A top-down approach to classify enzyme functional classes and sub-classes using random forest

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.     

The roles of contact residue disorder and domain composition in characterizing protein-ligand binding specificity and promiscuity

Most protein chains interact with only one ligand but a small number of protein chains can bind several ligands, and many examples are available in the protein-ligand complex database of PDB. Among these proteins, some show preferences for the ligands or types of ligands they bind; however, so far we have only poor understanding of what determines protein-ligand binding and its specificity. Here we investigate the structural and functional properties of proteins in protein-ligand complexes. Analysis of the protein-ligand complex dataset from the PDB structure database reveals that proteins with more interactions have more disordered contact residues. Those proteins containing few disordered contact residues that bind multiple ligands have a tendency to consist of several domains. Analysis of physicochemical properties of hub contact residues binding multiple ligands indicates that they are enriched for hydrophilic, charged, polar and His-Asp catalytic triad residues. Finally, in order to differentiate proteins binding different classes of ligands, we mapped the three most prominent classes of ligands onto different superfamily domains. Our results demonstrate that contact residue disorder and ordered multiple domains are complementary factors that play a crucial role in determining ligand binding specificity and promiscuity.     

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.     

A new method to detect related function among proteins independent of sequence and fold homology

A new method has been developed to detect functional relationships among proteins independent of a given sequence or fold homology. It is based on the idea that protein function is intimately related to the recognition and subsequent response to the binding of a substrate or an endogenous ligand in a well-characterized binding pocket. Thus, recognition of similar ligands, supposedly linked to similar function, requires conserved recognition features exposed in terms of common physicochemical interaction properties via the functional groups of the residues flanking a particular binding cavity. Following a technique commonly used in the comparison of small molecule ligands, generic pseudocenters coding for possible interaction properties were assigned for a large sample set of cavities extracted from the entire PDB and stored in the database Cavbase. Using a particular query cavity a series of related cavities of decreasing similarity is detected based on a clique detection algorithm. The detected similarity is ranked according to property-based surface patches shared in common by the different clique solutions. The approach either retrieves protein cavities accommodating the same (e.g. co-factors) or closely related ligands or it extracts proteins exhibiting similar function in terms of a related catalytic mechanism. Finally the new method has strong potential to suggest alternative molecular skeletons in de novo design. The retrieval of molecular building blocks accommodated in a particular sub-pocket that shares similarity with the pocket in a protein studied by drug design can inspire the discovery of novel ligands.     

Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions

Knowledge discovery from the exponentially growing body of structurally characterised protein-ligand complexes as a source of information in structure-based drug design is a major challenge in contemporary drug research. Given the need for powerful data retrieval, integration and analysis tools, Relibase was developed as a database system particularly designed to handle protein-ligand related problems and tasks. Here, we describe the design and functionality of the Relibase core database system. Features of Relibase include, e.g. the detailed analysis of superimposed ligand binding sites, ligand similarity and substructure searches, and 3D searches for protein-ligand and protein-protein interaction patterns. The broad range of functions provided in Relibase and its high level of data integration, along with its flexible and intuitive interface, makes Relibase an invaluable data mining tool which can significantly enhance the drug development process. An example, illustrating a 3D query for quarternary ligand nitrogen atoms interacting with aromatic ring systems in proteins, a pattern found in pharmaceutically relevant target proteins such as, e.g. acetylcholine-esterase, is discussed.     

Comparing protein sequence-based and predicted secondary structure-based methods for identification of remote homologs.

We have compared a novel sequence-structure matching technique, FORESST, for detecting remote homologs to three existing sequence based methods, including local amino acid sequence similarity by BLASTP, hidden Markov models (HMMs) of sequences of protein families using SAM, HMMs based on sequence motifs identified using meta-MEME. FORESST compares predicted secondary structures to a library of structural families of proteins, using HMMs. Altogether 45 proteins from nine structural families in the database CATH were used in a cross-validated test of the fold assignment accuracy of each method. Local sequence similarity of a query sequence to a protein family is measured by the highest segment pair (HSP) score. Each of the HMM-based approaches (FORESST, MEME, amino acid sequence-based HMM) yielded log-odds score for the query sequence. In order to make a fair comparison among these methods, the scores for each method were converted to Z-scores in a uniform way by comparing the raw scores of a query protein with the corresponding scores for a set of unrelated proteins. Z-Scores were analyzed as a function of the maximum pairwise sequence identity (MPSID) of the query sequence to sequences used in training the model. For MPSID above 20%, the Z-scores increase linearly with MPSID for the sequence-based methods but remain roughly constant for FORESST. Below 15%, average Z-scores are close to zero for the sequence-based methods, whereas the FORESST method yielded average Z-scores of 1.8 and 1.1, using observed and predicted secondary structures, respectively. This demonstrates the advantage of the sequence-structure method for detecting remote homologs.     

