1
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Wang X, Gao X, Fan X, Huai Z, Zhang G, Yao M, Wang T, Huang X, Lai L. WUREN: Whole-modal union representation for epitope prediction. Comput Struct Biotechnol J 2024; 23:2122-2131. [PMID: 38817963 PMCID: PMC11137340 DOI: 10.1016/j.csbj.2024.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.
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Affiliation(s)
| | | | - Xuezhe Fan
- XtalPi Innovation Center, Beijing, China
| | - Zhe Huai
- XtalPi Innovation Center, Beijing, China
| | | | | | | | | | - Lipeng Lai
- XtalPi Innovation Center, Beijing, China
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2
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Ahdritz G, Bouatta N, Floristean C, Kadyan S, Xia Q, Gerecke W, O'Donnell TJ, Berenberg D, Fisk I, Zanichelli N, Zhang B, Nowaczynski A, Wang B, Stepniewska-Dziubinska MM, Zhang S, Ojewole A, Guney ME, Biderman S, Watkins AM, Ra S, Lorenzo PR, Nivon L, Weitzner B, Ban YEA, Chen S, Zhang M, Li C, Song SL, He Y, Sorger PK, Mostaque E, Zhang Z, Bonneau R, AlQuraishi M. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat Methods 2024; 21:1514-1524. [PMID: 38744917 DOI: 10.1038/s41592-024-02272-z] [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] [Received: 08/14/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.
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Affiliation(s)
- Gustaf Ahdritz
- Department of Systems Biology, Columbia University, New York, NY, USA
- Harvard University, Cambridge, MA, USA
| | - Nazim Bouatta
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
| | | | - Sachin Kadyan
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Qinghui Xia
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - William Gerecke
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Daniel Berenberg
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Ian Fisk
- Flatiron Institute, New York, NY, USA
| | | | - Bo Zhang
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | | | | | | | | | | | | | - Stella Biderman
- EleutherAI, New York, NY, USA
- Booz Allen Hamilton, McLean, VA, USA
| | | | - Stephen Ra
- Prescient Design, Genentech, New York, NY, USA
| | | | | | | | | | | | - Minjia Zhang
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | | | | | | | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Zhao Zhang
- Rutgers University, New Brunswick, NJ, USA
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3
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Kinshuk S, Li L, Meckes B, Chan CTY. Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors. Int J Mol Sci 2024; 25:8320. [PMID: 39125888 PMCID: PMC11312098 DOI: 10.3390/ijms25158320] [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: 07/02/2024] [Revised: 07/23/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.
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Affiliation(s)
- Sahaj Kinshuk
- Department of Biomedical Engineering, College of Engineering, University of North Texas, 3940 N Elm Street, Denton, TX 76207, USA; (S.K.); (L.L.); (B.M.)
| | - Lin Li
- Department of Biomedical Engineering, College of Engineering, University of North Texas, 3940 N Elm Street, Denton, TX 76207, USA; (S.K.); (L.L.); (B.M.)
| | - Brian Meckes
- Department of Biomedical Engineering, College of Engineering, University of North Texas, 3940 N Elm Street, Denton, TX 76207, USA; (S.K.); (L.L.); (B.M.)
- BioDiscovery Institute, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203, USA
| | - Clement T. Y. Chan
- Department of Biomedical Engineering, College of Engineering, University of North Texas, 3940 N Elm Street, Denton, TX 76207, USA; (S.K.); (L.L.); (B.M.)
- BioDiscovery Institute, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203, USA
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4
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Martin J, Lequerica Mateos M, Onuchic JN, Coluzza I, Morcos F. Machine learning in biological physics: From biomolecular prediction to design. Proc Natl Acad Sci U S A 2024; 121:e2311807121. [PMID: 38913893 PMCID: PMC11228481 DOI: 10.1073/pnas.2311807121] [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: 06/26/2024] Open
Abstract
Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure.
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Affiliation(s)
- Jonathan Martin
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
| | - Marcos Lequerica Mateos
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Universidad del País Vasco/Euskal Herriko Unibertsitatea Science Park, Leioa48940, Spain
| | - José N. Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX77005
- Department of Physics and Astronomy, Rice University, Houston, TX77005
- Department of Chemistry, Rice University, Houston, TX77005
- Department of BioSciences, Rice University, Houston, TX77005
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Universidad del País Vasco/Euskal Herriko Unibertsitatea Science Park, Leioa48940, Spain
- Basque Foundation for Science, Ikerbasque, Bilbao48940, Spain
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX75080
- Department of Bioengineering, Center for Systems Biology, University of Texas at Dallas, Richardson, TX75080
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5
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Levine H, Tu Y. Machine learning meets physics: A two-way street. Proc Natl Acad Sci U S A 2024; 121:e2403580121. [PMID: 38913898 PMCID: PMC11228530 DOI: 10.1073/pnas.2403580121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024] Open
Affiliation(s)
- Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115
| | - Yuhai Tu
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY 10598
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6
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Jaafari H, Bueno C, Schafer NP, Martin J, Morcos F, Wolynes PG. The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes. Proc Natl Acad Sci U S A 2024; 121:e2322428121. [PMID: 38739795 PMCID: PMC11127006 DOI: 10.1073/pnas.2322428121] [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: 12/20/2023] [Accepted: 03/26/2024] [Indexed: 05/16/2024] Open
Abstract
Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.
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Affiliation(s)
- Hana Jaafari
- Center for Theoretical Biophysics, Rice University, Houston, TX77005
- Applied Physics Graduate Program, Smalley-Curl Institute, Rice University, Houston, TX77005
- Department of Chemistry, Rice University, Houston, TX77005
| | - Carlos Bueno
- Center for Theoretical Biophysics, Rice University, Houston, TX77005
| | | | - Jonathan Martin
- 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
| | - Peter G. Wolynes
- Center for Theoretical Biophysics, Rice University, Houston, TX77005
- Department of Chemistry, Rice University, Houston, TX77005
- Department of Physics and Astronomy, Rice University, Houston, TX77005
- Department of Biochemistry and Cell Biology, Rice University, Houston, TX77005
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7
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Hernández Berthet AS, Aptekmann AA, Tejero J, Sánchez IE, Noguera ME, Roman EA. Associating protein sequence positions with the modulation of quantitative phenotypes. Arch Biochem Biophys 2024; 755:109979. [PMID: 38583654 DOI: 10.1016/j.abb.2024.109979] [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: 12/13/2023] [Revised: 03/11/2024] [Accepted: 03/27/2024] [Indexed: 04/09/2024]
Abstract
Although protein sequences encode the information for folding and function, understanding their link is not an easy task. Unluckily, the prediction of how specific amino acids contribute to these features is still considerably impaired. Here, we developed a simple algorithm that finds positions in a protein sequence with potential to modulate the studied quantitative phenotypes. From a few hundred protein sequences, we perform multiple sequence alignments, obtain the per-position pairwise differences for both the sequence and the observed phenotypes, and calculate the correlation between these last two quantities. We tested our methodology with four cases: archaeal Adenylate Kinases and the organisms optimal growth temperatures, microbial rhodopsins and their maximal absorption wavelengths, mammalian myoglobins and their muscular concentration, and inhibition of HIV protease clinical isolates by two different molecules. We found from 3 to 10 positions tightly associated with those phenotypes, depending on the studied case. We showed that these correlations appear using individual positions but an improvement is achieved when the most correlated positions are jointly analyzed. Noteworthy, we performed phenotype predictions using a simple linear model that links per-position divergences and differences in the observed phenotypes. Predictions are comparable to the state-of-art methodologies which, in most of the cases, are far more complex. All of the calculations are obtained at a very low information cost since the only input needed is a multiple sequence alignment of protein sequences with their associated quantitative phenotypes. The diversity of the explored systems makes our work a valuable tool to find sequence determinants of biological activity modulation and to predict various functional features for uncharacterized members of a protein family.
