Measures to evaluate heteroaromaticity and their limitations: story of skeletally substituted benzenes

Ab initio HF, MP2, CCSD(T) and hybrid density functional B3LYP calculations were performed on a series of skeletally mono- and di-substituted benzenes, (CH)5Z and (CH)4Z2, Z = C-, N, O+, Si-, P, S+, Ge-, As, Se+, BH-, NH+, AlH-, SiH, PH+, GaH-, GeH and AsH+. Various measures of aromaticity such as the bond length equalization, homodesmic equations, singlet-triplet energy difference (DEs-t), chemical hardness (η) and out-of-plane distortive tendency are critically analysed. The relative energy ordering in skeletally disubstituted benzenes displays trends that are inexplicable based on conventional wisdom. In general, the orthoisomer is found to be the least stable when the substituent is from the second row, whereas if the substituent is from the fourth row, the ortho-isomer is the most stable. Various qualitative arguments, including (a) lone pair-lone pair repulsion, (b) the sum of bond strengths in the twin Kekule forms, and (c) the rule of topological charge stabilization (TCS), are used to explain the observed relative energy trends. The rule of TCS in conjunction with the sum of bond strengths is found to predict the relative energy ordering reasonably well. The reactivity of this class of compounds is assessed based on their singlet-triplet energy differences, chemical hardness and the frequencies corresponding to out-of-plane skeletal distortions. These reactivity indices show less kinetic stability for the compounds with substituents from the fourth row and point to the fact that the thermodynamically most stable compounds need not be the least reactive ones. The Δ Es-t values indicate that the Π-framework of benzene weakens upon skeletal substitutions

Dynamic ligand-based pharmacophore modeling and virtual screening to identify mycobacterial cyclopropane synthase inhibitors

Multidrug resistance in Mycobacterium tuberculosis (M. Tb) and its coexistence with HIV are the biggest therapeutic challenges in anti-M. Tb drug discovery. The current study reports a Virtual Screening (VS) strategy to identify potential inhibitors of Mycobacterial cyclopropane synthase (CmaA1), an important M. Tb target considering the above challenges. Five ligand-based pharmacophore models were generated from 40 different conformations of the cofactors of CmaA1 taken from molecular dynamics (MD) simulations trajectories of CmaA1. The screening abilities of these models were validated by screening 23 inhibitors and 1398 non-inhibitors of CmaA1. A VS protocol was designed with four levels of screening i.e., ligand-based pharmacophore screening, structure-based pharmacophore screening, docking and absorption, distribution, metabolism, excretion and the toxicity (ADMET) filters. In an attempt towards repurposing the existing drugs to inhibit CmaA1, 6,429 drugs reported in DrugBank were considered for screening. To find compounds that inhibit multiple targets of M. Tb as well as HIV, we also chose 701 and 11,109 compounds showing activity below 1 μM range on M. Tb and HIV cell lines, respectively, collected from ChEMBL database. Thus, a total of 18,239 compounds were screened against CmaA1, and 12 compounds were identified as potential hits for CmaA1 at the end of the fourth step. Detailed analysis of the structures revealed these compounds to interact with key active site residues of CmaA1

The bicyclo[2.1.1]hexan-2-one system: a new probe for the experimental and computational study of electronic effects in π-facial selectivity in nucleophilic additions

The remotely substituted 5-exo-bicyclo[2.1.1]hexan-2-one system is introduced as a new probe to study long range electronic effects on π -face selectivity during hydride reduction and a systematic computational study demonstrates good predictability at the semi-empirical level

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities

Characterization of ERK Docking Domain Inhibitors that Induce Apoptosis by Targeting Rsk-1 and Caspase-9

