Biology subjects

Cianferoni, D.

Publications and source records attributed to Cianferoni, D..

5 recordsLinked to original sources

bioRxiv2025

Artificial Intelligence And First Principle Methods In Protein Redesign: A Marriage Of Convenience?

Since AlphaFold2s rise, many deep learning methods for protein design have emerged. Here, we validate widely used and recognized tools, compare them with first-principle methods, and explore their combinations, focusing on their effectiveness in protein redesign and potential for therapeutic repurposing. We address two challenges: evaluating tools and combinations ability to detect the effects of multiple concurrent mutations in protein variants, and leveraging large-scale datasets to compare modeling-free methods, namely force fields, which handle point mutations well with limited backbone rearrangement, and inverse folding tools, which excel at native sequence recovery but may struggle with non-natural proteins. Debuting TriCombine, a tool that identifies residue triangles in input structures, matches them to a structural database, and scores mutants based on substitution frequencies, we shortlisted candidates, modeled them with FoldX, and generated 16 SH3 mutants carrying up to 9 concurrent substitutions. The dataset was expanded to include 36 mutants and 11 crystal structures (7 newly solved), along with a parallel set of multiple non-concurrent mutants from three additional proteins. For broader validation, we analyzed 160,000 four-site GB1 mutants and 163,555 (single and double) variants across 179 natural and de novo domains. We show that combining AI-based modeling tools with force field scoring functions yields the most reliable results. Inverse folding tools perform very well but lose accuracy on less-represented proteins. First-principle force fields like FoldX remain the most accurate for point mutations. All methods perform worse when applied to unsolved de novo models, underscoring the need for hybrid strategies in robust protein design.

bioinformatics↗

bioRxiv2024

De novo design of high-affinity single-domain antibodies

Antibody-based therapeutics have become indispensable in modern medicine, but traditional methods of antibody discovery often present with limitations in developability, cross-reactivity, and ethical concerns. While deep learning and generative approaches have shown promise in the design of high affinity protein binders, de novo antibody design remains challenging. Here, we present EvolveX, a structure-based computational pipeline for designing antibody fragments. EvolveX utilizes ModelX and empirical force field FoldX to optimize complementarity- determining regions (CDRs) and TANGO for aggregation analysis. We demonstrate the ability of EvolveX to redesign a single-domain VHH antibody fragment targeting mouse Vsig4 to address two challenges: enhancing stability and affinity for the original target and redesigning it for high affinity to the human ortholog. The redesigned variants of VHH fragments specific to mouse Vsig4 showed improved physicochemical properties, while retaining binding affinities comparable to the original version. Notably, EvolveX improved the binding affinity of VHHs to human Vsig4 by over 1000-fold, transforming low-affinity binders into nanomolar-affinity molecules. Structural analyses by X-ray crystallography and NMR confirmed the accuracy of the designs, which display optimized interactions with the antigen. NGS and re-modelling analysis further demonstrated the efficiency of FoldX-based design pipeline. Collectively, our study highlights EvolveXs potential to overcome current limitations in antibody design, offering a powerful tool for the development of next-generation therapeutics with enhanced specificity, stability, and efficacy.

bioinformatics↗

bioRxiv2024

Peptidoform analysis of IP-MS data allows detection of differentially present bait proteoforms

While it is recognised that protein functions are determined by their proteoform state, such as mutations and post-translational modifications, methods to determine their differential abundance between conditions are limited. Here, we present a novel workflow for classical immunoprecipitation coupled to mass spectrometry (IP-MS) data that focuses on identifying differential peptidoforms of the bait protein between conditions, providing additional information about protein function.

bioinformatics↗

bioRxiv2022

The BRD4S-LOXL2-MED1 interaction at the forefront of cell cycle transcriptional control in triple-negative breast cancer

Triple-negative breast cancer often develops resistance to single-agent treatments, which can be circumvented with targeted combinatorial approaches. Here, we demonstrate that the simultaneous inhibition of LOXL2 and BRD4 cooperate to reduce triple-negative breast cancer proliferation in vitro and in vivo. Mechanistically, we reveal that LOXL2 interacts in the nucleus with the short isoform of BRD4 and MED1 to control cell cycle progression at the gene expression level via sustaining the formation of BRD4-MED1 nuclear transcriptional foci. Indeed, the pharmacological or transcriptional repression of LOXL2 provokes downregulation of cell cycle gene expression, G1-S cell cycle arrest, and loss of BRD4-MED1 foci. Our results indicate that the BRD4S-LOXL2-MED1 interaction is fundamental for the proliferation of triple-negative breast cancer. Therefore, targeting such interaction holds potential for the development of novel triple-negative breast cancer therapies.

cancer biology↗

bioRxiv2021

Specialization of the photoreceptor transcriptome by Srrm3-dependent microexons is required for outer segment maintenance and vision

Retinal photoreceptors differ in their transcriptomic profiles from other neuronal subtypes, likely as a reflection of their unique cellular morphology and function in the detection of light thorough the ciliary outer segment. We discovered a new layer of this molecular specialization by revealing that the vertebrate retina expresses the largest number of tissue-enriched microexons of all tissue types. A subset of these microexons is included exclusively in photoreceptor transcripts, particularly in genes involved in cilia biogenesis and in vesicle-mediated transport. This microexon program is regulated by Srrm3, a paralog of the neural microexon regulator Srrm4. Despite both proteins positively regulate retina microexons in vitro, only Srrm3 is highly expressed in mature photoreceptors and its deletion in zebrafish results in widespread downregulation of microexon inclusion, severe photoreceptor alterations and blindness. These results shed light into photoreceptors transcriptomic specialization and functionality, uncovering new cell type-specific roles for Srrm3 and microexons with implication for retinal diseases.

systems biology↗