Nucleotide sequence of a cDNA for the dihydrolipoamide acetyltransferase component of human pyruvate dehydrogenase complex

Deoxynucleotide sequencing of a cDNA for the dihydrolipoamide acetyltransferase (PDC-E₂) component of human pyruvate dehydrogenase complex (PDC) revealed an open reading frame of 1848 base pairs corresponding to a leader sequence of 54 amino acids and a mature protein of 561 amino acids (59 551 Da). Both an amino-terminal lipoyl-bearing domain and a carboxy-terminal catalytic domain are present in the deduced amino acid sequence. The lipoyl-bearing domain contains two repeating units of 127 amino acids, each harboring one lipoic acid-binding lysine. Thus, mammalian PDC-E₂ differs as to the number of lipoic acid-binding sites from other dihydrolipoamide acyltransferases in both prokaryotic and eukaryotic organisms.     

A structural model for the prostate disease marker, human prostate-specific antigen.

Prostate-specific antigen (PSA) provides an excellent serum marker for prostate cancer, the most frequent form of cancer in American males. PSA is a 237-residue protease based on sequence homology to kallikrein-like enzymes. To predict the 3-dimensional structure of PSA, homology modeling studies were performed based on sequence and structural alignments with tonin, pancreatic kallikrein, chymotrypsin, and trypsin. The structurally conserved regions of the 4 reference X-ray proteins provided the core structure of PSA, whereas the loop structures were modeled on the loops of tonin and kallikrein. The unique "kallikrein loop" insert, between Ser 95b and Pro 95k of kallikrein, was constructed using molecular mechanics, dynamics, and electrostatics calculations. In the resulting PSA structure, the catalytic triad, involving residues His 57, Asp 102, and Ser 195, and hydrophobic and electrostatic interactions typical of serine proteases were extremely well conserved. Similarly, the 5-disulfide bonds of kallikrein were also conserved in PSA. These results, together with the fact that no major steric clashes arose during the modeling process, provide strong evidence for the validity of the PSA model. Calculation of the electrostatic potential contours of kallikrein and PSA was carried out using the finite difference Poisson-Boltzmann method. The calculations revealed matching areas of negative potential near the catalytic triad, but differences in the positive potential surrounding the active site. The PSA glycosylation site, Asn 61, is fully accessible to the solvent and is enclosed in a positive region of the isopotential map. The bottom of the substrate specificity pocket, residue S1, is a serine (Ser 189) as in chymotrypsin, rather than aspartate (Asp 189) as in tonin, kallikrein, and trypsin. This fact, plus other features of the S1 binding-pocket region, suggest that PSA would prefer substrates with hydrophobic residues at the P1 position. The location of a potential zinc ion binding site involving the side chain of histidines 91, 101, and 233 is also suggested. This PSA model should facilitate the understanding and prediction of structural and functional properties of this important cancer marker.     

A comparative study of available software for high-accuracy homology modeling: from sequence alignments to structural models

An open question in protein homology modeling is, how well do current modeling packages satisfy the dual criteria of quality of results and practical ease of use? To address this question objectively, we examined homology‐built models of a variety of therapeutically relevant proteins. The sequence identities across these proteins range from 19% to 76%. A novel metric, the difference alignment index (DAI), is developed to aid in quantifying the quality of local sequence alignments. The DAI is also used to construct the relative sequence alignment (RSA), a new representation of global sequence alignment that facilitates comparison of sequence alignments from different methods. Comparisons of the sequence alignments in terms of the RSA and alignment methodologies are made to better understand the advantages and caveats of each method. All sequence alignments and corresponding 3D models are compared to their respective structure‐based alignments and crystal structures. A variety of protein modeling software was used. We find that at sequence identities >40%, all packages give similar (and satisfactory) results; at lower sequence identities (<25%), the sequence alignments generated by Profit and Prime, which incorporate structural information in their sequence alignment, stand out from the rest. Moreover, the model generated by Prime in this low sequence identity region is noted to be superior to the rest. Additionally, we note that DSModeler and MOE, which generate reasonable models for sequence identities >25%, are significantly more functional and easier to use when compared with the other structure‐building software.     

Knowledge-based model building of proteins: concepts and examples.

