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Coronaviruses have posed a persistent threat to human health over the last two decades. Despite the accumulated knowledge about coronavirus-related pathogens, development of an effective treatment for its new variant COVID-19 is highly challenging. For the highly-conserved and main coronavirus protease 3CLpro, dimerization is known to be essential for its catalytic activity and thereby for virus proliferation. Here, we assess the potential of short peptide segments to disrupt dimerization of the 3CLpro protease as a route to block COVID-19 proliferation. Based on the X-ray structure of the 3CLpro dimer, we identified the SPSGVY126QCAMRP dodecapeptide segment as overlapping the hotspot regions on the 3CLpro dimer interface. Using computational blind docking of the peptide to the 3CLpro monomer, we found that the SPSGVY126QCAMRP peptide has favourable thermodynamic binding (ΔG= -5.93 kcal/mol) to the hotspot regions at the 3CLpro dimer interface. Importantly, the peptide was also found to preferentially bind to the hotspot regions compared to other potential binding sites lying away from the dimer interface (ΔΔG=-1.31 kcal/mol). Docking of peptides corresponding to systematic mutation of the V125 and Y126 residues led to the identification of seven peptides, SPSGHAQCAMRP, SPSGVTQCAMRP, SPSGKPQCAMRP, SPSGATQCAMRP, SPSGWLQCAMRP, SPSGAPQCAMRP and SPSGHPQCAMRP, that outperform the wild-type SPSGVY126QCAMRP peptide in terms of preferential binding to the 3CLpro dimer interface. These peptides have the potential to disrupt 3CLpro dimerization and therefore could provide lead structures for the development of broad spectrum COVID-19 inhibitors.Communicated by Ramaswamy H. Sarma.

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This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

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Peptides that inhibit MDM2 and attenuate MDM2-p53 interactions, thus activating p53, are currently being pursued as anticancer drug leads for tumors harboring wild type p53. The thermodynamic determinants of peptide-MDM2 interactions have been extensively studied. However, a detailed understanding of the dynamics that underlie these interactions is largely missing. In this study, we explore the kinetics of the binding of a set of peptides using Brownian dynamics simulations. We systematically investigate the effect of peptide C-terminal substitutions (Ser, Ala, Asn, Pro) of a Q16ETFSDLWKLLP27 p53-based peptide and a M1PRFMDYWEGLN12 12/1 phage-derived peptide on their interaction dynamics with MDM2. The substitutions modulate peptide residence times around the MDM2 protein. In particular, the highest affinity peptide, Q16ETFSDLWKLLS27, has the longest residence time (t ∼ 25 µs) around MDM2, suggesting its potentially important contribution to binding affinity. The binding of the p53-based peptides appears to be kinetically driven while that of the phage-derived series appears to be thermodynamically driven. The phage-derived peptides were found to adopt distinctly different modes of interaction with the MDM2 protein compared to their p53-based counterparts. The p53-based peptides approach the N-terminal region of the MDM2 protein with the peptide C-terminal end oriented toward the protein, while the M1PRFMDYWEGLN12-based peptides adopt the reverse orientation. To probe the determinants of this switch in orientation, a designed mutant of the phage-derived peptide, R3E (M1PEFMDYWEGLN12), was simulated and found to adopt the orientation adopted by the p53-based peptides and also to result in almost a 5-fold increase in the peptide residence time (∼120 µs) relative to the p53-based peptides. On this basis, we suggest that the R3E mutant phage-derived peptide has a higher affinity for MDM2 than the p53-based peptides and would therefore, competitively inhibit MDM2-p53. The study, therefore, provides a novel computational framework for kinetics-based lead optimization for anticancer drug development strategies.

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Human epithelia are organized in a hierarchical structure, where stem cells generate terminally differentiated cells via intermediate progenitors. This two-step differentiation process is conserved in all tissues, but it is not known whether a common gene set contributes to its regulation. Here, we show that retinoic acid (RA) regulates early human prostate epithelial differentiation by activating a tightly coexpressed set of 80 genes (e.g., TMPRSS2). Response kinetics suggested that some of these genes could be direct RA targets, whereas others are probably responding indirectly to RA stimulation. Comparative bioinformatic analyses of published tissue-specific microarrays and a large-scale transcriptomic data set revealed that these 80 genes are not only RA responsive but also significantly coexpressed in many human cell systems. The same gene set preferentially responds to androgens during terminal prostate epithelial differentiation, implying a cell-type-dependent interplay between RA and tissue-specific transcription factor-mediated signaling in regulating the two steps of epithelial differentiation.

