Computational Saturation Screen Reveals the Landscape of Mutations in Human Fumarate Hydratase

Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

K. Quadruple Primary Malignancies over 2 Years with Germline Mutation in Krebs Cycle Enzyme Gene Fumarate Hydratase

Multiple primary cancers (MPCs) are defined as the presence of more than one cancer in an individual that is not due to recurrence, metastasis, or local spread. Different factors such as copathogenic genetic mutations, environmental factors, lifestyle, and first cancer treatment increase the possible occurrence of subsequent malignancies. In recent years, the risk of MPCs has increased due to improved treatment; however, quadruple primary malignancies are still rare and require further investigation and treatment of the underlying cause. Here, we present a 64-year-old man with a 40-year history of cigarette smoking who developed quadruple primary malignancies of the epiglottis, kidney, pancreas, and lung. To investigate the possible genetic cause, we performed WES, and a variant of c.580G > A (Ala194Thr) was discovered in exon 5 of the Krebs cycle enzyme gene, fumarate hydratase (FH). This substitution was classified as VUS in Clinvar and likely pathogenic by Varsome and Franklin software. The structural analysis showed that the variation found was localized in a highly conserved alpha helix in the D2 domain near the FH hinge region (<6 Å), suggesting that enzyme activity was affected by a perturbation in protein quaternary structure. Because of the well-established role of FH mutations in renal cancer risk, it was possible that the FH mutation could have led to the development of renal cell carcinoma in this case. The biological mechanisms of MPCs suggest that subsequent primary malignancies are triggered by the combined effects of environmental factors, such as smoking and genetics.

Understanding large scale sequencing datasets through changes to protein folding

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens

Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

We present here an approach to protein design that combines (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learn a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force-field method (FoldX). Our approach is applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assess the functionality of 71 variants generated to explore a range of RBM and FoldX energies. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.

Mutations observed in somatic evolution reveal underlying gene mechanisms

Highly sensitive DNA sequencing techniques have allowed the discovery of large numbers of somatic mutations in normal tissues. Some mutations confer a competitive advantage over wild-type cells, generating expanding clones that spread through the tissue. Competition between mutant clones leads to selection. This process can be considered a large scale, in vivo screen for mutations increasing cell fitness. It follows that somatic missense mutations may offer new insights into the relationship between protein structure, function and cell fitness. We present a flexible statistical method for exploring the selection of structural features in data sets of somatic mutants. We show how this approach can evidence selection of specific structural features in key drivers in aged tissues. Finally, we show how drivers may be classified as fitness-enhancing and fitness-suppressing through different patterns of mutation enrichment. This method offers a route to understanding the mechanism of protein function through in vivo mutant selection.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

A Clinicopathological and Molecular Analysis of Fumarate Hydratase (FH)-deficient Renal Cell Carcinomas with Heterogeneous Loss of FH Expression

Aims. Fumarate hydratase (FH)-deficient renal cell carcinoma (RCC) is a rare aggressive renal malignancy associated with hereditary leiomyomatosis and RCC syndrome (HLRCC). Tumors exhibiting heterogeneous (ie, patchy) FH loss by immunohistochemistry have rarely been described, may be diagnostically challenging, and have never been the focus of a study. We aimed to investigate the FH mutational status of FH-deficient RCC with heterogeneous versus complete FH loss, to characterize additional genetic drivers, and to evaluate 2SC immunohistochemistry in this setting. Methods and Results. We studied FH-deficient RCC with heterogeneous ( n = 3) and complete ( n = 4) FH loss. Targeted next-generation sequencing (NGS) was performed on all tumors. No patients had a known history of HLRCC. All tumors had histological features within the morphologic spectrum described for FH-deficient RCC. All 7 tumors were immunoreactive for 2SC. Molecularly, all 7 tumors revealed multiple hits involving the FH locus resulting in complete loss of wild-type alleles. All tumors with heterogeneous FH loss harbored FH missense variants within domain 2 of the protein, and each had concomitant copy neutral loss of heterozygosity (CN-LOH). In complete FH loss tumors, FH alterations included splice variants with concomitant loss of heterozygosity ( n = 2) and homozygous gene deletions ( n = 2). Other non-recurrent alterations included biallelic alterations of TP53, NF2, SMAD4 and activation of PIK3CA. Conclusions. Our series highlights how heterogeneous FH loss (patchy positive staining) is an important staining pattern to recognize since it is compatible with a diagnosis of FH-deficient RCC and should prompt additional ancillary studies (confirmatory 2SC immunohistochemistry and/or molecular testing) and genetic evaluation.

SARS-CoV-2 Variants Are Selecting for Spike Protein Mutations That Increase Protein Stability

The emergence of variants of SARS-CoV-2 with mutations in their spike protein are a major cause for concern for the efficacy of vaccines and control of the pandemic. We show that mutations in the spike protein of SARS-CoV-2 are selecting for amino acid changes that result in a more thermodynamically stable protein than expected from background. We suggest that the computationally efficient analysis of mutational stability may aid in early screening of variants.

Cancer-causing BRCA2 missense mutations disrupt an intracellular protein assembly mechanism to disable genome maintenance

Cancer-causing missense mutations in the 3418 amino acid BRCA2 breast and ovarian cancer suppressor protein frequently affect a short (∼340 residue) segment in its carboxyl-terminal domain (DBD). Here, we identify a shared molecular mechanism underlying their pathogenicity. Pathogenic BRCA2 missense mutations cluster in the DBD’s helical domain (HD) and OB1-fold motifs, which engage the partner protein DSS1. Pathogenic - but not benign – DBD mutations weaken or abolish DSS1-BRCA2 assembly, provoking mutant BRCA2 oligomers that are excluded from the cell nucleus, and disable DNA repair by homologous DNA recombination (HDR). DSS1 inhibits the intracellular oligomerization of wildtype, but not mutant, forms of BRCA2. Remarkably, DSS1 expression corrects defective HDR in cells bearing pathogenic BRCA2 missense mutants with weakened, but not absent, DSS1 binding. Our findings identify a DSS1-mediated intracellular protein assembly mechanism that is disrupted by cancer-causing BRCA2 missense mutations, and suggest an approach for its therapeutic correction.