The Protein Data Bank: a historical perspective

Berman, Helen M.

doi:10.1107/s0108767307035623

Cited by 380 publications

(267 citation statements)

References 32 publications

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“…A homology model of Tpm3.1 was first built based on the solution NMR structure of the junction between tropomyosin molecules (15). The coordinates for the NMR structures were obtained from the PDB databank (16), PDB id 2g9j and a representative structure was chosen out of the 10 NMR models as the template structure. The homology model was created using SWISS-MODEL (17); however, the helical conformation and side chain packing were optimized using a simulated annealing procedure based on Smith and colleagues (18) using NAMD2 (19).…”

Section: Molecular Modelingmentioning

confidence: 99%

Identification of Cancer-Targeted Tropomyosin Inhibitors and Their Synergy with Microtubule Drugs

Currier

Stehn

Swain

et al. 2017

Molecular Cancer Therapeutics

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Actin filaments, with their associated tropomyosin polymers, and microtubules are dynamic cytoskeletal systems regulating numerous cell functions. While antimicrotubule drugs are wellestablished, antiactin drugs have been more elusive. We previously targeted actin in cancer cells by inhibiting the function of a tropomyosin isoform enriched in cancer cells, Tpm3.1, using a first-in-class compound, TR100. Here, we screened over 200 other antitropomyosin analogues for anticancer and ontarget activity using a series of in vitro cell-based and biochemical assays. ATM-3507 was selected as the new lead based on its ability to disable Tpm3.1-containing filaments, its cytotoxicity potency, and more favorable drug-like characteristics. We tested ATM-3507 and TR100 alone and in combination with antimicrotubule agents against neuroblastoma models in vitro and in vivo. Both ATM-3507 and TR100 showed a high degree of synergy in vitro with vinca alkaloid and taxane antimicrotubule agents. In vivo, combination-treated animals bearing human neuroblastoma xenografts treated with antitropomyosin combined with vincristine showed minimal weight loss, a significant and profound regression of tumor growth and improved survival compared with control and either drug alone. Antitropomyosin combined with vincristine resulted in G 2 -M phase arrest, disruption of mitotic spindle formation, and cellular apoptosis. Our data suggest that small molecules targeting the actin cytoskeleton via tropomyosin sensitize cancer cells to antimicrotubule agents and are tolerated together in vivo. This combination warrants further study.

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Section: Molecular Modelingmentioning

confidence: 99%

Identification of Cancer-Targeted Tropomyosin Inhibitors and Their Synergy with Microtubule Drugs

Currier

Stehn

Swain

et al. 2017

Molecular Cancer Therapeutics

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“…All the data used in this study can be downloaded from http://gila.bioengr.uic.edu/ resources/folding/Rate.html. We used the PDB identifiers to retrieve the structure data for these proteins from the PDB database (http://www.pdb.org/pdb/home/home.do) [22,23].…”

Section: Protein Folding Rate Datamentioning

confidence: 99%

The dynamical contact order: Protein folding rate parameters based on quantum conformational transitions

Zhang

Luo

2011

Sci. China Life Sci.

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Protein folding is regarded as a quantum transition between the torsion states of a polypeptide chain. According to the quantum theory of conformational dynamics, we propose the dynamical contact order (DCO) defined as a characteristic of the contact described by the moment of inertia and the torsion potential energy of the polypeptide chain between contact residues. Consequently, the protein folding rate can be quantitatively studied from the point of view of dynamics. By comparing theoretical calculations and experimental data on the folding rate of 80 proteins, we successfully validate the view that protein folding is a quantum conformational transition. We conclude that (i) a correlation between the protein folding rate and the contact inertial moment exists; (ii) multi-state protein folding can be regarded as a quantum conformational transition similar to that of two-state proteins but with an intermediate delay. We have estimated the order of magnitude of the time delay; (iii) folding can be classified into two types, exergonic and endergonic. Most of the two-state proteins with higher folding rate are exergonic and most of the multi-state proteins with low folding rate are endergonic. The folding speed limit is determined by exergonic folding. moment of inertia, dynamical contact order (DCO), protein folding rate Citation:Zhang Y, Luo L F. The dynamical contact order: Protein folding rate parameters based on quantum conformational transitions. Sci China Life Sci, 2011Sci, , 54: 386 -392, doi: 10.1007 Protein folding is a complex kinetic process by which a polypeptide changes from the denatured state to the native folding state. Although a great deal of theoretical work has been carried out, the fundamental physics underlying the folding remains unclear. The folding kinetics of a large number of proteins has been studied experimentally [1][2][3]. For many of these proteins, folding has been shown to be an all-or-none process with no clear intermediate state. These are called two-state proteins. However, some proteins require the accumulation of intermediates to complete the folding process. These are referred to as three-state or multi-state proteins. The rate of folding varies from milliseconds to hours. Some small proteins fold much faster, at rates in microsecond [2,3]. Experimental data has indicated that most of the ultrafast folders show a significant decrease in folding rates as the temperature is increased [4]. The wide range of folding rates and a possible folding speed limit are likely to be closely related to the inherent physics behind the phenomena. A deeper understanding of the folding mechanism and the accurate prediction of protein folding rates is an important topic in protein science. Since 1998, when the relative contact order (RCO) was proposed by Plaxco et al. [5], it has been widely accepted that folding rates and mechanisms are largely determined by the topology of the native state. Much theoretical work based on this concept has been proposed. For example, Ivankov et al. [6] pr...

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“…Built from the beginning as an open resource (Berman 2008;Berman et al 2012), its history is substantially different to that of the CCDC. The reasons for this difference are both historical and cultural, and beyond the scope of this paper, but the history and expectations of the community are important factors (Byrd et al 2016).…”

Section: Worldwide Protein Data Bankmentioning

confidence: 99%

Sustaining Scholarly Infrastructures through Collective Action: The Lessons that Olson can Teach us

Neylon

2017

KULA

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The infrastructures that underpin scholarship and research, including repositories, curation systems, aggregators, indexes and standards, are public goods. Finding sustainability models to support them is a challenge due to free-loading, where someone who does not contribute to the support of an infrastructure nonetheless gains the benefit of it. The work of Mancur Olson (1965) suggests that there are only three ways to address this for large groups: compelling all potential users, often through some form of taxation, to support the infrastructure; providing non-collective (club) goods to contributors that are created as a side-effect of providing the collective good; or implementing mechanisms that lower the effective number of participants in the negotiation (oligopoly).In this paper, I use Olson's framework to analyse existing scholarly infrastructures and proposals for the sustainability of new infrastructures. This approach provides some important insights. First, it illustrates that the problems of sustainability are not merely ones of finance but of political economy, which means that focusing purely on financial sustainability in the absence of considering governance principles and community is the wrong approach. The second key insight this approach yields is that the size of the community supported by an infrastructure is a critical parameter. Sustainability models will need to change over the life cycle of an infrastructure with the growth (or decline) of the community. In both cases, identifying patterns for success and creating templates for governance and sustainability could be of significant value. Overall, this analysis demonstrates a need to consider how communities, platforms, and finances interact and suggests that a political economic analysis has real value.

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The Protein Data Bank: a historical perspective

Cited by 380 publications

References 32 publications

Identification of Cancer-Targeted Tropomyosin Inhibitors and Their Synergy with Microtubule Drugs

Identification of Cancer-Targeted Tropomyosin Inhibitors and Their Synergy with Microtubule Drugs

The dynamical contact order: Protein folding rate parameters based on quantum conformational transitions

Sustaining Scholarly Infrastructures through Collective Action: The Lessons that Olson can Teach us

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