De novo variants (DNVs) are one of the most significant contributors to severe early-onset genetic disorders such as autism spectrum disorder, intellectual disability, and other developmental and neuropsychiatric (DNP) disorders. Presently, a plethora of DNVs have been identified using next-generation sequencing, and many efforts have been made to understand their impact at the gene level. However, there has been little exploration of the effects at the isoform level. The brain contains a high level of alternative splicing and regulation, and exhibits a more divergent splicing program than other tissues. Therefore, it is crucial to explore variants at the transcriptional regulation level to better interpret the mechanisms underlying DNP disorders. To facilitate a better usage and improve the isoform-level interpretation of variants, we developed NeuroPsychiatric Mutation Knowledge Base (PsyMuKB). It contains a comprehensive, carefully curated list of DNVs with transcriptional and translational annotations to enable identification of isoform-specific mutations. PsyMuKB allows a flexible search of genes or variants and provides both table-based descriptions and associated visualizations, such as expression, transcript genomic structures, protein interactions, and the mutation sites mapped on the protein structures. It also provides an easy-to-use web interface, allowing users to rapidly visualize the locations and characteristics of mutations and the expression patterns of the impacted genes and isoforms. PsyMuKB thus constitutes a valuable resource for identifying tissue-specific DNVs for further functional studies of related disorders. PsyMuKB is freely accessible at http://psymukb.net.

Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

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AbstractDe novo variants (DNVs) are one of the most significant contributors to severe early-onset genetic disorders such as autism spectrum disorder, intellectual disability, and other developmental and neuropsychiatric (DNP) disorders. Currently, a plethora of DNVs have being identified through the use of next-generation sequencing and much effort has been made to understand their impact at the gene level; however, there has been little exploration of the impact at the isoform level. The brain contains a high level of alternative splicing and regulation, and exhibits a more divergent splicing program than other tissues; therefore, it is crucial to explore variants at the transcriptional regulation level to better interpret the mechanisms underlying DNP disorders. To facilitate better usage and improve the isoform-level interpretation of variants, we developed the PsyMuKB (NeuroPsychiatric Mutation Knowledge Base), a knowledge base containing a comprehensive, carefully curated list of DNVs with transcriptional and translational annotations to enable identification of isoform-specific mutations. PsyMuKB allows a flexible search of genes or variants and provides both table-based descriptions and associated visualizations, such as expression, transcript genomic structures, protein interactions, and the mutation sites mapped on the protein structures. It also provides an easy-to-use web interface, allowing users to rapidly visualize the locations and characteristics of mutations and the expression patterns of the impacted genes and isoforms. PsyMuKB thus constitutes a valuable resource for identifying tissue-specific de novo mutations for further functional studies of related disorders. PsyMuKB is freely accessible at http://psymukb.net.

Protein-protein interactions play an important role in biological and cellular processes. Biochemistry experiment is the most reliable approach identifying protein-protein interactions, but it is time-consuming and expensive. It is one of the important reasons why there is only a little fraction of complete protein-protein interactions networks available by far. Hence, accurate computational methods are in a great need to predict protein-protein interactions. In this work, we proposed a new weighted feature fusion algorithm for protein-protein interactions prediction, which extracts both protein sequence feature and evolutionary feature, for the purpose to use both global and local information to identify protein-protein interactions. The method employs maximum margin criterion for feature selection and support vector machine for classification. Experimental results on 11188 protein pairs showed that our method had better performance and robustness. Performed on the independent database of Helicobacter pylori, the method achieved 99.59% sensitivity and 93.66% prediction accuracy, while the maximum margin criterion is 88.03%. The results indicated that our method was more efficient in predicting protein-protein interaction compared with other six state-of-the-art peer methods.