scholarly journals Transfer of Knowledge from Model Organisms to Evolutionarily Distant Non-Model Organisms: The Coral Pocillopora damicornis Membrane Signaling Receptome

2021 ◽  
Author(s):  
Lokender Kumar ◽  
Nathanael Brenner ◽  
Samuel Sledieski ◽  
Monsurat Olaosebikan ◽  
Matthew Lynn-Goin ◽  
...  

With the ease of gene sequencing and the technology available to study and manipulate non-model organisms, the need to translate our understanding of model organisms to non-model organisms has become an urgent problem. For example, mining of large coral and their symbiont sequence data is a challenge, but also provides an opportunity for understanding functionality and evolution of these and other non-model organisms. Much more information than for any other eukaryotic species is available for humans, especially related to signal transduction and diseases. However, the coral cnidarian host and human have diverged over 700 million years ago and homologies between proteins are therefore often in the gray zone or undetectable with traditional BLAST searches. We introduce a two-stage approach to identifying putative coral homologues of human proteins. First, through remote homology detection using Hidden Markov Models, we identify candidate human homologues in the cnidarian genome. However, for many proteins, the human genome alone contains multiple family members with similar or even more divergence in sequence. In the second stage, therefore, we filter the remote homology results based on the functional and structural plausibility of each coral candidate, shortlisting the coral proteins likely to be true human homologues. We demonstrate our approach with a pipeline for mapping membrane receptors in humans to membrane receptors in corals, with specific focus on the stony coral, P. damicornis. More than 1000 human membrane receptors mapped to 335 coral receptors, including 151 G protein coupled receptors (GPCRs). To validate specific sub-families, we chose opsin proteins, representative GPCRs that confer light sensitivity, and Toll-like receptors, representative non-GPCRs, which function in the immune response, and their ability to communicate with microorganisms. Through detailed structure-function analysis of their ligand-binding pockets and downstream signaling cascades, we selected those candidate remote homologues likely to carry out related functions in the corals. This pipeline may prove generally useful for other non-model organisms, such as to support the growing field of synthetic biology.

Author(s):  
Thomas Plötz ◽  
Gernot A. Fink

The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we propose feature based semi-continuous Profile HMMs. Based on a richer sequence representation consisting of features which capture the biochemical properties of residues in their local context, family specific semi-continuous models are estimated completely data-driven. Additionally, for substantially reducing the number of false predictions an explicit rejection model is estimated. Both the family specific semi-continuous Profile HMM and the non-target model are competitively evaluated. In the experimental evaluation of superfamily based screening of the SCOP database we demonstrate that semi-continuous Profile HMMs significantly outperform their discrete counterparts. Using the rejection model the number of false positive predictions could be reduced substantially which is an important prerequisite for target identification applications.


2018 ◽  
Author(s):  
Mohamed Baddar

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.


2018 ◽  
Author(s):  
Mohamed Baddar

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.


2018 ◽  
Author(s):  
Mohamed Baddar

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.


Author(s):  
Xiaopeng Jin ◽  
Qing Liao ◽  
Bin Liu

Abstract Protein remote homology detection is a fundamental and important task for protein structure and function analysis. Several search methods have been proposed to improve the detection performance of the remote homologues and the accuracy of ranking lists. The position-specific scoring matrix (PSSM) profile and hidden Markov model (HMM) profile can contribute to improving the performance of the state-of-the-art search methods. In this paper, we improved the profile-link (PL) information for constructing PSSM or HMM profiles, and proposed a PL-based search method (PL-search). In PL-search, more robust PLs are constructed through the double-link and iterative extending strategies, and an accurate similarity score of sequence pairs is calculated from the two-level Jaccard distance for remote homologues. We tested our method on two widely used benchmark datasets. Our results show that whether HHblits, JackHMMER or position-specific iterated-BLAST is used, PL-search obviously improves the search performance in terms of ranking quality as well as the number of detected remote homologues. For ease of use of PL-search, both its stand-alone tool and the web server are constructed, which can be accessed at http://bliulab.net/PL-search/.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.


2021 ◽  
Vol 16 ◽  
Author(s):  
Jinghao Peng ◽  
Jiajie Peng ◽  
Haiyin Piao ◽  
Zhang Luo ◽  
Kelin Xia ◽  
...  

Background: The open and accessible regions of the chromosome are more likely to be bound by transcription factors which are important for nuclear processes and biological functions. Studying the change of chromosome flexibility can help to discover and analyze disease markers and improve the efficiency of clinical diagnosis. Current methods for predicting chromosome flexibility based on Hi-C data include the flexibility-rigidity index (FRI) and the Gaussian network model (GNM), which have been proposed to characterize chromosome flexibility. However, these methods require the chromosome structure data based on 3D biological experiments, which is time-consuming and expensive. Objective: Generally, the folding and curling of the double helix sequence of DNA have a great impact on chromosome flexibility and function. Motivated by the success of genomic sequence analysis in biomolecular function analysis, we hope to propose a method to predict chromosome flexibility only based on genomic sequence data. Method: We propose a new method (named "DeepCFP") using deep learning models to predict chromosome flexibility based on only genomic sequence features. The model has been tested in the GM12878 cell line. Results: The maximum accuracy of our model has reached 91%. The performance of DeepCFP is close to FRI and GNM. Conclusion: The DeepCFP can achieve high performance only based on genomic sequence.


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