Mslar: Microbial synthetic lethal and rescue database.

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

Deep learning in preclinical antibody drug discovery and development.

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Self-binding peptides: Binding-upon-folding versus folding-upon-binding.

Self-binding peptide (SBP) represents a novel biomolecular phenomenon spanning between folding and binding. It is a structurally independent, short peptide segment within a monomeric protein and fulfills biological function by dynamically binding to/unbinding from its target domain in the same monomer. Here, four representative SBP systems, including mouse proto-oncogene Vav, human retinoic acid receptor RARγ, fruit fly scaffold module INAD and crypto 14-3-3 protein Cp14b, are investigated systematically by using atomistic molecular dynamics (MD) simulations and post binding energetics analyses. The native bound structure, artificial unbound state and isolated peptide segment of SBP moieties in the four systems were constructed, analyzed and compared in detail. It is revealed that the SBP interaction with their targets is almost a binding phenomenon at single-molecule level, but presence of a polypeptide linker between the SBP and target can promote the binding efficiency since the linker restriction largely increases the probability of SBP-target encounters in a statistical physics point of view. In this respect, unlike classical peptide-mediated interactions where the intrinsically disordered peptides are folded into an ordered structure upon binding to their protein partners (folding-upon-binding), we herein propose SBPs as a new and reversed biological event that is naturally a folding phenomenon but exhibits a typical binding behavior (binding-upon-folding).

In Silico Immunogenicity Assessment of Therapeutic Peptides.

The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made in silico evaluation an important component of immunogenicity assessment in therapeutic peptide development. In this review, we summarize the development of peptide-MHC-II binding prediction methods for antigenic peptides presented by MHC class II molecules and provide a systematic explanation of the most advanced ones, aiming to deepen our understanding of this field that requires particular attention.

DOTAD: A Database of Therapeutic Antibody Developability.

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Development and application of therapeutic antibodies against COVID-19.

The pandemic of Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2) continues to be a global health crisis. Fundamental studies at genome, transcriptome, proteome, and interactome levels have revealed many viral and host targets for therapeutic interventions. Hundreds of antibodies for treating COVID-19 have been developed at preclinical and clinical stages in the format of polyclonal antibodies, monoclonal antibodies, and cocktail antibodies. Four products, i.e., convalescent plasma, bamlanivimab, REGN-Cov2, and the cocktail of bamlanivimab and etesevimab have been authorized by the U.S. Food and Drug Administration (FDA) for emergency use. Hundreds of relevant clinical trials are ongoing worldwide. Therapeutic antibody therapies have been a very active and crucial part of COVID-19 treatment. In this review, we focus on the progress of therapeutic COVID-19 antibody development and application, discuss corresponding problems and challenges, suggesting new strategies and solutions.

Is protein context responsible for peptide-mediated interactions?

Many cell signaling pathways are orchestrated by the weak, transient, and reversible protein-protein interactions that are mediated by the binding of a short peptide segment in one protein (parent protein) to a globular domain in another (partner protein), known as peptide-mediated interactions (PMIs). Previous studies normally had an implicit hypothesis that a PMI is functionally equivalent or analogous to the protein-peptide interaction (PTI) involved in the PMI system, while ignoring parent context contribution to the peptide binding. Here, we perform a systematic investigation on the reasonability and applicability of the hypothesis at structural, energetic and dynamic levels. It is revealed that the context impacts PMIs primarily through conformational constraint of the peptide segments, which can (i) reduce the peptide flexibility and disorder in an unbound state, (ii) help the peptide conformational selection to fit the active pocket of partner proteins, and (iii) enhance the peptide packing tightness against the partners. Long, unstructured and/or middle-located peptide segments seem to be more vulnerable to their context than short, structured and/or terminal ones. The context is found to moderately or considerably improve both the binding affinity and specificity of PMIs as compared to their PTI counterparts; with the context support a peptide segment can contribute to ∼30-60% total binding energy of the whole PMI system, whereas the contribution is reduced to ∼5-50% when the context constraint is released. In addition, we also observe that peptide selectivity is largely impaired or even reversed upon stripping of their parent context (global selectivity decreases from 34.2 to 1.7-fold), by examining the crystal structures of full-length Src family kinases in an autoinhibitory state. Instead of the direct interaction and desolvation that are primarily concerned in traditional studies, peptide flexibility and the entropy penalty should also play a crucial role in the context effect on PMIs. Overall, we suggest that the context factor should not be ignored in most cases, particularly those with peptide segments that are long, highly disordered, and/or located at the middle region of their parent proteins.