Protein structure prediction by threading methods: Evaluation of current techniques

This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure. © 1995 Wiley-Liss, Inc.     

Threading a database of protein cores

We present an analysis of 10 blind predictions prepared for a recent conference, “Critical Assessment of Techniques for Protein Structure Prediction.”¹ The sequences of these proteins are not detectably similar to those of any protein in the structure database then available, but we attempted, by a threading method, to recognize similarity to known domain folds. Four of the 10 proteins, as we subsequently learned, do indeed show significant similarity to then-known structures. For 2 of these proteins the predictions were accurate, in the sense that a similar structure was at or near the top of the list of threading scores, and the threading alignment agreed well with the corresponding structural alignment. For the best predicted model mean alignment error relative to the optimal structural alignment was 2.7 residues, arising entirely from small “register shifts” of strands or helices. In the analysis we attempt to identify factors responsible for these successes and failures. Since our threading method does not use gap penalties, we may readily distinguish between errors arising from our prior definition of the “cores” of known structures and errors arising from inherent limitations in the threading potential. It would appear from the results that successful substructure recognition depends most critically on accurate definition of the “fold” of a database protein. This definition must correctly delineate substructures that are, and are not, likely to be conserved during protein evolution. © 1995 Wiley-Liss, Inc.     

A contact energy function considering residue hydrophobic environment and its application in protein fold recognition

The three-dimensional （3D） structure prediction of proteins ：is an important task in bioinformatics. Finding energy functions that can better represent residue-residue and residue-solvent interactions is a crucial way to improve the prediction accu- racy. The widely used contact energy functions mostly only consider the contact frequency between different types of residues; however, we find that the contact frequency also relates to the residue hydrophobic environment. Accordingly, we present an improved contact energy function to integrate the two factors, which can reflect the influence of hydrophobic interaction on the stabilization of protein 3D structure more effectively. Furthermore, a fold recognition （threading） approach based on this energy function is developed. The testing results obtained with 20 randomly selected proteins demonstrate that, compared with common contact energy functions, the proposed energy function can improve the accuracy of the fold template prediction from 20% to 50%, and can also improve the accuracy of the sequence-template alignment from 35% to 65%.     

Improving fold recognition without folds

The most reliable way to align two proteins of unknown structure is through sequence-profile and profile-profile alignment methods. If the structure for one of the two is known, fold recognition methods outperform purely sequence-based alignments. Here, we introduced a novel method that aligns generalised sequence and predicted structure profiles. Using predicted 1D structure (secondary structure and solvent accessibility) significantly improved over sequence-only methods, both in terms of correctly recognising pairs of proteins with different sequences and similar structures and in terms of correctly aligning the pairs. The scores obtained by our generalised scoring matrix followed an extreme value distribution; this yielded accurate estimates of the statistical significance of our alignments. We found that mistakes in 1D structure predictions correlated between proteins from different sequence-structure families. The impact of this surprising result was that our method succeeded in significantly out-performing sequence-only methods even without explicitly using structural information from any of the two. Since AGAPE also outperformed established methods that rely on 3D information, we made it available through. If we solved the problem of CPU-time required to apply AGAPE on millions of proteins, our results could also impact everyday database searches.     

The WWWH of remote homolog detection: the state of the art

The detection of remote homolog pairs of proteins using computational methods is a pivotal problem in structural bioinformatics, aiming to compute protein folds on the basis of information in the database of known structures. In the last 25 years, several methods have been developed to tackle this problem, based on different approaches including sequence-sequence alignments and/or structure comparison. In this article, we will briefly discuss When, Why, Where and How (WWWH) to perform remote homology search, reviewing some of the most widely adopted computational approaches. The specific aim is highlighting the basic criteria implemented by different research groups and commenting on the status of the art as well as on still-open questions.     

基于集成分类器的蛋白质折叠模式识别

蛋白质折叠问题被列为"21世纪的生物物理学"的重要课题,他是分子生物学中心法则尚未解决的一个重大生物学问题,因此预测蛋白质折叠模式是一个复杂、困难、和有挑战性的工作。为了解决该问题,我们引入了分类器集成,本文所采用的是三种分类器(LMT、RandomForest、SMO)进行集成以及188维组合理化特征来对蛋白质类别进行预测。实验证明,该方法可以有效表征蛋白质折叠模式的特性,对蛋白质序列数据实现精确分类;交叉验证和独立测试均证明本文预测准确率超过70%,比前人工作提高近10个百分点。     

Assessment of a protein fold recognition method that takes into account four physicochemical properties: Side-chain packing,solvation, hydrogen-bonding,and local conformation

A protein fold recognition method was tested by the blind prediction of the structures of a set of proteins. The method evaluates the compatibility of an amino acid sequence with a three-dimensional structure using the four evaluation functions: side-chain packing, solvation, hydrogen-bonding, and local conformation functions. The structures of 14 proteins containing 19 sequences were predicted. The predictions were compared with the experimental structures. The experimental results showed that 9 of the 19 target sequences have known folds or portions of known folds. Among them, the folds of Klebsiella aerogenes urease β subunit (KAUB) and pyruvate phosphate dikinase domain 4 (PPDK4) were successfully recognized; our method predicted that KAUB and PPDK4 would adopt the folds of macromomycin (Ig-fold) and phosphoribosylanthra-nilate isomerase:indoleglycerol-phosphate synthase (TIM barrel), respectively, and the experimental structure revealed that they actually adopt the predicted folds. The predictions for the other targets were not successful, but they often gave secondary structural patterns similar to those of the experimental structures. © 1995 Wiley-Liss, Inc.     

GLASS: A tool to visualize protein structure prediction data in three dimensions and evaluate their consistency

When a protein sequence does not share any significant sequence similarity with a protein of known structure, homology modeling cannot be applied. However, many novel and interesting methods, such as secondary structure prediction, fold recognition, and prediction of long-range interactions, are being developed and have been shown to be reasonably successful in predicting protein structures from sequence data and evolutionary information. The a priori evaluation of the correctness of a prediction obtained by one of these methods is however often problematic. Consequently, it is important to use all available information provided by as many different methods as possible and all the available experimental data about the protein of interest, since the consistency of the results is indicative of the reliability of the prediction. Hence the need has arisen for suitable tools able to compare results provided by different methods and evaluate their consistency. We have therefore constructed GLASS, a general platform to read, visualize, compare, and evaluate prediction results from many different sources and to project these prediction results into three dimensions. In addition, GLASS allows the comparison of selected parameters calculated for a model with the distribution observed in real protein structures, thus providing an easy way to test new methods for evaluating the likelihood of different structural models. GLASS can be considered as a “workbench” for structural predictions useful to both experimentalists and theoreticians. Proteins 30:339–351, 1998. © 1998 Wiley-Liss, Inc.