Prediction of functional tertiary interactions and intermolecular interfaces from primary sequence data

Pang, Phillip S.; Jankowsky, Eckhard; Wadley, Leven M.; Pyle, Anna Marie

doi:10.1002/jez.b.21024

Cited by 11 publications

(9 citation statements)

References 55 publications

(33 reference statements)

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“…For example, ORFans, orphan open reading frames that share no significant sequence similarity with any ORFs outside the genome in which they reside, represent 20%-30% of genes in sequenced genomes, but their origins and functions are largely mysterious (Fischer and Eisenberg 1999;Siew and Fischer 2004). Recently, several groups have demonstrated success in automatic prediction of protein functional interactions and intermolecular interfaces based on primary sequence information Pang et al 2004). However, when additional types of information are available (e.g., structural motifs, physical interactions, expression profiles, cellular localization, phylogenetic relationships), they can be incorporated to improve the accuracy of functional annotation.…”

Section: Discussionmentioning

confidence: 99%

Prediction of RNA binding sites in proteins from amino acid sequence

Terribilini¹,

Lee²,

Yan³

et al. 2006

RNA

164

196

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RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-oneout cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)

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Section: Discussionmentioning

confidence: 99%

Prediction of RNA binding sites in proteins from amino acid sequence

Terribilini¹,

Lee²,

Yan³

et al. 2006

RNA

164

196

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“…Correlated evolution of sequences has been documented quite often and has been used to detect physically interacting nucleotide and amino acid residues (Fariselli et al, 2001;Gobel et al, 1994;Pang et al, 2005;Pazos et al, 1997;Taylor and Hatrick, 1994). Correlated amino acid substitutions, however, are not limited to directly interacting amino acid residues, as recently been shown by Buck and Atchley (2005) in the case of Serpin proteins.…”

Section: Total Number Of Substitutions Inmentioning

confidence: 96%

“…Some adaptive substitutions at one locus can induce selection pressures at other loci in the same network causing them to undergo adaptive evolution in turn, similar to the amino acids in an enzyme, as exemplified in the work of DePristo et al (2005). This idea raises the possibility that functional modules may be detectable through co-evolutionary dynamics revealed by the comparison of genome sequences, similar to the detection of physically interacting residues in RNA and proteins (Fariselli et al, 2001;Gobel et al, 1994;Pang et al, 2005;Pazos et al, 1997;Taylor and Hatrick, 1994).…”

Section: Introductionmentioning

confidence: 93%

A simple model of co-evolutionary dynamics caused by epistatic selection

Schlosser

Wagner

2008

Journal of Theoretical Biology

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Epistasis is the dependency of the effect of a mutation on the genetic background in which it occurs. Epistasis has been widely documented and implicated in the evolution of species barriers and the evolution of genetic architecture. Here we propose a simple model to formalize the idea that epistasis can also lead to co-evolutionary patterns in molecular evolution of interacting genes. This model epistasis is represented by the influence of one gene substitution on the fitness rank of the resident allele at another locus. We assume that increasing or decreasing fitness rank occur equally likely. In simulations we show that this form of epistasis leads to co-evolution in the sense that the length of an adaptive walk between interacting loci is highly correlated. This effect is caused by episodes of elevated rate of evolution in both loci simultaneously. We find that the influence of epistasis on these measures of co-evolutionary dynamics is relatively robust to the details of the model. The main factor influencing the correlation in evolutionary rates is the probability that a substitution will have an epistatic effect, but the strength of epistasis or the asymmetry of the initial fitness ranks of the alleles have only a minor effect. We suggest that covariance in rates of evolution among loci could be used to detect epistasis among loci.

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“…The recent explosion of available homologous sequences for RNAs provides an exciting opportunity to detect tertiary contacts from sequence co-variation alone. However, existing methods -designed to detect local (pairwise) patterns of sequence co-variation -have had limited success 28,29,30 . Since RNA tertiary contacts often form complex networks 31 , it is possible that overlapping patterns of sequence constraint interfere with each other, obscuring true correlations and producing spurious transitive correlations when multiple contacts are chained together.…”

Section: Introductionmentioning

confidence: 99%

3D RNA from evolutionary couplings

Weinreb

Groß²,

Sander

et al. 2015

Preprint

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Non-protein-coding RNAs are ubiquitous in cell physiology, with a diverse repertoire of known functions. In fact, the majority of the eukaryotic genome does not code for proteins, and thousands of conserved long non-protein-coding RNAs of currently unkown function have been identified. When available, knowledge of their 3D structure is very helpful in elucidating the function of these RNAs. However, despite some outstanding structure elucidation of RNAs using X-ray crystallography, NMR and cryoEM, learning RNA 3D structures remains lowthroughput. RNA structure prediction in silico is a promising alternative approach and works well for double-helical stems, but full 3D structure determination requires tertiary contacts outside of secondary structures that are difficult to infer from sequence information. Here, based only on information from RNA multiple sequence alignments, we use a global statistical sequence probability model of co-variation in a pairs of nucleotide positions to detect 3D contacts, in analogy to recently developed breakthrough methods for computational protein folding. In blinded tests on 22 known RNA structures ranging in size from 65 to 1800 nucleotides, the predicted contacts matched physical nucleotide interactions with 65-95% true positive prediction accuracy. Importantly, we infer many long-range tertiary contacts, including non-Watson-Crick interactions, where secondary structure elements assemble in 3D. When used as restraints in molecular dynamics simulations, the inferred contacts improve RNA 3D structure prediction to a coordinate error as low as 6 -10 Å rmsd deviation in atom positions, with potential for further refinement by molecular dynamics. These contacts include functionally important interactions, such as those that distinguish the active and inactive conformations of four riboswitches. In blind prediction mode, we present evolutionary couplings suitable for folding simulations for 180 RNAs of unknown structure, available at https://marks.hms.harvard.edu/ev_rna/. We anticipate that this approach can help shed light on the structure and function of non-protein-coding RNAs as well as 3D-structured mRNAs.

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Prediction of functional tertiary interactions and intermolecular interfaces from primary sequence data

Cited by 11 publications

References 55 publications

Prediction of RNA binding sites in proteins from amino acid sequence

Prediction of RNA binding sites in proteins from amino acid sequence

A simple model of co-evolutionary dynamics caused by epistatic selection

3D RNA from evolutionary couplings

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