301
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Principles and Methods in Computational Membrane Protein Design. J Mol Biol 2021; 433:167154. [PMID: 34271008 DOI: 10.1016/j.jmb.2021.167154] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/03/2021] [Accepted: 07/06/2021] [Indexed: 01/13/2023]
Abstract
After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.
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302
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Contreras MA, Macaya L, Manrique V, Camacho F, González A, Montesinos R, Toledo JR, Sánchez O. A trivalent TNF-R2 as a new tumor necrosis factor alpha-blocking molecule. Proteins 2021; 89:1557-1564. [PMID: 34250652 DOI: 10.1002/prot.26177] [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: 01/25/2021] [Revised: 04/23/2021] [Accepted: 06/26/2021] [Indexed: 11/09/2022]
Abstract
The neutralization of tumor necrosis factor alpha (TNFα) with biopharmaceuticals is a successful therapy for inflammatory diseases. Currently, one of the main TNFα-antagonists is Etanercept, a dimeric TNF-R2 ectodomain. Considering that TNFα and its receptors are homotrimers, we proposed that a trimeric TNF-R2 ectodomain could be an innovative TNFα-antagonist. Here, the 3cTNFR2 protein was designed by the fusion of the TNF-R2 ectodomain with the collagen XV trimerization domain. 3cTNFR2 was produced in HEK293 cells and purified by immobilized metal affinity chromatography. Monomers, dimers, and trimers of 3cTNFR2 were detected. The interaction 3cTNFR2-TNFα was assessed. By microscale thermophoresis, the KD value for the interaction was 4.17 ± 0.88 nM, and complexes with different molecular weights were detected by size exclusion chromatography-high performance liquid chromatography. Moreover, 3cTNFR2 neutralized the TNFα-induced cytotoxicity totally in vitro. Although more studies are required to evaluate the anti-inflammatory effect, the results suggest that 3cTNFR2 could be a TNFα-antagonist agent.
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Affiliation(s)
- María A Contreras
- Recombinant Biopharmaceuticals Laboratory, Pharmacology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile.,Center for Biotechnology and Biomedicine Spa, Concepción, Chile
| | - Luis Macaya
- Recombinant Biopharmaceuticals Laboratory, Pharmacology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Viana Manrique
- Recombinant Biopharmaceuticals Laboratory, Pharmacology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Frank Camacho
- Recombinant Biopharmaceuticals Laboratory, Pharmacology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Alain González
- Recombinant Biopharmaceuticals Laboratory, Pharmacology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile.,Faculty of Basic Sciences, University of Medellin, Medellin, Colombia
| | - Raquel Montesinos
- Biotechnology and Biopharmaceutical Laboratory, Pathophysiology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Jorge R Toledo
- Biotechnology and Biopharmaceutical Laboratory, Pathophysiology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Oliberto Sánchez
- Recombinant Biopharmaceuticals Laboratory, Pharmacology Department, School of Biological Sciences, Universidad de Concepción, Concepción, Chile.,Center for Biotechnology and Biomedicine Spa, Concepción, Chile
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303
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Xia YH, Peng CX, Zhou XG, Zhang GJ. A Sequential Niche Multimodal Conformational Sampling Algorithm for Protein Structure Prediction. Bioinformatics 2021; 37:4357-4365. [PMID: 34245242 DOI: 10.1093/bioinformatics/btab500] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 06/23/2021] [Accepted: 07/05/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy. RESULTS A sequential niche multimodal conformational sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm overcome high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high-energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins, 24 CASP13 and 19 CASP14 FM targets. Results show that SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta restrained by distance (Rosetta-dist), SNfold achieves higher average TM-score and improves the sampling efficiency by more than 100 times. On several CASP FM targets, SNfold also shows good performance compared with four state-of-the-art servers in CASP. As a plug-in conformational sampling algorithm, SNfold can be extended to other protein structure prediction methods. AVAILABILITY The source code and executable versions are freely available at https://github.com/iobio-zjut/SNfold. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-Hao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Chun-Xiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Xiao-Gen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
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304
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Wu L, Qin L, Nie Y, Xu Y, Zhao YL. Computer-aided understanding and engineering of enzymatic selectivity. Biotechnol Adv 2021; 54:107793. [PMID: 34217814 DOI: 10.1016/j.biotechadv.2021.107793] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/26/2021] [Accepted: 06/28/2021] [Indexed: 12/26/2022]
Abstract
Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.
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Affiliation(s)
- Lunjie Wu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Lei Qin
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China.
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, 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
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305
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Zhao KL, Liu J, Zhou XG, Su JZ, Zhang Y, Zhang GJ. MMpred: a distance-assisted multimodal conformation sampling for de novo protein structure prediction. Bioinformatics 2021; 37:4350-4356. [PMID: 34185079 DOI: 10.1093/bioinformatics/btab484] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/22/2021] [Accepted: 06/28/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. RESULTS A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages. In the first modal exploration stage, a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. In the second modal maintaining stage, an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on 320 non-redundant proteins, where MMpred obtains models with TM-score ≥ 0.5 on 268 cases, which is 20.3% higher than that of Rosetta guided with the same distance constraints. In addition, on 320 benchmark proteins, the average TM-score of the enhanced version of MMpred (E-MMpred) is 0.732 on the best model, which is comparable to trRosetta (0.730). AVAILABILITY The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kai-Long Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jun Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xiao-Gen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw, Ann Arbor, MI 48109-2218, USA
| | - Jian-Zhong Su
- School of Biomedical Engineering, School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325011, Zhejiang, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw, Ann Arbor, MI 48109-2218, USA
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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306
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Liu S, Wang T, Xu Q, Shao B, Yin J, Liu TY. Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction. BMC Bioinformatics 2021; 22:351. [PMID: 34182922 PMCID: PMC8240311 DOI: 10.1186/s12859-021-04258-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fragment libraries play a key role in fragment-assembly based protein structure prediction, where protein fragments are assembled to form a complete three-dimensional structure. Rich and accurate structural information embedded in fragment libraries has not been systematically extracted and used beyond fragment assembly. METHODS To better leverage the valuable structural information for protein structure prediction, we extracted seven types of structural information from fragment libraries. We broadened the usage of such structural information by transforming fragment libraries into protein-specific potentials for gradient-descent based protein folding and encoding fragment libraries as structural features for protein property prediction. RESULTS Fragment libraires improved the accuracy of protein folding and outperformed state-of-the-art algorithms with respect to predicted properties, such as torsion angles and inter-residue distances. CONCLUSION Our work implies that the rich structural information extracted from fragment libraries can complement sequence-derived features to help protein structure prediction.