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Affiliation(s)
- Ayelén S Hernández Berthet
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Intendente Güiraldes 2160 - Ciudad Universitaria, 1428EGA, C.A.B.A., Argentina.
| | - Ariel A Aptekmann
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Buenos Aires, Argentina; Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, 08873, USA; Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, 08901, USA.
| | - Jesús Tejero
- Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA; Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Ignacio E Sánchez
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Facultad de Ciencias Exactas y Naturales, Laboratorio de Fisiología de Proteínas, Buenos Aires, Argentina.
| | - Martín E Noguera
- Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química y Fisicoquímica Biológicas Dr. Alejandro Paladini, Junín 956, 1113AAD, C.A.B.A., Argentina; Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Roque Saenz Peña 352, B1876BXD, Bernal, Argentina.
| | - Ernesto A Roman
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Intendente Güiraldes 2160 - Ciudad Universitaria, 1428EGA, C.A.B.A., Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Química y Fisicoquímica Biológicas Dr. Alejandro Paladini, Junín 956, 1113AAD, C.A.B.A., Argentina.
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8
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Biswas A, Choudhuri I, Arnold E, Lyumkis D, Haldane A, Levy RM. Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure. Proc Natl Acad Sci U S A 2024; 121:e2316662121. [PMID: 38557187 PMCID: PMC11009627 DOI: 10.1073/pnas.2316662121] [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: 09/25/2023] [Accepted: 02/23/2024] [Indexed: 04/04/2024] Open
Abstract
Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interaction between a DRM and other residues in HIV-1 protein sequences, is key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV-1 protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naive patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, RT, and integrase, the primary targets of antiretroviral therapy. The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV-1 and other infectious agents.
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Affiliation(s)
- Avik Biswas
- Center for Biophysics and Computational Biology, College of Science and Technology, Temple University, Philadelphia, PA19122
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA92037
- Department of Physics, University of California San Diego, La Jolla, CA92093
| | - Indrani Choudhuri
- Center for Biophysics and Computational Biology, College of Science and Technology, Temple University, Philadelphia, PA19122
- Department of Chemistry, Temple University, Philadelphia, PA19122
| | - Eddy Arnold
- Department of Chemistry and Chemical Biology, Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ08854
| | - Dmitry Lyumkis
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA92037
- Graduate School of Biological Sciences, Department of Molecular Biology, University of California San Diego, La Jolla, CA92093
| | - Allan Haldane
- Center for Biophysics and Computational Biology, College of Science and Technology, Temple University, Philadelphia, PA19122
- Department of Physics, Temple University, Philadelphia, PA19122
| | - Ronald M. Levy
- Center for Biophysics and Computational Biology, College of Science and Technology, Temple University, Philadelphia, PA19122
- Department of Chemistry, Temple University, Philadelphia, PA19122
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He R, Zhang J, Shao Y, Gu S, Song C, Qian L, Yin WB, Li Z. Knowledge-guided data mining on the standardized architecture of NRPS: Subtypes, novel motifs, and sequence entanglements. PLoS Comput Biol 2023; 19:e1011100. [PMID: 37186644 DOI: 10.1371/journal.pcbi.1011100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 05/25/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with re-engineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries.
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Affiliation(s)
- Ruolin He
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinyu Zhang
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, PR China
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yuanzhe Shao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shaohua Gu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Chen Song
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Long Qian
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Wen-Bing Yin
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, PR China
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing, PR China
| | - Zhiyuan Li
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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10
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Nchourupouo KWT, Nde J, Ngouongo YJW, Zekeng SS, Fongang B. Evolutionary Couplings and Molecular Dynamic Simulations Highlight Details of GPCRs Heterodimers' Interfaces. Molecules 2023; 28:1838. [PMID: 36838825 PMCID: PMC9966702 DOI: 10.3390/molecules28041838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 02/18/2023] Open
Abstract
A growing body of evidence suggests that only a few amino acids ("hot-spots") at the interface contribute most of the binding energy in transient protein-protein interactions. However, experimental protocols to identify these hot-spots are highly labor-intensive and expensive. Computational methods, including evolutionary couplings, have been proposed to predict the hot-spots, but they generally fail to provide details of the interacting amino acids. Here we showed that unbiased evolutionary methods followed by biased molecular dynamic simulations could achieve this goal and reveal critical elements of protein complexes. We applied the methodology to selected G-protein coupled receptors (GPCRs), known for their therapeutic properties. We used the structure-prior-assisted direct coupling analysis (SP-DCA) to predict the binding interfaces of A2aR/D2R, CB1R/D2R, A2aR/CB1R, 5HT2AR/D2R, and 5-HT2AR/mGluR2 receptor heterodimers, which all agreed with published data. In order to highlight details of the interactions, we performed molecular dynamic (MD) simulations using the newly developed AWSEM energy model. We found that these receptors interact primarily through critical residues at the C and N terminal domains and the third intracellular loop (ICL3). The MD simulations showed that these residues are energetically necessary for dimerization and revealed their native conformational state. We subsequently applied the methodology to the 5-HT2AR/5-HTR4R heterodimer, given its implication in drug addiction and neurodegenerative pathologies such as Alzheimer's disease (AD). Further, the SP-DCA analysis showed that 5-HT2AR and 5-HTR4R heterodimerize through the C-terminal domain of 5-HT2AR and ICL3 of 5-HT4R. However, elucidating the details of GPCR interactions would accelerate the discovery of druggable sites and improve our knowledge of the etiology of common diseases, including AD.
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Affiliation(s)
- Karim Widad Temgbet Nchourupouo
- Laboratory of Mechanics, Materials, and Structures, Department of Physics, Faculty of Science, University of Yaoundé I, Yaoundé P.O. Box 812, Cameroon
| | - Jules Nde
- Department of Physics, University of Washington Seattle, Seattle, WA 98105, USA
| | - Yannick Joel Wadop Ngouongo
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Serge Sylvain Zekeng
- Laboratory of Mechanics, Materials, and Structures, Department of Physics, Faculty of Science, University of Yaoundé I, Yaoundé P.O. Box 812, Cameroon
| | - Bernard Fongang
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
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11
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Krishnamohan A, Hamilton GL, Goutam R, Sanabria H, Morcos F. Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins. J Phys Chem B 2023; 127:884-898. [PMID: 36693159 PMCID: PMC9900596 DOI: 10.1021/acs.jpcb.2c06720] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.
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Affiliation(s)
- Aishwarya Krishnamohan
- Departments of Biological Sciences and Bioengineering, University of Texas at Dallas, Richardson, Texas75080, United States
| | - George L Hamilton
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina29634, United States
| | - Rajen Goutam
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina29634, United States
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina29634, United States
| | - Faruck Morcos
- Departments of Biological Sciences and Bioengineering, University of Texas at Dallas, Richardson, Texas75080, United States.,Center for Systems Biology, University of Texas at Dallas, Richardson, Texas75080, United States
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12
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Dietler N, Lupo U, Bitbol AF. Impact of phylogeny on structural contact inference from protein sequence data. J R Soc Interface 2023; 20:20220707. [PMID: 36751926 PMCID: PMC9905998 DOI: 10.1098/rsif.2022.0707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ancestry, their sequences also feature phylogenetic correlations, which can impair contact inference. We investigate this effect by generating controlled synthetic data from a minimal model where the importance of contacts and of phylogeny can be tuned. We demonstrate that global inference methods, specifically Potts models, are more resilient to phylogenetic correlations than local methods, based on covariance or mutual information. This holds whether or not phylogenetic corrections are used, and may explain the success of global methods. We analyse the roles of selection strength and of phylogenetic relatedness. We show that sites that mutate early in the phylogeny yield false positive contacts. We consider natural data and realistic synthetic data, and our findings generalize to these cases. Our results highlight the impact of phylogeny on contact prediction from protein sequences and illustrate the interplay between the rich structure of biological data and inference.
