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Judith Klein-Seetharaman

Publications and source records attributed to Judith Klein-Seetharaman.

At least 19 recordsLinked to original sources
PubMed2025

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC.

Despite the widespread availability of genome sequencing pipelines, many genes remain part of the genome's "dark matter," where existing inference tools cannot even begin to guess the biological function of their proteins from sequence alone. This challenge is especially pronounced in organisms that are highly evolutionarily distant from well-studied models, where homology-based methods break down. Here, we describe PHILHARMONIC, a computational method that combines deep learning-based de novo protein interaction network inference with robust unsupervised spectral clustering and remote homology to illuminate functional organization in any non-model organism. From only a sequenced proteome, we show PHILHARMONIC predicts protein functions, functional communities, and higher-order network structure with high accuracy. We validate its performance using experimental gene expression and pathway data in D. melanogaster, and we demonstrate its broad utility by analyzing temperature sensing and stress response pathways in the reef-building coral P. damicornis and its algal symbiont C. goreaui. PHILHARMONIC provides a general-purpose engine for functional discovery and biological hypothesis generation in non-model organisms, enabling systems-level insights across the full diversity of life.

Journal Article↗
PubMed2006

Predisposition of the dark state of rhodopsin to functional changes in structure.

As the only member of the family of G-protein-coupled receptors for which atomic coordinates are available, rhodopsin is widely studied for insight into the molecular mechanism of G-protein-coupled receptor activation. The currently available structures refer to the inactive, dark state, of rhodopsin, rather than the light-activated metarhodopsin II (Meta II) state. A model for the Meta II state is proposed here by analyzing elastic network normal modes in conjunction with experimental data. Key mechanical features and interactions broken/formed in the proposed model are found to be consistent with the experimental data. The model is further tested by using a set of Meta II fluorescence decay rates measured to empirically characterize the deactivation of rhodopsin mutants. The model is found to correctly predict 93% of the experimentally observed effects in 119 rhodopsin mutants for which the decay rates and misfolding data have been measured, including a systematic analysis of Cys-->Ser replacements reported here. Based on the detailed comparison between model and experiments, a cooperative activation mechanism is deduced that couples retinal isomerization to concerted changes in conformation, facilitated by the intrinsic dynamics of rhodopsin. A global hinge site is identified near the retinal-binding pocket that ensures the efficient propagation of signals from the central transmembrane region to both cytoplasmic and extracellular ends. The predicted activation mechanism opens the transmembrane helices at the critical G-protein binding cytoplasmic domain. This model provides a detailed, mechanistic description of the activation process, extending experimental observations and yielding new insights for further tests.

Algorithms↗
PubMed2006

Shape and oligomerization state of the cytoplasmic domain of the phototaxis transducer II from Natronobacterium pharaonis.

Phototaxis allows archaea to adjust flagellar motion in response to light. In the photophobic response of Natronobacterium pharaonis, light-activated sensory rhodopsin II causes conformational changes in the transducer II protein (pHtrII), initiating the two-component signaling system analogous to bacterial chemotaxis. pHtrII's cytoplasmic domain (pHtrII-cyt) is homologous to the cytoplasmic domains of eubacterial chemotaxis receptors. Chemotaxis receptors require dimerization for activity and are in vivo-organized in large clusters. In this study we investigated the oligomerization and aggregation states of pHtrII-cyt by using chemical cross-linking, analytical gel-filtration chromatography, and small-angle neutron scattering. We show that pHtrII-cyt is monomeric in dilute buffers, but forms dimers in 4 M KCl, the physiological salt concentration for halophilic archaea. At high ammonium sulfate concentration, the protein forms higher-order aggregates. The monomeric protein has a rod-like shape, 202 A in length and 14.4 A in diameter; upon dimerization the length increases to 248 A and the diameter to 18.2 A. These results suggest that under high salt concentration the shape and oligomerization state of pHtrII-cyt are comparable to those of chemotaxis receptors.

Archaeal Proteins↗
PubMed2006

Retinitis pigmentosa associated with rhodopsin mutations: Correlation between phenotypic variability and molecular effects.

Similar retinitis pigmentosa (RP) phenotypes can result from mutations affecting different rhodopsin regions, and distinct amino acid substitutions can cause different RP severity and progression rates. Specifically, both the R135L and R135W mutations (cytoplasmic end of H3) result in diffuse, severe disease (class A), but R135W causes more severe and more rapidly progressive RP than R135L. The P180A and G188R mutations (second intradiscal loop) exhibit a mild phenotype with regional variability (class B1) and diffuse disease of moderate severity (class B2), respectively. Computational and in vitro studies of these mutants provide molecular insights into this phenotypic variability.

