Biology subjects

Ofran, Y.

Publications and source records attributed to Ofran, Y..

6 recordsLinked to original sources

bioRxiv2026

Dual-Specific Antibody Design Using Artificial Intelligence

Multibodies, or "two-in-one" Immunoglobulin G (IgG) antibodies, are standard symmetrical IgG molecules engineered to competitively bind more than one antigen within a single variable fragment (Fv) binding surface. This format merges the functional advantages of bispecifics, such as multi-target binding and dynamic adaptation to target concentrations, with the superior manufacturing, developability, pharmacokinetics, and avidity of monospecific IgGs. Moreover, the co-accommodation of multiple paratopes on a single set of 6 CDRs introduces new functional possibilities that can improve efficacy and safety. Multibodies can, therefore, be thought of as force multipliers: for any format of antibodies, or fragments thereof, multibodies can bind double the number of epitopes compared to standard antibodies. While these advantages were recognized more than 15 years ago, the systematic design of multibodies has been intractable due to the challenge of optimizing two binding specificities into one Fv region, without having one of them compromising the other and without inducing poly-reactivity. To overcome this engineering barrier, we have developed an artificial intelligence (AI)-assisted computational platform that enables the design of functional multibodies against virtually any pair of targets. We applied the platform to design nine multibodies combining 15 different unrelated targets. We obtained therapeutic-grade multibodies that bind each desired pair of targets. We demonstrate that the generated multibodies possess excellent developability, high affinity, and stringent specificity, comparing favorably to clinical monospecific benchmarks. Critically, we show that these multibodies exhibit superior functional activity across a diverse range of mechanisms of action (MOAs), including internalization, T-cell engagement, and immune system modulation. This capability to reliably engineer versatile multibodies opens a new domain in antibody therapeutics, enabling complex multipharmacology and novel functions within a natural, cost-effective, and highly developable format. Two of these multibodies are currently in IND enabling studies, with first in human studies expected in 2026. The timeline from idea to a fully optimized, developable, lead candidate, ready for IND enabling studies, is 9 months.

bioengineering↗

bioRxiv2023

Computational Engineering of a Therapeutic Antibody to Inhibit Multiple Mutants of HER2 Without Compromising Inhibition of the Canonical HER2

Genomic germline and somatic variations may impact drug binding and even lead to resistance. However, designing a different drug for each mutant may not be feasible. In this study, we identified the most common cancer somatic mutations from the Catalogue of Somatic Mutations in Cancer (COSMIC) that occur in structurally characterized binding sites of approved therapeutic antibodies. We found two HER2 mutations, S310Y and S310F, that substantially compromise binding of Pertuzumab, a widely used therapeutics, and lead to drug resistance. To address these mutations, we designed a multi-specific version of Pertuzumab, that retains original function while also bindings these HER2 variants. This new antibody is stable and inhibits HER3 phosphorylation in a cell-based assay for all three variants, suggesting it can inhibit HER2-HER3 dimerization in patients with any of the variants. This study demonstrates how a small number of carefully selected mutations can add new specificities to an existing antibody without compromising its original function, creating a single therapeutic antibody that targets multiple common variants, making a drug that is not personalized yet its activity may be.

bioinformatics↗

bioRxiv2022

DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

SummaryRecently, Deep Learning models, initially developed in the field of Natural Language Processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, "distilled" models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet, and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91, and 0.87 respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e., shuffled sequences classified as proteins by DistilProtBert) we may be able to identify de-novo potential natural-like proteins based on random shuffling of amino acid sequences. Availabilityhttps://github.com/yarongef/DistilProtBert Contactyaron.geffen@biu.ac.il

bioinformatics↗

bioRxiv2020

RNF5 Regulation of RBBP4 Defines Acute Myeloid Leukemia Growth and Susceptibility to Histone Deacetylase Inhibitors

Acute myeloid leukemia (AML) remains incurable, largely due to its resistance to conventional treatments. Here, we found that increased expression and abundance of the ubiquitin ligase RNF5 contributes to AML development and survival. High RNF5 expression in AML patients correlated with poor prognosis. RNF5 inhibition decreased AML cell growth in culture and in vivo, and blocked development of MLL-AF9-driven leukemogenesis in mice, prolonging their survival. RNF5 inhibition led to transcriptional changes that overlapped with those seen upon HDAC1 inhibition. RNF5 induced the formation of K29 ubiquitin chains on the histone-binding protein RBBP4, promoting its recruitment and subsequent epigenetic regulation of genes involved in AML development and maintenance. Correspondingly, RNF5 or RBBP4 knockdown enhanced the sensitivity of AML cells to histone deacetylase (HDAC) inhibitors. Notably, low expression of RNF5 and HDAC coincided with a favorable prognosis. Our studies identified ERAD-independent role for RNF5, demonstrating that its control of RBBP4 constitutes an epigenetic pathway that drives AML while highlighting RNF5/RBBP4 as markers to stratify patients for treatment with HDAC inhibitors.

cancer biology↗

bioRxiv2018

High Resolution Comparison of Cancer-Related Developmental Processes Using Trajectory Alignment

Abnormal differentiation is a key feature of cancer, yet currently there is no framework that enables a comparative analysis of differentiation processes across patients while preserving their individual-level resolution. Here, we present devMap, an algorithm that uses high-dimensional trajectory alignment to anchor cancer-related developmental processes to a common backbone process, thus allowing for their systematic comparison. We applied devMap to bone marrow samples from healthy individuals and AML patients profiled by single-cell mass-cytometry at cancer diagnosis and following treatment. devMap standardization enabled us to infer the developmental status of the AML samples and characterize its evolution following treatment and in relapse. Application of devMap on an external dataset of AML bone marrow samples revealed conserved patterns of developmental signaling responses in AML that were obscured by traditional methodologies for developmental inference.

systems biology↗

bioRxiv2018

Immune cellular homeostasis in early life is determined by genetic variants of cellular production and turnover

Complex physiological functionality is often the outcome of multiple interacting cell-types, yet mechanistically how a large number of trait-associated genes yield a single multi-cellular network governing the phenotype has not been well defined. Individuals immune-cellular profiles at homeostasis show high heritability and inter-individual variation with functional and clinical implications. We profiled immune cellular variation by mass-cytometry in 55 genetically diverse mouse strains. We identify 788 genes associated with cellular homeostasis, supporting a polygenic model where 52% of genes correspond to core homeostatic functions whose genetic variants suffice to predict phenotype. Trait genes form a multi-cellular network architecture showing increased functional complexity over evolutionary timescales for shared regulation to all cells, specialized cell-specific programs, and between-cell synchronization. Contrasting to human studies suggests the regulatory network expands with environmental exposure history. Our findings shed light on the origin of immune-cellular variation and regulatory architectures that may generalize to other environmentally sensitive systems.

immunology↗