1
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Ruan S, Bourne CR. Escherichia coli cells evade inducible parE toxin expression by reducing plasmid copy number. Microbiol Spectr 2024; 12:e0397323. [PMID: 38700352 DOI: 10.1128/spectrum.03973-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Plasmids play important roles in microbial ecosystems, serving as carriers of antibiotic resistance and virulence. In the laboratory, they are essential tools for genetic manipulation and recombinant protein expression. We uncovered an intriguing survival phenotype in a fraction of the bacterial population while using plasmid-mediated arabinose-inducible gene expression to monitor the production of toxic ParE proteins. This phenotype was not correlated with changes to the plasmid sequence and could not be rescued by increasing arabinose uptake. Instead, survival correlates with a marked reduction in plasmid copy number (PCN). Reduced PCN is reproducible, not a function of the pre-existing population, and can be sequentially enriched by continual passage with induction. The reduction in PCN appears to allow mitigation of toxicity from the expression of ParE proteins while balancing the need to maintain a threshold PCN to withstand selection conditions. This indicates an adaptive cellular response to stressful conditions, likely by altering the regulation of plasmid replication. Furthermore, this survival mechanism appears to not be limited to a specific bacterial strain of Escherichia coli or ParE toxin family member, suggesting a generalized response. Finally, bacterial whole genome sequencing indicated an N845S residue substitution in DNA polymerase I, which correlates with the observed reduction in PCN and has been previously reported to impact plasmid replication. Further understanding this molecular mechanism has broader implications for this adaptive response of the dynamics of plasmid-mediated gene expression, microbial adaptation, and genetic engineering methodologies. IMPORTANCE This research has increased our understanding of how bacteria respond to the pressure from plasmid-borne toxic genes, such as those found in toxin-antitoxin systems. Surprisingly, we found that bacteria survived toxic ParE protein expression by reducing the number of these plasmids in the cells. This discovery reveals another way in which bacteria can balance toxin expression with antibiotic selection to attenuate the effects of deleterious genes. This insight is not only valuable for understanding bacterial survival strategies but may also influence the development of better tools in biotechnology, where plasmids are often used to study the functional roles of genes.
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Affiliation(s)
- Shengfeng Ruan
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma, USA
| | - Christina R Bourne
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma, USA
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2
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Metzger BPH, Park Y, Starr TN, Thornton JW. Epistasis facilitates functional evolution in an ancient transcription factor. eLife 2024; 12:RP88737. [PMID: 38767330 PMCID: PMC11105156 DOI: 10.7554/elife.88737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.
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Affiliation(s)
- Brian PH Metzger
- Department of Ecology and Evolution, University of ChicagoChicagoUnited States
| | - Yeonwoo Park
- Program in Genetics, Genomics, and Systems Biology, University of ChicagoChicagoUnited States
| | - Tyler N Starr
- Department of Biochemistry and Molecular Biophysics, University of ChicagoChicagoUnited States
| | - Joseph W Thornton
- Department of Ecology and Evolution, University of ChicagoChicagoUnited States
- Department of Human Genetics, University of ChicagoChicagoUnited States
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3
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Dibyachintan S, Dube AK, Bradley D, Lemieux P, Dionne U, Landry CR. Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.23.581840. [PMID: 38464075 PMCID: PMC10925128 DOI: 10.1101/2024.02.23.581840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Paralogous genes are often redundant for long periods of time before they diverge in function. While their functions are preserved, paralogous proteins can accumulate mutations that, through epistasis, could impact their fate in the future. By quantifying the impact of all single-amino acid substitutions on the binding of two myosin proteins to their interaction partners, we find that the future evolution of these proteins is highly contingent on their regulatory divergence and the mutations that have silently accumulated in their protein binding domains. Differences in the promoter strength of the two paralogs amplify the impact of mutations on binding in the lowly expressed one. While some mutations would be sufficient to non-functionalize one paralog, they would have minimal impact on the other. Our results reveal how functionally equivalent protein domains could be destined to specific fates by regulatory and cryptic coding sequence changes that currently have little to no functional impact.
