1
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Ohnuki J, Okazaki KI. Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK. J Phys Chem B 2024. [PMID: 39066727 DOI: 10.1021/acs.jpcb.4c02726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Transporter proteins carry their substrate across the cell membrane by changing their conformation. Thus, conformational dynamics are crucial for transport function. However, clarifying the complete transport cycle is challenging even with the current structural biology approach. Molecular dynamics (MD) simulation is a computational approach that can provide the time-resolved conformational dynamics of transporter proteins in atomic details but suffers from a high computational cost. Here, we integrate state-of-the-art protein structure prediction AI, AlphaFold2 (AF2), with MD simulation to reduce the computational cost. Focusing on the transporter protein NarK, we first show that AF2 sampled broad conformations of NarK, including the inward-open, occluded, and outward-open states. We also applied the coevolution-informed mutation in AF2, identifying state-shifting mutations. Then, we show that MD simulations from AF2-generated outward-open conformation, which is experimentally unresolved, captured the essence of the conformational state. We also found that MD simulations from AF2-generated intermediates showed transient dynamics like a transition state connecting two conformational states. This study paves the way for efficient conformational sampling of transporter proteins.
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Affiliation(s)
- Jun Ohnuki
- Research Center for Computational Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, Aichi 444-8585, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Okazaki, Aichi 444-8585, Japan
| | - Kei-Ichi Okazaki
- Research Center for Computational Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, Aichi 444-8585, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Okazaki, Aichi 444-8585, Japan
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2
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Swapna GVT, Dube N, Roth MJ, Montelione GT. Modeling Alternative Conformational States of Pseudo-Symmetric Solute Carrier Transporters using Methods from Machine Learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.15.603529. [PMID: 39071413 PMCID: PMC11275918 DOI: 10.1101/2024.07.15.603529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The Solute Carrier (SLC) superfamily of integral membrane proteins function to transport a wide array of solutes across the plasma and organelle membranes. SLC proteins also function as important drug transporters and as viral receptors. Despite being classified as a single superfamily, SLC proteins do not share a single common fold classification; however, most belong to multi-pass transmembrane helical protein fold families. SLC proteins populate different conformational states during the solute transport process, including outward open, intermediate (occluded), and inward open conformational states. For some SLC fold families this structural "flipping" corresponds to swapping between conformations of their N-terminal and C-terminal symmetry-related sub-structures. Conventional AlphaFold2 or Evolutionary Scale Modeling methods typically generate models for only one of these multiple conformational states of SLC proteins. Here we describe a fast and simple approach for modeling multiple conformational states of SLC proteins using a combined ESM - AF2 process. The resulting multi-state models are validated by comparison with sequence-based evolutionary co-variance data (ECs) that encode information about contacts present in the various conformational states adopted by the protein. We also explored the impact of mutations on conformational distributions of SLC proteins modeled by AlphaFold2 using both conventional and enhanced sampling methods. This approach for modeling conformational landscapes of pseudo-symmetric SLC proteins is demonstrated for several integral membrane protein transporters, including SLC35F2 the receptor of a feline leukemia virus envelope protein required for viral entry into eukaryotic cells.
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Affiliation(s)
- G V T Swapna
- Dept. of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway NJ 08854 USA
| | - Namita Dube
- Dept. of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
| | - Monica J Roth
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway NJ 08854 USA
| | - Gaetano T Montelione
- Dept. of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
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3
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Raisinghani N, Alshahrani M, Gupta G, Verkhivker G. Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602810. [PMID: 39026832 PMCID: PMC11257589 DOI: 10.1101/2024.07.09.602810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested that the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that enable to protect and restore ACE2 binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class-1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.
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4
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Ivashchenko SD, Shulga DA, Ivashchenko VD, Zinovev EV, Vlasov AV. In silico studies of the open form of human tissue transglutaminase. Sci Rep 2024; 14:15981. [PMID: 38987418 PMCID: PMC11236986 DOI: 10.1038/s41598-024-66348-8] [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: 02/07/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024] Open
Abstract
Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.
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Affiliation(s)
- S D Ivashchenko
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701
- Laboratory of Microbiology, BIOTECH University, Moscow, Russia, 125080
| | - D A Shulga
- Department of Chemistry, Moscow State University, Moscow, Russia, 119991
| | - V D Ivashchenko
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701
| | - E V Zinovev
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701
| | - A V Vlasov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701.
- Laboratory of Microbiology, BIOTECH University, Moscow, Russia, 125080.
- Joint Institute for Nuclear Research, Dubna, Russia, 141980.
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5
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Herrington NB, Li YC, Stein D, Pandey G, Schlessinger A. A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures. PLoS Comput Biol 2024; 20:e1012302. [PMID: 39046952 PMCID: PMC11268620 DOI: 10.1371/journal.pcbi.1012302] [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: 02/09/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024] Open
Abstract
Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.
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Affiliation(s)
- Noah B. Herrington
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yan Chak Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - David Stein
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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6
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Basu S, Subedi U, Tonelli M, Afshinpour M, Tiwari N, Fuentes EJ, Chakravarty S. Assessing the functional roles of coevolving PHD finger residues. Protein Sci 2024; 33:e5065. [PMID: 38923615 PMCID: PMC11201814 DOI: 10.1002/pro.5065] [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: 10/31/2023] [Revised: 04/21/2024] [Accepted: 05/16/2024] [Indexed: 06/28/2024]
Abstract
Although in silico folding based on coevolving residue constraints in the deep-learning era has transformed protein structure prediction, the contributions of coevolving residues to protein folding, stability, and other functions in physical contexts remain to be clarified and experimentally validated. Herein, the PHD finger module, a well-known histone reader with distinct subtypes containing subtype-specific coevolving residues, was used as a model to experimentally assess the contributions of coevolving residues and to clarify their specific roles. The results of the assessment, including proteolysis and thermal unfolding of wildtype and mutant proteins, suggested that coevolving residues have varying contributions, despite their large in silico constraints. Residue positions with large constraints were found to contribute to stability in one subtype but not others. Computational sequence design and generative model-based energy estimates of individual structures were also implemented to complement the experimental assessment. Sequence design and energy estimates distinguish coevolving residues that contribute to folding from those that do not. The results of proteolytic analysis of mutations at positions contributing to folding were consistent with those suggested by sequence design and energy estimation. Thus, we report a comprehensive assessment of the contributions of coevolving residues, as well as a strategy based on a combination of approaches that should enable detailed understanding of the residue contributions in other large protein families.