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Affiliation(s)
- Siyuan Liu
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
- Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
- Microsoft Research Asia, Beijing, China
| | - Tong Wang
- Microsoft Research Asia, Beijing, China.
| | | | - Bin Shao
- Microsoft Research Asia, Beijing, China
| | - Jian Yin
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
- Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
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307
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Sun T, Qiang S, Lu C, Xu F. Composition-dependent energetic contribution of complex salt bridges to collagen stability. Biophys J 2021; 120:3429-3436. [PMID: 34181903 DOI: 10.1016/j.bpj.2021.05.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/14/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
Complex salt bridges, on which three or more charged residues interplay simultaneously, cannot be considered as addition of individual salt bridges. This is still an intriguing problem in protein folding and stability. Here, we used an obligated ABC-type collagen heterotrimer as a platform to study the relationship between energetic contributions and conformational details of three-body complex salt bridges anchored by positively charged residues, K and R. Eight complex salt bridges were constructed by engineering point mutations in the heterotrimer. The circular dichroism measurements showed that the K-anchored complex salt bridges were stronger than the R-anchored ones. The molecular dynamics simulation revealed that both types of salt bridges had distinct dynamic features. The energetic contribution of K-anchored salt bridges was mainly determined by strong single bridges. In the R-anchored complex salt bridges, both side-chain electrostatic interactions and side-chain-backbone hydrogen bonding were involved. An empirical equation was proposed to predict the energetic contributions with high accuracy (R2 = 0.93). This work could help us take insights into the mechanisms of composition-dependent behaviors of the complex salt bridges on protein surface.
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Affiliation(s)
- Tiantian Sun
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Shumin Qiang
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Cheng Lu
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China.
| | - Fei Xu
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China.
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308
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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309
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Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity. Interdiscip Sci 2021; 13:693-702. [PMID: 34143353 DOI: 10.1007/s12539-021-00448-1] [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: 12/28/2020] [Revised: 05/31/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
Transmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, namely Prediction of TransMembrane protein Crystallization propensity (PTMC), to predict the propensity of transmembrane protein crystallization. First, we obtained several general sequence features and the specific encoded features of relative solvent accessibility and hydrophobicity. Second, feature selection was employed to filter out redundant and irrelevant features, and the optimal feature subset is composed of hydrophobicity, amino acid composition and relative solvent accessibility. Finally, we chose extreme gradient boosting by comparing with other several machine learning methods. Comparative results on the independent test set indicate that PTMC outperforms state-of-the-art sequence-based methods in terms of sensitivity, specificity, accuracy, Matthew's Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC). In comparison with two competitors, Bcrystal and TMCrys, PTMC achieves an improvement by 0.132 and 0.179 for sensitivity, 0.014 and 0.127 for specificity, 0.037 and 0.192 for accuracy, 0.128 and 0.362 for MCC, and 0.027 and 0.125 for AUC, respectively.
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310
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Lindquist P, Madsen JS, Bräuner-Osborne H, Rosenkilde MM, Hauser AS. Mutational Landscape of the Proglucagon-Derived Peptides. Front Endocrinol (Lausanne) 2021; 12:698511. [PMID: 34220721 PMCID: PMC8248487 DOI: 10.3389/fendo.2021.698511] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/24/2021] [Indexed: 12/18/2022] Open
Abstract
Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.
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Affiliation(s)
- Peter Lindquist
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob S. Madsen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hans Bräuner-Osborne
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette M. Rosenkilde
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alexander S. Hauser
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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311
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Neal EA, Nakanishi T. Alkyl-Fullerene Materials of Tunable Morphology and Function. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2021. [DOI: 10.1246/bcsj.20210129] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Edward A. Neal
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Takashi Nakanishi
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
- Division of Soft Matter, Graduate School of Life Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
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312
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Scherer M, Fleishman SJ, Jones PR, Dandekar T, Bencurova E. Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals. Front Bioeng Biotechnol 2021; 9:673005. [PMID: 34211966 PMCID: PMC8239229 DOI: 10.3389/fbioe.2021.673005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.
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Affiliation(s)
- Marc Scherer
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Patrik R Jones
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Thomas Dandekar
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
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313
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Bagci EZ, Senguler-Ciftci F, Ciftci U, Demir A. A novel measure to analyze protein structures: Aspect ratio in protein alpha shapes. Proteins 2021; 89:1270-1276. [PMID: 33993533 DOI: 10.1002/prot.26148] [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: 10/08/2020] [Revised: 03/01/2021] [Accepted: 05/03/2021] [Indexed: 11/10/2022]
Abstract
Proteins' three-dimensional (3D) structures are analyzed traditionally using geometric descriptors such as torsional angles and inter-atomic distances. In this study a measure that is borrowed from computational geometry, aspect ratio of each tetrahedron in alpha shapes of proteins, is utilized. This geometric descriptor differentiates alpha and beta structural classes of proteins when combined with principal components analysis. The method converts the structures of individual proteins, 3D coordinates of the atoms, to points on a plane. It has a high degree of accuracy in differentiating R and T structures of hemoglobin. Therefore, it is anticipated that the geometric measure can be used successfully in a method that is extended to solve classification problems in machine learning.
