Multiple mapping method: a novel approach to the sequence-to-structure alignment problem in comparative protein structure modeling

Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments

MOTIVATION

Two major bottlenecks in advancing comparative protein structure modeling are the efficient combination of multiple template structures and the generation of a correct input target-template alignment.

RESULTS

A novel method, Multiple Mapping Method with Multiple Templates (M4T) is introduced that implements an algorithm to automatically select and combine Multiple Template structures (MT) and an alignment optimization protocol (Multiple Mapping Method, MMM). The MT module of M4T selects and combines multiple template structures through an iterative clustering approach that takes into account the 'unique' contribution of each template, their sequence similarity among themselves and to the target sequence, and their experimental resolution. MMM is a sequence-to-structure alignment method that optimally combines alternatively aligned regions according to their fit in the structural environment of the template structure. The resulting M4T alignment is used as input to a comparative modeling module. The performance of M4T has been benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set, and showed favorable performance to current state of the art methods.

M4T: a comparative protein structure modeling server

Multiple Mapping Method with Multiple Templates (M4T) (http://www.fiserlab.org/servers/m4t) is a fully automated comparative protein structure modeling server. The novelty of M4T resides in two of its major modules, Multiple Templates (MT) and Multiple Mapping Method (MMM). The MT module of M4T selects and optimally combines the sequences of multiple template structures through an iterative clustering approach that takes into account the ‘unique’ contribution of each template, its sequence similarity to other template sequences and to the target sequences, and the quality of its experimental resolution. MMM module is a sequence-to-structure alignment method that is aimed at improving the alignment accuracy, especially at lower sequence identity levels. The current implementation of MMM takes inputs from three profile-to-profile-based alignment methods and iteratively compares and ranks alternatively aligned regions according to their fit in the structural environment of the template structure. The performance of M4T was benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set and showed a favorable performance to current state-of-the-art methods.

Methods for optimizing the structure alphabet sequences of proteins

Protein structure prediction based on fragment assemble has made great progress in recent years. Local protein structure prediction is receiving increased attention. One essential step of local protein structure prediction method is that the three-dimensional conformations must be compressed into one-dimensional series of letters of a structural alphabet. The traditional method assigns each structure fragment the structure alphabet that has the best local structure similarity. However, such locally optimal structure alphabet sequence does not guarantee to produce the globally optimal structure. This study presents two efficient methods trying to find the optimal structure alphabet sequence, which can model the native structures as accuracy as possible. First, a 28-letter structure alphabet is derived by clustering fragment in Cartesian space with fragment length of seven residues. The average quantization error of the 28 letters is 0.82 A in term of root mean square deviation. Then, two efficient methods are presented to encode the protein structures into series of structure alphabet letters, that is, the greedy and dynamic programming algorithm. They are tested on PDB database using the structure alphabet developed in Cartesian coordinates space (our structure alphabet) and in torsion angles space (the PB structure alphabet), respectively. The experimental results show that these two methods can find the approximately optimal structure alphabet sequences by searching a small fraction of the modeling space. The traditional local-optimization method achieves 26.27 A root mean square deviations between the reconstructed structures and the native one, while the modeling accuracy is improved to 3.28 A by the greedy algorithm. The results are helpful for local protein structure prediction.

Structural basis of inducible costimulator ligand costimulatory function: determination of the cell surface oligomeric state and functional mapping of the receptor binding site of the protein

Inducible costimulator (ICOS) ligand (ICOSL), a B7-related transmembrane glycoprotein with extracellular IgV and IgC domains, binds to ICOS on activated T cells and delivers a positive costimulatory signal for optimal T cell function. Toward determining the structural features of ICOSL crucial for its costimulatory function, the present study shows that ICOSL displays a marked oligomerization potential, resembling more like B7-1 than B7-2. Use of ICOSL constructs lacking either the IgC or IgV domain demonstrates that receptor binding is mediated solely by the IgV domain but requires the IgC domain for maintaining the structural integrity of the protein. To map further the receptor recognition surface on ICOSL, a homology-based protein structure model of the ICOS:ICOSL complex was constructed. Based on predictions from the model, a series of mutations were generated targeting the potential receptor binding surface on ICOSL, and the mutants were tested for their biological function in terms of ICOS binding and T cell costimulation ability. The results provide experimental validation of the model and show that the receptor binding site on ICOSL is constituted chiefly by aromatic/hydrophobic residues. Critical ICOSL residues essential for ICOS binding map to the GFCC'C'' beta-sheet face of the IgV domain and approximately overlap with the B7-1/B7-2 motif(s) that recognize CD28/CTLA-4. Altogether, similar structural features of ICOSL and B7 isoforms suggest a close evolutionary relationship between these costimulatory ligands, yet differences at the same time explain their unique specificity for the cognate binding partners, ICOS and CD28/CTLA-4, respectively.

Interactions of Brf1 peptides with the tetratricopeptide repeat-containing subunit of TFIIIC inhibit and promote preinitiation complex assembly

The binding of Brf1 to the tetratricopeptide repeat (TPR)-containing transcription factor IIIC (TFIIIC) subunit (Tfc4) represents a rate-limiting step in the ordered assembly of the RNA polymerase III initiation factor TFIIIB. Tfc4 contains multiple binding sites for Brf1 within its amino terminus and adjacent TPR arrays, but the access of Brf1 to these sites is limited by autoinhibition. Moreover, the Brf1 binding sites in Tfc4 overlap with sites important for the subsequent recruitment of another TFIIIB subunit, Bdp1, implying that repositioning of Brf1 is required after its initial interaction with Tfc4. As a starting point for dissecting the steps in TFIIIC-directed assembly of TFIIIB, we conducted yeast two-hybrid screens of Brf1 peptide libraries against different TPR-containing Tfc4 fragments. Short, biochemically active peptides were identified in three distinct regions of Brf1. Two peptides defined conserved but distal regions of Brf1 that participate in stable binding of Brf1 to TFIIIC-DNA. Remarkably, a third peptide that binds specifically to TPR6-9 of Tfc4 was found to promote the formation of both TFIIIC-DNA and Brf1-TFIIIC-DNA complexes and to reduce the mobility of these complexes in native gels. The data are consistent with this peptide causing a conformational change in TFIIIC that overcomes Tfc4 autoinhibition of Brf1 binding and suggest a structural model for the Brf1-Tfc4 interaction.

MMM: a sequence-to-structure alignment protocol

MOTIVATION

Accurate alignment of a target sequence to a template structure continues to be a bottleneck in producing good quality comparative protein structure models.

RESULTS

Multiple Mapping Method (MMM) is a comparative protein structure modeling server with an emphasis on a novel alignment optimization protocol. MMM takes inputs from five profile-to-profile based alignment methods. The alternatively aligned regions from the input alignment set are combined according to their fit in the structural environment of the template structure. The resulting, optimally spliced MMM alignment is used as input to an automated comparative modeling module to produce a full atom model.

AVAILABILITY

The MMM server is freely accessible at http://www.fiserlab.org/servers/mmm