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Deep learning-based design and experimental validation of a medicine-like human antibody library.

Brief Bioinform

November 2024

Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.

Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).

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Local adaptation and phenotypic plasticity in two forest understorey herbs in response to forest management intensity.

AoB Plants

January 2025

Plant Evolutionary Ecology, Faculty of Biological Sciences, Goethe University Frankfurt, Max-von-Laue-Str. 13, 60438 Frankfurt am Main, Germany.

Local adaptation is a common phenomenon that helps plant populations to adjust to broad-scale environmental heterogeneity. Given the strong effect of forest management on the understorey microenvironment and often long-term effects of forest management actions, it seems likely that understorey herbs may have locally adapted to the practiced management regime and induced environmental variation. We investigated the response of and to forest management using a transplant experiment along a silvicultural management intensity gradient.

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Genetic determinants of COVID-19 severity and mortality: Alu 287 bp polymorphism and , , expression in hospitalized patients.

PeerJ

January 2025

Departamento de genética, ecologia e evolução, Laboratório de biologia integrativa, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.

Background: The angiotensin-converting enzyme 2 (ACE2) and the transmembrane serine protease 2 (TMPRSS2) are central human molecules in the SARS-CoV-2 virus-host interaction. Evidence indicates that may influence expression. This study aims to determine whether ACE1, ACE2, and TMPRSS2 mRNA expression levels, along with the ACE1 Alu 287 bp polymorphism (rs4646994), contribute to the severity and mortality of COVID-19.

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Assessments of genetic diversity, structure, history, and effective population size ( ) are critical for the conservation of imperiled populations. The lesser prairie-chicken () has experienced declines due to habitat loss, degradation, and fragmentation in addition to substantial population fluctuations with unknown effects on genetic diversity. Our objectives were to: (i) compare genetic diversity across three temporally discrete sampling periods (2002, 2007-2010, and 2013-2014) that are characterized by low or high population abundance; (ii) examine genetic diversity at lek and lek cluster spatial scales; (ii) identify potential bottlenecks and characterize genetic structure and relatedness; and (iii) estimate the regional .

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MTIOT: Identifying HPV subtypes from multiple infection data.

Comput Struct Biotechnol J

December 2024

Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.

Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis.

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