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1. Case Studies of Orphan Domain Reclassification in ECOD by Expert Curation

   https://doi.org/10.1002/prot.26840  

   Pei, Jimin; Schaeffer, R_Dustin; Cong, Qian; Grishin, Nick_V (May 2025, Proteins: Structure, Function, and Bioinformatics)

   ABSTRACT Homology‐based protein domain classification is a powerful tool for gaining biological insights into protein function. This classification process has been significantly enhanced by the availability of experimental structures and high‐accuracy structural models generated by advanced tools such as AlphaFold. Our Evolutionary Classification of protein Domains (ECOD) database provides a continuously updated and refined domain classification system. Isolated (“orphan”) protein domain families, which have a limited distribution in the protein universe, present a unique challenge in this classification process. These families lack clear or identifiable evolutionary relationships with other sequence families. While some isolated domain families may have emerged through de novo evolution, others potentially share common evolutionary origins with existing domain families but represent difficult cases for traditional classification methods. In this study, we conducted a manual analysis of a set of isolated families of small domains in ECOD. By exploring sequence, structural, and functional evidence, we uncovered distant members and likely homologous relationships between different isolated domain families that were previously unrecognized. Our analysis provides valuable insights into the evolution of isolated domain families and has led to improved classification within ECOD. This work enhances our understanding of protein evolution and underscores the importance of continuous refinement in domain classification systems as new data and analytical methods become available. 

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2. Refinement and curation of homologous groups facilitated by structure prediction

   https://doi.org/10.1002/pro.70074  

   Schaeffer, Richard_Dustin; Pei, Jimin; Zhang, Jing; Cong, Qian; Grishin, Nick_V (February 2025, Protein Science)

   Abstract Domain classification of protein predictions released in the AlphaFold Database (AFDB) has been a recent focus of the Evolutionary Classification of protein Domains (ECOD). Although a primary focus of our recent work has been the partition and assignment of domains from these predictions, we here show how these diverse predictions can be used to examine the reference domain set more closely. Using results from DPAM, our AlphaFold‐specific domain parsing algorithm, we examine hierarchical groupings that share significant levels of homologous links, both between groups that were not previously assessed to be definitively homologous and between groups that were not previously observed to share significant homologous links. Combined with manual analysis, these large datasets of structural and sequence similarities allow us to merge homologous groups in multiple cases which we detail within. These domains tend to be families of domains from families that are either small, previously had few experimental representatives, or had unknown function. The exception to this is the chromodomains, a large homologous group which were increased from “possibly homologous” to “definitely homologous” to increase the consistency of ECOD based their strong homologous links to the SH3 domains. 

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3. A new algorithm to train hidden Markov models for biological sequences with partial labels

   https://doi.org/10.1186/s12859-021-04080-0  

   Li, Jiefu; Lee, Jung-Youn; Liao, Li (December 2021, BMC Bioinformatics)

   null (Ed.)

   Abstract Background Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when sequences are labelled or by expectation maximization, such as the Baum–Welch algorithm, when sequences are unlabelled. However, increasingly there are situations where sequences are just partially labelled. In this paper, we designed a new training method based on the Baum–Welch algorithm to train HMMs for situations in which only partial labeling is available for certain biological problems. Results Compared with a similar method previously reported that is designed for the purpose of active learning in text mining, our method achieves significant improvements in model training, as demonstrated by higher accuracy when the trained models are tested for decoding with both synthetic data and real data. Conclusions A novel training method is developed to improve the training of hidden Markov models by utilizing partial labelled data. The method will impact on detecting de novo motifs and signals in biological sequence data. In particular, the method will be deployed in active learning mode to the ongoing research in detecting plasmodesmata targeting signals and assess the performance with validations from wet-lab experiments. 

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4. SAXSDom: Modeling multidomain protein structures using small‐angle X‐ray scattering data

   https://doi.org/10.1002/prot.25865  

   Hou, Jie; Adhikari, Badri; Tanner, John J.; Cheng, Jianlin (December 2019, Proteins: Structure, Function, and Bioinformatics)

   Abstract Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small‐angle X‐ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input‐Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS‐based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain‐domain assembly. The source code and tool packages are available athttps://github.com/jianlin-cheng/SAXSDom. 

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5. Fast multiple sequence alignment via multi-armed bandits

   https://doi.org/10.1093/bioinformatics/btae225  

   Mazooji, Kayvon; Shomorony, Ilan (June 2024, Bioinformatics)

   Abstract SummaryMultiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences. Availability and implementationThe code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA. 

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