Secondary structure-based profiles: use of structure-conserving scoring tables in searching protein sequence databases for structural similarities

The profile method, for detecting distantly related proteins by sequence comparison, has been extended to incorporate secondary structure information from known X-ray structures. The sequence of a known structure is aligned to sequences of other members of a given folding class. From the known structure, the secondary structure (alpha-helix, beta-strand or "other") is assigned to each position of the aligned sequences. As in the standard profile method, a position-dependent scoring table, termed a profile, is calculated from the aligned sequences. However, rather than using the standard Dayhoff mutation table in calculating the profile, we use distinct amino acid mutation tables for residues in alpha-helices, beta-strands or other secondary structures to calculate the profile. In addition, we also distinguish between internal and external residues. With this new secondary structure-based profile method, we created a profile for eight-stranded, antiparallel beta barrels of the insecticyanin folding class. It is based on the sequences of retinol-binding protein, insecticyanin and beta-lactoglobulin. Scanning the sequence database with this profile, it was possible to detect the sequence of avidin. The structure of streptavidin is known, and it appears to be distantly related to the antiparallel beta barrels. Also detected is the sequence of complement component C8, which we therefore predict to be a member of this folding class.     

Evolutionary information hidden in a single protein structure

The knowledge of conserved sequences in proteins is valuable in identifying functionally or structurally important residues. Generating the conservation profile of a sequence requires aligning families of homologous sequences and having knowledge of their evolutionary relationships. Here, we report that the conservation profile at the residue level can be quantitatively derived from a single protein structure with only backbone information. We found that the reciprocal packing density profiles of protein structures closely resemble their sequence conservation profiles. For a set of 554 nonhomologous enzymes, 74% (408/554) of the proteins have a correlation coefficient > 0.5 between these two profiles. Our results indicate that the three-dimensional structure, instead of being a mere scaffold for positioning amino acid residues, exerts such strong evolutionary constraints on the residues of the protein that its profile of sequence conservation essentially reflects that of its structural characteristics.     

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles

Locating sequences compatible with a protein structural fold is the well‐known inverse protein‐folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy‐optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment‐derived sequence profiles and structure‐derived energy profiles. SPIN improves over the fragment‐derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild‐type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single‐body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks‐lab.org . Proteins 2014; 82:2565–2573. © 2014 Wiley Periodicals, Inc.     

Protein homology detection and fold inference through multiple alignment entropy profiles

Homology detection and protein structure prediction are central themes in bioinformatics. Establishment of relationship between protein sequences or prediction of their structure by sequence comparison methods finds limitations when there is low sequence similarity. Recent works demonstrate that the use of profiles improves homology detection and protein structure prediction. Profiles can be inferred from protein multiple alignments using different approaches. The "Conservatism-of-Conservatism" is an effective profile analysis method to identify structural features between proteins having the same fold but no detectable sequence similarity. The information obtained from protein multiple alignments varies according to the amino acid classification employed to calculate the profile. In this work, we calculated entropy profiles from PSI-BLAST-derived multiple alignments and used different amino acid classifications summarizing almost 500 different attributes. These entropy profiles were converted into pseudocodes which were compared using the FASTA program with an ad-hoc matrix. We tested the performance of our method to identify relationships between proteins with similar fold using a nonredundant subset of sequences having less than 40% of identity. We then compared our results using Coverage Versus Error per query curves, to those obtained by methods like PSI-BLAST, COMPASS and HHSEARCH. Our method, named HIP (Homology Identification with Profiles) presented higher accuracy detecting relationships between proteins with the same fold. The use of different amino acid classifications reflecting a large number of amino acid attributes, improved the recognition of distantly related folds. We propose the use of pseudocodes representing profile information as a fast and powerful tool for homology detection, fold assignment and analysis of evolutionary information enclosed in protein profiles.     

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.     

