Genetic Algorithm Approach for the Optimization of Protein Antifreeze Activity Using Molecular Simulations

Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies

Nanobody (Nb)-induced disassembly of surface array protein (Sap) S-layers, a two-dimensional paracrystalline protein lattice from Bacillus anthracis, has been presented as a therapeutic intervention for lethal anthrax infections. However, only a subset of existing Nbs with affinity to Sap exhibit depolymerization activity, suggesting that affinity and epitope recognition are not enough to explain inhibitory activity. In this study, we performed all-atom molecular dynamics simulations of each Nb bound to the Sap binding site and trained a collection of machine learning classifiers to predict whether each Nb induces depolymerization. We used feature importance analysis to filter out unnecessary features and engineered remaining features to regularize the feature landscape and encourage learning of the depolymerization mechanism. We find that, while not enforced in training, a gradient-boosting decision tree is able to reproduce the experimental activities of inhibitory Nbs while maintaining high classification accuracy, whereas neural networks were only able to discriminate between classes. Further feature analysis revealed that inhibitory Nbs restrain Sap motions toward an inhibitory conformational state described by domain-domain clamping and induced twisting of domains normal to the lattice plane. We believe these motions drive Sap lattice depolymerization and can be used as design targets for improved Sap-inhibitory Nbs. Finally, we expect our method of study to apply to S-layers that serve as virulence factors in other pathogens, paving the way forward for Nb therapeutics that target depolymerization mechanisms.

Recent Advances in Antifreeze Peptide Preparation: A Review

Antifreeze agents play a critical role in various fields including tissue engineering, gene therapy, therapeutic protein production, and transplantation. Commonly used antifreeze agents such as DMSO and other organic substances are known to have cytotoxic effects. Antifreeze proteins sourced from cold-adapted organisms offer a promising solution by inhibiting ice crystal formation; however, their effectiveness is hindered by a dynamic ice-shaping (DIS) effect and thermal hysteresis (TH) properties. In response to these limitations, antifreeze peptides (AFPs) have been developed as alternatives to antifreeze proteins, providing similar antifreeze properties without the associated drawbacks. This review explores the methods for acquiring AFPs, with a particular emphasis on chemical synthesis. It aims to offer valuable insights and practical implications to drive the realm of sub-zero storage.

Computational Design of Peptides for Biomaterials Applications

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. We show that using a self-termination method with a minimum Spearman's rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

Setmelanotide optimization through fragment-growing, molecular docking in-silico method targeting MC4 receptor

Obesity has emerged as a global issue, but with the complex structures of multiple related important targets and their agonists or antagonists determined, the mechanism of ligand-protein interaction may offer new chances for developing new generation agonists anti-obesity. Based on the molecule surface of the cryo-EM protein structure 7AUE, we tried to replace D-Ala3 with D-Met in setmelanotide as the linker site for fragment-growing with De novo evolution. The simulation results indicate that the derivatives could improve the binding abilities with the melanocortin 4 receptor and the selectivity over the melanocortin 1 receptor. The improved selectivity of the newly designed derivatives is mainly due to the shape difference of the molecular surface at the orthosteric peptide-binding pocket between melanocortin 4 receptor and melanocortin 1 receptor. The new extended fragments could not only enhance the binding affinities but also function as a gripper to seize the pore, making it easier to balance and stabilize the other component of the new derivatives. Although it is challenging to synthesize the compounds designed in silico, this study may perhaps serve as a trigger for additional anti-obesity research.Communicated by Ramaswamy H. Sarma.