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Affiliation(s)
- Shraddha Basu
- Department of Chemistry & BiochemistrySouth Dakota State UniversityBrookingsSouth DakotaUSA
| | - Ujwal Subedi
- Department of Chemistry & BiochemistrySouth Dakota State UniversityBrookingsSouth DakotaUSA
| | - Marco Tonelli
- National Magnetic Resonance Facility at Madison (NMRFAM), University of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Maral Afshinpour
- Department of Chemistry & BiochemistrySouth Dakota State UniversityBrookingsSouth DakotaUSA
| | - Nitija Tiwari
- Department of Biochemistry & Molecular BiologyUniversity of IowaIowa CityIowaUSA
| | - Ernesto J. Fuentes
- Department of Biochemistry & Molecular BiologyUniversity of IowaIowa CityIowaUSA
| | - Suvobrata Chakravarty
- Department of Chemistry & BiochemistrySouth Dakota State UniversityBrookingsSouth DakotaUSA
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7
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Hallam TM, Andreadi A, Sharp SJ, Brocklebank V, Gardenal E, Dreismann A, Lotery AJ, Marchbank KJ, Harris CL, Jones AV, Kavanagh D. Comprehensive functional characterization of complement factor I rare variant genotypes identified in the SCOPE geographic atrophy cohort. J Biol Chem 2024; 300:107452. [PMID: 38852887 PMCID: PMC11277764 DOI: 10.1016/j.jbc.2024.107452] [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: 04/10/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/11/2024] Open
Abstract
Rare variants (RVs) in the gene encoding the regulatory enzyme complement factor I (CFI; FI) that reduce protein function or levels increase age-related macular degeneration risk. A total of 3357 subjects underwent screening in the SCOPE natural history study for geographic atrophy secondary to age-related macular degeneration, including CFI sequencing and serum FI measurement. Eleven CFI RV genotypes that were challenging to categorize as type I (low serum level) or type II (normal serum level, reduced enzymatic function) were characterized in the context of pure FI protein in C3b and C4b fluid phase cleavage assays and a novel bead-based functional assay (BBFA) of C3b cleavage. Four variants predicted or previously characterized as benign were analyzed by BBFA for comparison. In all, three variants (W51S, C67R, and I370T) resulted in low expression. Furthermore, four variants (P64L, R339Q, G527V, and P528T) were identified as being highly deleterious with IC50s for C3b breakdown >1 log increased versus the WT protein, while two variants (K476E and R474Q) were ∼1 log reduced in function. Meanwhile, six variants (P50A, T203I, K441R, E548Q, P553S, and S570T) had IC50s similar to WT. Odds ratios and BBFA IC50s were positively correlated (r = 0.76, p < 0.01), while odds ratios versus combined annotation dependent depletion (CADD) scores were not (r = 0.43, p = 0.16). Overall, 15 CFI RVs were functionally characterized which may aid future patient stratification for complement-targeted therapies. Pure protein in vitro analysis remains the gold standard for determining the functional consequence of CFI RVs.
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Affiliation(s)
- Thomas M Hallam
- Gyroscope Therapeutics Limited, A Novartis Company, London, UK.
| | - Anneliza Andreadi
- Complement Therapeutics Research Group, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK; National Renal Complement Therapeutics Centre, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Scott J Sharp
- Gyroscope Therapeutics Limited, A Novartis Company, London, UK
| | - Vicky Brocklebank
- Complement Therapeutics Research Group, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK; National Renal Complement Therapeutics Centre, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | | | - Anna Dreismann
- Gyroscope Therapeutics Limited, A Novartis Company, London, UK
| | - Andrew J Lotery
- Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Kevin J Marchbank
- Complement Therapeutics Research Group, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK; National Renal Complement Therapeutics Centre, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Claire L Harris
- Gyroscope Therapeutics Limited, A Novartis Company, London, UK; Complement Therapeutics Research Group, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Amy V Jones
- Gyroscope Therapeutics Limited, A Novartis Company, London, UK
| | - David Kavanagh
- Complement Therapeutics Research Group, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK; National Renal Complement Therapeutics Centre, Royal Victoria Infirmary, Newcastle upon Tyne, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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8
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Huang YJ, Montelione GT. Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600902. [PMID: 38979209 PMCID: PMC11230435 DOI: 10.1101/2024.06.26.600902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.
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Affiliation(s)
- Yuanpeng J. Huang
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
| | - Gaetano T. Montelione
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
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9
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations. Phys Chem Chem Phys 2024; 26:17720-17744. [PMID: 38869513 DOI: 10.1039/d4cp01372g] [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: 06/14/2024]
Abstract
In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamics (MD) simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and MD simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and MD simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, 75275, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, 75275, USA
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
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10
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Raisinghani N, Alshahrani M, Gupta G, Tian H, Xiao S, Tao P, Verkhivker GM. Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase. J Chem Theory Comput 2024; 20:5317-5336. [PMID: 38865109 DOI: 10.1021/acs.jctc.4c00222] [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: 06/13/2024]
Abstract
Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Hao Tian
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Gennady M Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
- Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
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11
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Duran C, Casadevall G, Osuna S. Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign. Faraday Discuss 2024. [PMID: 38910409 DOI: 10.1039/d3fd00156c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Enzymes exhibit diverse conformations, as represented in the free energy landscape (FEL). Such conformational diversity provides enzymes with the ability to evolve towards novel functions. The challenge lies in identifying mutations that enhance specific conformational changes, especially if located in distal sites from the active site cavity. The shortest path map (SPM) method, which we developed to address this challenge, constructs a graph based on the distances and correlated motions of residues observed in nanosecond timescale molecular dynamics (MD) simulations. We recently introduced a template based AlphaFold2 (tAF2) approach coupled with 10 nanosecond MD simulations to quickly estimate the conformational landscape of enzymes and assess how the FEL is shifted after mutation. In this study, we evaluate the potential of SPM when coupled with tAF2-MD in estimating conformational heterogeneity and identifying key conformationally-relevant positions. The selected model system is the beta subunit of tryptophan synthase (TrpB). We compare how the SPM pathways differ when integrating tAF2 with different MD simulation lengths from as short as 10 ns until 50 ns and considering two distinct Amber forcefield and water models (ff14SB/TIP3P versus ff19SB/OPC). The new methodology can more effectively capture the distal mutations found in laboratory evolution, thus showcasing the efficacy of tAF2-MD-SPM in rapidly estimating enzyme dynamics and identifying the key conformationally relevant hotspots for computational enzyme engineering.
