1
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Johnston KE, Almhjell PJ, Watkins-Dulaney EJ, Liu G, Porter NJ, Yang J, Arnold FH. A combinatorially complete epistatic fitness landscape in an enzyme active site. Proc Natl Acad Sci U S A 2024; 121:e2400439121. [PMID: 39074291 PMCID: PMC11317637 DOI: 10.1073/pnas.2400439121] [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: 01/20/2024] [Accepted: 06/17/2024] [Indexed: 07/31/2024] Open
Abstract
Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.
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Affiliation(s)
- Kadina E. Johnston
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
| | - Patrick J. Almhjell
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
| | - Ella J. Watkins-Dulaney
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
| | - Grace Liu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
| | - Nicholas J. Porter
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
| | - Jason Yang
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
| | - Frances H. Arnold
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA91125
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA91125
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2
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Chitra U, Arnold BJ, Raphael BJ. Quantifying higher-order epistasis: beware the chimera. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603976. [PMID: 39071303 PMCID: PMC11275791 DOI: 10.1101/2024.07.17.603976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.
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3
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Bhutani G, Verma P, Paul S, Dhamija S, Chattopadhyay K, De AK. Elucidating photocycle in large Stokes shift red fluorescent proteins: Focus on mKeima. Photochem Photobiol 2024; 100:897-909. [PMID: 38752609 DOI: 10.1111/php.13964] [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: 03/25/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 07/30/2024]
Abstract
Large Stokes shift red fluorescent proteins (LSS-RFPs) are genetically encoded and exhibit a significant difference of a few hundreds of nanometers between their excitation and emission peak maxima (i.e., the Stokes shift). These LSS-RFPs (absorbing blue light and emitting red light) feature a unique photocycle responsible for their significant Stokes shift. The photocycle associated with this LSS characteristic in certain RFPs is quite perplexing, hinting at the complex nature of excited-state photophysics. This article provides a brief review on the fundamental mechanisms governing the photocycle of various LSS-RFPs, followed by a discussion on experimental results on mKeima emphasizing its relaxation pathways which garnered attention due to its >200 nm Stokes shift. Corroborating steady-state spectroscopy with computational studies, four different forms of chromophore of mKeima contributing to the cis-trans conformers of the neutral and anionic forms were identified in a recent study. Furthering these findings, in this account a detailed discussion on the photocycle of mKeima, which encompasses sequential excited-state isomerization, proton transfer, and subsequent structural reorganization involving three isomers, leading to an intriguing temperature and pH-dependent dual fluorescence, is explored using broadband femtosecond transient absorption spectroscopy.
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Affiliation(s)
- Garima Bhutani
- Condensed Phase Dynamics Group, Department of Chemical Sciences, Indian Institute of Science Education and Research Mohali, SAS Nagar, Punjab, India
| | - Pratima Verma
- Cytolysin Study Group, Department of Biological Sciences, Indian Institute of Science Education and Research Mohali, SAS Nagar, Punjab, India
| | - Sasthi Paul
- Condensed Phase Dynamics Group, Department of Chemical Sciences, Indian Institute of Science Education and Research Mohali, SAS Nagar, Punjab, India
| | - Shaina Dhamija
- Condensed Phase Dynamics Group, Department of Chemical Sciences, Indian Institute of Science Education and Research Mohali, SAS Nagar, Punjab, India
| | - Kausik Chattopadhyay
- Cytolysin Study Group, Department of Biological Sciences, Indian Institute of Science Education and Research Mohali, SAS Nagar, Punjab, India
| | - Arijit K De
- Condensed Phase Dynamics Group, Department of Chemical Sciences, Indian Institute of Science Education and Research Mohali, SAS Nagar, Punjab, India
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4
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Meger AT, Spence MA, Sandhu M, Matthews D, Chen J, Jackson CJ, Raman S. Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors. Cell Syst 2024; 15:374-387.e6. [PMID: 38537640 PMCID: PMC11299162 DOI: 10.1016/j.cels.2024.03.002] [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: 01/26/2023] [Revised: 09/08/2023] [Accepted: 03/05/2024] [Indexed: 04/20/2024]
Abstract
How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.