We describe how to build protein models from structural templates. Methods to identify structural similarities between proteins in cases of significant, moderate to low, or virtually absent sequence similarity are discussed. The detection and evaluation of structural relationships is emphasized as a central aspect of protein modeling, distinct from the more technical aspects of model building. Computational techniques to generate and complement comparative protein models are also reviewed. Two examples, P-selectin and gp39, are presented to illustrate the derivation of protein model structures and their use in experimental studies.     

Potential for dramatic improvement in sequence alignment against structures of remote homologous proteins by extracting structural information from multiple structure alignment

A novel method has been developed for acquiring the correct alignment of a query sequence against remotely homologous proteins by extracting structural information from profiles of multiple structure alignment. A systematic search algorithm combined with a group of score functions based on sequence information and structural information has been introduced in this procedure. A limited number of top solutions (15,000) with high scores were selected as candidates for further examination. On a test-set comprising 301 proteins from 75 protein families with sequence identity less than 30%, the proportion of proteins with completely correct alignment as first candidate was improved to 39.8% by our method, whereas the typical performance of existing sequence-based alignment methods was only between 16.1% and 22.7%. Furthermore, multiple candidates for possible alignment were provided in our approach, which dramatically increased the possibility of finding correct alignment, such that completely correct alignments were found amongst the top-ranked 1000 candidates in 88.3% of the proteins. With the assistance of a sequence database, completely correct alignment solutions were achieved amongst the top 1000 candidates in 94.3% of the proteins. From such a limited number of candidates, it would become possible to identify more correct alignment using a more time-consuming but more powerful method with more detailed structural information, such as side-chain packing and energy minimization, etc. The results indicate that the novel alignment strategy could be helpful for extending the application of highly reliable methods for fold identification and homology modeling to a huge number of homologous proteins of low sequence similarity. Details of the methods, together with the results and implications for future development are presented.     

Structural similarity to link sequence space: new potential superfamilies and implications for structural genomics

The current pace of structural biology now means that protein three-dimensional structure can be known before protein function, making methods for assigning homology via structure comparison of growing importance. Previous research has suggested that sequence similarity after structure-based alignment is one of the best discriminators of homology and often functional similarity. Here, we exploit this observation, together with a merger of protein structure and sequence databases, to predict distant homologous relationships. We use the Structural Classification of Proteins (SCOP) database to link sequence alignments from the SMART and Pfam databases. We thus provide new alignments that could not be constructed easily in the absence of known three-dimensional structures. We then extend the method of Murzin (1993b) to assign statistical significance to sequence identities found after structural alignment and thus suggest the best link between diverse sequence families. We find that several distantly related protein sequence families can be linked with confidence, showing the approach to be a means for inferring homologous relationships and thus possible functions when proteins are of known structure but of unknown function. The analysis also finds several new potential superfamilies, where inspection of the associated alignments and superimpositions reveals conservation of unusual structural features or co-location of conserved amino acids and bound substrates. We discuss implications for Structural Genomics initiatives and for improvements to sequence comparison methods.     

A structural model for the large subunit of the mammalian mitochondrial ribosome

Protein translation is essential for all forms of life and is conducted by a macromolecular complex, the ribosome. Evolutionary changes in protein and RNA sequences can affect the 3D organization of structural features in ribosomes in different species. The most dramatic changes occur in animal mitochondria, whose genomes have been reduced and altered significantly. The RNA component of the mitochondrial ribosome (mitoribosome) is reduced in size, with a compensatory increase in protein content. Until recently, it was unclear how these changes affect the 3D structure of the mitoribosome. Here, we present a structural model of the large subunit of the mammalian mitoribosome developed by combining molecular modeling techniques with cryo-electron microscopic data at 12.1A resolution. The model contains 93% of the mitochondrial rRNA sequence and 16 mitochondrial ribosomal proteins in the large subunit of the mitoribosome. Despite the smaller mitochondrial rRNA, the spatial positions of RNA domains known to be involved directly in protein synthesis are essentially the same as in bacterial and archaeal ribosomes. However, the dramatic reduction in rRNA content necessitates evolution of unique structural features to maintain connectivity between RNA domains. The smaller rRNA sequence also limits the likelihood of tRNA binding at the E-site of the mitoribosome, and correlates with the reduced size of D-loops and T-loops in some animal mitochondrial tRNAs, suggesting co-evolution of mitochondrial rRNA and tRNA structures.     

