Modularity-based parallel protein design algorithm with an implementation using shared memory programming

Insights into the recognition mechanism of shark-derived single-domain antibodies with high affinity and specificity targeting fluoroquinolones

In this study, we investigated the molecular recognition mechanisms of shark-derived single-domain antibodies (ssdAbs) targeting fluoroquinolones using an integrated approach that combines in silico homologous modeling, molecular dynamics simulations, molecular docking, and alanine scanning mutagenesis. Three ssdAbs-2E6, 1N9, and 1O17-specific to enrofloxacin, norfloxacin, and ofloxacin, respectively, were selected based on previous work. Through AlphaFold2 and GalaxyWEB, the protein structures of these ssdAbs were predicted and optimized, followed by molecular dynamics simulations to emulate realistic protein behavior in a solvent environment. Molecular docking, alanine scanning mutagenesis, and subsequent verifications identified 30N and 93W of 2E6; 30N, 89R, 98Y, and 99D of 1N9; 100W and 101R of 1O17, all located within the complementarity determining region 3 loop, as critical for antigen binding. These residues primarily interact with their targets through hydrogen bonds, salt bridges, π-π stackings, and cation-π interactions. This study revealed, for the first time, the binding mechanism of ssdAbs to fluoroquinolones from a theoretical perspective, emphasizing the importance of aromatic and polar residues in recognizing characteristic epitopes, such as the carboxyl group at the C3 position and the 1-piperazinyl group at the C7 position. Our findings provide valuable insights for the rational design and enhancement of ssdAbs for detecting small molecule hazards in aquaculture.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-024-00277-3.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.