Position-specific Scoring Matrices Research Articles

Master Blaster: an approach to sensitive identification of remotely related proteins

Genome sequencing projects unearth sequences of all the protein sequences encoded in a genome. As the first step, homology detection is employed to obtain clues to structure and function of these proteins. However, high evolutionary divergence between homologous proteins challenges our ability to detect distant relationships. In the past, an approach involving multiple Position Specific Scoring Matrices (PSSMs) was found to be more effective than traditional single PSSMs. Cascaded search is another successful approach where hits of a search are queried to detect more homologues. We propose a protocol, ‘Master Blaster’, which combines the principles adopted in these two approaches to enhance our ability to detect remote homologues even further. Assessment of the approach was performed using known relationships available in the SCOP70 database, and the results were compared against that of PSI-BLAST and HHblits, a hidden Markov model-based method. Compared to PSI-BLAST, Master Blaster resulted in 10% improvement with respect to detection of cross superfamily connections, nearly 35% improvement in cross family and more than 80% improvement in intra family connections. From the results it was observed that HHblits is more sensitive in detecting remote homologues compared to Master Blaster. However, there are true hits from 46-folds for which Master Blaster reported homologs that are not reported by HHblits even using the optimal parameters indicating that for detecting remote homologues, use of multiple methods employing a combination of different approaches can be more effective in detecting remote homologs. Master Blaster stand-alone code is available for download in the supplementary archive.

RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures.

MotivationApplied research in machine learning progresses faster when a clean dataset is available and ready to use. Several datasets have been proposed and released over the years for specific tasks such as image classification, speech-recognition and more recently for protein structure prediction. However, for the fundamental problem of RNA structure prediction, information is spread between several databases depending on the level we are interested in: sequence, secondary structure, 3D structure or interactions with other macromolecules. In order to speed-up advances in machine-learning based approaches for RNA secondary and/or 3D structure prediction, a dataset integrating all this information is required, to avoid spending time on data gathering and cleaning.ResultsHere, we propose the first attempt of a standardized and automatically generated dataset dedicated to RNA combining together: RNA sequences, homology information (under the form of position-specific scoring matrices) and information derived by annotation of available 3D structures (including secondary structure, canonical and non-canonical interactions and backbone torsion angles). The data are retrieved from public databases PDB, Rfam and SILVA. The paper describes the procedure to build such dataset and the RNA structure descriptors we provide. Some statistical descriptions of the resulting dataset are also provided.Availability and implementationThe dataset is updated every month and available online (in flat-text file format) on the EvryRNA software platform (https://evryrna.ibisc.univ-evry.fr/evryrna/rnanet). An efficient parallel pipeline to build the dataset is also provided for easy reproduction or modification.Supplementary information Supplementary data are available at Bioinformatics online.

Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries

Proteolysis is a major posttranslational regulator of biology inside and outside of cells. Broad identification of optimal cleavage sites and natural substrates of proteases is critical for drug discovery and to understand protease biology. Here, we present a method that employs two genetically encoded substrate phage display libraries coupled with next generation sequencing (SPD-NGS) that allows up to 10,000-fold deeper sequence coverage of the typical six- to eight-residue protease cleavage sites compared to state-of-the-art synthetic peptide libraries or proteomics. We applied SPD-NGS to two classes of proteases, the intracellular caspases, and the ectodomains of the sheddases, ADAMs 10 and 17. The first library (Lib 10AA) allowed us to identify 104 to 105 unique cleavage sites over a 1,000-fold dynamic range of NGS counts and produced consensus and optimal cleavage motifs based position-specific scoring matrices. A second SPD-NGS library (Lib hP), which displayed virtually the entire human proteome tiled in contiguous 49 amino acid sequences with 25 amino acid overlaps, enabled us to identify candidate human proteome sequences. We identified up to 104 natural linear cut sites, depending on the protease, and captured most of the examples previously identified by proteomics and predicted 10- to 100-fold more. Structural bioinformatics was used to facilitate the identification of candidate natural protein substrates. SPD-NGS is rapid, reproducible, simple to perform and analyze, inexpensive, and renewable, with unprecedented depth of coverage for substrate sequences, and is an important tool for protease biologists interested in protease specificity for specific assays and inhibitors and to facilitate identification of natural protein substrates.

Identification of Cancer Biomarkers in Human Body Fluids by Using Enhanced Physicochemical-incorporated Evolutionary Conservation Scheme.

Cancer is one of the most serious diseases affecting human health. Among all current cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for labor-intensive and time-consuming biochemical experiments. In this work, we propose a novel method for high-throughput identification of cancer biomarkers in human body fluids. We incorporate physicochemical properties into the weighted observed percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature subset. The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed model. We also test our proposed method on independent testing datasets and apply it to the identification of potential cancer biomarkers in human body fluids. The testing results promise a good generalization capability of our approach.

Identifying Antifreeze Proteins Based on Key Evolutionary Information.

Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein–ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein–ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

PeSA: A software tool for peptide specificity analysis

The discovery and characterization of molecular interactions is crucial towards a better understanding of complex biological processes. Particularly protein-protein interactions (i.e., PPIs), which are responsible for a variety of cellular functions from intracellular signaling to enzyme-substrate specificity, have been studied broadly over the past decades. Position-specific scoring matrices (PSSM) in particular are used extensively to help determine interaction specificity or candidate interaction motifs at the residue level. However, not all studies successfully report their results as a candidate interaction motif. In many cases, this may be due to a lack of suitable tools for simple analysis and motif generation. Peptide Specificity Analyst (PeSA) was developed with the goal of filling this information gap and providing an easy to use software to aid peptide array analysis and motif generation. PeSA utilizes two models of motif creation: (1) frequency-based using a user-defined peptide list, and (2) weight-based using experimental binding results. The ability to produce motifs effortlessly will make studying, interpreting and disseminating peptide specificity results in an effortless and straightforward process.

DeepETC: A deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes

An electron transport chain is a series of protein complexes embedded in the transport protein, which is an important process to transfer electrons and other macromolecules throughout the cell. It is the primary process to extract energy via redox reactions in the case of oxidation of sugars in cellular respiration. According to the molecular functions, the components of the electron transport chain could be formed with five complexes and with several different electron carriers. The functional loss of a specific molecular function in electron transport chain has been implicated in a variety of human diseases such as diabetes, neurodegenerative disorders, Parkinson, and Alzheimer's disease. Therefore, creating a precise model to identify its functions is pertinent to the understanding of human diseases and designing of drug targets. Previous bioinformatics studies have almost exclusively focused on the electron transport proteins without information on the five complexes. Here we present DeepETC, a deep learning model that uses a two-dimensional convolutional neural network and position-specific scoring matrices profiles to classify electron transport proteins into the five complexes. DeepETC can classify the electron transporters with the independent test accuracy of 99.6%, 99.7%, 99.7%, 99.1% and 99.8% for complex I, II, III, IV, and V, respectively. Our performance results are significantly more accurate than the state-of-the-art traditional neural networks in all typical measurement metrics. Throughout the proposed study, we provide an effective tool for investigating electron transport proteins and our achievement could promote the use of deep learning in bioinformatics and computational biology. DeepETC can be freely accessible via http://www.biologydeep.com/deepetc/.

Protein Sequences Features Extraction for Predicting Beta- Turns and their Types: A Review

Beta-turns are considered to be important secondary structure types that have essential role in molecular recognition, protein folding and stability. They represent 25% of protein structures, therefore they are the most common type of non-repetitive or tight turns structures. Their prediction is considered to be an important issue in bioinformatics and molecular biology, because it provides valuable information and inputs for the fold recognition and drug design. There are many machine learning and statistical based approaches that were designed to predict beta-turns. Among the successful approaches that are based on machine learning are the approaches that used Neural Networks (NNs) and Support Vector Machines (SVMs) methods. These approaches used different features and features organizations. Among the most usable features in beta-turns prediction are the Position Specific Scoring Matrices (PSSMs) and the predicted secondary structure. This work gives a review of the most successful methods that are used for beta-turns prediction and the features as well as the organizations of these features that they used.

RawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments

In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years. Availability: datasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa.

Improved Prediction of Protein-Protein Interactions Using Descriptors Derived From PSSM via Gray Level Co-Occurrence Matrix

A better exploring biological processes, means, and functions demands trusted information about Protein-protein interactions (PPIs). High-throughput technologies have produced a large number of PPIs data for various species, however, they are resource-expensive and often suffer from high error rates. To supplement the limitations of the traditional methods, in this paper, a sequence-based computational method is proposed to insight whether two proteins interact or not. The proposed method divides the novel PPIs prediction process into three stages: first, the position-specific scoring matrices (PSSMs) are produced by incorporating the evolutionary information; second, the 352-dimensional feature vector is constructed for each protein pair; third, effective parameters for the ensemble learning algorithm rotation forest (RF) are selected. In the proposed model, the evolutionary features are extracted from PSSM for each protein without considering any protein annotations. In addition, by using more accurate and diverse classifiers constructed by RF algorithm to avoid yielding coincident errors, one sample incorrectly divided by one classifier will be corrected by another classifier. The proposed method is evaluated in terms of accuracy, precision, sensitivity, and so on using Yeast, Human, and Pylori datasets and finds that its performance is superior to that of the competing methods. Specifically, the average accuracies achieved by the proposed method are 97.06% (Yeast), 98.95% (Human), and 89.69% (H.pylori), which improves the accuracy of PPIs prediction by 0.54%similar to 3.89% (Yeast), 1.29%similar to 3.85% (Human), and 0.22%similar to 4.85% (H.pylori). The experimental results prove that the proposed method is an effective alternative approach for predicting novel PPIs.

Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1

Rare variants in the cardiac potassium channel KV7.1 (KCNQ1) and sodium channel NaV1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of NaV1.5 (n = 356) and KV7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported NaV1.5 and KV7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting NaV1.5 peak current (adj. R2 = 0.27) and KV7.1 + KCNE1 half-maximal voltage of activation (adj. R2 = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers.

IDP⁻CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields.

Accurate prediction of intrinsically disordered proteins/regions is one of the most important tasks in bioinformatics, and some computational predictors have been proposed to solve this problem. How to efficiently incorporate the sequence-order effect is critical for constructing an accurate predictor because disordered region distributions show global sequence patterns. In order to capture these sequence patterns, several sequence labelling models have been applied to this field, such as conditional random fields (CRFs). However, these methods suffer from certain disadvantages. In this study, we proposed a new computational predictor called IDP–CRF, which is trained on an updated benchmark dataset based on the MobiDB database and the DisProt database, and incorporates more comprehensive sequence-based features, including PSSMs (position-specific scoring matrices), kmer, predicted secondary structures, and relative solvent accessibilities. Experimental results on the benchmark dataset and two independent datasets show that IDP–CRF outperforms 25 existing state-of-the-art methods in this field, demonstrating that IDP–CRF is a very useful tool for identifying IDPs/IDRs (intrinsically disordered proteins/regions). We anticipate that IDP–CRF will facilitate the development of protein sequence analysis.

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains.

Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)—SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks

Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks.

IAPSL-IF: Identification of Apoptosis Protein Subcellular Location Using Integrative Features Captured from Amino Acid Sequences.

Apoptosis proteins (APs) control normal tissue homeostasis by regulating the balance between cell proliferation and death. The function of APs is strongly related to their subcellular location. To date, computational methods have been reported that reliably identify the subcellular location of APs, however, there is still room for improvement of the prediction accuracy. In this study, we developed a novel method named iAPSL-IF (identification of apoptosis protein subcellular location—integrative features), which is based on integrative features captured from Markov chains, physicochemical property matrices, and position-specific score matrices (PSSMs) of amino acid sequences. The matrices with different lengths were transformed into fixed-length feature vectors using an auto cross-covariance (ACC) method. An optimal subset of the features was chosen using a recursive feature elimination (RFE) algorithm method, and the sequences with these features were trained by a support vector machine (SVM) classifier. Based on three datasets ZD98, CL317, and ZW225, the iAPSL-IF was examined using a jackknife cross-validation test. The resulting data showed that the iAPSL-IF outperformed the known predictors reported in the literature: its overall accuracy on the three datasets was 98.98% (ZD98), 94.95% (CL317), and 97.33% (ZW225), respectively; the Matthews correlation coefficient, sensitivity, and specificity for several classes of subcellular location proteins (e.g., membrane proteins, cytoplasmic proteins, endoplasmic reticulum proteins, nuclear proteins, and secreted proteins) in the datasets were 0.92–1.0, 94.23–100%, and 97.07–100%, respectively. Overall, the results of this study provide a high throughput and sequence-based method for better identification of the subcellular location of APs, and facilitates further understanding of programmed cell death in organisms.

Constrained De Novo Sequencing of neo-Epitope Peptides using Tandem Mass Spectrometry.

Neoepitope peptides are newly formed antigens presented by major histocompatibility complex class I (MHC-I) on cell surfaces. The cells presenting neoepitope peptides are recognized and subsequently killed by cytotoxic T-cells. Immunopeptidomic approaches aim to characterize the peptide repertoire (including neoepitope) associated with the MHC-I molecules on the surface of tumor cells using proteomic technologies, providing critical information for designing effective immunotherapy strategies. We developed a novel constrained de novo sequencing algorithm to identify neo-epitope peptides from tandem mass spectra acquired in immunopeptidomic analyses. Our method incorporates prior probabilities to putative peptides according to position specific scoring matrices (PSSMs) representing the sequence preferences recognized by MHC-I molecules. We implemented a dynamic programming algorithm to determine the peptide sequences with an optimal posterior matching score for each given MS/MS spectrum. Similar to the de novo peptide sequencing, the dynamic programming algorithm allows an efficient searching in the entire peptide sequence space. On an LC-MS/MS dataset, we demonstrated the performance of our algorithm in detecting the neoepitope peptides bound by the HLA-C*0501 molecules that were superior to database search approaches and existing general purpose de novo peptide sequencing algorithms.