Structure‐based classification of FAD binding sites: A comparative study of structural alignment tools

A total of six different structural alignment tools (TM‐Align, TriangleMatch, CLICK, ProBis, SiteEngine and GA‐SI) were assessed for their ability to perform two particular tasks: (i) discriminating FAD (flavin adenine dinucleotide) from non‐FAD binding sites, and (ii) performing an all‐to‐all comparison on a set of 883 FAD binding sites for the purpose of classifying them. For the first task, the consistency of each alignment method was evaluated, showing that every method is able to distinguish FAD and non‐FAD binding sites with a high Matthews correlation coefficient. Additionally, GA‐SI was found to provide alignments different from those of the other approaches. The results obtained for the second task revealed more significant differences among alignment methods, as reflected in the poor correlation of their results and highlighted clearly by the independent evaluation of the structural superimpositions generated by each method. The classification itself was performed using the combined results of all methods, using the best result found for each comparison of binding sites. A number of different clustering methods (Single‐linkage, UPGMA, Complete‐linkage, SPICKER and k‐Means clustering) were also used. The groups of similar binding sites (proteins) or clusters generated by the best performing method were further analyzed in terms of local sequence identity, local structural similarity and conservation of analogous contacts with the FAD ligands. Each of the clusters was characterized by a unique set of structural features or patterns, demonstrating that the groups generated truly reflect the structural diversity of FAD binding sites. Proteins 2016; 84:1728–1747. © 2016 Wiley Periodicals, Inc.     

A hybrid genetic-neural model for predicting protein structural classes

A genetic algorithm (GA) for feature selection in conjunction with neural network was applied to predict protein structural classes based on single amino acid and all dipeptide composition frequencies. These sequence parameters were encoded as input features for a GA in feature selection procedure and classified with a three-layered neural network to predict protein structural classes. The system was established through optimization of the classification performance of neural network which was used as evaluation function. In this study, self-consistency and jackknife tests on a database containing 498 proteins were used to verify the performance of this hybrid method, and were compared with some of prior works. The adoption of a hybrid model, which encompasses genetic and neural technologies, demonstrated to be a promising approach in the task of protein structural class prediction.     

Motifs and structural fold of the cofactor binding site of human glutamate decarboxylase.

The pyridoxal-P binding sites of the two isoforms of human glutamate decarboxylase (GAD65 and GAD67) were modeled by using PROBE (a recently developed algorithm for multiple sequence alignment and database searching) to align the primary sequence of GAD with pyridoxal-P binding proteins of known structure. GAD's cofactor binding site is particularly interesting because GAD activity in the brain is controlled in part by a regulated interconversion of the apo- and holoenzymes. PROBE identified six motifs shared by the two GADs and four proteins of known structure: bacterial ornithine decarboxylase, dialkylglycine decarboxylase, aspartate aminotransferase, and tyrosine phenol-lyase. Five of the motifs corresponded to the alpha/beta elements and loops that form most of the conserved fold of the pyridoxal-P binding cleft of the four enzymes of known structure; the sixth motif corresponded to a helical element of the small domain that closes when the substrate binds. Eight residues that interact with pyridoxal-P and a ninth residue that lies at the interface of the large and small domains were also identified. Eleven additional conserved residues were identified and their functions were evaluated by examining the proteins of known structure. The key residues that interact directly with pyridoxal-P were identical in ornithine decarboxylase and the two GADs, thus allowing us to make a specific structural prediction of the cofactor binding site of GAD. The strong conservation of the cofactor binding site in GAD indicates that the highly regulated transition between apo- and holoGAD is accomplished by modifications in this basic fold rather than through a novel folding pattern.     

Classifier ensembles for protein structural class prediction with varying homology

Structural class characterizes the overall folding type of a protein or its domain. A number of computational methods have been proposed to predict structural class based on primary sequences; however, the accuracy of these methods is strongly affected by sequence homology. This paper proposes, an ensemble classification method and a compact feature-based sequence representation. This method improves prediction accuracy for the four main structural classes compared to competing methods, and provides highly accurate predictions for sequences of widely varying homologies. The experimental evaluation of the proposed method shows superior results across sequences that are characterized by entire homology spectrum, ranging from 25% to 90% homology. The error rates were reduced by over 20% when compared with using individual prediction methods and most commonly used composition vector representation of protein sequences. Comparisons with competing methods on three large benchmark datasets consistently show the superiority of the proposed method.     

