eQuant - A Server for Fast Protein Model Quality Assessment by Integrating High-Dimensional Data and Machine Learning

Bittrich, Sebastian; Heinke, Florian; Labudde, Dirk

doi:10.1007/978-3-319-34099-9_32

Cited by 8 publications

(10 citation statements)

References 29 publications

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“…B, public methods currently available in CAMEO. New methods are constantly emerging, such as QMEANDisCo, eQuant2, and ModFOLD6, with QMEANDisCo currently being in narrow lead over ModFOLD6. The insets show the lDDT distribution of the underlying 3D models serving as targets for the QE category…”

Section: Resultsmentioning

confidence: 99%

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

et al. 2017

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Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.

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Section: Resultsmentioning

confidence: 99%

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

et al. 2017

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“…To assess the energetic contribution of EFR to the native structure, the proteins of the 143 dataset were transformed by the Energy Profiling approach [3,28]. Computed energy remarkable.…”

Section: Early Folding Residues Constitute Stable Local Conformationsmentioning

confidence: 99%

“…Computed energy by amino acid. A knowledge-based potential [3,28] was used to characterize the surrounding of each residue. Hydrophobic and aromatic amino acids have a high tendency to be located in the buried core of a protein.…”

Section: Early Folding Residues Constitute Stable Local Conformationsmentioning

confidence: 99%

“…28 These local structures form long-range contacts and assemble the global 29 structure [14,18,22,26,27]. The formation of a native structure causes a further 30 decrease in free energy [3,17,28]. Long-range contacts are especially important for the 31 stability of the hydrophobic core of the native structure [29].…”

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confidence: 99%

“…67 Representing proteins by Energy Profiling and graphs 68 A protein's native structure exhibits minimal free energy [14]. Thus, knowledge-based 69 potentials are a potent tool to describe the process of protein folding [28] and have been 70 previously employed for the quality assessment of protein structures [3]. In an approach 71 called Energy Profiling the surroundings of each residue are expressed as energy value.…”

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confidence: 99%

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Functional and Early Folding Residues are separated in proteins to increase evolvability and robustness

Bittrich

Schroeder

Labudde

2018

Preprint

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The three-dimensional structure of proteins captures evolutionary ancestry, and serves as starting point to understand the origin of diseases. Proteins adopt their structure autonomously by the process of protein folding. Over the last decades, the folding process of several proteins has been studied with temporal and spatial resolution which allowed the identification of so-called Early Folding Residues (EFR) in the folding process. These structurally relevant residues become affected early in the folding process and initiate the formation of secondary structure elements and guide their assembly.Using a dataset of 30 proteins and 3,337 residues provided by the Start2Fold database, discriminative features of EFR were identified by a systematical characterization. Therefore, proteins were represented as graphs in order to analyze topological descriptors of EFR. They constitute crucial connectors of protein regions which are distant at sequence level. Especially, these residues exhibit a high number of non-covalent contacts such as hydrogen bonds and hydrophobic interactions. This tendency also manifest as energetically stable local regions in a knowledge-based potential. Conclusively, these features are not only characteristic for EFR but also differ significantly with respect to functional residues. This unveils a split between structurally and functionally relevant residues in proteins which can drastically improve their evolvability and robustness.The characteristics of EFR cannot be attributed to trivial features such as the accessible surface area. Thus, the presented features are novel descriptors for EFR of the folding process. Potentially, these features can be used to design classifiers to predict EFR from structure or to implement structure quality assessment programs. The shown division of labor between functional and EFR has implications for the prediction of mutation effects as well as protein design and can provide insights into the evolution of proteins. Finally, EFR allow to further the understanding of the protein folding process due to their pivotal role. Author summaryProteins are chains of amino acids which adopt a three-dimensional structure and are then able to catalyze chemical reactions or propagate signals in organisms. Without external influence, most proteins fold into their correct structure, and a small number of Early Folding Residues (EFR) have been shown to become affected at the very start of 1/18 the process. We demonstrated that these residues are located in energetically stable local conformations. EFR are in contact to many other residues of a protein and act as hubs between sequentially distant regions of a proteins. These distinct characteristics can give insights into what causes certain residues to initiate and guide the folding process. Furthermore, it can help our understanding regarding diseases such as Alzheimer's or amyotrophic lateral sclerosis which are the result of protein folding gone wrong. We further found that the structurally relevant EFR are almost exclusivel...

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Improved protein structure refinement guided by deep learning based accuracy estimation

et al. 2021

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We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.

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eQuant - A Server for Fast Protein Model Quality Assessment by Integrating High-Dimensional Data and Machine Learning

Cited by 8 publications

References 29 publications

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

Functional and Early Folding Residues are separated in proteins to increase evolvability and robustness

Improved protein structure refinement guided by deep learning based accuracy estimation

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