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Article ; Online: Structure-function correlates of fibrinogen binding by

Tamadonfar, Kevin O / Di Venanzio, Gisela / Pinkner, Jerome S / Dodson, Karen W / Kalas, Vasilios / Zimmerman, Maxwell I / Bazan Villicana, Jesus / Bowman, Gregory R / Feldman, Mario F / Hultgren, Scott J

Proceedings of the National Academy of Sciences of the United States of America

2023 Volume 120, Issue 4, Page(s) e2212694120

Abstract: Multidrug- ... ...

Abstract	Multidrug-resistant
MeSH term(s)	Humans ; Adhesins, Bacterial/genetics ; Adhesins, Bacterial/metabolism ; Urinary Tract Infections/microbiology ; Catheters ; Acinetobacter baumannii/genetics ; Fibrinogen/metabolism
Chemical Substances	Adhesins, Bacterial ; Fibrinogen (9001-32-5)
Language	English
Publishing date	2023-01-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2212694120
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Article ; Online: Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.

Ward, Michael D / Zimmerman, Maxwell I / Meller, Artur / Chung, Moses / Swamidass, S J / Bowman, Gregory R

Nature communications

2021 Volume 12, Issue 1, Page(s) 3023

Abstract: Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an ... ...

Abstract	Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Computer Simulation ; Deep Learning ; Molecular Dynamics Simulation ; Myosins ; Protein Conformation ; Proteins/chemistry ; Proteins/metabolism ; Software
Chemical Substances	Proteins ; Myosins (EC 3.6.4.1)
Language	English
Publishing date	2021-05-21
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-23246-1
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Article ; Online: Apolipoprotein E4 has extensive conformational heterogeneity in lipid-free and lipid-bound forms.

Stuchell-Brereton, Melissa D / Zimmerman, Maxwell I / Miller, Justin J / Mallimadugula, Upasana L / Incicco, J Jeremías / Roy, Debjit / Smith, Louis G / Cubuk, Jasmine / Baban, Berevan / DeKoster, Gregory T / Frieden, Carl / Bowman, Gregory R / Soranno, Andrea

Proceedings of the National Academy of Sciences of the United States of America

2023 Volume 120, Issue 7, Page(s) e2215371120

Abstract: The ε4-allele variant of apolipoprotein E (ApoE4) is the strongest genetic risk factor for Alzheimer's disease, although it only differs from its neutral counterpart ApoE3 by a single amino acid substitution. While ApoE4 influences the formation of ... ...

Abstract	The ε4-allele variant of apolipoprotein E (ApoE4) is the strongest genetic risk factor for Alzheimer's disease, although it only differs from its neutral counterpart ApoE3 by a single amino acid substitution. While ApoE4 influences the formation of plaques and neurofibrillary tangles, the structural determinants of pathogenicity remain undetermined due to limited structural information. Previous studies have led to conflicting models of the C-terminal region positioning with respect to the N-terminal domain across isoforms largely because the data are potentially confounded by the presence of heterogeneous oligomers. Here, we apply a combination of single-molecule spectroscopy and molecular dynamics simulations to construct an atomically detailed model of monomeric ApoE4 and probe the effect of lipid association. Importantly, our approach overcomes previous limitations by allowing us to work at picomolar concentrations where only the monomer is present. Our data reveal that ApoE4 is far more disordered and extended than previously thought and retains significant conformational heterogeneity after binding lipids. Comparing the proximity of the N- and C-terminal domains across the three major isoforms (ApoE4, ApoE3, and ApoE2) suggests that all maintain heterogeneous conformations in their monomeric form, with ApoE2 adopting a slightly more compact ensemble. Overall, these data provide a foundation for understanding how ApoE4 differs from nonpathogenic and protective variants of the protein.
MeSH term(s)	Apolipoprotein E4/genetics ; Apolipoprotein E3/chemistry ; Apolipoprotein E2 ; Apolipoproteins E ; Protein Conformation ; Protein Isoforms/metabolism
Chemical Substances	Apolipoprotein E4 ; Apolipoprotein E3 ; Apolipoprotein E2 ; Apolipoproteins E ; Protein Isoforms
Language	English
Publishing date	2023-02-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2215371120
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Article ; Online: FAST Conformational Searches by Balancing Exploration/Exploitation Trade-Offs.

Zimmerman, Maxwell I / Bowman, Gregory R

Journal of chemical theory and computation

2015 Volume 11, Issue 12, Page(s) 5747–5757

Abstract: Molecular dynamics simulations are a powerful means of understanding conformational changes. However, it is still difficult to simulate biologically relevant time scales without the use of specialized supercomputers. Here, we introduce a goal-oriented ... ...

