDPs. This newly developed water model can be used to insight into the research of sequence-disordered-function paradigm for IDPs.

Molecular insights into the structural and dynamical changes of calcium channel TRPV6 induced by its interaction with phosphatidylinositol 4,5-bisphosphate

Transient receptor potential vanilloid subfamily member 6 (TRPV6) is an epithelial calcium channel that regulates the initial step of the transcellular calcium transport pathway. TRPV6 is expressed in the kidney, intestine, placenta, and other tissues, and the dysregulation of the channel is implicated in several human cancers. It has been reported that phosphatidylinositol 4,5-bisphosphate (PIP2) activates TRPV6 and its close homologue TRPV5; however, the underlying molecular mechanism is less clear. Recently, a structure of rabbit TRPV5 in complex with dioctanoyl (diC8) PIP2, a soluble form of PIP2, was determined by cryo-electron microscopy. Based on this structure, the structural model of human TRPV6 with PIP2 was set up, and then molecular dynamics simulations were performed for TRPV6 with and without PIP2. Simulation results show that the positively charged residues responsible for TRPV5 binding of diC8 PIP2 are conserved in the interactions between TRPV6 and PIP2. The binding of PIP2 to TRPV6 increases the distance between the diagonally opposed residues D542 in the selectivity filter and that between the diagonally opposed M578 residues in the lower gate of TRPV6. A secondary structural analysis reveals that residues M578 in TRPV6 undergo structural and position changes during the binding of PIP2 with TRPV6. In addition, principal component analysis indicates that the binding of PIP2 increases the dynamical motions of both the selectivity filter and the lower gate of TRPV6. These changes induced by PIP2 favor the channel opening. Thus, this study provides a basis for understanding the mechanism underlying the PIP2-induced TRPV6 channel activation.Communicated by Ramaswamy H. Sarma.

pyCHARMM: Embedding CHARMM Functionality in a Python Framework

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

Structural modeling of cytokine-receptor-JAK2 signaling complexes using AlphaFold Multimer

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). They are cell-surface single-pass transmembrane (TM) glycoproteins that regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to TM α-helices of the active TPOR dimer was proposed. The models also help elucidating the molecular basis of oncogenic mutations that may involve non-canonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.

Unraveling the catalytic mechanism of SARS-CoV-2 papain-like protease with allosteric modulation of C270 mutation using multiscale computational approaches

Papain-like protease (PL^pro) is a promising therapeutic target against SARS-CoV-2, but its restricted S1/S2 subsites pose an obstacle in developing active site-directed inhibitors. We have recently identified C270 as a novel covalent allosteric site for SARS-CoV-2 PL^pro inhibitors. Here we present a theoretical investigation of the proteolysis reaction catalyzed by the wild-type SARS-CoV-2 PL^pro as well as the C270R mutant. Enhanced sampling MD simulations were first performed to explore the influence of C270R mutation on the protease dynamics, and sampled thermodynamically favorable conformations were then submitted to MM/PBSA and QM/MM MD simulations for thorough characterization of the protease-substrate binding and covalent reactions. The disclosed proteolysis mechanism of PL^pro, as characterized by the occurrence of proton transfer from the catalytic C111 to H272 prior to the substrate binding and with deacylation being the rate-determining step of the whole proteolysis process, is not completely identical to that of the 3C-like protease, another key cysteine protease of coronaviruses. The C270R mutation alters the structural dynamics of the BL2 loop that indirectly impairs the catalytic function of H272 and reduces the binding of the substrate with the protease, ultimately showing an inhibitory effect on PL^pro. Together, these results provide a comprehensive understanding at the atomic level of the key aspects of SARS-CoV-2 PL^pro proteolysis, including the catalytic activity allosterically regulated by C270 modification, which is crucial to the follow-up inhibitor design and development.

