Guillen-Gosalbez, G. | bioRxiv Science

bioRxiv2025

De novo design of peptides localizing at the interface of biomolecular condensates

The interface of biomolecular condensates has been shown to play an important role in processes such as protein aggregation and biochemical reactions. Targeted modulation of these interfaces could, therefore, serve as an effective strategy for engineering condensates and modifying aberrant behaviors. However, the molecular grammar driving the preferential localization of molecules at condensate interfaces remains largely unknown. In this study, we developed a computational pipeline that combines highthroughput coarse-grained simulations, machine learning, and mixed-integer linear programming to design peptides that selectively partition at the interfaces of specific condensate targets. Using this workflow, we designed and synthesized peptides that localize at the interface of three distinct condensates formed by different intrinsically disordered protein regions (IDRs). These peptides exhibit surfactant-like architectures, with one tail incorporated into the condensate and the other excluded from the dense phase. In all cases, the tail entering the condensates is enriched in aromatic residues, while the sequence of the excluded tail varies among the IDRs. For hnRNPA1- and LAF1-IDRs, the excluded tail is enriched in lysines and matches the net charge of the condensate-forming protein, promoting electrostatic repulsion. In the case of DDX4-IDR, which exhibits the lowest charge density, the excluded tail mainly consists of uncharged valine residues, which exhibit negligible interactions with the scaffold protein. These results highlight the importance of the net charge of the scaffold as a key physicochemical parameter for designing peptides with preferential interfacial localization. Overall, our pipeline represents a promising strategy for the rational design of interface-localizing peptides and the identification of the corresponding molecular grammar. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/653111v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@eab174org.highwire.dtl.DTLVardef@2852d6org.highwire.dtl.DTLVardef@156960corg.highwire.dtl.DTLVardef@1952e1d_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Functional-Hybrid Modeling through automated adaptive symbolic regression for interpretable mathematical expressions

Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.

bioengineering↗