Abstract	Molecular dynamics simulations are a powerful means of understanding conformational changes. However, it is still difficult to simulate biologically relevant time scales without the use of specialized supercomputers. Here, we introduce a goal-oriented sampling method, called fluctuation amplification of specific traits (FAST), for extending the capabilities of commodity hardware. This algorithm rapidly searches conformational space for structures with desired properties by balancing trade-offs between focused searches around promising solutions (exploitation) and trying novel solutions (exploration). FAST was inspired by the hypothesis that many physical properties have an overall gradient in conformational space, akin to the energetic gradients that are known to guide proteins to their folded states. For example, we expect that transitioning from a conformation with a small solvent-accessible surface area to one with a large surface area will require passing through a series of conformations with steadily increasing surface areas. We demonstrate that such gradients are common through retrospective analysis of existing Markov state models (MSMs). Then we design the FAST algorithm to exploit these gradients to find structures with desired properties by (1) recognizing and amplifying structural fluctuations along gradients that optimize a selected physical property whenever possible, (2) overcoming barriers that interrupt these overall gradients, and (3) rerouting to discover alternative paths when faced with insurmountable barriers. To test FAST, we compare its performance to other methods for three common types of problems: (1) identifying unexpected binding pockets, (2) discovering the preferred paths between specific structures, and (3) folding proteins. Our conservative estimate is that FAST outperforms conventional simulations and an adaptive sampling algorithm by at least an order of magnitude. Furthermore, FAST yields both the proper thermodynamics and kinetics, allowing for a direct connection with kinetic experiments that is impossible with many other advanced sampling algorithms because they provide only thermodynamic information. Therefore, we expect FAST to be of great utility for a wide range of applications.
MeSH term(s)	Algorithms ; Markov Chains ; Molecular Dynamics Simulation ; Protein Structure, Tertiary ; Proteins/chemistry ; Proteins/metabolism ; Thermodynamics ; beta-Lactamases/chemistry ; beta-Lactamases/metabolism
Chemical Substances	Proteins ; beta-Lactamases (EC 3.5.2.6)
Language	English
Publishing date	2015-12-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1549-9626
ISSN (online)	1549-9626
DOI	10.1021/acs.jctc.5b00737
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Article ; Online: Computing and optimizing over all fixed-points of discrete systems on large networks.

Riehl, James R / Zimmerman, Maxwell I / Singh, Matthew F / Bowman, Gregory R / Ching, ShiNung

Journal of the Royal Society, Interface

2020 Volume 17, Issue 170, Page(s) 20200126

Abstract: Equilibria, or fixed points, play an important role in dynamical systems across various domains, yet finding them can be computationally challenging. Here, we show how to efficiently compute all equilibrium points of discrete-valued, discrete-time ... ...

Abstract	Equilibria, or fixed points, play an important role in dynamical systems across various domains, yet finding them can be computationally challenging. Here, we show how to efficiently compute all equilibrium points of discrete-valued, discrete-time systems on sparse networks. Using graph partitioning, we recursively decompose the original problem into a set of smaller, simpler problems that are easy to compute, and whose solutions combine to yield the full equilibrium set. This makes it possible to find the fixed points of systems on arbitrarily large networks meeting certain criteria. This approach can also be used without computing the full equilibrium set, which may grow very large in some cases. For example, one can use this method to check the existence and total number of equilibria, or to find equilibria that are optimal with respect to a given cost function. We demonstrate the potential capabilities of this approach with examples in two scientific domains: computing the number of fixed points in brain networks and finding the minimal energy conformations of lattice-based protein folding models.
MeSH term(s)	Algorithms ; Protein Folding
Language	English
Publishing date	2020-09-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2020.0126
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Article ; Online: Conformational distributions of isolated myosin motor domains encode their mechanochemical properties.

Porter, Justin R / Meller, Artur / Zimmerman, Maxwell I / Greenberg, Michael J / Bowman, Gregory R

eLife

2020 Volume 9

Abstract: Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it ... ...

