1
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Agarwal V, McShan AC. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol 2024:10.1038/s41589-024-01638-w. [PMID: 38907110 DOI: 10.1038/s41589-024-01638-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 04/29/2024] [Indexed: 06/23/2024]
Abstract
Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.
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Affiliation(s)
- Vinayak Agarwal
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
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2
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Zhao K, Zhao P, Wang S, Xia Y, Zhang G. FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe. Genome Biol 2024; 25:152. [PMID: 38862984 PMCID: PMC11167914 DOI: 10.1186/s13059-024-03291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024] Open
Abstract
Protein folding has become a tractable problem with the significant advances in deep learning-driven protein structure prediction. Here we propose FoldPAthreader, a protein folding pathway prediction method that uses a novel folding force field model by exploring the intrinsic relationship between protein evolution and folding from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring potential intermediates. On 30 example targets, FoldPAthreader successfully predicts 70% of the proteins whose folding pathway is consistent with biological experimental data.
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Affiliation(s)
- Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Pengxin Zhao
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Suhui Wang
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Yuhao Xia
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China.
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3
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Redvanly TW, Pielak GJ. Quantitative entropy-enthalpy compensation in intraprotein interactions from model compound data. Protein Sci 2024; 33:e5013. [PMID: 38808964 PMCID: PMC11135021 DOI: 10.1002/pro.5013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/18/2024] [Accepted: 04/21/2024] [Indexed: 05/30/2024]
Abstract
Many small globular proteins exist in only two states-the physiologically relevant folded state and an inactive unfolded state. The active state is stabilized by numerous weak attractive contacts, including hydrogen bonds, other polar interactions, and the hydrophobic effect. Knowledge of these interactions is key to understanding the fundamental equilibrium thermodynamics of protein folding and stability. We focus on one such interaction, that between amide and aromatic groups. We provide a statistically convincing case for quantitative, linear entropy-enthalpy compensation in forming aromatic-amide interactions using published model compound transfer-free energy data.
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Affiliation(s)
- Thomas W. Redvanly
- Department of ChemistryUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Gary J. Pielak
- Department of ChemistryUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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4
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Ding Y, Xue X. Medicinal Chemistry Strategies for the Modification of Bioactive Natural Products. Molecules 2024; 29:689. [PMID: 38338433 PMCID: PMC10856770 DOI: 10.3390/molecules29030689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
Natural bioactive compounds are valuable resources for drug discovery due to their diverse and unique structures. However, these compounds often lack optimal drug-like properties. Therefore, structural optimization is a crucial step in the drug development process. By employing medicinal chemistry principles, targeted molecular operations can be applied to natural products while considering their size and complexity. Various strategies, including structural fragmentation, elimination of redundant atoms or groups, and exploration of structure-activity relationships, are utilized. Furthermore, improvements in physicochemical properties, chemical and metabolic stability, biophysical properties, and pharmacokinetic properties are sought after. This article provides a concise analysis of the process of modifying a few marketed drugs as illustrative examples.
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Affiliation(s)
- Yuyang Ding
- Shenzhen Borui Pharmaceutical Technology Co., Ltd., Shenzhen 518055, China;
| | - Xiaoqian Xue
- Medi-X Pingshan, Southern University of Science and Technology, Shenzhen 518055, China
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5
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Galano‐Frutos JJ, Sancho J. Energy, water, and protein folding: A molecular dynamics-based quantitative inventory of molecular interactions and forces that make proteins stable. Protein Sci 2024; 33:e4905. [PMID: 38284492 PMCID: PMC10804899 DOI: 10.1002/pro.4905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/12/2023] [Accepted: 01/05/2024] [Indexed: 01/30/2024]
Abstract
Protein folding energetics can be determined experimentally on a case-by-case basis but it is not understood in sufficient detail to provide deep control in protein design. The fundamentals of protein stability have been outlined by calorimetry, protein engineering, and biophysical modeling, but these approaches still face great difficulty in elucidating the specific contributions of the intervening molecules and physical interactions. Recently, we have shown that the enthalpy and heat capacity changes associated to the protein folding reaction can be calculated within experimental error using molecular dynamics simulations of native protein structures and their corresponding unfolded ensembles. Analyzing in depth molecular dynamics simulations of four model proteins (CI2, barnase, SNase, and apoflavodoxin), we dissect here the energy contributions to ΔH (a key component of protein stability) made by the molecular players (polypeptide and solvent molecules) and physical interactions (electrostatic, van der Waals, and bonded) involved. Although the proteins analyzed differ in length, isoelectric point and fold class, their folding energetics is governed by the same quantitative pattern. Relative to the unfolded ensemble, the native conformations are enthalpically stabilized by comparable contributions from protein-protein and solvent-solvent interactions, and almost equally destabilized by interactions between protein and solvent molecules. The native protein surface seems to interact better with water than the unfolded one, but this is outweighed by the unfolded surface being larger. From the perspective of physical interactions, the native conformations are stabilized by van de Waals and Coulomb interactions and destabilized by conformational strain arising from bonded interactions. Also common to the four proteins, the sign of the heat capacity change is set by interactions between protein and solvent molecules or, from the alternative perspective, by Coulomb interactions.
