Predicting fold novelty based on ProtoNet hierarchical classification

MOTIVATION: Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. RESULTS: We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies.     

Predicting drug targets based on protein domains

The identification of interactions between drugs and proteins plays key roles in understanding mechanisms underlying drug actions and can lead to new drug design strategies. Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains. The known target proteins of those drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to identification of potential drug-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed PDTD method.     

The leucine-rich repeat as a protein recognition motif

Leucine-rich repeats (LRRs) are 20-29-residue sequence motifs present in a number of proteins with diverse functions. The primary function of these motifs appears to be to provide a versatile structural framework for the formation of protein-protein interactions. The past two years have seen an explosion of new structural information on proteins with LRRs. The new structures represent different LRR subfamilies and proteins with diverse functions, including GTPase-activating protein rna1p from the ribonuclease-inhibitor-like subfamily; spliceosomal protein U2A', Rab geranylgeranyltransferase, internalin B, dynein light chain 1 and nuclear export protein TAP from the SDS22-like subfamily; Skp2 from the cysteine-containing subfamily; and YopM from the bacterial subfamily. The new structural information has increased our understanding of the structural determinants of LRR proteins and our ability to model such proteins with unknown structures, and has shed new light on how these proteins participate in protein-protein interactions.     

Modularity of the slit protein. Characterization of a conserved carboxy-terminal sequence in secreted proteins and a motif implicated in extracellular protein interactions.

Since our characterization of the slit cDNA sequence, encoding a protein secreted by glial cells and involved in the formation of axonal pathways in Drosophila, we have discovered that the protein contains two additional sequence motifs that are highly conserved in a variety of proteins. A search of the GenPept database with the 73 amino acids at the carboxy terminus of slit revealed that this region contains significant similarity to a carboxy-terminal domain found in six other exported proteins. This observation has allowed us to define a new carboxy-terminal protein motif. In addition, comparisons with a 202 amino acid domain residing between epidermal growth factor (EGF) repeats in slit shows this region to be conserved in laminin, agrin and perlecan and, strikingly, also to lie between EGF repeats in both agrin and perlecan. Our analysis suggests this motif is involved in mediating interactions among extracellular proteins. Consistent with our previous characterization of the slit protein, both new motifs are found only in extracellular proteins. The identification of these two conserved motifs in slit reveals that the entire 1469 amino acids of the protein are made up of modular regions similar to those conserved in other extracellular proteins.     

Novel search method for the discovery of functional relationships

MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.     

Lighting up cells: labelling proteins with fluorophores

During the past decade, rapid improvements have been made in the tools available for labelling proteins within cells, which has increased our ability to unravel the finer details of cellular events. One significant reason for these advances has been the development of fluorescent proteins that can be incorporated into proteins by genetic fusion to produce a fluorescent label. In addition, new techniques have made it possible to label proteins with small organic fluorophores and semiconductor nanocrystals.     

Validating annotations for uncharacterized proteins in Shewanella oneidensis

Proteins of unknown function are a barrier to our understanding of molecular biology. Assigning function to these "uncharacterized" proteins is imperative, but challenging. The usual approach is similarity searches using annotation databases, which are useful for predicting function. However, since the performance of these databases on uncharacterized proteins is basically unknown, the accuracy of their predictions is suspect, making annotation difficult. To address this challenge, we developed a benchmark annotation dataset of 30 proteins in Shewanella oneidensis. The proteins in the dataset were originally uncharacterized after the initial annotation of the S. oneidensis proteome in 2002. In the intervening 5 years, the accumulation of new experimental evidence has enabled specific functions to be predicted. We utilized this benchmark dataset to evaluate several commonly utilized annotation databases. According to our criteria, six annotation databases accurately predicted functions for at least 60% of proteins in our dataset. Two of these six even had a "conditional accuracy" of 90%. Conditional accuracy is another evaluation metric we developed which excludes results from databases where no function was predicted. Also, 27 of the 30 proteins' functions were correctly predicted by at least one database. These represent one of the first performance evaluations of annotation databases on uncharacterized proteins. Our evaluation indicates that these databases readily incorporate new information and are accurate in predicting functions for uncharacterized proteins, provided that experimental function evidence exists.     