A phosphoinositide-binding sequence is shared by PH domain target molecules—a model for the binding of PH domains to proteins

Pleckstrin homology (PH) domains have been proven to bind phosphoinositides (PI) and inositolphosphates (IP). On the other hand, a binding of PH domains to proteins is still a matter of debate. The goal of this work was to identify potential PH domain protein target sites and to build a model for PH domain–protein binding. A candidate sequence, called HIKE, was identified by sequence homology analysis of the proteins that are considered the strongest PH binding candidates, i.e., G_β, PKC, and Akt. HIKE contains a PI binding sequence and fulfills several criteria for a potential PH-binding site, i.e., it is present in other PH-binding candidates, lies in regulatory regions independently predicted to bind PH domains, and is conserved in 3-D structure among different molecules. These findings and the similarities with the mode of binding of PTB and PDZ domains suggest a β strand–β strand coordination model for PH–protein binding. The HIKE model predicts that membrane anchoring of PH domains and their targets could be a critical step in their interaction, which would consistently explain why PH–protein binding has only been detected in the presence of PI. Proteins 31:1–9, 1998. © 1998 Wiley-Liss, Inc.     

A novel parameterization scheme for energy equations and its use to calculate the structure of protein molecules

A novel scheme for the parameterization of a type of “potential energy” function for protein molecules is introduced. The function is parameterized based on the known conformations of previously determined protein structures and their sequence similarity to a molecule whose conformation is to be calculated. Once parameterized, minima of the potential energy function can be located using a version of simulated annealing which has been previously shown to locate global and near-global minima with the given functional form. As a test problem, the potential was parameterized based on the known structures of the rubredoxins from Desulfovibrio vulgaris, Desulfovibrio desulfuricans, and Clostridium pasteurianum, which vary from 45 to 54 amino acids in length, and the sequence alignments of these molecules with the rubredoxin sequence from Desulfovibrio gigas. Since the Desulfovibrio gigas rubredeoxin conformation has also been determined, it is possible to check the accuracy of the results. Ten simulated-annealing runs from random starting conformations were performed. Seven of the 10 resultant conformations have an all-C_α rms deviation from the crystallographically determined conformation of less than 1.7 Å. For five of the structures, the rms deviation is less than 0.8 Å. Four of the structures have conformations which are virtually identical to each other except for the position of the carboxy-terminal residue. This is also the conformation which is achieved if the determined crystal structure is minimized with the same potential. The all-C_α rms difference between the crystal and minimized crystal structures is 0.6 Å. It is further observed that the “energies” of the structures according to the potential function exhibit a strong correlation with rms deviation from the native structure. The conformations of the individual model structures and the computational aspects of the modeling procedure are discussed. © 1993 Wiley-Liss, Inc.     

A probabilistic network model for structural transitions in biomolecules

Biological macromolecules often undergo large conformational rearrangements during a functional cycle. To simulate these structural transitions with full atomic detail typically demands extensive computational resources. Moreover, it is unclear how to incorporate, in a principled way, additional experimental information that could guide the structural transition. This article develops a probabilistic model for conformational transitions in biomolecules. The model can be viewed as a network of anharmonic springs that break, if the experimental data support the rupture of bonds. Hamiltonian Monte Carlo in internal coordinates is used to infer structural transitions from experimental data, thereby sampling large conformational transitions without distorting the structure. The model is benchmarked on a large set of conformational transitions. Moreover, we demonstrate the use of the probabilistic network model for integrative modeling of macromolecular complexes based on data from crosslinking followed by mass spectrometry.     