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Affiliation(s)
- Soham Dibyachintan
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
| | - Alexandre K Dube
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| | - David Bradley
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| | - Pascale Lemieux
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
| | - Ugo Dionne
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Current affiliation: Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Christian R Landry
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
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4
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Ding D, Shaw AY, Sinai S, Rollins N, Prywes N, Savage DF, Laub MT, Marks DS. Protein design using structure-based residue preferences. Nat Commun 2024; 15:1639. [PMID: 38388493 PMCID: PMC10884402 DOI: 10.1038/s41467-024-45621-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/29/2024] [Indexed: 02/24/2024] Open
Abstract
Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues-without accounting for mutation interactions-explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.
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Affiliation(s)
- David Ding
- Innovative Genomics Institute, University of California, Berkeley, CA, 94720, USA.
| | - Ada Y Shaw
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sam Sinai
- Dyno Therapeutics, Watertown, MA, 02472, USA
| | - Nathan Rollins
- Seismic Therapeutics, Lab Central, Cambridge, MA, 02142, USA
| | - Noam Prywes
- Innovative Genomics Institute, University of California, Berkeley, CA, 94720, USA
| | - David F Savage
- Innovative Genomics Institute, University of California, Berkeley, CA, 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, 94720, USA
| | - Michael T Laub
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
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5
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Alvarez S, Nartey CM, Mercado N, de la Paz JA, Huseinbegovic T, Morcos F. In vivo functional phenotypes from a computational epistatic model of evolution. Proc Natl Acad Sci U S A 2024; 121:e2308895121. [PMID: 38285950 PMCID: PMC10861889 DOI: 10.1073/pnas.2308895121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 12/19/2023] [Indexed: 01/31/2024] Open
Abstract
Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.
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Affiliation(s)
- Sophia Alvarez
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
| | - Charisse M. Nartey
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
| | - Nicholas Mercado
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
| | | | - Tea Huseinbegovic
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX75080
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX75080
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6
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Duan B, Qiu C, Lockless SW, Sze SH, Kaplan CD. Higher-order epistasis within Pol II trigger loop haplotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576280. [PMID: 38293233 PMCID: PMC10827151 DOI: 10.1101/2024.01.20.576280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
RNA polymerase II (Pol II) has a highly conserved domain, the trigger loop (TL), that controls transcription fidelity and speed. We previously probed pairwise genetic interactions between residues within and surrounding the TL and identified widespread incompatibility between TLs of different species when placed in the Saccharomyces cerevisiae Pol II context, indicating epistasis between the TL and its surrounding context. We sought to understand the nature of this incompatibility and probe higher order epistasis internal to the TL. We have employed deep mutational scanning with selected natural TL variants ("haplotypes"), and all possible intermediate substitution combinations between them and the yeast Pol II TL. We identified both positive and negative higher-order residue interactions within example TL haplotypes. Intricate higher-order epistasis formed by TL residues was sometimes only apparent from analysis of intermediate genotypes, emphasizing complexity of epistatic interactions. Furthermore, we distinguished TL substitutions with distinct classes of epistatic patterns, suggesting specific TL residues that potentially influence TL evolution. Our examples of complex residue interactions suggest possible pathways for epistasis to facilitate Pol II evolution.
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Affiliation(s)
- Bingbing Duan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
| | - Chenxi Qiu
- Department of Genetics, Harvard Medical School, Boston, MA 02215
| | - Steve W Lockless
- Department of Biology, Texas A&M University, College Station, TX 77843
| | - Sing-Hoi Sze
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX 77843
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX 77843
| | - Craig D Kaplan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
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7
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Avizemer Z, Martí-Gómez C, Hoch SY, McCandlish DM, Fleishman SJ. Evolutionary paths that link orthogonal pairs of binding proteins. RESEARCH SQUARE 2023:rs.3.rs-2836905. [PMID: 37131620 PMCID: PMC10153392 DOI: 10.21203/rs.3.rs-2836905/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Some protein binding pairs exhibit extreme specificities that functionally insulate them from homologs. Such pairs evolve mostly by accumulating single-point mutations, and mutants are selected if their affinity exceeds the threshold required for function1-4. Thus, homologous and high-specificity binding pairs bring to light an evolutionary conundrum: how does a new specificity evolve while maintaining the required affinity in each intermediate5,6? Until now, a fully functional single-mutation path that connects two orthogonal pairs has only been described where the pairs were mutationally close thus enabling experimental enumeration of all intermediates2. We present an atomistic and graph-theoretical framework for discovering low molecular strain single-mutation paths that connect two extant pairs, enabling enumeration beyond experimental capability. We apply it to two orthogonal bacterial colicin endonuclease-immunity pairs separated by 17 interface mutations7. We were not able to find a strain-free and functional path in the sequence space defined by the two extant pairs. But including mutations that bridge amino acids that cannot be exchanged through single-nucleotide mutations led us to a strain-free 19-mutation trajectory that is completely viable in vivo. Our experiments show that the specificity switch is remarkably abrupt, resulting from only one radical mutation on each partner. Furthermore, each of the critical specificity-switch mutations increases fitness, demonstrating that functional divergence could be driven by positive Darwinian selection. These results reveal how even radical functional changes in an epistatic fitness landscape may evolve.