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder

Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are closely associated with tumors, cardiovascular diseases, and neurodegeneration, which have no fixed three-dimensional structure under physiological conditions. Due to the characteristic of conformational diversity, conventional experimental methods of structural biology, such as NMR, X-ray diffraction, and CryoEM, are unable to capture conformational ensembles. Molecular dynamics (MD) simulation can sample the dynamic conformations at the atomic level, which has become an effective method for studying the structure and function of IDPs. However, the high computational cost prevents MD simulations from being widely used for IDPs conformational sampling. In recent years, significant progress has been made in artificial intelligence, which makes it possible to solve the conformational reconstruction problem of IDP with fewer computational resources. Here, based on short MD simulations of different IDPs systems, we use variational autoencoders (VAEs) to achieve the generative reconstruction of IDPs structures and include a wider range of sampled conformations from longer simulations. Compared with the generative autoencoder (AEs), VAEs add an inference layer between the encoder and decoder in the latent space, which can cover the conformational landscape of IDPs more comprehensively and achieve the effect of enhanced sampling. Through experimental verification, the Cα RMSD between VAE-generated and MD simulation sampling conformations in the 5 IDPs test systems was significantly lower than that of AE. The Spearman correlation coefficient on the structure was higher than that of AE. VAE can also achieve excellent performance regarding structured proteins. In summary, VAEs can be used to effectively sample protein structures.

Varying molecular interactions explain aspects of crowder-dependent enzyme function of a viral protease

Biochemical processes in cells, including enzyme-catalyzed reactions, occur in crowded conditions with various background macromolecules occupying up to 40% of cytoplasm's volume. Viral enzymes in the host cell also encounter such crowded conditions as they often function at the endoplasmic reticulum membranes. We focus on an enzyme encoded by the hepatitis C virus, the NS3/4A protease, which is crucial for viral replication. We have previously found experimentally that synthetic crowders, polyethylene glycol (PEG) and branched polysucrose (Ficoll), differently affect the kinetic parameters of peptide hydrolysis catalyzed by NS3/4A. To gain understanding of the reasons for such behavior, we perform atomistic molecular dynamics simulations of NS3/4A in the presence of either PEG or Ficoll crowders and with and without the peptide substrates. We find that both crowder types make nanosecond long contacts with the protease and slow down its diffusion. However, they also affect the enzyme structural dynamics; crowders induce functionally relevant helical structures in the disordered parts of the protease cofactor, NS4A, with the PEG effect being more pronounced. Overall, PEG interactions with NS3/4A are slightly stronger but Ficoll forms more hydrogen bonds with NS3. The crowders also interact with substrates; we find that the substrate diffusion is reduced much more in the presence of PEG than Ficoll. However, contrary to NS3, the substrate interacts more strongly with Ficoll than with PEG crowders, with the substrate diffusion being similar to crowder diffusion. Importantly, crowders also affect the substrate-enzyme interactions. We observe that both PEG and Ficoll enhance the presence of substrates near the active site, especially near catalytic H57 but Ficoll crowders increase substrate binding more than PEG molecules.

Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning

Catalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo- and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder model based on the MD data allowed the clustering of mutants with different phenotypes based on structural details. Overall, we found that a subset of loss-of-function (LOF) and lethal mutations tended to increase distances of TL residues to the NTP substrate, while another subset of LOF and lethal substitutions tended to confer an increase in distances between TL and bridge helix (BH). In contrast, some of the gain-of-function (GOF) mutants appear to cause disruption of hydrophobic contacts among TL and nearby helices.

Direct generation of protein conformational ensembles via machine learning

Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle we train a generative adversarial network based on a transformer architecture with self-attention on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent coarse-grained ensembles for sequences that are not present in the training set demonstrating that transferability can be achieved beyond the limited training data. We also retrain idpGAN on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.

Assessing protein homology models with docking reproducibility

Results of the recent Critical Assessment of Protein Structure (CASP) competitions demonstrate that protein backbones can be predicted with very high accuracy. In particular, the artificial intelligence methods of AlphaFold 2 from DeepMind were able to produce structures that were similar enough to experimental structures that many described the problem of protein prediction solved. However, for such structures to be used for drug docking studies requires precision in the placement of side chain atoms as well. Here we built a library of 1334 small molecules and examined how reproducibly they bound to the same site on a protein using QuickVina-W, a branch of the program Autodock that is optimized for blind searches. We discovered that the higher the backbone quality of the homology model the greater the similarity between the small molecule docking to the experimental and modeled structures. Furthermore, we found that specific subsets of this library were particularly useful for identifying small differences between the best of the best modeled structures. Specifically, when the number of rotatable bonds in the small molecule increased, differences in binding sites became more apparent.

Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles

We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.