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Affiliation(s)
- Nicola Dietler
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Umberto Lupo
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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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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Chandra A, Tünnermann L, Löfstedt T, Gratz R. Transformer-based deep learning for predicting protein properties in the life sciences. eLife 2023; 12:82819. [PMID: 36651724 PMCID: PMC9848389 DOI: 10.7554/elife.82819] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.
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Affiliation(s)
- Abel Chandra
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Laura Tünnermann
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Regina Gratz
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
- Department of Forest Ecology and Management, Swedish University of Agricultural SciencesUmeåSweden
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15
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Choudhuri I, Biswas A, Haldane A, Levy RM. Contingency and Entrenchment of Drug-Resistance Mutations in HIV Viral Proteins. J Phys Chem B 2022; 126:10622-10636. [PMID: 36493468 PMCID: PMC9841799 DOI: 10.1021/acs.jpcb.2c06123] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ability of HIV-1 to rapidly mutate leads to antiretroviral therapy (ART) failure among infected patients. Drug-resistance mutations (DRMs), which cause a fitness penalty to intrinsic viral fitness, are compensated by accessory mutations with favorable epistatic interactions which cause an evolutionary trapping effect, but the kinetics of this overall process has not been well characterized. Here, using a Potts Hamiltonian model describing epistasis combined with kinetic Monte Carlo simulations of evolutionary trajectories, we explore how epistasis modulates the evolutionary dynamics of HIV DRMs. We show how the occurrence of a drug-resistance mutation is contingent on favorable epistatic interactions with many other residues of the sequence background and that subsequent mutations entrench DRMs. We measure the time-autocorrelation of fluctuations in the likelihood of DRMs due to epistatic coupling with the sequence background, which reveals the presence of two evolutionary processes controlling DRM kinetics with two distinct time scales. Further analysis of waiting times for the evolutionary trapping effect to reverse reveals that the sequences which entrench (trap) a DRM are responsible for the slower time scale. We also quantify the overall strength of epistatic effects on the evolutionary kinetics for different mutations and show these are much larger for DRM positions than polymorphic positions, and we also show that trapping of a DRM is often caused by the collective effect of many accessory mutations, rather than a few strongly coupled ones, suggesting the importance of multiresidue sequence variations in HIV evolution. The analysis presented here provides a framework to explore the kinetic pathways through which viral proteins like HIV evolve under drug-selection pressure.
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Affiliation(s)
| | | | - Allan Haldane
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania 19122, United States; Department of Physics, Temple University, Philadelphia, Pennsylvania 19122-6008, United States
| | - Ronald M. Levy
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States; Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania 19122, United States
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16
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Lupo U, Sgarbossa D, Bitbol AF. Protein language models trained on multiple sequence alignments learn phylogenetic relationships. Nat Commun 2022; 13:6298. [PMID: 36273003 PMCID: PMC9588007 DOI: 10.1038/s41467-022-34032-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/07/2022] [Indexed: 12/25/2022] Open
Abstract
Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily related proteins as inputs. Simple combinations of MSA Transformer's row attentions have led to state-of-the-art unsupervised structural contact prediction. We demonstrate that similarly simple, and universal, combinations of MSA Transformer's column attentions strongly correlate with Hamming distances between sequences in MSAs. Therefore, MSA-based language models encode detailed phylogenetic relationships. We further show that these models can separate coevolutionary signals encoding functional and structural constraints from phylogenetic correlations reflecting historical contingency. To assess this, we generate synthetic MSAs, either without or with phylogeny, from Potts models trained on natural MSAs. We find that unsupervised contact prediction is substantially more resilient to phylogenetic noise when using MSA Transformer versus inferred Potts models.
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Affiliation(s)
- Umberto Lupo
- grid.5333.60000000121839049Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Damiano Sgarbossa
- grid.5333.60000000121839049Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Anne-Florence Bitbol
- grid.5333.60000000121839049Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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17
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Townsley TD, Wilson JT, Akers H, Bryant T, Cordova S, Wallace TL, Durston KK, Deweese JE. PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure. BIOINFORMATICS ADVANCES 2022; 2:vbac058. [PMID: 36699404 PMCID: PMC9710643 DOI: 10.1093/bioadv/vbac058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/08/2022] [Accepted: 08/16/2022] [Indexed: 01/28/2023]
Abstract
Motivation AlphaFold has been a major advance in predicting protein structure, but still leaves the problem of determining which sub-molecular components of a protein are essential for it to carry out its function within the cell. Direct coupling analysis predicts two- and three-amino acid contacts, but there may be essential interdependencies that are not proximal within the 3D structure. The problem to be addressed is to design a computational method that locates and ranks essential non-proximal interdependencies within a protein involving five or more amino acids, using large, multiple sequence alignments (MSAs) for both globular and intrinsically unstructured proteins. Results We developed PSICalc (Protein Subdomain Interdependency Calculator), a laptop-friendly, pattern-discovery, bioinformatics software tool that analyzes large MSAs for both structured and unstructured proteins, locates both proximal and non-proximal inter-dependent sites, and clusters them into pairwise (second order), third-order and higher-order clusters using a k-modes approach, and provides ranked results within minutes. To aid in visualizing these interdependencies, we developed a graphical user interface that displays these subdomain relationships as a polytree graph. To demonstrate, we provide examples of both proximal and non-proximal interdependencies documented for eukaryotic topoisomerase II including between the unstructured C-terminal domain and the N-terminal domain. Availability and implementation https://github.com/jdeweeselab/psicalc-package. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Thomas D Townsley
- Department of Computational Sciences, College of Computing & Technology, Lipscomb University, Nashville, TN 37204, USA,Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA
| | - James T Wilson
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA
| | - Harrison Akers
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA
| | - Timothy Bryant
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA
| | | | - T L Wallace
- Department of Computational Sciences, College of Computing & Technology, Lipscomb University, Nashville, TN 37204, USA,School of Applied Computational Sciences, Department of Biomedical Data Science, Meharry Medical College, Nashville, TN 37208, USA
| | - Kirk K Durston
- Department of Research and Publications, Digital Strategies, Langley, BC V2Y 1N5, Canada
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18
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van Keulen SC, Martin J, Colizzi F, Frezza E, Trpevski D, Diaz NC, Vidossich P, Rothlisberger U, Hellgren Kotaleski J, Wade RC, Carloni P. Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Siri C. van Keulen
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Science for Life, Faculty of Science – Chemistry Utrecht University Utrecht The Netherlands
| | - Juliette Martin
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry University of Lyon Lyon France
| | - Francesco Colizzi
- Molecular Ocean Laboratory, Department of Marine Biology and Oceanography Institute of Marine Sciences, ICM‐CSIC Barcelona Spain
| | - Elisa Frezza
- Université Paris Cité, CiTCoM, CNRS Paris France
| | - Daniel Trpevski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm
| | - Nuria Cirauqui Diaz
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry University of Lyon Lyon France
| | - Pietro Vidossich
- Molecular Modeling and Drug Discovery Lab Istituto Italiano di Tecnologia Genoa Italy
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm
- Department of Neuroscience Karolinska Institute Stockholm
| | - Rebecca C. Wade
- Molecular and Cellular Modeling Group Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany
- Center for Molecular Biology (ZMBH), DKFZ‐ZMBH Alliance, and Interdisciplinary Center for Scientific Computing (IWR) Heidelberg University Heidelberg Germany
| | - Paolo Carloni
- Institute for Neuroscience and Medicine (INM‐9) and Institute for Advanced Simulations (IAS‐5) “Computational biomedicine” Forschungszentrum Jülich Jülich Germany
- INM‐11 JARA‐Institute: Molecular Neuroscience and Neuroimaging Forschungszentrum Jülich Jülich Germany
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19
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Braberg H, Echeverria I, Kaake RM, Sali A, Krogan NJ. From systems to structure - using genetic data to model protein structures. Nat Rev Genet 2022; 23:342-354. [PMID: 35013567 PMCID: PMC8744059 DOI: 10.1038/s41576-021-00441-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 12/11/2022]
Abstract
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Gladstone Institutes, San Francisco, CA, USA.
