Best templates outperform homology models in predicting the impact of mutations on protein stability

Targeting TRIM26: Unveiling an Oncogene and Identification of Plant Metabolites as a Potential Therapeutics for Breast Cancer

Breast cancer is the major cause of cancer-related mortality and frequent malignancies among women worldwide. The TRIM (Tripartite Motif) protein family is a broad and diverse set of proteins that contain a conserved structural motif known as the tripartite motif, which comprises of three different domains, B-box domain, Coiled-coil domain and RBR (Ring-finger, B-box, and coiled-coil) domain. TRIM proteins are involved in regulating cancer growth and metastasis. However, TRIM proteins are still unexplored in cancer cell regulation. In this study, by using a cancer database expression of all TRIM proteins was determined in breast cancer. Out of 77 TRIM genes, 16 genes were upregulated in breast cancer. Here, the upregulated TRIM26 gene's role is not yet explored in breast cancer. Indeed, TRIM26 is upregulated in 21 cancer types out of 33 cancer types. To investigate the role of TRIM26 in breast cancer, siRNA-mediated gene silencing was carried out in MCF-7 and MDA-MB 231 breast cancer cells. Reduced expression of TRIM 26 decreased cancer cell proliferation, migration and invasion with simultaneous reduction of various proliferative, cell cycle and mesenchymal markers and upregulation of epithelial markers. Further, docking studies found potential novel plant metabolites. Thus, targeting TRIM26 may provide a novel therapeutic approach for breast cancer treatment.

Identification and mechanism determination of the efflux pump subunit amrB gene mutations linked to gentamicin susceptibility in clinical Burkholderia pseudomallei from Malaysian Borneo

The bacterium Burkholderia pseudomallei is typically resistant to gentamicin but rare susceptible strains have been isolated in certain regions, such as Thailand and Sarawak, Malaysia. Recently, several amino acid substitutions have been reported in the amrB gene (a subunit of the amrAB-oprA efflux pump gene) that confer gentamicin susceptibility. However, information regarding the mechanism of the substitutions conferring the susceptibility is lacking. To understand the mechanism of amino acid substitution that confers susceptibility, this study identifies the corresponding mutations in clinical gentamicin-susceptible B. pseudomallei isolates from the Malaysian Borneo (n = 46; Sarawak: 5; Sabah: 41). Three phenotypically confirmed gentamicin-susceptible (GENs) strains from Sarawak, Malaysia, were screened for mutations in the amrB gene using gene sequences of gentamicin-resistant (GENr) strains (QEH 56, QEH 57, QEH20, and QEH26) and publicly available sequences (AF072887.1 and BX571965.1) as the comparator. The effect of missense mutations on the stability of the AmrB protein was determined by calculating the average energy change value (ΔΔG). Mutagenesis analysis identified a polymorphism-associated mutation, g.1056 T > G, a possible susceptible-associated in-frame deletion, Delta V412, and a previously confirmed susceptible-associated amino acid substitution, T368R, in each of the three GENs isolates. The contribution of Delta V412 needs further confirmation by experimental mutagenesis analysis. The mechanism by which T368R confers susceptibility, as elucidated by in silico mutagenesis analysis using AmrB-modeled protein structures, is proposed to be due to the location of T368R in a highly conserved region, rather than destabilization of the AmrB protein structure.

Design of stable circular permutants of the GroEL chaperone apical domain

Enhancing protein stability holds paramount significance in biotechnology, therapeutics, and the food industry. Circular permutations offer a distinctive avenue for manipulating protein stability while keeping intra-protein interactions intact. Amidst the creation of circular permutants, determining the optimal placement of the new N- and C-termini stands as a pivotal, albeit largely unexplored, endeavor. In this study, we employed PONDR-FIT's predictions of disorder propensity to guide the design of circular permutants for the GroEL apical domain (residues 191-345). Our underlying hypothesis posited that a higher predicted disorder value would correspond to reduced stability in the circular permutants, owing to the increased likelihood of fluctuations in the novel N- and C-termini. To substantiate this hypothesis, we engineered six circular permutants, positioning glycines within the loops as locations for the new N- and C-termini. We demonstrated the validity of our hypothesis along the set of the designed circular permutants, as supported by measurements of melting temperatures by circular dichroism and differential scanning microcalorimetry. Consequently, we propose a novel computational methodology that rationalizes the design of circular permutants with projected stability. Video Abstract.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

PROSTATA: a framework for protein stability assessment using transformers

MOTIVATION

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.