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Affiliation(s)
- Cristina Duran
- Departament de Química, Institut de Química Computacional i Catàlisi, Universitat de Girona, c/Maria Aurèlia Capmany 69, 17003, Girona, Spain.
| | - Guillem Casadevall
- Departament de Química, Institut de Química Computacional i Catàlisi, Universitat de Girona, c/Maria Aurèlia Capmany 69, 17003, Girona, Spain.
| | - Sílvia Osuna
- Departament de Química, Institut de Química Computacional i Catàlisi, Universitat de Girona, c/Maria Aurèlia Capmany 69, 17003, Girona, Spain.
- ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain
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12
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Urvas L, Chiesa L, Bret G, Jacquemard C, Kellenberger E. Benchmarking AlphaFold-Generated Structures of Chemokine-Chemokine Receptor Complexes. J Chem Inf Model 2024; 64:4587-4600. [PMID: 38809680 DOI: 10.1021/acs.jcim.3c01835] [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: 05/31/2024]
Abstract
AlphaFold and AlphaFold-Multimer have become two essential tools for the modeling of unknown structures of proteins and protein complexes. In this work, we extensively benchmarked the quality of chemokine-chemokine receptor structures generated by AlphaFold-Multimer against experimentally determined structures. Our analysis considered both the global quality of the model, as well as key structural features for chemokine recognition. To study the effects of template and multiple sequence alignment parameters on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer correctly predicted differences in chemokine binding orientation and accurately reproduced the unique binding orientation of the CXCL12-ACKR3 complex. Further, the predictions of the full receptor N-terminus provided insights into a putative chemokine recognition site 0.5. The accuracy of chemokine N-terminus binding mode prediction varied between complexes, but the confidence score permitted the distinguishing of residues that were very likely well positioned. Finally, we generated a high-confidence model of the unsolved CXCL12-CXCR4 complex, which agreed with experimental mutagenesis and cross-linking data.
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Affiliation(s)
- Lauri Urvas
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Luca Chiesa
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Célien Jacquemard
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400 Illkirch, France
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13
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Gampp O, Kadavath H, Riek R. NMR tools to detect protein allostery. Curr Opin Struct Biol 2024; 86:102792. [PMID: 38428364 DOI: 10.1016/j.sbi.2024.102792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/06/2024] [Accepted: 02/14/2024] [Indexed: 03/03/2024]
Abstract
Allostery is a fundamental mechanism of cellular homeostasis by intra-protein communication between distinct functional sites. It is an internal process of proteins to steer interactions not only with each other but also with other biomolecules such as ligands, lipids, and nucleic acids. In addition, allosteric regulation is particularly important in enzymatic activities. A major challenge in structural and molecular biology today is unraveling allosteric sites in proteins, to elucidate the detailed mechanism of allostery and the development of allosteric drugs. Here we summarize the recently developed tools and approaches which enable the elucidation of regulatory hotspots and correlated motion in biomolecules, focusing primarily on solution-state nuclear magnetic resonance spectroscopy (NMR). These tools open an avenue towards a rational understanding of the mechanism of allostery and provide essential information for the design of allosteric drugs.
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Affiliation(s)
- Olivia Gampp
- Laboratory of Physical Chemistry, ETH Zurich, Switzerland
| | - Harindranath Kadavath
- Laboratory of Physical Chemistry, ETH Zurich, Switzerland; St. Jude Children's Research Hospital, 262 Danny Thomas Place, 38105 Memphis, Tennessee, USA. https://twitter.com/harijik
| | - Roland Riek
- Laboratory of Physical Chemistry, ETH Zurich, Switzerland.
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14
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Winnifrith A, Outeiral C, Hie BL. Generative artificial intelligence for de novo protein design. Curr Opin Struct Biol 2024; 86:102794. [PMID: 38663170 DOI: 10.1016/j.sbi.2024.102794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/31/2024] [Accepted: 02/19/2024] [Indexed: 05/19/2024]
Abstract
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion processes, seem adept at generating novel, yet realistic proteins that display desirable properties and perform specified functions. State-of-the-art design protocols now achieve experimental success rates nearing 20%, thus widening the access to de novo designed proteins. Despite extensive progress, there are clear field-wide challenges, for example, in determining the best in silico metrics to prioritise designs for experimental testing, and in designing proteins that can undergo large conformational changes or be regulated by post-translational modifications. With an increase in the number of models being developed, this review provides a framework to understand how these tools fit into the overall process of de novo protein design. Throughout, we highlight the power of incorporating biochemical knowledge to improve performance and interpretability.
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Affiliation(s)
- Adam Winnifrith
- Department of Biochemistry, University of Oxford, South Parks Rd, Oxford, OX1 3QU, United Kingdom; Evolvere Biosciences, Innovation Building, Old Road Campus, Oxford, OX3 7FZ, United Kingdom.
| | - Carlos Outeiral
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, United Kingdom.
| | - Brian L Hie
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, CA 94305, USA; Stanford Data Science, 475 Via Ortega, Stanford CA 94305, USA; Arc Institute, 3181 Porter Dr, Palo Alto, CA, USA.
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15
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Raisinghani N, Alshahrani M, Gupta G, Tian H, Xiao S, Tao P, Verkhivker G. Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594786. [PMID: 38798650 PMCID: PMC11118581 DOI: 10.1101/2024.05.17.594786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture of the effects of single point mutations that induced significant structural changes. We systematically examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. On the other hand, the predicted conformational ensembles for the G269E/M309L/T334I and M309L/L320I/T334I triple ABL mutants that share activating T334I gate-keeper substitution are dominated by the active ABL form. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric mediating centers that often directly correspond to state-switching mutational sites or reside in their immediate local structural proximity, which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.
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16
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity. J Phys Chem B 2024; 128:4696-4715. [PMID: 38696745 DOI: 10.1021/acs.jpcb.4c01341] [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: 05/04/2024]
Abstract
In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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17
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Ellaway JIJ, Anyango S, Nair S, Zaki HA, Nadzirin N, Powell HR, Gutmanas A, Varadi M, Velankar S. Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2024; 11:034701. [PMID: 38774441 PMCID: PMC11106648 DOI: 10.1063/4.0000251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/08/2024] [Indexed: 05/24/2024]
Abstract
Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.
