Dataset Information

Data mining patented antibody sequences.

ABSTRACT: The patent literature should reflect the past 30 years of engineering efforts directed toward developing monoclonal antibody therapeutics. Such information is potentially valuable for rational antibody design. Patents, however, are designed not to convey scientific knowledge, but to provide legal protection. It is not obvious whether antibody information from patent documents, such as antibody sequences, is useful in conveying engineering know-how, rather than as a legal reference only. To assess the utility of patent data for therapeutic antibody engineering, we quantified the amount of antibody sequences in patents destined for medicinal purposes and how well they reflect the primary sequences of therapeutic antibodies in clinical use. We identified 16,526 patent families covering major jurisdictions (e.g., US Patent and Trademark Office (USPTO) and World Intellectual Property Organization) that contained antibody sequences. These families held 245,109 unique antibody chains (135,397 heavy chains and 109,712 light chains) that we compiled in our Patented Antibody Database (PAD, http://naturalantibody.com/pad). We find that antibodies make up a non-trivial proportion of all patent amino acid sequence depositions (e.g., 11% of USPTO Full Text database). Our analysis of the 16,526 families demonstrates that the volume of patent documents with antibody sequences is growing, with the majority of documents classified as containing antibodies for medicinal purposes. We further studied the 245,109 antibody chains from patent literature to reveal that they very well reflect the primary sequences of antibody therapeutics in clinical use. This suggests that the patent literature could serve as a reference for previous engineering efforts to improve rational antibody design.

SUBMITTER: Krawczyk K

PROVIDER: S-EPMC7971238 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets.ResultsWithin the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases.ConclusionNumerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets.

Project description:BackgroundNew chemotherapeutic agents against Schistosoma mansoni, an etiological agent of human schistosomiasis, are a priority due to the emerging drug resistance and the inability of current drug treatments to prevent reinfection. Proteases have been under scrutiny as targets of immunological or chemotherapeutic anti-Schistosoma agents because of their vital role in many stages of the parasitic life cycle. Function has been established for only a handful of identified S. mansoni proteases, and the vast majority of these are the digestive proteases; very few of the conserved classes of regulatory proteases have been identified from Schistosoma species, despite their vital role in numerous cellular processes. To that end, we identified protease protein coding genes from the S. mansoni genome project and EST library.ResultsWe identified 255 protease sequences from five catalytic classes using predicted proteins of the S. mansoni genome. The vast majority of these show significant similarity to proteins in KEGG and the Conserved Domain Database. Proteases include calpains, caspases, cytosolic and mitochondrial signal peptidases, proteases that interact with ubiquitin and ubiquitin-like molecules, and proteases that perform regulated intramembrane proteolysis. Comparative analysis of classes of important regulatory proteases find conserved active site domains, and where appropriate, signal peptides and transmembrane helices. Phylogenetic analysis provides support for inferring functional divergence among regulatory aspartic, cysteine, and serine proteases.ConclusionNumerous proteases are identified for the first time in S. mansoni. We characterized important regulatory proteases and focus analysis on these proteases to complement the growing knowledge base of digestive proteases. This work provides a foundation for expanding knowledge of proteases in Schistosoma species and examining their diverse function and potential as targets for new chemotherapies.

Project description:BACKGROUND: Common bean (Phaseolus vulgaris L.) is one of the most important legumes in the world. Several diseases severely reduce bean production and quality; therefore, it is very important to better understand disease resistance in common bean in order to prevent these losses. More than 70 resistance (R) genes which confer resistance against various pathogens have been cloned from diverse plant species. Most R genes share highly conserved domains which facilitates the identification of new candidate R genes from the same species or other species. The goals of this study were to isolate expressed R gene-like sequences (RGLs) from 454-derived transcriptomic sequences and expressed sequence tags (ESTs) of common bean, and to develop RGL-tagged molecular markers. RESULTS: A data-mining approach was used to identify tentative P. vulgaris R gene-like sequences from approximately 1.69 million 454-derived sequences and 116,716 ESTs deposited in GenBank. A total of 365 non-redundant sequences were identified and named as common bean (P. vulgaris = Pv) resistance gene-like sequences (PvRGLs). Among the identified PvRGLs, about 60% (218 PvRGLs) were from 454-derived sequences. Reverse transcriptase-polymerase chain reaction (RT-PCR) analysis confirmed that PvRGLs were actually expressed in the leaves of common bean. Upon comparison to P. vulgaris genomic sequences, 105 (28.77%) of the 365 tentative PvRGLs could be integrated into the existing common bean physical map. Based on the syntenic blocks between common bean and soybean, 237 (64.93%) PvRGLs were anchored on the P. vulgaris genetic map and will need to be mapped to determine order. In addition, 11 sequence-tagged-site (STS) and 19 cleaved amplified polymorphic sequence (CAPS) molecular markers were developed for 25 unique PvRGLs. CONCLUSIONS: In total, 365 PvRGLs were successfully identified from 454-derived transcriptomic sequences and ESTs available in GenBank and about 65% of PvRGLs were integrated into the common bean genetic map. A total of 30 RGL-tagged markers were developed for 25 unique PvRGLs, including 11 STS and 19 CAPS markers. The expressed PvRGLs identified in this study provide a large sequence resource for development of RGL-tagged markers that could be used further for genetic mapping of disease resistant candidate genes and quantitative trait locus/loci (QTLs). This work also represents an additional method for identifying expressed RGLs from next generation sequencing data.

Project description:Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI). However, confirming the patent status of these generated molecules has been a challenge because there are no free and easy-to-use tools that can be used to determine the novelty of the generated compounds in terms of patents in a timely manner; additionally, there are no appropriate reference databases for pharmaceutical patents in the world. In this study, two public databases, SureChEMBL and Google Patents Public Datasets, were used to create a reference database of drug-related patented compounds using international patent classification. An exact structure search system was constructed using InChIKey and a relational database system to rapidly search for compounds in the reference database. Because drug-related patented compounds are a good source for generative AI to learn useful chemical structures, they were used as the training data. Furthermore, molecule generation was successfully directed by increasing and decreasing the number of generated patented compounds through incorporation of patent status (i.e., patented or not) into learning. The use of patent status enabled generation of novel molecules with high drug-likeness. The generation using generative AI with patent information would help efficiently propose novel compounds in terms of pharmaceutical patents. Scientific contribution: In this study, a new molecule-generation method that takes into account the patent status of molecules, which has rarely been considered but is an important feature in drug discovery, was developed. The method enables the generation of novel molecules based on pharmaceutical patents with high drug-likeness and will help in the efficient development of effective drug compounds.

Project description:BackgroundAutomatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.ResultsWildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm.ConclusionsThe mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan).

Dataset Information

Data mining patented antibody sequences.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets