Explicit Solvent Molecular Dynamics Simulations of Aβ Peptide Interacting with Ibuprofen Ligands

Using all-atom explicit water model and replica exchange molecular dynamics, we study the interactions between Aβ monomer and nonsteroidal anti-inflammatory drug ibuprofen, which is known to reduce the risk of Alzheimer’s disease. Ibuprofen binding to Aβ is largely governed by hydrophobic effect, and its binding site in Aβ peptide is entirely composed of hydrophobic amino acids. Electrostatic interactions between negatively charged ibuprofen ligands and positively charged side chains make a relatively small contribution to binding. This outcome is explained by the competition of ligand–peptide electrostatic interactions with intrapeptide salt bridges. Consistent with the experiments, the S-isomer of ibuprofen binds with stronger affinity to Aβ than the R-isomer. Conformational ensemble of Aβ monomer in ibuprofen solution reveals two structured regions, 19–25 (R1) and 29–35 (R2), composed of turn/helix and helix structure, respectively. The clustering technique and free energy analysis suggest that Aβ conformational ensemble is mainly determined by the formation of Asp23-Lys28 salt bridge and the hydrophobic interactions between R1 and R2. Control simulations of Aβ peptide in ligand-free water show that ibuprofen binding changes Aβ structure by promoting the formation of helix and Asp23-Lys28 salt bridge. Implications of our findings for Aβ amyloidogenesis are discussed

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors

Biofilm community structure and the associated drag penalties of a groomed fouling release ship hull coating

<p>Grooming is a proactive method to keep a ship’s hull free of fouling. This approach uses a frequent and gentle wiping of the hull surface to prevent the recruitment of fouling organisms. A study was designed to compare the community composition and the drag associated with biofilms formed on a groomed and ungroomed fouling release coating. The groomed biofilms were dominated by members of the Gammaproteobacteria and Alphaproteobacteria as well the diatoms <i>Navicula</i>, <i>Gomphonemopsis</i>, <i>Cocconeis</i>, and <i>Amphora.</i> Ungroomed biofilms were characterized by Phyllobacteriaceae, Xenococcaceae, Rhodobacteraceae, and the pennate diatoms <i>Cyclophora</i>, <i>Cocconeis</i>, and <i>Amphora.</i> The drag forces associated with a groomed biofilm (0.75 ± 0.09 N) were significantly less than the ungroomed biofilm (1.09 ± 0.06 N). Knowledge gained from this study has helped the design of additional testing which will improve grooming tool design, minimizing the growth of biofilms and thus lowering the frictional drag forces associated with groomed surfaces.</p