Topology improves phylogenetic motif functional site predictions

Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites

Proteins use their functional regions to exploit various activities, including binding to other proteins, nucleic acids, or drugs. Functional sites of the proteins have a tendency to be more conserved than the rest of the protein surface. Therefore, detection of the conserved residues using phylogenetic analysis is a general approach to predict functionally critical residues. In this chapter, we describe some of the available methods to predict functional sites and demonstrate a complete pipeline with tool alternatives at several steps. We explain the standard procedure and all intermediate stages including homology detection with BLAST search, multiple sequence alignment (MSA) and the construction of a phylogenetic tree for a given query sequence. Additionally, we demonstrate the prediction results of these methods on a case study. Finally, we discuss the possible challenges and bottlenecks throughout the pipeline. Our step-by-step description about the functional site prediction could be a helpful resource for the researchers interested in finding protein functional sites, to be used in drug discovery research.

Unrealistic phylogenetic trees may improve phylogenetic footprinting

Motivation

The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.

Results

Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.

Availability and Implementation

The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo

Supplementary information

Supplementary data are available at Bioinformatics online.

Rheostats and toggle switches for modulating protein function

The millions of protein sequences generated by genomics are expected to transform protein engineering and personalized medicine. To achieve these goals, tools for predicting outcomes of amino acid changes must be improved. Currently, advances are hampered by insufficient experimental data about nonconserved amino acid positions. Since the property “nonconserved” is identified using a sequence alignment, we designed experiments to recapitulate that context: Mutagenesis and functional characterization was carried out in 15 LacI/GalR homologs (rows) at 12 nonconserved positions (columns). Multiple substitutions were made at each position, to reveal how various amino acids of a nonconserved column were tolerated in each protein row. Results showed that amino acid preferences of nonconserved positions were highly context-dependent, had few correlations with physico-chemical similarities, and were not predictable from their occurrence in natural LacI/GalR sequences. Further, unlike the “toggle switch” behaviors of conserved positions, substitutions at nonconserved positions could be rank-ordered to show a “rheostatic”, progressive effect on function that spanned several orders of magnitude. Comparisons to various sequence analyses suggested that conserved and strongly co-evolving positions act as functional toggles, whereas other important, nonconserved positions serve as rheostats for modifying protein function. Both the presence of rheostat positions and the sequence analysis strategy appear to be generalizable to other protein families and should be considered when engineering protein modifications or predicting the impact of protein polymorphisms.

Structure-based Methods for Computational Protein Functional Site Prediction

Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details.