Recent advances in sequence-based protein structure prediction: Table 1

Kc, Dukka B.

doi:10.1093/bib/bbw070

Cited by 21 publications

(16 citation statements)

References 103 publications

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“…As well as improvements in residue-contact prediction, there have also been improvements in the structural refinement category, with improved energy functions and MD simulations (for a recent review on structural refinement, see Feig, 2017). There are also promising recent developments in template-free modelling (for a review, see Kc, 2016). Finally, there has been an increase in the performance of single modelbased model-quality assessment methods, thanks to the use of…”

Section: Discussionmentioning

confidence: 99%

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

Das

Sillitoe

et al. 2017

Acta Cryst Sect D Struct Biol

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Computational modelling of proteins has been a major catalyst in structural biology. Bioinformatics groups have exploited the repositories of known structures to predict high-quality structural models with high efficiency at low cost. This article provides an overview of comparative modelling, reviews recent developments and describes resources dedicated to large-scale comparative modelling of genome sequences. The value of subclustering protein domain superfamilies to guide the template-selection process is investigated. Some recent cases in which structural modelling has aided experimental work to determine very large macromolecular complexes are also cited.

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Section: Discussionmentioning

confidence: 99%

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

Das

Sillitoe

et al. 2017

Acta Cryst Sect D Struct Biol

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“…For decades, the preferred choice in high-resolution protein structure determination have been two well-established structural biology techniques: X-ray crystallography and Nuclear Magnetic resonance (NMR) spectroscopy 11 . X-ray and NMR have helped to determine over 115,000 high-resolution protein structures over the last four decades 12–16 .…”

Section: Introductionmentioning

confidence: 99%

Iterative Molecular Dynamics–Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities

Leelananda

Lindert

2017

J. Chem. Theory Comput.

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Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR and cryo-EM are the preferred form of protein structure determination and have achieved great success over the last decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick superior models than with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high resolution density maps (~ 4 Å) we were able to more significantly improve the quality of the models than when medium resolution maps (6.9 Å) were used. Beginning from an average starting structure RMSD to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used.

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“…Thus, if a target sequence has some degree of similarity with a protein sequence having known 3D structure, then that structure can be used to precisely model the target protein. A plethora of review articles are available on the strategies and challenges of computational protein structure prediction [8,26].…”

Section: Homology Modelingmentioning

confidence: 99%

Role of Force Fields in Protein Function Prediction

Hazarika¹,

Rajkhowa²,

Jha³

2021

Homology Molecular Modeling - Perspectives and Applications

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The world today, although, has developed an elaborate health system to fortify against known and unknown diseases, it continues to be challenged by new as well as emerging, and re-emerging infectious disease threats with severity and probable fluctuations. These threats also have varying costs for morbidity and mortality, as well as for a complex set of socioeconomic outcomes. Some of these diseases are often caused by pathogens which use humans as host. In such cases, it becomes paramount responsibility to dig out the source of pathogen survival to stop their population growth. Sequencing genomes has been finessed so much in the 21st century that complete genomes of any pathogen can be sequenced in a matter of days following which; different potential drug targets are needed to be identified. Structure modeling of the selected sequences is an initial step in structure-based drug design (SBDD). Dynamical study of predicted models provides a stable target structure. Results of these in-silico techniques greatly depend on force field (FF) parameters used. Thus, in this chapter, we intend to discuss the role of FF parameters used in protein structure prediction and molecular dynamics simulation to provide a brief overview on this area.

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Recent advances in sequence-based protein structure prediction: Table 1

Cited by 21 publications

References 103 publications

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

Iterative Molecular Dynamics–Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities

Role of Force Fields in Protein Function Prediction

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