Biochemical functional predictions for protein structures of unknown or uncertain function

Abstract

With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.

Keywords: Computational chemistry; Local structure methods; Protein function prediction; Structural genomics.

Fig. 1

Three histidine residues from histidinol phosphate phosphatase (HPP) (PDB 2yz5) were analyzed by THEMATICS to produce theoretical titration curves (A), which plot the mean net charge of a given residue of a large ensemble of protein molecules as a function of pH, and first derivative plots (B).The titration curves of two non-catalytic residues, H84 and H150, show sigmoidal curve shapes with a small buffer range, while the catalytic H226 displays a curve with an anomalous shape, shallow slope, and larger buffer range. When analyzing the first derivatives of the titration curves, non-catalytic residues display symmetrical, highly peaked plots. However, active site residues such as H226 shown here display broad, asymmetric derivative plots and may have multiple peaks.

Fig. 2

(A) The metal binding pocket of YP_910028.1, containing a PHP (Polymerase and Histidinol Phosphatase) domain (PDB ID 3e0f, shown in dark blue) aligns well with that of DNA Pol III alpha subunit (PDB ID 2hpi, shown in magenta). However, C145 and Y74 of DNA Pol III are mismatched with a histidine and threonine, respectively in YP_910028.1. (B) On the other hand, the metal binding pocket of YP_910028.1 (PDB 3e0f) aligns perfectly with the pocket of histidinol phosphate phosphatase (HPP) (PDB 2yz5), shown in green.

Fig. 3

Schematic diagram outlining the different methods utilized in ProFunc. HMM: Hidden Markov Model; SSM: Secondary Structure Matching; HTH: Helix–Turn–Helix.

Fig. 4

Schematic diagram outlining the SALSA method of annotating protein function.

Fig. 5

The metabolites above dock in silico into Tm0936 and are substrates of the enzyme Tm0936. The general structure of these three metabolites is the same with the exception of the moieties shown in the boxes.