Ligand‐Induced Conformational Changes in HSP90 Monitored Time Resolved and Label Free—Towards a Conformational Activity Screening for Drug Discovery

Güldenhaupt, Jörn; Amaral, Marta; Kötting, Carsten; Nabers, Andreas; Müsil, Djordje; Frech, Matthias; Gerwert, Klaus

doi:10.1002/anie.201802603

Cited by 15 publications

(28 citation statements)

References 24 publications

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“…Simulations were performed using the open-source Gromacs software 47 v2018 using the AM-BER99SB forcefield 48,49 and the TIP3P water model. 50 We performed simulations on four Hsp90-binding compounds named 1, 2, 3 and 4, which are compounds 1b (PDB ID 5J20), 51 1j (PDB ID 6FCJ), 52,53 1f (modeled based on PDB ID 5J9X) 51 and 1g (PDB ID 5J27) 10 in Ref., 53 respectively. The simulation systems and topologies were taken from Ref.…”

Section: Simulations Of Hsp90-ligand Complexesmentioning

confidence: 99%

Non-equilibrium biomolecular simulation pathway analysis assisted by machine learning and graph methods

Bray¹,

Tänzel²,

Wolf³

2022

Preprint

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Employing biased molecular dynamics simulations to enforce protein-ligand unbinding provides the path-dependent work required to effect dissociation, rather than a path-independent unbinding free energy. Analysis of such simulations therefore requires detection of paths, as well as of reaction coordinates the paths appear in. We here present two methods to identify paths and to cluster trajectories into path ensembles accordingly. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of pathways and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the relevant reaction coordinate is a single intra-ligand hydrogen bond, highlighting the challenges involved in path reaction coordinate detection.

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Section: Simulations Of Hsp90-ligand Complexesmentioning

confidence: 99%

Non-equilibrium biomolecular simulation pathway analysis assisted by machine learning and graph methods

Bray¹,

Tänzel²,

Wolf³

2022

Preprint

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“…Ligand binding induces conformational changes in the protein structure. 63 The stability of protein-ligand complexes would impact on protein function. As revealed in Table 14, structures with the lowest global fluctuation are indicative of the most stable protein-ligand complexes.…”

Section: Normal Mode Analysismentioning

confidence: 99%

The Structure-Based Virtual Screening for Natural Compounds that Bind with the Activating Receptors of Natural Killer Cells

2021

TJNPR

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Cancers are a group of diseases characterized by erratic cell growth which invade and spread into other parts of the body. 1,2 They are caused by DNA damage and an ineffective DNA repair mechanism. According to a 2015 WHO report, cancer is the second leading cause of death globally and there were 90.5 million incidences of cancer in 2015 which accounted for 8.8 million deaths. 3 Cancers are caused by a persistent damage to DNA which culminates into mutations of certain gene sequences in the human genome. 4 Expression of these mutant sequences lead to an autonomous and unregulated hyper-proliferation of cells; insufficient apoptosis; altered differentiation and metabolism; genomic instability; and immortalization. 5 The abnormal proliferation of cells is due to alterations in the cell cycle replication mechanism due to nuclear and cytoplasm distortions. These changes include hyperchromatism, increased telomerase expression, prominent nucleoli, irregular chromatin distribution within nuclei, and increased size of nucleus, pleomorphism, and chromosomal translocations. 6,7 Available cancer therapies such as chemotherapy are non-selective as other normal rapidly dividing cells (including immune cells) are destroyed. Another major frustration faced by clinicians and researchers includes the evasive nature of cancer cells as they beat the

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“…The determination of protein secondary structures or identifying their constituent amino acids is important across a broad range of topics, for example: protein misfolding that is strongly linked with neurodegenerative disorders, tumor cells, red blood cells in sickle cell disease, etc. [1][2][3][4][5][6][7][8][9][10] The vibrational amide I band in the infrared has proved to be of great significance in the spectroscopy of biological species due to its abundance in constituent proteins and hence amino acid residues. [11][12][13] Confidence in assigning the peak positions that contribute to the overall amide I band as well as accurately fitting the spectrum is of great significance since it allows a more robust determination of the secondary structures that are present as well as their associated quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts

2020

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The challenge of deriving quantitative information from the infrared spectra of proteins arises from the large number of secondary structures and amino acid side-chain functional groups that all contribute to the spectral intensity, such as within the amide I band (1600–1700 cm–1). The band is invariably heavily convoluted from overlapping spectral features, thereby making interpretation difficult such that deconvolution is usually required. This work critically examines the methods available to deconvolute the spectra and assesses the commonly used methods and algorithms applied to vibrational spectra for smoothing and peak identification. We show that unless their spectra have very high signal-to-noise ratios, quantitative analysis to decipher protein constituents is not feasible. The advantages and disadvantages of spectral smoothing using adjacent averaging, the Savitzky–Golay filter and the fast Fourier transform filter are examined in detail. The use of derivative spectra to identify peaks is described with particular reference to the influence and reduction of interfering water bands in the amide I region. The reliability of band narrowing techniques such as second-derivative analysis or Fourier deconvolution that lead to the identification of the contributing protein peaks is investigated. Both methods are shown to be limited in their capacity to resolve features with very similar frequencies. Additionally, the presence of narrow bands arising from high-frequency noise whether from atmospheric water vapor, acoustic vibrations, or electrical interference results in both methods becoming increasingly unusable as narrow bands are preferentially enhanced at the expense of broad ones such as the amide I bands. An optimal strategy is critically developed to allow accurate determination and quantification of protein constituents and their conformations. Additionally, quantitative methods are proposed to account for baseline shifts, which would otherwise introduce significant errors in similarity indices.

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Ligand‐Induced Conformational Changes in HSP90 Monitored Time Resolved and Label Free—Towards a Conformational Activity Screening for Drug Discovery

Cited by 15 publications

References 24 publications

Non-equilibrium biomolecular simulation pathway analysis assisted by machine learning and graph methods

Non-equilibrium biomolecular simulation pathway analysis assisted by machine learning and graph methods

The Structure-Based Virtual Screening for Natural Compounds that Bind with the Activating Receptors of Natural Killer Cells

Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts

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