Will my protein crystallize? A sequence‐based predictor

We propose a machine-learning approach to sequence-based prediction of protein crystallizability in which we exploit subtle differences between proteins whose structures were solved by X-ray analysis [or by both X-ray and nuclear magnetic resonance (NMR) spectroscopy] and those proteins whose structures were solved by NMR spectroscopy alone. Because the NMR technique is usually applied on relatively small proteins, sequence length distributions of the X-ray and NMR datasets were adjusted to avoid predictions biased by protein size. As feature space for classification, we used frequencies of mono-, di-, and tripeptides represented by the original 20-letter amino acid alphabet as well as by several reduced alphabets in which amino acids were grouped by their physicochemical and structural properties. The classification algorithm was constructed as a two-layered structure in which the output of primary support vector machine classifiers operating on peptide frequencies was combined by a second-level Naive Bayes classifier. Due to the application of metamethods for cost sensitivity, our method is able to handle real datasets with unbalanced class representation. An overall prediction accuracy of 67% [65% on the positive (crystallizable) and 69% on the negative (noncrystallizable) class] was achieved in a 10-fold cross-validation experiment, indicating that the proposed algorithm may be a valuable tool for more efficient target selection in structural genomics. A Web server for protein crystallizability prediction called SECRET is available at http://webclu.bio.wzw.tum.de:8080/secret.     

PROMPT: a protein mapping and comparison tool

Background  

Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available.     

Structural mechanisms of the degenerate sequence recognition by Bse634I restriction endonuclease

Restriction endonuclease Bse634I recognizes and cleaves the degenerate DNA sequence 5'-R/CCGGY-3' (R stands for A or G; Y for T or C, '/' indicates a cleavage position). Here, we report the crystal structures of the Bse634I R226A mutant complexed with cognate oligoduplexes containing ACCGGT and GCCGGC sites, respectively. In the crystal, all potential H-bond donor and acceptor atoms on the base edges of the conserved CCGG core are engaged in the interactions with Bse634I amino acid residues located on the α6 helix. In contrast, direct contacts between the protein and outer base pairs are limited to van der Waals contact between the purine nucleobase and Pro203 residue in the major groove and a single H-bond between the O2 atom of the outer pyrimidine and the side chain of the Asn73 residue in the minor groove. Structural data coupled with biochemical experiments suggest that both van der Waals interactions and indirect readout contribute to the discrimination of the degenerate base pair by Bse634I. Structure comparison between related enzymes Bse634I (R/CCGGY), NgoMIV (G/CCGGC) and SgrAI (CR/CCGGYG) reveals how different specificities are achieved within a conserved structural core.     

New Insights into the Role of DNA Shape on Its Recognition by p53 Proteins

1. Download high-res image (346KB)
2. Download full-size image

     

Protein function annotation by homology-based inference

With many genomes now sequenced, computational annotation methods to characterize genes and proteins from their sequence are increasingly important. The BioSapiens Network has developed tools to address all stages of this process, and here we review progress in the automated prediction of protein function based on protein sequence and structure.     

TargetSpy: a supervised machine learning approach for microRNA target prediction

Background  

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.     

The ancient cell death suppressor BAX inhibitor-1

Bax inhibitor-1 (BI-1) was initially identified for its ability to inhibit BAX-induced apoptosis in yeast cells and is the founding member of a family of highly hydrophobic proteins localized in diverse cellular membranes. It is evolutionarily conserved and orthologues from plants can substitute for mammalian BI-1 in regard to its anti-apoptotic function suggesting a high degree of functional conservation. BI-1 interacts with BCL-2 and BCL-XL and, similar to these two anti-apoptotic proteins, the effect of BI-1 on cell death involves changes in the amount of Ca²⁺ releasable from intracellular stores. However, BI-1 is also a negative regulator of the endoplasmic reticulum stress sensor IRE1 α, it interacts with G-actin and increases actin polymerization, enhances cancer metastasis by altering glucose metabolism and activating the sodium-hydrogen exchanger, and reduces the production of reactive oxygen species through direct interaction with NADPH-P450 reductase. In this contribution, we summarize what is known about the expression, intracellular localization and structure of BI-1 and specifically illuminate its effects on the intracellular Ca²⁺ homeostasis and how this might relate to its other functions. We also present a thorough phylogenetic analysis of BI-1 proteins from major phyla together with paralogues from all BI-1 family members.