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Affiliation(s)
- Joseph I. J. Ellaway
- Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom
| | - Stephen Anyango
- Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom
| | - Sreenath Nair
- Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom
| | - Hossam A. Zaki
- The Warren Alpert Medical School of Brown University, Providence, Rhode Island 02903, USA
| | - Nurul Nadzirin
- Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom
| | - Harold R. Powell
- Imperial College London, Department of Life Sciences, London, United Kingdom
| | - Aleksandras Gutmanas
- WaveBreak Therapeutics Ltd., Clarendon House, Clarendon Road, Cambridge, United Kingdom
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom
| | - Sameer Velankar
- Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom
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18
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Xie T, Huang J. Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters? J Chem Inf Model 2024; 64:3524-3536. [PMID: 38564295 DOI: 10.1021/acs.jcim.3c01936] [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: 04/04/2024]
Abstract
Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods.
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Affiliation(s)
- Tengyu Xie
- College of Life Science, Zhejiang University, HangZhou Zhejiang 310058, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, HangZhou Zhejiang 310024, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, HangZhou Zhejiang 310024, China
| | - Jing Huang
- College of Life Science, Zhejiang University, HangZhou Zhejiang 310058, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, HangZhou Zhejiang 310024, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, HangZhou Zhejiang 310024, China
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19
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations and Molecular Dynamics Simulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587850. [PMID: 38617283 PMCID: PMC11014522 DOI: 10.1101/2024.04.02.587850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles and binding mechanisms of convergent evolution for the SARS-CoV-2 Spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron Spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamic simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron Spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and molecular dynamics simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and molecular dynamics simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.
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20
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Monteiro da Silva G, Cui JY, Dalgarno DC, Lisi GP, Rubenstein BM. High-throughput prediction of protein conformational distributions with subsampled AlphaFold2. Nat Commun 2024; 15:2464. [PMID: 38538622 PMCID: PMC10973385 DOI: 10.1038/s41467-024-46715-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/28/2024] [Indexed: 04/12/2024] Open
Abstract
This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.
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Affiliation(s)
| | - Jennifer Y Cui
- Brown University Department of Molecular and Cell Biology and Biochemistry, Providence, RI, USA
| | | | - George P Lisi
- Brown University Department of Molecular and Cell Biology and Biochemistry, Providence, RI, USA
- Brown University Department of Chemistry, Providence, RI, USA
| | - Brenda M Rubenstein
- Brown University Department of Molecular and Cell Biology and Biochemistry, Providence, RI, USA.
- Brown University Department of Chemistry, Providence, RI, USA.
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21
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Carbery A, Buttenschoen M, Skyner R, von Delft F, Deane CM. Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures. J Cheminform 2024; 16:32. [PMID: 38486231 PMCID: PMC10941399 DOI: 10.1186/s13321-024-00821-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/01/2024] [Indexed: 03/17/2024] Open
Abstract
Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.
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Affiliation(s)
- Anna Carbery
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
| | - Martin Buttenschoen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Rachael Skyner
- OMass Therapeutics, Building 4000, Chancellor Court, John Smith Drive, ARC Oxford, OX4 2GX, UK
| | - Frank von Delft
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Centre for Medicines Discovery, University of Oxford, Oxford, OX3 7DQ, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, United Kingdom
- Department of Biochemistry, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
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22
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape. J Chem Inf Model 2024; 64:1657-1681. [PMID: 38373700 DOI: 10.1021/acs.jcim.3c01857] [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: 02/21/2024]
Abstract
The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both the structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that the AlphaFold2-predicted structural ensemble of the BA.2.86 spike protein complex with ACE2 can accurately capture the main conformational states of the Omicron variant. Complementary to AlphaFold2 structural predictions, microsecond molecular dynamics simulations reveal the details of the conformational landscape and produced equilibrium ensembles of the BA.2.86 structures that are used to perform mutational scanning of spike residues and characterize structural stability and binding energy hotspots. The ensemble-based mutational profiling of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 revealed a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 convergent mutational hotspots R403K, F486P, and R493Q. To examine the immune evasion properties of BA.2.86 in atomistic detail, we performed structure-based mutational profiling of the spike protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against the BA.2.86 variant. The results revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have evolved to outcompete other Omicron subvariants by improving immune evasion while preserving binding affinity with ACE2 via through a compensatory effect of R493Q and F486P convergent mutational hotspots. This study demonstrated that an integrative approach combining AlphaFold2 predictions with complementary atomistic molecular dynamics simulations and robust ensemble-based mutational profiling of spike residues can enable accurate and comprehensive characterization of structure, dynamics, and binding mechanisms of newly emerging Omicron variants.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States of America
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23
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Jänes J, Beltrao P. Deep learning for protein structure prediction and design-progress and applications. Mol Syst Biol 2024; 20:162-169. [PMID: 38291232 PMCID: PMC10912668 DOI: 10.1038/s44320-024-00016-x] [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: 06/26/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 02/01/2024] Open
Abstract
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.
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Affiliation(s)
- Jürgen Jänes
- Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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24
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Yao H, Wang X, Chi J, Chen H, Liu Y, Yang J, Yu J, Ruan Y, Xiang X, Pi J, Xu JF. Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures. Molecules 2024; 29:964. [PMID: 38474476 DOI: 10.3390/molecules29050964] [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: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.
