Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases.We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking > 900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures.Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naïve for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.     

Crystal structure of YbaK protein from Haemophilus influenzae (HI1434) at 1.8 A resolution: functional implications

Structural genomics of proteins of unknown function most straightforwardly assists with assignment of biochemical activity when the new structure resembles that of proteins whose functions are known. When a new fold is revealed, the universe of known folds is enriched, and once the function is determined by other means, novel structure-function relationships are established. The previously unannotated protein HI1434 from H. influenzae provides a hybrid example of these two paradigms. It is a member of a microbial protein family, labeled in SwissProt as YbaK and ebsC. The crystal structure at 1.8 A resolution reported here reveals a fold that is only remotely related to the C-lectin fold, in particular to endostatin, and thus is not sufficiently similar to imply that YbaK proteins are saccharide binding proteins. However, a crevice that may accommodate a small ligand is evident. The putative binding site contains only one invariant residue, Lys46, which carries a functional group that could play a role in catalysis, indicating that YbaK is probably not an enzyme. Detailed sequence analysis, including a number of newly sequenced microbial organisms, highlights sequence homology to an insertion domain in prolyl-tRNA synthetases (proRS) from prokaryote, a domain whose function is unknown. A HI1434-based model of the insertion domain shows that it should also contain the putative binding site. Being part of a tRNA synthetases, the insertion domain is likely to be involved in oligonucleotide binding, with possible roles in recognition/discrimination or editing of prolyl-tRNA. By analogy, YbaK may also play a role in nucleotide or oligonucleotide binding, the nature of which is yet to be determined.     

The Structure of an NDR/LATS Kinase–Mob Complex Reveals a Novel Kinase–Coactivator System and Substrate Docking Mechanism

Eukaryotic cells commonly use protein kinases in signaling systems that relay information and control a wide range of processes. These enzymes have a fundamentally similar structure, but achieve functional diversity through variable regions that determine how the catalytic core is activated and recruited to phosphorylation targets. “Hippo” pathways are ancient protein kinase signaling systems that control cell proliferation and morphogenesis; the NDR/LATS family protein kinases, which associate with “Mob” coactivator proteins, are central but incompletely understood components of these pathways. Here we describe the crystal structure of budding yeast Cbk1–Mob2, to our knowledge the first of an NDR/LATS kinase–Mob complex. It shows a novel coactivator-organized activation region that may be unique to NDR/LATS kinases, in which a key regulatory motif apparently shifts from an inactive binding mode to an active one upon phosphorylation. We also provide a structural basis for a substrate docking mechanism previously unknown in AGC family kinases, and show that docking interaction provides robustness to Cbk1’s regulation of its two known in vivo substrates. Co-evolution of docking motifs and phosphorylation consensus sites strongly indicates that a protein is an in vivo regulatory target of this hippo pathway, and predicts a new group of high-confidence Cbk1 substrates that function at sites of cytokinesis and cell growth. Moreover, docking peptides arise in unstructured regions of proteins that are probably already kinase substrates, suggesting a broad sequential model for adaptive acquisition of kinase docking in rapidly evolving intrinsically disordered polypeptides.     

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 comparison of sequence and structure protein domain families as a basis for structural genomics.

MOTIVATION: Protein families can be defined based on structure or sequence similarity. We wanted to compare two protein family databases, one based on structural and one on sequence similarity, to investigate to what extent they overlap, the similarity in definition of corresponding families, and to create a list of large protein families with unknown structure as a resource for structural genomics. We also wanted to increase the sensitivity of fold assignment by exploiting protein family HMMs. RESULTS: We compared Pfam, a protein family database based on sequence similarity, to Scop, which is based on structural similarity. We found that 70% of the Scop families exist in Pfam while 57% of the Pfam families exist in Scop. Most families that occur in both databases correspond well to each other, but in some cases they are different. Such cases highlight situations in which structure and sequence approaches differ significantly. The comparison enabled us to compile a list of the largest families that do not occur in Scop; these are suitable targets for structure prediction and determination, and may be useful to guide projects in structural genomics. It can be noted that 13 out of the 20 largest protein families without a known structure are likely transmembrane proteins. We also exploited Pfam to increase the sensitivity of detecting homologs of proteins with known structure, by comparing query sequences to Pfam HMMs that correspond to Scop families. For SWISSPROT+TREMBL, this yielded an increase in fold assignment from 31% to 42% compared to using FASTA only. This method assigned a structure to 22% of the proteins in Saccharomyces cerevisiae, 24% in Escherichia coli, and 16% in Methanococcus jannaschii.     