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Affiliation(s)
- Elife Z Bagci
- Department of Biology, Tekirdag Namik Kemal University, Tekirdag, Turkey
| | | | - Unver Ciftci
- Department of Mathematics, Tekirdag Namik Kemal University, Tekirdag, Turkey
| | - Ayhan Demir
- Department of Projects Management and Support, Turkish Health Institutes (TÜSEB), Ankara, Turkey
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314
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Di Lena P, Baldi P. Fold recognition by scoring protein maps using the congruence coefficient. Bioinformatics 2021; 37:506-513. [PMID: 32976564 DOI: 10.1093/bioinformatics/btaa833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/07/2020] [Accepted: 09/10/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Protein fold recognition is a key step for template-based modeling approaches to protein structure prediction. Although closely related folds can be easily identified by sequence homology search in sequence databases, fold recognition is notoriously more difficult when it involves the identification of distantly related homologs. Recent progress in residue-residue contact and distance prediction opens up the possibility of improving fold recognition by using structural information contained in predicted distance and contact maps. RESULTS Here we propose to use the congruence coefficient as a metric of similarity between maps. We prove that this metric has several interesting mathematical properties which allow one to compute in polynomial time its exact mean and variance over all possible (exponentially many) alignments between two symmetric matrices, and assess the statistical significance of similarity between aligned maps. We perform fold recognition tests by recovering predicted target contact/distance maps from the two most recent Critical Assessment of Structure Prediction editions and over 27 000 non-homologous structural templates from the ECOD database. On this large benchmark, we compare fold recognition performances of different alignment tools with their own similarity scores against those obtained using the congruence coefficient. We show that the congruence coefficient overall improves fold recognition over other methods, proving its effectiveness as a general similarity metric for protein map comparison. AVAILABILITY AND IMPLEMENTATION The congruence coefficient software CCpro is available as part of the SCRATCH suite at: http://scratch.proteomics.ics.uci.edu/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Bologna 40126, Italy
| | - Pierre Baldi
- Department of Computer Science, University of California, Irvine, CA 92697, USA.,Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA
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315
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Suh D, Lee JW, Choi S, Lee Y. Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction. Int J Mol Sci 2021; 22:6032. [PMID: 34199677 PMCID: PMC8199773 DOI: 10.3390/ijms22116032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 05/29/2021] [Accepted: 05/29/2021] [Indexed: 01/23/2023] Open
Abstract
The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.
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Affiliation(s)
- Donghyuk Suh
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Jai Woo Lee
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Sun Choi
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Korea
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316
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Atsavapranee B, Stark CD, Sunden F, Thompson S, Fordyce PM. Fundamentals to function: Quantitative and scalable approaches for measuring protein stability. Cell Syst 2021; 12:547-560. [PMID: 34139165 DOI: 10.1016/j.cels.2021.05.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/16/2021] [Accepted: 05/07/2021] [Indexed: 12/11/2022]
Abstract
Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.
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Affiliation(s)
| | - Catherine D Stark
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA; ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Fanny Sunden
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - Samuel Thompson
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Polly M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA.
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317
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Computational Study on Temperature Driven Structure-Function Relationship of Polysaccharide Producing Bacterial Glycosyl Transferase Enzyme. Polymers (Basel) 2021; 13:polym13111771. [PMID: 34071348 PMCID: PMC8198650 DOI: 10.3390/polym13111771] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/24/2021] [Accepted: 05/26/2021] [Indexed: 12/12/2022] Open
Abstract
Glycosyltransferase (GTs) is a wide class of enzymes that transfer sugar moiety, playing a key role in the synthesis of bacterial exopolysaccharide (EPS) biopolymer. In recent years, increased demand for bacterial EPSs has been observed in pharmaceutical, food, and other industries. The application of the EPSs largely depends upon their thermal stability, as any industrial application is mainly reliant on slow thermal degradation. Keeping this in context, EPS producing GT enzymes from three different bacterial sources based on growth temperature (mesophile, thermophile, and hyperthermophile) are considered for in silico analysis of the structural–functional relationship. From the present study, it was observed that the structural integrity of GT increases significantly from mesophile to thermophile to hyperthermophile. In contrast, the structural plasticity runs in an opposite direction towards mesophile. This interesting temperature-dependent structural property has directed the GT–UDP-glucose interactions in a way that thermophile has finally demonstrated better binding affinity (−5.57 to −10.70) with an increased number of hydrogen bonds (355) and stabilizing amino acids (Phe, Ala, Glu, Tyr, and Ser). The results from this study may direct utilization of thermophile-origin GT as best for industrial-level bacterial polysaccharide production.
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318
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Pakhrin SC, Shrestha B, Adhikari B, KC DB. Deep Learning-Based Advances in Protein Structure Prediction. Int J Mol Sci 2021; 22:5553. [PMID: 34074028 PMCID: PMC8197379 DOI: 10.3390/ijms22115553] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/12/2021] [Accepted: 05/18/2021] [Indexed: 12/29/2022] Open
Abstract
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.
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Affiliation(s)
- Subash C. Pakhrin
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA;
| | - Bikash Shrestha
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Badri Adhikari
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Dukka B. KC
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA;
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319
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Revolutionizing enzyme engineering through artificial intelligence and machine learning. Emerg Top Life Sci 2021; 5:113-125. [PMID: 33835131 DOI: 10.1042/etls20200257] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 12/20/2022]
Abstract
The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.
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320
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Bouchiba Y, Cortés J, Schiex T, Barbe S. Molecular flexibility in computational protein design: an algorithmic perspective. Protein Eng Des Sel 2021; 34:6271252. [PMID: 33959778 DOI: 10.1093/protein/gzab011] [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: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022] Open
Abstract
Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.