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Affiliation(s)
- Anthony T Meger
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Matthew A Spence
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Mahakaran Sandhu
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Dana Matthews
- Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
| | - Jackie Chen
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Colin J Jackson
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia; ARC Centre of Excellence for Innovations in Peptide & Protein Science, Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia; ARC Centre of Excellence for Innovations in Synthetic Biology, Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia.
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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5
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Prywes N, Philips NR, Oltrogge LM, Lindner S, Candace Tsai YC, de Pins B, Cowan AE, Taylor-Kearney LJ, Chang HA, Hall LN, Bellieny-Rabelo D, Nisonoff HM, Weissman RF, Flamholz AI, Ding D, Bhatt AY, Shih PM, Mueller-Cajar O, Milo R, Savage DF. A map of the rubisco biochemical landscape. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.27.559826. [PMID: 38645011 PMCID: PMC11030240 DOI: 10.1101/2023.09.27.559826] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Rubisco is the primary CO2 fixing enzyme of the biosphere yet has slow kinetics. The roles of evolution and chemical mechanism in constraining the sequence landscape of rubisco remain debated. In order to map sequence to function, we developed a massively parallel assay for rubisco using an engineered E. coli where enzyme function is coupled to growth. By assaying >99% of single amino acid mutants across CO2 concentrations, we inferred enzyme velocity and CO2 affinity for thousands of substitutions. We identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data suggest that non-trivial kinetic improvements are readily accessible and provide a comprehensive sequence-to-function mapping for enzyme engineering efforts.
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Affiliation(s)
- Noam Prywes
- Innovative Genomics Institute, University of California; Berkeley, California 94720, USA
- Howard Hughes Medical Institute, University of California; Berkeley, California 94720, USA
| | - Naiya R. Philips
- Department of Molecular and Cell Biology, University of California; Berkeley, California 94720, USA
| | - Luke M. Oltrogge
- Howard Hughes Medical Institute, University of California; Berkeley, California 94720, USA
- Department of Molecular and Cell Biology, University of California; Berkeley, California 94720, USA
| | | | - Yi-Chin Candace Tsai
- School of Biological Sciences, Nanyang Technological University; Singapore 637551, Singapore
| | - Benoit de Pins
- Department of Plant and Environmental Sciences, Weizmann Institute of Science; Rehovot 76100, Israel
| | - Aidan E. Cowan
- Department of Molecular and Cell Biology, University of California; Berkeley, California 94720, USA
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory; Emeryville, CA 94608, USA
| | - Leah J. Taylor-Kearney
- Department of Plant and Microbial Biology, University of California, Berkeley; Berkeley, CA 94720, USA
| | - Hana A. Chang
- Department of Plant and Microbial Biology, University of California, Berkeley; Berkeley, CA 94720, USA
| | - Laina N. Hall
- Biophysics, University of California, Berkeley; Berkeley, CA 94720, USA
| | - Daniel Bellieny-Rabelo
- Innovative Genomics Institute, University of California; Berkeley, California 94720, USA
- California Institute for Quantitative Biosciences (QB3), University of California; Berkeley, CA 94720, USA
| | - Hunter M. Nisonoff
- Center for Computational Biology, University of California, Berkeley; Berkeley, CA, USA
| | - Rachel F. Weissman
- Department of Molecular and Cell Biology, University of California; Berkeley, California 94720, USA
| | - Avi I. Flamholz
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA 91125
| | - David Ding
- Innovative Genomics Institute, University of California; Berkeley, California 94720, USA
- Howard Hughes Medical Institute, University of California; Berkeley, California 94720, USA
| | - Abhishek Y. Bhatt
- Department of Molecular and Cell Biology, University of California; Berkeley, California 94720, USA
- School of Medicine, University of California, San Diego; La Jolla, CA 92092, USA
| | - Patrick M. Shih
- Innovative Genomics Institute, University of California; Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley; Berkeley, CA 94720, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA
- Feedstocks Division, Joint BioEnergy Institute; Emeryville, CA 94608, USA
| | - Oliver Mueller-Cajar
- School of Biological Sciences, Nanyang Technological University; Singapore 637551, Singapore
| | - Ron Milo
- Department of Plant and Environmental Sciences, Weizmann Institute of Science; Rehovot 76100, Israel
| | - David F. Savage
- Innovative Genomics Institute, University of California; Berkeley, California 94720, USA
- Howard Hughes Medical Institute, University of California; Berkeley, California 94720, USA
- Department of Molecular and Cell Biology, University of California; Berkeley, California 94720, USA
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6
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Case M, Smith M, Vinh J, Thurber G. Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space. Proc Natl Acad Sci U S A 2024; 121:e2311726121. [PMID: 38451939 PMCID: PMC10945751 DOI: 10.1073/pnas.2311726121] [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: 07/24/2023] [Accepted: 12/27/2023] [Indexed: 03/09/2024] Open
Abstract
Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.
