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.     

Enhanced protein fold recognition using secondary structure information from NMR.

NMR offers the possibility of accurate secondary structure for proteins that would be too large for structure determination. In the absence of an X-ray crystal structure, this information should be useful as an adjunct to protein fold recognition methods based on low resolution force fields. The value of this information has been tested by adding varying amounts of artificial secondary structure data and threading a sequence through a library of candidate folds. Using a literature test set, the threading method alone has only a one-third chance of producing a correct answer among the top ten guesses. With realistic secondary structure information, one can expect a 60-80% chance of finding a homologous structure. The method has then been applied to examples with published estimates of secondary structure. This implementation is completely independent of sequence homology, and sequences are optimally aligned to candidate structures with gaps and insertions allowed. Unlike work using predicted secondary structure, we test the effect of differing amounts of relatively reliable data.     

Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments

Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence-based and structure-based methods as well as their combinations have been developed. Here, we propose a fold-recognition method that incorporates structural information without the need of sequence-to-structure threading. This is accomplished by generating sequence profiles from protein structural fragments. The structure-derived sequence profiles allow a simple integration with evolution-derived sequence profiles and secondary-structural information for an optimized alignment by efficient dynamic programming. The resulting method (called SP(3)) is found to make a statistically significant improvement in both sensitivity of fold recognition and accuracy of alignment over the method based on evolution-derived sequence profiles alone (SP) and the method based on evolution-derived sequence profile and secondary structure profile (SP(2)). SP(3) was tested in SALIGN benchmark for alignment accuracy and Lindahl, PROSPECTOR 3.0, and LiveBench 8.0 benchmarks for remote-homology detection and model accuracy. SP(3) is found to be the most sensitive and accurate single-method server in all benchmarks tested where other methods are available for comparison (although its results are statistically indistinguishable from the next best in some cases and the comparison is subjected to the limitation of time-dependent sequence and/or structural library used by different methods.). In LiveBench 8.0, its accuracy rivals some of the consensus methods such as ShotGun-INBGU, Pmodeller3, Pcons4, and ROBETTA. SP(3) fold-recognition server is available on http://theory.med.buffalo.edu.     

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.     

Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition

An elaborate knowledge-based energy function is designed for fold recognition. It is a residue-level single-body potential so that highly efficient dynamic programming method can be used for alignment optimization. It contains a backbone torsion term, a buried surface term, and a contact-energy term. The energy score combined with sequence profile and secondary structure information leads to an algorithm called SPARKS (Sequence, secondary structure Profiles and Residue-level Knowledge-based energy Score) for fold recognition. Compared with the popular PSI-BLAST, SPARKS is 21% more accurate in sequence-sequence alignment in ProSup benchmark and 10%, 25%, and 20% more sensitive in detecting the family, superfamily, fold similarities in the Lindahl benchmark, respectively. Moreover, it is one of the best methods for sensitivity (the number of correctly recognized proteins), alignment accuracy (based on the MaxSub score), and specificity (the average number of correctly recognized proteins whose scores are higher than the first false positives) in LiveBench 7 among more than twenty servers of non-consensus methods. The simple algorithm used in SPARKS has the potential for further improvement. This highly efficient method can be used for fold recognition on genomic scales. A web server is established for academic users on http://theory.med.buffalo.edu.     

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.     

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.     

A proposed architecture for the central domain of the bacterial enhancer-binding proteins based on secondary structure prediction and fold recognition

