Tài liệu Báo cáo khoa học: A knowledge-based potential function predicts the specificity and relative

A knowledge-based potential function predicts the

specificity and relative binding energy of RNA-binding

proteins

Suxin Zheng1,*, Timothy A. Robertson2,* and Gabriele Varani1,2

1 Department of Chemistry, University of Washington, Seattle, WA, USA

2 Department of Biochemistry, University of Washington, Seattle, WA, USA

The sequence-specific recognition of RNA by proteins

plays a fundamental role in gene expression by direct￾ing different cellular RNAs to specific processing path￾ways or subcellular locations. Many experimental

studies have explored the molecular basis for the

sequence dependence of protein–RNA recognition [1–

4]; more recently, a few studies have explored this prob￾lem from a computational perspective as well [5–16].

However, these early studies have emphasized qualita￾tive descriptions of the recognition process; relatively

few attempts have been made to quantify the character￾istics of protein–RNA interactions using computational

approaches [17]. Here, we present a new approach for

predicting the specificity of RNA-binding proteins and

to evaluate the contribution of individual amino acids

to the energetic of protein–RNA complexes.

Knowledge-based potential functions have been

employed in protein structure prediction [18–27], as

well as in the prediction of protein–protein [25,28–30]

and protein–ligand interactions [30–33]. A few studies

have explored the use of knowledge-based methods for

the prediction of protein–DNA interactions from

structure [30,34,35]. More recently, our group [36] and

others [37] have independently demonstrated that

knowledge-based potentials can provide quantitative

descriptions of protein–DNA interfaces comparable to

those provided using molecular mechanics force fields

[37].

The relative scarcity of high-resolution structures of

protein–RNA complexes has represented an under￾standable barrier to the quantitative application of

computational approaches to the problem of protein–

RNA recognition. However, we have previously dem￾onstrated that a statistical hydrogen bonding potential

can discriminate native structures of protein–RNA

complexes from docking decoy sets [17]. As hydrogen

Keywords

distance-dependent potential; protein–RNA

interaction; RRM recognition; statistical

potential

Correspondence

G. Varani, Department of Chemistry and

Department of Biochemistry, University of

Washington, Seattle, WA 98195, USA

Fax: +1 206 685 8665

Tel: +1 206 543 7113

E-mail: [email protected]

*These authors contributed equally to this

work

(Received 25 July 2007, revised 22 Septem￾ber 2007, accepted 19 October 2007)

doi:10.1111/j.1742-4658.2007.06155.x

RNA–protein interactions are fundamental to gene expression. Thus, the

molecular basis for the sequence dependence of protein–RNA recognition

has been extensively studied experimentally. However, there have been very

few computational studies of this problem, and no sustained attempt has

been made towards using computational methods to predict or alter the

sequence-specificity of these proteins. In the present study, we provide a

distance-dependent statistical potential function derived from our previous

work on protein–DNA interactions. This potential function discriminates

native structures from decoys, successfully predicts the native sequences

recognized by sequence-specific RNA-binding proteins, and recapitulates

experimentally determined relative changes in binding energy due to muta￾tions of individual amino acids at protein–RNA interfaces. Thus, this work

demonstrates that statistical models allow the quantitative analysis of

protein–RNA recognition based on their structure and can be applied to

modeling protein–RNA interfaces for prediction and design purposes.

Abbreviations

KH, K homology; MD, molecular dynamics; PDB, Protein Data Bank; RRM, RNA recognition motif; SRP, signal recognition particle.

6378 FEBS Journal 274 (2007) 6378–6391 ª 2007 The Authors Journal compilation ª 2007 FEBS