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Affiliation(s)
- Younes Bouchiba
- Toulouse Biotechnology Institute, TBI, CNRS, INRAE, INSA, ANITI, Toulouse 31400, France.,Laboratoire d'Analyse et d'Architecture des Systèmes, LAAS CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
| | - Juan Cortés
- Laboratoire d'Analyse et d'Architecture des Systèmes, LAAS CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
| | - Thomas Schiex
- Université de Toulouse, ANITI, INRAE, UR MIAT, F-31320, Castanet-Tolosan, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, TBI, CNRS, INRAE, INSA, ANITI, Toulouse 31400, France
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321
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Ju F, Zhu J, Shao B, Kong L, Liu TY, Zheng WM, Bu D. CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction. Nat Commun 2021; 12:2535. [PMID: 33953201 PMCID: PMC8100175 DOI: 10.1038/s41467-021-22869-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/28/2021] [Indexed: 11/29/2022] Open
Abstract
Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA. The key elements of CopulaNet include: (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures.
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Affiliation(s)
- Fusong Ju
- Key Lab of Intelligent Information Processing, State Key Lab of Computer Architecture, Big-data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Bin Shao
- Microsoft Research Asia, Beijing, China
| | - Lupeng Kong
- Key Lab of Intelligent Information Processing, State Key Lab of Computer Architecture, Big-data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Wei-Mou Zheng
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, State Key Lab of Computer Architecture, Big-data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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322
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Kapla J, Rodríguez-Espigares I, Ballante F, Selent J, Carlsson J. Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models? PLoS Comput Biol 2021; 17:e1008936. [PMID: 33983933 PMCID: PMC8186765 DOI: 10.1371/journal.pcbi.1008936] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 06/08/2021] [Accepted: 04/02/2021] [Indexed: 01/14/2023] Open
Abstract
The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.
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Affiliation(s)
- Jon Kapla
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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323
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Devaux F, Li X, Sluysmans D, Maurizot V, Bakalis E, Zerbetto F, Huc I, Duwez AS. Single-molecule mechanics of synthetic aromatic amide helices: Ultrafast and robust non-dissipative winding. Chem 2021. [DOI: 10.1016/j.chempr.2021.02.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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324
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Jain A, Terashi G, Kagaya Y, Maddhuri Venkata Subramaniya SR, Christoffer C, Kihara D. Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction. Sci Rep 2021; 11:7574. [PMID: 33828153 PMCID: PMC8027171 DOI: 10.1038/s41598-021-87204-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 03/25/2021] [Indexed: 12/12/2022] Open
Abstract
Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA's feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.
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Affiliation(s)
- Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Yuki Kagaya
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | | | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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325
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Le KH, Adolf-Bryfogle J, Klima JC, Lyskov S, Labonte J, Bertolani S, Burman SSR, Leaver-Fay A, Weitzner B, Maguire J, Rangan R, Adrianowycz MA, Alford RF, Adal A, Nance ML, Wu Y, Willis J, Kulp DW, Das R, Dunbrack RL, Schief W, Kuhlman B, Siegel JB, Gray JJ. PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. BIOPHYSICIST (ROCKVILLE, MD.) 2021; 2:108-122. [PMID: 35128343 DOI: 10.35459/tbp.2019.000147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.
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Affiliation(s)
- Kathy H Le
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Jason C Klima
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jason Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania, United States
| | - Steven Bertolani
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Brian Weitzner
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Ramya Rangan
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Matt A Adrianowycz
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aleexsan Adal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Yuanhan Wu
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Jordan Willis
- RubrYc Therapeutics, San Ramon, California, United States
| | - Daniel W Kulp
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Rhiju Das
- Program in Biophysics, Stanford University, Stanford, California, United States
| | | | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.,Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Justin B Siegel
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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326
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Ochoa R, Laskowski RA, Thornton JM, Cossio P. Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders. Front Mol Biosci 2021; 8:636562. [PMID: 34222328 PMCID: PMC8253603 DOI: 10.3389/fmolb.2021.636562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/15/2021] [Indexed: 11/23/2022] Open
Abstract
The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
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327
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Ferrando J, Solomon LA. Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions. Life (Basel) 2021; 11:life11030225. [PMID: 33802210 PMCID: PMC7999464 DOI: 10.3390/life11030225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 12/14/2022] Open
Abstract
De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.
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Affiliation(s)
- Juan Ferrando
- Department of Biology, George Mason University, 4400 University Dr, Fairfax, VA 22030, USA;
| | - Lee A. Solomon
- Department of Chemistry and Biochemistry, George Mason University, 10920 George Mason Circle, Manassas, VA 20110, USA
- Correspondence: ; Tel.: +703-993-6418
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328
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Guardiola S, Varese M, Roig X, Sánchez-Navarro M, García J, Giralt E. Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1. Chem Sci 2021; 12:5164-5170. [PMID: 34163753 PMCID: PMC8179567 DOI: 10.1039/d1sc01031j] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 02/25/2021] [Indexed: 01/22/2023] Open
Abstract
Peptides are a rapidly growing class of therapeutics with various advantages over traditional small molecules, especially for targeting difficult protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing bioactive cyclic topologies that go beyond natural l-amino acids. Here, we report a generalizable framework that exploits the computational power of Rosetta, in terms of large-scale backbone sampling, side-chain composition and energy scoring, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we developed two new inhibitors (PD-i3 and PD-i6) of programmed cell death 1 (PD-1), a key immune checkpoint in oncology. A comprehensive biophysical evaluation was performed to assess their binding to PD-1 as well as their blocking effect on the endogenous PD-1/PD-L1 interaction. Finally, NMR elucidation of their in-solution structures confirmed our de novo design approach.
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Affiliation(s)
- Salvador Guardiola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
| | - Monica Varese
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
| | - Xavier Roig
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
| | | | - Jesús García
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
| | - Ernest Giralt
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
- Department of Inorganic and Organic Chemistry, University of Barcelona Spain
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329
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Bhadra A, Yeturu K. Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abad88] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).
