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L Rychlewski

Publications and source records attributed to L Rychlewski.

35 records · Page 2Linked to original sources
PubMed2000

From fold to function predictions: an apoptosis regulator protein BID.

With the rapidly increasing pace of genome sequencing projects and the resulting flood of predicted amino acid sequences of uncharacterized proteins, protein sequence analysis, and in particular, protein structure prediction is quickly gaining in importance. Prediction algorithms can be used for preliminary annotation of newly sequenced proteins and, at least in some cases, provide insights into their function and specific mode of action. Such annotations for several microbial genomes were performed by several groups and placed in public domain for evaluation. An example presented in this work comes from a related project of structural and functional predictions for proteins involved in the process of controlled cell death (apoptosis). The BID protein belongs to an important class of regulators of apoptosis identified by short sequence motifs. Here, several fold prediction methods are used to build a series of three-dimensional models. Structure analysis of the models with reference to the biological data available allows selection of the most appropriate model. It is found that the most likely structural model of BID is built on the structure of Bcl-X(L). The model is discussed in terms of experimental data on specific proteolytic cleavage of BID and its effect on BID interactions with other proteins and membranes.

Algorithms↗
PubMed2000

MaxSub: an automated measure for the assessment of protein structure prediction quality.

MOTIVATION: Evaluating the accuracy of predicted models is critical for assessing structure prediction methods. Because this problem is not trivial, a large number of different assessment measures have been proposed by various authors, and it has already become an active subfield of research (Moult et al. (1997,1999) and CAFASP (Fischer et al. 1999) prediction experiments have demonstrated that it has been difficult to choose one single, 'best' method to be used in the evaluation. Consequently, the CASP3 evaluation was carried out using an extensive set of especially developed numerical measures, coupled with human-expert intervention. As part of our efforts towards a higher level of automation in the structure prediction field, here we investigate the suitability of a fully automated, simple, objective, quantitative and reproducible method that can be used in the automatic assessment of models in the upcoming CAFASP2 experiment. Such a method should (a) produce one single number that measures the quality of a predicted model and (b) perform similarly to human-expert evaluations. RESULTS: MaxSub is a new and independently developed method that further builds and extends some of the evaluation methods introduced at CASP3. MaxSub aims at identifying the largest subset of C(alpha) atoms of a model that superimpose 'well' over the experimental structure, and produces a single normalized score that represents the quality of the model. Because there exists no evaluation method for assessment measures of predicted models, it is not easy to evaluate how good our new measure is. Even though an exact comparison of MaxSub and the CASP3 assessment is not straightforward, here we use a test-bed extracted from the CASP3 fold-recognition models. A rough qualitative comparison of the performance of MaxSub vis-a-vis the human-expert assessment carried out at CASP3 shows that there is a good agreement for the more accurate models and for the better predicting groups. As expected, some differences were observed among the medium to poor models and groups. Overall, the top six predicting groups ranked using the fully automated MaxSub are also the top six groups ranked at CASP3. We conclude that MaxSub is a suitable method for the automatic evaluation of models.

Algorithms↗
PubMed2000

The 2000 Olympic Games of protein structure prediction; fully automated programs are being evaluated vis-à-vis human teams in the protein structure prediction experiment CAFASP2.

In this commentary, we describe two new protein structure prediction experiments being run in parallel with the CASP experiment, which together may be regarded as the 2000 Olympic Games of structure prediction. The first new experiment is CAFASP, the Critical Assessment of Fully Automated Structure Prediction. In CAFASP, the participants are fully automated programs or Internet servers, and here the automated results of the programs are evaluated, without any human intervention. The second new experiment, named LiveBench, follows the CAFASP ideology in that it is aimed towards the evaluation of automatic servers only, while it runs on a large set of prediction targets and in a continuous fashion. Researchers will be watching the 2000 protein structure prediction Olympic Games, to be held in December, in order to learn about the advances in the classical 'human-plus-machine' CASP category, the fully automated CAFASP category, and the comparison between the two.

Amino Acid Sequence↗
PubMed2000

Comparison of sequence profiles. Strategies for structural predictions using sequence information.

