Biology subjects

Janusz, B.

Publications and source records attributed to Janusz, B..

8 recordsLinked to original sources

bioRxiv2026

RePaRank: An Efficient Architecture for Antibody-Antigen Interface Prediction by Proximity Ranking

The prediction of protein-protein interactions is central to structural biology, yet leading models are often computationally expensive, creating an accessibility gap for many high-throughput applications. Furthermore, common evaluation metrics such as binary contact prediction can be unreliable. In this work, we address both challenges. We introduce RePaRank, a computationally efficient deep learning architecture with 39.4 million parameters that predicts antibody-antigen interfaces by reframing the problem as a proximity ranking task in a learned embedding space. We also propose the Precision AUC, a robust, ranking-based metric that provides a more stable assessment of model performance than traditional binary methods. Our experiments show that RePaRank consistently outperforms benchmark models in paratope prediction and is highly competitive in epitope prediction among models that do not require external resources such as Multiple Sequence Alignments (MSA). RePaRank offers a practical and powerful tool for the immunoinformatics community.

immunology↗

bioRxiv2025

Benchmarking antigen-aware inverse folding methods for antibody design.

Computational antibody design has seen many recent advances pioneered via the use of language models and advanced structure prediction tools. Developing a de novo antibody against a specific antigen requires structural awareness that most language models lack. A prominent class of machine learning methods combining the best of language model and structural worlds is inverse folding. This approach aims to predict a sequence that would fit a given structure. Such methods are now increasingly used to predict alternate sequences given a structure of a binder. It is known that, just like language models, such methods have certain predictive power in identifying binders. Here we performed a set of tests to reveal where, if at all, such methods provide value in the realistic setting of antibody discovery.

bioinformatics↗

bioRxiv2025

AbDesign: Database of point mutants of antibodies with associated structures reveals poor generalization of binding predictions from machine learning models.

Antibodies are naturally evolved molecular recognition scaffolds that can bind a variety of surfaces. Their designability is crucial to the development of biologics, with computational methods holding promise in accelerating the delivery of medicines to the clinic. Modeling antibody-antigen recognition is prohibitively difficult, with data paucity being one of the biggest hurdles. Current affinity datasets comprise a small number of experimental measurements, which are often not standardized between molecules. Here, we address these issues by creating a dataset of seven antigens with two antibodies each, for which we introduce a heterogeneous set of mutations to the CDR-H3 measured by ELISA. Each of the parental complexes has a known crystal structure. We perform benchmarking of state-of-the-art affinity prediction algorithms to gauge their effectiveness. Current computational methods exhibit significant limitations in accurately predicting the effects of single-point mutations. In contrast, the older empirical, physics-based method FoldX, performs well in identifying mutants that retain binding. These findings highlight the need for more resources like the one presented here -- large, molecularly diverse, and experimentally consistent datasets.

bioinformatics↗

bioRxiv2025

nanoFOLD : sequence design of nanobodies via inverse folding

Antibodies devoid of light chains are a promising class of biotherapeutics. Computational methods that address these molecules are crucially needed to accelerate the traditional, long and expensive experimental process of their discovery. Inverse folding, wherein one is tasked to predict a sequence given molecular coordinates, is an established method in scaffold-based protein design. Here we develop an inverse folding method speci[fi]c to nanobodies. We demonstrate its application in nanobody-engineering scenarios of enriching binders from next-generation sequencing experiments and novel binder design.

bioinformatics↗

bioRxiv2024

Conserved heavy/light contacts and germline preferences revealed by a large-scale analysis of natively paired human antibody sequences and structural data.