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330
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Pannecoucke E, Van Trimpont M, Desmet J, Pieters T, Reunes L, Demoen L, Vuylsteke M, Loverix S, Vandenbroucke K, Alard P, Henderikx P, Deroo S, Baatz F, Lorent E, Thiolloy S, Somers K, McGrath Y, Van Vlierberghe P, Lasters I, Savvides SN. Cell-penetrating Alphabody protein scaffolds for intracellular drug targeting. SCIENCE ADVANCES 2021; 7:7/13/eabe1682. [PMID: 33771865 PMCID: PMC7997521 DOI: 10.1126/sciadv.abe1682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 02/05/2021] [Indexed: 05/02/2023]
Abstract
The therapeutic scope of antibody and nonantibody protein scaffolds is still prohibitively limited against intracellular drug targets. Here, we demonstrate that the Alphabody scaffold can be engineered into a cell-penetrating protein antagonist against induced myeloid leukemia cell differentiation protein MCL-1, an intracellular target in cancer, by grafting the critical B-cell lymphoma 2 homology 3 helix of MCL-1 onto the Alphabody and tagging the scaffold's termini with designed cell-penetration polypeptides. Introduction of an albumin-binding moiety extended the serum half-life of the engineered Alphabody to therapeutically relevant levels, and administration thereof in mouse tumor xenografts based on myeloma cell lines reduced tumor burden. Crystal structures of such a designed Alphabody in complex with MCL-1 and serum albumin provided the structural blueprint of the applied design principles. Collectively, we provide proof of concept for the use of Alphabodies against intracellular disease mediators, which, to date, have remained in the realm of small-molecule therapeutics.
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Affiliation(s)
- Erwin Pannecoucke
- VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, 9052 Ghent, Belgium
| | - Maaike Van Trimpont
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | | | - Tim Pieters
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Lindy Reunes
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Lisa Demoen
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | | | | | | | | | | | | | | | | | | | | | | | - Pieter Van Vlierberghe
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | | | - Savvas N Savvides
- VIB Center for Inflammation Research, 9052 Ghent, Belgium.
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, 9052 Ghent, Belgium
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331
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Iwabuchi S, Kawamata I, Murata S, Nomura SIM. A large, square-shaped, DNA origami nanopore with sealing function on a giant vesicle membrane. Chem Commun (Camb) 2021; 57:2990-2993. [PMID: 33587063 DOI: 10.1039/d0cc07412h] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Intaking molecular information from the external environment is essential for the normal functioning of artificial cells/molecular robots. Herein, we report the design and function of a membrane nanopore using a DNA origami square tube with a cross-section of 100 nm2. When the nanopore is added to a giant vesicle that mimics a cell membrane, the permeation of large external hydrophilic fluorescent molecules is observed. Furthermore, the addition of up to four ssDNA strands enables size-based selective transport of molecules. A controllable artificial nanopore should facilitate the communication between the vesicle components and their environment.
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Affiliation(s)
- Shoji Iwabuchi
- Department of Robotics, Tohoku University, Miyagi 980-8579, Japan.
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332
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Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 2021; 22:71-88. [PMID: 33168968 PMCID: PMC7649713 DOI: 10.1038/s41576-020-00292-x] [Citation(s) in RCA: 480] [Impact Index Per Article: 160.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.
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Affiliation(s)
- Erick Armingol
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Adam Officer
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Harismendy
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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333
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Abstract
QM/MM simulations have become an indispensable tool in many chemical and biochemical investigations. Considering the tremendous degree of success, including recognition by a 2013 Nobel Prize in Chemistry, are there still "burning challenges" in QM/MM methods, especially for biomolecular systems? In this short Perspective, we discuss several issues that we believe greatly impact the robustness and quantitative applicability of QM/MM simulations to many, if not all, biomolecules. We highlight these issues with observations and relevant advances from recent studies in our group and others in the field. Despite such limited scope, we hope the discussions are of general interest and will stimulate additional developments that help push the field forward in meaningful directions.
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Affiliation(s)
- Qiang Cui
- Departments of Chemistry, Physics, and Biomedical Engineering, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Tanmoy Pal
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Luke Xie
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
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334
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Pinheiro MP, Reis RA, Dupree P, Ward RJ. Plant cell wall architecture guided design of CBM3-GH11 chimeras with enhanced xylanase activity using a tandem repeat left-handed β-3-prism scaffold. Comput Struct Biotechnol J 2021; 19:1108-1118. [PMID: 33680354 PMCID: PMC7890094 DOI: 10.1016/j.csbj.2021.01.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 01/19/2023] Open
Abstract
Effective use of plant biomass as an abundant and renewable feedstock for biofuel production and biorefinery requires efficient enzymatic mobilization of cell wall polymers. Knowledge of plant cell wall composition and architecture has been exploited to develop novel multifunctional enzymes with improved activity against lignocellulose, where a left-handed β-3-prism synthetic scaffold (BeSS) was designed for insertion of multiple protein domains at the prism vertices. This allowed construction of a series of chimeras fusing variable numbers of a GH11 β-endo-1,4-xylanase and the CipA-CBM3 with defined distances and constrained relative orientations between catalytic domains. The cellulose binding and endoxylanase activities of all chimeras were maintained. Activity against lignocellulose substrates revealed a rapid 1.6- to 3-fold increase in total reducing saccharide release and increased levels of all major oligosaccharides as measured by polysaccharide analysis using carbohydrate gel electrophoresis (PACE). A construct with CBM3 and GH11 domains inserted in the same prism vertex showed highest activity, demonstrating interdomain geometry rather than number of catalytic sites is important for optimized chimera design. These results confirm that the BeSS concept is robust and can be successfully applied to the construction of multifunctional chimeras, which expands the possibilities for knowledge-based protein design.