Adolescent↗
PubMed2006

Evaluation of different biological data and computational classification methods for use in protein interaction prediction.

Protein-protein interactions play a key role in many biological systems. High-throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false-positive and false-negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co-complex relationship, and (3) pathway co-membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity-based k-Nearest-Neighbor, Naïve Bayes, Decision Tree, Logistic Regression, and Support Vector Machine. For all classifiers, the three prediction tasks had different success rates, and co-complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consistently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting function of the RF tree structure, the Gini index, to estimate feature importance. Second, we determined classification accuracy when only the top-ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast-2-hybrid system were not among the top-ranking features under any condition.

Computational Biology↗
PubMed2005

Glutamic acid-rich proteins of rod photoreceptors are natively unfolded.

The outer segment of vertebrate photoreceptors is a specialized compartment that hosts all the signaling components required for visual transduction. Specific to rod photoreceptors is an unusual set of three glutamic acid-rich proteins (GARPs) as follows: two soluble forms, GARP1 and GARP2, and the N-terminal cytoplasmic domain (GARP' part) of the B1 subunit of the cyclic GMP-gated channel. GARPs have been shown to interact with proteins at the rim of the disc membrane. Here we characterized native GARP1 and GARP2 purified from bovine rod photoreceptors. Amino acid sequence analysis of GARPs revealed structural features typical of "natively unfolded" proteins. By using biophysical techniques, including size-exclusion chromatography, dynamic light scattering, NMR spectroscopy, and circular dichroism, we showed that GARPs indeed exhibit a large degree of intrinsic disorder. Analytical ultracentrifugation and chemical cross-linking showed that GARPs exist in a monomer/multimer equilibrium. The results suggested that the function of GARP proteins is linked to their structural disorder. They may provide flexible spacers or linkers tethering the cyclic GMP-gated channel in the plasma membrane to peripherin at the disc rim to produce a stack of rings of these protein complexes along the long axis of the outer segment. GARP proteins could then provide the environment needed for protein interactions in the rim region of discs.

Amino Acid Sequence↗
PubMed2005

Protein classification based on text document classification techniques.

The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM) using alignment-based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naive Bayes classifiers with chi-square feature selection on counts of n-grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naive Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively.

Algorithms↗
PubMed2005

Dual role of interactions between membranous and soluble portions of helical membrane receptors for folding and signaling.

Dramatic advances in the understanding of the molecular mechanisms of membrane receptor activation for several prototypic members of different families of receptors have taken place during the past 2-3 years. The new structures of receptor fragments or full-length receptors in different conformations have been reviewed previously in light of the large bodies of available structure-function data. However, in this article, we will compare, among different receptor families, the emerging paradigms for conformational changes during signaling. Recent advances in the understanding of membrane-protein folding suggest that these paradigms are closely related to those that describe the folding and structural stability of membrane proteins. These relate in particular to long-range interactions not only within but also between soluble or membrane-embedded parts of proteins.

Animals↗
PubMed2005

Random forest similarity for protein-protein interaction prediction from multiple sources.

One of the most important, but often ignored, parts of any clustering and classification algorithm is the computation of the similarity matrix. This is especially important when integrating high throughput biological data sources because of the high noise rates and the many missing values. In this paper we present a new method to compute such similarities for the task of classifying pairs of proteins as interacting or not. Our method uses direct and indirect information about interaction pairs to constructs a random forest (a collection of decision tress) from a training set. The resulting forest is used to determine the similarity between protein pairs and this similarity is used by a classification algorithm (a modified kNN) to classify protein pairs. Testing the algorithm on yeast data indicates that it is able to improve coverage to 20% of interacting pairs with a false positive rate of 50%. These results compare favorably with all previously suggested methods for this task indicating the importance of robust similarity estimates.

Algorithms↗
PubMed2004

The optimal fraction of hydrophobic residues required to ensure protein collapse.

