Prediction of beta-strand packing interactions using the signature product

An amino acid code for β-sheet packing structure

To understand the relationship between protein sequence and structure, this work extends the knob-socket model in an investigation of β-sheet packing. Over a comprehensive set of β-sheet folds, the contacts between residues were used to identify packing cliques: sets of residues that all contact each other. These packing cliques were then classified based on size and contact order. From this analysis, the two types of four-residue packing cliques necessary to describe β-sheet packing were characterized. Both occur between two adjacent hydrogen bonded β-strands. First, defining the secondary structure packing within β-sheets, the combined socket or XY:HG pocket consists of four residues i, i+2 on one strand and j, j+2 on the other. Second, characterizing the tertiary packing between β-sheets, the knob-socket XY:H+B consists of a three-residue XY:H socket (i, i+2 on one strand and j on the other) packed against a knob B residue (residue k distant in sequence). Depending on the packing depth of the knob B residue, two types of knob-sockets are found: side-chain and main-chain sockets. The amino acid composition of the pockets and knob-sockets reveal the sequence specificity of β-sheet packing. For β-sheet formation, the XY:HG pocket clearly shows sequence specificity of amino acids. For tertiary packing, the XY:H+B side-chain and main-chain sockets exhibit distinct amino acid preferences at each position. These relationships define an amino acid code for β-sheet structure and provide an intuitive topological mapping of β-sheet packing.

A method to predict edge strands in beta-sheets from protein sequences

There is a need for rules allowing three-dimensional structure information to be derived from protein sequences. In this work, consideration of an elementary protein folding step allows protein sub-sequences which optimize folding to be derived for any given protein sequence. Classical mechanics applied to this system and the energy conservation law during the elementary folding step yields an equation whose solutions are taken over the field of rational numbers. This formalism is applied to beta-sheets containing two edge strands and at least two central strands. The number of protein sub-sequences optimized for folding per amino acid in beta-strands is shown in particular to predict edge strands from protein sequences. Topological information on beta-strands and loops connecting them is derived for protein sequences with a prediction accuracy of 75%. The statistical significance of the finding is given. Applications in protein structure prediction are envisioned such as for the quality assessment of protein structure models.

A survey of machine learning methods for secondary and supersecondary protein structure prediction

In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.

BetaSearch: a new method for querying β-residue motifs

BACKGROUND

Searching for structural motifs across known protein structures can be useful for identifying unrelated proteins with similar function and characterising secondary structures such as β-sheets. This is infeasible using conventional sequence alignment because linear protein sequences do not contain spatial information. β-residue motifs are β-sheet substructures that can be represented as graphs and queried using existing graph indexing methods, however, these approaches are designed for general graphs that do not incorporate the inherent structural constraints of β-sheets and require computationally-expensive filtering and verification procedures. 3D substructure search methods, on the other hand, allow β-residue motifs to be queried in a three-dimensional context but at significant computational costs.

FINDINGS

We developed a new method for querying β-residue motifs, called BetaSearch, which leverages the natural planar constraints of β-sheets by indexing them as 2D matrices, thus avoiding much of the computational complexities involved with structural and graph querying. BetaSearch exhibits faster filtering, verification, and overall query time than existing graph indexing approaches whilst producing comparable index sizes. Compared to 3D substructure search methods, BetaSearch achieves 33 and 240 times speedups over index-based and pairwise alignment-based approaches, respectively. Furthermore, we have presented case-studies to demonstrate its capability of motif matching in sequentially dissimilar proteins and described a method for using BetaSearch to predict β-strand pairing.

CONCLUSIONS

We have demonstrated that BetaSearch is a fast method for querying substructure motifs. The improvements in speed over existing approaches make it useful for efficiently performing high-volume exploratory querying of possible protein substructural motifs or conformations. BetaSearch was used to identify a nearly identical β-residue motif between an entirely synthetic (Top7) and a naturally-occurring protein (Charcot-Leyden crystal protein), as well as identifying structural similarities between biotin-binding domains of avidin, streptavidin and the lipocalin gamma subunit of human C8.

Using product kernels to predict protein interactions

There is a wide variety of experimental methods for the identification of protein interactions. This variety has in turn spurred the development of numerous different computational approaches for modeling and predicting protein interactions. These methods range from detailed structure-based methods capable of operating on only a single pair of proteins at a time to approximate statistical methods capable of making predictions on multiple proteomes simultaneously. In this chapter, we provide a brief discussion of the relative merits of different experimental and computational methods available for identifying protein interactions. Then we focus on the application of our particular (computational) method using Support Vector Machine product kernels. We describe our method in detail and discuss the application of the method for predicting protein-protein interactions, beta-strand interactions, and protein-chemical interactions.