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Affiliation(s)
- Matheus P. Pinheiro
- Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP CEP 14040-901, Brazil
| | - Renata A.G. Reis
- Departamento de Física e Química, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, Ribeirão Preto, SP CEP 14040-901, Brazil
| | - Paul Dupree
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, United Kingdom
| | - Richard J. Ward
- Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP CEP 14040-901, Brazil
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335
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Yudenko A, Smolentseva A, Maslov I, Semenov O, Goncharov IM, Nazarenko VV, Maliar NL, Borshchevskiy V, Gordeliy V, Remeeva A, Gushchin I. Rational Design of a Split Flavin-Based Fluorescent Reporter. ACS Synth Biol 2021; 10:72-83. [PMID: 33325704 DOI: 10.1021/acssynbio.0c00454] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein-fragment complementation assays are used ubiquitously for probing protein-protein interactions. Most commonly, the reporter protein is split in two parts, which are then fused to the proteins of interest and can reassemble and provide a readout if the proteins of interest interact with each other. The currently known split fluorescent proteins either can be used only in aerobic conditions and assemble irreversibly, or require addition of exogenous chromophores, which complicates the design of experiments. In recent years, light-oxygen-voltage (LOV) domains of several photoreceptor proteins have been developed into flavin-based fluorescent proteins (FbFPs) that, under some circumstances, can outperform commonly used fluorescent proteins such as GFP. Here, we show that CagFbFP, a small thermostable FbFP based on a LOV domain-containing protein from Chloroflexus aggregans, can serve as a split fluorescent reporter. We use the available genetic and structural information to identify three loops between the conserved secondary structure elements, Aβ-Bβ, Eα-Fα, and Hβ-Iβ, that tolerate insertion of flexible poly-Gly/Ser segments and eventually splitting. We demonstrate that the designed split pairs, when fused to interacting proteins, are fluorescent in vivo in E. coli and human cells and have low background fluorescence. Our results enable probing protein-protein interactions in anaerobic conditions without using exogenous fluorophores and provide a basis for further development of LOV and PAS (Per-Arnt-Sim) domain-based fluorescent reporters and optogenetic tools.
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Affiliation(s)
- Anna Yudenko
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Anastasia Smolentseva
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Ivan Maslov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Oleg Semenov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Ivan M. Goncharov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Vera V. Nazarenko
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Nina L. Maliar
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Valentin Borshchevskiy
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Valentin Gordeliy
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Institut de Biologie Structurale J.-P. Ebel, Université Grenoble Alpes-CEA-CNRS, 38044 Grenoble, France
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, 52425 Jülich, Germany
- JuStruct: Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Alina Remeeva
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Ivan Gushchin
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
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336
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Warkentin R, Kwan DH. Resources and Methods for Engineering "Designer" Glycan-Binding Proteins. Molecules 2021; 26:E380. [PMID: 33450899 PMCID: PMC7828330 DOI: 10.3390/molecules26020380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 01/04/2021] [Accepted: 01/10/2021] [Indexed: 12/11/2022] Open
Abstract
This review provides information on available methods for engineering glycan-binding proteins (GBP). Glycans are involved in a variety of physiological functions and are found in all domains of life and viruses. Due to their wide range of functions, GBPs have been developed with diagnostic, therapeutic, and biotechnological applications. The development of GBPs has traditionally been hindered by a lack of available glycan targets and sensitive and selective protein scaffolds; however, recent advances in glycobiology have largely overcome these challenges. Here we provide information on how to approach the design of novel "designer" GBPs, starting from the protein scaffold to the mutagenesis methods, selection, and characterization of the GBPs.
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Affiliation(s)
- Ruben Warkentin
- Department of Biology, Centre for Applied Synthetic Biology, and Centre for Structural and Functional Genomics, Concordia University, 7141 Sherbrooke Street West, Montreal, QC H4B 1R6, Canada;
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Quebec City, QC G1V 0A6, Canada
| | - David H. Kwan
- Department of Biology, Centre for Applied Synthetic Biology, and Centre for Structural and Functional Genomics, Concordia University, 7141 Sherbrooke Street West, Montreal, QC H4B 1R6, Canada;
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Quebec City, QC G1V 0A6, Canada
- Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street West, Montreal, QC H4B 1R6, Canada
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337
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Azmi MB, Sultana S, Naeem S, Qureshi SA. In silico investigation on alkaloids of Rauwolfia serpentina as potential inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase. Saudi J Biol Sci 2021; 28:731-737. [PMID: 33424361 PMCID: PMC7783793 DOI: 10.1016/j.sjbs.2020.10.066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/20/2020] [Accepted: 10/29/2020] [Indexed: 11/26/2022] Open
Abstract
Present work aimed to investigate the in silico activity of the alkaloids of roots of Rauwolfia serpentina as inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR). For this purpose, the three-dimensional (3D) structure of the protein HMGCR (PDB ID: 1HW9) was downloaded from Protein Data Bank (PDB) database, as a target enzyme. The structures of twelve alkaloids from the roots of R. serpentina were selected as ligands and docked with the selected HMGCR enzyme using Molegro Virtual Docker (MVD) software. The software ‘MVD’ computes the binding (atom) energies of selected protein (enzyme) and each ligand at minimum energetic conformation state by using the PLP (Piecewise Linear Potential) scoring mechanism. Docking results of twelve tested alkaloids showed that five alkaloids including compound 1 (ajmalicine), 2 (reserpine), 3 (indobinine), 4 (yohimbine), and 5 (indobine) have displayed the highest MolDock scores and best fit within the prominent active site residues (positioned between 684 and 692 of cis-loop) of HMGCR. According to the lowest MolDock energies obtained through non-covalent interactions of alkaloids with HMGCR, these are characterized to be the potential inhibitors of HMGCR. Therefore, the alkaloids from R. serpentina can effectively suppress the cholesterol biosynthesis pathway through inhibition of HMGCR and can serve as potential lead compounds for the development of new drugs for the treatment of hyperlipidaemia.