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Affiliation(s)
- Marshall Case
- Chemical Engineering, University of Michigan, Ann Arbor, MI48109
| | - Matthew Smith
- Chemical Engineering, University of Michigan, Ann Arbor, MI48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109
| | - Jordan Vinh
- Biomedical Engineering, University of Michigan, Ann Arbor, MI48109
| | - Greg Thurber
- Chemical Engineering, University of Michigan, Ann Arbor, MI48109
- Biomedical Engineering, University of Michigan, Ann Arbor, MI48109
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7
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Kolesnik VV, Nurtdinov RF, Oloruntimehin ES, Karabelsky AV, Malogolovkin AS. Optimization strategies and advances in the research and development of AAV-based gene therapy to deliver large transgenes. Clin Transl Med 2024; 14:e1607. [PMID: 38488469 PMCID: PMC10941601 DOI: 10.1002/ctm2.1607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Adeno-associated virus (AAV)-based therapies are recognized as one of the most potent next-generation treatments for inherited and genetic diseases. However, several biological and technological aspects of AAV vectors remain a critical issue for their widespread clinical application. Among them, the limited capacity of the AAV genome significantly hinders the development of AAV-based gene therapy. In this context, genetically modified transgenes compatible with AAV are opening up new opportunities for unlimited gene therapies for many genetic disorders. Recent advances in de novo protein design and remodelling are paving the way for new, more efficient and targeted gene therapeutics. Using computational and genetic tools, AAV expression cassette and transgenic DNA can be split, miniaturized, shuffled or created from scratch to mediate efficient gene transfer into targeted cells. In this review, we highlight recent advances in AAV-based gene therapy with a focus on its use in translational research. We summarize recent research and development in gene therapy, with an emphasis on large transgenes (>4.8 kb) and optimizing strategies applied by biomedical companies in the research pipeline. We critically discuss the prospects for AAV-based treatment and some emerging challenges. We anticipate that the continued development of novel computational tools will lead to rapid advances in basic gene therapy research and translational studies.
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Affiliation(s)
- Valeria V. Kolesnik
- Martsinovsky Institute of Medical ParasitologyTropical and Vector‐Borne Diseases, Sechenov UniversityMoscowRussia
| | - Ruslan F. Nurtdinov
- Martsinovsky Institute of Medical ParasitologyTropical and Vector‐Borne Diseases, Sechenov UniversityMoscowRussia
| | - Ezekiel Sola Oloruntimehin
- Martsinovsky Institute of Medical ParasitologyTropical and Vector‐Borne Diseases, Sechenov UniversityMoscowRussia
| | | | - Alexander S. Malogolovkin
- Martsinovsky Institute of Medical ParasitologyTropical and Vector‐Borne Diseases, Sechenov UniversityMoscowRussia
- Center for Translational MedicineSirius University of Science and TechnologySochiRussia
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8
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Zhang S, Ma Z, Li W, Shen Y, Xu Y, Liu G, Chang J, Li Z, Qin H, Tian B, Gong H, Liu D, Thuronyi B, Voigt C. EvoAI enables extreme compression and reconstruction of the protein sequence space. RESEARCH SQUARE 2024:rs.3.rs-3930833. [PMID: 38464127 PMCID: PMC10925456 DOI: 10.21203/rs.3.rs-3930833/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here, we first establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.