<p>Abstract</p> <p>Background</p> <p>The extracellular signal-regulated kinase-1 and 2 (ERK1/2) proteins play an important role in cancer cell proliferation and survival. ERK1/2 proteins also are important for normal cell functions. Thus, anti-cancer therapies that block all ERK1/2 signaling may result in undesirable toxicity to normal cells. As an alternative, we have used computational and biological approaches to identify low-molecular weight compounds that have the potential to interact with unique ERK1/2 docking sites and selectively inhibit interactions with substrates involved in promoting cell proliferation.</p> <p>Methods</p> <p>Colony formation and water soluble tetrazolium salt (WST) assays were used to determine the effects of test compounds on cell proliferation. Changes in phosphorylation and protein expression in response to test compound treatment were examined by immunoblotting and <it>in vitro </it>kinase assays. Apoptosis was determined with immunoblotting and caspase activity assays.</p> <p>Results</p> <p><it>In silico </it>modeling was used to identify compounds that were structurally similar to a previously identified parent compound, called <b>76</b>. From this screen, several compounds, termed <b>76.2</b>, <b>76.3</b>, and <b>76.4 </b>sharing a common thiazolidinedione core with an aminoethyl side group, inhibited proliferation and induced apoptosis of HeLa cells. However, the active compounds were less effective in inhibiting proliferation or inducing apoptosis in non-transformed epithelial cells. Induction of HeLa cell apoptosis appeared to be through intrinsic mechanisms involving caspase-9 activation and decreased phosphorylation of the pro-apoptotic Bad protein. Cell-based and <it>in vitro </it>kinase assays indicated that compounds <b>76.3 </b>and <b>76.4 </b>directly inhibited ERK-mediated phosphorylation of caspase-9 and the p90Rsk-1 kinase, which phosphorylates and inhibits Bad, more effectively than the parent compound <b>76</b>. Further examination of the test compound's mechanism of action showed little effects on related MAP kinases or other cell survival proteins.</p> <p>Conclusion</p> <p>These findings support the identification of a class of ERK-targeted molecules that can induce apoptosis in transformed cells by inhibiting ERK-mediated phosphorylation and inactivation of pro-apoptotic proteins.</p

BiRDS - Binding Residue Detection from Protein Sequences using Deep ResNets

Protein-drug interactions play important roles in many biological processes and therapeutics. Prediction of the active binding site of a protein helps discover and optimise these interactions leading to the design of better ligand molecules. The tertiary structure of a protein determines the binding sites available to the drug molecule. A quick and accurate prediction of the binding site from sequence alone without utilising the three-dimensional structure is challenging. Deep Learning has been used in a variety of biochemical tasks and has been hugely successful. In this paper, a Residual Neural Network (leveraging skip connections) is implemented to predict a protein\u27s most active binding site. An Annotated Database of Druggable Binding Sites from the Protein DataBank, sc-PDB, is used for training the network. Features extracted from the Multiple Sequence Alignments (MSAs) of the protein generated using DeepMSA, such as Position-Specific Scoring Matrix (PSSM), Secondary Structure (SS3), and Relative Solvent Accessibility (RSA), are provided as input to the network. A weighted binary cross-entropy loss function is used to counter the substantial imbalance in the two classes of binding and non-binding residues. The network performs very well on single-chain proteins, providing a pocket that has good interactions with a ligand

TorRNA - Improved Prediction of Backbone Torsion Angles of RNA by Leveraging Large Language Models

RNA molecules play a significant role in many biological pathways and have diverse functional roles, which is a result of their structural flexibility to fold into diverse conformations. This structural flexibility makes it challenging to obtain the structures of RNAs experimentally. Deep learning can be used to predict the secondary structures of RNA and other properties such as the backbone torsion angles, to be used as restraints for the computational optimization of the tertiary structures of RNA. TorRNA is a transformer encoder-decoder model, that takes an input RNA sequence and predicts the (pseudo)torsion angles of each nucleotide with a pre-trained RNA-FM model as the encoder. TorRNA is able to achieve a performance boost of 2% − 16% over the previous (pseudo)torsion angle prediction method for RNAs. We also demonstrate that TorRNA can used as a tool for model quality assessment of candidate RNA structures