The expression of genes transcribed by the RNA polymerase with the alternative sigma factor <r54 (Ecr54) is absolutely dependent on activator proteins that bind to enhancer-like sites, located far upstream from the promoter. These unique prokaryotic proteins, known as enhancer-binding proteins (EBP), mediate open promoter complex formation in a reaction dependent on NTP hydrolysis. The best characterized proteins of this family of regulators are NtrC and Nif A, which activate genes required for ammonia assimilation and nitrogen fixation, respectively. In a recent IRBM course (“Frontiers of protein structure prediction,” IRBM, Pomezia, Italy, 1995; see web site http://www.mrc-cpe.cam.uk/ irbm-course95/), one of us (J.O.) participated in the elaboration of the proposal that the Central domain of the EBPs might adopt the classical mononucleotide-binding fold. This suggestion was based on the results of a new protein fold recognition algorithm (Map) and in the mapping of correlated mutations calculated for the sequence family on the same mononucleotide-binding fold topology. In this work, we present new data that support the previous conclusion. The results from a number of different secondary structure prediction programs suggest that the Central domain could adopt an alfi topology. The fold recognition programs ProFIT 0.9, 3D PROFILE combined with secondary structure prediction, and 123D suggest a mononucleotide-binding fold topology for the Central domain amino acid sequence. Finally, and most importantly, three of five reported residue alterations that impair the Central domain ATPase activity of the Eo-54 activators are mapped to polypeptide regions that might be playing equivalent roles as those involved in nucleotide-binding in the mononucleotide-binding proteins. Furthermore, the known residue substitutions that alter the function of the Ecr54 activators, leaving intact the Central domain ATPase activity, are mapped on a region proposed to play an equivalent role as the effector region of the GTPase superfamily.     

Fold recognition by concurrent use of solvent accessibility and residue depth

Recognizing the structural similarity without significant sequence identity (called fold recognition) is the key for bridging the gap between the number of known protein sequences and the number of structures solved. Previously, we developed a fold-recognition method called SP(3) which combines sequence-derived sequence profiles, secondary-structure profiles and residue-depth dependent, structure-derived sequence profiles. The use of residue-depth-dependent profiles makes SP(3) one of the best automatic predictors in CASP 6. Because residue depth (RD) and solvent accessible surface area (solvent accessibility) are complementary in describing the exposure of a residue to solvent, we test whether or not incorporation of solvent-accessibility profiles into SP(3) could further increase the accuracy of fold recognition. The resulting method, called SP(4), was tested in SALIGN benchmark for alignment accuracy and Lindahl, LiveBench 8 and CASP7 blind prediction for fold recognition sensitivity and model-structure accuracy. For remote homologs, SP(4) is found to consistently improve over SP(3) in the accuracy of sequence alignment and predicted structural models as well as in the sensitivity of fold recognition. Our result suggests that RD and solvent accessibility can be used concurrently for improving the accuracy and sensitivity of fold recognition. The SP(4) server and its local usage package are available on http://sparks.informatics.iupui.edu/SP4.     

Bioinformatics methods to predict protein structure and function. A practical approach

Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Not all protein structure prediction projects involve the use of all these techniques. A central part of a typical protein structure prediction is the identification of a suitable structural target from which to extrapolate three-dimensional information for a query sequence. The way in which this is done defines three types of projects. The first involves the use of standard and well-understood techniques. If a structural template remains elusive, a second approach using nontrivial methods is required. If a target fold cannot be reliably identified because inconsistent results have been obtained from nontrivial data analyses, the project falls into the third type of project and will be virtually impossible to complete with any degree of reliability. In this article, a set of protocols to predict protein structure from sequence is presented and distinctions among the three types of project are given. These methods, if used appropriately, can provide valuable indicators of protein structure and function.     

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.     

