HAL (Le Centre pour la Communication Scientifique Directe), Jun 22, 2021
Constraint Networks: Boolean tables (tensors) for domains and constraints Constraint Programming:... more Constraint Networks: Boolean tables (tensors) for domains and constraints Constraint Programming: interval variables, specialized constraints, control Global constraints Names for specific (useful) constraints Most famous A D S 2 54 F CSP CP Languages for domains and constraints Constraint Networks: Boolean tables (tensors) for domains and constraints Constraint Programming: interval variables, specialized constraints, control Application domains: NP and beyond Excel at the analysis of complex perfectly known systems Digital circuit verification, scheduling and other resource management problems, planning, so ware verification, theorem proving,. . .
Theobroma cacao L., is a diploid tree fruit species (2n = 2x = 20), originated from the South Ame... more Theobroma cacao L., is a diploid tree fruit species (2n = 2x = 20), originated from the South American rainforests. A criollo genotype, providing a high quality chocolate and highly homozygous, has been choosing by the ICGS to sequence the cocoa genome. A combination of Sanger and NGS technologies was chosen for sequencing the criollo genotype, with a genome coverage of 16,7 X. The assembly corresponds to 76% of the estimated genome size of the criollo genotype (430 Mb) and appears to cover a very large proportion of the euchromatin of the T. cacao genome, allowing to recover 97.8% of the transcriptome unigene resource. Annotations revealed 28,798 protein-coding genes. This first cocoa genome sequence was the support to inventory in the cocoa genome important gene families potentially involved in resistance and quality traits. Their localisation in the genome and their comparison with QTLs involved in these traits provide a large set of candidate genes. The inferred paleohistory of ...
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
bioRxiv (Cold Spring Harbor Laboratory), Feb 23, 2021
The extant complex proteins must have evolved from ancient short and simple ancestors. Neverthele... more The extant complex proteins must have evolved from ancient short and simple ancestors. Nevertheless, how such prototype proteins emerged on the primitive earth remains enigmatic. The double-psi beta-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its evolutionary pathway started by "interlacing homo-
Australian & New Zealand Journal of Statistics, Jun 1, 2019
Probabilistic graphical models offer a powerful framework to account for the dependence structure... more Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so-called treewidth of the graph characterises this algorithmic complexity: low-treewidth graphs can be processed efficiently. The first message that we illustrate is therefore the idea that for inference in graphical model, the number of variables is not the limiting factor, and it is worth checking for the treewidth before turning to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to perform exact inference. The second message is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte-Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.
Motivation: Structure-based Computational Protein design (CPD) plays a critical role in advancing... more Motivation: Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results: We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wildtype sequence recovery rate achieved 93%.
Journal of the American Chemical Society, Sep 24, 2021
The extant complex proteins must have evolved from ancient short and simple ancestors. Neverthele... more The extant complex proteins must have evolved from ancient short and simple ancestors. Nevertheless, how such prototype proteins emerged on the primitive earth remains enigmatic. The double-psi beta-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its evolutionary pathway started by "interlacing homo
Plant-parasitic nematodes are major agricultural pests worldwide and novel approaches to control ... more Plant-parasitic nematodes are major agricultural pests worldwide and novel approaches to control them are sorely needed. We report the draft genome sequence of the root-knot nematode Meloidogyne incognita, a biotrophic parasite of many crops, including tomato, cotton and coffee. Most of the assembled sequence of this asexually reproducing nematode, totaling 86 Mb, exists in pairs of homologous but divergent segments. This suggests that ancient allelic regions in M. incognita are evolving toward effective haploidy, permitting new mechanisms of adaptation. The number and diversity of plant cell wall-degrading enzymes in M. incognita is unprecedented in any animal for which a genome sequence is available, and may derive from multiple horizontal gene transfers from bacterial sources. Our results provide insights into the adaptations required by metazoans to successfully parasitize immunocompetent plants, and open the way for discovering new antiparasitic strategies. Plant-parasitic nematodes are responsible for global agricultural losses amounting to an estimated $157 billion annually. Although chemical nematicides are the most reliable means of controlling root-knot nematodes, they are increasingly being withdrawn owing to their toxicity to humans and the environment. Novel and specific targets are thus needed to develop new strategies against these pests. The Southern root-knot nematode Meloidogyne incognita is able to infect the roots of almost all cultivated plants, making it perhaps the