The hydrophobic interaction is the main driving force for protein folding. Here, we address the question of what is the optimal fraction, f of hydrophobic (H) residues required to ensure protein collapse. For very small f (say f<0.1), the protein chain is expected to behave as a random coil, where the H residues are "wrapped" locally by polar (P) residues. However, for large enough f this local coverage cannot be achieved and the thermodynamic alternative to avoid contact with water is burying the H residues in the interior of a compact chain structure. The interior also contains P residues that are known to be clustered to optimize their electrostatic interactions. This means that the H residues are clustered as well, i.e. they effectively attract each other like the H-monomers in Dill's HP lattice model. Previously, we asked the question: assuming that the H monomers in the HP model are distributed randomly along the chain, what fraction of them is required to ensure a compact ground state? We claimed there that f approximately p(c), where p(c) is the site percolation threshold of the lattice (in a percolation experiment, each site of an initially empty lattice is visited and a particle is placed there with a probability p. The interest is in the critical (minimal) value, p(c), for which percolation occurs, i.e. a cluster connecting the opposite sides of the lattice is created). Due to the above correspondence between the HP model and real proteins (and assuming that the H residues are distributed at random) we suggest that the experimental f should lead to percolating clusters of H residues over the highly dense protein core, i.e. clusters of the core size. To check this theory, we treat a simplified model consisting of H and P residues represented by their alpha-carbon atoms only. The structure is defined by the C(alpha)-C(alpha) virtual bond lengths, angles and dihedral angles, and the X-ray structure is best-fitted onto a face-centered cubic lattice. Percolation experiments are carried out for 103 single-chain proteins using six different hydrophobic sets of residues. Indeed, on average, percolating clusters are generated, which supports our theory; however, some sets lead to a better core coverage than others. We also calculate the largest actual hydrophobic cluster of each protein and show that, on average, these clusters span the core, again in accord with our theory. We discuss the effect of protein size, deviations from the average picture, and implications of this study for defining reliable simplified models of proteins.

Crystallography, X-Ray↗
PubMed2004

Comparison of probabilistic combination methods for protein secondary structure prediction.

MOTIVATION: Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction. RESULTS: We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction.

Algorithms↗
PubMed2004

Identification of core amino acids stabilizing rhodopsin.

Rhodopsin is the only G protein-coupled receptor (GPCR) whose 3D structure is known; therefore, it serves as a prototype for studies of the GPCR family of proteins. Rhodopsin dysfunction has been linked to misfolding, caused by chemical modifications that affect the naturally occurring disulfide bond between C110 and C187. Here, we identify the structural elements that stabilize rhodopsin by computational analysis of the rhodopsin structure and comparison with data from previous in vitro mutational studies. We simulate the thermal unfolding of rhodopsin by breaking the native-state hydrogen bonds sequentially in the order of their relative strength, using the recently developed Floppy Inclusion and Rigid Substructure Topography (FIRST) method [Jacobs, D. J., Rader, A. J., Kuhn, L. A. & Thorpe, M. F. (2001) Proteins 44, 150-165]. Residues most stable under thermal denaturation are part of a core, which is assumed to be important for the formation and stability of folded rhodopsin. This core includes the C110-C187 disulfide bond at the center of residues forming the interface between the transmembrane and the extracellular domains near the retinal binding pocket. Fast mode analysis of rhodopsin using the Gaussian network model also identifies the disulfide bond and the retinal ligand binding pocket to be the most rigid region in rhodopsin. Experiments confirm that 90% of the amino acids predicted by the FIRST method to be part of the core cause misfolding upon mutation. The observed high degree of conservation (78.9%) of this disulfide bond across all GPCR classes suggests that it is critical for the stability and function of GPCRs.

Amino Acid Sequence↗
PubMed2004

Differential dynamics in the G protein-coupled receptor rhodopsin revealed by solution NMR.

G protein-coupled receptors are cell-surface seven-helical membrane proteins that undergo conformational changes on activation. The mammalian photoreceptor, rhodopsin, is the best-studied member of this superfamily. Here, we provide the first evidence that activation in rhodopsin may involve differential dynamic properties of side-chain versus backbone atoms. High-resolution NMR studies of alpha-(15)N-labeled receptor revealed large backbone motions in the inactive dark state. In contrast, indole side-chain (15)N groups of tryptophans showed well resolved, equally intense NMR signals, suggesting restriction to a single specific conformation.

Amino Acid Sequence↗
PubMed2004

NMR spectroscopy of phosphorylated wild-type rhodopsin: mobility of the phosphorylated C-terminus of rhodopsin in the dark and upon light activation.

Binding of arrestin to light-activated rhodopsin involves recognition of the phosphorylated C-terminus and several residues on the cytoplasmic surface of the receptor. These sites are in close proximity in dark, unphosphorylated rhodopsin. To address the position and mobility of the phosphorylated C-terminus in the active and inactive receptor, we combined high-resolution solution and solid state NMR spectroscopy of the intact mammalian photoreceptor rhodopsin in detergent micelles as a function of temperature. The (31)P NMR resonance of rhodopsin phosphorylated by rhodopsin kinase at the C-terminal tail was observable with single pulse excitation using magic angle spinning until the sample temperature reached -40 degrees C. Below this temperature, the (31)P resonance broadened and was only observable using cross polarization. These results indicate that the phosphorylated C-terminus is highly mobile above -40 degrees C and immobilized at lower temperature. To probe the relative position of the immobilized phosphorylated C-terminus with respect to the cytoplasmic domain of rhodopsin, (19)F labels were introduced at positions 140 and 316 by the reaction of rhodopsin with 2,2,2-trifluoroethanethiol (TET). Solid state rotational-echo double-resonance (REDOR) NMR was used to probe the internuclear distance between the (19)F and the (31)P-labels. The REDOR technique allows (19)F...(31)P distances to be measured out to approximately 12 A with high resolution, but no significant dephasing was observed in the REDOR experiment in the dark or upon light activation. This result indicates that the distances between the phosphorylated sites on the C-terminus and the (19)F sites on helix 8 (Cys 316) and in the second cytoplasmic loop (Cys140) are greater than 12 A in phosphorylated rhodopsin.