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Affiliation(s)
- Juan José Galano‐Frutos
- Biocomputation and Complex Systems Physics Institute (BIFI)‐Joint Unit GBsC‐CSICUniversity of ZaragozaZaragozaSpain
- Departamento de Bioquímica y Biología Molecular y Celular, Facultad de CienciasUniversity of ZaragozaZaragozaSpain
| | - Javier Sancho
- Biocomputation and Complex Systems Physics Institute (BIFI)‐Joint Unit GBsC‐CSICUniversity of ZaragozaZaragozaSpain
- Departamento de Bioquímica y Biología Molecular y Celular, Facultad de CienciasUniversity of ZaragozaZaragozaSpain
- Aragon Health Research Institute (IIS Aragón)ZaragozaSpain
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6
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Ohnuki J, Jaunet-Lahary T, Yamashita A, Okazaki KI. Accelerated Molecular Dynamics and AlphaFold Uncover a Missing Conformational State of Transporter Protein OxlT. J Phys Chem Lett 2024; 15:725-732. [PMID: 38215403 DOI: 10.1021/acs.jpclett.3c03052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Transporter proteins change their conformations to carry their substrate across the cell membrane. The conformational dynamics is vital to understanding the transport function. We have studied the oxalate transporter (OxlT), an oxalate:formate antiporter from Oxalobacter formigenes, significant in avoiding kidney stone formation. The atomic structure of OxlT has been recently solved in the outward-open and occluded states. However, the inward-open conformation is still missing, hindering a complete understanding of the transporter. Here, we performed a Gaussian accelerated molecular dynamics simulation to sample the extensive conformational space of OxlT and successfully predicted the inward-open conformation where cytoplasmic substrate formate binding was preferred over oxalate binding. We also identified critical interactions for the inward-open conformation. The results were complemented by an AlphaFold2 structure prediction. Although AlphaFold2 solely predicted OxlT in the outward-open conformation, mutation of the identified critical residues made it partly predict the inward-open conformation, identifying possible state-shifting mutations.
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Affiliation(s)
- Jun Ohnuki
- Research Center for Computational Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki 444-8585, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Okazaki, Aichi 444-8585, Japan
| | - Titouan Jaunet-Lahary
- Research Center for Computational Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki 444-8585, Japan
| | - Atsuko Yamashita
- Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8530, Japan
| | - Kei-Ichi Okazaki
- Research Center for Computational Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki 444-8585, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Okazaki, Aichi 444-8585, Japan
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7
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Peng CX, Liang F, Xia YH, Zhao KL, Hou MH, Zhang GJ. Recent Advances and Challenges in Protein Structure Prediction. J Chem Inf Model 2024; 64:76-95. [PMID: 38109487 DOI: 10.1021/acs.jcim.3c01324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.