RESULTS

In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.

AVAILABILITY AND IMPLEMENTATION

PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.

Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion

Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability

Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach is to use protein structural information to model the physical impacts of variants and predict their likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of stability predictors in reproducing thermodynamically accurate values and evaluated their ability to distinguish between known pathogenic and benign mutations. Here, we take an alternate approach, and explore how well stability predictor scores correlate with functional impacts derived from deep mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein stability-based tools against mutant protein fitness values from 49 independent DMS datasets, covering 170,940 unique single amino acid variants. We find that FoldX and Rosetta show the strongest correlations with DMS-based functional scores, similar to their previous top performance in distinguishing between pathogenic and benign variants. For both methods, performance is considerably improved when considering intermolecular interactions from protein complex structures, when available. Furthermore, using these two predictors, we derive a "Foldetta" consensus score, which improves upon the performance of both, and manages to match dedicated variant effect predictors in reflecting variant functional impacts. Finally, we also highlight that predicted stability effects show consistently higher correlations with certain DMS experimental phenotypes, particularly those based upon protein abundance, and, in certain cases, can significantly outcompete sequence-based variant effect prediction methodologies for predicting functional scores from DMS experiments.

Clinical Characteristics and Genetic Analyses of Patients with Idiopathic Hypogonadotropic Hypogonadism

Objective

Idiopathic hypogonadotropic hypogonadism (IHH) is classified into two groups-Kalman syndrome and normosmic IHH (nIHH). Half of all cases can be explained by mutations in >50 genes. Targeted gene panel testing with nexrt generation sequencing (NGS) is required for patients without typical phenotypic findings. The aim was to determine the genetic etiologies of patients with IHH using NGS, including 54 IHH-associated genes, and to present protein homology modeling and protein stability analyzes of the detected variations.

Methods

Clinical and demographic data of 16 patients (eight female), aged between 11.6-17.8 years, from different families were assessed. All patients were followed up for a diagnosis of nIHH, had normal cranial imaging, were without anterior pituitary hormone deficiency other than gonadotropins, had no sex chromosome anomaly, had no additional disease, and underwent genetic analysis with NGS between the years 2008-2021. Rare variants were classified according to the variant interpretation framework of the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology. Changes in protein structure caused by variations were modeled using RoseTTAFold and changes in protein stability resulting from variation were analyzed.

Results

Half of the 16 had no detectable variation. Three (18.75%) had a homozygous (pathogenic) variant in the GNRHR gene, one (6.25%) had a compound heterozygous [likely pathogenic-variants of uncertain significance (VUS)] variant in PROK2 and four (25%) each had a heterozygous (VUS) variant in HESX1, FGF8, FLRT3 and DMXL2. Protein models showed that variants interpreted as VUS according to ACMG could account for the clinical IHH.

Conclusion

The frequency of variation detection was similar to the literature. Modelling showed that the variant in five different genes, interpreted as VUS according to ACMG, could explain the clinical IHH.

Enhanced Thermostability and Catalytic Activity of Streptomyces mobaraenesis Transglutaminase by Rationally Engineering Its Flexible Regions

Streptomyces mobaraenesis transglutaminase can catalyze the cross-linking of proteins, which has been widely used in food processing. In this study, we rationally modified flexible regions to further improve the thermostability of FRAPD-TGm2 (S2P-S23V-Y24N-E28T-S199A-A265P-A287P-K294L), a stable mutant of the transglutaminase constructed in our previous study. First, five flexible regions of FRAPD-TGm2 were identified by molecular dynamics simulations at 330 and 360 K. Second, a script based on Rosetta Cartesian_ddg was developed for virtual saturation mutagenesis within the flexible regions far from the substrate binding pocket, generating the top 18 mutants with remarkable decreases in folding free energy. Third, from the top 18 mutants, we identified two mutants (S116A and S179L) with increased thermostability and activity. Finally, the above favorable mutations were combined to obtain FRAPD-TGm2-S116A-S179L (FRAPD-TGm2A), exhibiting a half-life of 132.38 min at 60 °C (t_1/2(60 °C)) and a specific activity of 79.15 U/mg, 84 and 21% higher than those of FRAPD-TGm2, respectively. Therefore, the current result may benefit the application of S. mobaraenesis transglutaminase at high temperatures.

Using AlphaFold to predict the impact of single mutations on protein stability and function

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.

Accurate protein stability predictions from homology models

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.