Computational protein design: engineering molecular diversity, nonnatural enzymes, nonbiological cofactor complexes, and membrane proteins

Computational and theoretical methods are advancing protein design as a means to create and investigate proteins. Such efforts further our capacity to control, design and understand biomolecular structure, sequence and function. Herein, the focus is on some recent applications that involve using theoretical and computational methods to guide the design of protein sequence ensembles, new enzymes, proteins with novel cofactors, and membrane proteins.     

Expansins

Biochemical dissection of the "acid-growth" process of plant cell walls led to the isolation of a new class of wall loosening proteins, called expansins. These proteins affect the rheology of growing walls by permitting the microfibril matrix network to slide, thereby enabling the wall to expand. Molecular sequence analysis suggests that expansins might have a cryptic glycosyl transferase activity, but biochemical results suggest that expansins disrupt noncovalent bonding between microfibrils and the matrix. Recent discoveries of a new expansin family and gene expression in fruit meristems and cotton fibers have enlarged our view of the developmental functions of this group of wall loosening proteins.     

A framework for incorporating functional interrelationships into protein function prediction algorithms

The functional annotation of proteins is one of the most important tasks in the post-genomic era. Although many computational approaches have been developed in recent years to predict protein function, most of these traditional algorithms do not take interrelationships among functional terms into account, such as different GO terms usually coannotate with some common proteins. In this study, we propose a new functional similarity measure in the form of Jaccard coefficient to quantify these interrelationships and also develop a framework for incorporating GO term similarity into protein function prediction process. The experimental results of cross-validation on S. cerevisiae and Homo sapiens data sets demonstrate that our method is able to improve the performance of protein function prediction. In addition, we find that small size terms associated with a few of proteins obtain more benefit than the large size ones when considering functional interrelationships. We also compare our similarity measure with other two widely used measures, and results indicate that when incorporated into function prediction algorithms, our proposed measure is more effective. Experiment results also illustrate that our algorithms outperform two previous competing algorithms, which also take functional interrelationships into account, in prediction accuracy. Finally, we show that our method is robust to annotations in the database which are not complete at present. These results give new insights about the importance of functional interrelationships in protein function prediction.     

Understand the Functions of Scaffold Proteins in Cell Signaling by a Mesoscopic Simulation Method

Scaffold proteins are central players in regulating the spatial-temporal organization of many important signaling pathways in cells. They offer physical platforms to downstream signaling proteins so that their transient interactions in a crowded and heterogeneous environment of cytosol can be greatly facilitated. However, most scaffold proteins tend to simultaneously bind more than one signaling molecule, which leads to the spatial assembly of multimeric protein complexes. The kinetics of these protein oligomerizations are difficult to quantify by traditional experimental approaches. To understand the functions of scaffold proteins in cell signaling, we developed a, to our knowledge, new hybrid simulation algorithm in which both spatial organization and binding kinetics of proteins were implemented. We applied this new technique to a simple network system that contains three molecules. One molecule in the network is a scaffold protein, whereas the other two are its binding targets in the downstream signaling pathway. Each of the three molecules in the system contains two binding motifs that can interact with each other and are connected by a flexible linker. By applying the new simulation method to the model, we show that the scaffold proteins will promote not only thermodynamics but also kinetics of cell signaling given the premise that the interaction between the two signaling molecules is transient. Moreover, by changing the flexibility of the linker between two binding motifs, our results suggest that the conformational fluctuations in a scaffold protein play a positive role in recruiting downstream signaling molecules. In summary, this study showcases the capability of computational simulation in understanding the general principles of scaffold protein functions.     

Recent developments in the intracellular degradation of oxidized proteins

The accumulation of oxidized proteins in cells and tissues is a feature of a number of age-related diseases and may also occur as a result of the aging process itself. In this article we review recent advances in our understanding of the cellular degradation of oxidized proteins directing our attention primarily to information which directly bears on the behavior of intact eukaryotic cells. We summarize new work on the key intracellular degradative machineries, proteasomes and lysosomes and examine evidence implicating an increase in protein hydrophobicity as the primary signal to the proteasome to initiate degradation. The data identifying the proteasome as the main route of degradation of oxidized proteins is examined, as well as recent data investigating changes in proteasome function after exposure of cells to oxidants and the altered catabolism of oxidized proteins in aging cells. Evidence for the cooperation between the lysosomal and proteasomal systems in the degradation of oxidized proteins is discussed. We conclude that the cellular catabolism of oxidized proteins may be a more complex process than it first appeared and suggest key issues that need to be resolved to improve our understanding of this important process.     