Modeling Concentration-dependent Phase Separation Processes Involving Peptides and RNA via Residue-Based Coarse-Graining

Biomolecular condensation, especially liquid-liquid phase separation, is an important physical process with relevance for a number of different aspects of biological functions. Key questions of what drives such condensation, especially in terms of molecular composition, can be addressed via computer simulations, but the development of computationally efficient yet physically realistic models has been challenging. Here, the coarse-grained model COCOMO is introduced that balances the polymer behavior of peptides and RNA chains with their propensity to phase separate as a function of composition and concentration. COCOMO is a residue-based model that combines bonded terms with short- and long-range terms, including a Debye-Hückel solvation term. The model is highly predictive of experimental data on phase-separating model systems. It is also computationally efficient and can reach the spatial and temporal scales on which biomolecular condensation is observed with moderate computational resources.

A NR2E1-interacting peptide of LSD1 inhibits the proliferation of brain tumour initiating cells

OBJECTIVES

Elimination of brain tumour initiating cells (BTICs) is important for the good prognosis of malignant brain tumour treatment. To develop a novel strategy targeting BTICs, we studied NR2E1(TLX) involved self-renewal mechanism of BTICs and explored the intervention means.

MATERIALS AND METHODS

NR2E1 and its interacting protein-LSD1 in BTICs were studied by gene interference combined with cell growth, tumour sphere formation, co-immunoprecipitation and chromatin immunoprecipitation assays. NR2E1 interacting peptide of LSD1 was identified by Amide Hydrogen/Deuterium Exchange and Mass Spectrometry (HDX-MS) and analysed by in vitro functional assays. The in vivo function of the peptide was examined with intracranial mouse model by transplanting patient-derived BTICs.

RESULTS

We found NR2E1 recruits LSD1, a lysine demethylase, to demethylate mono- and di-methylated histone 3 Lys4 (H3K4me/me2) at the Pten promoter and repress its expression, thereby promoting BTIC proliferation. Using Amide Hydrogen/Deuterium Exchange and Mass Spectrometry (HDX-MS) method, we identified four LSD1 peptides that may interact with NR2E1. One of the peptides, LSD1-197-211 that locates at the LSD1 SWIRM domain, strongly inhibited BTIC proliferation by promoting Pten expression through interfering NR2E1 and LSD1 function. Furthermore, overexpression of this peptide in human BTICs can inhibit intracranial tumour formation.

CONCLUSION

Peptide LSD1-197-211 can repress BTICs by interfering the synergistic function of NR2E1 and LSD1 and may be a promising lead peptide for brain tumour therapy in future.

Recognition and release of uridine and hCNT3: From multivariate interactions to molecular design

As a vital target for the development of novel anti-cancer drugs, human concentrative nucleoside transporter 3 (hCNT3) has been widely concerned. Nevertheless, the lack of a comprehensive understanding of molecular interactions and motion mechanism has greatly hindered the development of novel inhibitors against hCNT3. In this paper, molecular recognition of hCNT3 with uridine was investigated with molecular docking, conventional molecular dynamics (CMD) simulations and adaptive steered molecular dynamics (ASMD) simulations; and then, the uridine derivatives with possibly highly inhibitory activity were designed. The result of CMD showed that more water-mediated H-bonds and lower binding free energy both explained higher recognition ability and transported efficiency of hCNT3. While during the ASMD simulation, nucleoside transport process involved the significant side-chain flip of residues F321 and Q142, a typical substrate-induced conformational change. By considering electronegativity, atomic radius, functional group and key H-bonds factors, 25 novel uridine derivatives were constructed. Subsequently, the receptor-ligand binding free energy was predicted by solvated interaction energy (SIE) method to determine the inhibitor c8 with the best potential performance. This work not only revealed molecular recognition and release mechanism of uridine with hCNT3, but also designed a series of uridine derivatives to obtain lead compounds with potential high activity.