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Affiliation(s)
- Muhammad Bilal Azmi
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi 74200, Pakistan
- Corresponding author.
| | - Saleha Sultana
- Department of Biochemistry, University of Karachi, Karachi 75270, Pakistan
| | - Sadaf Naeem
- Department of Biochemistry, University of Karachi, Karachi 75270, Pakistan
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338
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Ai R, Jin X, Tang B, Yang G, Niu Z, Fang EF. Ageing and Alzheimer’s Disease. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_74-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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339
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Hu J, Yang W, Dong R, Li Y, Li X, Li S, Siriwardane EMD. Contact map based crystal structure prediction using global optimization. CrystEngComm 2021. [DOI: 10.1039/d0ce01714k] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Crystal structure prediction is now playing an increasingly important role in the discovery of new materials or crystal engineering.
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Affiliation(s)
- Jianjun Hu
- Department of Computer Science and Engineering
- University of South Carolina
- Columbia
- USA
| | - Wenhui Yang
- School of Mechanical Engineering
- Guizhou University
- Guiyang 550050
- China
| | - Rongzhi Dong
- School of Mechanical Engineering
- Guizhou University
- Guiyang 550050
- China
| | - Yuxin Li
- School of Mechanical Engineering
- Guizhou University
- Guiyang 550050
- China
| | - Xiang Li
- School of Mechanical Engineering
- Guizhou University
- Guiyang 550050
- China
| | - Shaobo Li
- School of Mechanical Engineering
- Guizhou University
- Guiyang 550050
- China
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340
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Ferreira SS, Antunes MS. Re-engineering Plant Phenylpropanoid Metabolism With the Aid of Synthetic Biosensors. FRONTIERS IN PLANT SCIENCE 2021; 12:701385. [PMID: 34603348 PMCID: PMC8481569 DOI: 10.3389/fpls.2021.701385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/23/2021] [Indexed: 05/03/2023]
Abstract
Phenylpropanoids comprise a large class of specialized plant metabolites with many important applications, including pharmaceuticals, food nutrients, colorants, fragrances, and biofuels. Therefore, much effort has been devoted to manipulating their biosynthesis to produce high yields in a more controlled manner in microbial and plant systems. However, current strategies are prone to significant adverse effects due to pathway complexity, metabolic burden, and metabolite bioactivity, which still hinder the development of tailor-made phenylpropanoid biofactories. This gap could be addressed by the use of biosensors, which are molecular devices capable of sensing specific metabolites and triggering a desired response, as a way to sense the pathway's metabolic status and dynamically regulate its flux based on specific signals. Here, we provide a brief overview of current research on synthetic biology and metabolic engineering approaches to control phenylpropanoid synthesis and phenylpropanoid-related biosensors, advocating for the use of biosensors and genetic circuits as a step forward in plant synthetic biology to develop autonomously-controlled phenylpropanoid-producing plant biofactories.
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341
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Wang J, Li Y, Nie G. Multifunctional biomolecule nanostructures for cancer therapy. NATURE REVIEWS. MATERIALS 2021; 6:766-783. [PMID: 34026278 PMCID: PMC8132739 DOI: 10.1038/s41578-021-00315-x] [Citation(s) in RCA: 179] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/06/2021] [Indexed: 05/08/2023]
Abstract
Biomolecule-based nanostructures are inherently multifunctional and harbour diverse biological activities, which can be explored for cancer nanomedicine. The supramolecular properties of biomolecules can be precisely programmed for the design of smart drug delivery vehicles, enabling efficient transport in vivo, targeted drug delivery and combinatorial therapy within a single design. In this Review, we discuss biomolecule-based nanostructures, including polysaccharides, nucleic acids, peptides and proteins, and highlight their enormous design space for multifunctional nanomedicines. We identify key challenges in cancer nanomedicine that can be addressed by biomolecule-based nanostructures and survey the distinct biological activities, programmability and in vivo behaviour of biomolecule-based nanostructures. Finally, we discuss challenges in the rational design, characterization and fabrication of biomolecule-based nanostructures, and identify obstacles that need to be overcome to enable clinical translation.
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Affiliation(s)
- Jing Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, China, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Yiye Li
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, China, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Guangjun Nie
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, China, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- GBA Research Innovation Institute for Nanotechnology, Guangdong, China
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, China
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342
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Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
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343
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Abstract
Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.
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344
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Mehlich A, Fang J, Pelz B, Li H, Stigler J. Slow Transition Path Times Reveal a Complex Folding Barrier in a Designed Protein. Front Chem 2020; 8:587824. [PMID: 33365300 PMCID: PMC7750197 DOI: 10.3389/fchem.2020.587824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/26/2020] [Indexed: 11/30/2022] Open
Abstract
De-novo designed proteins have received wide interest as potential platforms for nano-engineering and biomedicine. While much work is being done in the design of thermodynamically stable proteins, the folding process of artificially designed proteins is not well-studied. Here we used single-molecule force spectroscopy by optical tweezers to study the folding of ROSS, a de-novo designed 2x2 Rossmann fold. We measured a barrier crossing time in the millisecond range, much slower than what has been reported for other systems. While long transition times can be explained by barrier roughness or slow diffusion, we show that isotropic roughness cannot explain the measured transition path time distribution. Instead, this study shows that the slow barrier crossing of ROSS is caused by the population of three short-lived high-energy intermediates. In addition, we identify incomplete and off-pathway folding events with different barrier crossing dynamics. Our results hint at the presence of a complex transition barrier that may be a common feature of many artificially designed proteins.
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Affiliation(s)
- Alexander Mehlich
- Physics Department E22, Technische Universität München, Garching, Germany
| | - Jie Fang
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Benjamin Pelz
- Physics Department E22, Technische Universität München, Garching, Germany
| | - Hongbin Li
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Johannes Stigler
- Gene Center Munich, Ludwig-Maximilians-Universität München, Munich, Germany
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345
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Woodley SM, Day GM, Catlow R. Structure prediction of crystals, surfaces and nanoparticles. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190600. [PMID: 33100162 DOI: 10.1098/rsta.2019.0600] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'.
