Evidence for Shaping of Light Chain Repertoire by Structural Selection

Toledano, Adar; Elhanati, Yuval; Benichou, Jennifer I. C.; Walczak, Aleksandra M.; Mora, Thierry; Louzoun, Yoram

doi:10.3389/fimmu.2018.01307

Cited by 10 publications

(14 citation statements)

References 35 publications

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“…In its current state OLGA can compute the probabilities of CDR3 sequences and motifs, with or without V/J restriction, of four chain loci (human and mouse TRB, human TRA and human IGH), but the list can readily be expanded by learning recombination models for other loci and species using IGoR (Marcou et al , 2018) which shares the same model format. Obvious additions include the light chains of BCR (Toledano et al , 2018), and more mouse models. While the algorithm evaluates the probability of single chains, recent analyses show that chain pairing in TCR is close to independent (Dupic et al , 2018; Grigaityte et al , 2017).…”

Section: Discussionmentioning

confidence: 99%

OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

et al. 2019

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Motivation High-throughput sequencing of large immune repertoires has enabled the development of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence. These generation probabilities are very non-homogeneous, ranging over 20 orders of magnitude in real repertoires. Since the function of a receptor really depends on its protein sequence, it is important to be able to predict this probability of generation at the amino acid level. However, brute-force summation over all the nucleotide sequences with the correct amino acid translation is computationally intractable. The purpose of this paper is to present a solution to this problem. Results We use dynamic programming to construct an efficient and flexible algorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin Amino-acid sequences), for calculating the probability of generating a given CDR3 amino acid sequence or motif, with or without V/J restriction, as a result of V(D)J recombination in B or T cells. We apply it to databases of epitope-specific T-cell receptors to evaluate the probability that a typical human subject will possess T cells responsive to specific disease-associated epitopes. The model prediction shows an excellent agreement with published data. We suggest that OLGA may be a useful tool to guide vaccine design. Availability and implementation Source code is available at https://github.com/zsethna/OLGA. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

et al. 2019

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“…To highlight the importance of the encoding, we first tested whether the encoder reveals the reported selection against specific AA in CDR3 sequences[26]. Samples composed of 100 TCR per sample from the Benny Chain dataset were projected using the AE.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, if distances are maintained, similar length CDR sequences should have close projections. Moreover, CDR3 with much longer or shorter than average sequences should be again projected to the periphery, as they are also less frequent and selected against[26, 27]. Again, the AE projects the mid-length CDR3 sequences to the center of the distribution and the long and short CDR3 sequences to the periphery (Fig.…”

Section: Resultsmentioning

confidence: 99%

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

Dvorkin

Louzoun

2020

Preprint

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10Recent advances in T cell repertoire (TCR) sequencing allow for characterization of repertoire 11 properties, as well as the frequency and sharing of specific TCR. However, there is no good 12 measure for the local density of a given TCR. TCRs are often described using their 13 Complementary Determining region 3 (CDR3) sequences, V/J usage, and clone size. We here 14 show that the local repertoire density can be estimated using a combined representation of these 15 components through distance conserving autoencoders and Kernel Density Estimates (KDE). 16We present ELATE -an Encoder based LocAl Tcr dEnsity and show that the resulting density 17 of a sample can be used as a novel measure to study repertoire properties. The cross-density 18 between two samples can be used as a similarity matrix to fully characterize samples from the 19 same host. Finally, the same projection in combination with machine learning algorithms can 20 be used to predict TCR-peptide binding through the local density of known TCRs binding a 21 specific target. 22 23 3 24 101 vectors, representing a CDR3 sequence. Encoder's output (decoder's input): the 102 embedded representation with 30 dimensions. Decoder's output: the reconstructed 103 vector. The autoencoder is trained by minimizing the reconstruction MSE (Mean 104 Squared Error). The classifier is a combination of the encoder (with fixed weights) and 6 105 4 more fully connected layers. The classifier is trained by minimizing the BCE loss 106 (Binary Cross Entropy). 107In all the observed datasets, very few pairs of CDR3 sequences differ by less than 2 AA one 108 from each other (more precisely have an edit distance of less than 2). Indeed, when the 109 distribution of edit distance between every pair of sequences from pairs of random samples is 110 calculated, the distributions are consistently normal, with practically no values below 2 (see a 111representing example in Fig.2B). Thus, even an AE producing 1 or 2 errors would lead to a 112 representation closer to the original CDR3 than any other sequence in a different sample. We 113 thus report the fraction of sequences successfully reconstructed with 0, 1, or 2 errors (Fig. 2). 114 Fig 2. A. Edit distances distribution. Histogram of edit distances between all pairs of 115 CDR3 sequences from two samples. Few sequences have 2 or fewer mismatches. B. 116 Encoder's accuracies. All accuracies of AE for the different datasets with no 117 mismatches (fully matched, red), 1 mismatch (orange), and 2 mismatches (blue). C. 118 Encoder's accuracy combined with distances. All accuracies of AE+DIS for the 119 different datasets. D. Encoder and CDR3 distances correlation. Pairwise Euclidean 120 distances in the set of CDR3 one-hot vectors (Xs) in the x axis and pairwise Euclidean 121 distances in the embedded set (Zs) predicted by AE in the y axis. The legend contains 122 the Spearman correlation between the axes. E. Encoder's distances correlation when 123 combined with distances. Pairwise Euclidean distances in the set of CDR3 one-hot 124 vec...

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“…XII E 1 for details about Maximum Likelihood and Expectation-Maximization). This inference procedure has been applied to variety of immune receptors and species, TCR β [169] and α [171] chains, BCR heavy [170] and light chains [172] in humans, as well as TCRβ in mouse [173] and BCR heavy chains in trout [174]. It is implemented in the IGoR software [175] which can be used to learn additional models from other species and locus combinations.…”

Section: B Inference Of the Stochastic Repertoire Generation Processmentioning

confidence: 99%

“…XII E 1). This approach has been applied to the initial selection process of β-chain sequences [188], α-chain sequences [171], and BCR heavy chain [170] and light chain [172] sequences. For many of these cases, the Q(s) selection factors did not differ substantially from individual to individual.…”

Section: Thymic Selection and Central Tolerancementioning

confidence: 99%

Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. CONTENTS Dissociation ratesAs discussed below, immunological interactions span the range of very short lived interactions, k off > 10 s −1 or τ off = (k off ) −1 < 0.1 s for antibody binding to its target in the initial phase of an immune response, to extremely long-lived interactions, k off < 10 −4 s −1 or τ off > 3 hours for cytokines interacting with their receptors. These estimates set a huge range of time scales the immune system must deal with, even before taking into consideration delays in cellular responses and how these affect the ligand environment on time scales ranging from hours to days. These considerations form the crux of the matter for quantitative immunology at the cellular scale: while the physical chemistry of ligand-receptor interactions is straightforward, the immune system builds a response of devilish complexity from such elementary interactions. Numbers of receptors per cellIn some situations, such as in T cell antigen recognition, where both the T cell receptor and the antigen exist in a membrane-bound form. All dynamics of interactions must be estimated taking into account the surface concentrations of molecules, with proper adjustment for slower diffusion in the association rates.

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Evidence for Shaping of Light Chain Repertoire by Structural Selection

Cited by 10 publications

References 35 publications

OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

Quantitative Immunology for Physicists

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