Structural and Functional Similarity between the Bacterial Type III Secretion System Needle Protein PrgI and the Eukaryotic Apoptosis Bcl-2 Proteins

Background

Functional similarity is challenging to identify when global sequence and structure similarity is low. Active-sites or functionally relevant regions are evolutionarily more stable relative to the remainder of a protein structure and provide an alternative means to identify potential functional similarity between proteins. We recently developed the FAST-NMR methodology to discover biochemical functions or functional hypotheses of proteins of unknown function by experimentally identifying ligand binding sites. FAST-NMR utilizes our CPASS software and database to assign a function based on a similarity in the structure and sequence of ligand binding sites between proteins of known and unknown function.

Methodology/Principal Findings

The PrgI protein from Salmonella typhimurium forms the needle complex in the type III secretion system (T3SS). A FAST-NMR screen identified a similarity between the ligand binding sites of PrgI and the Bcl-2 apoptosis protein Bcl-xL. These ligand binding sites correlate with known protein-protein binding interfaces required for oligomerization. Both proteins form membrane pores through this oligomerization to release effector proteins to stimulate cell death. Structural analysis indicates an overlap between the PrgI structure and the pore forming motif of Bcl-xL. A sequence alignment indicates conservation between the PrgI and Bcl-xL ligand binding sites and pore formation regions. This active-site similarity was then used to verify that chelerythrine, a known Bcl-xL inhibitor, also binds PrgI.

Conclusions/Significance

A structural and functional relationship between the bacterial T3SS and eukaryotic apoptosis was identified using our FAST-NMR ligand affinity screen in combination with a bioinformatic analysis based on our CPASS program. A similarity between PrgI and Bcl-xL is not readily apparent using traditional global sequence and structure analysis, but was only identified because of conservation in ligand binding sites. These results demonstrate the unique opportunity that ligand-binding sites provide for the identification of functional relationships when global sequence and structural information is limited.     

Prediction of protein function from protein sequence and structure

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.     

A Statistical Model of Protein Sequence Similarity and Function Similarity Reveals Overly-Specific Function Predictions

Background

Predicting protein function from primary sequence is an important open problem in modern biology. Not only are there many thousands of proteins of unknown function, current approaches for predicting function must be improved upon. One problem in particular is overly-specific function predictions which we address here with a new statistical model of the relationship between protein sequence similarity and protein function similarity.

Methodology

Our statistical model is based on sets of proteins with experimentally validated functions and numeric measures of function specificity and function similarity derived from the Gene Ontology. The model predicts the similarity of function between two proteins given their amino acid sequence similarity measured by statistics from the BLAST sequence alignment algorithm. A novel aspect of our model is that it predicts the degree of function similarity shared between two proteins over a continuous range of sequence similarity, facilitating prediction of function with an appropriate level of specificity.

Significance

Our model shows nearly exact function similarity for proteins with high sequence similarity (bit score >244.7, e-value >1e⁻⁶², non-redundant NCBI protein database (NRDB)) and only small likelihood of specific function match for proteins with low sequence similarity (bit score <54.6, e-value <1e⁻⁰⁵, NRDB). For sequence similarity ranges in between our annotation model shows an increasing relationship between function similarity and sequence similarity, but with considerable variability. We applied the model to a large set of proteins of unknown function, and predicted functions for thousands of these proteins ranging from general to very specific. We also applied the model to a data set of proteins with previously assigned, specific functions that were electronically based. We show that, on average, these prior function predictions are more specific (quite possibly overly-specific) compared to predictions from our model that is based on proteins with experimentally determined function.     

Ligand binding and homology modelling of insect odorant‐binding proteins

This review describes the main characteristics of odorant‐binding proteins (OBPs) for homology modelling and presents a summary of structure prediction studies on insect OBPs, along with the steps involved and some limitations and improvements. The technique involves a computing approach to model protein structures and is based on a comparison between a target (unknown structure) and one or more templates (experimentally determined structures). As targets for structure prediction, OBPs are considered to play a functional role for recognition, desorption, scavenging, protection and transportation of hydrophobic molecules (odourants) across an aqueous environment (lymph) to olfactory receptor neurones (ORNs) located in sensilla, the main olfactory units of insect antennae. Lepidopteran pheromone‐binding proteins, a subgroup of OBPs, are characterized by remarkable structural features, in which high sequence identities (approximately 30%) among these OBPs and a large number of available templates can facilitate the prediction of precise homology models. Approximately 30 studies have been performed on insect OBPs using homology modelling as a tool to predict their structures. Although some of the studies have assessed ligand‐binding affinity using structural information and biochemical measurements, few have performed docking and molecular dynamic (MD) simulations as a virtual method to predict best ligands. Docking and MD simulations are discussed in the context of discovery of novel semiochemicals (super‐ligands) using homology modelling to conceive further strategies in insect management.     