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The stereoselective affinity of small-molecule binding to proteins is typically broadly explained in terms of the thermodynamics of the final bound complex. Using Brownian dynamics simulations, we show that the preferential binding of the MDM2 protein to the geometrical isomers of Nutlin-3, an effective anticancer lead that works by inhibiting the interaction between the proteins p53 and MDM2, can be explained by kinetic arguments related to the formation of the MDM2:Nutlin-3 encounter complex. This is a diffusively bound state that forms prior to the final bound complex. We find that the MDM2 protein stereoselectivity for the Nutlin-3a enantiomer stems largely from the destabilization of the encounter complex of its mirror image enantiomer Nutlin-3b, by the K70 residue that is located away from the binding site. On the other hand, the trans-Nutlin-3a diastereoisomer exhibits a shorter residence time in the vicinity of MDM2 compared with Nutlin-3a due to destabilization of its encounter complex by the collective interaction of pairs of charged residues on either side of the binding site: Glu25 and Lys51 on one side, and Lys94 and Arg97 on the other side. This destabilization is largely due to the electrostatic potential of the trans-Nutlin-3a isomer being largely positive over extended continuous regions around its structure, which are otherwise well-identified into positive and negative regions in the case of the Nutlin-3a isomer. Such rich insight into the binding processes underlying biological selectivity complements the static view derived from the traditional thermodynamic analysis of the final bound complex. This approach, based on an explicit consideration of the dynamics of molecular association, suggests new avenues for kinetics-based anticancer drug development and discovery.

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Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

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The interaction of p53 with its regulators MDM2 and MDMX plays a major role in regulating the cell cycle. Inhibition of this interaction has become an important therapeutic strategy in oncology. Although MDM2 and MDMX share a very high degree of sequence/structural similarity, the small-molecule inhibitor nutlin appears to be an efficient inhibitor only of the p53-MDM2 interaction. Here, we investigate the mechanism of interaction of nutlin with these two proteins and contrast it with that of p53 using Brownian dynamics simulations. In contrast to earlier attempts to examine the bound states of the partners, here we locate initial reaction events in these interactions by identifying the regions of space around MDM2/MDMX, where p53/nutlin experience associative encounters with prolonged residence times relative to that in bulk solution. We find that the initial interaction of p53 with MDM2 is long-lived relative to nutlin, but, unlike nutlin, it takes place at the N- and C termini of the MDM2 protein, away from the binding site, suggestive of an allosteric mechanism of action. In contrast, nutlin initially interacts with MDM2 directly at the clefts of the binding site. The interaction of nutlin with MDMX, however, is very short-lived compared with MDM2 and does not show such direct initial interactions with the binding site. Comparison of the topology of the electrostatic potentials of MDM2 and MDMX and the locations of the initial encounters with p53/nutlin in tandem with structure-based sequence alignment revealed that the origin of the diminished activity of nutlin toward MDMX relative to MDM2 may stem partly from the differing topologies of the electrostatic potentials of the two proteins. Glu25 and Lys51 residues underpin these topological differences and appear to collectively play a key role in channelling nutlin directly toward the binding site on the MDM2 surface and are absent in MDMX. The results, therefore, provide new insight into the mechanism of p53/nutlin interactions with MDM2 and MDMX and could potentially have a broader impact on anticancer drug optimization strategies.

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We investigated the potential of small peptide segments to function as broad-spectrum antiviral drug leads. We extracted the α-helical peptide segments that share common secondary-structure environments in the capsid protein-protein interfaces of three unrelated virus classes (PRD1-like, HK97-like, and BTV-like) that encompass different levels of pathogenicity to humans, animals, and plants. The potential for the binding of these peptides to the individual capsid proteins was then investigated using blind docking simulations. Most of the extracted α-helical peptides were found to interact favorably with one or more of the protein-protein interfaces within the capsid in all three classes of virus. Moreover, binding of these peptides to the interface regions was found to block one or more of the putative "hot spot" regions on the protein interface, thereby providing the potential to disrupt virus capsid assembly via competitive interaction with other capsid proteins. In particular, binding of the GDFNALSN peptide was found to block interface "hot spot" regions in most of the viruses, providing a potential lead for broad-spectrum antiviral drug therapy.

