Identification of a novel IVD mutation in a consanguineous family with isovaleric acidemia

Isovaleric acidemia (IVA) is a rare autosomal recessive disorder caused by a deficiency of isovaleryl-CoA dehydrogenase encoded by IVD gene. In this case study we report the first Saudi IVA patients from a consanguineous family with a novel transversion (p.G362V) and briefly discuss likely phenotype–genotype correlation of the disease in the Saudi population. We explored the functional consequences of the mutation by using various bioinformatics prediction algorithms and discussed the likely mechanism of the disease caused by the mutation.     

Protein family clustering for structural genomics

A major goal of structural genomics is the provision of a structural template for a large fraction of protein domains. The magnitude of this task depends on the number and nature of protein sequence families. With a large number of bacterial genomes now fully sequenced, it is possible to obtain improved estimates of the number and diversity of families in that kingdom. We have used an automated clustering procedure to group all sequences in a set of genomes into protein families. Bench-marking shows the clustering method is sensitive at detecting remote family members, and has a low level of false positives. This comprehensive protein family set has been used to address the following questions. (1) What is the structure coverage for currently known families? (2) How will the number of known apparent families grow as more genomes are sequenced? (3) What is a practical strategy for maximizing structure coverage in future? Our study indicates that approximately 20% of known families with three or more members currently have a representative structure. The study indicates also that the number of apparent protein families will be considerably larger than previously thought: We estimate that, by the criteria of this work, there will be about 250,000 protein families when 1000 microbial genomes have been sequenced. However, the vast majority of these families will be small, and it will be possible to obtain structural templates for 70-80% of protein domains with an achievable number of representative structures, by systematically sampling the larger families.     

Comparison of sequence and structure-based datasets for nonredundant structural data mining

Structural data mining studies attempt to deduce general principles of protein structure from solved structures deposited in the protein data bank (PDB). The entire database is unsuitable for such studies because it is not representative of the ensemble of protein folds. Given that novel folds continue to be unearthed, some folds are currently unrepresented in the PDB while other folds are overrepresented. Overrepresentation can easily be avoided by filtering the dataset. PDB_SELECT is a well-used representative subset of the PDB that has been deduced by sequence comparison. Specifically, structures with sequences that exhibit a pairwise sequence identity above a threshold value are weeded from the dataset. Although length criteria for pairwise alignments have a structural basis, this automated method of pruning is essentially sequence-based and runs into problems in the twilight zone, possibly resulting in some folds being overrepresented. The value-added structure databases SCOP and CATH are also a potential source of a nonredundant dataset. Here we compare the sequence-derived dataset PDB_SELECT with the structural databases SCOP (Structural Classification Of Proteins) and CATH (Class-Architecture-Topology-Homology). We show that some folds remain overrepresented in the PDB_SELECT dataset while other folds are not represented at all. However, SCOP and CATH also have their own problems such as the labor-intensiveness of the update process and the problem of determining whether all folds are equally or sufficiently distant. We discuss areas where further work is required.     

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.     

Dynamics alignment: comparison of protein dynamics in the SCOP database

A novel methodology for comparison of protein dynamics is presented. Protein dynamics is calculated using the Gaussian network model and the modes of motion are globally aligned using the dynamic programming algorithm of Needleman and Wunsch, commonly used for sequence alignment. The alignment is fast and can be used to analyze large sets of proteins. The methodology is applied to the four major classes of the SCOP database: "all alpha proteins," "all beta proteins," "alpha and beta proteins," and "alpha/beta proteins". We show that different domains may have similar global dynamics. In addition, we report that the dynamics of "all alpha proteins" domains are less specific to structural variations within a given fold or superfamily compared with the other classes. We report that domain pairs with the most similar and the least similar global dynamics tend to be of similar length. The significance of the methodology is that it suggests a new and efficient way of mapping between the global structural features of protein families/subfamilies and their encoded dynamics.     

Predicting protein structural class by SVM with class-wise optimized features and decision probabilities

Determination of protein structural class solely from sequence information is a challenging task. Several attempts to solve this problem using various methods can be found in literature. We present support vector machine (SVM) approach where probability-based decision is used along with class-wise optimized feature sets. This approach has two distinguishing characteristics from earlier attempts: (1) it uses class-wise optimized features and (2) decisions of different SVM classifiers are coupled with probability estimates to make the final prediction. The algorithm was tested on three datasets, containing 498 domains, 1092 domains and 5261 domains. Ten-fold external cross-validation was performed to assess the performance of the algorithm. Significantly high accuracy of 92.89% was obtained for the 498-dataset. We achieved 54.67% accuracy for the dataset with 1092 domains, which is better than the previously reported best accuracy of 53.8%. We obtained 59.43% prediction accuracy for the larger and less redundant 5261-dataset. We also investigated the advantage of using class-wise features over union of these features (conventional approach) in one-vs.-all SVM framework. Our results clearly show the advantage of using class-wise optimized features. Brief analysis of the selected class-wise features indicates their biological significance.     