Structure and Functional Analysis of YcfD,a Novel 2-Oxoglutarate/Fe-Dependent Oxygenase Involved in Translational Regulation in Escherichia coli

The 2-oxoglutarate (2OG)/Fe^2 +-dependent oxygenases (2OG oxygenases) are a large family of proteins that share a similar overall three-dimensional structure and catalyze a diverse array of oxidation reactions. The Jumonji C (JmjC)-domain-containing proteins represent an important subclass of the 2OG oxygenase family that typically catalyze protein hydroxylation; however, recently, other reactions have been identified, such as tRNA modification. The Escherichia coli gene, ycfD, was predicted to be a JmjC-domain-containing protein of unknown function based on primary sequence. Recently, YcfD was determined to act as a ribosomal oxygenase, hydroxylating an arginine residue on the 50S ribosomal protein L-16 (RL-16). We have determined the crystal structure of YcfD at 2.7 Å resolution, revealing that YcfD is structurally similar to known JmjC proteins and possesses the characteristic double-stranded β-helix fold or cupin domain. Separate from the cupin domain, an additional globular module termed α-helical arm mediates dimerization of YcfD. We further have shown that 2OG binds to YcfD using isothermal titration calorimetry and identified key binding residues using mutagenesis that, together with the iron location and structural similarity with other cupin family members, allowed identification of the active site. Structural homology to ribosomal assembly proteins combined with GST (glutathione S-transferase)-YcfD pull-down of a ribosomal protein and docking of RL-16 to the YcfD active site support the role of YcfD in regulation of bacterial ribosome assembly. Furthermore, overexpression of YcfD is shown to inhibit cell growth signifying a toxic effect on ribosome assembly.     

Pathogenesis-related protein 4 is structurally homologous to the carboxy-terminal domains of hevein, Win-1 and Win-2

Summary The extracellular, acidic pathogenesis-related protein, PR-4, was purified to homogeneity from leaves of Nicotiana tabacum infected with tobacco mosaic virus (TMV) and characterized by partial amino acid sequencing. Complementary DNA clones encoding PR-4 were isolated using an oligonucleotide probe based on the sequence of one of the peptides. The deduced PR-4 protein sequence was found to be related to a family of proteins including hevein and Win-1, which have an amino-terminal lectin domain and a carboxy-terminal domain of unknown function. PR-4 is homologous to the carboxy-terminus of these proteins but does not contain the lectin domain. Thus, the organization of the PR-4 family of proteins is similar to that of the plant chitinase family, in that both contain structural subclasses characterized by the presence or absence of an amino-terminal lectin domain. This observation is consistent with the proposal that the DNA encoding the lectin domain may be capable of transposing to form new genes encoding proteins of more complex, multi-domain structure. The expression of PR-4 mRNA was found to increase dramatically in response to TMV infection and the time course of RNA accumulation was similar to that of other PR proteins.     

Mouse glandular kallikrein genes. Structure and partial sequence analysis of the kallikrein gene locus

Mouse glandular kallikreins are encoded by a family of closely linked genes which are located on chromosome 7 at a site corresponding to the genetically defined Tam-1, Prt-4, and Prt-5 loci. We have characterized 24 kallikrein genes by genomic cloning and restriction mapping of 310 kilobase pairs of BALB/c mouse DNA. Most of these genes are highly homologous, have the same exon/intron organization, and are linked in clusters of up to 11 genes. Partial sequence analysis of the kallikrein genes has facilitated identification of those members of the family for which protein sequence data exist and assignment of those which are pseudogenes or encode proteins of unknown function. We find that a maximum of 14 mouse kallikrein genes have the potential to encode functional proteins.     

Molecular modeling of protein function regions

Experimental protein structures often provide extensive insight into the mode and specificity of small molecule binding, and this information is useful for understanding protein function and for the design of drugs. We have performed an analysis of the reliability with which ligand-binding information can be deduced from computer model structures, as opposed to experimentally derived ones. Models produced as part of the CASP experiments are used. The accuracy of contacts between protein model atoms and experimentally determined ligand atom positions is the main criterion. Only comparative models are included (i.e., models based on a sequence relationship between the protein of interest and a known structure). We find that, as expected, contact errors increase with decreasing sequence identity used as a basis for modeling. Analysis of the causes of errors shows that sequence alignment errors between model and experimental template have the most deleterious effect. In general, good, but not perfect, insight into ligand binding can be obtained from models based on a sequence relationship, providing there are no alignment errors in the model. The results support a structural genomics strategy based on experimental sampling of structure space so that all protein domains can be modeled on the basis of 30% or higher sequence identity.     

