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Madsen JJ, Yu W. Dynamic Nature of Staphylococcus aureus Type I Signal Peptidases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576923. [PMID: 38328037 PMCID: PMC10849702 DOI: 10.1101/2024.01.23.576923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Molecular dynamics simulations are used to interrogate the dynamic nature of Staphylococcus aureus Type I signal peptidases, SpsA and SpsB, including the impact of the P29S mutation of SpsB. Fluctuations and plasticity- rigidity characteristics vary among the proteins, particularly in the extracellular domain. Intriguingly, the P29S mutation, which influences susceptibility to arylomycin antibiotics, affect the mechanically coupled motions in SpsB. The integrity of the active site is crucial for catalytic competency, and variations in sampled structural conformations among the proteins are consistent with diverse peptidase capabilities. We also explored the intricate interactions between the proteins and the model S. aureus membrane. It was observed that certain membrane-inserted residues in the loop around residue 50 (50s) and C-terminal loops, beyond the transmembrane domain, give rise to direct interactions with lipids in the bilayer membrane. Our findings are discussed in the context of functional knowledge about these signal peptidases, offering additional understanding of dynamic aspects relevant to some cellular processes with potential implications for drug targeting strategies.
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Affiliation(s)
- Jesper J. Madsen
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612, United States of America
- Center for Global Health and Infectious Diseases Research, Global and Planetary Health, College of Public Health, University of South Florida, Tampa, Florida 33612, United States of America
| | - Wenqi Yu
- Department of Molecular Biosciences, College of Arts and Sciences, University of South Florida, Tampa, Florida 33612, United States of America
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Ahalawat N, Sahil M, Mondal J. Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning. J Chem Theory Comput 2023; 19:2644-2657. [PMID: 37068044 DOI: 10.1021/acs.jctc.2c00932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.
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Affiliation(s)
- Navjeet Ahalawat
- Department of Bioinformatics and Computational Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar 125 004, Haryana, India
| | - Mohammad Sahil
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Jagannath Mondal
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
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Sorensen AB, Greisen PJ, Madsen JJ, Lund J, Andersen G, Wulff-Larsen PG, Pedersen AA, Gandhi PS, Overgaard MT, Østergaard H, Olsen OH. A systematic approach for evaluating the role of surface-exposed loops in trypsin-like serine proteases applied to the 170 loop in coagulation factor VIIa. Sci Rep 2022; 12:3747. [PMID: 35260627 PMCID: PMC8904457 DOI: 10.1038/s41598-022-07620-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/14/2022] [Indexed: 12/27/2022] Open
Abstract
Proteases play a major role in many vital physiological processes. Trypsin-like serine proteases (TLPs), in particular, are paramount in proteolytic cascade systems such as blood coagulation and complement activation. The structural topology of TLPs is highly conserved, with the trypsin fold comprising two β-barrels connected by a number of variable surface-exposed loops that provide a surprising capacity for functional diversity and substrate specificity. To expand our understanding of the roles these loops play in substrate and co-factor interactions, we employ a systematic methodology akin to the natural truncations and insertions observed through evolution of TLPs. The approach explores a larger deletion space than classical random or directed mutagenesis. Using FVIIa as a model system, deletions of 1–7 amino acids through the surface exposed 170 loop, a vital allosteric regulator, was introduced. All variants were extensively evaluated by established functional assays and computational loop modelling with Rosetta. The approach revealed detailed structural and functional insights recapitulation and expanding on the main findings in relation to 170 loop functions elucidated over several decades using more cumbersome crystallization and single deletion/mutation methodologies. The larger deletion space was key in capturing the most active variant, which unexpectedly had a six-amino acid truncation. This variant would have remained undiscovered if only 2–3 deletions were considered, supporting the usefulness of the methodology in general protease engineering approaches. Our findings shed further light on the complex role that surface-exposed loops play in TLP function and supports the important role of loop length in the regulation and fine-tunning of enzymatic function throughout evolution.
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Affiliation(s)
- Anders B Sorensen
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark.,Department of Chemistry and Bioscience, Aalborg University, 9220, Ålborg, Denmark
| | | | - Jesper J Madsen
- Global and Planetary Health, College of Public Health, University of South Florida, Tampa, FL, 33612, USA.,Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA
| | - Jacob Lund
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark
| | - Gorm Andersen
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark
| | | | | | | | - Michael T Overgaard
- Department of Chemistry and Bioscience, Aalborg University, 9220, Ålborg, Denmark
| | | | - Ole H Olsen
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark. .,Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology, University of Copenhagen, Blegdamsvej 3b, 2200, Copenhagen, Denmark.
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