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9
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Notin P, Kollasch AW, Ritter D, van Niekerk L, Paul S, Spinner H, Rollins N, Shaw A, Weitzman R, Frazer J, Dias M, Franceschi D, Orenbuch R, Gal Y, Marks DS. ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570727. [PMID: 38106144 PMCID: PMC10723403 DOI: 10.1101/2023.12.07.570727] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.
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Affiliation(s)
| | | | | | | | | | | | | | - Ada Shaw
- Applied Mathematics, Harvard University
| | | | | | - Mafalda Dias
- Centre for Genomic Regulation, Universitat Pompeu Fabra
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
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10
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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: 3] [Impact Index Per Article: 3.0] [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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11
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Yu T, Boob AG, Singh N, Su Y, Zhao H. In vitro continuous protein evolution empowered by machine learning and automation. Cell Syst 2023; 14:633-644. [PMID: 37224814 DOI: 10.1016/j.cels.2023.04.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/19/2022] [Accepted: 04/20/2023] [Indexed: 05/26/2023]
Abstract
Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.
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Affiliation(s)
- Tianhao Yu
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, Urbana, IL, USA; NSF Molecule Maker Lab Institute, Urbana, IL, USA
| | - Aashutosh Girish Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, Urbana, IL, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nilmani Singh
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Yufeng Su
- NSF Molecule Maker Lab Institute, Urbana, IL, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, Urbana, IL, USA; NSF Molecule Maker Lab Institute, Urbana, IL, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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12
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Chen L, Zhang Z, Li Z, Li R, Huo R, Chen L, Wang D, Luo X, Chen K, Liao C, Zheng M. Learning protein fitness landscapes with deep mutational scanning data from multiple sources. Cell Syst 2023; 14:706-721.e5. [PMID: 37591206 DOI: 10.1016/j.cels.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 08/19/2023]
Abstract
One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme that leverages the existing deep mutational scanning data from diverse proteins to aid in understanding the fitness landscape of a new protein. Proof-of-concept trials are designed to validate this training scheme in three aspects: random and positional extrapolation for single-variant effects, zero-shot fitness predictions for new proteins, and extrapolation for higher-order variant effects from single-variant effects. Moreover, our study identified previously overlooked strong baselines, and their unexpectedly good performance brings our attention to the pitfalls of MLDE. Overall, these results may improve our understanding of the association between different protein fitness profiles and shed light on developing better machine learning-assisted approaches to the directed evolution of proteins. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Lin Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zehong Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenghao Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Rui Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Ruifeng Huo
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Cangsong Liao
- University of Chinese Academy of Sciences, Beijing 100049, China; Chemical Biology Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Science, Shanghai 201203, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China.
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13
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Weinstein JY, Martí-Gómez C, Lipsh-Sokolik R, Hoch SY, Liebermann D, Nevo R, Weissman H, Petrovich-Kopitman E, Margulies D, Ivankov D, McCandlish DM, Fleishman SJ. Designed active-site library reveals thousands of functional GFP variants. Nat Commun 2023; 14:2890. [PMID: 37210560 DOI: 10.1038/s41467-023-38099-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/13/2023] [Indexed: 05/22/2023] Open
Abstract
Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.
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Affiliation(s)
| | - Carlos Martí-Gómez
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Rosalie Lipsh-Sokolik
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Shlomo Yakir Hoch
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Demian Liebermann
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Reinat Nevo
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Haim Weissman
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | | | - David Margulies
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Dmitry Ivankov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel.
