SAM-T04: what is new in protein-structure prediction for CASP6.

The SAM-T04 method for predicting protein structures uses a single protocol across the entire range of targets, from comparative modeling to new folds. This protocol is similar to the SAM-T02 protocol used in CASP5, but has improvements in the iterative search for similar sequences in finding and aligning templates, in creating fragment libraries, in generating protein conformations, and in scoring the conformations. The automatic procedure made some improvements over simply selecting an alignment to the highest-scoring template, and human intervention made substantial improvements over the automatic procedure. The main improvements made by human intervention were from adding constraints to build (or retain) beta-sheets and from splitting multidomain proteins into separate domains. The uniform protocol was moderately successful across the entire range of target difficulty, but was somewhat less successful than other approaches in CASP6 on the comparative modeling targets.

Combining local-structure, fold-recognition, and new fold methods for protein structure prediction.

This article presents an overview of the SAM-T02 method for protein fold recognition and the UNDERTAKER program for ab initio predictions. The SAM-T02 server is an automatic method that uses two-track hidden Markov models (HMMS) to find and align template proteins from PDB to the target protein. The two-track HMMs use an amino acid alphabet and one of several different local structure alphabets. The UNDERTAKER program is a new fragment-packing program that can use short or long fragments and alignments to create protein conformations. The HMMs and fold-recognition alignments from the SAM-T02 method were used to generate the fragment and alignment libraries used by UNDERTAKER. We present results on a few selected targets for which this combined method worked particularly well: T0129, T0181, T0135, T0130, and T0139.

Calibrating E-values for hidden Markov models using reverse-sequence null models.

MOTIVATION: Hidden Markov models (HMMs) calculate the probability that a sequence was generated by a given model. Log-odds scoring provides a context for evaluating this probability, by considering it in relation to a null hypothesis. We have found that using a reverse-sequence null model effectively removes biases owing to sequence length and composition and reduces the number of false positives in a database search. Any scoring system is an arbitrary measure of the quality of database matches. Significance estimates of scores are essential, because they eliminate model- and method-dependent scaling factors, and because they quantify the importance of each match. Accurate computation of the significance of reverse-sequence null model scores presents a problem, because the scores do not fit the extreme-value (Gumbel) distribution commonly used to estimate HMM scores' significance. RESULTS: To get a better estimate of the significance of reverse-sequence null model scores, we derive a theoretical distribution based on the assumption of a Gumbel distribution for raw HMM scores and compare estimates based on this and other distribution families. We derive estimation methods for the parameters of the distributions based on maximum likelihood and on moment matching (least-squares fit for Student's t-distribution). We evaluate the modeled distributions of scores, based on how well they fit the tail of the observed distribution for data not used in the fitting and on the effects of the improved E-values on our HMM-based fold-recognition methods. The theoretical distribution provides some improvement in fitting the tail and in providing fewer false positives in the fold-recognition test. An ad hoc distribution based on assuming a stretched exponential tail does an even better job. The use of Student's t to model the distribution fits well in the middle of the distribution, but provides too heavy a tail. The moment-matching methods fit the tails better than maximum-likelihood methods. AVAILABILITY: Information on obtaining the SAM program suite (free for academic use), as well as a server interface, is available at http://www.soe.ucsc.edu/research/compbio/sam.html and the open-source random sequence generator with varying compositional biases is available at http://www.soe.ucsc.edu/research/compbio/gen_sequence

Alkaline hemolysis fragility is dependent on cell shape: results from a morphology tracker.

BACKGROUND: The morphometric analysis of red blood cells (RBCs) is an important area of study and has been performed previously for fixed samples. We present a novel method for the analysis of morphologic changes of live erythrocytes as a function of time. We use this method to extract information on alkaline hemolysis fragility. Many other toxins lyse cells by membrane poration, which has been studied by averaging over cell populations. However, no quantitative data are available for changes in the morphology of individual cells during membrane poration-driven hemolysis or for the relation between cell shape and fragility. METHODS: Hydroxide, a porating agent, was generated in a microfluidic enclosure containing RBCs in suspension. Automatic cell recognition, tracking, and morphometric measurements were done by using a custom image analysis program. Cell area and circular shape factor (CSF) were measured over time for individual cells. Implementations were developed in MATLAB and on Kestrel, a parallel computer that affords higher speed that approaches real-time processing. RESULTS: The average CSF went through a first period of fast increase, corresponding to the conversion of discocytes to spherocytes under internal osmotic pressure, followed by another period of slow increase until the fast lysis event. For individual cells, the initial CSF was shown to be inversely correlated to cell lifetime (linear regression factor R=0.44), with discocytes surviving longer than spherocytes. The inflated cell surface area to volume ratio was also inversely correlated to lifetime (R=0.43) but not correlated to the CSF. Lifetime correlated best to the ratio of cell inflation volume (Vfinal-Vinitial) to surface area (R=0.65). CONCLUSIONS: RBCs inflate at a rate proportional to their surface area, in agreement with a constant flux model, and lyse after attaining a spherical morphology. Spherical RBCs display increased alkaline hemolysis fragility (shorter lifetimes), providing an explanation for the increased osmotic fragility of RBCs from patients who have spherocytosis.

Predicting reliable regions in protein sequence alignments.

MOTIVATION: Protein sequence alignments have a myriad of applications in bioinformatics, including secondary and tertiary structure prediction, homology modeling, and phylogeny. Unfortunately, all alignment methods make mistakes, and mistakes in alignments often yield mistakes in their application. Thus, a method to identify and remove suspect alignment positions could benefit many areas in protein sequence analysis. RESULTS: We tested four predictors of alignment position reliability, including near-optimal alignment information, column score, and secondary structural information. We validated each predictor against a large library of alignments, removing positions predicted as unreliable. Near-optimal alignment information was the best predictor, removing 70% of the substantially-misaligned positions and 58% of the over-aligned positions, while retaining 86% of those aligned accurately.