Inaccurate secondary structure predictions often indicate protein fold switching

Although most proteins conform to the classical one‐structure/one‐function paradigm, an increasing number of proteins with dual structures and functions have been discovered. In response to cellular stimuli, such proteins undergo structural changes sufficiently dramatic to remodel even their secondary structures and domain organization. This “fold‐switching” capability fosters protein multi‐functionality, enabling cells to establish tight control over various biochemical processes. Accurate predictions of fold‐switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Recently, we developed a prediction method for fold‐switching proteins using structure‐based thermodynamic calculations and discrepancies between predicted and experimentally determined protein secondary structure (Porter and Looger, Proc Natl Acad Sci U S A 2018; 115:5968–5973). Here we seek to leverage the negative information found in these secondary structure prediction discrepancies. To do this, we quantified secondary structure prediction accuracies of 192 known fold‐switching regions (FSRs) within solved protein structures found in the Protein Data Bank (PDB). We find that the secondary structure prediction accuracies for these FSRs vary widely. Inaccurate secondary structure predictions are strongly associated with fold‐switching proteins compared to equally long segments of non‐fold‐switching proteins selected at random. These inaccurate predictions are enriched in helix‐to‐strand and strand‐to‐coil discrepancies. Finally, we find that most proteins with inaccurate secondary structure predictions are underrepresented in the PDB compared with their alternatively folded cognates, suggesting that unequal representation of fold‐switching conformers within the PDB could be an important cause of inaccurate secondary structure predictions. These results demonstrate that inconsistent secondary structure predictions can serve as a useful preliminary marker of fold switching.     

Successful protein fold recognition by optimal sequence threading validated by rigorous blind testing

Analysis of the results of the recent protein structure prediction experiment for our method shows that we achieved a high level of success, Of the 18 available prediction targets of known structure, the assessors have identified 11 chains which either entirely match a previously known fold, or which partially match a substantial region of a known fold. Of these 11 chains, we made predictions for 9, and correctly assigned the folds in 5 cases. We have also identified a further 2 chains which also partially match known folds, and both of these were correctly predicted. The success rate for our method under blind testing is therefore 7 out of 11 chains. A further 2 folds could have easily been recognized but failed due to either overzealous filtering of potential matches, or to simple human error on our part. One of the two targets for which we did not submit a prediction, prosubtilisin, would not have been recognized by our usual criteria, but even in this case, it is possible that a correct prediction could have been made by considerin a combination of pairwise energy and solvation energy Z-scores. Inspection of the threading alignments for the (αβ)₈ barrels provides clues as to how fold recognition by threading works, in that these folds are recognized by parts rather than as a whole. The prospects for developing sequence threading technology further is discussed. © 1995 Wiley-Liss, Inc.     

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。     

蛋白质折叠识别方法综述

蛋白质折叠识别算法是蛋白质三维结构预测的重要方法之一,该方法在生物科学的许多方面得到卓有成效的应用。在过去的十年中,我们见证了一系列基于不同计算方式的蛋白质折叠识别方法。在这些计算方法中,机器学习和序列谱-序列谱比对是两种在蛋白质折叠中应用较为广泛和有效的方法。除了计算方法的进展外,不断增大的蛋白质结构数据库也是蛋白质折叠识别的预测精度不断提高的一个重要因素。在这篇文章中,我们将简要地回顾蛋白质折叠中的先进算法。另外,我们也将讨论一些可能可以应用于改进蛋白质折叠算法的策略。     

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%.     

A novel fold recognition method using composite predicted secondary structures

In this work, we introduce a new method for fold recognition using composite secondary structures assembled from different secondary structure prediction servers for a given target sequence. An automatic, complete, and robust way of finding all possible combinations of predicted secondary structure segments (SSS) for the target sequence and clustering them into a few flexible clusters, each containing patterns with the same number of SSS, is developed. This program then takes two steps in choosing plausible homologues: (i) a SSS-based alignment excludes impossible templates whose SSS patterns are very different from any of those of the target; (ii) a residue-based alignment selects good structural templates based on sequence similarity and secondary structure similarity between the target and only those templates left in the first stage. The secondary structure of each residue in the target is selected from one of the predictions to find the best match with the template. Truncation is applied to a target where different predictions vary. In most cases, a target is also divided into N-terminal and C-terminal fragments, each of which is used as a separate subsequence. Our program was tested on the fold recognition targets from CASP3 with known PDB codes and some available targets from CASP4. The results are compared with a structural homologue list for each target produced by the CE program (Shindyalov and Bourne, Protein Eng 1998;11:739-747). The program successfully locates homologues with high Z-score and low root-mean-score deviation within the top 30-50 predictions in the overwhelming majority of cases.