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14
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Reiter F, de Almeida BP, Stark A. Enhancers display constrained sequence flexibility and context-specific modulation of motif function. Genome Res 2023; 33:346-358. [PMID: 36941077 PMCID: PMC10078294 DOI: 10.1101/gr.277246.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023]
Abstract
The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, whereas the enhancer syntax, that is, the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remains poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important TF motifs by all possible 65,536 eight-nucleotide-long sequences and (2) paste eight important TF motif types into 763 positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but these are only a fraction of all possible sequences and motif types. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, the presence and diversity of other motif types, and the distance between motifs), such that not all motif types can work in all positions. The context-specific modulation of motif function is also a hallmark of human enhancers, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution, and in disease.
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Affiliation(s)
- Franziska Reiter
- Research Institute of Molecular Pathology, Vienna BioCenter, Campus-Vienna-BioCenter 1, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, 1030 Vienna, Austria
| | - Bernardo P de Almeida
- Research Institute of Molecular Pathology, Vienna BioCenter, Campus-Vienna-BioCenter 1, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, 1030 Vienna, Austria
| | - Alexander Stark
- Research Institute of Molecular Pathology, Vienna BioCenter, Campus-Vienna-BioCenter 1, 1030 Vienna, Austria;
- Medical University of Vienna, Vienna BioCenter, 1030 Vienna, Austria
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15
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Wittmund M, Cadet F, Davari MD. Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Marcel Wittmund
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - Frederic Cadet
- Laboratory of Excellence LABEX GR, DSIMB, Inserm UMR S1134, University of Paris city & University of Reunion, Paris 75014, France
| | - Mehdi D. Davari
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
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16
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Abstract
One core goal of genetics is to systematically understand the mapping between the DNA sequence of an organism (genotype) and its measurable characteristics (phenotype). Understanding this mapping is often challenging because of interactions between mutations, where the result of combining several different mutations can be very different than the sum of their individual effects. Here we provide a statistical framework for modeling complex genetic interactions of this type. The key idea is to ask how fast the effects of mutations change when introducing the same mutation in increasingly distant genetic backgrounds. We then propose a model for phenotypic prediction that takes into account this tendency for the effects of mutations to be more similar in nearby genetic backgrounds. Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype–phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype–phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA 5′ splice sites, for which we also validate our model predictions via additional low-throughput experiments.
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17
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Gabzi T, Pilpel Y, Friedlander T. Fitness landscape analysis of a tRNA gene reveals that the wild type allele is sub-optimal, yet mutationally robust. Mol Biol Evol 2022; 39:6670756. [PMID: 35976926 PMCID: PMC9447856 DOI: 10.1093/molbev/msac178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Fitness landscape mapping and the prediction of evolutionary trajectories on these landscapes are major tasks in evolutionary biology research. Evolutionary dynamics is tightly linked to the landscape topography, but this relation is not straightforward. Here, we analyze a fitness landscape of a yeast tRNA gene, previously measured under four different conditions. We find that the wild type allele is sub-optimal, and 8–10% of its variants are fitter. We rule out the possibilities that the wild type is fittest on average on these four conditions or located on a local fitness maximum. Notwithstanding, we cannot exclude the possibility that the wild type might be fittest in some of the many conditions in the complex ecology that yeast lives at. Instead, we find that the wild type is mutationally robust (“flat”), while more fit variants are typically mutationally fragile. Similar observations of mutational robustness or flatness have been so far made in very few cases, predominantly in viral genomes.
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Affiliation(s)
- Tzahi Gabzi
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yitzhak Pilpel
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tamar Friedlander
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture Faculty of Agriculture, Hebrew University of Jerusalem, 229 Herzl St., Rehovot 7610001, Israel
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18
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Abstract
Using a neural network to predict how green fluorescent proteins respond to genetic mutations illuminates properties that could help design new proteins.
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Affiliation(s)
- Grzegorz Kudla
- MRC Human Genetics Unit, The University of Edinburgh, Edinburgh, United Kingdom
| | - Marcin Plech
- MRC Human Genetics Unit, The University of Edinburgh, Edinburgh, United Kingdom
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