Shao-Wei Huang

CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.

CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.

Deriving protein dynamical properties from weighted protein contact number

It has recently been shown that in proteins the atomic mean‐square displacement (or B‐factor) can be related to the number of the neighboring atoms (or protein contact number), and that this relationship allows one to compute the B‐factor profiles directly from protein contact number. This method, referred to as the protein contact model, is appealing, since it requires neither trajectory integration nor matrix diagonalization. As a result, the protein contact model can be applied to very large proteins and can be implemented as a high‐throughput computational tool to compute atomic fluctuations in proteins. Here, we show that this relationship can be further refined to that between the atomic mean‐square displacement and the weighted protein contact‐number, the weight being the square of the reciprocal distance between the contacting pair. In addition, we show that this relationship can be utilized to compute the cross‐correlation of atomic motion (the B‐factor is essentially the auto‐correlation of atomic motion). For a nonhomologous dataset comprising 972 high‐resolution X‐ray protein structures (resolution <2.0 Å and sequence identity <25%), the mean correlation coefficient between the X‐ray and computed B‐factors based on the weighted protein contact‐number model is 0.61, which is better than those of the original contact‐number model (0.51) and other methods. We also show that the computed correlation maps based on the weighted contact‐number model are globally similar to those computed through normal model analysis for some selected cases. Our results underscore the relationship between protein dynamics and protein packing. We believe that our method will be useful in the study of the protein structure‐dynamics relationship. Proteins 2008.

Crystal structure of Helicobacter pylori formamidase AmiF reveals a cysteine-glutamate-lysine catalytic triad

Helicobacter pylori AmiF formamidase that hydrolyzes formamide to produce formic acid and ammonia belongs to a member of the nitrilase superfamily. The crystal structure of AmiF was solved to 1.75Å resolution using single-wavelength anomalous dispersion methods. The structure consists of a homohexamer related by 3-fold symmetry in which each subunit has an α-β-β-α four-layer architecture characteristic of the nitrilase superfamily. One exterior α layer faces the solvent, whereas the other one associates with that of the neighbor subunit, forming a tight α-β-β-α-α-β-β-α dimer. The apo and liganded crystal structures of an inactive mutant C166S were also determined to 2.50 and 2.30Å, respectively. These structures reveal a small formamide-binding pocket that includes Cys166, Glu60, and Lys133 catalytic residues, in which Cys166 acts as a nucleophile. Analysis of the liganded AmiF and N-carbamoyl d-amino acid amidohydrolase binding pockets reveals a common Cys-Glu-Lys triad, another conserved glutamate, and different subsets of ligand-binding residues. Molecular dynamic simulations show that the conserved triad has minimal fluctuations, catalyzing the hydrolysis of a specific nitrile or amide in the nitrilase superfamily efficiently.

On the relationship between the protein structure and protein dynamics.

Recently, we have developed a method (Shih et al., Proteins: Structure, Function, and Bioinformatics 2007;68: 34–38) to compute correlation of fluctuations of proteins. This method, referred to as the protein fixed‐point (PFP) model, is based on the positional vectors of atoms issuing from the fixed point, which is the point of the least fluctuations in proteins. One corollary from this model is that atoms lying on the same shell centered at the fixed point will have the same thermal fluctuations. In practice, this model provides a convenient way to compute the average dynamical properties of proteins directly from the geometrical shapes of proteins without the need of any mechanical models, and hence no trajectory integration or sophisticated matrix operations are needed. As a result, it is more efficient than molecular dynamics simulation or normal mode analysis. Though in the previous study the PFP model has been successfully applied to a number of proteins of various folds, it is not clear to what extent this model will be applied. In this article, we have carried out the comprehensive analysis of the PFP model for a dataset comprising 972 high‐resolution X‐ray structures with pairwise sequence identity ≤25%. We found that in most cases the PFP model works well. However, in case of proteins comprising multiple domains, each domain should be treated separately as an independent dynamical module with its own fixed point; and in case of the protein complex comprising a number of subunits, if functioning as a biological unit, the whole complex should be considered as one single dynamical module with one fixed point. Under such considerations, the resultant correlation coefficient between the computed and the X‐ray structural B‐factors for the data set is 0.59 and 75% (727/972) of proteins with a correlation coefficient ≥0.5. Our result shows that the fixed‐point model is indeed quite general and will be a useful tool for high throughput analysis of dynamical properties of proteins. Proteins 2008.

A simple way to compute protein dynamics without a mechanical model

We found that in proteins the average atomic fluctuation is linearly related to the square of the atomic distance from the center of mass of the protein. Using this simple relation, we can accurately compute the temperature factors of proteins of a wide range of sizes and folds, and the correlation of the fluctuations in proteins. This simple relation provides a direct link between protein dynamics and the static proteins geometrical shape and offers a simple way to compute protein dynamics without either long time trajectory integration or any matrix operations. Proteins 2007.

On the Relationship Between Catalytic Residues and their Protein Contact Number

Due to advances in structural biology, an increasing number of protein structures of unknown function have been deposited in Protein Data Bank (PDB). These proteins are usually characterized by novel structures and sequences. Conventional comparative methodology (such as sequence alignment, structure comparison, or template search) is unable to determine their function. Thus, it is important to identify proteins function directly from its structure, but this is not an easy task. One of the strategies used is to analyze whether there are distinctive structure-derived features associated with functional residues. If so, one may be able to identify the functional residues directly from a single structure. Recently, we have shown that protein weighted contact number is related to atomic thermal fluctuations and can be used to derive motional correlations in proteins. In this report, we analyze the weighted contact-number profiles of both catalytic residues and non-catalytic residues for a dataset of 760 structures. We found that catalytic residues have distinct distributions of weighted contact numbers from those of non-catalytic residues. Using this feature, we are able to effectively differentiate catalytic residues from other residues with a single optimized threshold value. Our method is simple to implement and compares favourably with other more sophisticated methods. In addition, we discuss the physics behind the relationship between catalytic residues and their contact numbers as well as other features (such as residue centrality or B-factors) associated with catalytic residues.