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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20
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Gerardos A, Dietler N, Bitbol AF. Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences. PLoS Comput Biol 2022; 18:e1010147. [PMID: 35576238 PMCID: PMC9135348 DOI: 10.1371/journal.pcbi.1010147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/26/2022] [Accepted: 04/27/2022] [Indexed: 11/19/2022] Open
Abstract
Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.
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Affiliation(s)
- Andonis Gerardos
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicola Dietler
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- * E-mail:
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21
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Ding D, Green AG, Wang B, Lite TLV, Weinstein EN, Marks DS, Laub MT. Co-evolution of interacting proteins through non-contacting and non-specific mutations. Nat Ecol Evol 2022; 6:590-603. [PMID: 35361892 PMCID: PMC9090974 DOI: 10.1038/s41559-022-01688-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 01/31/2022] [Indexed: 01/08/2023]
Abstract
Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact and promote tolerance non-specifically to many different antitoxin mutations, despite covariation in homologues occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.
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Affiliation(s)
- David Ding
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Anna G Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Boyuan Wang
- Department of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Thuy-Lan Vo Lite
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Michael T Laub
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
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22
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Chi H, Zhou Q, Tutol JN, Phelps SM, Lee J, Kapadia P, Morcos F, Dodani SC. Coupling a Live Cell Directed Evolution Assay with Coevolutionary Landscapes to Engineer an Improved Fluorescent Rhodopsin Chloride Sensor. ACS Synth Biol 2022; 11:1627-1638. [PMID: 35389621 PMCID: PMC9184236 DOI: 10.1021/acssynbio.2c00033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Our understanding of chloride in biology has been accelerated through the application of fluorescent protein-based sensors in living cells. These sensors can be generated and diversified to have a range of properties using laboratory-guided evolution. Recently, we established that the fluorescent proton-pumping rhodopsin wtGR from Gloeobacter violaceus can be converted into a fluorescent sensor for chloride. To unlock this non-natural function, a single point mutation at the Schiff counterion position (D121V) was introduced into wtGR fused to cyan fluorescent protein (CFP) resulting in GR1-CFP. Here, we have integrated coevolutionary analysis with directed evolution to understand how the rhodopsin sequence space can be explored and engineered to improve this starting point. We first show how evolutionary couplings are predictive of functional sites in the rhodopsin family and how a fitness metric based on a sequence can be used to quantify the known proton-pumping activities of GR-CFP variants. Then, we couple this ability to predict potential functional outcomes with a screening and selection assay in live Escherichia coli to reduce the mutational search space of five residues along the proton-pumping pathway in GR1-CFP. This iterative selection process results in GR2-CFP with four additional mutations: E132K, A84K, T125C, and V245I. Finally, bulk and single fluorescence measurements in live E. coli reveal that GR2-CFP is a reversible, ratiometric fluorescent sensor for extracellular chloride with an improved dynamic range. We anticipate that our framework will be applicable to other systems, providing a more efficient methodology to engineer fluorescent protein-based sensors with desired properties.
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23
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Biswas A, Haldane A, Levy RM. Limits to detecting epistasis in the fitness landscape of HIV. PLoS One 2022; 17:e0262314. [PMID: 35041711 PMCID: PMC8765623 DOI: 10.1371/journal.pone.0262314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023] Open
Abstract
The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence “prevalences” expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.
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Affiliation(s)
- Avik Biswas
- Department of Physics, Temple University, Philadelphia, PA, United States of America
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, PA, United States of America
| | - Allan Haldane
- Department of Physics, Temple University, Philadelphia, PA, United States of America
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, PA, United States of America
| | - Ronald M. Levy
- Department of Physics, Temple University, Philadelphia, PA, United States of America
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, PA, United States of America
- Department of Chemistry, Temple University, Philadelphia, PA, United States of America
- * E-mail:
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24
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Do HN, Haldane A, Levy RM, Miao Y. Unique features of different classes of G-protein-coupled receptors revealed from sequence coevolutionary and structural analysis. Proteins 2022; 90:601-614. [PMID: 34599827 PMCID: PMC8738117 DOI: 10.1002/prot.26256] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/21/2021] [Accepted: 09/27/2021] [Indexed: 02/03/2023]
Abstract
G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and represent the primary targets of about one third of currently marketed drugs. Despite the critical importance, experimental structures have been determined for only a limited portion of GPCRs and functional mechanisms of GPCRs remain poorly understood. Here, we have constructed novel sequence coevolutionary models of the A and B classes of GPCRs and compared them with residue contact frequency maps generated with available experimental structures. Significant portions of structural residue contacts were successfully detected in the sequence-based covariational models. "Exception" residue contacts predicted from sequence coevolutionary models but not available structures added missing links that were important for GPCR activation and allosteric modulation. Moreover, we identified distinct residue contacts involving different sets of functional motifs for GPCR activation, such as the Na+ pocket, CWxP, DRY, PIF, and NPxxY motifs in the class A and the HETx and PxxG motifs in the class B. Finally, we systematically uncovered critical residue contacts tuned by allosteric modulation in the two classes of GPCRs, including those from the activation motifs and particularly the extracellular and intracellular loops in class A GPCRs. These findings provide a promising framework for rational design of ligands to regulate GPCR activation and allosteric modulation.
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Affiliation(s)
- Hung N Do
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Allan Haldane
- Department of Chemistry, Center for Biophysics and Computational Biology, Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122,Corresponding authors: and
| | - Ronald M Levy
- Department of Chemistry, Center for Biophysics and Computational Biology, Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122
| | - Yinglong Miao
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047,Corresponding authors: and
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25
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Miyazawa S. Boltzmann Machine Learning and Regularization Methods for Inferring Evolutionary Fields and Couplings From a Multiple Sequence Alignment. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:328-342. [PMID: 32396099 DOI: 10.1109/tcbb.2020.2993232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We study regularization and learning methods and how to tune regularization parameters to correctly infer interactions in Boltzmann machine learning. Using L2 regularization for fields, group L1 for couplings is shown to be very effective for sparse couplings in comparison with L2 and L1. Two regularization parameters are tuned to yield equal values for both the sample and ensemble averages of evolutionary energy. Both averages smoothly change and converge, but their learning profiles are very different between learning methods. The Adam method is modified to make stepsize proportional to the gradient for sparse couplings and to use a soft-thresholding function for group L1. It is shown by first inferring interactions from protein sequences and then from Monte Carlo samples that the fields and couplings can be well recovered, but that recovering the pairwise correlations in the resolution of a total energy is harder for the natural proteins than for the protein-like sequences. Selective temperature for folding/structural constrains in protein evolution is also estimated.
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26
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Chu WT, Yan Z, Chu X, Zheng X, Liu Z, Xu L, Zhang K, Wang J. Physics of biomolecular recognition and conformational dynamics. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2021; 84:126601. [PMID: 34753115 DOI: 10.1088/1361-6633/ac3800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/09/2021] [Indexed: 06/13/2023]
Abstract
Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.
