Kyung Dae Ko

Phylogenetic profiles reveal evolutionary relationships within the “twilight zone” of sequence similarity

Inferring evolutionary relationships among highly divergent protein sequences is a daunting task. In particular, when pairwise sequence alignments between protein sequences fall <25% identity, the phylogenetic relationships among sequences cannot be estimated with statistical certainty. Here, we show that phylogenetic profiles generated with the Gestalt Domain Detection Algorithm–Basic Local Alignment Tool (GDDA-BLAST) are capable of deriving, ab initio, phylogenetic relationships for highly divergent proteins in a quantifiable and robust manner. Notably, the results from our computational case study of the highly divergent family of retroelements accord with previous estimates of their evolutionary relationships. Taken together, these data demonstrate that GDDA-BLAST provides an independent and powerful measure of evolutionary relationships that does not rely on potentially subjective sequence alignment. We demonstrate that evolutionary relationships can be measured with phylogenetic profiles, and therefore propose that these measurements can provide key insights into relationships among distantly related and/or rapidly evolving proteins.

PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences

Both multiple sequence alignment and phylogenetic analysis are problematic in the “twilight zone” of sequence similarity (≤25% amino acid identity). Herein we explore the accuracy of phylogenetic inference at extreme sequence divergence using a variety of simulated data sets. We evaluate four leading multiple sequence alignment (MSA) methods (MAFFT, T-COFFEE, CLUSTAL, and MUSCLE) and six commonly used programs of tree estimation (Distance-based: Neighbor-Joining; Character-based: PhyML, RAxML, GARLI, Maximum Parsimony, and Bayesian) against a novel MSA-independent method (PHYRN) described here. Strikingly, at “midnight zone” genetic distances (∼7% pairwise identity and 4.0 gaps per position), PHYRN returns high-resolution phylogenies that outperform traditional approaches. We reason this is due to PHRYNs capability to amplify informative positions, even at the most extreme levels of sequence divergence. We also assess the applicability of the PHYRN algorithm for inferring deep evolutionary relationships in the divergent DANGER protein superfamily, for which PHYRN infers a more robust tree compared to MSA-based approaches. Taken together, these results demonstrate that PHYRN represents a powerful mechanism for mapping uncharted frontiers in highly divergent protein sequence data sets.

Predicting Protein Folds with Fold-Specific PSSM Libraries

Accurately assigning folds for divergent protein sequences is a major obstacle to structural studies. Herein, we outline an effective method for fold recognition using sets of PSSMs, each of which is constructed for different protein folds. Our analyses demonstrate that FSL (Fold-specific Position Specific Scoring Matrix Libraries) can predict/relate structures given only their amino acid sequences of highly divergent proteins. This ability to detect distant relationships is dependent on low-identity sequence alignments obtained from FSL. Results from our experiments demonstrate that FSL perform well in recognizing folds from the “twilight-zone” SABmark dataset. Further, this method is capable of accurate fold prediction in newly determined structures. We suggest that by building complete PSSM libraries for all unique folds within the Protein Database (PDB), FSL can be used to rapidly and reliably annotate a large subset of protein folds at proteomic level. The related programs and fold-specific PSSMs for our FSL are publicly available at: http://ccp.psu.edu/download/FSLv1.0/.

Phylogenetic profiles reveal structural/functional determinants of TRPC3 signal-sensing antennae.

Biochemical assessment of channel structure/function is incredibly challenging. Developing computational tools that provide these data would enable translational research, accelerating mechanistic experimentation for the bench scientist studying ion channels. Starting with the premise that protein sequence encodes information about structure, function and evolution (SF&E), we developed a unified framework for inferring SF&E from sequence information using a knowledge-based approach. The Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST) provides phylogenetic profiles that can model, ab initio, SF&E relationships of biological sequences at the whole protein, single domain, and single-amino acid level.1,2 In our recent paper,4 we have applied GDDA-BLAST analysis to study canonical TRP (TRPC) channels1 and empirically validated predicted lipid-binding and trafficking activities contained within the TRPC3 TRP_2 domain of unknown function. Overall, our in silico, in vitro, and in vivo experiments support a model in which TRPC3 has signal-sensing antennae which are adorned with lipid-binding, trafficking, and calmodulin regulatory domains. In this Addendum, we correlate our functional domain analysis with the cryo-EM structure of TRPC3.3 In addition, we synthesize recent studies with our new findings to provide a refined model on the mechanism(s) of TRPC3 activation/deactivation.

The development of a proteomic analyzing pipeline to identify proteins with multiple RRMs and predict their domain boundaries

The RNA-recognition motif (RRM) is the most abundant RNA-binding domain involved in many post-transcriptional processes. Since RRM-containing proteins have different functions with similar domain architecture, it is challenging to implement an automated annotation tool for these proteins in proteomic analysis. In this study, we implemented a proteomic analyzing pipeline to identify proteins with multiple RRMs and predict their domain boundaries using specific PSSMs, domain architectures, and proteins with the same entity name. After clustering sequences on the basis of their evolutionary distances, a reference group is selected comparing domain architectures. Then, candidate proteins are collected in a proteome using specific PSSMs from seed alignments in PFAM. Finally, target proteins are identified using multiple alignments and phyolgenetic trees between candidate and reference proteins. Therefore, we identified 33 proteins close to 12 types of RRM containing proteins and their domain boundaries among 508 candidates from 33610 sequences in a human proteome.

The classification of a protein from its primary sequence using functional and structural-specific PSSMs in quantitative measurement

In principle, the amino acid sequence of a protein contains structural, functional, and evolutionary characteristics [1]. Investigation of these characteristics using computational methods provides a powerful resource. However, these methods have limitations in their ability to annotate the characteristics of proteins accurately [2]. In an attempt to overcome this drawback, we have developed a unified computational pipeline, called the Gestalt Domain Detection Algorithm Basic Local Alignment Tool (GDDA-BLAST), for measuring the structural, functional and evolutionary characteristics of a protein [3]. The performance of GDDA-BLAST is better than those of other method such as SAM and psi-BLAST in homology detection.