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Article ; Online: Inference of annealed protein fitness landscapes with AnnealDCA.

Sesta, Luca / Pagnani, Andrea / Fernandez-de-Cossio-Diaz, Jorge / Uguzzoni, Guido

2024 Volume 20, Issue 2, Page(s) e1011812

Abstract: The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a ... ...

Abstract	The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence space in-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning framework to learn the protein fitness landscape from sequencing data derived from a broad range of experiments that use selection and sequencing to quantify protein activity. We demonstrate the effectiveness of our method by applying it to antibody Rep-Seq data of immunized mice and screening experiments, assessing the quality of the fitness landscape reconstructions. Our method can be applied to several experimental cases where a population of protein variants undergoes various rounds of selection and sequencing, without relying on the computation of variants enrichment ratios, and thus can be used even in cases of disjoint sequence samples.
MeSH term(s)	Animals ; Mice ; Machine Learning ; Mutation ; Genetic Fitness/genetics
Language	English
Publishing date	2024-02-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011812
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Statistical mechanics of interacting metabolic networks.

Fernandez-de-Cossio-Diaz, Jorge / Mulet, Roberto

Physical review. E

2020 Volume 101, Issue 4-1, Page(s) 42401

Abstract: We cast the metabolism of interacting cells within a statistical mechanics framework considering both the actual phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of high-dimensional spin ... ...

Abstract	We cast the metabolism of interacting cells within a statistical mechanics framework considering both the actual phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of high-dimensional spin vectors, whose values will be constrained by the stochiometry and the energy requirements of the metabolism. Within this picture, finding the phenotypic states of the population turns out to be equivalent to searching for the equilibrium states of a disordered spin model. We provide a general solution of this problem for arbitrary metabolic networks and interactions. We apply this solution to a simplified model of metabolism and to a complex metabolic network, the central core of Escherichia coli, and demonstrate that the combination of selective pressure and interactions defines a complex phenotypic space. We also present numerical results for cells fixed in a grid. These results reproduce the qualitative picture discussed for the mean-field model. Cells may specialize in producing or consuming metabolites complementing each other, and this is described by an equilibrium phase space with multiple minima, like in a spin-glass model.
MeSH term(s)	Metabolic Networks and Pathways ; Models, Biological
Language	English
Publishing date	2020-05-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2844562-4
ISSN	2470-0053 ; 2470-0045
ISSN (online)	2470-0053
ISSN	2470-0045
DOI	10.1103/PhysRevE.101.042401
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Article ; Online: Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information.

Pereiro-Morejón, José Antonio / Fernandez-de-Cossio-Diaz, Jorge / Mulet, Roberto

iScience

2022 Volume 25, Issue 12, Page(s) 105450

Abstract: The study of cellular metabolism is limited by the amount of experimental data available. Formulations able to extract relevant predictions from accessible measurements are needed. Maximum Entropy (ME) inference has been successfully applied to genome- ... ...

Abstract	The study of cellular metabolism is limited by the amount of experimental data available. Formulations able to extract relevant predictions from accessible measurements are needed. Maximum Entropy (ME) inference has been successfully applied to genome-scale models of cellular metabolism, and recent data-driven studies have suggested that in chemostat cultures of
Language	English
Publishing date	2022-10-29
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2022.105450
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Article ; Online: A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.

Bravi, Barbara / Di Gioacchino, Andrea / Fernandez-de-Cossio-Diaz, Jorge / Walczak, Aleksandra M / Mora, Thierry / Cocco, Simona / Monasson, Rémi

eLife

2023 Volume 12

Abstract: Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build ... ...

Abstract	Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
MeSH term(s)	T-Cell Antigen Receptor Specificity ; Learning ; Amino Acids ; Cell Membrane ; Mitochondrial Membranes
Chemical Substances	Amino Acids
Language	English
Publishing date	2023-09-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.85126
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Article ; Online: A self-consistent probabilistic formulation for inference of interactions.

Fernandez-de-Cossio, Jorge / Fernandez-de-Cossio-Diaz, Jorge / Perera-Negrin, Yasser

Scientific reports

2020 Volume 10, Issue 1, Page(s) 21435

Abstract: Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding ... ...

Abstract	Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.
Language	English
Publishing date	2020-12-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-78496-8
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Article ; Online: Maximum entropy and population heterogeneity in continuous cell cultures.

Fernandez-de-Cossio-Diaz, Jorge / Mulet, Roberto

PLoS computational biology

2019 Volume 15, Issue 2, Page(s) e1006823

Abstract: Continuous cultures of mammalian cells are complex systems displaying hallmark phenomena of nonlinear dynamics, such as multi-stability, hysteresis, as well as sharp transitions between different metabolic states. In this context mathematical models may ... ...

Abstract	Continuous cultures of mammalian cells are complex systems displaying hallmark phenomena of nonlinear dynamics, such as multi-stability, hysteresis, as well as sharp transitions between different metabolic states. In this context mathematical models may suggest control strategies to steer the system towards desired states. Although even clonal populations are known to exhibit cell-to-cell variability, most of the currently studied models assume that the population is homogeneous. To overcome this limitation, we use the maximum entropy principle to model the phenotypic distribution of cells in a chemostat as a function of the dilution rate. We consider the coupling between cell metabolism and extracellular variables describing the state of the bioreactor and take into account the impact of toxic byproduct accumulation on cell viability. We present a formal solution for the stationary state of the chemostat and show how to apply it in two examples. First, a simplified model of cell metabolism where the exact solution is tractable, and then a genome-scale metabolic network of the Chinese hamster ovary (CHO) cell line. Along the way we discuss several consequences of heterogeneity, such as: qualitative changes in the dynamical landscape of the system, increasing concentrations of byproducts that vanish in the homogeneous case, and larger population sizes.
MeSH term(s)	Animals ; Batch Cell Culture Techniques/methods ; Batch Cell Culture Techniques/statistics & numerical data ; Bioreactors ; CHO Cells ; Cell Culture Techniques/methods ; Cell Survival ; Cricetulus ; Entropy ; Metabolic Networks and Pathways ; Models, Theoretical ; Nonlinear Dynamics
Language	English
Publishing date	2019-02-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1006823
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Book ; Online: Disentangling representations in Restricted Boltzmann Machines without adversaries