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Affiliation(s)
- Wen-Ting Chu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Zhiqiang Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Xiakun Chu
- Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, NY 11794, United States of America
| | - Xiliang Zheng
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Zuojia Liu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Li Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Kun Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Jin Wang
- Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, NY 11794, United States of America
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27
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The generative capacity of probabilistic protein sequence models. Nat Commun 2021; 12:6302. [PMID: 34728624 PMCID: PMC8563988 DOI: 10.1038/s41467-021-26529-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 09/23/2021] [Indexed: 01/10/2023] Open
Abstract
Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general. Generative models have become increasingly popular in protein design, yet rigorous metrics that allow the comparison of these models are lacking. Here, the authors propose a set of such metrics and use them to compare three popular models.
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28
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Gu J, Sim BR, Li J, Yu Y, Qin L, Wu L, Shen Y, Nie Y, Zhao YL, Xu Y. Evolutionary coupling-inspired engineering of alcohol dehydrogenase reveals the influence of distant sites on its catalytic efficiency for stereospecific synthesis of chiral alcohols. Comput Struct Biotechnol J 2021; 19:5864-5873. [PMID: 34815831 PMCID: PMC8572861 DOI: 10.1016/j.csbj.2021.10.031] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 01/02/2023] Open
Abstract
Alcohol dehydrogenase (ADH) has attracted much attention due to its ability to catalyze the synthesis of important chiral alcohol pharmaceutical intermediates with high stereoselectivity. ADH protein engineering efforts have generally focused on reshaping the substrate-binding pocket. However, distant sites outside the pocket may also affect its activity, although the underlying molecular mechanism remains unclear. The current study aimed to apply evolutionary coupling-inspired engineering to the ADH CpRCR and to identify potential mutation sites. Through conservative analysis, phylogenic analysis and residues distribution analysis, the co-evolution hotspots Leu34 and Leu137 were confirmed to be highly evolved under the pressure of natural selection and to be possibly related to the catalytic function of the protein. Hence, Leu34 and Leu137, far away from the active center, were selected for mutation. The generated CpRCR-L34A and CpRCR-L137V variants showed high stereoselectivity and 1.24-7.81 fold increase in k cat /K m value compared with that of the wild type, when reacted with 8 aromatic ketones or β-ketoesters. Corresponding computational study implied that L34 and L137 may extend allosteric fluctuation in the protein structure from the distal mutational site to the active site. Moreover, the L34 and L137 mutations modified the pre-reaction state in multiple ways, in terms of position of the hydride with respect to the target carbonyl. These findings provide insights into the catalytic mechanism of the enzyme and facilitate its regulation from the perspective of the site interaction network.
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Affiliation(s)
- Jie Gu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Byu Ri Sim
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiarui Li
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yangqing Yu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Lei Qin
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Lunjie Wu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yu Shen
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
- Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Xu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
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29
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Mehrabiani KM, Cheng RR, Onuchic JN. Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data. J Phys Chem B 2021; 125:11408-11417. [PMID: 34618469 DOI: 10.1021/acs.jpcb.1c07145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Direct coupling analysis (DCA) is a global statistical approach that uses information encoded in protein sequence data to predict spatial contacts in a three-dimensional structure of a folded protein. DCA has been widely used to predict the monomeric fold at amino acid resolution and to identify biologically relevant interaction sites within a folded protein. Going beyond single proteins, DCA has also been used to identify spatial contacts that stabilize the interaction in protein complex formation. However, extracting this higher order information necessary to predict dimer contacts presents a significant challenge. A DCA evolutionary signal is much stronger at the single protein level (intraprotein contacts) than at the protein-protein interface (interprotein contacts). Therefore, if DCA-derived information is to be used to predict the structure of these complexes, there is a need to identify statistically significant DCA predictions. We propose a simple Z-score measure that can filter good predictions despite noisy, limited data. This new methodology not only improves our prediction ability but also provides a quantitative measure for the validity of the prediction.
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Affiliation(s)
- Kareem M Mehrabiani
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - Ryan R Cheng
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States.,Department of Physics & Astronomy, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States
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30
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Laine E, Eismann S, Elofsson A, Grudinin S. Protein sequence-to-structure learning: Is this the end(-to-end revolution)? Proteins 2021; 89:1770-1786. [PMID: 34519095 DOI: 10.1002/prot.26235] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 01/08/2023]
Abstract
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.
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Affiliation(s)
- Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France
| | - Stephan Eismann
- Department of Computer Science and Applied Physics, Stanford University, Stanford, California, USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
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31
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Colizzi F, Orozco M. Probing allosteric regulations with coevolution-driven molecular simulations. SCIENCE ADVANCES 2021; 7:eabj0786. [PMID: 34516882 PMCID: PMC8442858 DOI: 10.1126/sciadv.abj0786] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
Protein-mediated allosteric regulations are essential in biology, but their quantitative characterization continues to posit formidable challenges for both experiments and computations. Here, we combine coevolutionary information, multiscale molecular simulations, and free-energy methods to interrogate and quantify the allosteric regulation of functional changes in protein complexes. We apply this approach to investigate the regulation of adenylyl cyclase (AC) by stimulatory and inhibitory G proteins—a prototypical allosteric system that has long escaped from in-depth molecular characterization. We reveal a surprisingly simple ON/OFF regulation of AC functional dynamics through multiple pathways of information transfer. The binding of G proteins reshapes the free-energy landscape of AC following the classical population-shift paradigm. The model agrees with structural and biochemical data and reveals previously unknown experimentally consistent intermediates. Our approach showcases a general strategy to explore uncharted functional space in complex biomolecular regulations.
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Affiliation(s)
- Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Carrer de Baldiri Reixac 10, Barcelona 08028, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Carrer de Baldiri Reixac 10, Barcelona 08028, Spain
- Departament de Bioquímica i Biomedicina, Facultat de Biologia, Universitat de Barcelona, Avinguda Diagonal 647, Barcelona 08028, Spain
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32
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Reza MS, Zhang H, Hossain MT, Jin L, Feng S, Wei Y. COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization. MEMBRANES 2021; 11:membranes11070503. [PMID: 34209399 PMCID: PMC8305966 DOI: 10.3390/membranes11070503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 11/17/2022]
Abstract
Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein’s function; therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a Mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue–residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant α-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods; therefore, COMTOP is more robust against different types of test sets. COMTOP also showed better/comparable predictions when compared with the state-of-the-art predictors.
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Affiliation(s)
- Md. Selim Reza
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (M.S.R.); (H.Z.); (M.T.H.)
- Centre for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Huiling Zhang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (M.S.R.); (H.Z.); (M.T.H.)
- Centre for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Md. Tofazzal Hossain
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (M.S.R.); (H.Z.); (M.T.H.)
- Centre for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Langxi Jin
- Department of Computer Science and Technology, School of Computer Science and Technology, Harbin University of Science and Technology, 52 Xuefu Road, Nangang District, Harbin 150080, China;
| | - Shengzhong Feng
- Centre for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Yanjie Wei
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; (M.S.R.); (H.Z.); (M.T.H.)
- Centre for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
- Correspondence:
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33
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Machine learning in protein structure prediction. Curr Opin Chem Biol 2021; 65:1-8. [PMID: 34015749 DOI: 10.1016/j.cbpa.2021.04.005] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/10/2021] [Indexed: 12/31/2022]
Abstract
Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing "neuralization" of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence-structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.
