Biology subjects

Nakhaee-Zadeh Gutierrez, D.

Publications and source records attributed to Nakhaee-Zadeh Gutierrez, D..

2 recordsLinked to original sources

bioRxiv2023

Cartography of the developability landscapes of native and human-engineered antibodies

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibodys ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We found lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. Sequence DPs were more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies were localized within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

bioinformatics↗

bioRxiv2023

ProteinFlow: a Python Library to Pre-Process Protein Structure Data for Deep Learning Applications

Over the past few years, deep learning tools for protein design have made significant advances in the field of bioengineering, opening up new opportunities for drug discovery, disease prevention or industrial biotechnology. However, despite the growing interest and excitement surrounding these tools, progress in the field is hindered by a lack of standardized datasets for benchmarking. Most models are trained on data from the Protein Data Bank (PDB), the largest repository of experimentally determined biological macromolecular structures. But filtering and processing this data involves many hyperparameter choices that are often not harmonized across the research community. Moreover, the task of splitting protein data into training and validation subsets with minimal data leakage is not trivial and often overlooked. Here we present ProteinFlow, a computational pipeline to pre-process protein sequence and structural data for deep learning applications. The pipeline is fully configurable and allows the extraction of all levels of protein organization (primary to quaternary), allowing end-users to cater the dataset for a multitude of downstream tasks, such as protein sequence design, protein folding modeling or protein-protein interaction prediction. In addition, we curate a feature-rich benchmarking dataset based on the latest annual release of the PDB and a selection of preprocessing parameters that are widely used across the research community. We showcase its utility by benchmarking a state-of-the-art (SOTA) deep learning model for protein sequence design. The open source code is packaged as a python library and can be accessed on https://github.com/adaptyvbio/ProteinFlow.

bioinformatics↗