Antibody next-generation sequencing (NGS) datasets have become crucial to develop computational models addressing this successful class of therapeutics. Although antibodies are composed of both heavy and light chains, most NGS sequencing depositions provide them in unpaired form, reducing their utility. Here we introduce PairedAbNGS, a novel database with paired heavy/light antibody chains. To the best of our knowledge, this is the largest resource for paired natural antibody sequences with 58 bioprojects and over 14 million assembled productive sequences. We make the database accessible at http://naturalantibody.com/paired-ngs as a valuable tool for biological and machine-learning applications. Using this dataset, we investigated heavy and light chain variable (V) gene pairing preferences and found significant biases beyond gene usage frequencies, possibly due to receptor editing favoring less autoreactive combinations. Analyzing the available antibody structures from the Protein Data Bank, we studied conserved contact residues between heavy and light chains, particularly interactions between the CDR3 region of one chain and the FWR2 region of the opposite chain. Examination of amino acid pairs at key contact sites revealed significant deviations of amino acids distributions compared to random pairings, in the heavy chains CDR3 region contacting the opposite chain, indicating specific interactions might be crucial for proper chain pairing. This observation is further reinforced by preferential IGHV-IGLJ and IGLV-IGHJ pairing preferences. We hope that both our resources and the findings would contribute to improving the engineering of biological drugs.

immunology↗

bioRxiv2024

RIOT - Rapid Immunoglobulin Overview Tool - annotation of nucleotide and amino acid immunoglobulin sequences using an open germline database.

Antibodies are a cornerstone of the immune system, playing a pivotal role in identifying and neutralizing infections caused by bacteria, viruses, and other pathogens. Understanding their structure, and function, can provide insights into both the bodys natural defenses and the principles behind many therapeutic interventions, including vaccines and antibody-based drugs. The analysis and annotation of antibody sequences, including the identification of variable, diversity, joining, and constant genes, as well as the delineation of framework regions and complementarity-determining regions, is essential for understanding their structure and function. Currently analyzing large volumes of antibody sequences is routine in antibody discovery, requiring fast and accurate tools. While there are existing tools designed for the annotation and numbering of antibody sequences, they often have limitations such as being restricted to either nucleotide or amino acid sequences, reliance on non-uniform germline databases, or slow execution times. Here we present Rapid Immunoglobulin Overview Tool (RIOT), a novel open-source solution for antibody numbering that addresses these shortcomings. RIOT handles nucleotide and amino acid sequence processing, comes with a free germline database, and is computationally efficient. We hope the tool will facilitate rapid annotation of antibody sequencing outputs for the benefit of understanding antibody biology and discovering novel therapeutics. AvailabilityRIOT is available at https://github.com/NaturalAntibody/riot_na.

bioinformatics↗

bioRxiv2024

nanoBERT: A deep learning model for gene agnostic navigation of the nanobody mutational space

Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling - positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules. Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies. Availabilityhttps://huggingface.co/NaturalAntibody/

bioinformatics↗

bioRxiv2022

Structural pre-training improves physical accuracy of antibody structure prediction using deep learning.

Protein folding problem obtained a practical solution recently, owing to advances in deep learning. There are classes of proteins though, such as antibodies, that are structurally unique, where the general solution still lacks. In particular, the prediction of the CDR-H3 loop, which is an instrumental part of an antibody in its antigen recognition abilities, remains a challenge. Antibody-specific deep learning frameworks were proposed to tackle this problem noting great progress, both on accuracy and speed fronts. Oftentimes though, the original networks produce physically implausible bond geometries that then need to undergo a time-consuming energy minimization process. Here we hypothesized that pre-training the network on a large, augmented set of models with correct physical geometries, rather than a small set of real antibody X-ray structures, would allow the network to learn better bond geometries. We show that fine-tuning such a pre-trained network on a task of shape prediction on real X-ray structures improves the number of correct peptide bond distances. We further demonstrate that pre-training allows the network to produce physically plausible shapes on an artificial set of CDR-H3s, showing the ability to generalize to the vast antibody sequence space. We hope that our strategy will benefit the development of deep learning antibody models that rapidly generate physically plausible geometries, without the burden of time-consuming energy minimization.

bioinformatics↗