Biomedical subjects

Kevin Karplus

Publications and source records attributed to Kevin Karplus.

10 recordsLinked to original sources

PubMed2006

Identification and characterization of RbmA, a novel protein required for the development of rugose colony morphology and biofilm structure in Vibrio cholerae.

Phase variation between smooth and rugose colony variants of Vibrio cholerae is predicted to be important for the pathogen's survival in its natural aquatic ecosystems. The rugose variant forms corrugated colonies, exhibits increased levels of resistance to osmotic, acid, and oxidative stresses, and has an enhanced capacity to form biofilms. Many of these phenotypes are mediated in part by increased production of an exopolysaccharide termed VPS. In this study, we compared total protein profiles of the smooth and rugose variants using two-dimensional gel electrophoresis and identified one protein that is present at a higher level in the rugose variant. A mutation in the gene encoding this protein, which does not have any known homologs in the protein databases, causes cells to form biofilms that are more fragile and sensitive to sodium dodecyl sulfate than wild-type biofilms. The results indicate that the gene, termed rbmA (rugosity and biofilm structure modulator A), is required for rugose colony formation and biofilm structure integrity in V. cholerae. Transcription of rbmA is positively regulated by the response regulator VpsR but not VpsT.

Bacterial Proteins↗

PubMed2005

Methods of translating NMR proton distances into their corresponding heavy atom distances for protein structure prediction with limited experimental data.

This paper proposes a strategy to translate experimental 1H NMR proton distance restraints into their corresponding heavy atom distance restraints for the purpose of protein structure prediction. The relationships between interproton distances and the corresponding heavy atom distances are determined by studying well-resolved X-ray protein structures. The data from the interproton distances of amide protons, alpha-protons, beta-protons and side chain methyl protons are plotted against the corresponding heavy atoms in scatter plots and then fitted with linear equations for lower bounds, upper bounds and optimal fits. We also transform the scatter plots into two-dimensional heat maps and three-dimensional histograms, which identify the regions where data points concentrate. The common interproton distances between amide protons, alpha-protons, beta-protons in alpha-helices, anti-parallel beta-sheets and parallel beta-sheets are also tabulated. We have found several patterns emerging from the distance relationships between heavy atom pairs and their corresponding proton pairs. All our upper bound, lower bound and optimal fit results for translating the interproton distance into their corresponding heavy atom distances are tabulated.

Algorithms↗

PubMed2005

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

Algorithms↗

PubMed2005

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.

Algorithms↗

PubMed2004

Evaluation of local structure alphabets based on residue burial.

Residue burial, which describes a protein residue's exposure to solvent and neighboring atoms, is key to protein structure prediction, modeling, and analysis. We assessed 21 alphabets representing residue burial, according to their predictability from amino acid sequence, conservation in structural alignments, and utility in one fold-recognition scenario. This follows upon our previous work in assessing nine representations of backbone geometry.1 The alphabet found to be most effective overall has seven states and is based on a count of C(beta) atoms within a 14 A-radius sphere centered at the C(beta) of a residue of interest. When incorporated into a hidden Markov model (HMM), this alphabet gave us a 38% performance boost in fold recognition and 23% in alignment quality.

Amino Acids↗

PubMed2003

Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry.

An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE.

Algorithms↗

PubMed2003

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.

Algorithms↗

PubMed2002

Information-theoretic dissection of pairwise contact potentials.

Pairwise contact potentials have a long, successful history in protein structure prediction. They provide an easily-estimated representation of many attributes of protein structures, such as the hydrophobic effect. In order to improve on existing potentials, one should develop a clear understanding of precisely what information they convey. Here, using mutual information, we quantified the information in amino acid potentials, and the importance of hydropathy, charge, disulfide bonding, and burial. Sampling error in mutual information was controlled for by estimating how much information cannot be attributed to sampling bias. We found the information in amino acid contacts to be modest: 0.04 bits per contact. Of that, only 0.01 bits of information could not be attributed to hydropathy, charge, disulfide bonding, or burial.

Amino Acids↗

PubMed2002

Classifying G-protein coupled receptors with support vector machines.

MOTIVATION: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are found in a wide range of organisms and are central to a cellular signalling network that regulates many basic physiological processes. They are the focus of a significant amount of current pharmaceutical research because they play a key role in many diseases. However, their tertiary structures remain largely unsolved. The methods described in this paper use only primary sequence information to make their predictions. We compare a simple nearest neighbor approach (BLAST), methods based on multiple alignments generated by a statistical profile Hidden Markov Model (HMM), and methods, including Support Vector Machines (SVMs), that transform protein sequences into fixed-length feature vectors. RESULTS: The last is the most computationally expensive method, but our experiments show that, for those interested in annotation-quality classification, the results are worth the effort. In two-fold cross-validation experiments testing recognition of GPCR subfamilies that bind a specific ligand (such as a histamine molecule), the errors per sequence at the Minimum Error Point (MEP) were 13.7% for multi-class SVMs, 17.1% for our SVMtree method of hierarchical multi-class SVM classification, 25.5% for BLAST, 30% for profile HMMs, and 49% for classification based on nearest neighbor feature vector Kernel Nearest Neighbor (kernNN). The percentage of true positives recognized before the first false positive was 65% for both SVM methods, 13% for BLAST, 5% for profile HMMs and 4% for kernNN.

Algorithms↗

PubMed2002

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.

Algorithms↗