Distant homologies between proteins are often discovered only after three-dimensional structures of both proteins are solved. The sequence divergence for such proteins can be so large that simple comparison of their sequences fails to identify any similarity. New generation of sensitive alignment tools use averaged sequences of entire homologous families (profiles) to detect such homologies. Several algorithms, including the newest generation of BLAST algorithms and BASIC, an algorithm used in our group to assign fold predictions for proteins from several genomes, are compared to each other on the large set of structurally similar proteins with little sequence similarity. Proteins in the benchmark are classified according to the level of their similarity, which allows us to demonstrate that most of the improvement of the new algorithms is achieved for proteins with strong functional similarities, with almost no progress in recognizing distant fold similarities. It is also shown that details of profile calculation strongly influence its sensitivity in recognizing distant homologies. The most important choice is how to include information from diverging members of the family, avoiding generating false predictions, while accounting for entire sequence divergence within a family. PSI-BLAST takes a conservative approach, deriving a profile from core members of the family, providing a solid improvement without almost any false predictions. BASIC strives for better sensitivity by increasing the weight of divergent family members and paying the price in lower reliability. A new FFAS algorithm introduced here uses a new procedure for profile generation that takes into account all the relations within the family and matches BASIC sensitivity with PSI-BLAST like reliability.

Algorithms↗
PubMed2000

Improving the quality of twilight-zone alignments.

Several recent publications illustrated advantages of using sequence profiles in recognizing distant homologies between proteins. At the same time, the practical usefulness of distant homology recognition depends not only on the sensitivity of the algorithm, but also on the quality of the alignment between a prediction target and the template from the database of known proteins. Here, we study this question for several supersensitive protein algorithms that were previously compared in their recognition sensitivity (Rychlewski et al., 2000). A database of protein pairs with similar structures, but low sequence similarity is used to rate the alignments obtained with several different methods, which included sequence-sequence, sequence-profile, and profile-profile alignment methods. We show that incorporation of evolutionary information encoded in sequence profiles into alignment calculation methods significantly increases the alignment accuracy, bringing them closer to the alignments obtained from structure comparison. In general, alignment quality is correlated with recognition and alignment score significance. For every alignment method, alignments with statistically significant scores correlate with both correct structural templates and good quality alignments. At the same time, average alignment lengths differ in various methods, making the comparison between them difficult. For instance, the alignments obtained by FFAS, the profile-profile alignment algorithm developed in our group are always longer that the alignments obtained with the PSI-BLAST algorithms. To address this problem, we develop methods to truncate or extend alignments to cover a specified percentage of protein lengths. In most cases, the elongation of the alignment by profile-profile methods is reasonable, adding fragments of similar structure. The examples of erroneous alignment are examined and it is shown that they can be identified based on the model quality.

Algorithms↗
PubMed2000

Sensitive sequence comparison as protein function predictor.

Protein function assignments based on postulated homology as recognized by high sequence similarity are used routinely in genome analysis. Improvements in sensitivity of sequence comparison algorithms got to the point, that proteins with previously undetectable sequence similarity, such as for instance 10-15% of identical residues, sometimes can be classified as similar. What is the relation between such proteins? Is it possible that they are homologous? What is the practical significance of detecting such similarities? A simplified analysis of the relation between sequence similarity and function similarity is presented here for the well-characterized proteins from the E. coli genome. Using a simple measure of functional similarity based on E.C. classification of enzymes, it is shown that it correlates well with sequence similarity measured by statistical significance of the alignment score. Proteins, similar by this standard, even in cases of low sequence identity, have a much larger chance of having similar function than the randomly chosen protein pairs. Interesting exceptions to these rules are discussed.

Algorithms↗
PubMed2000

Prediction of a novel RNA 2'-O-ribose methyltransferase subfamily encoded by the Escherichia coli YgdE open reading frame and its orthologs.