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Affiliation(s)
- Hanbo Yao
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaodong Wang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaxin Chi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Haorong Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yilin Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiayi Yang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaqi Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yongdui Ruan
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
| | - Xufu Xiang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jun-Fa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
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25
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Raisinghani N, Alshahrani M, Gupta G, Tian H, Xiao S, Tao P, Verkhivker G. Interpretable Atomistic Prediction and Functional Analysis of Conformational Ensembles and Allosteric States in Protein Kinases Using AlphaFold2 Adaptation with Randomized Sequence Scanning and Local Frustration Profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580591. [PMID: 38496487 PMCID: PMC10942451 DOI: 10.1101/2024.02.15.580591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The groundbreaking achievements of AlphaFold2 (AF2) approaches in protein structure modeling marked a transformative era in structural biology. Despite the success of AF2 tools in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and fold-switching systems. The recent NMR-based structural determination of the unbound ABL kinase in the active state and two inactive low-populated functional conformations that are unique for ABL kinase presents an ideal challenge for AF2 approaches. In the current study we employ several implementations of AF2 methods to predict protein conformational ensembles and allosteric states of the ABL kinase including (a) multiple sequence alignments (MSA) subsampling approach; (b) SPEACH_AF approach in which alanine scanning is performed on generated MSAs; and (c) introduced in this study randomized full sequence mutational scanning for manipulation of sequence variations combined with the MSA subsampling. We show that the proposed AF2 adaptation combined with local frustration mapping of conformational states enable accurate prediction of the ABL active and intermediate structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AF2 predictions and limitations in detecting hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AF2 methods. This study uncovered previously unappreciated, fundamental connections between distinct patterns of local frustration in functional kinase states and AF2 successes/limitations in detecting low-populated frustrated conformations, providing a better understanding of benefits and limitations of current AF2-based adaptations in modeling of conformational ensembles.
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26
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Brown BP, Stein RA, Meiler J, Mchaourab HS. Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations. J Chem Theory Comput 2024; 20:1434-1447. [PMID: 38215214 PMCID: PMC10867840 DOI: 10.1021/acs.jctc.3c01081] [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: 09/29/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024]
Abstract
Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, computational and experimental methods capable of quantifying the energetic determinants of these equilibria are limited to systems of modest size. Recently, it has been demonstrated that the artificial intelligence system AlphaFold2 can be manipulated to produce structurally valid protein conformational ensembles. Here, we extend these studies and explore the extent to which AlphaFold2 contact distance distributions can approximate projections of the conformational Boltzmann distributions. For this purpose, we examine the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Our studies suggest that AlphaFold2 normalized contact distance distributions can correlate with conformation probabilities obtained with other methods but that they suffer from peak broadening. We also find that the AlphaFold2 contact distance distributions can be sensitive to point mutations. Overall, we anticipate that our findings will be valuable as the community seeks to model the thermodynamics of conformational changes in large biomolecular systems.
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Affiliation(s)
- Benjamin P. Brown
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
| | - Richard A. Stein
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig, SAC 04103, Germany
| | - Hassane S. Mchaourab
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
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27
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Choudhury J, Yonezawa K, Anu A, Shimizu N, Chaudhuri B. SAXS/WAXS data of conformationally flexible ribose binding protein. Data Brief 2024; 52:109932. [PMID: 38178847 PMCID: PMC10764985 DOI: 10.1016/j.dib.2023.109932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 01/06/2024] Open
Abstract
Modern artificial intelligence-based protein structure prediction methods, such as Alphafold2, can predict structures of folded proteins with reasonable accuracy. However, Alphafold2 provides a static view of a protein, which does not show the conformational variability of the protein, domain movement in a multi-domain protein, or ligand-induced conformational changes it might undergo in solution. Small-angle X-ay scattering (SAXS) and wide-angle X-ray scattering (WAXS) are solution techniques that can aid in integrative modeling of conformationally flexible proteins, or in validating their predicted ensemble structures. While SAXS is sensitive to global structural features, WAXS can expand the scope of structural modeling by including information about local structural changes. We present SAXS and WAXS datasets obtained from conformationally flexible d-ribose binding protein (RBP) from Escherichia coli in the ribose bound and unbound forms. SAXS/WAXS datasets of RBP provided here may aid in method development efforts for more accurate prediction of structural ensembles of conformationally flexible proteins, and their conformational changes.
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Affiliation(s)
- Jagrity Choudhury
- GN Ramachandran Protein Center, CSIR Institute of Microbial Technology, Chandigarh 160036, India
| | - Kento Yonezawa
- Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801, Japan
| | - Anu Anu
- GN Ramachandran Protein Center, CSIR Institute of Microbial Technology, Chandigarh 160036, India
- Academy of Scientific and Innovative Research (AcSIR), Anusandhan Bhawan, 2 Rafi Marg, New Delhi 110001, India
| | - Nobutaka Shimizu
- Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801, Japan
- Photon Factory, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801, Japan
| | - Barnali Chaudhuri
- GN Ramachandran Protein Center, CSIR Institute of Microbial Technology, Chandigarh 160036, India
- Academy of Scientific and Innovative Research (AcSIR), Anusandhan Bhawan, 2 Rafi Marg, New Delhi 110001, India
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28
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Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
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Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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29
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Zhang S, Li J, Chen SJ. Machine learning in RNA structure prediction: Advances and challenges. Biophys J 2024:S0006-3495(24)00067-5. [PMID: 38297836 DOI: 10.1016/j.bpj.2024.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.
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Affiliation(s)
- Sicheng Zhang
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Jun Li
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Shi-Jie Chen
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri; Department of Biochemistry, University of Missouri, Columbia, Missouri.
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30
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Stein RA, Mchaourab HS. Rosetta Energy Analysis of AlphaFold2 models: Point Mutations and Conformational Ensembles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.05.556364. [PMID: 37732281 PMCID: PMC10508732 DOI: 10.1101/2023.09.05.556364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
There has been an explosive growth in the applications of AlphaFold2, and other structure prediction platforms, to accurately predict protein structures from a multiple sequence alignment (MSA) for downstream structural analysis. However, two outstanding questions persist in the field regarding the robustness of AlphaFold2 predictions of the consequences of point mutations and the completeness of its prediction of protein conformational ensembles. We combined our previously developed method SPEACH_AF with model relaxation and energetic analysis with Rosetta to address these questions. SPEACH_AF introduces residue substitutions across the MSA and not just within the input sequence. With respect to conformational ensembles, we combined SPEACH_AF and a new MSA subsampling method, AF_cluster, and for a benchmarked set of proteins, we found that the energetics of the conformational ensembles generated by AlphaFold2 correspond to those of experimental structures and explored by standard molecular dynamic methods. With respect to point mutations, we compared the structural and energetic consequences of having the mutation(s) in the input sequence versus in the whole MSA (SPEACH_AF). Both methods yielded models different from the wild-type sequence, with more robust changes when the mutation(s) were in the whole MSA. While our findings demonstrate the robustness of AlphaFold2 in analyzing point mutations and exploring conformational ensembles, they highlight the need for multi parameter structural and energetic analyses of these models to generate experimentally testable hypotheses.
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Affiliation(s)
- Richard A Stein
- Department of Molecular Physiology and Biophysics and Center for Applied AI in Protein Dynamics Vanderbilt University
| | - Hassane S Mchaourab
- Department of Molecular Physiology and Biophysics and Center for Applied AI in Protein Dynamics Vanderbilt University
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31
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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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32
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Amaya-Rodriguez CA, Carvajal-Zamorano K, Bustos D, Alegría-Arcos M, Castillo K. A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel. Front Pharmacol 2024; 14:1251061. [PMID: 38328578 PMCID: PMC10847257 DOI: 10.3389/fphar.2023.1251061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024] Open
Abstract
The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.
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Affiliation(s)
- Cesar A. Amaya-Rodriguez
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Departamento de Fisiología y Comportamiento Animal, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - Karina Carvajal-Zamorano
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Daniel Bustos
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca, Chile
| | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago, Chile
| | - Karen Castillo
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
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33
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Bret H, Gao J, Zea DJ, Andreani J, Guerois R. From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2. Nat Commun 2024; 15:597. [PMID: 38238291 PMCID: PMC10796318 DOI: 10.1038/s41467-023-44288-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 12/07/2023] [Indexed: 01/22/2024] Open
Abstract
The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. The analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we manage to raise this success rate up to 90%. We obtain similar success rates using a much larger dataset of protein complexes taken from the ELM database. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.
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Affiliation(s)
- Hélène Bret
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Jinmei Gao
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Diego Javier Zea
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
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34
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Versini R, Sritharan S, Aykac Fas B, Tubiana T, Aimeur SZ, Henri J, Erard M, Nüsse O, Andreani J, Baaden M, Fuchs P, Galochkina T, Chatzigoulas A, Cournia Z, Santuz H, Sacquin-Mora S, Taly A. A Perspective on the Prospective Use of AI in Protein Structure Prediction. J Chem Inf Model 2024; 64:26-41. [PMID: 38124369 DOI: 10.1021/acs.jcim.3c01361] [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: 12/23/2023]
Abstract
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.
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Affiliation(s)
- Raphaelle Versini
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sujith Sritharan
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Burcu Aykac Fas
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Thibault Tubiana
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Sana Zineb Aimeur
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Julien Henri
- Sorbonne Université, CNRS, Laboratoire de Biologie, Computationnelle et Quantitative UMR 7238, Institut de Biologie Paris-Seine, 4 Place Jussieu, F-75005 Paris, France
| | - Marie Erard
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Oliver Nüsse
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Patrick Fuchs
- Sorbonne Université, École Normale Supérieure, PSL University, CNRS, Laboratoire des Biomolécules, LBM, 75005 Paris, France
- Université de Paris, UFR Sciences du Vivant, 75013 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Hubert Santuz
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Antoine Taly
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
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35
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Li J, Wang L, Zhu Z, Song C. Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction. J Chem Inf Model 2024; 64:301-315. [PMID: 38117138 PMCID: PMC10777399 DOI: 10.1021/acs.jcim.3c01381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Abstract
The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.
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Affiliation(s)
- Jiaxuan Li
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Lei Wang
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Zefeng Zhu
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Chen Song
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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36
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Ghafouri H, Lazar T, Del Conte A, Tenorio Ku LG, Tompa P, Tosatto SCE, Monzon AM. PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins. Nucleic Acids Res 2024; 52:D536-D544. [PMID: 37904608 PMCID: PMC10767937 DOI: 10.1093/nar/gkad947] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023] Open
Abstract
The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.
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Affiliation(s)
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Alessio Del Conte
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Institute of Enzymology, Research Centre for Natural Sciences (RCNS), Budapest, Hungary
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37
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Terwilliger TC, Liebschner D, Croll TI, Williams CJ, McCoy AJ, Poon BK, Afonine PV, Oeffner RD, Richardson JS, Read RJ, Adams PD. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat Methods 2024; 21:110-116. [PMID: 38036854 PMCID: PMC10776388 DOI: 10.1038/s41592-023-02087-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/11/2023] [Indexed: 12/02/2023]
Abstract
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.
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Affiliation(s)
- Thomas C Terwilliger
- New Mexico Consortium, Los Alamos, NM, USA.
- Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Dorothee Liebschner
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Tristan I Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Airlie J McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Billy K Poon
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Robert D Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
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38
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Winski A, Ludwiczak J, Orlowska M, Madaj R, Kaminski K, Dunin‐Horkawicz S. AlphaFold2 captures the conformational landscape of the HAMP signaling domain. Protein Sci 2024; 33:e4846. [PMID: 38010737 PMCID: PMC10731501 DOI: 10.1002/pro.4846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/30/2023] [Accepted: 11/19/2023] [Indexed: 11/29/2023]
Abstract
In this study, we present a conformational landscape of 5000 AlphaFold2 models of the Histidine kinases, Adenyl cyclases, Methyl-accepting proteins and Phosphatases (HAMP) domain, a short helical bundle that transduces signals from sensors to effectors in two-component signaling proteins such as sensory histidine kinases and chemoreceptors. The landscape reveals the conformational variability of the HAMP domain, including rotations, shifts, displacements, and tilts of helices, many combinations of which have not been observed in experimental structures. HAMP domains belonging to a single family tend to occupy a defined region of the landscape, even when their sequence similarity is low, suggesting that individual HAMP families have evolved to operate in a specific conformational range. The functional importance of this structural conservation is illustrated by poly-HAMP arrays, in which HAMP domains from families with opposite conformational preferences alternate, consistent with the rotational model of signal transduction. The only poly-HAMP arrays that violate this rule are predicted to be of recent evolutionary origin and structurally unstable. Finally, we identify a family of HAMP domains that are likely to be dynamic due to the presence of a conserved pi-helical bulge. All code associated with this work, including a tool for rapid sequence-based prediction of the rotational state in HAMP domains, is deposited at https://github.com/labstructbioinf/HAMPpred.
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Affiliation(s)
- Aleksander Winski
- Laboratory of Structural Bioinformatics, Centre of New TechnologiesUniversity of WarsawWarsawPoland
| | - Jan Ludwiczak
- Institute of Evolutionary Biology, Faculty of Biology, Biological and Chemical Research CentreUniversity of WarsawWarsawPoland
- Present address:
Prescient Design, Genentech Research & Early DevelopmentRoche GroupBaselSwitzerland
| | - Malgorzata Orlowska
- Institute of Evolutionary Biology, Faculty of Biology, Biological and Chemical Research CentreUniversity of WarsawWarsawPoland
| | - Rafal Madaj
- Institute of Evolutionary Biology, Faculty of Biology, Biological and Chemical Research CentreUniversity of WarsawWarsawPoland
| | - Kamil Kaminski
- Institute of Evolutionary Biology, Faculty of Biology, Biological and Chemical Research CentreUniversity of WarsawWarsawPoland
| | - Stanislaw Dunin‐Horkawicz
- Institute of Evolutionary Biology, Faculty of Biology, Biological and Chemical Research CentreUniversity of WarsawWarsawPoland
- Department of Protein EvolutionMax Planck Institute for Biology TübingenTübingenGermany
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39
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Kleiman DE, Nadeem H, Shukla D. Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning. J Phys Chem B 2023; 127:10669-10681. [PMID: 38081185 DOI: 10.1021/acs.jpcb.3c04843] [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: 12/22/2023]
Abstract
Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings.
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Affiliation(s)
- Diego E Kleiman
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hassan Nadeem
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Diwakar Shukla
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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40
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da Silva GM, Cui JY, Dalgarno DC, Lisi GP, Rubenstein BM. Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550545. [PMID: 37546747 PMCID: PMC10402055 DOI: 10.1101/2023.07.25.550545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, NMR analysis, and evolution.
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Affiliation(s)
- Gabriel Monteiro da Silva
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Providence, RI, USA
| | - Jennifer Y Cui
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Providence, RI, USA
| | | | - George P Lisi
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University Department of Chemistry, Providence, RI, USA
| | - Brenda M Rubenstein
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University Department of Chemistry, Providence, RI, USA
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41
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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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42
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2-Enabled Atomistic Modeling of Epistatic Binding Mechanisms for the SARS-CoV-2 Spike Omicron XBB.1.5, EG.5 and FLip Variants: Convergent Evolution Hotspots Cooperate to Control Stability and Conformational Adaptability in Balancing ACE2 Binding and Antibody Resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.11.571185. [PMID: 38168257 PMCID: PMC10760024 DOI: 10.1101/2023.12.11.571185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
In this study, we combined AI-based atomistic structural modeling and microsecond molecular simulations of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for XBB.1.5+L455F, XBB.1.5+F456L(EG.5) and XBB.1.5+L455F/F456L (FLip) lineages to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and physics-based rigorous computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of Q493 hotspot in synchronization of epistatic couplings between L455F and F456L mutations providing a quantitative insight into the mechanism underlying differences between XBB lineages. Mutational profiling is combined with network-based model of epistatic couplings showing that the Q493, L455 and F456 sites mediate stable communities at the binding interface with ACE2 and can serve as stable mediators of non-additive couplings. Structure-based mutational analysis of Spike protein binding with the class 1 antibodies quantified the critical role of F456L and F486P mutations in eliciting strong immune evasion response. The results of this analysis support a mechanism in which the emergence of EG.5 and FLip variants may have been dictated by leveraging strong epistatic effects between several convergent revolutionary hotspots that provide synergy between the improved ACE2 binding and broad neutralization resistance. This interpretation is consistent with the notion that functionally balanced substitutions which simultaneously optimize immune evasion and high ACE2 affinity may continue to emerge through lineages with beneficial pair or triplet combinations of RBD mutations involving mediators of epistatic couplings and sites in highly adaptable RBD regions.
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43
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Ramelot TA, Tejero R, Montelione GT. Representing structures of the multiple conformational states of proteins. Curr Opin Struct Biol 2023; 83:102703. [PMID: 37776602 PMCID: PMC10841472 DOI: 10.1016/j.sbi.2023.102703] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/18/2023] [Accepted: 08/23/2023] [Indexed: 10/02/2023]
Abstract
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.
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Affiliation(s)
- Theresa A Ramelot
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Roberto Tejero
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Gaetano T Montelione
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
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44
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Ahmed M, Maldonado AM, Durrant JD. From Byte to Bench to Bedside: Molecular Dynamics Simulations and Drug Discovery. ARXIV 2023:arXiv:2311.16946v1. [PMID: 38076508 PMCID: PMC10705576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware and software improvements. Given these advancements, MD simulations are poised to become even more powerful tools for investigating the dynamic interactions between potential small-molecule drugs and their target proteins, with significant implications for pharmacological research.
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Affiliation(s)
- Mayar Ahmed
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Alex M. Maldonado
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jacob D. Durrant
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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45
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Vögele M, Zhang BW, Kaindl J, Wang L. Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding? J Chem Theory Comput 2023; 19:8414-8422. [PMID: 37943175 DOI: 10.1021/acs.jctc.3c00899] [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: 11/10/2023]
Abstract
For an effective drug, strong binding to the target protein is a prerequisite, but it is not enough. To produce a particular functional response, drugs need to either block the proteins' functions or modulate their activities by changing their conformational equilibrium. The binding free energy of a compound to its target is routinely calculated, but the timescales for the protein conformational changes are prohibitively long to be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that the binding free energies of the ligands with different receptor conformations may infer their efficacy. However, this hypothesis has not been thoroughly validated. We present an actionable protocol and a comprehensive study to show that binding thermodynamics provides a strong predictor of the efficacy of a ligand. We apply the absolute binding free energy perturbation method to ligands bound to active and inactive states of eight G protein-coupled receptors and a nuclear receptor and then compare the resulting binding free energies. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state. Our method achieves unprecedented performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.
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Affiliation(s)
- Martin Vögele
- Schrödinger, Inc., 1540 Broadway 24th Floor, New York, New York 10036, United States
| | - Bin W Zhang
- Schrödinger, Inc., 1540 Broadway 24th Floor, New York, New York 10036, United States
| | - Jonas Kaindl
- Schrödinger GmbH, Glücksteinallee 25, Mannheim 68163, Germany
| | - Lingle Wang
- Schrödinger, Inc., 1540 Broadway 24th Floor, New York, New York 10036, United States
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Buller R, Lutz S, Kazlauskas RJ, Snajdrova R, Moore JC, Bornscheuer UT. From nature to industry: Harnessing enzymes for biocatalysis. Science 2023; 382:eadh8615. [PMID: 37995253 DOI: 10.1126/science.adh8615] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/17/2023] [Indexed: 11/25/2023]
Abstract
Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology.