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Affiliation(s)
- Chun-Xiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fang Liang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yu-Hao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kai-Long Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ming-Hua Hou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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8
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Santhouse JR, Leung JMG, Chong LT, Horne WS. Effects of altered backbone composition on the folding kinetics and mechanism of an ultrafast-folding protein. Chem Sci 2024; 15:675-682. [PMID: 38179541 PMCID: PMC10763558 DOI: 10.1039/d3sc03976e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024] Open
Abstract
Sequence-encoded protein folding is a ubiquitous biological process that has been successfully engineered in a range of oligomeric molecules with artificial backbone chemical connectivity. A remarkable aspect of protein folding is the contrast between the rapid rates at which most sequences in nature fold and the vast number of conformational states possible in an unfolded chain with hundreds of rotatable bonds. Research efforts spanning several decades have sought to elucidate the fundamental chemical principles that dictate the speed and mechanism of natural protein folding. In contrast, little is known about how protein mimetic entities transition between an unfolded and folded state. Here, we report effects of altered backbone connectivity on the folding kinetics and mechanism of the B domain of Staphylococcal protein A (BdpA), an ultrafast-folding sequence. A combination of experimental biophysical analysis and atomistic molecular dynamics simulations performed on the prototype protein and several heterogeneous-backbone variants reveal the interplay among backbone flexibility, folding rates, and structural details of the transition state ensemble. Collectively, these findings suggest a significant degree of plasticity in the mechanisms that can give rise to ultrafast folding in the BdpA sequence and provide atomic level insights into how protein mimetic chains adopt an ordered folded state.
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Affiliation(s)
| | - Jeremy M G Leung
- Department of Chemistry, University of Pittsburgh Pittsburgh PA 15260 USA
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh Pittsburgh PA 15260 USA
| | - W Seth Horne
- Department of Chemistry, University of Pittsburgh Pittsburgh PA 15260 USA
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9
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Moss MJ, Chamness LM, Clark PL. The Effects of Codon Usage on Protein Structure and Folding. Annu Rev Biophys 2023; 53:10.1146/annurev-biophys-030722-020555. [PMID: 38134335 PMCID: PMC11227313 DOI: 10.1146/annurev-biophys-030722-020555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
The rate of protein synthesis is slower than many folding reactions and varies depending on the synonymous codons encoding the protein sequence. Synonymous codon substitutions thus have the potential to regulate cotranslational protein folding mechanisms, and a growing number of proteins have been identified with folding mechanisms sensitive to codon usage. Typically, these proteins have complex folding pathways and kinetically stable native structures. Kinetically stable proteins may fold only once over their lifetime, and thus, codon-mediated regulation of the pioneer round of protein folding can have a lasting impact. Supporting an important role for codon usage in folding, conserved patterns of codon usage appear in homologous gene families, hinting at selection. Despite these exciting developments, there remains few experimental methods capable of quantifying translation elongation rates and cotranslational folding mechanisms in the cell, which challenges the development of a predictive understanding of how biology uses codons to regulate protein folding. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- McKenze J Moss
- Department of Chemistry & Biochemistry, University of Notre Dame, Notre Dame, Indiana, USA; , ,
| | - Laura M Chamness
- Department of Chemistry & Biochemistry, University of Notre Dame, Notre Dame, Indiana, USA; , ,
| | - Patricia L Clark
- Department of Chemistry & Biochemistry, University of Notre Dame, Notre Dame, Indiana, USA; , ,
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10
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Chakravarty D, Schafer JW, Chen EA, Thole JR, Porter LL. AlphaFold2 has more to learn about protein energy landscapes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571380. [PMID: 38168383 PMCID: PMC10760193 DOI: 10.1101/2023.12.12.571380] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Recent work suggests that AlphaFold2 (AF2)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. Using several implementations of AF2, including two published enhanced sampling approaches, we generated >280,000 models of 93 fold-switching proteins whose experimentally determined conformations were likely in AF2's training set. Combining all models, AF2 predicted fold switching with a modest success rate of ~25%, indicating that it does not readily sample both experimentally characterized conformations of most fold switchers. Further, AF2's confidence metrics selected against models consistent with experimentally determined fold-switching conformations in favor of inconsistent models. Accordingly, these confidence metrics-though suggested to evaluate protein energetics reliably-did not discriminate between low and high energy states of fold-switching proteins. We then evaluated AF2's performance on seven fold-switching proteins outside of its training set, generating >159,000 models in total. Fold switching was accurately predicted in one of seven targets with moderate confidence. Further, AF2 demonstrated no ability to predict alternative conformations of two newly discovered targets without homologs in the set of 93 fold switchers. These results indicate that AF2 has more to learn about the underlying energetics of protein ensembles and highlight the need for further developments of methods that readily predict multiple protein conformations.
