SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent

Abstract

Many important interactions of proteins are facilitated by short, linear motifs (SLiMs) within a protein's primary sequence. Our aim was to establish robust methods for discovering putative functional motifs. The strongest evidence for such motifs is obtained when the same motifs occur in unrelated proteins, evolving by convergence. In practise, searches for such motifs are often swamped by motifs shared in related proteins that are identical by descent. Prediction of motifs among sets of biologically related proteins, including those both with and without detectable similarity, were made using the TEIRESIAS algorithm. The number of motif occurrences arising through common evolutionary descent were normalized based on treatment of BLAST local alignments. Motifs were ranked according to a score derived from the product of the normalized number of occurrences and the information content. The method was shown to significantly outperform methods that do not discount evolutionary relatedness, when applied to known SLiMs from a subset of the eukaryotic linear motif (ELM) database. An implementation of Multiple Spanning Tree weighting outperformed two other weighting schemes, in a variety of settings.

From protein sequence to dynamics and disorder with DynaMine

“…DynaMine has the ability to outline molten globule regions (for example, NCBD or CBP) embedded in a more disordered structural environment. IDPs often recognize their binding partners via short continuous sequence motifs, which are frequently defined by local sequence conservation 38,39 and structural bias towards the bound conformational state [6][7][8] . DynaMine seems to be capable of picking up locally reduced dynamics in these regions, which appear as peaks in the prediction.…”

Exploring the binding diversity of intrinsically disordered proteins involved in one‐to‐many binding

“…With regard to IDP regions involved in binding, various descriptors have been used, such as eukaryotic linear motif (ELMs), 34,35 linear motifs (LMs), 36 short linear motif (SLiMs), 37,38 regions of increased structural propensity (RISPs), 39 and molecular recognition features (MoRFs). 40 All of these describe similar phenomena, despite differing approaches used by the various researchers for identification of binding segments.…”

“…41,42 Predicting binding sites by sequence matches to the motifs of ELMs, 34,35 LMs, 36 SLiMs, 37,38 or other collections of sequence patterns [43][44][45] provides one strategy for identifying potential binding sites located within IDPs or IDP regions. Using sequence characteristics that indicate short binding regions within longer regions of disorder offers a second strategy that does not depend on specific motifs, and several predictors have been developed that use this second strategy.…”

Computational identification and analysis of protein short linear motifs

“…SLiMDisc (90,91) is also built on the basic pattern discovery abilities of the TEIRESIAS algorithm (81). Motifs are scored using an information content-based scoring scheme which use evolutionary weighted support (those SLiMs present in evolutionarily distant sequences are up-weighted and those primarily arising due to common evolutionary descent are down-weighted).…”

“…SLiMFinder (62) is a probabilistic SLiM discovery program building on the principles of the SLiMDisc algorithm (91). The TEIRESIAS raw motif discovery tool is (81) replaced by SLiMBuild (62) allowing flexible and ambiguous motifs to be returned.…”