Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence

Classification of missense variants is challenging. Lacking compelling clinical and/or functional data, ACMG/AMP lines of evidence are restricted to PM2 (rarity code applied at supporting level) and PP3/BP4 (computational evidence based mostly on multiple-sequence-alignment conservation tools). Currently, the ClinGen ENIGMA BRCA1/2 Variant Curation Expert Panel uses BayesDel to apply PP3/BP4 to missense variants located in the BRCA1 RING/BRCT domains. The ACMG/AMP framework does not refer explicitly to protein structure as a putative source of pathogenic/benign evidence. Here, we tested the value of incorporating structure-based evidence such as relative solvent accessibility (RSA), folding stability (ΔΔG), and/or AlphaMissense pathogenicity to the classification of BRCA1 missense variants. We used MAVE functional scores as proxies for pathogenicity/benignity. We computed RSA and FoldX5.0 ΔΔG predictions using as alternative input templates for either PDB files or AlphaFold2 models, and we retrieved pre-computed AlphaMissense and BayesDel scores. We calculated likelihood ratios toward pathogenicity/benignity provided by the tools (individually or combined). We performed a clinical validation of major findings using the large-scale BRIDGES case-control dataset. AlphaMissense outperforms ΔΔG and BayesDel, providing similar PP3/BP4 evidence strengths with lower rate of variants in the uninformative score range. AlphaMissense combined with ΔΔG increases evidence strength granularity. AlphaFold2 models perform well as input templates for ΔΔG predictions. Regardless of the tool, BP4 (but not PP3) is highly dependent on RSA, with benignity evidence provided only to variants targeting buried or partially buried residues (RSA ≤ 60%). Stratification by functional domain did not reveal major differences. In brief, structure-based analysis improves PP3/BP4 assessment, uncovering a relevant role for RSA.

Predicting the structure-altering mechanisms of disease variants

Missense variants can affect the severity of disease, choice of treatment, and treatment outcomes. While the number of known variants has been increasing at a rapid pace, available evidence of their clinical effect has been lagging behind, constituting a challenge for clinicians and researchers. Multiplexed assays of variant effects (MAVEs) are important to close the gap; nonetheless, computational predictions of pathogenicity are still often the only available data for scoring variants. Such methods are not designed to provide a mechanistic explanation for the effect of amino acid substitutions. To this purpose, we propose structure-based frameworks as ensemble methodologies, with each method tailored to predict a different aspect among those exerted by amino acid substitutions to link predicted pathogenicity to mechanistic indicators. We review available frameworks, as well as advancements in underlying structure-based methods that predict variant effects on several protein features, such as protein stability, biomolecular interactions, allostery, post-translational modifications, and more.

VUStruct: a compute pipeline for high throughput and personalized structural biology

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High-Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Shared-weight graph framework for comprehensive protein stability prediction across diverse mutation types

Research on protein stability changes is vital for understanding disease mechanisms and optimizing industrial enzymes. Protein thermal stability can be modified by variants leading to changes in ΔΔG values between wild-type and mutant proteins. Despite advances, most models focus on single-point mutations, overlooking multipoint and indel mutations. Typically, the single-point mutation is expected to have a relatively limited impact on the function of a protein, necessitating more drastic modifications to meet new challenges. Current methods for multipoint mutations yield poor results, and no method exists for any length of indel mutations. To address this, we introduce UniMutStab, a shared-graph convolutional network leveraging protein language models and residue interaction networks to access any type of mutation. An embedded edge weight module enhances the integration of residue node features and interactions, improving prediction accuracy. Trained on the "Mega-scale" dataset with ~780 000 mutations, UniMutStab surpasses existing methods in predicting protein stability changes. It is a purely sequence-based approach to predict arbitrary mutation types, demonstrating robust generalization across multiple tasks and potentially contributing significantly to protein engineering, personalized therapeutics, and diagnostic methodologies.

VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance

Various computational methods have been developed to predict the pathogenicity of missense variants, which is crucial for diagnosing rare diseases. Recently, we introduced VarMeter, a diagnostic tool for predicting variant pathogenicity based on normalized solvent-accessible surface area (nSASA) and mutation energy calculated from AlphaFold 3D models, and validated it on arylsulfatase L. To evaluate the broader applicability of VarMeter and enhance its predictive accuracy, here we analyzed 296 pathogenic and 240 benign variants extracted from the ClinVar database. By comparing structural features including nSASA, mutation energy, and predicted local distance difference test (pLDDT) score, we identified distinct characteristics between pathogenic and benign variants. These features were used to develop VarMeter2, which classifies variants based on Mahalanobis distance. VarMeter2 achieved a prediction accuracy of 82 % for the ClinVar dataset, a marked improvement over the original VarMeter (74 %), and 84 % for published missense variants of N-sulphoglucosamine sulphohydrolase (SGSH), an enzyme associated with Sanfillippo syndrome A. Application of VarMeter 2 to SGSH variants in our clinical database identified a novel SGSH variant, Q365P, as pathogenic. The recombinant Q365P protein lacked enzymatic activity as compared with wild-type SGSH. Furthermore, it was largely retained in the endoplasmic reticulum and failed to reach the Golgi, probably due to misfolding. Protein stability assays confirmed reduced stability of the variant, further explaining its loss of function. Consistently, the patient homozygous for this variant was diagnosed with Sanfilippo syndrome A. These results underscore the predictive power and versatility of VarMeter2 in assessing the pathogenicity of missense variants.

When "loss-of-function" means proteostasis burden: Thinking again about coding DNA variants

Each human genome has approximately 5 million DNA variants. Even for complete loss-of-function variants causing inherited, monogenic diseases, current understanding based on gene-specific molecular function does not adequately predict variability observed between people with identical mutations or fluctuating disease trajectories. We present a parallel paradigm for loss-of-function variants based on broader consequences to the cell when aberrant polypeptide chains of amino acids are translated from mutant RNA to generate mutated proteins. Missense variants that modify primary amino acid sequence, and nonsense/frameshift variants that generate premature termination codons (PTCs), are placed in context alongside emergent themes of chaperone binding, protein quality control capacity, and cellular adaptation to stress. Relatively stable proteostasis burdens are contrasted with rapid changes after induction of gene expression, or stress responses that suppress nonsense mediated decay (NMD) leading to higher PTC transcript levels where mutant proteins can augment cellular stress. For known disease-causal mutations, an adjunctive variant categorization system enhances clinical predictive power and precision therapeutic opportunities. Additionally, with typically more than 100 nonsense and frameshift variants, and ∼10,000 missense variants per human DNA, the paradigm focuses attention on all protein-coding DNA variants, and their potential contributions to multimorbid states beyond classically designated inherited diseases. Experimental testing in clinically relevant systems is encouraged to augment current atlases of protein expression at single-cell resolution, and high-throughput experimental data and deep-learning models that predict which amino acid substitutions generate enhanced degradative burdens. Incorporating additional dimensions such as pan-proteome competition for chaperones, and age-related loss of proteostasis capacity, should further accelerate health impacts.

SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions

The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments. We show that by integrating both types of information we obtain a variant effect prediction model that is robust when sequence information is scarce. We also show that SSEmb learns embeddings of the sequence and structure that are useful for other downstream tasks such as to predict protein-protein binding sites. We envisage that SSEmb may be useful both for variant effect predictions and as a representation for learning to predict protein properties that depend on sequence and structure.

ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

Destabilization and Degradation of a Disease-Linked PGM1 Protein Variant

PGM1-linked congenital disorder of glycosylation (PGM1-CDG) is an autosomal recessive disease characterized by several phenotypes, some of which are life-threatening. Research focusing on the disease-related variants of the α-D-phosphoglucomutase 1 (PGM1) protein has shown that several are insoluble in vitro and expressed at low levels in patient fibroblasts. Due to these observations, we hypothesized that some disease-linked PGM1 protein variants are structurally destabilized and subject to protein quality control (PQC) and rapid intracellular degradation. Employing yeast-based assays, we show that a disease-associated human variant, PGM1 L516P, is insoluble, inactive, and highly susceptible to ubiquitylation and rapid degradation by the proteasome. In addition, we show that PGM1 L516P forms aggregates in S. cerevisiae and that both the aggregation pattern and the abundance of PGM1 L516P are chaperone-dependent. Finally, using computational methods, we perform saturation mutagenesis to assess the impact of all possible single residue substitutions in the PGM1 protein. These analyses identify numerous missense variants with predicted detrimental effects on protein function and stability. We suggest that many disease-linked PGM1 variants are subject to PQC-linked degradation and that our in silico site-saturated data set may assist in the mechanistic interpretation of PGM1 variants.

Deep mutational scanning reveals a correlation between degradation and toxicity of thousands of aspartoacylase variants

Unstable proteins are prone to form non-native interactions with other proteins and thereby may become toxic. To mitigate this, destabilized proteins are targeted by the protein quality control network. Here we present systematic studies of the cytosolic aspartoacylase, ASPA, where variants are linked to Canavan disease, a lethal neurological disorder. We determine the abundance of 6152 of the 6260 ( ~ 98%) possible single amino acid substitutions and nonsense ASPA variants in human cells. Most low abundance variants are degraded through the ubiquitin-proteasome pathway and become toxic upon prolonged expression. The data correlates with predicted changes in thermodynamic stability, evolutionary conservation, and separate disease-linked variants from benign variants. Mapping of degradation signals (degrons) shows that these are often buried and the C-terminal region functions as a degron. The data can be used to interpret Canavan disease variants and provide insight into the relationship between protein stability, degradation and cell fitness.

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

BACKGROUND

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity.

RESULTS

Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity.

CONCLUSIONS

In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

A mutational atlas for Parkin proteostasis

Proteostasis can be disturbed by mutations affecting folding and stability of the encoded protein. An example is the ubiquitin ligase Parkin, where gene variants result in autosomal recessive Parkinsonism. To uncover the pathological mechanism and provide comprehensive genotype-phenotype information, variant abundance by massively parallel sequencing (VAMP-seq) is leveraged to quantify the abundance of Parkin variants in cultured human cells. The resulting mutational map, covering 9219 out of the 9300 possible single-site amino acid substitutions and nonsense Parkin variants, shows that most low abundance variants are proteasome targets and are located within the structured domains of the protein. Half of the known disease-linked variants are found at low abundance. Systematic mapping of degradation signals (degrons) reveals an exposed degron region proximal to the so-called "activation element". This work provides examples of how missense variants may cause degradation either via destabilization of the native protein, or by introducing local signals for degradation.

Exploring the "misfolding problem" by systematic discovery and analysis of functional-but-degraded proteins

In both health and disease, the ubiquitin-proteasome system (UPS) degrades point mutants that retain partial function but have decreased stability compared with their wild-type counterparts. This class of UPS substrate includes routine translational errors and numerous human disease alleles, such as the most common cause of cystic fibrosis, ΔF508-CFTR. Yet, there is no systematic way to discover novel examples of these "minimally misfolded" substrates. To address that shortcoming, we designed a genetic screen to isolate functional-but-degraded point mutants, and we used the screen to study soluble, monomeric proteins with known structures. These simple parent proteins yielded diverse substrates, allowing us to investigate the structural features, cytotoxicity, and small-molecule regulation of minimal misfolding. Our screen can support numerous lines of inquiry, and it provides broad access to a class of poorly understood but biomedically critical quality-control substrates.

Lynch syndrome-associated endometrial cancer patient with a rare novel germline likely pathogenic variant of MSH2 gene: A case report

The Lynch syndrome (LS) is an autosomal dominant condition usually characterized by germline pathogenic variants in DNA mismatch repair (MMR) genes. Despite the guidelines now available, determining the pathogenicity of rare variants remains challenging, as the clinical significance of a genetic variant could be uncertain, but it may represent a disease-associated variation in the aforementioned genes. In this case report we will describe the case of a 47 years-old female affected by endometrial cancer (EC) with an extremely rare germline heterozygous variant in the MSH2 gene (c.562G > T p. (Glu188Ter), exon 3) that is likely pathogenic, and a family history consistent with LS.