Fernandez-de-Cossio-Diaz, Jorge / Cocco, Simona / Monasson, Remi

2022

Abstract: A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make easier to interpret the significant latent factors of variation in the ...

Abstract	A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make easier to interpret the significant latent factors of variation in the data, as well as to generate new data with desirable features. Methods for disentangling representations often rely on an adversarial scheme, in which representations are tuned to avoid discriminators from being able to reconstruct information about the data properties (labels). Unfortunately adversarial training is generally difficult to implement in practice. Here we propose a simple, effective way of disentangling representations without any need to train adversarial discriminators, and apply our approach to Restricted Boltzmann Machines (RBM), one of the simplest representation-based generative models. Our approach relies on the introduction of adequate constraints on the weights during training, which allows us to concentrate information about labels on a small subset of latent variables. The effectiveness of the approach is illustrated with four examples: the CelebA dataset of facial images, the two-dimensional Ising model, the MNIST dataset of handwritten digits, and the taxonomy of protein families. In addition, we show how our framework allows for analytically computing the cost, in terms of log-likelihood of the data, associated to the disentanglement of their representations. Comment: Minor corrections. Accepted for publication in Physical Review X
Keywords	Computer Science - Machine Learning ; Condensed Matter - Disordered Systems and Neural Networks
Subject code	006
Publishing date	2022-06-23
Publishing country	us
Document type	Book ; Online
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Article ; Online: Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan.

Fernandez-de-Cossio-Diaz, Jorge / Uguzzoni, Guido / Pagnani, Andrea

Molecular biology and evolution

2020 Volume 38, Issue 1, Page(s) 318–328

Abstract: The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large ... ...

Abstract	The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.
MeSH term(s)	Epistasis, Genetic ; Evolution, Molecular ; Genetic Fitness ; Mutation ; Unsupervised Machine Learning
Language	English
Publishing date	2020-08-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Validation Study
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msaa204
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Article ; Online: AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape.

Sesta, Luca / Uguzzoni, Guido / Fernandez-de-Cossio-Diaz, Jorge / Pagnani, Andrea

International journal of molecular sciences

2021 Volume 22, Issue 20

Abstract: We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in ... ...

Abstract	We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution via multiple rounds of mutation and selection for a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental conditions and as a relevant testing ground to develop accurate statistical models and inference algorithms (thanks to high-throughput screening and sequencing). Fitness landscape modeling either uses the enrichment of variants abundances as input, thus requiring the observation of the same variants at different rounds or assuming the last sequenced round as being sampled from an equilibrium distribution. AMaLa aims at effectively leveraging the information encoded in the whole time evolution. To do so, while assuming statistical sampling independence between sequenced rounds, the possible trajectories in sequence space are gauged with a time-dependent statistical weight consisting of two contributions: (i) an energy term accounting for the selection process and (ii) a generalized Jukes-Cantor model for the purely mutational step. This simple scheme enables accurately describing the Directed Evolution dynamics and inferring a fitness landscape that correctly reproduces the measures of the phenotype under selection (e.g., antibiotic drug resistance), notably outperforming widely used inference strategies. In addition, we assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Directed Molecular Evolution/methods ; Evolution, Molecular ; Genetic Fitness ; High-Throughput Nucleotide Sequencing ; Models, Genetic ; Mutation ; Sequence Analysis, DNA
Language	English
Publishing date	2021-10-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms222010908
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Book ; Online: Inferring metabolic fluxes in nutrient-limited continuous cultures

Pereiro-Morejón, Jose A. / Fernández-de-Cossio-Díaz, Jorge / Mulet, R.

A Maximum Entropy Approach with minimum information

2021

Abstract: We propose a new scheme to infer the metabolic fluxes of cell cultures in a chemostat. Our approach is based on the Maximum Entropy Principle and exploits the understanding of the chemostat dynamics and its connection with the actual metabolism of cells. ...

Abstract	We propose a new scheme to infer the metabolic fluxes of cell cultures in a chemostat. Our approach is based on the Maximum Entropy Principle and exploits the understanding of the chemostat dynamics and its connection with the actual metabolism of cells. We show that, in continuous cultures with limiting nutrients, the inference can be done with {\it limited information about the culture}: the dilution rate of the chemostat, the concentration in the feed media of the limiting nutrient and the cell concentration at steady state. Also, we remark that our technique provides information, not only about the mean values of the fluxes in the culture, but also its heterogeneity. We first present these results studying a computational model of a chemostat. Having control of this model we can test precisely the quality of the inference, and also unveil the mechanisms behind the success of our approach. Then, we apply our method to E. coli experimental data from the literature and show that it outperforms alternative formulations that rest on a Flux Balance Analysis framework. Comment: 35 pages, 11 figues
Keywords	Condensed Matter - Statistical Mechanics ; Quantitative Biology - Quantitative Methods
Subject code	612
Publishing date	2021-09-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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