Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site.

MOTIVATION: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arise from a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as ΔΔG. RESULTS: We evaluated our algorithm extensively using cross-validation on two previously utilized datasets of single amino acid mutations and a (third) validation set. The results indicate that using known ΔΔG values of mutations at the query position improves the accuracy of ΔΔG predictions for other mutations in that position. The accuracy of our predictions in such cases significantly surpasses that of similar methods, achieving, e.g. a Pearson's correlation coefficient of 0.79 and a root mean square error of 0.96 on the validation set. Because Pro-Maya uses a diverse set of features, including predictions using two other methods, it also performs slightly better than other methods in the absence of additional experimental data on the query positions. AVAILABILITY: Pro-Maya is freely available via web server at http://bental.tau.ac.il/ProMaya. CONTACT: nirb@tauex.tau.ac.il; wolf@cs.tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MuD: an interactive web server for the prediction of non-neutral substitutions using protein structural data.

The discrimination between functionally neutral amino acid substitutions and non-neutral mutations, affecting protein function, is very important for our understanding of diseases. The rapidly growing amounts of experimental data enable the development of computational tools to facilitate the annotation of these substitutions. Here, we describe a Random Forests-based classifier, named Mutation Detector (MuD) that utilizes structural and sequence-derived features to assess the impact of a given substitution on the protein function. In its automatic mode, MuD is comparable to alternative tools in performance. However, the uniqueness of MuD is that user-reported protein-specific structural and functional information can be added at run-time, thereby enhancing the prediction accuracy further. The MuD server, available at http://mud.tau.ac.il, assigns a reliability score to every prediction, thus offering a useful tool for the prioritization of substitutions in proteins with an available 3D structure.

Mutations of JAK2 in acute lymphoblastic leukaemias associated with Down's syndrome.

BACKGROUND: Children with Down's syndrome have a greatly increased risk of acute megakaryoblastic and acute lymphoblastic leukaemias. Acute megakaryoblastic leukaemia in Down's syndrome is characterised by a somatic mutation in GATA1. Constitutive activation of the JAK/STAT (Janus kinase and signal transducer and activator of transcription) pathway occurs in several haematopoietic malignant diseases. We tested the hypothesis that mutations in JAK2 might be a common molecular event in acute lymphoblastic leukaemia associated with Down's syndrome. METHODS: JAK2 DNA mutational analysis was done on diagnostic bone marrow samples obtained from 88 patients with Down's syndrome-associated acute lymphoblastic leukaemia; and 216 patients with sporadic acute lymphoblastic leukaemia, Down's syndrome-associated acute megakaryoblastic leukaemia, and essential thrombocythaemia. Functional consequences of identified mutations were studied in mouse haematopoietic progenitor cells. FINDINGS: Somatically acquired JAK2 mutations were identified in 16 (18%) patients with Down's syndrome-associated acute lymphoblastic leukaemia. The only patient with non-Down's syndrome-associated leukaemia but with a JAK2 mutation had an isochromosome 21q. Children with a JAK2 mutation were younger (mean [SE] age 4.5 years [0.86] vs 8.6 years [0.59], p<0.0001) at diagnosis. Five mutant alleles were identified, each affecting a highly conserved arginine residue (R683). These mutations immortalised primary mouse haematopoietic progenitor cells in vitro, and caused constitutive Jak/Stat activation and cytokine-independent growth of BaF3 cells, which was sensitive to pharmacological inhibition with JAK inhibitor I. In modelling studies of the JAK2 pseudokinase domain, R683 was situated in an exposed conserved region separated from the one implicated in myeloproliferative disorders. INTERPRETATION: A specific genotype-phenotype association exists between the type of somatic mutation within the JAK2 pseudokinase domain and the development of B-lymphoid or myeloid neoplasms. Somatically acquired R683 JAK2 mutations define a distinct acute lymphoblastic leukaemia subgroup that is uniquely associated with trisomy 21. JAK2 inhibitors could be useful for treatment of this leukaemia. FUNDING: Israel Trade Ministry, Israel Science Ministry, Jewish National Fund UK, Sam Waxman Cancer Research Foundation, Israel Science Foundation, Israel Cancer Association, Curtis Katz, Constantiner Institute for Molecular Genetics, German-Israel Foundation, and European Commission FP6 Integrated Project EUROHEAR.

A hierarchical protein folding scheme based on the building block folding model.

The building block protein folding model states that the native protein structure is the product of a combinatorial assembly of relatively structurally independent contiguous parts of the protein that possess a hydrophobic core, i.e., building blocks (BBs). According to this model, our group proposed a three-stage scheme for a feasible time-wise semi ab-intio protein structure prediction. Given a protein sequence, at the first stage of the prediction scheme, we propose cutting the sequence into structurally assigned BBs. Next, we perform a combinatorial assembly and attempt to predict the relative three-dimensional arrangement of the BBs. In the third stage, we refine and rank the assemblies. The scheme has proven to be very promising in reducing the complexity of the protein folding problem and gaining insight into the protein folding process. In this chapter, we describe the different stages of the scheme and discuss a possible application of the model to protein design.

A permissive secondary structure-guided superposition tool for clustering of protein fragments toward protein structure prediction via fragment assembly.

MOTIVATION: Secondary-Structure Guided Superposition tool (SSGS) is a permissive secondary structure-based algorithm for matching of protein structures and in particular their fragments. The algorithm was developed towards protein structure prediction via fragment assembly. RESULTS: In a fragment-based structural prediction scheme, a protein sequence is cut into building blocks (BBs). The BBs are assembled to predict their relative 3D arrangement. Finally, the assemblies are refined. To implement this prediction scheme, a clustered structural library representing sequence patterns for protein fragments is essential. To create a library, BBs generated by cutting proteins from the PDB are compared and structurally similar BBs are clustered. To allow structural comparison and clustering of the BBs, which are often relatively short with flexible loops, we have devised SSGS. SSGS maintains high similarity between cluster members and is highly efficient. When it comes to comparing BBs for clustering purposes, the algorithm obtains better results than other, non-secondary structure guided protein superimposition algorithms.