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Affiliation(s)
- Scott M Woodley
- Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton SO17 1BJ, UK
| | - R Catlow
- Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
- School of Chemistry, Cardiff University, Park Place, Cardiff CF10 3AT, UK
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346
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Gao W, Mahajan SP, Sulam J, Gray JJ. Deep Learning in Protein Structural Modeling and Design. PATTERNS (NEW YORK, N.Y.) 2020; 1:100142. [PMID: 33336200 PMCID: PMC7733882 DOI: 10.1016/j.patter.2020.100142] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
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Affiliation(s)
- Wenhao Gao
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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347
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Maguire JB, Haddox HK, Strickland D, Halabiya SF, Coventry B, Griffin JR, Pulavarti SVSRK, Cummins M, Thieker DF, Klavins E, Szyperski T, DiMaio F, Baker D, Kuhlman B. Perturbing the energy landscape for improved packing during computational protein design. Proteins 2020; 89:436-449. [PMID: 33249652 DOI: 10.1002/prot.26030] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 10/04/2020] [Accepted: 11/21/2020] [Indexed: 01/04/2023]
Abstract
The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.
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Affiliation(s)
- Jack B Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Hugh K Haddox
- Department of Biochemistry, University of Washington, Seattle, Washington, USA.,Institute for Protein Design, University of Washington, Seattle, Washington, USA
| | - Devin Strickland
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Samer F Halabiya
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Brian Coventry
- Institute for Protein Design, University of Washington, Seattle, Washington, USA.,Molecular Engineering PhD Program, University of Washington, Seattle, Washington, USA
| | - Jermel R Griffin
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, USA
| | | | - Matthew Cummins
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - David F Thieker
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eric Klavins
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, Washington, USA.,Institute for Protein Design, University of Washington, Seattle, Washington, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington, USA.,Institute for Protein Design, University of Washington, Seattle, Washington, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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348
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Hu J, Yang W, Dilanga Siriwardane EM. Distance Matrix-Based Crystal Structure Prediction Using Evolutionary Algorithms. J Phys Chem A 2020; 124:10909-10919. [DOI: 10.1021/acs.jpca.0c08775] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jianjun Hu
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29201, United States
| | - Wenhui Yang
- School of Mechanical Engineering, Guizhou University, Guiyang 550050, China
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349
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Li M, Hua B, Liang H, Liu J, Shao L, Huang F. Supramolecular Tessellations via Pillar[ n]arenes-Based Exo-Wall Interactions. J Am Chem Soc 2020; 142:20892-20901. [PMID: 33242958 DOI: 10.1021/jacs.0c11037] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Supramolecular tessellation is a newly emerging and promising area in supramolecular chemistry because of its unique structural aesthetics and potential applications. Herein, we investigate the "exo-wall" interactions of pillar[n]arenes and prepare fantastic hexagonal supramolecular tessellations based on perethylated pillar[6]arenes (EtP6) with electron-deficient molecules 1,5-difluoro-2,4-dinitrobenzene (DFN) and tetrafluoro-1,4-benzoquinone (TFB). The crystal structures clearly confirm that EtP6 can form highly ordered hexagonal 2D tiling patterns with DFN/TFB as linkers through cocrystallization. Moreover, the self-assembled packing arrangements in the ultimate cocrystal superstructures can be adjusted under different crystallization conditions. This work not only explores the rare exo-wall interactions based on pillar[n]arenes but also reports the fabrication of supramolecular tessellations based on pillararenes for the first time, showing a new perspective in supramolecular chemistry.
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Affiliation(s)
- Ming Li
- State Key Laboratory of Chemical Engineering, Center for Chemistry of High-Performance & Novel Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, P. R. China
| | - Bin Hua
- State Key Laboratory of Chemical Engineering, Center for Chemistry of High-Performance & Novel Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, P. R. China
| | - Haozhong Liang
- State Key Laboratory of Chemical Engineering, Center for Chemistry of High-Performance & Novel Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, P. R. China
| | - Jiyong Liu
- State Key Laboratory of Chemical Engineering, Center for Chemistry of High-Performance & Novel Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, P. R. China
| | - Li Shao
- State Key Laboratory of Chemical Engineering, Center for Chemistry of High-Performance & Novel Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, P. R. China
| | - Feihe Huang
- State Key Laboratory of Chemical Engineering, Center for Chemistry of High-Performance & Novel Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, P. R. China.,Green Catalysis Center and College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
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350
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Doukov T, Herschlag D, Yabukarski F. Instrumentation and experimental procedures for robust collection of X-ray diffraction data from protein crystals across physiological temperatures. J Appl Crystallogr 2020; 53:1493-1501. [PMID: 33312102 DOI: 10.1107/s1600576720013503] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 10/08/2020] [Indexed: 11/10/2022] Open
Abstract
Traditional X-ray diffraction data collected at cryo-temperatures have delivered invaluable insights into the three-dimensional structures of proteins, providing the backbone of structure-function studies. While cryo-cooling mitigates radiation damage, cryo-temperatures can alter protein conformational ensembles and solvent structure. Furthermore, conformational ensembles underlie protein function and energetics, and recent advances in room-temperature X-ray crystallography have delivered conformational heterogeneity information that can be directly related to biological function. Given this capability, the next challenge is to develop a robust and broadly applicable method to collect single-crystal X-ray diffraction data at and above room temperature. This challenge is addressed herein. The approach described provides complete diffraction data sets with total collection times as short as ∼5 s from single protein crystals, dramatically increasing the quantity of data that can be collected within allocated synchrotron beam time. Its applicability was demonstrated by collecting 1.09-1.54 Å resolution data over a temperature range of 293-363 K for proteinase K, thaumatin and lysozyme crystals at BL14-1 at the Stanford Synchrotron Radiation Lightsource. The analyses presented here indicate that the diffraction data are of high quality and do not suffer from excessive dehydration or radiation damage.
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Affiliation(s)
- Tzanko Doukov
- SMB, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
| | - Daniel Herschlag
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA.,Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.,Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Filip Yabukarski
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
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