Combining evolutionary and structural information for local protein structure prediction

In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia

Background:

Acute myeloid leukemia (AML) is an extremely heterogeneous malignant disorder; AML has been reported as one of the main causes of death in children. The objective of this work was to classify the most deleterious mutation in CCAAT/enhancer-binding protein-alpha (CEBPA) and to predict their influence on the functional, structural, and expression levels by various Bioinformatics analysis tools.

Methods:

The single nucleotide polymorphisms (SNPs) were claimed from the National Center for Biotechnology Information (NCBI) database and then submitted into various functional analysis tools, which were done to predict the influence of each SNP, followed by structural analysis of modeled protein followed by predicting the mutation effect on energy stability; the most damaging mutations were chosen for additional investigation by Mutation3D, Project hope, ConSurf, BioEdit, and UCSF Chimera tools.

Results:

A total of 5 mutations out of 248 were likely to be responsible for the structural and functional variations in CEBPA protein, whereas in the 3′-untranslated region (3′-UTR) the result showed that among 350 SNPs in the 3′-UTR of CEBPA gene, about 11 SNPs were predicted. Among these 11 SNPs, 65 alleles disrupted a conserved miRNA site and 22 derived alleles created a new site of miRNA.

Conclusions:

In this study, the impact of functional mutations in the CEBPA gene was investigated through different bioinformatics analysis techniques, which determined that R339W, R288P, N292S, N292T, and D63N are pathogenic mutations that have a possible functional and structural influence, therefore, could be used as genetic biomarkers and may assist in genetic studies with a special consideration of the large heterogeneity of AML.

Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.

From local structure to a global framework: recognition of protein folds

Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/

Convergent evolution in structural elements of proteins investigated using cross profile analysis

Background

Evolutionary relations of similar segments shared by different protein folds remain controversial, even though many examples of such segments have been found. To date, several methods such as those based on the results of structure comparisons, sequence-based classifications, and sequence-based profile-profile comparisons have been applied to identify such protein segments that possess local similarities in both sequence and structure across protein folds. However, to capture more precise sequence-structure relations, no method reported to date combines structure-based profiles, and sequence-based profiles based on evolutionary information. The former are generally regarded as representing the amino acid preferences at each position of a specific conformation of protein segment. They might reflect the nature of ancient short peptide ancestors, using the results of structural classifications of protein segments.

Results

This report describes the development and use of "Cross Profile Analysis" to compare sequence-based profiles and structure-based profiles based on amino acid occurrences at each position within a protein segment cluster. Using systematic cross profile analysis, we found structural clusters of 9-residue and 15-residue segments showing remarkably strong correlation with particular sequence profiles. These correlations reflect structural similarities among constituent segments of both sequence-based and structure-based profiles. We also report previously undetectable sequence-structure patterns that transcend protein family and fold boundaries, and present results of the conformational analysis of the deduced peptide of a segment cluster. These results suggest the existence of ancient short-peptide ancestors.

Conclusions

Cross profile analysis reveals the polyphyletic and convergent evolution of β-hairpin-like structures, which were verified both experimentally and computationally. The results presented here give us new insights into the evolution of short protein segments.

A new prediction strategy for long local protein structures using an original description

A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions.

Prediction of protein local structures and folding fragments based on building-block library

In recent years, protein structure prediction using local structure information has made great progress. In this study, a novel and effective method is developed to predict the local structure and the folding fragments of proteins. First, the proteins with known structures are split into fragments. Second, these fragments, represented by dihedrals, are clustered to produce the building blocks (BBs). Third, an efficient machine learning method is used to predict the local structures of proteins from sequence profiles. Finally, a bi-gram model, trained by an iterated algorithm, is introduced to simulate the interactions of these BBs. For test proteins, the building-block lattice is constructed, which contains all the folding fragments of the proteins. The local structures and the optimal fragments are then obtained by the dynamic programming algorithm. The experiment is performed on a subset of the PDB database with sequence identity less than 25%. The results show that the performance of the method is better than the method that uses only sequence information. When multiple paths are returned, the average classification accuracy of local structures is 72.27% and the average prediction accuracy of local structures is 67.72%, which is a significant improvement in comparison with previous studies. The method can predict not only the local structures but also the folding fragments of proteins. This work is helpful for the ab initio protein structure prediction and especially, the understanding of the folding process of proteins.

"Pinning strategy": a novel approach for predicting the backbone structure in terms of protein blocks from sequence

The description of protein 3D structures can be performed through a library of 3D fragments, named a structural alphabet. Our structural alphabet is composed of 16 small protein fragments of 5 C alpha in length, called protein blocks (PBs). It allows an efficient approximation of the 3D protein structures and a correct prediction of the local structure. The 72 most frequent series of 5 consecutive PBs, called structural words (SWs)are able to cover more than 90% of the 3D structures. PBs are highly conditioned by the presence of a limited number of transitions between them. In this study, we propose a new method called "pinning strategy" that used this specific feature to predict long protein fragments. Its goal is to define highly probable successions of PBs. It starts from the most probable SW and is then extended with overlapping SWs. Starting from an initial prediction rate of 34.4%, the use of the SWs instead of the PBs allows a gain of 4.5%. The pinning strategy simply applied to the SWs increases the prediction accuracy to 39.9%. In a second step, the sequence-structure relationship is optimized, the prediction accuracy reaches 43.6%.

Involvement of some large immunophilins and their ligands in the protection and regeneration of neurons: a hypothetical mode of action

The powerful immunosuppressive drugs such as FK506 and its derivatives induce some regeneration and protection of neurons from ischaemic brain injury and some other neurological disorders. The drugs form complexes with diverse FKBPs but apparently the FKBP52/FK506 complex was shown to be involved in the protection and regeneration of neurons. We used several different sequence attributes in searching diverse genomic databases for similar motifs as those present in the FKBPs. A Fortran library of algorithms (Par_Seq) has been designed and used in searching for the similarity of sequence motifs extracted from the multiple sequence alignments of diverse groups of proteins (query motifs) and the target motifs which are encoded in various genomes. The following sequence attributes were used in the establishment of the degree of convergence between: (A) amino acid (AA) sequence similarity (ID) of the query/target motifs and (B) their: (1) AA composition (AAC); (2) hydrophobicity (HI); (3) Jensen-Shannon entropy; and (4) AA propensity to form a particular secondary structure. The sequence hallmark of two different groups of peptidylprolyl cis/trans isomerases (PPIases), namely tetratricopetide repeat (TPR) motifs, which are present in the heat-shock cyclophilins and in the large FK506-binding proteins (FKBPs) were used to search various genomic databases. The Par_Seq algorithm has revealed that the TPR motifs have similar sequence attributes as a number of hydrophobic sequence segments of functionally unrelated membrane proteins, including some of the TMs from diverse G protein-coupled receptors (GPCRs). It is proposed that binding of the FKBP52/FK506 complex to the membranes via the TPR motifs and its interaction with some membrane proteins could be in part responsible for some neuro-regeneration and neuro-protection of the brain during some ischaemia-induced stresses.

MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information

We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10 000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments.

Empirical potential function for simplified protein models: combining contact and local sequence-structure descriptors

An effective potential function is critical for protein structure prediction and folding simulation. Simplified protein models such as those requiring only Calpha or backbone atoms are attractive because they enable efficient search of the conformational space. We show residue-specific reduced discrete-state models can represent the backbone conformations of proteins with small RMSD values. However, no potential functions exist that are designed for such simplified protein models. In this study, we develop optimal potential functions by combining contact interaction descriptors and local sequence-structure descriptors. The form of the potential function is a weighted linear sum of all descriptors, and the optimal weight coefficients are obtained through optimization using both native and decoy structures. The performance of the potential function in a test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. Our potential function requiring only backbone atoms or Calpha atoms have comparable or better performance than several residue-based potential functions that require additional coordinates of side-chain centers or coordinates of all side-chain atoms. By reducing the residue alphabets down to size 10 for contact descriptors, the performance of the potential function can be further improved. Our results also suggest that local sequence-structure correlation may play important role in reducing the entropic cost of protein folding.

