s<scp>DFIRE</scp>: Sequence‐specific statistical energy function for protein structure prediction by decoy selections

Hoque, Tamjidul; Yang, Yuedong; Mishra, Avdesh; Zhou, Yaoqi

doi:10.1002/jcc.24298

Cited by 16 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we used two commonly employed decoy sets: the I-TASSER Decoy Set-II generated by the Zhang lab [ 33 ] and the Rosetta decoy set by the Baker lab [ 34 ]. These have been broadly used to test a variety of scoring methods [ 35 – 42 ]. The decoys in these two datasets were generated differently, and therefore represent different test cases for a scoring function.…”

Section: Resultsmentioning

confidence: 99%

Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials

Holland¹,

Pan²,

Grigoryan³

2018

PLoS ONE

View full text Add to dashboard Cite

Co-evolution between pairs of residues in a multiple sequence alignment (MSA) of homologous proteins has long been proposed as an indicator of structural contacts. Recently, several methods, such as direct-coupling analysis (DCA) and MetaPSICOV, have been shown to achieve impressive rates of contact prediction by taking advantage of considerable sequence data. In this paper, we show that prediction success rates are highly sensitive to the structural definition of a contact, with more permissive definitions (i.e., those classifying more pairs as true contacts) naturally leading to higher positive predictive rates, but at the expense of the amount of structural information contributed by each contact. Thus, the remaining limitations of contact prediction algorithms are most noticeable in conjunction with geometrically restrictive contacts—precisely those that contribute more information in structure prediction. We suggest that to improve prediction rates for such “informative” contacts one could combine co-evolution scores with additional indicators of contact likelihood. Specifically, we find that when a pair of co-varying positions in an MSA is occupied by residue pairs with favorable statistical contact energies, that pair is more likely to represent a true contact. We show that combining a contact potential metric with DCA or MetaPSICOV performs considerably better than DCA or MetaPSICOV alone, respectively. This is true regardless of contact definition, but especially true for stricter and more informative contact definitions. In summary, this work outlines some remaining challenges to be addressed in contact prediction and proposes and validates a promising direction towards improvement.

show abstract

Section: Resultsmentioning

confidence: 99%

Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials

Holland¹,

Pan²,

Grigoryan³

2018

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The energy of interaction between protein residues ensures protein structural stability, and the energy contribution of residue interactions can be approximated by an energy function extracted from known structures ( Hoque et al, 2016 ; Mishra et al, 2016 ). Dosztanyi et al (2005) performed the least square fit of the contact energy derived from the primary sequences of 674 proteins to the tertiary structures of 785 proteins and constructed the RCEM matrix, a 20×20 dimensional matrix with rows and columns representing the 20 standard amino acids.…”

Section: Datasets and Methodsmentioning

confidence: 99%

Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework

Chen

Li²,

Li³

2022

Front. Plant Sci.

View full text Add to dashboard Cite

Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction has achieved many research results. Recently, some machine learning-based methods have emerged to identify plant R proteins. Still, most of them only rely on protein sequence features, which ignore inter-amino acid features, thus limiting the further improvement of plant R protein prediction performance. In this manuscript, we propose a method called StackRPred to predict plant R proteins. Specifically, the StackRPred first obtains plant R protein feature information from the pairwise energy content of residues; then, the obtained feature information is fed into the stacking framework for training to construct a prediction model for plant R proteins. The results of both the five-fold cross-validation and independent test validation show that our proposed method outperforms other state-of-the-art methods, indicating that StackRPred is an effective tool for predicting plant R proteins. It is expected to bring some favorable contribution to the study of plant R proteins.

show abstract

“…The profiles of unpaired/paired bases generated by SHAPE and DMS experiments have already been used for improving secondary structure prediction [ 93 ] and tertiary structure prediction as a part of integrative modelling [ 94 ]. Thus, the results of SASA from LASER-Seq or icLASER experiments as well as from computational prediction will be likely integrated in 3D structure modelling in near future, similar to the use of SASA in ab initio protein structure prediction [ 95 ], template-based structure modelling [ 96 ] and native structure discrimination [ 97 ].…”

Section: Future Perspective and Conclusionmentioning

confidence: 99%

Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives

Solayman

Litfin

Singh

et al. 2022

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure–function relations of RNAs.

show abstract

sDFIRE: Sequence‐specific statistical energy function for protein structure prediction by decoy selections

Cited by 16 publications

References 47 publications

Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials

Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials

Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework

Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives

Contact Info

Product

Resources

About