A comparison of SCOP and CATH with respect to domain-domain interactions

The analysis and prediction of protein-protein interaction sites from structural data are restricted by the limited availability of structural complexes that represent the complete protein-protein interaction space. The domain classification schemes CATH and SCOP are normally used independently in the analysis and prediction of protein domain-domain interactions. In this article, the effect of different domain classification schemes on the number and type of domain-domain interactions observed in structural data is systematically evaluated for the SCOP and CATH hierarchies. Although there is a large overlap in domain assignments between SCOP and CATH, 23.6% of CATH interfaces had no SCOP equivalent and 37.3% of SCOP interfaces had no CATH equivalent in a nonredundant set. Therefore, combining both classifications gives an increase of between 23.6 and 37.3% in domain-domain interfaces. It is suggested that if possible, both domain classification schemes should be used together, but if only one is selected, SCOP provides better coverage than CATH. Employing both SCOP and CATH reduces the false negative rate of predictive methods, which employ homology matching to structural data to predict protein-protein interaction by an estimated 6.5%.     

OPUS-Dom: Applying the Folding-Based Method VECFOLD to Determine Protein Domain Boundaries

In this article, we present a de novo method for predicting protein domain boundaries, called OPUS-Dom. The core of the method is a novel coarse-grained folding method, VECFOLD, which constructs low-resolution structural models from a target sequence by folding a chain of vectors representing the predicted secondary-structure elements. OPUS-Dom generates a large ensemble of folded structure decoys by VECFOLD and labels the domain boundaries of each decoy by a domain parsing algorithm. Consensus domain boundaries are then derived from the statistical distribution of the putative boundaries and three empirical sequence-based domain profiles. OPUS-Dom generally outperformed several state-of-the-art domain prediction algorithms over various benchmark protein sets. Even though each VECFOLD-generated structure contains large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary-structure segments, rather than sequence-specific constraints.     

Domain-Based and Family-Specific Sequence Identity Thresholds Increase the Levels of Reliable Protein Function Transfer

Divergence in function of homologous proteins is based on both sequence and structural changes. Overall enzyme function has been reported to diverge earlier (50% sequence identity) than overall structure (35%). We herein study the functional conservation of enzymes and non-enzyme sequences using the protein domain families in CATH-Gene3D. Despite the rapid increase in sequence data since the last comprehensive study by Tian and Skolnick, our findings suggest that generic thresholds of 40% and 60% aligned sequence identity are still sufficient to safely inherit third-level and full Enzyme Commission numbers, respectively. This increases to 50% and 70% on the domain level, unless the multi-domain architecture matches. Assignments from the Kyoto Encyclopedia of Genes and Genomes and the Munich Information Center for Protein Sequences Functional Catalogue seem to be less conserved with sequence, probably due to a more pathway-centric view: 80% domain sequence identity is required for safe function transfer. Comparing domains (more pairwise relationships) and the use of family-specific thresholds (varying evolutionary speeds) yields the highest coverage rates when transferring functions to model proteomes. An average twofold increase in enzyme annotations is seen for 523 proteomes in Gene3D. As simple ‘rules of thumb’, sequence identity thresholds do not require a bioinformatics background. We will provide and update this information with future releases of CATH-Gene3D.     

Insulin receptor kinase‐independent signaling via tyrosine phosphorylation of phosphatase PHLPP1

Most insulin responses correlate well with insulin receptor (IR) Tyr kinase activation; however, critical exceptions to this concept have been presented. Specific IR mutants and stimulatory IR antibodies demonstrate a lack of correlation between IR kinase activity and specific insulin responses in numerous independent studies. IR conformation changes in response to insulin observed with various IR antibodies define an IR kinase‐independent signal that alters the C‐terminus. IR‐related receptors in lower eukaryotes that lack a Tyr kinase point to an alternative mechanism of IR signaling earlier in evolution. However, the implied IR kinase‐independent signaling mechanism remained obscure at the molecular level. Here we begin to define the molecular basis of an IR‐dependent but IR kinase‐independent insulin signal that is equally transmitted by a kinase‐inactive mutant IR. This insulin signal results in Tyr phosphorylation and catalytic activation of phosphatase PHLPP1 via a PI 3‐kinase‐independent, wortmannin‐insensitive signaling pathway. Dimerized SH2B1/PSM is a critical activator of the IR kinase and the resulting established insulin signal. In contrast it is an inhibitor of the IR kinase‐independent insulin signal and disruption of SH2B1/PSM dimer binding to IR potentiates this signal. Dephosphorylation of Akt2 by PHLPP1 provides an alternative, SH2B1/PSM‐regulated insulin‐signaling pathway from IR to Akt2 of opposite polarity and distinct from the established PI 3‐kinase‐dependent signaling pathway via IRS proteins. In combination, both pathways should allow the opposing regulation of Akt2 activity at two phosphorylation sites to specifically define the insulin signal in the background of interfering Akt‐regulating signals, such as those controlling cell proliferation and survival. J. Cell. Biochem. 107: 65–75, 2009. © 2009 Wiley‐Liss, Inc.