HAL (Le Centre pour la Communication Scientifique Directe), Jun 30, 2015
Probabilistic graphical models offer a powerful framework to account for the dependence structure... more Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which can be represented as a graph. The dependence between variables may render inference tasks such as computing normalizing constant, marginalization or optimization intractable. The objective of this paper is to review techniques exploiting the graph structure for exact inference borrowed from optimization and computer science. They are not yet standard in the statistician toolkit, and we specify under which conditions they are efficient in practice. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated in the graph. The so-called treewidth of the graph characterizes this algorithmic complexity: low-treewidth graphs can be processed efficiently. Algorithmic solutions derived from variable elimination and the notion of treewidth are illustrated on problems of treewidth computation and inference in challenging benchmarks from optimization competitions. We also review how efficient techniques for approximate inference such as loopy belief propagation and variational approaches can be linked to variable elimination and we illustrate them in the context of Expectation-Maximisation procedures for parameter estimation in coupled Hidden Markov Models.
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monocl... more Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.
Discrete Graphical Models (GMs) are widely used in Artificial Intelligence to describe complex sy... more Discrete Graphical Models (GMs) are widely used in Artificial Intelligence to describe complex systems through a joint function of interest. Probabilistic GMs such as Markov Random Fields (MRFs) define a joint non-normalized probability distribution while deterministic GMs such as Cost Function Networks (CFNs) define a joint cost function. A typical query on GMs consists in finding the joint state that optimizes this joint function, a problem denoted as the Maximum a Posteriori or Weighted Constraint Satisfaction Problem respectively. In practice, more than one function of interest may need to be optimized at the same time. In this paper, we develop a two-phase scalarization method for solving bi-objective discrete graphical model optimization, with the aim of computing a set of non-dominated solutions -the Pareto frontier -representing different compromises between two GM-defined objectives. For this purpose, we introduce a dedicated higher-order constraint, which bounds the value of one GM-defined objective while minimizing another GM on the same variables. Discrete GM optimization is NP-hard, and its bi-objective variants are even harder. We show how existing GM global lower and upper bounds can be exploited to provide anytime bounds of the exact Pareto frontier. We benchmark it on various instances from Operations Research and Probabilistic Graphical Models.
Modern technologies and especially next generation sequencing facilities are giving a cheaper acc... more Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth ''Dialogue for Reverse Engineering Assessments and Methods'' (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on ''Systems Genetics'' proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Understanding how proteins evolve under selective pressure is a longstanding challenge. The immen... more Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride towards achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.
The species Salmonella enterica is one of the most prevalent human and animal pathogens, it inclu... more The species Salmonella enterica is one of the most prevalent human and animal pathogens, it includesNon Typhoïdal Salmonella (NTS) serovars like Typhimurium and Enteridis, that are generalist pathogenswith broad host specificity and Typhoïdal Salmonella (TS) serovars, like Typhi and Paratyphi A, that arespecialized pathogens strictly adapted to the human host and the cause of an invasive, dangerous diseaseknown as enteric (typhoid) fever [1,2,3].The SalHostTrop project aims at identifying, characterizing and understanding the human-restrictedtropism of Typhoidal Salmonella (TS) using comparative dual-RNAseq sequencing and othercomplementary approaches.We combine state of the art genome and transcriptome sequencing methods to decipher the molecularbasis of host-tropism in clinical strains. We contrast the comparative genomics and differential expressionanalyses to explore and assess the variability and plasticity of pathogenesis routes among and betweentyphoidal and non-typhoidal ser...