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34
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Zou T, Woodrum BW, Halloran N, Campitelli P, Bobkov AA, Ghirlanda G, Ozkan SB. Local Interactions That Contribute Minimal Frustration Determine Foldability. J Phys Chem B 2021; 125:2617-2626. [PMID: 33687216 DOI: 10.1021/acs.jpcb.1c00364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Earlier experiments suggest that the evolutionary information (conservation and coevolution) encoded in protein sequences is necessary and sufficient to specify the fold of a protein family. However, there is no computational work to quantify the effect of such evolutionary information on the folding process. Here we explore the role of early folding steps for sequences designed using coevolution and conservation through a combination of computational and experimental methods. We simulated a repertoire of native and designed WW domain sequences to analyze early local contact formation and found that the N-terminal β-hairpin turn would not form correctly due to strong non-native local contacts in unfoldable sequences. Through a maximum likelihood approach, we identified five local contacts that play a critical role in folding, suggesting that a small subset of amino acid pairs can be used to solve the "needle in the haystack" problem to design foldable sequences. Thus, using the contact probability of those five local contacts that form during the early stage of folding, we built a classification model that predicts the foldability of a WW sequence with 81% accuracy. This classification model was used to redesign WW domain sequences that could not fold due to frustration and make them foldable by introducing a few mutations that led to the stabilization of these critical local contacts. The experimental analysis shows that a redesigned sequence folds and binds to polyproline peptides with a similar affinity as those observed for native WW domains. Overall, our analysis shows that evolutionary-designed sequences should not only satisfy the folding stability but also ensure a minimally frustrated folding landscape.
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Affiliation(s)
- Taisong Zou
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona 85287, United States
| | - Brian W Woodrum
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Nicholas Halloran
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Paul Campitelli
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona 85287, United States
| | - Andrey A Bobkov
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California 92037, United States
| | - Giovanna Ghirlanda
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Sefika Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona 85287, United States
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35
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Haldane A, Levy RM. Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein covariation analysis. COMPUTER PHYSICS COMMUNICATIONS 2021; 260:107312. [PMID: 33716309 PMCID: PMC7944406 DOI: 10.1016/j.cpc.2020.107312] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. We introduce Mi3-GPU ("mee-three", for MCMC Inverse Ising Inference) software for solving the inverse Ising problem for protein-sequence datasets with few analytic approximations, by parallel Markov-Chain Monte-Carlo sampling on GPUs. We also provide tools for analysis and preparation of protein-family Multiple Sequence Alignments (MSAs) to account for finite-sampling issues, which are a major source of error or bias in inverse Ising inference. Our method is "generative" in the sense that the inferred model can be used to generate synthetic MSAs whose mutational statistics (marginals) can be verified to match the dataset MSA statistics up to the limits imposed by the effects of finite sampling. Our GPU implementation enables the construction of models which reproduce the covariation patterns of the observed MSA with a precision that is not possible with more approximate methods. The main components of our method are a GPU-optimized algorithm to greatly accelerate MCMC sampling, combined with a multi-step Quasi-Newton parameter-update scheme using a "Zwanzig reweighting" technique. We demonstrate the ability of this software to produce generative models on typical protein family datasets for sequence lengths L ~ 300 with 21 residue types with tens of millions of inferred parameters in short running times.
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Affiliation(s)
- Allan Haldane
- Center for Biophysics and Computational Biology and Department of Physics, Temple University, Philadelphia, Pennsylvania 19122
| | - Ronald M. Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122
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36
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Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
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37
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Thadani NN, Zhou Q, Reyes Gamas K, Butler S, Bueno C, Schafer NP, Morcos F, Wolynes PG, Suh J. Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus. Biophys J 2020; 120:489-503. [PMID: 33359833 DOI: 10.1016/j.bpj.2020.12.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/08/2020] [Accepted: 12/08/2020] [Indexed: 01/03/2023] Open
Abstract
Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.
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Affiliation(s)
| | - Qin Zhou
- Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas
| | | | - Susan Butler
- Department of Bioengineering, Rice University, Houston, Texas
| | - Carlos Bueno
- Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas
| | - Nicholas P Schafer
- Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemistry, Rice University, Houston, Texas
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, University of Texas at Dallas, Richardson, Texas; Department of Bioengineering, University of Texas at Dallas, Richardson, Texas
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemistry, Rice University, Houston, Texas; Department of Biosciences, Rice University, Houston, Texas; Department of Physics, Rice University, Houston, Texas
| | - Junghae Suh
- Department of Bioengineering, Rice University, Houston, Texas; Department of Biosciences, Rice University, Houston, Texas; Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas; Systems, Synthetic, and Physical Biology Program, Rice University, Houston, Texas.
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38
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Muntoni AP, Pagnani A, Weigt M, Zamponi F. Aligning biological sequences by exploiting residue conservation and coevolution. Phys Rev E 2020; 102:062409. [PMID: 33465950 DOI: 10.1103/physreve.102.062409] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 11/12/2020] [Indexed: 11/07/2022]
Abstract
Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among the most important computational problems is that of sequence alignment, i.e., arranging sequences from different organisms in such a way to identify similar regions, to detect evolutionary relationships between sequences, and to predict biomolecular structure and function. This is typically addressed through profile models, which capture position specificities like conservation in sequences but assume an independent evolution of different positions. Over recent years, it has been well established that coevolution of different amino-acid positions is essential for maintaining three-dimensional structure and function. Modeling approaches based on inverse statistical physics can catch the coevolution signal in sequence ensembles, and they are now widely used in predicting protein structure, protein-protein interactions, and mutational landscapes. Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information. The potential of DCAlign is carefully explored using well-controlled simulated data, as well as real protein and RNA sequences.
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Affiliation(s)
- Anna Paola Muntoni
- Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy
- Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biologie Computationnelle et Quantitative LCQB, F-75005 Paris, France
| | - Andrea Pagnani
- Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy
- Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060 Candiolo (TO), Italy
- INFN, Sezione di Torino, Via Giuria 1, I-10125 Torino, Italy
| | - Martin Weigt
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biologie Computationnelle et Quantitative LCQB, F-75005 Paris, France
| | - Francesco Zamponi
- Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
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39
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Yan Z, Wang J. Funneled energy landscape unifies principles of protein binding and evolution. Proc Natl Acad Sci U S A 2020; 117:27218-27223. [PMID: 33067388 PMCID: PMC7959555 DOI: 10.1073/pnas.2013822117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Most proteins have evolved to spontaneously fold into native structure and specifically bind with their partners for the purpose of fulfilling biological functions. According to Darwin, protein sequences evolve through random mutations, and only the fittest survives. The understanding of how the evolutionary selection sculpts the interaction patterns for both biomolecular folding and binding is still challenging. In this study, we incorporated the constraint of functional binding into the selection fitness based on the principle of minimal frustration for the underlying biomolecular interactions. Thermodynamic stability and kinetic accessibility were derived and quantified from a global funneled energy landscape that satisfies the requirements of both the folding into the stable structure and binding with the specific partner. The evolution proceeds via a bowl-like evolution energy landscape in the sequence space with a closed-ring attractor at the bottom. The sequence space is increasingly reduced until this ring attractor is reached. The molecular-interaction patterns responsible for folding and binding are identified from the evolved sequences, respectively. The residual positions participating in the interactions responsible for folding are highly conserved and maintain the hydrophobic core under additional evolutionary constraints of functional binding. The positions responsible for binding constitute a distributed network via coupling conservations that determine the specificity of binding with the partner. This work unifies the principles of protein binding and evolution under minimal frustration and sheds light on the evolutionary design of proteins for functions.
