Using quaternions to calculate RMSD

Coutsias, Evangelos A.; Seok, Chaok; Dill, Ken A.

doi:10.1002/jcc.20110

Cited by 325 publications

(351 citation statements)

References 20 publications

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“…The rotation matrix for optimal superimposition was calculated using the quaternion method developed by us previously. 15,16 To calculate the rotation matrix, the alpha carbons of the residues 8-127 for FT and 11-130 for TFL1 were superimposed excluding the external loop and its C-terminal region because the crystal structures of FT and TFL1 are similar and the major structural difference occurs at the external loop. Using the calculated rotation matrix, the whole protein structures were superimposed, and then RMSD was calculated between the alpha carbon coordinates at equivalent positions.…”

Section: Methodsmentioning

confidence: 99%

Structural Basis of Functional Conversion of a Floral Repressor to an Activator: A Molecular Dynamics Simulation Study

Kang¹,

Lee²,

Lee³

et al. 2008

Bulletin of the Korean Chemical Society

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FLOWERING LOCUS T (FT) and TERMINAL FLOWER 1 (TFL1) inArabidopsis are homologous proteins that perform opposite functions: FT is an activator of flowering, and TFL1 is a repressor. It was shown before that change of a single amino acid (His88) of TFL1 to the corresponding amino acid (Tyr) of FT is enough to convert the floral repressor to an activator. However, structural basis of the functional conversion has not been understood. In our molecular dynamics simulations on modified TFL1 proteins, a hydrogen bond present in native TFL1 between the His88 residue and a residue (Asp144) in a neighboring external loop became broken by change of His88 to Tyr. This breakage induced conformational change of the external loop whose structure was previously reported to be another key functional determinant. These findings reveal that the two important factors determining the functional specificities of the floral regulators, the key amino acid (His88) and the external loop, are correlated, and the key amino acid determines the functional specificity indirectly by affecting the conformation of the external loop.

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Section: Methodsmentioning

confidence: 99%

Structural Basis of Functional Conversion of a Floral Repressor to an Activator: A Molecular Dynamics Simulation Study

Kang¹,

Lee²,

Lee³

et al. 2008

Bulletin of the Korean Chemical Society

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“…The RMSD represented the structural changes during the simulation [47,48]. Analysis for the time evolution-RMSD resulted from the simulation at 300K and revealed that the structure was fluctuated at around 4 to 6 Å from the initial structure, as shown in Fig.…”

Section: Conformational Dynamics Of Dna Pol I Itb-1mentioning

confidence: 99%

The Role of Interface Domain Interactions on Thermal Stability of DNA polymerase I ITB-1

Nurbaiti¹,

Hertadi²,

Martoprawiro³

et al. 2009

TOSBJ

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Temperature-induced unfolding of Klenow-like DNA Pol I ITB-1 was investigated by molecular dynamic simulation, focusing on the key factors that stabilizing the protein. The result showed that the protein unfolded initially by disruptions of the interface between of 5' 3'polymerase and 3' 5' exonuclease domains. Several amino acid residues, Lys374-Glu489 and Lys381-Glu487, form salt bridges at the interface domain and played an important role in the contact between the two domains. These interactions were examined through in silico mutation by comparing the free-energy solvation changes between the wild type and the mutants. The disruption of salt bridges by replacing Glu to Gln at position 487 and 489 caused positive value of G solv , suggesting that the proteins were more unstable. While the substitution of Glu to Asp at position 487 and 489 preserved the electrostatic interaction. The last two mutants showed negative value of G solv , suggesting that the proteins were more stable. All the data suggested that the salt bridges between Lys374-Glu489 and Lys381-Glu487 have an essential role in maintaining the stability of the interface domain of DNA Pol I ITB-1, and thus, the whole structure of the protein.

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“…The MaxSub score is the three dimensional similarity between two protein structures. The learning algorithm can be extended easily to predict ranks based on additional similarity measures such as the RMSD score [8], the GDT score [11], and the TM score [10]. However, we have decided to use the MaxSub score due to the high correlation among these similarity scores.…”

Section: A Data and Preprocessingmentioning

confidence: 99%

“…Related model quality assessment programs [3]- [7] are based on machine learning algorithm. The learning algorithm learns a function that maps a combination of a set of features extracted form the computationally generated structures to a quality score such as the RMSD score [8], the MaxSub score [9], the TM score [10], and the GDT score [11]. The developers of these methods formulated the problem as a regression problem.…”

Section: Introductionmentioning

confidence: 99%

On-line hierarchy of general linear models for selecting and ranking the best predicted protein structures

Girgis

Corso

Fischer

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-To predict the three dimensional structure of proteins, many computational methods sample the conformational space, generating a large number of candidate structures. Subsequently, such methods rank the generated structures using a variety of model quality assessment programs in order to obtain a small set of structures that are most likely to resemble the unknown experimentally determined structure. Model quality assessment programs suffer from two main limitations: (i) the rank-one structure is not always the best predicted structure; in other words, the best predicted structure could be ranked as the 10th structure (ii) no single assessment method can correctly rank the predicted structures for all target proteins. However, because often at least some of the methods achieve a good ranking, a model quality assessment method that is based on a consensus of a number of model quality assessment methods is likely to perform better. We have devised the STPdata algorithm, a consensus method based on five model quality assessment programs. We have applied it to build an on-line "custom-trained" hierarchy of general linear models to select and rank the best predicted structures. By "custom-trained", we mean for each target protein the STPdata algorithm trains a unique model on data related to the input target protein. To evaluate our method we participated in CASP8 as human predictors. In CASP8, the STPdata algorithm has trained 128 hierarchical models for each of the 128 target proteins. Based on the official results of CASP8 our method outperformed the best server by 6% and won the fourth position among human predictors. Our CASP results are purely based on computational methods without any human intervention.

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Using quaternions to calculate RMSD

Cited by 325 publications

References 20 publications

Structural Basis of Functional Conversion of a Floral Repressor to an Activator: A Molecular Dynamics Simulation Study

Structural Basis of Functional Conversion of a Floral Repressor to an Activator: A Molecular Dynamics Simulation Study

The Role of Interface Domain Interactions on Thermal Stability of DNA polymerase I ITB-1

On-line hierarchy of general linear models for selecting and ranking the best predicted protein structures

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