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Affiliation(s)
- Devlina Chakravarty
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894
| | - Joseph W. Schafer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894
| | - Ethan A. Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894
| | - Joseph R. Thole
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894
- Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892
| | - Lauren L. Porter
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894
- Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892
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11
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James JK, Norland K, Johar AS, Kullo IJ. Deep generative models of LDLR protein structure to predict variant pathogenicity. J Lipid Res 2023; 64:100455. [PMID: 37821076 PMCID: PMC10696256 DOI: 10.1016/j.jlr.2023.100455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/16/2023] [Accepted: 10/05/2023] [Indexed: 10/13/2023] Open
Abstract
The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.
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Affiliation(s)
- Jose K James
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kristjan Norland
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Angad S Johar
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.
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12
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Tiong E, Koo YS, Bi J, Koduru L, Koh W, Lim YH, Wong FT. Expression and engineering of PET-degrading enzymes from Microbispora, Nonomuraea, and Micromonospora. Appl Environ Microbiol 2023; 89:e0063223. [PMID: 37943056 PMCID: PMC10686063 DOI: 10.1128/aem.00632-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
IMPORTANCE Mismanagement of PET plastic waste significantly threatens human and environmental health. Together with the relentless increase in plastic production, plastic pollution is an issue of rising concern. In response to this challenge, scientists are investigating eco-friendly approaches, such as bioprocessing and microbial factories, to sustainably manage the growing quantity of plastic waste in our ecosystem. Industrial applicability of enzymes capable of degrading PET is limited by numerous factors, including their scarcity in nature. The objective of this study is to enhance our understanding of this group of enzymes by identifying and characterizing novel enzymes that can facilitate the breakdown of PET waste. This data will expand the enzymatic repertoire and provide valuable insights into the prerequisites for successful PET degradation.
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Grants
- C211917006 Agency for Science, Technology, and Research (A*STAR)
- C211917006 Agency for Science, Technology, and Research (A*STAR)
- C211917003 Agency for Science, Technology, and Research (A*STAR)
- A*STAR Graduate Academy Agency for Science, Technology, and Research (A*STAR)
- C233017006 Agency for Science, Technology, and Research (A*STAR)
- C233017004 Agency for Science, Technology, and Research (A*STAR)
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Affiliation(s)
- Elaine Tiong
- Molecular Engineering Lab, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology, and Research (A*STAR), Proteos, Singapore
| | - Ying Sin Koo
- Chemical Biotechnology and Biocatalysis, Institute of Sustainability for Chemicals, Energy, and Environment (ISCE), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
| | - Jiawu Bi
- Molecular Engineering Lab, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology, and Research (A*STAR), Proteos, Singapore
| | - Lokanand Koduru
- Molecular Engineering Lab, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology, and Research (A*STAR), Proteos, Singapore
| | - Winston Koh
- Chemical Biotechnology and Biocatalysis, Institute of Sustainability for Chemicals, Energy, and Environment (ISCE), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
| | - Yee Hwee Lim
- Chemical Biotechnology and Biocatalysis, Institute of Sustainability for Chemicals, Energy, and Environment (ISCE), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
- Synthetic Biology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Fong Tian Wong
- Molecular Engineering Lab, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology, and Research (A*STAR), Proteos, Singapore
- Chemical Biotechnology and Biocatalysis, Institute of Sustainability for Chemicals, Energy, and Environment (ISCE), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
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13
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Borges-Araújo L, Patmanidis I, Singh AP, Santos LHS, Sieradzan AK, Vanni S, Czaplewski C, Pantano S, Shinoda W, Monticelli L, Liwo A, Marrink SJ, Souza PCT. Pragmatic Coarse-Graining of Proteins: Models and Applications. J Chem Theory Comput 2023; 19:7112-7135. [PMID: 37788237 DOI: 10.1021/acs.jctc.3c00733] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.