Probabilistic alignment detects remote homology in a pair of protein sequences without homologous sequence information

Dynamic programming (DP) and its heuristic algorithms are the most fundamental methods for similarity searches of amino acid sequences. Their detection power has been improved by including supplemental information, such as homologous sequences in the profile method. Here, we describe a method, probabilistic alignment (PA), that gives improved detection power, but similarly to the original DP, uses only a pair of amino acid sequences. Receiver operating characteristic (ROC) analysis demonstrated that the PA method is far superior to BLAST, and that its sensitivity and selectivity approach to those of PSI-BLAST. Particularly for orphan proteins having few homologues in the database, PA exhibits much better performance than PSI-BLAST. On the basis of this observation, we applied the PA method to a homology search of two orphan proteins, Latexin and Resuscitation-promoting factor domain. Their molecular functions have been described based on structural similarities, but sequence homologues have not been identified by PSI-BLAST. PA successfully detected sequence homologues for the two proteins and confirmed that the observed structural similarities are the result of an evolutional relationship.     

Prediction of amino acid sequence from structure

We have developed a method for the prediction of an amino acid sequence that is compatible with a three-dimensional backbone structure. Using only a backbone structure of a protein as input, the algorithm is capable of designing sequences that closely resemble natural members of the protein family to which the template structure belongs. In general, the predicted sequences are shown to have multiple sequence profile scores that are dramatically higher than those of random sequences, and sometimes better than some of the natural sequences that make up the superfamily. As anticipated, highly conserved but poorly predicted residues are often those that contribute to the functional rather than structural properties of the protein. Overall, our analysis suggests that statistical profile scores of designed sequences are a novel and valuable figure of merit for assessing and improving protein design algorithms.     

Improving computational protein design by using structure‐derived sequence profile

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main‐chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five‐residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino‐acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign‐SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild‐type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi‐blast. More importantly, the sequences designed by RosettaDesign‐SR have 2–3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild‐type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign‐SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Ethanol utilization regulatory protein: profile alignments give no evidence of origin through aldehyde and alcohol dehydrogenase gene fusion.

The suggestion that the ethanol regulatory protein from Aspergillus has its evolutionary origin in a gene fusion between aldehyde and alcohol dehydrogenase genes (Hawkins AR, Lamb HK, Radford A, Moore JD, 1994, Gene 146:145-158) has been tested by profile analysis with aldehyde and alcohol dehydrogenase family profiles. We show that the degree and kind of similarity observed between these profiles and the ethanol regulatory protein sequence is that expected from random sequences of the same composition. This level of similarity fails to support the suggested gene fusion.     

Protein subcellular localization based on PSI-BLAST and machine learning

Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.     

Designing gene libraries from protein profiles for combinatorial protein experiments

Protein combinatorial libraries provide new ways to probe the determinants of folding and to discover novel proteins. Such libraries are often constructed by expressing an ensemble of partially random gene sequences. Given the intractably large number of possible sequences, some limitation on diversity must be imposed. A non-uniform distribution of nucleotides can be used to reduce the number of possible sequences and encode peptide sequences having a predetermined set of amino acid probabilities at each residue position, i.e., the amino acid sequence profile. Such profiles can be determined by inspection, multiple sequence alignment or physically-based computational methods. Here we present a computational method that takes as input a desired sequence profile and calculates the individual nucleotide probabilities among partially random genes. The calculated gene library can be readily used in the context of standard DNA synthesis to generate a protein library with essentially the desired profile. The fidelity between the desired profile and the calculated one coded by these partially random genes is quantitatively evaluated using the linear correlation coefficient and a relative entropy, each of which provides a measure of profile agreement at each position of the sequence. On average, this method of identifying such codon frequencies performs as well or better than other methods with regard to fidelity to the original profile. Importantly, the method presented here provides much better yields of complete sequences that do not contain stop codons, a feature that is particularly important when all or large fractions of a gene are subject to combinatorial mutation.     

Multicoil2: predicting coiled coils and their oligomerization states from sequence in the twilight zone

The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu.     

Scoring profile-to-profile sequence alignments

Sequence alignment profiles have been shown to be very powerful in creating accurate sequence alignments. Profiles are often used to search a sequence database with a local alignment algorithm. More accurate and longer alignments have been obtained with profile-to-profile comparison. There are several steps that must be performed in creating profile-profile alignments, and each involves choices in parameters and algorithms. These steps include (1) what sequences to include in a multiple alignment used to build each profile, (2) how to weight similar sequences in the multiple alignment and how to determine amino acid frequencies from the weighted alignment, (3) how to score a column from one profile aligned to a column of the other profile, (4) how to score gaps in the profile-profile alignment, and (5) how to include structural information. Large-scale benchmarks consisting of pairs of homologous proteins with structurally determined sequence alignments are necessary for evaluating the efficacy of each scoring scheme. With such a benchmark, we have investigated the properties of profile-profile alignments and found that (1) with optimized gap penalties, most column-column scoring functions behave similarly to one another in alignment accuracy; (2) some functions, however, have much higher search sensitivity and specificity; (3) position-specific weighting schemes in determining amino acid counts in columns of multiple sequence alignments are better than sequence-specific schemes; (4) removing positions in the profile with gaps in the query sequence results in better alignments; and (5) adding predicted and known secondary structure information improves alignments.     