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Affiliation(s)
- Zhiqiang Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, P. R. China
| | - Jin Wang
- Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, NY 11790
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40
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Reinartz I, Weiel M, Schug A. FRET Dyes Significantly Affect SAXS Intensities of Proteins. Isr J Chem 2020. [DOI: 10.1002/ijch.202000007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Ines Reinartz
- Institute for Automation and Applied InformaticsKarlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany
- HIDSS4Health – Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany
| | - Marie Weiel
- Department of PhysicsKarlsruhe Institute of Technology Wolfgang-Gaede-Str. 1 76131 Karlsruhe Germany
- Steinbuch Centre for ComputingKarlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany
| | - Alexander Schug
- Institute for Advanced Simulation Jülich Supercomputing Center Wilhelm-Johnen-Straße 52428 Jülich Germany
- Faculty of BiologyUniversity of Duisburg-Essen Germany
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41
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Cooper CJ, Zheng K, Rush KW, Johs A, Sanders BC, Pavlopoulos GA, Kyrpides NC, Podar M, Ovchinnikov S, Ragsdale SW, Parks JM. Structure determination of the HgcAB complex using metagenome sequence data: insights into microbial mercury methylation. Commun Biol 2020; 3:320. [PMID: 32561885 PMCID: PMC7305189 DOI: 10.1038/s42003-020-1047-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/27/2020] [Indexed: 11/09/2022] Open
Abstract
Bacteria and archaea possessing the hgcAB gene pair methylate inorganic mercury (Hg) to form highly toxic methylmercury. HgcA consists of a corrinoid binding domain and a transmembrane domain, and HgcB is a dicluster ferredoxin. However, their detailed structure and function have not been thoroughly characterized. We modeled the HgcAB complex by combining metagenome sequence data mining, coevolution analysis, and Rosetta structure calculations. In addition, we overexpressed HgcA and HgcB in Escherichia coli, confirmed spectroscopically that they bind cobalamin and [4Fe-4S] clusters, respectively, and incorporated these cofactors into the structural model. Surprisingly, the two domains of HgcA do not interact with each other, but HgcB forms extensive contacts with both domains. The model suggests that conserved cysteines in HgcB are involved in shuttling HgII, methylmercury, or both. These findings refine our understanding of the mechanism of Hg methylation and expand the known repertoire of corrinoid methyltransferases in nature. Connor J. Cooper et al. expressed HgcA and HgcB in Escherichia coli and modeled the structure of the HgcAB complex by combining metagenome sequence data, coevolution analysis, and ab initio structure calculations. This study provides insights into the biochemical mechanism of mercury (Hg) methylation.
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Affiliation(s)
- Connor J Cooper
- Graduate School of Genome Science and Technology, University of Tennessee, F225 Walters Life Science, Knoxville, TN, 37996, USA.,Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831-6038, USA
| | - Kaiyuan Zheng
- Department of Biological Chemistry, University of Michigan Medical School, 1150 West Medical Center Drive, Ann Arbor, MI, 48109-0606, USA
| | - Katherine W Rush
- Department of Biological Chemistry, University of Michigan Medical School, 1150 West Medical Center Drive, Ann Arbor, MI, 48109-0606, USA
| | - Alexander Johs
- Environmental Sciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831-6038, USA
| | - Brian C Sanders
- Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831-6038, USA
| | - Georgios A Pavlopoulos
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.,Institute for Fundamental Biomedical Research, Biomedical Science Research Center "Alexander Fleming", 34 Fleming Street, 16672, Vari, Greece
| | - Nikos C Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory Berkeley, California, USA
| | - Mircea Podar
- Graduate School of Genome Science and Technology, University of Tennessee, F225 Walters Life Science, Knoxville, TN, 37996, USA.,Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831-6038, USA
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, 02138, USA
| | - Stephen W Ragsdale
- Department of Biological Chemistry, University of Michigan Medical School, 1150 West Medical Center Drive, Ann Arbor, MI, 48109-0606, USA
| | - Jerry M Parks
- Graduate School of Genome Science and Technology, University of Tennessee, F225 Walters Life Science, Knoxville, TN, 37996, USA. .,Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831-6038, USA.
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42
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Epistatic contributions promote the unification of incompatible models of neutral molecular evolution. Proc Natl Acad Sci U S A 2020; 117:5873-5882. [PMID: 32123092 PMCID: PMC7084075 DOI: 10.1073/pnas.1913071117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Mathematical models of evolution help us understand mechanisms driving protein-sequence change. Previous models recapitulate a disjoint subset of statistical features of natural sequences. We present a neutral evolution model that unifies features including extreme variance of the molecular clock’s tick rate and the observation of an evolutionary Stokes shift, an irreversible effect of mutations in the fitness landscape during sequence evolution. We show that interactions between amino acid sites, which inform our fitness metric, are required to observe these features. These interactions are inferred by using direct coupling analysis, which has been successfully utilized to predict protein structures, dynamics, and complexes from coevolutionary information. We anticipate our model will have applications in phylogenetics, ancestral reconstruction of sequences, and protein design. We introduce a model of amino acid sequence evolution that accounts for the statistical behavior of real sequences induced by epistatic interactions. We base the model dynamics on parameters derived from multiple sequence alignments analyzed by using direct coupling analysis methodology. Known statistical properties such as overdispersion, heterotachy, and gamma-distributed rate-across-sites are shown to be emergent properties of this model while being consistent with neutral evolution theory, thereby unifying observations from previously disjointed evolutionary models of sequences. The relationship between site restriction and heterotachy is characterized by tracking the effective alphabet dynamics of sites. We also observe an evolutionary Stokes shift in the fitness of sequences that have undergone evolution under our simulation. By analyzing the structural information of some proteins, we corroborate that the strongest Stokes shifts derive from sites that physically interact in networks near biochemically important regions. Perspectives on the implementation of our model in the context of the molecular clock are discussed.
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43
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Nerattini F, Figliuzzi M, Cardelli C, Tubiana L, Bianco V, Dellago C, Coluzza I. Identification of Protein Functional Regions. Chemphyschem 2020; 21:335-347. [PMID: 31944517 DOI: 10.1002/cphc.201900898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 11/01/2019] [Indexed: 11/12/2022]
Abstract
Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co-evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co-evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co-evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.
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Affiliation(s)
- Francesca Nerattini
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria
| | - Matteo Figliuzzi
- Sorbonne Universites, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative UMR, 7238, Paris, France
| | - Chiara Cardelli
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria
| | - Luca Tubiana
- Physics Department, Universitá degli studi di Trento, via Sommarive 14, 38123, Trento, IT
| | - Valentino Bianco
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria.,Faculty of Chemistry, Chemical Physics Department, Universidad Complutense de Madrid, Plaza de las Ciencias, Ciudad Universitaria, Madrid, 28040, Spain
| | - Christoph Dellago
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria
| | - Ivan Coluzza
- CIC biomaGUNE, Paseo Miramon 182, 20014 San Sebastian, Spain, and IKERBASQUE, Basque Foundation for Science, 48013, Bilbao, Spain
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44
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Rizzato F, Coucke A, de Leonardis E, Barton JP, Tubiana J, Monasson R, Cocco S. Inference of compressed Potts graphical models. Phys Rev E 2020; 101:012309. [PMID: 32069678 DOI: 10.1103/physreve.101.012309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Indexed: 06/10/2023]
Abstract
We consider the problem of inferring a graphical Potts model on a population of variables. This inverse Potts problem generally involves the inference of a large number of parameters, often larger than the number of available data, and, hence, requires the introduction of regularization. We study here a double regularization scheme, in which the number of Potts states (colors) available to each variable is reduced and interaction networks are made sparse. To achieve the color compression, only Potts states with large empirical frequency (exceeding some threshold) are explicitly modeled on each site, while the others are grouped into a single state. We benchmark the performances of this mixed regularization approach, with two inference algorithms, adaptive cluster expansion (ACE) and pseudolikelihood maximization (PLM), on synthetic data obtained by sampling disordered Potts models on Erdős-Rényi random graphs. We show in particular that color compression does not affect the quality of reconstruction of the parameters corresponding to high-frequency symbols, while drastically reducing the number of the other parameters and thus the computational time. Our procedure is also applied to multisequence alignments of protein families, with similar results.