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Affiliation(s)
- Luís Borges-Araújo
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
| | - Ilias Patmanidis
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Akhil P Singh
- Department of Biology, University of Fribourg, Chemin du Musée 10, Fribourg CH-1700, Switzerland
| | - Lucianna H S Santos
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Adam K Sieradzan
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Stefano Vanni
- Department of Biology, University of Fribourg, Chemin du Musée 10, Fribourg CH-1700, Switzerland
- Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d'Azur, Inserm, CNRS, 06560 Valbonne, France
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Sergio Pantano
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Wataru Shinoda
- Research Institute for Interdisciplinary Science, Okayama University, 3-1-1 Tsushima-naka, Kita, Okayama 700-8530, Japan
| | - Luca Monticelli
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
| | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Paulo C T Souza
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, 7 Passage du Vercors, 69007 Lyon, France
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14
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Mishra S, Sharma M, Singh MK, Pati S, Dehury B. Dissecting the Molecular Basis of Host Leucine-Rich Repeat Containing 15 Mediated Interaction with Receptor Binding Domain of SARS-CoV-2 Spike Protein: A Computational Approach. J Phys Chem Lett 2023; 14:8994-9001. [PMID: 37781985 DOI: 10.1021/acs.jpclett.3c01443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
The detection of leucine-rich repeat containing 15 (LRRC15) as a connecting link with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the possibility of its involvement in differential restriction activity of SARS-CoV-2 pathways. However, the structure-function mechanism of LRRC15 involving the receptor binding domain (RBD) of the SARS-CoV-2 spike protein and their mode of interaction is largely unknown. Using state-of-the-art AlphaFold2 and all-atom molecular dynamics simulations, our findings provide evidences of alternative binding modes of RBD with LRR units of LRRC15 having varied affinities. Contribution of both the receptor binding regions in RBD, including receptor binding motif in accommodating the LRR domain, towards the C-terminal region, emphasizes its differential role in modulating host cell receptiveness for SARS-CoV-2, the innate immune system, as well as antiviral tone. However, further experimental validations are necessary for unravelling the unknown mechanism and distinctive features of this host receptor in the COVID-19 pandemic, involving both the transmembrane as well as cytoplasmic domain.
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Affiliation(s)
- Sarbani Mishra
- Bioinformatics Division, ICMR-Regional Medical Research Centre, Nalco Square, Chandrasekharpur, Bhubaneswar 751023, Odisha, India
| | - Mansi Sharma
- KIIT School of Biotechnology, Sikharchandi Vihar, Bhubaneswar 751024, Odisha, India
| | - Mahendra Kumar Singh
- Data Science Laboratory, National Brain Research Centre, Gurgaon, Haryana 122052, India
| | - Sanghamitra Pati
- Bioinformatics Division, ICMR-Regional Medical Research Centre, Nalco Square, Chandrasekharpur, Bhubaneswar 751023, Odisha, India
| | - Budheswar Dehury
- Bioinformatics Division, ICMR-Regional Medical Research Centre, Nalco Square, Chandrasekharpur, Bhubaneswar 751023, Odisha, India
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15
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Rothman JE. Starting at Go: Protein structure prediction succumbs to machine learning. Proc Natl Acad Sci U S A 2023; 120:e2311128120. [PMID: 37732752 PMCID: PMC10523586 DOI: 10.1073/pnas.2311128120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023] Open
Abstract
This year's Lasker Basic Science Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acids in the vast majority of proteins on a genomic scale on the basis of sequence alone [J. Jumper et al., Nature 596, 583-589 (2021) and K. Tunyasuvunakool et al., Nature 596, 590-596 (2021)]. This extraordinary achievement by Demis Hassabis and John Jumper and their coworkers at Google's DeepMind and other collaborators was built on decades of experimental protein structure determination (structural biology) as well as the gradual development of multiple strategies incorporating biologically inspired statistical approaches. But when Jumper and Hassabis added a brew of innovative neural network-based machine learning approaches to the mix, the results were explosive. Realizing the half-century-old dream of predicting protein structure has already accelerated the pace and creativity of many areas of Chemistry, Biology, and Medicine.
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16
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Sosnick TR. AlphaFold developers Demis Hassabis and John Jumper share the 2023 Albert Lasker Basic Medical Research Award. J Clin Invest 2023:e174915. [PMID: 37731359 DOI: 10.1172/jci174915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023] Open
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17
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Huang Z, Cui X, Xia Y, Zhao K, Zhang G. Pathfinder: Protein folding pathway prediction based on conformational sampling. PLoS Comput Biol 2023; 19:e1011438. [PMID: 37695768 PMCID: PMC10513300 DOI: 10.1371/journal.pcbi.1011438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/21/2023] [Accepted: 08/17/2023] [Indexed: 09/13/2023] Open
Abstract
The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than β-strands.
