Protein structure determination using metagenome sequence data

Овчинников, С. Г.; Park, Hahnbeom; Varghese, Neha; Huang, Po-Ssu; Pavlopoulos, Georgios A.; Kim, David E.; Kamisetty, Hetunandan; Kyrpides, Nikos C.; Baker, David

doi:10.1126/science.aah4043

Cited by 492 publications

(594 citation statements)

References 57 publications

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“…Although application of these free energies requires the use of a structure, this is changing because of recent advances in predicting contact pairs from sequence correlations (66,67). Improved next-generation sequencing technologies are leading to the rapid growth in protein sequence data so that these sequence-based predictions of contacts may eventually overwhelm structure determination studies.…”

Section: Discussionmentioning

confidence: 99%

Knowledge-based entropies improve the identification of native protein structures

Sankar

Jia

Jernigan

2017

Proc. Natl. Acad. Sci. U.S.A.

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Evaluating protein structures requires reliable free energies with good estimates of both potential energies and entropies. Although there are many demonstrated successes from using knowledge-based potential energies, computing entropies of proteins has lagged far behind. Here we take an entirely different approach and evaluate knowledge-based conformational entropies of proteins based on the observed frequencies of contact changes between amino acids in a set of 167 diverse proteins, each of which has two alternative structures. The results show that charged and polar interactions break more often than hydrophobic pairs. This pattern correlates strongly with the average solvent exposure of amino acids in globular proteins, as well as with polarity indices and the sizes of the amino acids. Knowledgebased entropies are derived by using the inverse Boltzmann relationship, in a manner analogous to the way that knowledge-based potentials have been extracted. Including these new knowledge-based entropies almost doubles the performance of knowledge-based potentials in selecting the native protein structures from decoy sets. Beyond the overall energy-entropy compensation, a similar compensation is seen for individual pairs of interacting amino acids. The entropies in this report have immediate applications for 3D structure prediction, protein model assessment, and protein engineering and design.knowledge-based | entropies | free energy | native structure | contact changes K nowledge of a protein's structure is required to understand its dynamics and function; so, improvements in protein structure prediction, especially template-free methods, are essential if the whole protein universe it to be fully comprehended. Computational methods of structure prediction typically yield large numbers of possible structure models (decoys), then require challenging discrimination in determining which of these models is most likely to be the native structure. This well-known bottleneck in protein structure prediction suffers from presentday limitations in structure evaluation. Because the folding of a protein into its native structure is dictated by its free-energy landscape (1), the development of accurate free-energy functions for native structure evaluation is an area of active research. The free energy ΔG of a protein structure can be represented as ΔG = ΔV -TΔS, where ΔV and ΔS represent the energetic (enthalpic) and entropic components, respectively, and T the temperature. In the conventional folding funnel hypothesis of protein folding, the energies and entropies are captured by the depth and by the width of the well (1). Both the energetic and entropic components are combinations of large numbers of contributions, and hence the accurate prediction of free energies is limited by the reliable ability to assess all of these contributions.The energetic contribution to free energy of proteins is usually captured by potential functions, either physics-based or knowledgebased. Physics-based force fields, such as CHARMM, AMBER, GROMOS...

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Section: Discussionmentioning

confidence: 99%

Knowledge-based entropies improve the identification of native protein structures

Sankar

Jia

Jernigan

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…By comparing residue-residue coevolution strengths computed from an alignment of Cyc2-like proteins with the Gremlin server, 7,8 it appears that the predicted contacts match best with the contacts present in model #3 (Fig. 2B and different cutoffs of the coevolution data in Fig.…”

Section: Resultsmentioning

confidence: 99%

Structural model of Cyc2, the primary electron acceptor of Acidithiobacillus ferrooxidans’ respiratory chain, as a modular cytochrome - β-barrel fusion protein, and mechanistic proposals based on this model

Abriata

2018

Preprint

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Abstract. Acidithiobacillus ferrooxidans oxidizes Fe(II) to Fe(III) to feed electrons into its respiratory chain. The primary electron acceptor of this complex system is Cyc2, an outer membrane protein of unknown structure. This work proposes a feasible model of Cyc2's global structure, based on homology modeling, residue-residue coevolution data, bioinformatics predictions and limited knowledge about Cyc2's function. The proposal is that the sequence segment spanning residues ~30 to ~90 folds as a cytochrome-like domain that contains a heme group which would presumably bind and oxidize external Fe(II), whereas the remaining segment from residue ~90 until the end adopts a β-barrel fold similar to that of most outer membrane proteins. Such model differs strongly from a published model, but is backed up by more data and is more compatible with the known topology of outer membrane proteins and with Cyc2's function of internalizing reducing equivalents. The small size of the cytochrome-like domain would allow it to reside inside, and/or slide through, the β-barrel domain, thus communicating in a controlled fashion the extracellular medium with the periplasm to import electrons through the outer membrane. All the models discussed are provided as PyMOL session files in the Supporting Information and can be visualized online at

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“…Ovchinnikov and co-workers have used Big Data techniques to better predict 3D protein structures. [28] Big Data tools are also valuable in chemical toxicology, where the use of high-throughput screening produces both structured and unstructured information that is so large and complex that it is difficult to analyze using traditional methods. [29] Just managing the chemistry data (in terms of chemical compounds and challenges of chemical structure detail vs. the myriad associated identifiers) is enough of a problem.…”

Section: Unitmentioning

confidence: 99%

The Future of Chemical Information Is Now

Williams¹,

Pence²

2017

Chemistry International

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Search and retrieval of chemical information has been dramatically changed by the application of “Big Data” techniques. This development continues to be driven by the massive growth of chemical scientific literature and of online data and databases. Not only is there an expansion of the traditional avenues of publication, but many new contributing resources, such as open access journals, MOOCs (Massive Open Online Courses), Wikis, and blogs have arisen. Powerful tools, like APIs (application programming interfaces) and Big Data interrogation are providing innovative ways to retrieve and analyze data and connect different databases. Materials, pharmaceutical, and environmental research, to name just a few, are especially challenged by the need to organize and access vast amounts of data. What skill-sets will need to be developed in order to get the greatest value out of the available data? Will it be coding and information technology skills, or awareness and better delivery of the data by the available systems? We believe that, in the short term, efforts are needed to expand awareness and training.

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Protein structure determination using metagenome sequence data

Cited by 492 publications

References 57 publications

Knowledge-based entropies improve the identification of native protein structures

Knowledge-based entropies improve the identification of native protein structures

Structural model of Cyc2, the primary electron acceptor of Acidithiobacillus ferrooxidans’ respiratory chain, as a modular cytochrome - β-barrel fusion protein, and mechanistic proposals based on this model

The Future of Chemical Information Is Now

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