Biology subjects

Ljubetic, A.

Publications and source records attributed to Ljubetic, A..

7 recordsLinked to original sources

bioRxiv2025

De-novo design of a random protein walker

Molecular machines hold great potential. Design of static monomeric and oligomeric protein structures has advanced tremendously, but few dynamic protein systems have been designed. Here we present the design and characterization of a random protein walker that diffuses along a designed protein track. For the track, we designed micro-meter long fibres and developed a method to rigidly decorate them with arbitrary proteins. The walkers consist of homo-oligomers that reversibly bind the track using heterodimeric feet; we tested multiple heterodimer interfaces for reversibility and designed six walkers with different numbers of feet. Cryo-EM experiments confirmed the structure of the track and walkers. We performed detailed single molecule tracking and kinetics characterisation and found that the walkers with more feet diffuse along the track faster. The system represents a tuneable starting point for future powered protein molecular robots.

synthetic biology↗

bioRxiv2025

Single-chain permuted proteins for dimerization-based control of protein activity and cellular processes

Strategies for detecting and controlling protein interactions play a critical role in gaining insight into molecular mechanisms of biological processes and for the control of cellular processes. Conditional protein reconstitution allows control of the selected protein function based on the proximity, defined by the genetically fused domain pairs, which may be regulated by chemical or biological signals. This typically requires two protein components in a stoichiometric ratio, which increases the complexity and genetic footprint with split segments often being unstable and prone to aggregation. To overcome this limitation, we developed an approach based on a permuted protein reconstitution by conditional dimerization (PROPER). According to this strategy, the N- and C-terminal domains of selected proteins are swapped and a loop is replaced by a short linker that prevents the functionality of a monomeric protein, which reconstitutes only upon di- or oligomerization, controlled by a genetically fused domain that dimerizes by a chemical signal or senses a dimeric target. This design principle was demonstrated on three proteins with diverse functions: a protease, luciferase, and a cytokine. We demonstrate chemically and biologically inducible systems that enable controllable induction of cell death, virus detection, and immune cell stimulation. The PROPER platform expands the chemical biology toolbox, with the benefits of split proteins accomplished by a single component.

synthetic biology↗

bioRxiv2023

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

synthetic biology↗

bioRxiv2021

Multivalent designed proteins protect against SARS-CoV-2 variants of concern

Escape variants of SARS-CoV-2 are threatening to prolong the COVID-19 pandemic. To address this challenge, we developed multivalent protein-based minibinders as potential prophylactic and therapeutic agents. Homotrimers of single minibinders and fusions of three distinct minibinders were designed to geometrically match the SARS-CoV-2 spike (S) trimer architecture and were optimized by cell-free expression and found to exhibit virtually no measurable dissociation upon binding. Cryo-electron microscopy (cryoEM) showed that these trivalent minibinders engage all three receptor binding domains on a single S trimer. The top candidates neutralize SARS-CoV-2 variants of concern with IC50 values in the low pM range, resist viral escape, and provide protection in highly vulnerable human ACE2-expressing transgenic mice, both prophylactically and therapeutically. Our integrated workflow promises to accelerate the design of mutationally resilient therapeutics for pandemic preparedness. One-Sentence SummaryWe designed, developed, and characterized potent, trivalent miniprotein binders that provide prophylactic and therapeutic protection against emerging SARS-CoV-2 variants of concern.

synthetic biology↗

bioRxiv2021

Interpreting Neural Networks for Biological Sequences by Learning Stochastic Masks

Sequence-based neural networks can learn to make accurate predictions from large biological datasets, but model interpretation remains challenging. Many existing feature attribution methods are optimized for continuous rather than discrete input patterns and assess individual feature importance in isolation, making them ill-suited for interpreting non-linear interactions in molecular sequences. Building on work in computer vision and natural language processing, we developed an approach based on deep generative modeling - Scrambler networks - wherein the most salient sequence positions are identified with learned input masks. Scramblers learn to generate Position-Specific Scoring Matrices (PSSMs) where unimportant nucleotides or residues are scrambled by raising their entropy. We apply Scramblers to interpret the effects of genetic variants, uncover non-linear interactions between cis-regulatory elements, explain binding specificity for protein-protein interactions, and identify structural determinants of de novo designed proteins. We show that interpretation based on a generative model allows for efficient attribution across large datasets and results in high-quality explanations, often outperforming state-of-the-art methods.

genomics↗

bioRxiv2021

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

bioinformatics↗

bioRxiv2020

A Multiplexed Bacterial Two-Hybrid for Rapid Characterization of Protein-Protein Interactions and Iterative Protein Design

Myriad biological functions require protein-protein interactions (PPIs), and engineered PPIs are crucial for applications ranging from drug design to synthetic cell circuits. Understanding and engineering specificity in PPIs is particularly challenging as subtle sequence changes can drastically alter specificity. Coiled-coils are small protein domains that have long served as a simple model for studying the sequence-determinants of specificity and have been used as modular building blocks to build large protein nanostructures and synthetic circuits. Despite their simple rules and long-time use, building large sets of well-behaved orthogonal pairs that can be used together is still challenging because predictions are often inaccurate, and, as the library size increases, it becomes difficult to test predictions at scale. To address these problems, we first developed a method called the Next-Generation Bacterial Two-Hybrid (NGB2H), which combines gene synthesis, a bacterial two-hybrid assay, and a high-throughput next-generation sequencing readout, allowing rapid exploration of interactions of programmed protein libraries in a quantitative and scalable way. After validating the NGB2H system on previously characterized libraries, we designed, built, and tested large sets of orthogonal synthetic coiled-coils. In an iterative set of experiments, we assayed more than 8,000 PPIs, used the dataset to train a novel linear model-based coiled-coil scoring algorithm, and then characterized nearly 18,000 interactions to identify the largest set of orthogonal PPIs to date with twenty-two on-target interactions.

molecular biology↗