Function annotation for pseudoknot using structure similarity

Many raw biological sequence data have been generated by the human genome project and related efforts. The understanding of structural information encoded by biological sequences is important to acquire knowledge of their biochemical functions but remains a fundamental challenge. Recent interest in RNA regulation has resulted in a rapid growth of deposited RNA secondary structures in varied databases. However, a functional classification and characterization of the RNA structure have only been partially addressed. This article aims to introduce a novel interval-based distance metric for structure-based RNA function assignment. The characterization of RNA structures relies on distance vectors learned from a collection of predicted structures. The distance measure considers the intersected, disjoint, and inclusion between intervals. A set of RNA pseudoknotted structures with known function are applied and the function of the query structure is determined by measuring structure similarity. This not only offers sequence distance criteria to measure the similarity of secondary structures but also aids the functional classification of RNA structures with pesudoknots.     

Similarity graphing and enzyme-reaction database: methods to detect sequence regions of importance for recognition of chemical structures

We developed a new method which searches sequence segments responsible for the recognition of a given chemical structure. These segments are detected as those locally conserved among a sequence to be analyzed (target sequence) and a set of sequences (reference sequences). Reference sequences are the sequences of functionally related proteins, ligands of which contain a common chemical substructure in their molecular structures. 'Similarity graphing' cuts target sequences into segments, aligns them with reference sequence pairwise, calculates the degree of similarity for each alignment, and shows graphically cumulative similarity values on target sequence. Any locally conserved regions, short or long in length and weak or strong in similarity, are detected at their optimal conditions by adjusting three parameters. The 'enzyme-reaction database' contains chemical structures and their related enzymes. When a chemical substructure is input into the database, sequences of the enzymes related to the input substructure are systematically searched from the NBRF sequence database and output as reference sequences. Examples of analysis using similarity graphing in combination with the enzyme-reaction database showed a great potentiality in the systematic analysis of the relationships between sequences and molecular recognitions for protein engineering.     

Comparison of intrinsic dynamics of cytochrome p450 proteins using normal mode analysis

Cytochrome P450 enzymes are hemeproteins that catalyze the monooxygenation of a wide‐range of structurally diverse substrates of endogenous and exogenous origin. These heme monooxygenases receive electrons from NADH/NADPH via electron transfer proteins. The cytochrome P450 enzymes, which constitute a diverse superfamily of more than 8,700 proteins, share a common tertiary fold but < 25% sequence identity. Based on their electron transfer protein partner, cytochrome P450 proteins are classified into six broad classes. Traditional methods of protein classification are based on the canonical paradigm that attributes proteins’ function to their three‐dimensional structure, which is determined by their primary structure that is the amino acid sequence. It is increasingly recognized that protein dynamics play an important role in molecular recognition and catalytic activity. As the mobility of a protein is an intrinsic property that is encrypted in its primary structure, we examined if different classes of cytochrome P450 enzymes display any unique patterns of intrinsic mobility. Normal mode analysis was performed to characterize the intrinsic dynamics of five classes of cytochrome P450 proteins. The present study revealed that cytochrome P450 enzymes share a strong dynamic similarity (root mean squared inner product > 55% and Bhattacharyya coefficient > 80%), despite the low sequence identity (< 25%) and sequence similarity (< 50%) across the cytochrome P450 superfamily. Noticeable differences in C_α atom fluctuations of structural elements responsible for substrate binding were noticed. These differences in residue fluctuations might be crucial for substrate selectivity in these enzymes.     

