How do I get the most out of my protein sequence using bioinformatics tools?

Abstract

Biochemical and biophysical experiments are essential for uncovering the three-dimensional structure and biological role of a protein of interest. However, meaningful predictions can frequently also be made using bioinformatics resources that transfer knowledge from a well studied protein to an uncharacterized protein based on their evolutionary relatedness. These predictions are helpful in developing specific hypotheses to guide wet-laboratory experiments. Commonly used bioinformatics resources include methods to identify and predict conserved sequence motifs, protein domains, transmembrane segments, signal sequences, and secondary as well as tertiary structure. Here, several such methods available through the MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) are described and how their combined use can provide meaningful information on a protein of unknown function is demonstrated. In particular, the identification of homologs of known structure using HHpred, internal repeats using HHrepID, coiled coils using PCOILS and DeepCoil, and transmembrane segments using Quick2D are focused on.

SEQUENCE SLIDER: integration of structural and genetic data to characterize isoforms from natural sources

“…Estimation of sequence conservation and the amino acid frequency of each residue position based on homologue sequences (illustrated by Genetic information ellipse in Figure 1 ) is extracted by SLIDER from the results from ConSurf, a bioinformatics tool to estimate evolutionary conservation based on phylogenetic relations among homologues ( 26 , 27 ), giving as input sequence the most probable sequence using results from the previous techniques (arrow ‘Input seq’ connecting either Structural data or Mass spectrometry data ellipses to the Genetic information ellipse in Figure 1 ). Various tools are available for remote homologue sequence detection ( 76 ), e.g. HH-suite3 ( 77 ).…”

Pangenomic analysis reveals plant NAD+manipulation as an important virulence activity of bacterial pathogen effectors

“…The HMMER approach searched all 2,867,287 P. syringae protein sequences with high sensitivity and allowed specific searching of all alleles within each of the > 35,000 orthogroups for similarity to a curated set of sequences of defined domains. However, this analysis relied on what is already in the pfam-seed alignment and may have missed remote homologues that do not share significant sequence similarity (Pereira and Alva, 2021). For example, the HopAG1 ART domain, OG5256, OG18195, OG4578, and OG18056 were not identified by the HMMER search.…”

What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and folds

“…Structural homologs for selected representatives (those with a length close to the median length in the component) in the PDB or the AFDB90Communities set were searched with Foldseek using the TM-align mode (van Kempen et al, n.d.). Remote sequence homologs were detected for selected representatives by HHPred searches over the PDB, ECOD and Pfam databases through the MPI Bioinformatics toolkit using default settings (Gabler et al, 2020; Pereira and Alva, 2021). Further case-by-case analyses were also carried out as described below.…”