SDADB: a functional annotation database of protein structural domains.

Annotating functional terms with individual domains is essential for understanding the functions of full-length proteins. We describe SDADB, a functional annotation database for structural domains. SDADB provides associations between gene ontology (GO) terms and SCOP domains calculated with an integrated framework. GO annotations are assigned probabilities of being correct, which are estimated with a Bayesian network by taking advantage of structural neighborhood mappings, SCOP-InterPro domain mapping information, position-specific scoring matrices (PSSMs) and sequence homolog features, with the most substantial contribution coming from high-coverage structure-based domain-protein mappings. The domain-protein mappings are computed using large-scale structure alignment. SDADB contains ontological terms with probabilistic scores for more than 214 000 distinct SCOP domains. It also provides additional features include 3D structure alignment visualization, GO hierarchical tree view, search, browse and download options.Database URL: http://sda.denglab.org

3DCONS-DB: A Database of Position-Specific Scoring Matrices in Protein Structures.

Many studies have used position-specific scoring matrices (PSSM) profiles to characterize residues in protein structures and to predict a broad range of protein features. Moreover, PSSM profiles of Protein Data Bank (PDB) entries have been recalculated in many works for different purposes. Although the computational cost of calculating a single PSSM profile is affordable, many statistical studies or machine learning-based methods used thousands of profiles to achieve their goals, thereby leading to a substantial increase of the computational cost. In this work we present a new database compiling PSSM profiles for the proteins of the PDB. Currently, the database contains 333,532 protein chain profiles involving 123,135 different PDB entries.

Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini.

Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set.

Abstract 556: CDD: functional insights into orphan GPCRs via subfamily domain architectures

Abstract NCBI’s Conserved Domain Database (CDD, https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) is a resource that aims at classifying ancient conserved domain families and capturing what is known about their functional diversity. It is a collection of multiple sequence alignments (MSAs) and derived position-specific score matrices (PSSMs). RPS-BLAST allows for the rapid identification of conserved protein domain footprints in protein sequences along with the location of annotated functional sites. CDD is a redundant collection, it imports external domain model collections (Pfam, COGs etc.) and supplement these with manually-curated domain models that are organized into hierarchical classifications, which facilitate annotation of functionally distinct protein families and subfamilies. More recently CDD has been curating information on conserved domain architectures to support functional labeling of proteins that fall into characterized families: SPARCLE (Subfamily Protein ArCHitecture Labeling Engine) is both a curation tool and a public service available at https://www.ncbi.nlm.nih.gov/sparcle. Architecture summaries provide interactive interfaces to lists of proteins that share the same characteristic domain architecture, and to their assigned names, functional labels, and supporting evidence. Here we present the functionally annotated, evolutionary hierarchical classification of the seven-transmembrane G-protein coupled receptors (7TM GPCRs), which includes the largest family of promising therapeutic targets, with more than 140 human orphan GPCRs of unknown function. Across the three kingdoms of life, GPCRs are involved in a wide array of critical physiological processes, and have been implicated in tumor initiation, tumor progression, angiogenesis and metastasis. Orphan subfamilies with unknown endogenous ligand/function are often found to have poor sequence similarity to canonical GPCRs. They have been assigned putative functions with predicted ligand-binding sites, downstream interacting partners, and/or the location of 7TM helices annotated by inference from the molecular and physiological functions of known related GPCR proteins, utilizing more than 25 available 3D-structures, phylogenetic relationships, and the published literature. We hope that this updated classification, together with NCBI’s software tools (Cn3D/CDTree and SPARCLE), will aid researchers in the discovery of molecular therapeutic targets for cancer treatment by providing insights regarding as-yet-unidentified intra-and intermolecular interactions as well as functional mechanisms of GPCR signaling. Acknowledgement: This research was supported by the Intramural Research Program of the National Library of Medicine, NIH. Citation Format: James S. Song, Noreen R. Gonzales, Roxanne A. Yamashita, Stephen H. Bryant, Aron Marchler-Bauer. CDD: functional insights into orphan GPCRs via subfamily domain architectures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 556. doi:10.1158/1538-7445.AM2017-556