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Affiliation(s)
- Zhaohong Huang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xinyue Cui
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuhao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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18
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Porter LL. Fluid protein fold space and its implications. Bioessays 2023; 45:e2300057. [PMID: 37431685 PMCID: PMC10529699 DOI: 10.1002/bies.202300057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/12/2023]
Abstract
Fold-switching proteins, which remodel their secondary and tertiary structures in response to cellular stimuli, suggest a new view of protein fold space. For decades, experimental evidence has indicated that protein fold space is discrete: dissimilar folds are encoded by dissimilar amino acid sequences. Challenging this assumption, fold-switching proteins interconnect discrete groups of dissimilar protein folds, making protein fold space fluid. Three recent observations support the concept of fluid fold space: (1) some amino acid sequences interconvert between folds with distinct secondary structures, (2) some naturally occurring sequences have switched folds by stepwise mutation, and (3) fold switching is evolutionarily selected and likely confers advantage. These observations indicate that minor amino acid sequence modifications can transform protein structure and function. Consequently, proteomic structural and functional diversity may be expanded by alternative splicing, small nucleotide polymorphisms, post-translational modifications, and modified translation rates.
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Affiliation(s)
- Lauren L. Porter
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
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19
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Banavar JR, Giacometti A, Hoang TX, Maritan A, Škrbić T. A geometrical framework for thinking about proteins. Proteins 2023. [PMID: 37565735 DOI: 10.1002/prot.26567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/16/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023]
Abstract
We present a model, based on symmetry and geometry, for proteins. Using elementary ideas from mathematics and physics, we derive the geometries of discrete helices and sheets. We postulate a compatible solvent-mediated emergent pairwise attraction that assembles these building blocks, while respecting their individual symmetries. Instead of seeking to mimic the complexity of proteins, we look for a simple abstraction of reality that yet captures the essence of proteins. We employ analytic calculations and detailed Monte Carlo simulations to explore some consequences of our theory. The predictions of our approach are in accord with experimental data. Our framework provides a rationalization for understanding the common characteristics of proteins. Our results show that the free energy landscape of a globular protein is pre-sculpted at the backbone level, sequences and functionalities evolve in the fixed backdrop of the folds determined by geometry and symmetry, and that protein structures are unique in being simultaneously characterized by stability, diversity, and sensitivity.
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Affiliation(s)
- Jayanth R Banavar
- Department of Physics and Institute for Fundamental Science, University of Oregon, Eugene, Oregon, USA
| | - Achille Giacometti
- Ca' Foscari University of Venice, Department of Molecular Sciences and Nanosystems, Venice, Italy
- European Centre for Living Technology (ECLT), Venice, Italy
| | - Trinh X Hoang
- Vietnam Academy of Science and Technology, Institute of Physics, Hanoi, Vietnam
| | - Amos Maritan
- University of Padua, Department of Physics and Astronomy, Padua, Italy
| | - Tatjana Škrbić
- Department of Physics and Institute for Fundamental Science, University of Oregon, Eugene, Oregon, USA
- Ca' Foscari University of Venice, Department of Molecular Sciences and Nanosystems, Venice, Italy
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20
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Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
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21
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Vila JA. Protein structure prediction from the complementary science perspective. Biophys Rev 2023; 15:439-445. [PMID: 37681107 PMCID: PMC10480374 DOI: 10.1007/s12551-023-01107-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/25/2023] [Indexed: 09/09/2023] Open
Abstract
A comparative analysis between two problems-apparently unrelated-which are solved in a period of ~400 years, viz., the accurate prediction of both the planetary orbits and the protein structures, leads to inferred conjectures that go far beyond the existence of a common path in their resolution, i.e., observation → pattern recognition → modeling. The preliminary results from this analysis indicate that complementary science, together with a new perspective on protein folding, may help us discover common features that could contribute to a more in-depth understanding of still-unsolved problems such as protein folding.
