The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment

Abstract

As biomolecular sequencing is becoming the main technique in life sciences, functional interpretation of sequences in terms of biomolecular mechanisms with in silico approaches is getting increasingly significant. Function prediction tools are most powerful for protein-coding sequences; yet, the concepts and technologies used for this purpose are not well reflected in bioinformatics textbooks. Notably, protein sequences typically consist of globular domains and non-globular segments. The two types of regions require cardinally different approaches for function prediction. Whereas the former are classic targets for homology-inspired function transfer based on remnant, yet statistically significant sequence similarity to other, characterized sequences, the latter type of regions are characterized by compositional bias or simple, repetitive patterns and require lexical analysis and/or empirical sequence pattern-function correlations. The recipe for function prediction recommends first to find all types of non-globular segments and, then, to subject the remaining query sequence to sequence similarity searches. We provide an updated description of the ANNOTATOR software environment as an advanced example of a software platform that facilitates protein sequence-based function prediction.

Charged residues next to transmembrane regions revisited: “Positive-inside rule” is complemented by the “negative inside depletion/outside enrichment rule”

“…The prediction accuracy for true positives and negatives is reported to be close to 100%, and the remaining main cause of false positive prediction is hydrophobic α-helices completely buried in the hydrophobic core of proteins. Note that reliable prediction of TMHs and protein topology is a strong restriction for protein function of even otherwise non-characterised proteins [26–28] and thus provides very valuable information.…”

xHMMER3x2: Utilizing HMMER3’s speed and HMMER2’s sensitivity and specificity in the glocal alignment mode for improved large-scale protein domain annotation

“…To resolve the annotation discrepancies, the sequences containing the 389 domain hits were subjected to a dissectHMMER [13–15, 24] analysis where pairs of HMMER E-values are dissected into their fold-critical and remnant contributions. Briefly, it is the fold-critical parts (the supposedly 3D structural part) of a sequence-to-domain alignment that argues for a similar overall fold, hence similar function whereas the remnant part represents disordered, fibrillary or other non-globular regions that might be not obligatory for the domain fold [13, 14, 24].…”

Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy

“…SIFT results were first retrieved using “SIFT dbSNP batch tool” which was run on 21 March 2012 to pre-screen the results. After that, orthologue sequences (select only “1:1 orthologs”) were retrieved from either OMA browser [ 24 ] or Orthologue search against NCBI Non-redundant protein set on ANNOTATOR [ 25 ] and were used to create a multiple sequence alignment with MAFFT (L-INS-I settings) [ 26 ]. We deleted those sequences that have large gaps using Jalview [ 27 ].…”

“… A SNP is located in the functional domain of a protein. We used the amino acid sequence of the gene that the SNP is located in as input to do “Prim-Seq-An w/Pfam” analysis in ANNOTATOR [ 25 ] using default settings which include HMMER against many protein domain databases e.g. SMART, Pfam to retrieve functional domain information of the protein.…”