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

Analysis of genome instability and implications for the consequent phenotype in Plasmodium falciparum containing mutated MSH2-1 (P513T)

Malarial parasites exhibit extensive genomic plasticity, which induces the antigen diversification and the development of antimalarial drug resistance. Only a few studies have examined the genome maintenance mechanisms of parasites. The study aimed at elucidating the impact of a mutation in a DNA mismatch repair gene on genome stability by maintaining the mutant and wild-type parasites through serial in vitro cultures for approximately 400 days and analysing the subsequent spontaneous mutations. A P513T mutant of the DNA mismatch repair protein PfMSH2-1 from Plasmodium falciparum 3D7 was created. The mutation did not influence the base substitution rate but significantly increased the insertion/deletion (indel) mutation rate in short tandem repeats (STRs) and minisatellite loci. STR mutability was affected by allele size, genomic category and certain repeat motifs. In the mutants, significant telomere healing and homologous recombination at chromosomal ends caused extensive gene loss and generation of chimeric genes, resulting in large-scale chromosomal alteration. Additionally, the mutant showed increased tolerance to N-methyl-N'-nitro-N-nitrosoguanidine, suggesting that PfMSH2-1 was involved in recognizing DNA methylation damage. This work provides valuable insights into the role of PfMSH2-1 in genome stability and demonstrates that the genomic destabilization caused by its dysfunction may lead to antigen diversification.

Lynch syndrome, molecular mechanisms and variant classification

Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.

GSNOR deficiency promotes tumor growth via FAK1 S-nitrosylation

Nitric oxide (NO) production in the tumor microenvironment is a common element in cancer. S-nitrosylation, the post-translational modification of cysteines by NO, is emerging as a key transduction mechanism sustaining tumorigenesis. However, most oncoproteins that are regulated by S-nitrosylation are still unknown. Here we show that S-nitrosoglutathione reductase (GSNOR), the enzyme that deactivates S-nitrosylation, is hypo-expressed in several human malignancies. Using multiple tumor models, we demonstrate that GSNOR deficiency induces S-nitrosylation of focal adhesion kinase 1 (FAK1) at C658. This event enhances FAK1 autophosphorylation and sustains tumorigenicity by providing cancer cells with the ability to survive in suspension (evade anoikis). In line with these results, GSNOR-deficient tumor models are highly susceptible to treatment with FAK1 inhibitors. Altogether, our findings advance our understanding of the oncogenic role of S-nitrosylation, define GSNOR as a tumor suppressor, and point to GSNOR hypo-expression as a therapeutically exploitable vulnerability in cancer.

Interpreting the molecular mechanisms of disease variants in human transmembrane proteins

Next-generation sequencing of human genomes reveals millions of missense variants, some of which may lead to loss of protein function and ultimately disease. Here, we investigate missense variants in membrane proteins-key drivers in cell signaling and recognition. We find enrichment of pathogenic variants in the transmembrane region across 19,000 functionally classified variants in human membrane proteins. To accurately predict variant consequences, one fundamentally needs to understand the underlying molecular processes. A key mechanism underlying pathogenicity in missense variants of soluble proteins has been shown to be loss of stability. Membrane proteins, however, are widely understudied. Here, we interpret variant effects on a larger scale by performing structure-based estimations of changes in thermodynamic stability using a membrane-specific energy function and analyses of sequence conservation during evolution of 15 transmembrane proteins. We find evidence for loss of stability being the cause of pathogenicity in more than half of the pathogenic variants, indicating that this is a driving factor also in membrane-protein-associated diseases. Our findings show how computational tools aid in gaining mechanistic insights into variant consequences for membrane proteins. To enable broader analyses of disease-related and population variants, we include variant mappings for the entire human proteome.

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.

The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.

