Biology subjects

Zielinski, M.

Publications and source records attributed to Zielinski, M..

4 recordsLinked to original sources

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↗

bioRxiv2023

Cryo-EM Structures of Amyloid-β Fibrils from Alzheimer's Disease Mouse Models

The development of novel drugs for Alzheimers disease has proven difficult, with a high failure rate in clinical trials. Typically, transgenic mice displaying amyloid-{beta} peptide brain pathology are used to develop therapeutic options and to test their efficacy in preclinical studies. However, the properties of A{beta} in such mice have not been systematically compared to A{beta} from the patient brains. Here, we determined the structures of nine ex vivo A{beta} fibrils from six different mouse models by cryo-EM. We found novel A{beta} fibril structures in the APP/PS1, ARTE10, and tg-SwDI models, whereas the human familial type II fibril fold was found in the ARTE10, tg-APPSwe, and APP23 models. The tg-APPArcSwe mice showed an A{beta} fibril whose structure resembles the human sporadic type I fibril. These structural elucidations are key to the selection of adequate mouse models for the development of novel plaque-targeting therapeutics and PET imaging tracers. One Sentence SummaryCryo-EM structures of A{beta} fibrils extracted from brains of mouse models used for Alzheimers disease preclinical research are presented.

neuroscience↗

bioRxiv2021

Protein complex prediction with AlphaFold-Multimer

While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] [≥] 0.49) on 13 targets and high accuracy (DockQ [≥] 0.8) on 7 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,446 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ [≥] 0.23) in 70% of cases, and produce high accuracy predictions (DockQ [≥] 0.8) in 26% of cases, an improvement of +27 and +14 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric inter-faces we successfully predict the interface in 72% of cases, and produce high accuracy predictions in 36% of cases, an improvement of +8 and +7 percentage points respectively.

bioinformatics↗

bioRxiv2020

Endo-lysosomal Aβ concentration and pH enable formation of Aβ oligomers that potently induce Tau missorting

Amyloid-β peptide (Aβ) forms metastable oligomers >50 kD, termed AβOs or protofibrils, that are more effective than Aβ amyloid fibrils at triggering Alzheimer’s disease-related processes such as synaptic dysfunction and Tau pathology, including Tau mislocalization. In neurons, Aβ accumulates in endo-lysosomal vesicles at low pH. Here, we show that the rate of AβO assembly is accelerated 8,000-fold upon pH reduction from extracellular to endo-lysosomal pH, at the expense of amyloid fibril formation. The pH-induced promotion of AβO formation and the high endo-lysosomal Aβ concentration together enable extensive AβO formation of Aβ42 under physiological conditions. Exploiting the enhanced AβO formation of the dimeric Aβ variant dimAβ we furthermore demonstrate targeting of AβOs to dendritic spines, potent induction of Tau missorting, a key factor in tauopathies, and impaired neuronal activity. The results suggest that the endosomal/lysosomal system is a major site for the assembly of pathomechanistically relevant AβOs.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