Amino Acid Sequence↗
PubMed2004

A sequence alignment-independent method for protein classification.

Annotation of the rapidly accumulating body of sequence data relies heavily on the detection of remote homologues and functional motifs in protein families. The most popular methods rely on sequence alignment. These include programs that use a scoring matrix to compare the probability of a potential alignment with random chance and programs that use curated multiple alignments to train profile hidden Markov models (HMMs). Related approaches depend on bootstrapping multiple alignments from a single sequence. However, alignment-based programs have limitations. They make the assumption that contiguity is conserved between homologous segments, which may not be true in genetic recombination or horizontal transfer. Alignments also become ambiguous when sequence similarity drops below 40%. This has kindled interest in classification methods that do not rely on alignment. An approach to classification without alignment based on the distribution of contiguous sequences of four amino acids (4-grams) was developed. Interest in 4-grams stemmed from the observation that almost all theoretically possible 4-grams (20(4)) occur in natural sequences and the majority of 4-grams are uniformly distributed. This implies that the probability of finding identical 4-grams by random chance in unrelated sequences is low. A Bayesian probabilistic model was developed to test this hypothesis. For each protein family in Pfam-A and PIR-PSD, a feature vector called a probe was constructed from the set of 4-grams that best characterised the family. In rigorous jackknife tests, unknown sequences from Pfam-A and PIR-PSD were compared with the probes for each family. A classification result was deemed a true positive if the probe match with the highest probability was in first place in a rank-ordered list. This was achieved in 70% of cases. Analysis of false positives suggested that the precision might approach 85% if selected families were clustered into subsets. Case studies indicated that the 4-grams in common between an unknown and the best matching probe correlated with functional motifs from PRINTS. The results showed that remote homologues and functional motifs could be identified from an analysis of 4-gram patterns.

Algorithms↗
PubMed2004

Inferring property selection pressure from positional residue conservation.

In this study, we attempt to understand and explain positional selection pressure in terms of underlying physical and chemical properties. We propose a set of constraining assumptions about how these pressures behave, then describe a procedure for analysing and explaining the distribution of residues at a particular position in a multiple sequence alignment. In contrast to previous approaches, our model takes into account both amino acid frequencies and a large number of physical-chemical properties. By analysing each property separately, it is possible to identify positions where distinct conservation patterns are present. In addition, the model can easily incorporate sequence weights that adjust for bias in the sample sequences. Finally, a test of statistical significance is provided for our conservation measure. The applicability of this method is demonstrated on two HIV-1 proteins: Nef and Env. The tools, data and results presented in this article are available at http://flan.blm.cs.cmu.edu.

Algorithms↗
PubMed2004

BLMT: statistical sequence analysis using N-grams.

UNLABELLED: Statistical analysis of amino acid and nucleotide sequences, especially sequence alignment, is one of the most commonly performed tasks in modern molecular biology. However, for many tasks in bioinformatics, the requirement for the features in an alignment to be consecutive is restrictive and "n-grams" (aka k-tuples) have been used as features instead. N-grams are usually short nucleotide or amino acid sequences of length n, but the unit for a gram may be chosen arbitrarily. The n-gram concept is borrowed from language technologies where n-grams of words form the fundamental units in statistical language models. Despite the demonstrated utility of n-gram statistics for the biology domain, there is currently no publicly accessible generic tool for the efficient calculation of such statistics. Most sequence analysis tools will disregard matches because of the lack of statistical significance in finding short sequences. This article presents the integrated Biological Language Modeling Toolkit (BLMT) that allows efficient calculation of n-gram statistics for arbitrary sequence datasets. AVAILABILITY: BLMT can be downloaded from http://www.cs.cmu.edu/~blmt/source and installed for standalone use on any Unix platform or Unix shell emulation such as Cygwin on the Windows platform. Specific tools and usage details are described in a "readme" file. The n-gram computations carried out by the BLMT are part of a broader set of tools borrowed from language technologies and modified for statistical analysis of biological sequences; these are available at http://flan.blm.cs.cmu.edu/.

Algorithms↗