Computation of Conformational Entropy from Protein Sequences Using the Machine-Learning Method—Application to the Study of the Relationship between Structural Conservation and Local Structural Stability

A complete protein sequence can usually determine a unique conformation; however, the situation is different for shorter subsequences—some of them are able to adopt unique conformations, independent of context; while others assume diverse conformations in different contexts. The conformations of subsequences are determined by the interplay between local and nonlocal interactions. A quantitative measure of such structural conservation or variability will be useful in the understanding of the sequence–structure relationship. In this report, we developed an approach using the support vector machine method to compute the conformational variability directly from sequences, which is referred to as the sequence structural entropy. As a practical application, we studied the relationship between sequence structural entropy and the hydrogen exchange for a set of well‐studied proteins. We found that the slowest exchange cores usually comprise amino acids of the lowest sequence structural entropy. Our results indicate that structural conservation is closely related to the local structural stability. This relationship may have interesting implications in the protein folding processes, and may be useful in the study of the sequence–structure relationship. Proteins 2005.

Accurate prediction of protein catalytic residues by side chain orientation and residue contact density.

Prediction of protein catalytic residues provides useful information for the studies of protein functions. Most of the existing methods combine both structure and sequence information but heavily rely on sequence conservation from multiple sequence alignments. The contribution of structure information is usually less than that of sequence conservation in existing methods. We found a novel structure feature, residue side chain orientation, which is the first structure-based feature that achieves prediction results comparable to that of evolutionary sequence conservation. We developed a structure-based method, Enzyme Catalytic residue SIde-chain Arrangement (EXIA), which is based on residue side chain orientations and backbone flexibility of protein structure. The prediction that uses EXIA outperforms existing structure-based features. The prediction quality of combing EXIA and sequence conservation exceeds that of the state-of-the-art prediction methods. EXIA is designed to predict catalytic residues from single protein structure without needing sequence or structure alignments. It provides invaluable information when there is no sufficient or reliable homology information for target protein. We found that catalytic residues have very special side chain orientation and designed the EXIA method based on the newly discovered feature. It was also found that EXIA performs well for a dataset of enzymes without any bounded ligand in their crystallographic structures.

EXIA2: Web Server of Accurate and Rapid Protein Catalytic Residue Prediction

We propose a method (EXIA2) of catalytic residue prediction based on protein structure without needing homology information. The method is based on the special side chain orientation of catalytic residues. We found that the side chain of catalytic residues usually points to the center of the catalytic site. The special orientation is usually observed in catalytic residues but not in noncatalytic residues, which usually have random side chain orientation. The method is shown to be the most accurate catalytic residue prediction method currently when combined with PSI-Blast sequence conservation. It performs better than other competing methods on several benchmark datasets that include over 1,200 enzyme structures. The areas under the ROC curve (AUC) on these benchmark datasets are in the range from 0.934 to 0.968.

On the Structural Characteristics of the Protein Active Sites and Their Relation to Thermal Fluctuations

Due to the advances in structural biology research, a large number of protein structures have been solved in the last decade. In the same time, we also witness a rapidly growing number of structures of unknown function being deposited in the PDB. As a result, the ability to predict protein function from its structure becomes increasingly important in computational biology. The conventional comparative methods(for example, Laskowski, Watson et al. 2005; Watson, Sanderson et al. 2007) for identifying functional sites rely on evolutional information like homologous structures of known function or the known catalytic templates. However, these approaches are not applicable to these novel structures. One needs to develop ingenious approaches that do not rely on evolutionary information. Recently, several groups(Amitai, Shemesh et al. 2004; Ben-Shimon & Eisenstein 2005; Sacquin-Mora, Laforet et al. 2007; Huang, Yu et al. 2011) developed novel approaches to predict the active sites of enzymes from a single structure without using any homologous structures or known catalytic templates. The basic idea of their approaches is simple: they first identify certain structural or dynamical features that are unique to the active sites; they then further refine this relationship such that it can be used to accurately predict the enzyme catalytic sites. For example, Pietrokovski and co-workers(Amitai, Shemesh et al. 2004) transformed the protein structure into residue interaction graphs, with each amino acid residue represented as a graph node and the interaction between them as a graph edge. They then computed the network closeness of each residue. They found that most catalytic residues are associated with the network centrality. Ben-Shimon and Eisenstein(Ben-Shimon & Eisenstein 2005), analyzing 175 enzymes, observed that most catalytic residues are near the enzyme centroid. Based on these results, they developed novel methods to predict catalytic sites from a single structure. The aim of this review is to show that this peculiar relationship between catalytic sites and the structure centroid or its network centrality can be accounted for by the dynamical properties of the catalytic residues, which are in general more rigid than other residues. In addition, we will discuss the recent studies (Halle 2002; Shih, Huang et al. 2007; Huang, Shih et al. 2008; Lin, Huang et al. 2008; Lu, Huang et al. 2008) on the surprisingly close link