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space. The backbone string of a query can be accurately predicted by two innovative technologies: a knowledge-driven sequence alignment and encoding of a backbone string element profile. Then, the predicted backbone string is employed to align against a backbone string database and retrieve a set of backbone string neighbors. The backbone string neighbors were shown to be close to native structures of query proteins. BS-align was successfully employed to predict models of 10 membrane proteins with lengths ranging between 229 and 595 residues, and whose high-resolution structural determinations were difficult to elucidate both by experiment and prediction. The obtained TM-scores and root mean square deviations of the models confirmed that the models based on the backbone string neighbors retrieved by the BS-align were very close to the native membrane structures although the query and the neighbor shared a very low sequence identity. The backbone string system represents a new road for the prediction of protein structure from sequence, and suggests that the similarity of the backbone string would be more informative than describing a protein as belonging to a fold.     

A Similarity-Based Method for Predicting Enzymatic Functions in Yeast Uncovers a New AMP Hydrolase

Despite decades of research and the availability of the full genomic sequence of the baker’s yeast Saccharomyces cerevisiae, still a large fraction of its genome is not functionally annotated. This hinders our ability to fully understand cellular activity and suggests that many additional processes await discovery. The recent years have shown an explosion of high-quality genomic and structural data from multiple organisms, ranging from bacteria to mammals. New computational methods now allow us to integrate these data and extract meaningful insights into the functional identity of uncharacterized proteins in yeast. Here, we created a database of sensitive sequence similarity predictions for all yeast proteins. We use this information to identify candidate enzymes for known biochemical reactions whose enzymes are unidentified, and show how this provides a powerful basis for experimental validation. Using one pathway as a test case we pair a new function for the previously uncharacterized enzyme Yhr202w, as an extra-cellular AMP hydrolase in the NAD degradation pathway. Yhr202w, which we now term Smn1 for Scavenger MonoNucleotidase 1, is a highly conserved protein that is similar to the human protein E5NT/CD73, which is associated with multiple cancers. Hence, our new methodology provides a paradigm, that can be adopted to other organisms, for uncovering new enzymatic functions of uncharacterized proteins.     

Estimating protein-ligand binding free energy: atomic solvation parameters for partition coefficient and solvation free energy calculation

Solvation energy calculation is one of the main difficulties for the estimation of protein-ligand binding free energy and the correct scoring in docking studies. We have developed a new solvation energy estimation method for protein-ligand binding based on atomic solvation parameter (ASP), which has been shown to improve the power of protein-ligand binding free energy predictions. The ASP set, designed to handle both proteins and organic compounds and derived from experimental n-octanol/water partition coefficient (log P) data, contains 100 atom types (united model that treats hydrogen atoms implicitly) or 119 atom types (all-atom model that treats hydrogen atoms explicitly). By using this unified ASP set, an algorithm was developed for solvation energy calculation and was further integrated into a score function for predicting protein-ligand binding affinity. The score function reproduced the absolute binding free energies of a test set of 50 protein-ligand complexes with a standard error of 8.31 kJ/mol. As a byproduct, a conformation-dependent log P calculation algorithm named ASPLOGP was also implemented. The predictive results of ASPLOGP for a test set of 138 compounds were r = 0.968, s = 0.344 for the all-atom model and r = 0.962, s = 0.367 for the united model, which were better than previous conformation-dependent approaches and comparable to fragmental and atom-based methods. ASPLOGP also gave good predictive results for small peptides. The score function based on the ASP model can be applied widely in protein-ligand interaction studies and structure-based drug design.     

Restriction endonucleases: natural and directed evolution

Type II restriction endonucleases (REs) are highly sequence-specific compared with other classes of nucleases. PD-(D/E)XK nucleases, initially represented by only type II REs, now comprise a large and extremely diverse superfamily of proteins and, although sharing a structurally conserved core, typically display little or no detectable sequence similarity except for the active site motifs. Sequence similarity can only be observed in methylases and few isoschizomers. As a consequence, REs are classified according to combinations of functional properties rather than on the basis of genetic relatedness. New alignment matrices and classification systems based on structural core connectivity and cleavage mechanisms have been developed to characterize new REs and related proteins. REs recognizing more than 300 distinct specificities have been identified in RE database (REBASE: ) but still the need for newer specificities is increasing due to the advancement in molecular biology and applications. The enzymes have undergone constant evolution through structural changes in protein scaffolds which include random mutations, homologous recombinations, insertions, and deletions of coding DNA sequences but rational mutagenesis or directed evolution delivers protein variants with new functions in accordance with defined biochemical or environmental pressures. Redesigning through random mutation, addition or deletion of amino acids, methylation-based selection, synthetic molecules, combining recognition and cleavage domains from different enzymes, or combination with domains of additional functions change the cleavage specificity or substrate preference and stability. There is a growing number of patents awarded for the creation of engineered REs with new and enhanced properties.     