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Affiliation(s)
- R Buller
- Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, 8820 Wädenswil, Switzerland
| | - S Lutz
- Codexis Incorporated, Redwood City, CA 94063, USA
| | - R J Kazlauskas
- Department of Biochemistry, Molecular Biology and Biophysics, Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
| | - R Snajdrova
- Novartis Institutes for BioMedical Research, Global Discovery Chemistry, 4056 Basel, Switzerland
| | - J C Moore
- MRL, Merck & Co., Rahway, NJ 07065, USA
| | - U T Bornscheuer
- Institute of Biochemistry, Dept. of Biotechnology and Enzyme Catalysis, Greifswald University, Greifswald, Germany
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. Accurate Characterization of Conformational Ensembles and Binding Mechanisms of the SARS-CoV-2 Omicron BA.2 and BA.2.86 Spike Protein with the Host Receptor and Distinct Classes of Antibodies Using AlphaFold2-Augmented Integrative Computational Modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.18.567697. [PMID: 38045395 PMCID: PMC10690158 DOI: 10.1101/2023.11.18.567697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The latest wave SARS-CoV-2 Omicron variants displayed a growth advantage and the increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with all-atom MD simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that AlphaFold2-predicted conformational ensemble of the BA.2.86 spike protein complex can accurately capture the main dynamics signatures obtained from microscond molecular dynamics simulations. The ensemble-based dynamic mutational scanning of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 dissected the role of the BA.2 and BA.2.86 backgrounds in modulating binding free energy changes revealing a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 mutational sites R403K, F486P and R493Q. To examine immune evasion properties of BA.2.86 in atomistic detail, we performed large scale structure-based mutational profiling of the S protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against BA.2.86 variant. The results quantified specific function of the BA.2.86 mutations to ensure broad resistance against different classes of RBD antibodies. This study revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have primarily evolved to improve immune escape while modulating binding affinity with ACE2 through cooperative effect of R403K, F486P and R493Q mutations. The study supports a hypothesis that the impact of the increased ACE2 binding affinity on viral fitness is more universal and is mediated through cross-talk between convergent mutational hotspots, while the effect of immune evasion could be more variant-dependent.
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Varadi M, Tsenkov M, Velankar S. Challenges in bridging the gap between protein structure prediction and functional interpretation. Proteins 2023. [PMID: 37850517 DOI: 10.1002/prot.26614] [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: 06/28/2023] [Revised: 09/26/2023] [Accepted: 10/04/2023] [Indexed: 10/19/2023]
Abstract
The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is essential to note that they are not validated and cannot be considered the ground truth. Thus, challenges persist, particularly in capturing protein dynamics, predicting multi-chain structures, interpreting protein function, and assessing model quality. Interdisciplinary collaborations are crucial to overcoming these obstacles. Databases like the AlphaFold Protein Structure Database, the ESM Metagenomic Atlas, and initiatives like the 3D-Beacons Network provide FAIR access to these data, enabling their interpretation and application across a broader scientific community. Whilst substantial advancements have been made in protein structure prediction, further progress is required to address the remaining challenges. Developing training materials, nurturing collaborations, and ensuring open data sharing will be paramount in this pursuit. The continued evolution of these tools and methodologies will deepen our understanding of protein function and accelerate disease pathogenesis and drug development discoveries.
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Affiliation(s)
- Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Maxim Tsenkov
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
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Guo X, Chen R, Cao L. Molecular docking-based virtual screening and dynamics simulation study of novel and potential SIRT7 inhibitors. Chem Biol Drug Des 2023; 102:707-717. [PMID: 37287091 DOI: 10.1111/cbdd.14277] [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: 01/10/2023] [Revised: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
In cancer cells, short for sirtuin (SIRT7) stabilizes the transformed state via its nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase activity. Epigenetic factor SIRT7 plays important roles in cancer biology, reversing cancer phenotypes and suppressing tumor growth when inactive. In the present study, we got the SIRT7 protein structure from Alpha Fold2 Database and performed structure-based virtual screening to develop specific SIRT7 inhibitors using the SIRT7 inhibitor 97,491 interaction mechanism. As candidates for specific SIRT7 inhibitors, compounds with high affinities to SIRT7 were chosen. ZINC000001910616 and ZINC000014708529, two of our leading compounds, showed strong interactions with SIRT7. Our MD simulation results also revealed that the 5-hydroxy-4H-thioxen-4-one group and terminal carboxyl group were critical groups responsible for interaction of small molecules with SIRT7. In our study, we demonstrated that targeting SIRT7 may offer novel therapeutic options for cancer treatment. Compounds ZINC000001910616 and ZINC000014708529 can serve as chemical probes to investigate SIRT7 biological functions and provide starting points for the development of novel therapeutics against cancers.
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Affiliation(s)
- Xinli Guo
- Department of Operating Room, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Rui Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Liping Cao
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Research Center of Cognitive Healthcare, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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50
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Shui S, Buckley S, Scheller L, Correia BE. Rational design of small-molecule responsive protein switches. Protein Sci 2023; 32:e4774. [PMID: 37656809 PMCID: PMC10510469 DOI: 10.1002/pro.4774] [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: 04/18/2023] [Revised: 08/26/2023] [Accepted: 08/29/2023] [Indexed: 09/03/2023]
Abstract
Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches.
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Affiliation(s)
- Sailan Shui
- Laboratory of Protein Design and Immunoengineering (LPDI)STI, EPFLLausanneSwitzerland
- Swiss Institute of Bioinformatics (SIB)LausanneSwitzerland
| | - Stephen Buckley
- Laboratory of Protein Design and Immunoengineering (LPDI)STI, EPFLLausanneSwitzerland
- Swiss Institute of Bioinformatics (SIB)LausanneSwitzerland
| | - Leo Scheller
- Laboratory of Protein Design and Immunoengineering (LPDI)STI, EPFLLausanneSwitzerland
- Swiss Institute of Bioinformatics (SIB)LausanneSwitzerland
| | - Bruno E. Correia
- Laboratory of Protein Design and Immunoengineering (LPDI)STI, EPFLLausanneSwitzerland
- Swiss Institute of Bioinformatics (SIB)LausanneSwitzerland
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