A Novel Glycerol Kinase from Flavobacterium meningosepticum: Characterization,Gene Cloning and Primary Structure

A thermostable glycerol kinase (FGK) was purified 34-fold to homogeneity from Flavobacterium meningosepticum. The molecular masses of the enzyme were 200 kDa by gel filtration and 50 kDa by SDS-PAGE. The Km for glycerol and ATP were 0.088 and 0.030 mM, respectively. The enzyme was stable at 65°C for 10 min and at 37°C for two weeks. The enzyme gene was cloned into Escherichia coli and its complete DNA was sequenced. The FGK gene consists of an open reading frame of 1494-bp encoding a protein of 498 amino acids. The deduced amino acid sequence of the gene had 40-60% similarity to those of glycerol kinases from other origins and the amino acid sequence of the putative active site residue reported for E. coli GK is identical to the corresponding sequence of FGK except for one amino acid residue.     

Prediction of protein structural class for the twilight zone sequences

Structural class characterizes the overall folding type of a protein or its domain. This paper develops an accurate method for in silico prediction of structural classes from low homology (twilight zone) protein sequences. The proposed LLSC-PRED method applies linear logistic regression classifier and a custom-designed, feature-based sequence representation to provide predictions. The main advantages of the LLSC-PRED are the comprehensive representation that includes 58 features describing composition and physicochemical properties of the sequences and transparency of the prediction model. The representation also includes predicted secondary structure content, thus for the first time exploring synergy between these two related predictions. Based on tests performed with a large set of 1673 twilight zone domains, the LLSC-PRED's prediction accuracy, which equals over 62%, is shown to be better than accuracy of over a dozen recently published competing in silico methods and similar to accuracy of other, non-transparent classifiers that use the proposed representation.     

A database for human Y chromosome protein data

The human Y chromosome is the sex determining chromosome. The number of proteins associated with this chromosome is 196 and 107 of the 196 proteins have yet not been characterised. Here, we describe the analysis of these 107 proteins by computing various physicochemical properties using sequence and predicted structural data to elucidate molecular function. We present the derived data in the form a form a database made freely available for download, review, refinement and update.

Availability

http://puratham.googlepages.com/ or http://puratham.googlepages.com/ftpconnection     

3D structural model of the G-protein-coupled cannabinoid CB2 receptor

The potential for therapeutic specificity in regulating diseases and for reduced side effects has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets for drug discovery. The cannabinoid (CB) receptor subtype CB2 is of particular interest due to its involvement in signal transduction in the immune system and its increased characterization by mutational and other studies. However, our understanding of their mode of action has been limited by the absence of an experimental receptor structure. In this study, we have developed a 3D model of the CB2 receptor based on the recent crystal structure of a related GPCR, bovine rhodopsin. The model was developed using multiple sequence alignment of homologous receptor sub-types in humans and mammals, and compared with other GPCRs. Alignments were analyzed with mutation scores, pairwise hydrophobicity profiles and Kyte-Doolittle plots. The 3D model of the transmembrane segment was generated by mapping the CB2 sequence onto the homologous residues of the rhodopsin structure. The extra- and intracellular loop regions of the CB2 were generated by searching for homologous C(alpha) backbone sequences in published structures in the Brookhaven Protein Databank (PDB). Residue side chains were positioned through a combination of rotamer library searches, simulated annealing and minimization. Intermediate models of the 7TM helix bundles were analyzed in terms of helix tilt angles, hydrogen-bond networks, conserved residues and motifs, possible disulfide bonds. The amphipathic cytoplasmic helix domain was also correlated with biological and site-directed mutagenesis data. Finally, the model receptor-binding cavity was characterized using solvent-accessible surface approach.     

Homology model for the human GSTT2 theta class glutathione transferase

A tertiary model of the human GSTT2 Theta class glutathione transferase is presented based on the recently solved crystal structure of a related thetalike isoenzyme from Lucilia cuprina. Although the N-terminal domains are quite homologous, the C-terminal domains share less than about 20% identity. The model is used to consolidate the role of Ser 11 in the active site of the enzyme as well as to identify other residues and mechanisms of likely catalytic importance. The T2 subfamily of theta class enzymes have been shown to inactivate reactive sulfate esters arising from arylmethanols. A possible reaction pathway involving the conjugation of glutathione with one such sulfate ester, 1-menaphthyl-sulfate, is described. It is also proposed that the C-terminal region of the enzyme plays an important role in allowing substrate access to the active site. Proteins 27:118–130 © 1997 Wiley-Liss, Inc.