The amino acid sequence of the RNA 2'-O-ribose methyltranserase RrmJ was used as a probe for detecting putative homologs through iterative searches of genomic databases. We found a previously unannotated YgdE open reading frame (ORF) in the genome sequences of Escherichia coli and other gamma-Proteobacteria, which shares key features with RrmJ, despite the mutual sequence similarity of these proteins is relatively low. The predicted structural compatibility and the conservation of all functionally important residues between RrmJ and YgdE strongly suggests that the newly identified methyltranserase also modifies 2'-OH groups of ribose. The N-terminal region of YgdE, which has no counterpart in RrmJ, is predicted to form an independent domain, possibly involved in target recognition.

Amino Acid Sequence↗
PubMed1999

The Helicobacter pylori genome: from sequence analysis to structural and functional predictions.

Fold assignments for proteins from the Helicobacter pylori genome are carried out using BASIC, a profile-profile alignment algorithm recently tested on the Mycoplasma genitalium and Escherichia coli genomes. The fold assignments are followed by automated function evaluation, based on the multilevel description of functional sites in proteins. Over 40% of the proteins encoded in the H. pylori genome can be recognized as belonging to a protein family with known structure. Previous estimates suggested that only 10-15% of genome proteins could be characterized this way. This dramatic increase in the number of recognized homologies between H. pylori proteins and structurally characterized protein families is partly due to the rapid increase of the database of known protein structures, but mostly it is due to the significant improvement in prediction algorithms. Knowledge of a protein fold adds a new dimension to our understanding of its function and, similarly, structure prediction can also add to understanding, verification, and/or prediction of function for uncharacterized proteins. Several examples analyzed in more detail in this article illustrate insights that can be achieved from structure and detailed function prediction.

Algorithms↗
PubMed1999

CAFASP-1: critical assessment of fully automated structure prediction methods.

The results of the first Critical Assessment of Fully Automated Structure Prediction (CAFASP-1) are presented. The objective was to evaluate the success rates of fully automatic web servers for fold recognition which are available to the community. This study was based on the targets used in the third meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP-3). However, unlike CASP-3, the study was not a blind trial, as it was held after the structures of the targets were known. The aim was to assess the performance of methods without the user intervention that several groups used in their CASP-3 submissions. Although it is clear that "human plus machine" predictions are superior to automated ones, this CAFASP-1 experiment is extremely valuable for users of our methods; it provides an indication of the performance of the methods alone, and not of the "human plus machine" performance assessed in CASP. This information may aid users in choosing which programs they wish to use and in evaluating the reliability of the programs when applied to their specific prediction targets. In addition, evaluation of fully automated methods is particularly important to assess their applicability at genomic scales. For each target, groups submitted the top-ranking folds generated from their servers. In CAFASP-1 we concentrated on fold-recognition web servers only and evaluated only recognition of the correct fold, and not, as in CASP-3, alignment accuracy. Although some performance differences appeared within each of the four target categories used here, overall, no single server has proved markedly superior to the others. The results showed that current fully automated fold recognition servers can often identify remote similarities when pairwise sequence search methods fail. Nevertheless, in only a few cases outside the family-level targets has the score of the top-ranking fold been significant enough to allow for a confident fully automated prediction. Because the goals, rules, and procedures of CAFASP-1 were different from those used at CASP-3, the results reported here are not comparable with those reported in CASP-3. Nevertheless, it is clear that current automated fold recognition methods can not yet compete with "human-expert plus machine" predictions. Finally, CAFASP-1 has been useful in identifying the requirements for a future blind trial of automated served-based protein structure prediction.

Algorithms↗
PubMed1999

Functional insights from structural predictions: analysis of the Escherichia coli genome.