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The study of drug-receptor interactions has largely been framed in terms of the equilibrium thermodynamic binding affinity, an in vitro measure of the stability of the drug-receptor complex that is commonly used as a proxy measure of in vivo biological activity. In response to the growing realization of the importance of binding kinetics to in vivo drug activity we present a computational methodology for the kinetic characterization of drug-receptor interactions in terms of the encounter complex. Using trajectory data from multiple Brownian dynamics simulations of ligand diffusion, we derive the spatial density of the ligand around the receptor and show how it can be quantitatively partitioned into different basins of attraction. Numerical integration of the ligand densities within the basins can be used to estimate the residence time of the ligand within these diffusive binding sites. Simulations of two structurally similar inhibitors of Hsp90 exhibit diffusive binding sites with similar spatial structure but with different ligand residence times. In contrast, a pair of structurally dissimilar inhibitors of MDM2, a peptide and a small molecule, exhibit spatially distinct basins of attraction around the receptor, which in turn reveal differences in ligand orientational order. Thus, our kinetic approach provides microscopic details of drug-receptor dynamics that provide novel insight into the observed differences in the thermodynamic binding affinities for the two inhibitors, such as the differences in the entropic contributions to binding. The characterization of the encounter complex, in terms of the structure, topology, and dynamics of diffusive binding sites, offers a new perspective on ligand-receptor interactions and the potential for greater insight into drug action. The method, which requires no prior knowledge of the bound state, is a first step toward the incorporation of ligand kinetics into in silico drug development protocols.

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Single-stranded RNA (ssRNA) viruses form a major class that includes important human, animal, and plant pathogens. While the principles underlying the structures of their protein capsids are generally well understood, much less is known about the organization of their encapsulated genomic RNAs. Cryo-electron microscopy and x-ray crystallography have revealed striking evidence of order in the packaged genomes of a number of ssRNA viruses. The physical determinants of such order, however, are largely unknown. We study here the relative effect of different energetic contributions, as well as the role of confinement, on the genome packaging of a representative ssRNA virus, the bacteriophage MS2, via a series of biomolecular simulations in which different energy terms are systematically switched off. We show that the bimodal radial density profile of the packaged genome is a consequence of RNA self-repulsion in confinement, suggesting that it should be similar for all ssRNA viruses with a comparable ratio of capsid size/genome length. In contrast, the detailed structure of the outer shell of the RNA density depends crucially on steric contributions from the capsid inner surface topography, implying that the various different polyhedral RNA cages observed in experiment are largely due to differences in the inner surface topography of the capsid.

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A large number of single-stranded RNA viruses, which form a major class of all viruses, co-assemble their protein container and their genomic material. The multiple roles of the viral genome in this process are presently only partly understood. Recent experimental results indicate that RNA, in addition to its function as a repository for genetic information, could play important functional roles during the assembly of the viral protein containers. An investigation of the impact of genomic RNA on the association of the protein subunits may therefore provide further insights into the mechanism of virus assembly. We study here the impact of viral RNA on the association rates of the capsid proteins during virus assembly. As a case study, we consider the viral capsid of bacteriophage MS2, which is formed from 60 asymmetric (AB) and 30 symmetric (CC) protein dimers. Using Brownian dynamics simulations, we investigate the effect of the binding of an RNA stem-loop (the translational repressor) on the association rates of the capsid protein dimers. Our analysis shows that translational repressor binding results in self-association of AB dimers being inhibited, whilst association of AB with CC dimers is greatly enhanced. This provides an explanation for experimental results in which an alternating assembly pattern of AB and CC dimer addition to the growing assembly intermediate has been observed to be the dominant mode of assembly. The presence of the RNA hence dramatically decreases the number of dominant assembly pathways and thereby reduces the complexity of the self-assembly process of these viruses.

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An extensive computational analysis of available sequence and crystal structure data was used to identify functionally important residue interactions within the motor domain of the kinesin molecular motor. Principal component analysis revealed that all current kinesin crystal structures reside in one of two main conformations, which differ at the active site, and in the position of a microtubule-binding sub-domain relative to a rigid central core. This sub-domain consists of secondary structure elements alpha4-loop12-alpha5-loop13 and contains a conserved hydrophilic surface patch that may be involved in strong binding to microtubules. A hinge point for the sub-domain motion lies near a conserved glycine at position 292. Statistical coupling analysis revealed a network of co-evolving positions that link this region to the nucleotide-binding site, via a highly conserved histidine in the switch I loop. The data are consistent with a model in which the nucleotide status of the active site shifts kinesin between weak and strong binding conformations via reconfiguration of the identified sub-domain. Our data provide a statistically supported framework for further examination of this and other structure-function relationships in the kinesin family.