Abstract	Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it remains unclear how sequence encodes these differences, since biochemically distinct motors often have nearly indistinguishable crystal structures. We hypothesized that sequences produce distinct biochemical phenotypes by modulating the relative probabilities of an ensemble of conformations primed for different functional roles. To test this hypothesis, we modeled the distribution of conformations for 12 myosin motor domains by building Markov state models (MSMs) from an unprecedented two milliseconds of all-atom, explicit-solvent molecular dynamics simulations. Comparing motors reveals shifts in the balance between nucleotide-favorable and nucleotide-unfavorable P-loop conformations that predict experimentally measured duty ratios and ADP release rates better than sequence or individual structures. This result demonstrates the power of an ensemble perspective for interrogating sequence-function relationships.
MeSH term(s)	Adenosine Diphosphate/chemistry ; Adenosine Diphosphate/metabolism ; Animals ; Avian Proteins/chemistry ; Avian Proteins/genetics ; Avian Proteins/metabolism ; Biomechanical Phenomena/genetics ; Chickens ; Humans ; Kinetics ; Molecular Dynamics Simulation ; Myosins/chemistry ; Myosins/genetics ; Myosins/metabolism ; Protein Conformation ; Protein Domains ; Thermodynamics
Chemical Substances	Avian Proteins ; Adenosine Diphosphate (61D2G4IYVH) ; Myosins (EC 3.6.4.1)
Language	English
Publishing date	2020-05-29
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.55132
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Article ; Online: Conformational distributions of isolated myosin motor domains encode their mechanochemical properties

Justin R Porter / Artur Meller / Maxwell I Zimmerman / Michael J Greenberg / Gregory R Bowman

eLife, Vol

2020 Volume 9

Abstract	Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it remains unclear how sequence encodes these differences, since biochemically distinct motors often have nearly indistinguishable crystal structures. We hypothesized that sequences produce distinct biochemical phenotypes by modulating the relative probabilities of an ensemble of conformations primed for different functional roles. To test this hypothesis, we modeled the distribution of conformations for 12 myosin motor domains by building Markov state models (MSMs) from an unprecedented two milliseconds of all-atom, explicit-solvent molecular dynamics simulations. Comparing motors reveals shifts in the balance between nucleotide-favorable and nucleotide-unfavorable P-loop conformations that predict experimentally measured duty ratios and ADP release rates better than sequence or individual structures. This result demonstrates the power of an ensemble perspective for interrogating sequence-function relationships.
Keywords	energy landscapes ; machine learning ; markov sate models ; molecular dynamics ; conformational heterogeneity ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets

Michael D. Ward / Maxwell I. Zimmerman / Artur Meller / Moses Chung / S. J. Swamidass / Gregory R. Bowman

Nature Communications, Vol 12, Iss 1, Pp 1-

2021 Volume 12

Abstract: Comparing and contrasting structural ensembles of different protein variants helps connect specific structural features to a protein’s biochemical properties. Here, the authors propose DiffNets, a self-supervised, deep learning method that streamlines ... ...

Abstract	Comparing and contrasting structural ensembles of different protein variants helps connect specific structural features to a protein’s biochemical properties. Here, the authors propose DiffNets, a self-supervised, deep learning method that streamlines this process.
Keywords	Science ; Q
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential.

Vithani, Neha / Ward, Michael D / Zimmerman, Maxwell I / Novak, Borna / Borowsky, Jonathan H / Singh, Sukrit / Bowman, Gregory R

Biophysical journal

2021 Volume 120, Issue 14, Page(s) 2880–2889

Abstract: Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between 20 and 30 proteins to carry out their viral ... ...

Abstract	Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between 20 and 30 proteins to carry out their viral replication cycle, including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2'-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? Although experimentally derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over 1 ms of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16's conformational ensemble to activate it. Second, guided by this activation mechanism and Markov state models, we investigate whether Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV1 and MERS but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket.
Language	English
Publishing date	2021-03-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	218078-9
ISSN	1542-0086 ; 0006-3495
ISSN (online)	1542-0086
ISSN	0006-3495
DOI	10.1016/j.bpj.2021.03.024
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Article ; Online: A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Matthew A. Cruz / Thomas E. Frederick / Upasana L. Mallimadugula / Sukrit Singh / Neha Vithani / Maxwell I. Zimmerman / Justin R. Porter / Katelyn E. Moeder / Gaya K. Amarasinghe / Gregory R. Bowman

Nature Communications, Vol 13, Iss 1, Pp 1-

2022 Volume 10

Abstract: Many viral proteins are thought to be unlikely candidates for drug discovery as they lack obvious drug binding sites. Here, the authors use computational approaches followed by experimental validation to identify a cryptic pocket within the Ebola virus ... ...

Abstract	Many viral proteins are thought to be unlikely candidates for drug discovery as they lack obvious drug binding sites. Here, the authors use computational approaches followed by experimental validation to identify a cryptic pocket within the Ebola virus protein VP35.
Keywords	Science ; Q
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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