Biology subjects

Khan, J. A.

Publications and source records attributed to Khan, J. A..

3 recordsLinked to original sources

bioRxiv2023

In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding

Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures [1, 2, 3, 4, 5]. While these approaches have been applied in silico to designing antibody complementarity-determining regions (CDRs), they have yet to be validated in vitro for designing antibody binders, which is the true measure of success for antibody design. Here we describe IgDesign, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibodys variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the models training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both de novo antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. The data generated in this study serve as a useful benchmark of diverse antibody-antigen interactions. We use this data to benchmark self-consistency RMSD (scRMSD), using ABodyBuilder2 [6], ABodyBuilder3 [7], and ESMFold [8], as a metric for assessing binding. We open source the code for IgDesign and the SPR datasets. Code and datasets can be found at https://github.com/AbSciBio/igdesign.

synthetic biology↗

bioRxiv2023

Pulmonary Matrix Derived Hydrogels from Patients with Idiopathic Pulmonary Fibrosis Induce a Proinflammatory State in Lung Fibroblasts In Vitro

Idiopathic pulmonary fibrosis (IPF), one of the most common forms of interstitial lung disease, is a poorly understood, chronic, and often fatal fibroproliferative condition with only two FDA-approved medications. Understanding the pathobiology of the fibroblast in IPF is critical to evaluating and discovering novel therapeutics. Unfortunately, our ability to interrogate this biology in vitro is greatly limited by the well-documented effects of tissue culture plastic on the fibroblast phenotype. Using a decellularized lung matrix derived from IPF patients, we generate three-dimensional (3D) hydrogels as in vitro models of lung physiology and characterize the phenotype of fibroblasts seeded into the hydrogels. When cultured in our hydrogels, IPF fibroblasts display differential contractility compared to their normal counterparts, lose the classical myofibroblast marker -smooth muscle actin, and increase expression of proinflammatory cytokines compared to fibroblasts seeded two-dimensionally (2D) on tissue culture dishes. We validate this proinflammatory state in fibroblast conditioned media studies with monocytes and monocyte-derived macrophages. These findings add to a growing understanding of the lung microenvironment effect on fibroblast phenotypes, shed light on the potential role of fibroblasts as immune signaling hubs during lung fibrosis, and suggest intervention in fibroblast-immune cell crosstalk as a possible novel therapeutic avenue.

molecular biology↗

bioRxiv2017

Insights into regeneration from the genome, transcriptome and metagenome analysis of Eisenia fetida

Earthworms show a wide spectrum of regenerative potential with certain species like Eisenia fetida capable of regenerating more than two-thirds of their body while other closely related species, such as Paranais litoralis seem to have lost this ability. Earthworms belong to the phylum annelida, in which the genomes of the marine oligochaete Capitella telata, and the freshwater leech Helobdella robusta have been sequenced and studied. The terrestrial annelids, in spite of their ecological relevance and unique biochemical repertoire, are represented by a single rough genome draft of Eisenia fetida (North American isolate), which suggested that extensive duplications have led to a large number of HOX genes in this annelid. Herein, we report the draft genome sequence of Eisenia fetida (Indian isolate), a terrestrial redworm widely used for vermicomposting assembled using short reads and mate-pair reads. An in-depth analysis of the miRNome of the worm, showed that many miRNA gene families have also undergone extensive duplications. Genes for several important proteins such as sialidases and neurotrophins were identified by RNA sequencing of tissue samples. We also used de novo assembled RNA-Seq data to identify genes that are differentially expressed during regeneration, both in the newly regenerating cells and in the adjacent tissue. Sox4, a master regulator of TGF-beta induced epithelial-mesenchymal transition was induced in the newly regenerated tissue. The regeneration of the ventral nerve cord was also accompanied by the induction of nerve growth factor and neurofilament genes. The metagenome of the worm, characterized using 16S rRNA sequencing, revealed the identity of several bacterial species that reside in the nephridia of the worm. Comparison of the bodywall and cocoon metagenomes showed exclusion of hereditary symbionts in the regenerated tissue. In summary, we present extensive genome, transcriptome and metagenome data to establish the transcriptome and metagenome dynamics during regeneration.

genomics↗