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UNLABELLED: An automated procedure for the analysis of homologous protein structures has been developed. The method facilitates the characterization of internal conformational differences and inter-conformer relationships and provides a framework for the analysis of protein structural evolution. The method is implemented in bio3d, an R package for the exploratory analysis of structure and sequence data. AVAILABILITY: The bio3d package is distributed with full source code as a platform-independent R package under a GPL2 license from: http://mccammon.ucsd.edu/~bgrant/bio3d/

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A multivariate analysis of the backbone and sugar torsion angles of dinucleotide fragments was used to construct a 3D principal conformational subspace (PCS) of DNA duplex crystal structures. The potential energy surface (PES) within the PCS was mapped for a single-strand dinucleotide model using an empirical energy function. The low energy regions of the surface encompass known DNA forms and also identify previously unclassified conformers. The physical determinants of the conformational landscape are found to be predominantly steric interactions within the dinucleotide backbone, with medium-dependent backbone-base electrostatic interactions serving to tune the relative stability of the different local energy minima. The fidelity of the PES to duplex DNA properties is validated through a correspondence to the conformational distribution of duplex DNA crystal structures and the reproduction of observed sequence specific propensities for the formation of A-form DNA. The utility of the PES is demonstrated through its succinct and accurate description of complex conformational processes in simulations of duplex DNA. The study suggests that stereochemical considerations of the nucleic acid backbone play a role in determining conformational preferences of DNA which is analogous to the role of local steric interactions in determining polypeptide secondary structure.

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Atrial septal defect is one of the most common forms of congenital heart malformation. We identified a new locus linked with atrial septal defect on chromosome 14q12 in a large family with dominantly inherited atrial septal defect. The underlying mutation is a missense substitution, I820N, in alpha-myosin heavy chain (MYH6), a structural protein expressed at high levels in the developing atria, which affects the binding of the heavy chain to its regulatory light chain. The cardiac transcription factor TBX5 strongly regulates expression of MYH6, but mutant forms of TBX5, which cause Holt-Oram syndrome, do not. Morpholino knock-down of expression of the chick MYH6 homolog eliminates the formation of the atrial septum without overtly affecting atrial chamber formation. These data provide evidence for a link between a transcription factor, a structural protein and congenital heart disease.

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Uniquely, the asynchronous flight muscle myofibrils of many insects contain arthrin, a stable 1:1 conjugate between actin and ubiquitin. The function of arthrin is still unknown. Here we survey for the presence of arthrin in 63 species of insect across nine orders using Western blotting. Analysis of the evolutionary distribution shows that arthrin has evolved a limited number of times but at least once in the Diptera and once in the Hemiptera. However, the presence of arthrin does not correlate with any observed common features of flight mechanism, natural history, or morphology. We also identify the site of the isopeptide bond in arthrin from Drosophila melanogaster (Diptera) and Lethocerus griseus (Hemiptera) using mass spectrometry. In both species, the isopeptide bond is formed between lysine 118 of the actin and the C-terminal glycine 76 of ubiquitin. Thus, not only the ubiquitination of actin but also the site of the isopeptide bond has evolved convergently in Diptera and Hemiptera. In terms of the actin monomer, lysine 118 is near neither the binding sites of the major actin-binding proteins, myosin, tropomyosin, or the troponins, nor the actin polymerization sites. However, molecular modeling supports the idea that ubiquitin bound to an actin in one F-actin strand might be able to interact with tropomyosin bound to the actin monomers of the other strand and thereby interfere with thin filament regulation.

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Central to the study of a complex dynamical system is knowledge of its phase space behavior. Experimentally, it is rarely possible to record a system's (multidimensional) phase space variables. Rather, the system is observed via one (or few) scalar-valued signal(s) of emission or response. In dynamical systems analysis, the multidimensional phase space of a system can be reconstructed by manipulation of a one-dimensional signal. The trick is in the construction of a (higher-dimensional) space through the use of a time lag (or delay) on the signal time series. The trajectory in this embedding space can then be examined using phase portraits generated in selected subspaces. By contrast, in computer simulation, one has an embarrassment of riches: direct access to the complete multidimensional phase space variables, at arbitrary time resolution and precision. Here, the problem is one of reducing the dimensionality to make analysis tractable. This can be achieved through linear or nonlinear projection of the trajectory into subspaces containing high information content. This study considers trajectories of the small protein crambin from molecular dynamics simulations. The phase space behavior is examined using principal component analysis on the Cartesian coordinate covariance matrix of 138 dimensions. In addition, the phase space is reconstructed from a one dimensional signal, representing the radius of gyration of the structure along the trajectory. Comparison of low-dimensional phase portraits obtained from the two methods shows that the complete phase space distribution is well represented by the reconstruction. The study suggests that it may be possible to develop a deeper connection between the experimental and simulated dynamics of biomolecules via phase space reconstruction using data emerging from recent advances in single-molecule time-resolved biophysical techniques.

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