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Affiliation(s)
- Jorge A. Vila
- IMASL-CONICET, Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina
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22
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Puławski W, Koliński A, Koliński M. Integrative modeling of diverse protein-peptide systems using CABS-dock. PLoS Comput Biol 2023; 19:e1011275. [PMID: 37405984 DOI: 10.1371/journal.pcbi.1011275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/15/2023] [Indexed: 07/07/2023] Open
Abstract
The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.
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Affiliation(s)
- Wojciech Puławski
- Bioinformatics Laboratory, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
| | | | - Michał Koliński
- Bioinformatics Laboratory, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
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23
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Konkankit CC, Rackovsky S. Global Survey of Protein Dynamic Properties. J Phys Chem B 2023. [PMID: 37368985 DOI: 10.1021/acs.jpcb.3c02609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Using tools developed to study the dynamic bioinformatics of proteins, we are able to study the dynamic characteristics of very large numbers of protein sequences simultaneously. We study herein the distribution of protein sequences in a space determined by sequence mobility. It is shown that there are statistically significant differences in mobility distribution between folded sequences of different structural classes and between those and sequences of intrinsically disordered proteins. It is also shown that the several regions of mobility space differ significantly with respect to structural makeup. Helical proteins are shown to have distinctive dynamic characteristics at both extremes of the mobility spectrum.
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Affiliation(s)
- Chilaluck C Konkankit
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York 14853, United States
| | - S Rackovsky
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, United States
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York 14853, United States
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24
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discov Today 2023; 28:103551. [PMID: 36907321 PMCID: PMC10238671 DOI: 10.1016/j.drudis.2023.103551] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023]
Abstract
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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25
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Rajapaksa S, Konagurthu AS, Lesk AM. Sequence and structure alignments in post-AlphaFold era. Curr Opin Struct Biol 2023; 79:102539. [PMID: 36753924 DOI: 10.1016/j.sbi.2023.102539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 01/02/2023] [Indexed: 02/09/2023]
Abstract
Sequence alignment is fundamental for analyzing protein structure and function. For all but closely-related proteins, alignments based on structures are more accurate than alignments based purely on amino-acid sequences. However, the disparity between the large amount of sequence data and the relative paucity of experimentally-determined structures has precluded the general applicability of structure alignment. Based on the success of AlphaFold (and its likes) in producing high-quality structure predictions, we suggest that when aligning homologous proteins, lacking experimental structures, better results can be obtained by a structural alignment of predicted structures than by an alignment based only on amino-acid sequences. We present a quantitative evaluation, based on pairwise alignments of sequences and structures (both predicted and experimental) to support this hypothesis.
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Affiliation(s)
- Sandun Rajapaksa
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia
| | - Arun S Konagurthu
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia
| | - Arthur M Lesk
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA.
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26
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Chakravarty D, Schafer JW, Porter LL. Distinguishing features of fold-switching proteins. Protein Sci 2023; 32:e4596. [PMID: 36782353 PMCID: PMC9951197 DOI: 10.1002/pro.4596] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/30/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023]
Abstract
Though many folded proteins assume one stable structure that performs one function, a small-but-increasing number remodel their secondary and tertiary structures and change their functions in response to cellular stimuli. These fold-switching proteins regulate biological processes and are associated with autoimmune dysfunction, severe acute respiratory syndrome coronavirus-2 infection, and more. Despite their biological importance, it is difficult to computationally predict fold switching. With the aim of advancing computational prediction and experimental characterization of fold switchers, this review discusses several features that distinguish fold-switching proteins from their single-fold and intrinsically disordered counterparts. First, the isolated structures of fold switchers are less stable and more heterogeneous than single folders but more stable and less heterogeneous than intrinsically disordered proteins (IDPs). Second, the sequences of single fold, fold switching, and intrinsically disordered proteins can evolve at distinct rates. Third, proteins from these three classes are best predicted using different computational techniques. Finally, late-breaking results suggest that single folders, fold switchers, and IDPs have distinct patterns of residue-residue coevolution. The review closes by discussing high-throughput and medium-throughput experimental approaches that might be used to identify new fold-switching proteins.
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Affiliation(s)
- Devlina Chakravarty
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesdaMarylandUSA
| | - Joseph W. Schafer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesdaMarylandUSA
| | - Lauren L. Porter
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesdaMarylandUSA
- Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of HealthBethesdaMarylandUSA
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27
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Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [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: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
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Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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