Biology subjects

Bell, D. R.

Publications and source records attributed to Bell, D. R..

3 recordsLinked to original sources

bioRxiv2023

Apposed networks of interacting TCRs and BCRs exhibiting mosaicked CDR3 sequences made of fixed junctional motifs

T and B cells are the two arms of the adaptive immune system that mediate cellular and humoral immunity, respectively, using highly diverse repertoires of antigen receptors. T cells recognize antigens using the T cell receptor (TCR), whereas B cells use the B cell receptor (BCR or surface immunoglobulin). Complementary determining regions (CDR3) of TCRs and BCRs are randomly generated through somatic VDJ recombination and nucleotide deletions and insertions at the V-D and D-J junctions. Contrary to this paradigm, here we describe two networks of millions of TCR{beta} and IGH clonotypes that are made from only two CDR3 sequences and associated with more 63 diseases. The TCR{beta} network members bore either the prototypic signature CDR3 sequence (CASSPGTEAFF), its N-terminal VD motif (CASSPGT) recombined with various J{beta} segments (CASSPGT-J{beta}x) or its DJ{beta} motif recombined with various V{beta} (V{beta}x-PGTEAFF). The BCR network members exhibit one signature CDR3 sequence (CARx1-4DTAMVYYFYDW) made from an invariant DJH motif (DTAMVYYFDYW) combined with various VH genes. The prototypes of the two networks are apparently teleogically related as they were dually expressed on the rare population of dual expresser (DE) lymphocytes and molecular dynamic simulations show that they were interacting partners. We conclude that members of the two networks represent a core set of evolutionary-conserved primordial antigen receptors that play fundamental roles in host defense and autoimmune diseases.

immunology↗

bioRxiv2021

Towards guided mutagenesis: Gaussian process regression predicts MHC class II antigen mutant binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptide into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental dataset from the Immune Epitope Database, and theoretical datasets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of 9 neutral residues from a 6-residue subset with an average R2 coefficient of determination value of 0.62 {+/-} 0.04 ({+/-}95% CI), average error of 0.09 {+/-} 0.01 kcal/mol ({+/-}95% CI), and with an ROC AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 {+/-} 0.04, average error of 0.07 {+/-} 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting 7 neutral residues from an 8-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

biophysics↗

bioRxiv2018

Exploring the Binding Mechanism between Human Profilin (PFN1) and Polyproline-10 through Binding Mode Screening

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the timeconsuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133 and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement (RMSD) correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average rupture work calculated from SMD simulations correlates fairly well with the PMFs (R2 = 0.67), making it a promising PPI screening method.

biophysics↗