Protein function annotation by local binding site surface similarity

Hundreds of protein crystal structures exist for proteins whose function cannot be confidently determined from sequence similarity. Surflex‐PSIM, a previously reported surface‐based protein similarity algorithm, provides an alternative method for hypothesizing function for such proteins. The method now supports fully automatic binding site detection and is fast enough to screen comprehensive databases of protein binding sites. The binding site detection methodology was validated on apo/holo cognate protein pairs, correctly identifying 91% of ligand binding sites in holo structures and 88% in apo structures where corresponding sites existed. For correctly detected apo binding sites, the cognate holo site was the most similar binding site 87% of the time. PSIM was used to screen a set of proteins that had poorly characterized functions at the time of crystallization, but were later biochemically annotated. Using a fully automated protocol, this set of 8 proteins was screened against ～60,000 ligand binding sites from the PDB. PSIM correctly identified functional matches that predated query protein biochemical annotation for five out of the eight query proteins. A panel of 12 currently unannotated proteins was also screened, resulting in a large number of statistically significant binding site matches, some of which suggest likely functions for the podorly characterized proteins. Proteins 2014; 82:679–694. © 2013 Wiley Periodicals, Inc.     

Evolutionarily conserved linkage between enzyme fold, flexibility, and catalysis

Proteins are intrinsically flexible molecules. The role of internal motions in a protein''s designated function is widely debated. The role of protein structure in enzyme catalysis is well established, and conservation of structural features provides vital clues to their role in function. Recently, it has been proposed that the protein function may involve multiple conformations: the observed deviations are not random thermodynamic fluctuations; rather, flexibility may be closely linked to protein function, including enzyme catalysis. We hypothesize that the argument of conservation of important structural features can also be extended to identification of protein flexibility in interconnection with enzyme function. Three classes of enzymes (prolyl-peptidyl isomerase, oxidoreductase, and nuclease) that catalyze diverse chemical reactions have been examined using detailed computational modeling. For each class, the identification and characterization of the internal protein motions coupled to the chemical step in enzyme mechanisms in multiple species show identical enzyme conformational fluctuations. In addition to the active-site residues, motions of protein surface loop regions (>10 Å away) are observed to be identical across species, and networks of conserved interactions/residues connect these highly flexible surface regions to the active-site residues that make direct contact with substrates. More interestingly, examination of reaction-coupled motions in non-homologous enzyme systems (with no structural or sequence similarity) that catalyze the same biochemical reaction shows motions that induce remarkably similar changes in the enzyme–substrate interactions during catalysis. The results indicate that the reaction-coupled flexibility is a conserved aspect of the enzyme molecular architecture. Protein motions in distal areas of homologous and non-homologous enzyme systems mediate similar changes in the active-site enzyme–substrate interactions, thereby impacting the mechanism of catalyzed chemistry. These results have implications for understanding the mechanism of allostery, and for protein engineering and drug design.

Author''s Summary

Enzymes are nature''s molecular machines that catalyze biochemical reactions with remarkable efficiency. Recent evidence suggests that enzyme function may involve not only direct structural interactions between the enzyme and its substrate, but also internal motions of the enzyme itself. Here, we describe a computational investigation of three classes of enzymes that catalyze completely different biochemical reactions. Remarkably, the mobile enzyme regions and the nature of these motions are the same across species ranging from single-celled organisms to complex life-forms. Also surprisingly, non-homologous enzymes that catalyze the same chemical reaction but do not share sequence or structural similarity reveal a similar impact of enzyme motions on their reaction mechanisms. Flexible enzyme regions are found to be connected by conserved networks of coupled interactions that connect surface regions to active-site residues. These networks may provide a mechanism for the solvent on an enzyme''s surface to couple to the reaction catalyzed by the enzyme. These results have implications for understanding the mechanism of allostery (long-range effects), and for protein engineering and drug design.