Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility

Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences enjoys high levels of use and finds numerous applications in the development of a broad range of other bioinformatics tools. Numerous sequence-based predictors of SS and SSS were developed and published in recent years. We survey and analyze 45 SS predictors that were released since 2018, focusing on their inputs, predictive models, scope of their prediction, and availability. We also review 32 sequence-based SSS predictors, which primarily focus on predicting coiled coils and beta-hairpins and which include five methods that were published since 2018. Substantial majority of these predictive tools rely on machine learning models, including a variety of deep neural network architectures. They also frequently use evolutionary sequence profiles. We discuss details of several modern SS and SSS predictors that are currently available to the users and which were published in higher impact venues.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

Specific transcriptional programs differentiate ICOS from CD28 costimulatory signaling in human Naïve CD4+ T cells

Costimulatory molecules of the CD28 family play a crucial role in the activation of immune responses in T lymphocytes, complementing and modulating signals originating from the T-cell receptor (TCR) complex. Although distinct functional roles have been demonstrated for each family member, the specific signaling pathways differentiating ICOS- from CD28-mediated costimulation during early T-cell activation are poorly characterized. In the present study, we have performed RNA-Seq-based global transcriptome profiling of anti-CD3-treated naïve CD4+ T cells upon costimulation through either inducible costimulator (ICOS) or CD28, revealing a set of signaling pathways specifically associated with each signal. In particular, we show that CD3/ICOS costimulation plays a major role in pathways related to STAT3 function and osteoarthritis (OA), whereas the CD3/CD28 axis mainly regulates p38 MAPK signaling. Furthermore, we report the activation of distinct immunometabolic pathways, with CD3/ICOS costimulation preferentially targeting glycosaminoglycans (GAGs) and CD3/CD28 regulating mitochondrial respiratory chain and cholesterol biosynthesis. These data suggest that ICOS and CD28 costimulatory signals play distinct roles during the activation of naïve T cells by modulating distinct sets of immunological and immunometabolic genes.

The MitoAging Project: Single nucleotide polymorphisms (SNPs) in mitochondrial genes and their association to longevity

Mitochondrial dysfunction is a major hallmark of aging. Mitochondrial DNA (mtDNA) mutations (inherited or acquired) may cause a malfunction of the respiratory chain (RC), and thus negatively affect cell metabolism and function. In contrast, certain mtDNA single nucleotide polymorphisms (SNPs) may be beneficial to mitochondrial electron transport chain function and the extension of cellular health as well as lifespan. The goal of the MitoAging project is to detect key physiological characteristics and mechanisms that improve mitochondrial function and use them to develop therapies to increase longevity and a healthy lifespan. We chose to perform a systematic literature review (SLR) as a tool to collect key mtDNA SNPs associated with an increase in lifespan. Then validated our results by comparing them to the MitoMap database. Next, we assessed the effect of relevant SNPs on protein stability. A total of 28 SNPs were found in protein coding regions. These SNPs were reported in Japan, China, Turkey, and India. Among the studied SNPs, the C5178A mutation in the ND2 gene of Complex I of the RC was detected in all the reviewed reports except in Uygur Chinese centenarians. Then, we found that G9055A (ATP6 gene) and A10398G (ND3 gene) polymorphisms have been associated with a protective effect against Parkinson's disease (PD). Additionally, C8414T in ATP8 was significantly associated with longevity in three Japanese reports. Interestingly, using MitoMap we found that G9055A (ATP6 gene) was the only SNP promoting longevity not associated with any pathology. The identification of SNPs associated with an increase in lifespan opens the possibility to better understand individual differences regarding a decrease in illness susceptibility and find strategies that contribute to healthy aging.

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.