Biology subjects

Mier, P.

Publications and source records attributed to Mier, P..

4 recordsLinked to original sources

bioRxiv2025

Unraveling cooperative and competitive interactions within protein triplets in the human interactome

Knowledge about protein-protein interactions (PPIs) is crucial for our understanding of cellular functions. An important contribution to these comes from experimental high-throughput techniques such as yeast two-hybrid (Y2H), which provide evidence for direct, binary PPIs. Consequently, although protein function often emerges from interactions within multi-protein assemblies, most PPI networks focus on binary relationships. Higher-order motifs, such as protein triplets, can reflect cooperative or competitive binding events crucial for cellular function. However, distinguishing these interaction types systematically from a network of binary PPIs remains challenging. To address this issue, we present a computational framework to predict cooperative and competitive interactions among protein triplets in the human Protein Interaction Network (hPIN). By embedding the hPIN into two-dimensional hyperbolic space using the LaBNE+HM algorithm, we extracted latent geometric coordinates for each protein. Combined with topological features and biological annotations, these were used to train a Random Forest classifier on structurally validated triplets from Interactome3D. Our model achieved strong performance (AUC = 0.88), identifying angular and hyperbolic distances as two of the most predictive features. Cooperative triplets were characterized by longer common proteins and a lower frequency of paralogous partners, indicating distinct structural and evolutionary features compared to competitive cases. Structural evaluation using AlphaFold 3 confirmed that cooperative triplets exhibit distinct binding sites, whereas competitive ones share overlapping interfaces. Hyperbolic embeddings, in combination with machine learning, offer a powerful approach to characterize higher-order interaction motifs in protein networks. Our findings provide mechanistic insights into the organization of protein complexes and a predictive framework to support future studies in network biology.

bioinformatics↗

bioRxiv2022

Statistical tests to detect differences between codon-specific Ramachandran plots

In their recent work, Rosenberg et al. [1] studied the dependence between the identity of synonymous codons and the distribution of the backbone dihedral angles of the translated amino acids. In the past, it has been shown that the use of synonymous codons is highly relevant in multiple biological processes including, among others, mRNA splicing, translational rates and protein folding [2, 3]. While the correlation between synonymous codons and secondary structure in translated proteins has been widely studied [4-6], Rosenberg et al. evaluated the effect of codon identity on a finer scale, analyzing whether the distribution of ({phi}, {psi}) dihedral angles within secondary structure elements is significantly altered when synonymous codons are used. Their conclusion, showing significant differences, particularly for amino acid residues involved in {beta}-strands, would represent a new paradigm for the role played by synonymous codons in defining protein structure. However, the statistical methodology used in that study was formally incorrect, casting doubt on the obtained results. Besides, it is based on density estimates that might be imprecise for small sample sizes, yielding misleading comparisons. These methodological errors are described in the following section. Then, using an appropriate methodology, we reanalyzed the data presented in [1]. Our results show that the influence of the codon on the distribution of the dihedral angles is statistically non-significant for all types of secondary structures, contradicting the conclusion by Rosenberg et al.. These results were corroborated by repeating the analysis on structures extracted from the AlphaFold Database [7, 8] for the same set of proteins, and shown to be robust with respect to the definition secondary structural classes and also when considering the nature of the neighbor residues. Overall, our observations demonstrate that the influence of the synonymous codons on the backbone dihedral angles can not be inferred with current data.

bioinformatics↗

bioRxiv2021

Identification of functional residues using machine learning provides insights into the evolution of odorant receptor gene families in solitary and social insects

The gene family of insect odorant receptors (ORs) has greatly expanded in the course of evolution. ORs allow insects to detect volatile chemicals and therefore play an important role in social interactions, the detection of enemies and preys, and during foraging. The sequences of several thousand ORs are known, but their specific function or ligands have been identified only for very few of them. To advance the functional characterization of ORs, we compiled, curated and aligned the sequences of 3,902 ORs from 21 insect species. We identified the amino acid positions that best predict the response to ligands using machine learning on sets of functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator. We studied the conservation of these predicted relevant residues across all OR subfamilies and show that the subfamilies that expanded strongly in social insects exhibit high levels of conservation in their binding sites. This indicates that ORs of social insect families are typically finely tuned and exhibit a sensitivity to very similar odorants. Our novel approach provides a powerful tool to use functional information from a limited number of genes to investigate the functional evolution of large gene families.

bioinformatics↗

bioRxiv2017

The latent geometry of the human protein interaction network

To mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

systems biology↗