Relationships among amino acid sequences of animal,microbial and plant peroxidases

Summary Relationships among 18 peroxidases amino acid sequences of animal, microbial and plant origin were examined using optimum alignment of all pairwise sequence combinations to generate a total distance matrix. The matrix was used to cluster the sequences with complete linkage (farthest neighbour) procedures. Specific distances were calculated from the total distances matrix. The patterns of specific distances for each sequence were compared to evaluate the relationships between sequences, check their significance and construct subgroups of related sequences. The results were compared with those from clustering and its resultant dendrogram; good agreement was achieved. The 18 sequences fell into two principal groups, plant peroxidases and animal/microbial peroxidases. Within the plant peroxidases four subgroups were detected; the animal/microbial peroxidases formed a fifth subgroup. Profiles were constructed for the subgroups from lists of matching amino acids generated by the alignment calculations. Superimposed lists were realigned to recognise conserved areas and elements. Individual subgroup profiles for the plant peroxidases were then combined into a single profile which in turn was combined with profiles from the animal/microbial peroxidases. The final profile suggested that numerous sequence features (motifs) were common to peroxidases of widely different function and origins.     

The conservation profile of a protein bears the imprint of the molecule that is evolutionarily coupled to the protein

The conservation profile of a protein is a curve of the conservation levels of amino acids along the sequence. Biologists are usually more interested in individual points on the curve (namely, the conserved amino acids) than the overall shape of the curve. Here, we show that the conservation curves of proteins bear the imprints of molecules that are evolutionarily coupled to the proteins. Our method is based on recent studies that a sequence conservation profile is quantitatively linked to its structural packing profile. We find that the conservation profiles of nucleic acid (NA) binding proteins are better correlated with the packing profiles of the protein–NA complexes than those of the proteins alone. This indicates that a nucleic acid binding protein evolves to accommodate the nucleic acid in such a way that the residues involved in binding have their conservation levels closely coupled with the specific nucleotides. Proteins 2015; 83:1407–1413. © 2015 Wiley Periodicals, Inc.     

Modes of Action of Five Different Endopectate Lyases from Erwinia chrysanthemi 3937

Five endopectate lyases from the phytopathogenic bacterium Erwinia chrysanthemi, PelA, PelB, PelD, PelI, and PelL, were analyzed with respect to their modes of action on polymeric and oligomeric substrates (degree of polymerization, 2 to 8). On polygalacturonate, PelB showed higher reaction rates than PelD, PelI, and PelA, whereas the reaction rates for PelL were extremely low. The product progression during polygalacturonate cleavage showed a typical depolymerization profile for each enzyme and demonstrated their endolytic character. PelA, PelI, and PelL released oligogalacturonates of different sizes, whereas PelD and PelB released mostly unsaturated dimer and unsaturated trimer, respectively. Upon prolonged incubation, all enzymes degraded the primary products further, to unsaturated dimer and trimer, except for PelL, which degraded the primary products to unsaturated tetramer and pentamer in addition to unsaturated dimer and trimer. The bond cleavage frequencies on oligogalacturonates revealed differences in the modes of action of these enzymes that were commensurate with the product progression profiles. The preferential products formed from the oligogalacturonates were unsaturated dimer for PelD, unsaturated trimer for PelB, and unsaturated tetramer for PelI and PelL. For PelA, preferential products were dependent on the sizes of the oligogalacturonates. Whereas PelB and PelD displayed their highest activities on hexagalacturonate and tetragalacturonate, respectively, PelA, PelI, and PelL were most active on the octamer, the largest substrate used. The bond cleavage frequencies and reaction rates were used to estimate the number of subsites of each enzyme.