HAL (Le Centre pour la Communication Scientifique Directe), Jun 22, 2021
Constraint Networks: Boolean tables (tensors) for domains and constraints Constraint Programming:... more Constraint Networks: Boolean tables (tensors) for domains and constraints Constraint Programming: interval variables, specialized constraints, control Global constraints Names for specific (useful) constraints Most famous A D S 2 54 F CSP CP Languages for domains and constraints Constraint Networks: Boolean tables (tensors) for domains and constraints Constraint Programming: interval variables, specialized constraints, control Application domains: NP and beyond Excel at the analysis of complex perfectly known systems Digital circuit verification, scheduling and other resource management problems, planning, so ware verification, theorem proving,. . .
Theobroma cacao L., is a diploid tree fruit species (2n = 2x = 20), originated from the South Ame... more Theobroma cacao L., is a diploid tree fruit species (2n = 2x = 20), originated from the South American rainforests. A criollo genotype, providing a high quality chocolate and highly homozygous, has been choosing by the ICGS to sequence the cocoa genome. A combination of Sanger and NGS technologies was chosen for sequencing the criollo genotype, with a genome coverage of 16,7 X. The assembly corresponds to 76% of the estimated genome size of the criollo genotype (430 Mb) and appears to cover a very large proportion of the euchromatin of the T. cacao genome, allowing to recover 97.8% of the transcriptome unigene resource. Annotations revealed 28,798 protein-coding genes. This first cocoa genome sequence was the support to inventory in the cocoa genome important gene families potentially involved in resistance and quality traits. Their localisation in the genome and their comparison with QTLs involved in these traits provide a large set of candidate genes. The inferred paleohistory of ...
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
bioRxiv (Cold Spring Harbor Laboratory), Feb 23, 2021
The extant complex proteins must have evolved from ancient short and simple ancestors. Neverthele... more The extant complex proteins must have evolved from ancient short and simple ancestors. Nevertheless, how such prototype proteins emerged on the primitive earth remains enigmatic. The double-psi beta-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its evolutionary pathway started by "interlacing homo-
Australian & New Zealand Journal of Statistics, Jun 1, 2019
Probabilistic graphical models offer a powerful framework to account for the dependence structure... more Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so-called treewidth of the graph characterises this algorithmic complexity: low-treewidth graphs can be processed efficiently. The first message that we illustrate is therefore the idea that for inference in graphical model, the number of variables is not the limiting factor, and it is worth checking for the treewidth before turning to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to perform exact inference. The second message is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte-Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.
Motivation: Structure-based Computational Protein design (CPD) plays a critical role in advancing... more Motivation: Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results: We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wildtype sequence recovery rate achieved 93%.
Journal of the American Chemical Society, Sep 24, 2021
The extant complex proteins must have evolved from ancient short and simple ancestors. Neverthele... more The extant complex proteins must have evolved from ancient short and simple ancestors. Nevertheless, how such prototype proteins emerged on the primitive earth remains enigmatic. The double-psi beta-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its evolutionary pathway started by "interlacing homo
Plant-parasitic nematodes are major agricultural pests worldwide and novel approaches to control ... more Plant-parasitic nematodes are major agricultural pests worldwide and novel approaches to control them are sorely needed. We report the draft genome sequence of the root-knot nematode Meloidogyne incognita, a biotrophic parasite of many crops, including tomato, cotton and coffee. Most of the assembled sequence of this asexually reproducing nematode, totaling 86 Mb, exists in pairs of homologous but divergent segments. This suggests that ancient allelic regions in M. incognita are evolving toward effective haploidy, permitting new mechanisms of adaptation. The number and diversity of plant cell wall-degrading enzymes in M. incognita is unprecedented in any animal for which a genome sequence is available, and may derive from multiple horizontal gene transfers from bacterial sources. Our results provide insights into the adaptations required by metazoans to successfully parasitize immunocompetent plants, and open the way for discovering new antiparasitic strategies. Plant-parasitic nematodes are responsible for global agricultural losses amounting to an estimated $157 billion annually. Although chemical nematicides are the most reliable means of controlling root-knot nematodes, they are increasingly being withdrawn owing to their toxicity to humans and the environment. Novel and specific targets are thus needed to develop new strategies against these pests. The Southern root-knot nematode Meloidogyne incognita is able to infect the roots of almost all cultivated plants, making it perhaps the