Centromere and kinetochore structure.

Recent studies have begun to yield some insight into the structural and regulatory components of centromeres, and new assays have been developed that promise to be of use in advancing our understanding of centromere structure and function. In the budding yeast Saccharomyces cerevisiae new proteins that are required for centromere function have been identified and an in vitro microtubule-binding assay that should assist in dissecting the process of centromere microtubule attachment has been developed. The centromere-specific DNA sequences in the fission yeast Schizosaccharomyces pombe have been identified and partially characterized. In addition, several mammalian centromere proteins have been further characterized, and localization and inhibition studies suggest roles for these proteins in the regulation and assembly of a functional kinetochore.     

Kinesins in the spindle: an update

Eukaryotes contain a superfamily of microtubule-based motor proteins comprising kinesin and a number of related proteins that are thought to participate in various forms of intracellular motility, including cell division and organelle transport. The role of various members of the kinesin superfamily in chromosome segregation and spindle morphogenesis was described in TCB last year in parts of a series on cytoplasmic motor proteins. In this brief update, Helen Epstein and Jon Scholey comment on new findings that have improved our understanding of the functions of kinesin-related proteins in mitosis and meiosis.     

Expanisns

Biochemical dissection of the “acid-growth” process of plant cell walls led to the isolation of a new class of wall loosening proteins, called expansins. These proteins affect the rheology of growing walls by permitting the microfibril-matrix network to slide, thereby enabling the wall to expand. Molecular sequence analysis suggests that expansins might have a cryptic glycosyl transferase activity, but biochemical results suggest that expansins disrupt noncovalent bonding between microfibrils and the matrix. Recent discoveries of a new expansin family and gene expression in fruit, meristerms and cotton fibers have enlarged our view of the developmental functions of this group of wall loosening proteins.     

Coiled-Coil Proteins Facilitated the Functional Expansion of the Centrosome

Repurposing existing proteins for new cellular functions is recognized as a main mechanism of evolutionary innovation, but its role in organelle evolution is unclear. Here, we explore the mechanisms that led to the evolution of the centrosome, an ancestral eukaryotic organelle that expanded its functional repertoire through the course of evolution. We developed a refined sequence alignment technique that is more sensitive to coiled coil proteins, which are abundant in the centrosome. For proteins with high coiled-coil content, our algorithm identified 17% more reciprocal best hits than BLAST. Analyzing 108 eukaryotic genomes, we traced the evolutionary history of centrosome proteins. In order to assess how these proteins formed the centrosome and adopted new functions, we computationally emulated evolution by iteratively removing the most recently evolved proteins from the centrosomal protein interaction network. Coiled-coil proteins that first appeared in the animal–fungi ancestor act as scaffolds and recruit ancestral eukaryotic proteins such as kinases and phosphatases to the centrosome. This process created a signaling hub that is crucial for multicellular development. Our results demonstrate how ancient proteins can be co-opted to different cellular localizations, thereby becoming involved in novel functions.     

In silico chaperonin-like cycle helps folding of proteins for structure prediction

Currently, one of the most serious problems in protein-folding simulations for de novo structure prediction is conformational sampling of medium-to-large proteins. In vivo, folding of these proteins is mediated by molecular chaperones. Inspired by the functions of chaperonins, we designed a simple chaperonin-like simulation protocol within the framework of the standard fragment assembly method: in our protocol, the strength of the hydrophobic interaction is periodically modulated to help the protein escape from misfolded structures. We tested this protocol for 38 proteins and found that, using a certain defined criterion of success, our method could successfully predict the native structures of 14 targets, whereas only those of 10 targets were successfully predicted using the standard protocol. In particular, for non-α-helical proteins, our method yielded significantly better predictions than the standard approach. This chaperonin-inspired protocol that enhanced de novo structure prediction using folding simulations may, in turn, provide new insights into the working principles underlying the chaperonin system.