Prediction of cyclin-dependent kinase phosphorylation substrates

Protein phosphorylation, mediated by a family of enzymes called cyclin-dependent kinases (Cdks), plays a central role in the cell-division cycle of eukaryotes. Phosphorylation by Cdks directs the cell cycle by modifying the function of regulators of key processes such as DNA replication and mitotic progression. Here, we present a novel computational procedure to predict substrates of the cyclin-dependent kinase Cdc28 (Cdk1) in the Saccharomyces cerevisiae. Currently, most computational phosphorylation site prediction procedures focus solely on local sequence characteristics. In the present procedure, we model Cdk substrates based on both local and global characteristics of the substrates. Thus, we define the local sequence motifs that represent the Cdc28 phosphorylation sites and subsequently model clustering of these motifs within the protein sequences. This restraint reflects the observation that many known Cdk substrates contain multiple clustered phosphorylation sites. The present strategy defines a subset of the proteome that is highly enriched for Cdk substrates, as validated by comparing it to a set of bona fide, published, experimentally characterized Cdk substrates which was to our knowledge, comprehensive at the time of writing. To corroborate our model, we compared its predictions with three experimentally independent Cdk proteomic datasets and found significant overlap. Finally, we directly detected in vivo phosphorylation at Cdk motifs for selected putative substrates using mass spectrometry.     

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.     

Evolutionary significance and functional characterization of streptomycin adenylyltransferase from Serratia marcescens

Abstract

Complete functional annotations of proteins are essential to understand the role and mechanisms in pathogenesis. Aminoglycoside nucleotidyltransferases are the subclasses of aminoglycosides modifying enzymes conferring resistance to organisms. Insight into the structural and functional understanding of nucleotidyltransferase family protein provides vital information to combat pathogenesis. Phylogenetic analysis is employed to identify the evolutionary significance and common motif’s present among the homologs of nucleotidyltransferase family protein. Structure, sequence based approaches and molecular docking were implemented to predict the exact function of the protein. Wide distribution of the nucleotidyltransferase family protein in gram-positive and gram-negative organisms are evidenced from phylogenetic analysis. Five common motifs were present in all the homolog’s of nucleotidyltransferase family protein. Sequence-structure based functional annotations predicts that the targeted protein function as ATP-Mg dependent streptomycin adenylyltransferase. Structural comparisons and docking studies correlate well with the identified function. The complete function of nucleotidyltransferase family protein was identified as Streptomycin adenylyltransferase and it could be targeted as a potential therapeutic target to overcome antibiotic resistance.

Communicated by Ramaswamy H. Sarma

Abbreviations AAC aminoglycoside acetyltransferases

AME aminoglycoside modifying enzyme

ANT aminoglycoside nucleotidyltransferases

APH aminoglycoside phosphotransferases

ATP adenosine triphosphate

CASTp computer atlas and surface topography of proteins

DUF domains of unknown function

Glide grid-based ligand docking with energetic

HMM hidden Markov model

MAST motif alignment and search tool

MEGA molecular evolutionary genetics analysis

MEME multiple Em for motif elicitation

MSA multiple sequence alignment

NMP nucleoside monophosphate

NTP nucleoside triphosphate

NT nucleotidyltransferase

OPLS optimized potential for liquid simulation

XP extra precision

     

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.     

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes

We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.     

The NMR solution structure and function of RPA3313: a putative ribosomal transport protein from Rhodopseudomonas palustris

Protein function elucidation often relies heavily on amino acid sequence analysis and other bioinformatics approaches. The reliance is extended to structure homology modeling for ligand docking and protein–protein interaction mapping. However, sequence analysis of RPA3313 exposes a large, unannotated class of hypothetical proteins mostly from the Rhizobiales order. In the absence of sequence and structure information, further functional elucidation of this class of proteins has been significantly hindered. A high quality NMR structure of RPA3313 reveals that the protein forms a novel split ββαβ fold with a conserved ligand binding pocket between the first β‐strand and the N‐terminus of the α‐helix. Conserved residue analysis and protein–protein interaction prediction analyses reveal multiple protein binding sites and conserved functional residues. Results of a mass spectrometry proteomic analysis strongly point toward interaction with the ribosome and its subunits. The combined structural and proteomic analyses suggest that RPA3313 by itself or in a larger complex may assist in the transportation of substrates to or from the ribosome for further processing. Proteins 2016; 85:93–102. © 2016 Wiley Periodicals, Inc.