HAL (Le Centre pour la Communication Scientifique Directe), Jun 30, 2015
Probabilistic graphical models offer a powerful framework to account for the dependence structure... more Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which can be represented as a graph. The dependence between variables may render inference tasks such as computing normalizing constant, marginalization or optimization intractable. The objective of this paper is to review techniques exploiting the graph structure for exact inference borrowed from optimization and computer science. They are not yet standard in the statistician toolkit, and we specify under which conditions they are efficient in practice. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated in the graph. The so-called treewidth of the graph characterizes this algorithmic complexity: low-treewidth graphs can be processed efficiently. Algorithmic solutions derived from variable elimination and the notion of treewidth are illustrated on problems of treewidth computation and inference in challenging benchmarks from optimization competitions. We also review how efficient techniques for approximate inference such as loopy belief propagation and variational approaches can be linked to variable elimination and we illustrate them in the context of Expectation-Maximisation procedures for parameter estimation in coupled Hidden Markov Models.
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monocl... more Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.
Discrete Graphical Models (GMs) are widely used in Artificial Intelligence to describe complex sy... more Discrete Graphical Models (GMs) are widely used in Artificial Intelligence to describe complex systems through a joint function of interest. Probabilistic GMs such as Markov Random Fields (MRFs) define a joint non-normalized probability distribution while deterministic GMs such as Cost Function Networks (CFNs) define a joint cost function. A typical query on GMs consists in finding the joint state that optimizes this joint function, a problem denoted as the Maximum a Posteriori or Weighted Constraint Satisfaction Problem respectively. In practice, more than one function of interest may need to be optimized at the same time. In this paper, we develop a two-phase scalarization method for solving bi-objective discrete graphical model optimization, with the aim of computing a set of non-dominated solutions -the Pareto frontier -representing different compromises between two GM-defined objectives. For this purpose, we introduce a dedicated higher-order constraint, which bounds the value of one GM-defined objective while minimizing another GM on the same variables. Discrete GM optimization is NP-hard, and its bi-objective variants are even harder. We show how existing GM global lower and upper bounds can be exploited to provide anytime bounds of the exact Pareto frontier. We benchmark it on various instances from Operations Research and Probabilistic Graphical Models.
Modern technologies and especially next generation sequencing facilities are giving a cheaper acc... more Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth ''Dialogue for Reverse Engineering Assessments and Methods'' (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on ''Systems Genetics'' proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Understanding how proteins evolve under selective pressure is a longstanding challenge. The immen... more Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride towards achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.
The species Salmonella enterica is one of the most prevalent human and animal pathogens, it inclu... more The species Salmonella enterica is one of the most prevalent human and animal pathogens, it includesNon Typhoïdal Salmonella (NTS) serovars like Typhimurium and Enteridis, that are generalist pathogenswith broad host specificity and Typhoïdal Salmonella (TS) serovars, like Typhi and Paratyphi A, that arespecialized pathogens strictly adapted to the human host and the cause of an invasive, dangerous diseaseknown as enteric (typhoid) fever [1,2,3].The SalHostTrop project aims at identifying, characterizing and understanding the human-restrictedtropism of Typhoidal Salmonella (TS) using comparative dual-RNAseq sequencing and othercomplementary approaches.We combine state of the art genome and transcriptome sequencing methods to decipher the molecularbasis of host-tropism in clinical strains. We contrast the comparative genomics and differential expressionanalyses to explore and assess the variability and plasticity of pathogenesis routes among and betweentyphoidal and non-typhoidal ser...
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