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Affiliation(s)
- Ziv Avizemer
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Carlos Martí-Gómez
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
| | - Shlomo Yakir Hoch
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - David M. McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
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8
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Notin P, Marks DS, Weitzman R, Gal Y. ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570473. [PMID: 38106034 PMCID: PMC10723423 DOI: 10.1101/2023.12.06.570473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Protein design holds immense potential for optimizing naturally occurring proteins, with broad applications in drug discovery, material design, and sustainability. However, computational methods for protein engineering are confronted with significant challenges, such as an expansive design space, sparse functional regions, and a scarcity of available labels. These issues are further exacerbated in practice by the fact most real-life design scenarios necessitate the simultaneous optimization of multiple properties. In this work, we introduce ProteinNPT, a non-parametric transformer variant tailored to protein sequences and particularly suited to label-scarce and multi-task learning settings. We first focus on the supervised fitness prediction setting and develop several cross-validation schemes which support robust performance assessment. We subsequently reimplement prior top-performing baselines, introduce several extensions of these baselines by integrating diverse branches of the protein engineering literature, and demonstrate that ProteinNPT consistently outperforms all of them across a diverse set of protein property prediction tasks. Finally, we demonstrate the value of our approach for iterative protein design across extensive in silico Bayesian optimization and conditional sampling experiments.
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Affiliation(s)
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
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9
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Boss L, Kędzierska B. Bacterial Toxin-Antitoxin Systems' Cross-Interactions-Implications for Practical Use in Medicine and Biotechnology. Toxins (Basel) 2023; 15:380. [PMID: 37368681 DOI: 10.3390/toxins15060380] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Toxin-antitoxin (TA) systems are widely present in bacterial genomes. They consist of stable toxins and unstable antitoxins that are classified into distinct groups based on their structure and biological activity. TA systems are mostly related to mobile genetic elements and can be easily acquired through horizontal gene transfer. The ubiquity of different homologous and non-homologous TA systems within a single bacterial genome raises questions about their potential cross-interactions. Unspecific cross-talk between toxins and antitoxins of non-cognate modules may unbalance the ratio of the interacting partners and cause an increase in the free toxin level, which can be deleterious to the cell. Moreover, TA systems can be involved in broadly understood molecular networks as transcriptional regulators of other genes' expression or modulators of cellular mRNA stability. In nature, multiple copies of highly similar or identical TA systems are rather infrequent and probably represent a transition stage during evolution to complete insulation or decay of one of them. Nevertheless, several types of cross-interactions have been described in the literature to date. This implies a question of the possibility and consequences of the TA system cross-interactions, especially in the context of the practical application of the TA-based biotechnological and medical strategies, in which such TAs will be used outside their natural context, will be artificially introduced and induced in the new hosts. Thus, in this review, we discuss the prospective challenges of system cross-talks in the safety and effectiveness of TA system usage.
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Affiliation(s)
- Lidia Boss
- Department of Bacterial Molecular Genetics, Faculty of Biology, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Barbara Kędzierska
- Department of Bacterial Molecular Genetics, Faculty of Biology, University of Gdańsk, 80-309 Gdańsk, Poland
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10
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Alvarez S, Nartey CM, Mercado N, de la Paz A, Huseinbegovic T, Morcos F. In vivo functional phenotypes from a computational epistatic model of evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.24.542176. [PMID: 37292895 PMCID: PMC10245989 DOI: 10.1101/2023.05.24.542176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called Sequence Evolution with Epistatic Contributions. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo β -lactamase activity in E. coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes and facilitate vaccine development.
