Amyloidogenesis Abolished by Proline Substitutions but Enhanced by Lipid Binding

The influence of lipid molecules on the aggregation of a highly amyloidogenic segment of human islet amyloid polypeptide, hIAPP20–29, and the corresponding sequence from rat has been studied by all-atom replica exchange molecular dynamics (REMD) simulations with explicit solvent model. hIAPP20–29 fragments aggregate into partially ordered β-sheet oligomers and then undergo large conformational reorganization and convert into parallel/antiparallel β-sheet oligomers in mixed in-register and out-of-register patterns. The hydrophobic interaction between lipid tails and residues at positions 23–25 is found to stabilize the ordered β-sheet structure, indicating a catalysis role of lipid molecules in hIAPP20–29 self-assembly. The rat IAPP variants with three proline residues maintain unstructured micelle-like oligomers, which is consistent with non-amyloidogenic behavior observed in experimental studies. Our study provides the atomic resolution descriptions of the catalytic function of lipid molecules on the aggregation of IAPP peptides

Ligand Binding Induces Agonistic-Like Conformational Adaptations in Helix 12 of Progesterone Receptor Ligand Binding Domain

Progesterone receptor (PR) is a member of the nuclear receptor (NR) superfamily and plays a vital role in the female reproductive system. The malfunction of it would lead to several types of cancers. The understanding of conformational changes in its ligand binding domain (LBD) is valuable for both biological function studies and therapeutically intervenes. A key unsolved question is how the binding of a ligand (agonist, antagonist, or a selective modulator) induces conformational changes of PR LBD, especially its helix 12. We applied molecular dynamics (MD) simulations to explore the conformational adaptations of PR LBD with or without a ligand or the co-repressor peptides binding. From the simulations, both the agonist progesterone (P4) and the selective PR modulator (SPRM) asoprisnil induces agonistic-like helix 12 conformations (the “closed” states) in PR LBD and the complex of LBD-SPRM is less stable, comparing to the agonist-liganded PR LBD. The results, therefore, explain the partial agonism of the SPRM, which could induce weak agonistic effects in PR. We also found that co-repressor peptides could be stably associated with the LBD and stabilize the LBD in a “semi-open” state for helix 12. These findings would enhance our understanding of PR structural and functional relationships and would also be useful for future structure and knowledge-based drug discovery

Molecular Dynamics Simulation of Ligand Dissociation from Liver Fatty Acid Binding Protein

The mechanisms of how ligands enter and leave the binding cavity of fatty acid binding proteins (FABPs) have been a puzzling question over decades. Liver fatty acid binding protein (LFABP) is a unique family member which accommodates two molecules of fatty acids in its cavity and exhibits the capability of interacting with a variety of ligands with different chemical structures and properties. Investigating the ligand dissociation processes of LFABP is thus a quite interesting topic, which however is rather difficult for both experimental approaches and ordinary simulation strategies. In the current study, random expulsion molecular dynamics simulation, which accelerates ligand motions for rapid dissociation, was used to explore the potential egress routes of ligands from LFABP. The results showed that the previously hypothesized “portal region” could be readily used for the dissociation of ligands at both the low affinity site and the high affinity site. Besides, one alternative portal was shown to be highly favorable for ligand egress from the high affinity site and be related to the unique structural feature of LFABP. This result lends strong support to the hypothesis from the previous NMR exchange studies, which in turn indicates an important role for this alternative portal. Another less favored potential portal located near the N-terminal end was also identified. Identification of the dissociation pathways will allow further mechanistic understanding of fatty acid uptake and release by computational and/or experimental techniques

SARS-CoV-2 accessory protein 7b forms homotetramers in detergent

A global pandemic is underway caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 genome, like its predecessor SARS-CoV, contains open reading frames that encode accessory proteins involved in virus-host interactions active during infection and which likely contribute to pathogenesis. One of these accessory proteins is 7b, with only 44 (SARS-CoV) and 43 (SARS-CoV-2) residues. It has one predicted transmembrane domain fully conserved, which suggests a functional role, whereas most variability is contained in the predicted cytoplasmic C-terminus. In SARS-CoV, 7b protein is expressed in infected cells, and the transmembrane domain was necessary and sufficient for Golgi localization. Also, anti-p7b antibodies have been found in the sera of SARS-CoV convalescent patients. In the present study, we have investigated the hypothesis that SARS-2 7b protein forms oligomers with ion channel activity. We show that in both SARS viruses 7b is almost completely α-helical and has a single transmembrane domain. In SDS, 7b forms various oligomers, from monomers to tetramers, but only monomers when exposed to reductants. Combination of SDS gel electrophoresis and analytical ultracentrifugation (AUC) in both equilibrium and velocity modes suggests a dimer-tetramer equilibrium, but a monomer–dimer–tetramer equilibrium in the presence of reductant. This data suggests that although disulfide-linked dimers may be present, they are not essential to form tetramers. Inclusion of pentamers or higher oligomers in the SARS-2 7b model were detrimental to fit quality. Preliminary models of this association was generated with AlphaFold2, and two alternative models were exposed to a molecular dynamics simulation in presence of a model lipid membrane. However, neither of the two models provided any evident pathway for ions. To confirm this, SARS-2 p7b was studied using Planar Bilayer Electrophysiology. Addition of p7b to model membranes produced occasional membrane permeabilization, but this was not consistent with bona fide ion channels made of a tetrameric assembly of α-helices

Fast, accurate, and reliable molecular docking with QuickVina 2

Abstract Motivation: The need for efficient molecular docking tools for high-throughput screening is growing alongside the rapid growth of drug-fragment databases. AutoDock Vina (‘Vina’) is a widely used docking tool with parallelization for speed. QuickVina (‘QVina 1’) then further enhanced the speed via a heuristics, requiring high exhaustiveness. With low exhaustiveness, its accuracy was compromised. We present in this article the latest version of QuickVina (‘QVina 2’) that inherits both the speed of QVina 1 and the reliability of the original Vina. Results: We tested the efficacy of QVina 2 on the core set of PDBbind 2014. With the default exhaustiveness level of Vina (i.e. 8), a maximum of 20.49-fold and an average of 2.30-fold acceleration with a correlation coefficient of 0.967 for the first mode and 0.911 for the sum of all modes were attained over the original Vina. A tendency for higher acceleration with increased number of rotatable bonds as the design variables was observed. On the accuracy, Vina wins over QVina 2 on 30% of the data with average energy difference of only 0.58 kcal/mol. On the same dataset, GOLD produced RMSD smaller than 2 Å on 56.9% of the data while QVina 2 attained 63.1%. Availability and implementation: The C++ source code of QVina 2 is available at (www.qvina.org). Contact:  [email protected] Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p

OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity based on Residue-Atom Contacting Shells

One key task in virtual screening is to accurately predict the binding affinity ($\triangle$$G$) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict $\triangle$$G$. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.Comment: 7 pages, 4 figures, 1 tabl

Molecular dynamics simulation of multivalent ion mediated DNA attraction

All atom molecular dynamics simulations with explicit water were done to study the interaction between two parallel double-stranded DNA molecules in the presence of the multivalent counterions putrescine (2+), spermidine (3+), spermine (4+) and cobalt hexamine (3+). The inter-DNA interaction potential is obtained with the umbrella sampling technique. The attractive force is rationalized in terms of the formation of ion bridges, i.e. multivalent ions which are simultaneously bound to the two opposing DNA molecules. The lifetime of the ion bridges is short on the order of a few nanoseconds.Comment: 4 pages, 5 figures, to be published in Physical Review Letter