Table I Potential predictors of translation and protein degradation regulation

A total of 157 attributes were analyzed in their ability to explain membership of proteins in expression clusters as identified in the data in Figure 1. We assembled experimental data sets, as well as sequence features that are known to relate to post-transcriptional expression and protein degradation. These attributes include binding of RNA-binding proteins (putative regulators), protein stability estimates (experimental and theoretical), measurements of translation efficiency and transcript stability, sequence features, and a few other features outside these categories. References are provided in parentheses. DISEMBL, DISorder predictor from the European Molecular Biology Laboratory; MIPS, Munich Institute for Protein Sciences; uORF, upstream ORF; PARS, parallel analysis of RNA structure (experimental measure of double-strandedness in RNA); PEST, proline, glutamate, serine, threonine degradation signal; RBP, RNA-binding protein.

Data typeSource/comment
Target of RNA-binding proteinPossible regulators of transcript stability and/or translation efficiency
    Rab1MIPS (90)
    Bfr1, Cbc2, Gbp2, Khd1, Nab2, Nab3, Npl3, Nrd1, Nsr1, Pab1, Pub1, Puf4, Scp160, Sik1, and Yra2Targets chosen at <1% FDR (54)
    Yra1 and Mex67(91)
    Total number of RBP regulatorsAcross above studies
Protein stability
    Protein half-lifeMeasure of protein stability (92)
    PEST protein degradation signalMaximum score in ePESTfind (93)
    DISEMBL coils, DISEMBL hot loopsDisordered proteins tend to be less stable than folded proteins and vice versa. Disorder is measured by loops/coils and “hot loops” (loops with a high degree of mobility) as predicted by DisEMBL (94)
    Chaperones: APJ1, CAJ1, CCT2, CCT3, CCT4, CCT5, CCT6, CCT7, CCT8, CWC23, DJP1, ECM10, ERJ5, GIM3, GIM4, GIM5, HLJ1, HSC82, HSP104, HSP12, HSP26, HSP31, HSP42, HSP60, HSP78, HSP82, JAC1, JEM1, JID1, JJJ1, JJJ2, JJJ3, KAR2, LHS1, MCX1, MDJ1, PAC10, PFD1, SCJ1, SEC63, SIS1, SNO4, SSA1, SSA2, SSA3, SSA4, SSB1, SSB2, SSC1, SSE1, SSE2, SSQ1, SSZ1, SWA2, TCP1, TIM14, XDJ1, YDJ1, YKE2, and ZUO1; number of chaperones bound to proteinTargets of chaperones may be stabilized (85)
Translation and transcript stability
    Translation efficiency change (measured as ribosome profile: log10[PS + MS/PC + MC])Translational response to 0.2 mm H2O2 stress (16) (>2-fold change)
    Translation efficiency change (ribosome association as log2[stress/control])Translational response to menadione stress (30)
    Protein production rate (proteins/s); numbers of proteins per mRNA/section (protein production/transcription rate), log10[protein/mRNA]General translation efficiency (9597) (unperturbed system)
    mRNA half-life (poly-A length measurement)General transcript stability (98) (unperturbed system)
    Number of uORFs; Conserved uORFsInfluencing translation efficiency (99)
    Sequence lengths (UTRs, coding)Influencing protein expression: the shorter the sequence, the more protein (38)
    Number of motifs in 3′-UTRPossible regulators of transcript stability or translation efficiency (100)
    Minimum free energy (50 nucleotides at end of 5′-UTR, beginning of coding strand, and at beginning of 3′-UTR)RNA secondary structure influences transcript stability and/or accessibility to regulators and ribosomes (73)
    PI, CAI, relative amino acid frequencies, FOP score, GRAVY score, and AROMATICITY scoreSaccharomyces Genome Database (101)
Other features
    EssentialityGene knockout effect under normal conditions (102)
    Growth score during diamide treatment, sensitivity to diamide treatment, effect unique to diamide treatment Indicating role of gene in diamide resistance (growth)(71)
    PARS score in the coding region, 3′- and 5′-UTR. Calculated were the average, standard deviation, relative standard deviation, minimum, maximum, median score across the whole sequence, and the first and last ten nucleotides of the sequenceThe higher PARS score, the higher the probability of nucleotides in the sequence to be in double-stranded conformation. RNA secondary structure influences transcript stability and/or accessibility to regulators and ribosomes (103)