Cooperative dynamics of proteins unraveled by network models

Revisiting a classic carbocation - DFT, coupled-cluster, and ab initio molecular dynamics computations on barbaralyl cation formation and rearrangements

Density functional theory computations were used to model the formation and rearrangement of the barbaralyl cation (C9H+ 9). Two highly delocalized minima were located for C9H+ 9, one of C s symmetry and the other of D 3h symmetry, with the former having lower energy. Quantum chemistry-based NMR predictions affirm that the lower energy structure is the best match with experimental spectra. Partial scrambling was found to proceed through a C 2 symmetric transition structure associated with a barrier of only 2.3 kcal mol-1. The full scrambling was found to involve a C 2v symmetric transition structure associated with a 5.0 kcal mol-1 barrier. Ab initio molecular dynamics simulations initiated from the D 3h C9H+ 9 structure revealed its connection to six minima, due to the six-fold symmetry of the potential energy surface. The effects of tunneling and boron substitution on this complex reaction network were also examined.

Pnictogen bonding in imide derivatives for chiral folding and self-assembly

Pnictogen bonding (PnB) is an attraction interaction that originates from the anisotropic distribution of electron density of pnictogen elements, which however has been rarely found in nitrogen atoms. In this work, for the first time, we unveil the general presence of N-involved PnB in aromatic or aliphatic imide groups and reveal its implications in chiral self-assembly of folding. This long-neglected interaction was consolidated by Cambridge structural database (CSD) searching as well as subsequent computational studies. Though the presence of PnB has limited effects on spectroscopic properties in the solution phase, conformation locking effects are sufficiently expressed in the chiral folding and self-assembly behavior. PnB anchors the chiral conformation to control the emergence and inversion of chiroptical signals, while intramolecular PnB induces the formation of supramolecular tilt chirality. It also enables the chiral folding of imide-containing amino acid or peptide derivatives, which induces the formation of unique secondary structural sequences such as β-sheets. Finally, the effects of PnB in directing folded helical structures were revealed. Examples of cysteine and cystine derivatives containing multiple N⋯O and N⋯S PnBs constitute an α-helix like secondary structure with characteristic circular dichroism. This work discloses the comprehensive existence of imide-involved PnB, illustrates its important role in folding and self-assembly, and sheds light on the rational fabrication of conformation-locked compounds and polymers with controllable chiroptical activities.

Designing solvent systems using self-evolving solubility databases and graph neural networks

Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11 637 experimental solubilities to >900 000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal mol-1 for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.

Discovery of Novel Inhibitors of BRD4 for Treating Prostate Cancer: A Comprehensive Case Study for Considering Water Networks in Virtual Screening and Drug Design

Androgen receptor (AR) is the primary target for treating prostate cancer (PCa), which inevitably progresses due to drug-resistant mutations. Bromodomain-containing protein 4 (BRD4) has been a new potential drug target for PCa treatment. Herein, we report the rational design and discovery of novel BRD4 inhibitors through computer-aided drug design (CADD), and a hit compound SQ-1 (IC50 = 676 nM) was identified by structure-based virtual screening (SBVS) with the conserved water network. To optimize the structure of SQ-1, the free energy landscape was constructed, and the binding mechanism was explored by characterizing the water profile and the dissociation mechanism. Finally, the compound SQ-17 with improved inhibitory activity (IC50 < 100 nM) was discovered, which showed potent antiproliferative activity against LNCaP. These data highlighted a successful attempt to identify and optimize a small molecule by comprehensive CADD application and provided essential clues for developing novel therapeutics for PCa treatment.

Cooperative mechanics of PR65 scaffold underlies the allosteric regulation of the phosphatase PP2A

PR65, a horseshoe-shaped scaffold composed of 15 HEAT (observed in Huntingtin, elongation factor 3, protein phosphatase 2A, and the yeast kinase TOR1) repeats, forms, together with catalytic and regulatory subunits, the heterotrimeric protein phosphatase PP2A. We examined the role of PR65 in enabling PP2A enzymatic activity with computations at various levels of complexity, including hybrid approaches that combine full-atomic and elastic network models. Our study points to the high flexibility of this scaffold allowing for end-to-end distance fluctuations of 40-50 Å between compact and extended conformations. Notably, the intrinsic dynamics of PR65 facilitates complexation with the catalytic subunit and is retained in the PP2A complex enabling PR65 to engage the two domains of the catalytic subunit and provide the mechanical framework for enzymatic activity, with support from the regulatory subunit. In particular, the intra-repeat coils at the C-terminal arm play an important role in allosterically mediating the collective dynamics of PP2A, pointing to target sites for modulating PR65 function.