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Affiliation(s)
- Sophia Alvarez
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Charisse M. Nartey
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Nicholas Mercado
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Alberto de la Paz
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Tea Huseinbegovic
- School of Natural Sciences and Mathematics, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA
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11
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Duan B, Qiu C, Sze SH, Kaplan C. Widespread epistasis shapes RNA Polymerase II active site function and evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530048. [PMID: 36909581 PMCID: PMC10002619 DOI: 10.1101/2023.02.27.530048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Multi-subunit RNA Polymerases (msRNAPs) are responsible for transcription in all kingdoms of life. At the heart of these msRNAPs is an ultra-conserved active site domain, the trigger loop (TL), coordinating transcription speed and fidelity by critical conformational changes impacting multiple steps in substrate selection, catalysis, and translocation. Previous studies have observed several different types of genetic interactions between eukaryotic RNA polymerase II (Pol II) TL residues, suggesting that the TL's function is shaped by functional interactions of residues within and around the TL. The extent of these interaction networks and how they control msRNAP function and evolution remain to be determined. Here we have dissected the Pol II TL interaction landscape by deep mutational scanning in Saccharomyces cerevisiae Pol II. Through analysis of over 15000 alleles, representing all single mutants, a rationally designed subset of double mutants, and evolutionarily observed TL haplotypes, we identify interaction networks controlling TL function. Substituting residues creates allele-specific networks and propagates epistatic effects across the Pol II active site. Furthermore, the interaction landscape further distinguishes alleles with similar growth phenotypes, suggesting increased resolution over the previously reported single mutant phenotypic landscape. Finally, co-evolutionary analyses reveal groups of co-evolving residues across Pol II converge onto the active site, where evolutionary constraints interface with pervasive epistasis. Our studies provide a powerful system to understand the plasticity of RNA polymerase mechanism and evolution, and provide the first example of pervasive epistatic landscape in a highly conserved and constrained domain within an essential enzyme.
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Affiliation(s)
- Bingbing Duan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
| | - Chenxi Qiu
- Department of Genetics, Harvard Medical School, Boston, MA 02215
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX 77843
| | - Craig Kaplan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
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12
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Malinverni D, Babu MM. Data-driven design of orthogonal protein-protein interactions. Sci Signal 2023; 16:eabm4484. [PMID: 36853962 DOI: 10.1126/scisignal.abm4484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Engineering protein-protein interactions to generate new functions presents a challenge with great potential for many applications, ranging from therapeutics to synthetic biology. To avoid unwanted cross-talk with preexisting protein interaction networks in a cell, the specificity and selectivity of newly engineered proteins must be controlled. Here, we developed a computational strategy that mimics gene duplication and the divergence of preexisting interacting protein pairs to design new interactions. We used the bacterial PhoQ-PhoP two-component system as a model system to demonstrate the feasibility of this strategy and validated the approach with known experimental results. The designed protein pairs are predicted to exclusively interact with each other and to be insulated from potential cross-talk with their native partners. Thus, our approach enables exploration of uncharted regions of the protein sequence space and the design of new interacting protein pairs.
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Affiliation(s)
- Duccio Malinverni
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK.,Department of Structural Biology and Center of Excellence for Data Driven Discovery, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - M Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK.,Department of Structural Biology and Center of Excellence for Data Driven Discovery, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
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13
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Sun J, Kulandaisamy A, Liu J, Hu K, Gromiha MM, Zhang Y. Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications. Comput Struct Biotechnol J 2023; 21:1205-1226. [PMID: 36817959 PMCID: PMC9932300 DOI: 10.1016/j.csbj.2023.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Headington, Oxford OX3 7LD, UK
| | - Arulsamy Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Jacklyn Liu
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Kai Hu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India,Corresponding authors.
| | - Yuan Zhang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China,Corresponding authors.
