miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

Protein language model-based prediction for plant miRNA encoded peptides

Plant miRNA encoded peptides (miPEPs), which are short peptides derived from small open reading frames within primary miRNAs, play a crucial role in regulating diverse plant traits. Plant miPEPs identification is challenging due to limitations in the available number of known miPEPs for training. Existing prediction methods rely on manually encoded features, including miPEPPred-FRL, to infer plant miPEPs. Recent advances in deep learning modeling of protein sequences provide an opportunity to improve the representation of key features, leveraging large datasets of protein sequences. In this study, we propose an accurate prediction model, called pLM4PEP, which integrates ESM2 peptide embedding with machine learning methods. Our model not only demonstrates precise identification capabilities for plant miPEPs, but also achieves remarkable results across diverse datasets that include other bioactive peptides. The source codes, datasets of pLM4PEP are available at https://github.com/xialab-ahu/pLM4PEP.

Identification of multiple miRNA-encoded peptide reveals OsmiPEP162a putatively stabilizes OsMIR162 in rice

KEY MESSAGE

MiPEPs regulate growth, development and stress response. Identification of rice miPEPs plays a crucial role in elucidation of molecular functions of rice miPEPs and rice genetic improvement. MicroRNAs (miRNAs) are derivatives of primary miRNAs (pri-miRNAs) and govern the expression of target genes. Plant pri-miRNAs encode regulatory peptides known as miPEPs, which specifically boost the transcription of their originating pri-miRNA. Although there are hundreds of pri-miRNAs in rice, research on whether they encode functional peptides is limited. In this study, we identified 10 miPEPs using a transient protoplast expression system. Among these, we focused our attention on OsmiPEP162a, which influences growth. OsmiPEP162a-edited plants exhibited reduced plant height, similar to mature OsmiR162-edited plants. Transcriptome-focused molecular analysis unveiled significant alterations in transcription profiles following the depletion of OsmiPEP162a. In addition, knocking out OsmiPEP162a led to decreased expression levels of mature OsMIR162a and OsMIR162b. This study suggests that OsmiPEP162a potentially plays a crucial role in stabilizing mature OsMIR162.

Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis

Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in silico mutational scanning for all possible single mutations, along with 2 μs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. In sum, our findings highlight the potential of MuMi as a reliable and computationally efficient tool for predicting protein fitness landscapes, offering significant advantages over traditional methods. The methodologies and results presented in this study pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.