Sarcin/Ricin Domain RNA Retains Its Structure Better Than A-RNA in Adaptively Biased Molecular Dynamics Simulations

Less than one in thirty of the RNA sequences transcribed in humans are translated into protein. The noncoding RNA (ncRNA) functions in catalysis, structure, regulation, and more. However, for the most part, these functions are poorly characterized. RNA is modular and described by motifs that include helical A-RNA with canonical Watson-Crick base-pairing as well as structures with only noncanonical base pairs. Understanding the structure and dynamics of motifs will aid in deciphering functions of specific ncRNAs. We present computational studies on a standard sarcin/ricin domain (SRD), citrus bark cracking viroid SRD, as well as A-RNA. We have applied enhanced molecular dynamics techniques that construct an inverse free-energy surface (iFES) determined by collective variables that monitor base-pairing and backbone conformation. Each SRD RNA is flanked on each side by A-RNA, allowing comparison of the behavior of these motifs in the same molecule. The RNA iFESs have single peaks, indicating that the combined motifs should denature as a single cohesive unit, rather than by regional melting. Local root-mean-square deviation (RMSD) analysis and communication propensity (CProp, variance in distances between residue pairs) reveal distinct motif properties. Our analysis indicates that the standard SRD is more stable than the viroid SRD, which is more stable than A-RNA. Base pairs at SRD to A-RNA transitions have limited flexibility. Application of CProp reveals extraordinary stiffness of the SRD, allowing residues on opposite sides of the motif to sense each other's motions.

Soft Vibrational Modes Predict Breaking Events during Force-Induced Protein Unfolding

We investigate the correlation between soft vibrational modes and unfolding events in simulated force spectroscopy of proteins. Unfolding trajectories are obtained from molecular dynamics simulations of a Gō model of a monomer of a mutant of superoxide dismutase 1 protein containing all heavy atoms in the protein, and a normal mode analysis is performed based on the anisotropic network model. We show that a softness map constructed from the superposition of the amplitudes of localized soft modes correlates with unfolding events at different stages of the unfolding process. Soft residues are up to eight times more likely to undergo disruption of native structure than the average amino acid. The memory of the softness map is retained for extensions of up to several nanometers, but decorrelates more rapidly during force drops.

The anisotropic network model web server at 2015 (ANM 2.0)

SUMMARY

The anisotropic network model (ANM) is one of the simplest yet powerful tools for exploring protein dynamics. Its main utility is to predict and visualize the collective motions of large complexes and assemblies near their equilibrium structures. The ANM server, introduced by us in 2006 helped making this tool more accessible to non-sophisticated users. We now provide a new version (ANM 2.0), which allows inclusion of nucleic acids and ligands in the network model and thus enables the investigation of the collective motions of protein-DNA/RNA and -ligand systems. The new version offers the flexibility of defining the system nodes and the interaction types and cutoffs. It also includes extensive improvements in hardware, software and graphical interfaces.

AVAILABILITY AND IMPLEMENTATION

ANM 2.0 is available at http://anm.csb.pitt.edu

CONTACT

eran.eyal@sheba.health.gov.il, eyal.eran@gmail.com.

Generating conformational transitions using the euclidean distance matrix

Elastic network interpolation (ENI) is an efficient method for generating intermediate conformations between two end protein conformations. Its current formulation uses interatomic distance. We show how this can be generalized to interatomic distances-squared. This generalization is part of an effort to study protein dynamics on the set of positive semidefinite (PSD) matrices, which has a rich mathematical structure. We use lattice structures to test this interpolation scheme, and discuss some limitations observed. We conclude with some suggestions for future research.

Activation of PI3Kα by physiological effectors and by oncogenic mutations: structural and dynamic effects

PI3Kα, a heterodimeric lipid kinase, catalyzes the conversion of phosphoinositide-4,5-bisphosphate (PIP2) to phosphoinositide-3,4,5-trisphosphate (PIP3), a lipid that recruits to the plasma membrane proteins that regulate signaling cascades that control key cellular processes such as cell proliferation, carbohydrate metabolism, cell motility, and apoptosis. PI3Kα is composed of two subunits, p110α and p85, that are activated by binding to phosphorylated receptor tyrosine kinases (RTKs) or their substrates. The gene coding for p110α, PIK3CA, has been found to be mutated in a large number of tumors; these mutations result in increased PI3Kα kinase activity. The structure of the complex of p110α with a fragment of p85 containing the nSH2 and the iSH2 domains has provided valuable information about the mechanisms underlying the physiological activation of PI3Kα and its pathological activation by oncogenic mutations. This review discusses information derived from x-ray diffraction and theoretical calculations regarding the structural and dynamic effects of mutations in four highly mutated regions of PI3K p110α, as well as the proposed mechanisms by which these mutations increase kinase activity. During the physiological activation of PI3Kα, the phosphorylated tyrosine of RTKs binds to the nSH2 domain of p85, dislodging an inhibitory interaction between the p85 nSH2 and a loop of the helical domain of p110α. Several of the oncogenic mutations in p110α activate the enzyme by weakening this autoinhibitory interaction. These effects involve structural changes as well as changes in the dynamics of the enzyme. One of the most common p110α mutations, H1047R, activates PI3Kα by a different mechanism: it increases the interaction of the enzyme with the membrane, maximizing the access of the PI3Kα to its substrate PIP2, a membrane lipid.