Assessing a novel approach for predicting local 3D protein structures from sequence

We developed a novel approach for predicting local protein structure from sequence. It relies on the Hybrid Protein Model (HPM), an unsupervised clustering method we previously developed. This model learns three-dimensional protein fragments encoded into a structural alphabet of 16 protein blocks (PBs). Here, we focused on 11-residue fragments encoded as a series of seven PBs and used HPM to cluster them according to their local similarities. We thus built a library of 120 overlapping prototypes (mean fragments from each cluster), with good three-dimensional local approximation, i.e., a mean accuracy of 1.61 A Calpha root-mean-square distance. Our prediction method is intended to optimize the exploitation of the sequence-structure relations deduced from this library of long protein fragments. This was achieved by setting up a system of 120 experts, each defined by logistic regression to optimize the discrimination from sequence of a given prototype relative to the others. For a target sequence window, the experts computed probabilities of sequence-structure compatibility for the prototypes and ranked them, proposing the top scorers as structural candidates. Predictions were defined as successful when a prototype <2.5 A from the true local structure was found among those proposed. Our strategy yielded a prediction rate of 51.2% for an average of 4.2 candidates per sequence window. We also proposed a confidence index to estimate prediction quality. Our approach predicts from sequence alone and will thus provide valuable information for proteins without structural homologs. Candidates will also contribute to global structure prediction by fragment assembly.

A permissive secondary structure-guided superposition tool for clustering of protein fragments toward protein structure prediction via fragment assembly

MOTIVATION

Secondary-Structure Guided Superposition tool (SSGS) is a permissive secondary structure-based algorithm for matching of protein structures and in particular their fragments. The algorithm was developed towards protein structure prediction via fragment assembly.

RESULTS

In a fragment-based structural prediction scheme, a protein sequence is cut into building blocks (BBs). The BBs are assembled to predict their relative 3D arrangement. Finally, the assemblies are refined. To implement this prediction scheme, a clustered structural library representing sequence patterns for protein fragments is essential. To create a library, BBs generated by cutting proteins from the PDB are compared and structurally similar BBs are clustered. To allow structural comparison and clustering of the BBs, which are often relatively short with flexible loops, we have devised SSGS. SSGS maintains high similarity between cluster members and is highly efficient. When it comes to comparing BBs for clustering purposes, the algorithm obtains better results than other, non-secondary structure guided protein superimposition algorithms.

A structural alphabet for local protein structures: improved prediction methods

Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%.

Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method

MOTIVATION

The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data.

RESULTS

We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins.

AVAILABILITY

Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/

CONTACT

jul@ssu.ac.kr.

Estimates of statistical significance for comparison of individual positions in multiple sequence alignments

Background

Profile-based analysis of multiple sequence alignments (MSA) allows for accurate comparison of protein families. Here, we address the problems of detecting statistically confident dissimilarities between (1) MSA position and a set of predicted residue frequencies, and (2) between two MSA positions. These problems are important for (i) evaluation and optimization of methods predicting residue occurrence at protein positions; (ii) detection of potentially misaligned regions in automatically produced alignments and their further refinement; and (iii) detection of sites that determine functional or structural specificity in two related families.

Results

For problems (1) and (2), we propose analytical estimates of P-value and apply them to the detection of significant positional dissimilarities in various experimental situations. (a) We compare structure-based predictions of residue propensities at a protein position to the actual residue frequencies in the MSA of homologs. (b) We evaluate our method by the ability to detect erroneous position matches produced by an automatic sequence aligner. (c) We compare MSA positions that correspond to residues aligned by automatic structure aligners. (d) We compare MSA positions that are aligned by high-quality manual superposition of structures. Detected dissimilarities reveal shortcomings of the automatic methods for residue frequency prediction and alignment construction. For the high-quality structural alignments, the dissimilarities suggest sites of potential functional or structural importance.

Conclusion

The proposed computational method is of significant potential value for the analysis of protein families.