Fold assignments for proteins from the Escherichia coli genome are carried out using BASIC, a profile-profile alignment algorithm, recently tested on fold recognition benchmarks and on the Mycoplasma genitalium genome and PSI BLAST, the newest generation of the de facto standard in homology search algorithms. The fold assignments are followed by automated modeling and the resulting three-dimensional models are analyzed for possible function prediction. Close to 30% of the proteins encoded in the E. coli genome can be recognized as homologous to a protein family with known structure. Most of these homologies (23% of the entire genome) can be recognized both by PSI BLAST and BASIC algorithms, but the latter recognizes an additional 260 homologies. Previous estimates suggested that only 10-15% of E. coli proteins can be characterized this way. This dramatic increase in the number of recognized homologies between E. coli proteins and structurally characterized protein families is partly due to the rapid increase of the database of known protein structures, but mostly it is due to the significant improvement in prediction algorithms. Knowing protein structure adds a new dimension to our understanding of its function and the predictions presented here can be used to predict function for uncharacterized proteins. Several examples, analyzed in more detail in this paper, include the DPS protein protecting DNA from oxidative damage (predicted to be homologous to ferritin with iron ion acting as a reducing agent) and the ahpC/tsa family of proteins, which provides resistance to various oxidating agents (predicted to be homologous to glutathione peroxidase).

Algorithms↗
PubMed1999

From fold predictions to function predictions: automation of functional site conservation analysis for functional genome predictions.

A database of functional sites for proteins with known structures, SITE, is constructed and used in conjunction with a simple pattern matching program SiteMatch to evaluate possible function conservation in a recently constructed database of fold predictions for Escherichia coli proteins (Rychlewski L et al., 1999, Protein Sci 8:614-624). In this and other prediction databases, fold predictions are based on algorithms that can recognize weak sequence similarities and putatively assign new proteins into already characterized protein families. It is not clear whether such sequence similarities arise from distant homologies or general similarity of physicochemical features along the sequence. Leaving aside the important question of nature of relations within fold superfamilies, it is possible to assess possible function conservation by looking at the pattern of conservation of crucial functional residues. SITE consists of a multilevel function description based on structure annotations and structure analyses. In particular, active site residues, ligand binding residues, and patterns of hydrophobic residues on the protein surface are used to describe different functional features. SiteMatch, a simple pattern matching program, is designed to check the conservation of residues involved in protein activity in alignments generated by any alignment method. Here, this procedure is used to study conservation of functional features in alignments between protein sequences from the E. coli genome and their optimal structural templates. The optimal templates were identified and alignments taken from the database of genomic structural predictions was described in a previous publication (Rychlewski L et al., 1999, Protein Sci 8:614-624). An automated assessment of function conservation is used to analyze the relation between fold and function similarity for a large number of fold predictions. For instance, it is shown that identifying low significance predictions with a high level of functional residue conservations can be used to extend the prediction sensitivity for fold prediction methods. Over 100 new fold/function predictions in this class were obtained in the E. coli genome. At the same time, about 30% of our previous fold predictions are not confirmed as function predictions, further highlighting the problem of function divergence in fold superfamilies.

Algorithms↗
PubMed1998

Fold prediction by a hierarchy of sequence, threading, and modeling methods.

Several fold recognition algorithms are compared to each other in terms of prediction accuracy and significance. It is shown that on standard benchmarks, hybrid methods, which combine scoring based on sequence-sequence and sequence-structure matching, surpass both sequence and threading methods in the number of accurate predictions. However, the sequence similarity contributes most to the prediction accuracy. This strongly argues that most examples of apparently nonhomologous proteins with similar folds are actually related by evolution. While disappointing from the perspective of the fundamental understanding of protein folding, this adds a new significance to fold recognition methods as a possible first step in function prediction. Despite hybrid methods being more accurate at fold prediction than either the sequence or threading methods, each of the methods is correct in some cases where others have failed. This partly reflects a different perspective on sequence/structure relationship embedded in various methods. To combine predictions from different methods, estimates of significance of predictions are made for all methods. With the help of such estimates, it is possible to develop a "jury" method, which has accuracy higher than any of the single methods. Finally, building full three-dimensional models for all top predictions helps to eliminate possible false positives where alignments, which are optimal in the one-dimensional sequences, lead to unsolvable sterical conflicts for the full three-dimensional models.

Algorithms↗
PubMed1998

Fold and function predictions for Mycoplasma genitalium proteins.

BACKGROUND: Uncharacterized proteins from newly sequenced genomes provide perfect targets for fold and function prediction. RESULTS: For 38% of the entire genome of Mycoplasma genitalium, sequence similarity to a protein with a known structure can be recognized using a new sequence alignment algorithm. When comparing genomes of M. genitalium and Escherichia coli, > 80% of M. genitalium proteins have a significant sequence similarity to a protein in E. coli and there are > 40 examples that have not been recognized before. For all cases of proteins with significant profile similarities, there are strong analogies in their functions, if the functions of both proteins are known. The results presented here and other recent results strongly support the argument that such proteins are actually homologous. Assuming this homology allows one to make tentative functional assignments for > 50 previously uncharacterized proteins, including such intriguing cases as the putative beta-lactam antibiotic resistance protein in M. gentalium. CONCLUSIONS: Using a new profile-to-profile alignment algorithm, the three-dimensional fold can be predicted for almost 40% of proteins from a genome of the small bacterium M. genitalium, and tentative function can be assigned to almost 80% of the entire genome. Some predictions lead to new insights about known functions or point to hitherto unexpected features of M. genitalium.

Bacterial Proteins↗
PubMed1997

Similarities and differences between nonhomologous proteins with similar folds: evaluation of threading strategies.

BACKGROUND: There are many pairs and groups of proteins with similar folds and interaction patterns, but whose sequence similarity is below the threshold of easily recognizable sequence homology. The existence of multiple sequence solutions for a given fold has inspired fold prediction methods in which structural information from one protein is used to estimate the energy of another, putatively similar, structure. RESULTS: A set of 68 pairs of proteins with similar folds and sequence identity in the 8-30% range is identified from the literature. for each pair, the energy of one protein, calculated using knowledge-based statistical potentials, is compared to the estimated energy, calculated with the same potentials but using the structural information (burial status and interaction pattern) of another protein with the same fold. Different energy estimates, corresponding to approximations used in various fold recognition algorithms, are calculated and compared to each other, as well as to the correct energy. It is shown that the local energy terms, based on burial and secondary structure preferences, can be reliably estimated with an accuracy close to 70%. At the same time, the two-body nonlocal energy loses over 60% of its value due to the repacking of the structure. Further approximations, such as the 'frozen approximation', can bring it to an essentially random value. CONCLUSIONS: Local energy terms could be used safely to improve fold recognition algorithms. To utilize pair interaction information, specially designed pair potentials and/or a self-consistent description of pair interactions is necessary.

Algorithms↗
PubMed1997

Secondary structure prediction using segment similarity.

We present a secondary structure prediction method based on finding similarities between sequence segments from the target sequence and segments contained in the database of proteins with known structures. The similarity definition is optimized using a genetic algorithm and is based on a 21 x 40 similarity matrix, comparing a target sequence with the sequence and burial status of the proteins from the database. The three-state secondary structure prediction accuracy reaches 72.4% on a non homologous (maximum sequence identity <25%) data set derived from PDB and is reproduced on two independent testing sets, including the set of CASP2 prediction targets and a group of newly solved PDB structures. The prediction method was developed with simplicity and open architecture in mind, allowing for an easy extension to other types of predictions and to the analysis of the contributions to the local structure formation. For instance, the design of the prediction procedure allows us to trace back segments of the database that contributed to the prediction. It can be shown that those segments came from various structural classes and that even complete exclusion of related folds from the database does not result in a significant decrease in prediction accuracy.

Databases, Factual↗
PubMedDate not supplied

Fold predictions for bacterial genomes.

Fold assignments for newly sequenced genomes belong to the most important and interesting applications of the booming field of protein structure prediction. We present a brief survey and a discussion of such assignments completed to date, using as an example several fold assignment projects for proteins from the Escherichia coli genome. This review focuses on steps that are necessary to go beyond the simple assignment projects and into the development of tools extending our understanding of functions of proteins in newly sequenced genomes. This paper also discusses several problems seldom addressed in the literature, such as the problem of domain prediction and complementary predictions (e.g., transmembrane regions and flexible regions) and cross-correlation of predictions from different servers. The influence of sequence and structure database growth on prediction success is also addressed. Finally, we discuss the perspectives of the field in the context of massive sequence and structure determination projects, as well as the development of novel prediction methods.

Algorithms↗