AlphaFold2 in Molecular Discovery

Identification, saltiness-enhancing effect, and antioxidant properties of novel saltiness-enhancing peptides from peanut protein

In order to reduce the use of traditional salt (NaCl), this study aimed to rapidly identify novel peptides with salt-reducing effects from peanut protein. Four potential peptides were identified through virtual screening and molecular docking. The sensory evaluation and electronic tongue confirmed that the peptides SPDIY, DPSPR, QPGDY, and SPPGER had significant saltiness-enhancing effects, with saltiness enhancement thresholds ranging from 0.16 to 0.64 mmol L-1. Among them, DPSPR exhibited the most pronounced effect in enhancing saltiness, capable of replacing approximately 56.7% of NaCl. Molecular docking and dynamics simulation studies indicated that amino acid residues Arg272, Glu161, Gln279, Arg168, and Ser165 were found to play key roles in ligand-receptor binding. Additionally, antioxidant activity assays demonstrated that the peptide QPGDY contributed to free radical scavenging in a dose-dependent manner through the hydrogen atom transfer mechanism. The combination of virtual screening technology and experimental validation greatly improved the efficiency and accuracy of peptide discovery and functional characterization, offering a promising strategy for the development of low-sodium foods with antioxidant properties.

Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells

Macromolecular crowding in the cellular cytoplasm can potentially impact diffusion rates of proteins, their intrinsic structural stability, binding of proteins to their corresponding partners as well as biomolecular organization and phase separation. While such intracellular crowding can have a large impact on biomolecular structure and function, the molecular mechanisms and driving forces that determine the effect of crowding on dynamics and conformations of macromolecules are so far not well understood. At a molecular level, computational methods can provide a unique lens to investigate the effect of macromolecular crowding on biomolecular behavior, providing us with a resolution that is challenging to reach with experimental techniques alone. In this review, we focus on the various physics-based and data-driven computational methods developed in the past few years to investigate macromolecular crowding and intracellular protein condensation. We review recent progress in modeling and simulation of biomolecular systems of varying sizes, ranging from single protein molecules to the entire cellular cytoplasm. We further discuss the effects of macromolecular crowding on different phenomena, such as diffusion, protein-ligand binding, and mechanical and viscoelastic properties, such as surface tension of condensates. Finally, we discuss some of the outstanding challenges that we anticipate the community addressing in the next few years in order to investigate biological phenomena in model cellular environments by reproducing in vivo conditions as accurately as possible.

Genomic Analysis Reveals Novel Genes and Adaptive Mechanisms for Artificial Diet Utilization in the Silkworm Strain Guican No.5

The transition from traditional mulberry leaf feeding to artificial diet cultivation represents a major advancement in modern sericulture, yet the genetic mechanisms driving this adaptation remain largely unexplored. This study investigates the genomic basis of artificial diet adaptation in the silkworm strain Guican No.5 through whole-genome resequencing and transcriptome analysis. We identified 8,935,179 single-nucleotide polymorphisms (SNPs) across all chromosomes, accounting for 2.01% of the genome, with particularly high densities observed in chromosomes 23, 26, and 28. Our analysis also revealed 879 novel transcripts, many of which are involved in digestion, detoxification, and stress response pathways. Key novel genes, including three carboxylesterases, two cytochrome P450s, one heat shock protein, and one copper/zinc superoxide dismutase, exhibited varying degrees of sequence similarity to known proteins, suggesting modifications to existing genetic frameworks. Notably, one novel P450 gene displayed only 74.07% sequence identity with its closest homolog, indicating the emergence of a new protein sequence. Additionally, several key genes showed high similarity to wild silkworm (Bombyx mandarina) proteins, underscoring their evolutionary origins. These findings provide valuable insights into the molecular mechanisms underpinning artificial diet adaptation in silkworms and offer genomic resources to enhance artificial diet formulations and breeding programs in sericulture.

Evaluation of AlphaFold 3's Protein-Protein Complexes for Predicting Binding Free Energy Changes upon Mutation

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step toward comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. In this work, we evaluate AF3 complex structures using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations. AF3 complex structures when applied to the most advanced TDL model, MT-TopLap (MultiTask-Topological Laplacian), give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, which is slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.