Development of hidden Markov modeling method for molecular orientations and structure estimation from high-speed atomic force microscopy time-series images

High-speed atomic force microscopy (HS-AFM) is a powerful technique for capturing the time-resolved behavior of biomolecules. However, structural information in HS-AFM images is limited to the surface geometry of a sample molecule. Inferring latent three-dimensional structures from the surface geometry is thus important for getting more insights into conformational dynamics of a target biomolecule. Existing methods for estimating the structures are based on the rigid-body fitting of candidate structures to each frame of HS-AFM images. Here, we extend the existing frame-by-frame rigid-body fitting analysis to multiple frames to exploit orientational correlations of a sample molecule between adjacent frames in HS-AFM data due to the interaction with the stage. In the method, we treat HS-AFM data as time-series data, and they are analyzed with the hidden Markov modeling. Using simulated HS-AFM images of the taste receptor type 1 as a test case, the proposed method shows a more robust estimation of molecular orientations than the frame-by-frame analysis. The method is applicable in integrative modeling of conformational dynamics using HS-AFM data.

End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification

The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical properties of proteins, focused on high-throughput normal mode predictions directly from the amino acid sequence. Using neural network models within the family of Natural Language Processing and graph-based methods, we offer atomistically based mechanistic predictions of key protein mechanical features. The models include an end-to-end long short-term memory (LSTM) model, an end-to-end transformer model, a graph-based transformer model, and an equivariant graph neural network. All four models show exceptional performance, with the graph-based transformer architecture offering the best results but at the cost of requiring a graph structure as input. Conversely, the LSTM and transformer models offer end-to-end sequence-to-property prediction capabilities, providing efficient avenues for protein engineering, analysis, and design. We compare our results against published data based on a Principal Neighborhood Aggregation graph neural network, revealing that the transformer model offers better performance while also being able to predict a large set of the first 64 normal mode frequencies, simultaneously. The use of the end-to-end transformer model may facilitate other downstream applications through the use of transfer learning, and it offers a comprehensive prediction of dynamical properties without any structural knowledge, directly from the amino acid sequence. We demonstrate a potential application in scientific sonification, where the normal mode frequencies are transposed to generate audible signals for a detailed analysis of subtle changes of protein sequences.

Performance evaluation of the balanced force field ff03CMAP for intrinsically disordered and ordered proteins

Intrinsically disordered proteins (IDPs) have been found to be closely associated with various human diseases. Because IDPs have no fixed tertiary structure under physiological conditions, current experimental methods, such as X-ray spectroscopy, NMR, and CryoEM, cannot capture all the dynamic conformations. Molecular dynamics simulation is an useful tool that is widely used to study the conformer distributions of IDPs and has become an important complementary tool for experimental methods. However, the accuracy of MD simulations directly depends on utilizing a precise force field. Recently a CMAP optimized force field based on the Amber ff03 force field (termed ff03CMAP herein) was developed for a balanced sampling of IDPs and folded proteins. In order to further evaluate the performance, more types of disordered and ordered proteins were used to test the ability for conformer sampling. The results showed that simulated chemical shifts, J-coupling, and R_g distribution with the ff03CMAP force field were in better agreement with NMR measurements and were more accurate than those with the ff03 force field. The sampling conformations by ff03CMAP were more diverse than those of ff03. At the same time, ff03CMAP could stabilize the conformers of the ordered proteins. These findings indicate that ff03CMAP can be widely used to sample diverse conformers for proteins, including the intrinsically disordered regions.

GPU-Accelerated All-Atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber

Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH conditions; however, the current accuracy is limited due to the use of implicit-solvent models or a hybrid solvent scheme. Here, we report the first GPU-accelerated implementation, parametrization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Amber22 pmemd.cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replica-exchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pK_a's of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearson's correlation coefficient for the pK_a shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pK_a's and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pK_a upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pK_a downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD will offer proper pH control to improve the accuracies of MD simulations and enable mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.

Molecular Dynamics Simulations of Rhodamine B Zwitterion Diffusion in Polyelectrolyte Solutions