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Affiliation(s)
- Francesca Rizzato
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Alice Coucke
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Eleonora de Leonardis
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - John P Barton
- Department of Physics and Astronomy, University of California, Riverside, 900 University Avenue, Riverside, California 92521, USA
| | - Jérôme Tubiana
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Rémi Monasson
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Simona Cocco
- Laboratoire de Physique de l'Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
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45
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Dimas RP, Jiang XL, Alberto de la Paz J, Morcos F, Chan CTY. Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset. Nucleic Acids Res 2019; 47:5449-5463. [PMID: 31162606 PMCID: PMC6547410 DOI: 10.1093/nar/gkz280] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/08/2019] [Indexed: 12/17/2022] Open
Abstract
Engineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information.
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Affiliation(s)
- Rey P Dimas
- Department of Biology, The University of Texas at Tyler, Tyler, TX 75799, USA
| | - Xian-Li Jiang
- Department of Biological Sciences, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Jose Alberto de la Paz
- Department of Biological Sciences, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Faruck Morcos
- Department of Biological Sciences, The University of Texas at Dallas, Dallas, TX 75080, USA.,Department of Bioengineering, The University of Texas at Dallas, Dallas, TX 75080, USA.,Center for Systems Biology, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Clement T Y Chan
- Department of Biology, The University of Texas at Tyler, Tyler, TX 75799, USA.,Department of Chemistry and Biochemistry, The University of Texas at Tyler, Tyler, TX 75799, USA
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46
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Zerihun MB, Pucci F, Peter EK, Schug A. pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences. Bioinformatics 2019; 36:2264-2265. [DOI: 10.1093/bioinformatics/btz892] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 10/14/2019] [Accepted: 11/26/2019] [Indexed: 12/20/2022] Open
Abstract
Abstract
Motivation
The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction.
Results
Here, we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds.
Availability and implementation
pydca can be obtained from https://github.com/KIT-MBS/pydca or from the Python Package Index under the MIT License.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mehari B Zerihun
- Steinbuch Centre for Computing, Eggenstein-Leopoldshafen 76344
- Department of Physics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen 76344
| | - Fabrizio Pucci
- John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, Jülich 52428, Germany
| | - Emanuel K Peter
- John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, Jülich 52428, Germany
| | - Alexander Schug
- John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, Jülich 52428, Germany
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47
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Dos Santos RN, Bottino GF, Gozzo FC, Morcos F, Martínez L. Structural complementarity of distance constraints obtained from chemical cross-linking and amino acid coevolution. Proteins 2019; 88:625-632. [PMID: 31693206 DOI: 10.1002/prot.25843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/07/2019] [Accepted: 11/03/2019] [Indexed: 12/11/2022]
Abstract
The analysis of amino acid coevolution has emerged as a practical method for protein structural modeling by providing structural contact information from alignments of amino acid sequences. In parallel, chemical cross-linking/mass spectrometry (XLMS) has gained attention as a universally applicable method for obtaining low-resolution distance constraints to model the quaternary arrangements of proteins, and more recently even protein tertiary structures. Here, we show that the structural information obtained by XLMS and coevolutionary analysis are effectively complementary: the distance constraints obtained by each method are almost exclusively associated with non-coincident pairs of residues, and modeling results obtained by the combination of both sets are improved relative to considering the same total number of constraints of a single type. The structural rationale behind the complementarity of the distance constraints is discussed and illustrated for a representative set of proteins with different sizes and folds.
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Affiliation(s)
- Ricardo N Dos Santos
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, Brazil.,Center for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Guilherme F Bottino
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, Brazil.,Center for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Fábio C Gozzo
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, Brazil
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas.,Department of Bioengineering, University of Texas at Dallas, Richardson, Texas
| | - Leandro Martínez
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, Brazil.,Center for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo, Brazil
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48
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Wang X, Jing X, Deng Y, Nie Y, Xu F, Xu Y, Zhao YL, Hunt JF, Montelione GT, Szyperski T. Evolutionary coupling saturation mutagenesis: Coevolution-guided identification of distant sites influencing Bacillus naganoensis pullulanase activity. FEBS Lett 2019; 594:799-812. [PMID: 31665817 DOI: 10.1002/1873-3468.13652] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/15/2019] [Accepted: 10/25/2019] [Indexed: 01/20/2023]
Abstract
Pullulanases are well-known debranching enzymes hydrolyzing α-1,6-glycosidic linkages. To date, engineering of pullulanase is mainly focused on catalytic pocket or domain tailoring based on structure/sequence information. Saturation mutagenesis-involved directed evolution is, however, limited by the low number of mutational sites compatible with combinatorial libraries of feasible size. Using Bacillus naganoensis pullulanase as a target protein, here we introduce the 'evolutionary coupling saturation mutagenesis' (ECSM) approach: residue pair covariances are calculated to identify residues for saturation mutagenesis, focusing directed evolution on residue pairs playing important roles in natural evolution. Evolutionary coupling (EC) analysis identified seven residue pairs as evolutionary mutational hotspots. Subsequent saturation mutagenesis yielded variants with enhanced catalytic activity. The functional pairs apparently represent distant sites affecting enzyme activity.
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Affiliation(s)
- Xinye Wang
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Xiaoran Jing
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yi Deng
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Fei Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - John F Hunt
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Department of Chemistry and Chemical Biology, and Center for Biotechnology and Integrative Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, NY, USA
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49
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Li Y, De la Paz JA, Jiang X, Liu R, Pokkulandra AP, Bleris L, Morcos F. Coevolutionary Couplings Unravel PAM-Proximal Constraints of CRISPR-SpCas9. Biophys J 2019; 117:1684-1691. [PMID: 31648792 DOI: 10.1016/j.bpj.2019.09.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/25/2019] [Accepted: 09/30/2019] [Indexed: 01/07/2023] Open
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR) system, an immune system analog found in prokaryotes, allows a single-guide RNA to direct a CRISPR-associated protein (Cas) with combined helicase and nuclease activity to DNA. The presence of a specific protospacer adjacent motif (PAM) next to the DNA target site plays a crucial role in determining both efficacy and specificity of gene editing. Herein, we introduce a coevolutionary framework to computationally unveil nonobvious molecular interactions in CRISPR systems and experimentally probe their functional role. Specifically, we use direct coupling analysis, a statistical inference framework used to infer direct coevolutionary couplings, in the context of protein/nucleic acid interactions. Applied to Streptococcus pyogenes Cas9, a Hamiltonian metric obtained from coevolutionary relationships reveals, to our knowledge, novel PAM-proximal nucleotide preferences at the seventh position of S. pyogenes Cas9 PAM (5'-NGRNNNT-3'), which was experimentally confirmed by in vitro and functional assays in human cells. We show that coevolved and conserved interactions point to specific clues toward rationally engineering new generations of Cas9 systems and may eventually help decipher the diversity of this family of proteins.
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Affiliation(s)
- Yi Li
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas
| | - José A De la Paz
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Xianli Jiang
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Richard Liu
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas
| | - Adarsha P Pokkulandra
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Leonidas Bleris
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas; Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas.
| | - Faruck Morcos
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas; Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas.
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50
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Vajdi A, Zarringhalam K, Haspel N. Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores. Bioinformatics 2019; 36:1460-1467. [DOI: 10.1093/bioinformatics/btz791] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 09/30/2019] [Accepted: 10/15/2019] [Indexed: 01/07/2023] Open
Abstract
Abstract
Motivation
Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein–protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein–protein inter-residue contacts remains relatively limited.
Results
In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein–protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN.
Availability and implementation
https://github.com/BioMLBoston/PatchDCA
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Amir Vajdi
- Computer Science Department, University of Massachusetts Boston, Boston, MA, USA
- Department of Informatics and Analytics, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Nurit Haspel
- Computer Science Department, University of Massachusetts Boston, Boston, MA, USA
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