A high‐throughput predictive method for sequence‐similar fold switchers

Abstract

Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from α-helix <-> β-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that α-helix <-> β-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequencesimilar fold switchers with high statistical significance (P < 1.8*10 −19 ). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.

Predictable fold switching by theSARS‐CoV‐2 proteinORF9b

“…16 Since ORF9b undergoes a large α-helix <À> β-sheet transition, it is a suitable target for both the sequenceand structure-based predictive methods reported previously. 15,18 Consistent with experimental observations, both methods indicate that ORF9b switches folds. These methods were then tested on the SARS-CoV-1 ORF9b homolog, hereafter called ORF9b1, which is also binds Tom70 in situ 16 but has not been shown to switch folds.…”

“…The table reports secondary structure prediction accuracies of each algorithm referenced against each experimentally determined structure 2.2 | Sequence-based predictions suggest that SARS-CoV-2 ORF9b switches folds Given that the structure-based method correctly inferred that ORF9b switches folds, the next step was to determine whether ORF9b fold switching could be inferred from its sequence alone. It has been shown previously that JPred4 predicts fold switching from sequence more robustly than other secondary structure predictors, 18 and α-helix <À> β-strand prediction discrepancies from JPred4 between whole protein sequences and excised sequence fragments can be a robust indicator of fold switching. 15 Such a fragment (residues 59-80) was identified in ORF9b (Figure 3).…”

Many dissimilar protein domains switch between α-helix and β-sheet folds

“…JPred4 ( 18 ) predictions were carried out as in ( 12 ), sections 2.4 and 2.6. In further detail, they were first performed on all 50-residue CTD sequences using two databases: the JPred database (http://www.compbio.dundee.ac.uk/jpred/about_RETR_JNetv231_details.shtml) from 2014 and the Uniprot90 database from January 2021.…”

“…Sequences of each prediction were aligned against the E. coli NusG sequence (beginning with EMVRV ) using Biopython ( 39 ) Bio.pairwise2.localxs with gap opening/extension scores of -1.0/-0.5. Secondary structure predictions of the sequence in question and of E. coli NusG were reregistered according to the resulting pairwise alignments and compared as in ( 12 ). Predictions were considered high-confidence if at least 5 sequences were in the MView ( 40 )-generated alignments used by JPred.…”

“…Nevertheless, prediction of fold switchers seems achievable. Previous work has shown that discrepancies between homology-based secondary structure predictions can be an effective predictor of fold switching (15)(16)(17). We sought to leverage these discrepancies to discover dissimilar foldswitching sequences in the NusG protein superfamily, the only family of transcriptional regulators known to be conserved from bacteria to humans (18).…”