Polyelectrolytes continue to find wide interest and application in science and engineering, including areas such as water purification, drug delivery, and multilayer thin films. We have been interested in the dynamics of small molecules in a variety of polyelectrolyte (PE) environments; in this paper, we report simulations and analysis of the small dye molecule rhodamine B (RB) in several very simple polyelectrolyte solutions. Translational diffusion of the RB zwitterion has been measured in fully atomistic, 2 μs long molecular dynamics simulations in four different polyelectrolyte solutions. Two solutions contain the common polyanion sodium poly(styrene sulfonate) (PSS), one with a 30-mer chain and the other with 10 trimers. The other two solutions contain the common polycation poly(allyldimethylammonium) chloride (PDDA), one with two 15-mers and the other with 10 trimers. RB diffusion was also simulated in several polymer-free solutions to verify its known experimental value for the translational diffusion coefficient, D_RB, of 4.7 × 10^-6 cm²/s at 300 K. RB diffusion was slowed in all four simulated PE solutions, but to varying degrees. D_RB values of 3.07 × 10^-6 and 3.22 × 10^-6 cm²/s were found in PSS 30-mer and PSS trimer solutions, respectively, whereas PDDA 15-mer and trimer solutions yielded values of 2.19 × 10^-6 and 3.34 × 10^-6 cm²/s. Significant associations between RB and the PEs were analyzed and interpreted via a two-state diffusion model (bound and free diffusion) that describes the data well. Crowder size effects and anomalous diffusion were also analyzed. Finally, RB translation along the polyelectrolytes during association was characterized.

Mechanism of the distinct toxicity level of imidacloprid and thiacloprid against honey bees: An in silico study based on cytochrome P450 9Q3

The honey bee, Apis mellifera, shows variation in sensitivity to imidacloprid and thiacloprid, which does not reside at the target site but rather in the rapidly oxidative metabolism mediated by P450s (such as a single P450, CYP9Q3). An in silico study was conducted to investigate the various metabolism of imidacloprid and thiacloprid. The binding potency of thiacloprid was stronger and a stable π-π interaction with Phe121 and the N-H⋯N hydrogen bond with Asn214 are found in the CYP9Q3-thiacloprid system but absent in imidacloprid, which might affect the potential metabolic activity. Moreover, the values of highest occupied molecular orbit (HOMO) energy and the vertical ionization potential (IP) of two compounds demonstrated that thiacloprid is more likely to oxidation. The findings revealed the probable binding modes of imidacloprid and thiacloprid with CYP9Q3 and might facilitate future design of the low bee toxicity neonicotinoid insecticides.

Ligand-induced shifts in conformational ensembles that describe transcriptional activation

Nuclear receptors function as ligand-regulated transcription factors whose ability to regulate diverse physiological processes is closely linked with conformational changes induced upon ligand binding. Understanding how conformational populations of nuclear receptors are shifted by various ligands could illuminate strategies for the design of synthetic modulators to regulate specific transcriptional programs. Here, we investigate ligand-induced conformational changes using a reconstructed, ancestral nuclear receptor. By making substitutions at a key position, we engineer receptor variants with altered ligand specificities. We combine cellular and biophysical experiments to characterize transcriptional activity, as well as elucidate mechanisms underlying altered transcription in receptor variants. We then use atomistic molecular dynamics (MD) simulations with enhanced sampling to generate ensembles of wildtype and engineered receptors in combination with multiple ligands, followed by conformational analysis and correlation of MD-based predictions with functional ligand profiles. We determine that conformational ensembles accurately describe ligand responses based on observed population shifts. These studies provide a platform which will allow structural characterization of physiologically-relevant conformational ensembles, as well as provide the ability to design and predict transcriptional responses in novel ligands.

Enzyme activity engineering based on sequence co-evolution analysis

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3'-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substrate-binding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.

Fuzzy supertertiary interactions within PSD-95 enable ligand binding

The scaffold protein PSD-95 links postsynaptic receptors to sites of presynaptic neurotransmitter release. Flexible linkers between folded domains in PSD-95 enable a dynamic supertertiary structure. Interdomain interactions within the PSG supramodule, formed by PDZ3, SH3, and Guanylate Kinase domains, regulate PSD-95 activity. Here we combined discrete molecular dynamics and single molecule Förster resonance energy transfer (FRET) to characterize the PSG supramodule, with time resolution spanning picoseconds to seconds. We used a FRET network to measure distances in full-length PSD-95 and model the conformational ensemble. We found that PDZ3 samples two conformational basins, which we confirmed with disulfide mapping. To understand effects on activity, we measured binding of the synaptic adhesion protein neuroligin. We found that PSD-95 bound neuroligin well at physiological pH while truncated PDZ3 bound poorly. Our hybrid structural models reveal how the supertertiary context of PDZ3 enables recognition of this critical synaptic ligand.