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14
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Kennedy EN, Foster CA, Barr SA, Bourret RB. General strategies for using amino acid sequence data to guide biochemical investigation of protein function. Biochem Soc Trans 2022; 50:1847-1858. [PMID: 36416676 PMCID: PMC10257402 DOI: 10.1042/bst20220849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 11/24/2022]
Abstract
The rapid increase of '-omics' data warrants the reconsideration of experimental strategies to investigate general protein function. Studying individual members of a protein family is likely insufficient to provide a complete mechanistic understanding of family functions, especially for diverse families with thousands of known members. Strategies that exploit large amounts of available amino acid sequence data can inspire and guide biochemical experiments, generating broadly applicable insights into a given family. Here we review several methods that utilize abundant sequence data to focus experimental efforts and identify features truly representative of a protein family or domain. First, coevolutionary relationships between residues within primary sequences can be successfully exploited to identify structurally and/or functionally important positions for experimental investigation. Second, functionally important variable residue positions typically occupy a limited sequence space, a property useful for guiding biochemical characterization of the effects of the most physiologically and evolutionarily relevant amino acids. Third, amino acid sequence variation within domains shared between different protein families can be used to sort a particular domain into multiple subtypes, inspiring further experimental designs. Although generally applicable to any kind of protein domain because they depend solely on amino acid sequences, the second and third approaches are reviewed in detail because they appear to have been used infrequently and offer immediate opportunities for new advances. Finally, we speculate that future technologies capable of analyzing and manipulating conserved and variable aspects of the three-dimensional structures of a protein family could lead to broad insights not attainable by current methods.
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Affiliation(s)
- Emily N. Kennedy
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Clay A. Foster
- Department of Pediatrics, Section Hematology/Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Sarah A. Barr
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Robert B. Bourret
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
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15
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Evolutionary entrenchment in immune proteins selects against stabilizing mutations. Proc Natl Acad Sci U S A 2022; 119:e2216087119. [PMID: 36449544 PMCID: PMC9894131 DOI: 10.1073/pnas.2216087119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
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16
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Zhao XJG, Cao H. Linking research of biomedical datasets. Brief Bioinform 2022; 23:6712704. [PMID: 36151775 DOI: 10.1093/bib/bbac373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 12/14/2022] Open
Abstract
Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.
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Affiliation(s)
- Xiu-Ju George Zhao
- Wuhan Institute of Physics and Mathematics (WIPM), China.,Wuhan Polytechnic University, China
| | - Hui Cao
- Wuhan Polytechnic University, China
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17
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Wu C, Guo D. Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants. Int J Mol Sci 2022; 23:ijms232012688. [PMID: 36293547 PMCID: PMC9604239 DOI: 10.3390/ijms232012688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/16/2022] Open
Abstract
Proteins are modular functionalities regulating multiple cellular activities in prokaryotes and eukaryotes. As a consequence of higher plants adapting to arid and thermal conditions, C4 photosynthesis is the carbon fixation process involving multi-enzymes working in a coordinated fashion. However, how these enzymes interact with each other and whether they co-evolve in parallel to maintain interactions in different plants remain elusive to date. Here, we report our findings on the global protein co-evolution relationship and local dynamics of co-varying site shifts in key C4 photosynthetic enzymes. We found that in most of the selected key C4 photosynthetic enzymes, global pairwise co-evolution events exist to form functional couplings. Besides, protein-protein interactions between these enzymes may suggest their unknown functionalities in the carbon delivery process. For PEPC and PPCK regulation pairs, pocket formation at the interactive interface are not necessary for their function. This feature is distinct from another well-known regulation pair in C4 photosynthesis, namely, PPDK and PPDK-RP, where the pockets are necessary. Our findings facilitate the discovery of novel protein regulation types and contribute to expanding our knowledge about C4 photosynthesis.
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18
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Qiu J, Zhai Y, Wei M, Zheng C, Jiao X. Toxin–antitoxin systems: Classification, biological roles, and applications. Microbiol Res 2022; 264:127159. [DOI: 10.1016/j.micres.2022.127159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 11/28/2022]
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19
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Morcos F. Characterizing the landscape of evolvability. Nat Ecol Evol 2022; 6:500-501. [PMID: 35361891 PMCID: PMC9150722 DOI: 10.1038/s41559-022-01731-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A framework to experimentally traverse the large space of functionally neutral variants in a toxin–antitoxin protein complex reveals insights on evolvability and entrenchment of molecular interactions.
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Affiliation(s)
- Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA.
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA.
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA.
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