ABCA1 is an extracellular phospholipid translocase

Production of high density lipoprotein (HDL) requires ATP-binding cassette transporter A1 (ABCA1) to drive phospholipid (PL) from the plasma membrane into extracellular apolipoprotein A-I. Here, we use simulations to show that domains of ABCA1 within the plasma membrane remove PL from the membrane’s outer leaflet. In our simulations, after the lipid diffuses into the interior of ABCA1’s outward-open cavity, PL extracted by the gateway passes through a ring-shaped domain, the annulus orifice, which forms the base of an elongated hydrophobic tunnel in the transporter’s extracellular domain. Engineered mutations in the gateway and annulus strongly inhibit lipid export by ABCA1 without affecting cell-surface expression levels. Our finding that ABCA1 extracts lipid from the outer face of the plasma membrane and forces it through its gateway and annulus into an elongated hydrophobic tunnel contrasts with the alternating access model, which proposes that ABCA1 flops PL substrate from the inner leaflet to the outer leaflet of the membrane. Consistent with our model, ABCA1 lacks the charged amino acid residues in the transmembrane domain found in the floppase members of the ABC transporter family.

ATP-binding cassette transporter A1 (ABCA1) drives phospholipid (PL) from the plasma membrane into extracellular apolipoprotein A-I, for the production of high density lipoprotein (HDL). Here, the authors use simulations to assess the mechanism of ABCA1 function and show that ABCA1 extracts lipid from the outer face of the plasma membrane.

Using Selectively Scaled Molecular Dynamics Simulations to Assess Ligand Poses in RNA Aptamers

Predicting the structure (or pose) of RNA-ligand complexes is an important problem in RNA structural biology. Although one could use computational docking to rapidly sample putative poses of RNA-ligand complexes, accurately discriminating the native-like poses from non-native, decoy poses remains a formidable challenge. Here, we started from the assumption that native-like RNA-ligand poses are less likely to dissociate during molecular dynamics simulations, and then we used enhanced simulations to promote ligand unbinding for diverse poses of a handful of RNA aptamer-ligand complexes. By fitting unbinding profiles obtained from the simulations to a single exponential, we identified the characteristic decay time (τ) as particularly effective at resolving native poses from decoys. We also found that a simple regression model trained to predict the simulation-derived parameters directly from structure could also discriminate ligand poses for similar RNA aptamers. Characterizing the unbinding properties of individual poses may thus be an effective strategy for enhancing pose prediction for ligands interacting with RNA aptamers. A similar strategy might be applicable to other ligandable RNAs; however, further analysis will be required to confirm this hypothesis.

Recognition between CD147 and cyclophilin A deciphered by accelerated molecular dynamics simulations

CD147 functions as the receptor of extracellular cyclophilin A (CypA) in various diseases, and CD147-CypA binding ulteriorly underlies the pathological process of various viral infections including HIV-1, SARS, and SARS-CoV-2. Although CyPA has been identified as a key intermediate pro-inflammatory factor, the mechanism by which CD147 cooperates with CypA in the development of the cytokine storm remains largely unknown, and the binding profile of CD147 with CypA remains to be elucidated as well. Here, we prepared three binding models of the CD147-CypA complex, including the active site of CypA severally binding to the groove bound by the Ig1 and Ig2 domains (model-0), P180-G181 (model-1), and P211 (model-2) of CD147, as well as introducing mutations P180A-G181A and P211A individually in each model. All systems were studied using accelerated molecular dynamics simulations and the molecular mechanics generalized Born surface area (MM/GBSA) method. For model-0, CypA bound to the ectodomain of CD147 with the highest binding affinity. Moreover, mutations P180A-G181A of CD147 in model-0 decreased the binding affinity and weakened the dynamic correlation between CD147 and CypA, which resulted in CypA shifting from the initial binding location. Other residue mutations of CD147 did not significantly affect the CD147-CypA binding, as reflected by the energy and structural analyses. Compared with surface plasmon resonance results and nuclear magnetic resonance shift signals, CypA should tend to reciprocally bind to the groove of CD147, and the binding process might be modulated by P180-G181 rather than P211. Besides, residue R201 of CD147 is critical for CD147-CypA binding and needs further experimental verification. These findings further our understanding of the recruitment between CD147 and CypA and its potential role in the development of inflammation and viral infection.

Covalent docking in CDOCKER

Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keserű group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.