Identification and Mutation Analysis of Nonconserved Residues on the TIM-Barrel Surface of GH5_5 Cellulases for Catalytic Efficiency and Stability Improvement

Exploring the potential functions of nonconserved residues on the outer side of α-helices and systematically optimizing them are pivotal for their application in protein engineering. Based on the evolutionary structural conservation analysis of GH5_5 cellulases, a practical molecular improvement strategy was developed. Highly variable sites on the outer side of the α-helices of the GH5_5 cellulase from Aspergillus niger (AnCel5A) were screened, and 14 out of the 34 highly variable sites were confirmed to exert a positive effect on the activity. After the modular combination of the positive mutations, the catalytic efficiency of the mutants was further improved. By using CMC-Na as the substrate, the catalytic efficiency and specific activity of variant AnCel5A_N193A/T300P/D307P were approximately 2.0-fold that of AnCel5A (227 ± 21 versus 451 ± 43 ml/s/mg and 1,726 ± 19 versus 3,472 ± 42 U/mg, respectively). The half-life (t_1/2) of variant AnCel5A_N193A/T300P/D307P at 75°C was 2.36 times that of AnCel5A. The role of these sites was successfully validated in other GH5_5 cellulases. Computational analyses revealed that the flexibility of the loop 6-loop 7-loop 8 region was responsible for the increased catalytic performance. This work not only illustrated the important role of rapidly evolving positions on the outer side of the α-helices of GH5_5 cellulases but also revealed new insights into engineering the proteins that nature left as clues for us to find. IMPORTANCE A comprehensive understanding of the residues on the α-helices of the GH5_5 cellulases is important for catalytic efficiency and stability improvement. The main objective of this study was to use the evolutionary conservation and plasticity of the TIM-barrel fold to probe the relationship between nonconserved residues on the outer side of the α-helices and the catalytic efficiency of GH5_5 cellulases by conducting structure-guided protein engineering. By using a four-step nonconserved residue screening strategy, the functional role of nonconserved residues on the outer side of the α-helices was effectively identified, and a variant with superior performance and capability was constructed. Hence, this study proved the effectiveness of this strategy in engineering GH5_5 cellulases and provided a potential competitor for industrial applications. Furthermore, this study sheds new light on engineering TIM-barrel proteins.

Assessing the clinical utility of protein structural analysis in genomic variant classification: experiences from a diagnostic laboratory

BACKGROUND

The widespread clinical application of genome-wide sequencing has resulted in many new diagnoses for rare genetic conditions, but testing regularly identifies variants of uncertain significance (VUS). The remarkable rise in the amount of genomic data has been paralleled by a rise in the number of protein structures that are now publicly available, which may have clinical utility for the interpretation of missense and in-frame insertions or deletions.

METHODS

Within a UK National Health Service genomic medicine diagnostic laboratory, we investigated the number of VUS over a 5-year period that were evaluated using protein structural analysis and how often this analysis aided variant classification.

RESULTS

We found 99 novel missense and in-frame variants across 67 genes that were initially classified as VUS by our diagnostic laboratory using standard variant classification guidelines and for which further analysis of protein structure was requested. Evidence from protein structural analysis was used in the re-assessment of 64 variants, of which 47 were subsequently reclassified as pathogenic or likely pathogenic and 17 remained as VUS. We identified several case studies where protein structural analysis aided variant interpretation by predicting disease mechanisms that were consistent with the observed phenotypes, including loss-of-function through thermodynamic destabilisation or disruption of ligand binding, and gain-of-function through de-repression or escape from proteasomal degradation.

CONCLUSIONS

We have shown that using in silico protein structural analysis can aid classification of VUS and give insights into the mechanisms of pathogenicity. Based on our experience, we propose a generic evidence-based workflow for incorporating protein structural information into diagnostic practice to facilitate variant classification.

Cancer-related Mutations with Local or Long-range Effects on an Allosteric Loop of p53

The tumor protein 53 (p53) is involved in transcription-dependent and independent processes. Several p53 variants related to cancer have been found to impact protein stability. Other variants, on the contrary, might have little impact on structural stability and have local or long-range effects on the p53 interactome. Our group previously identified a loop in the DNA binding domain (DBD) of p53 (residues 207-213) which can recruit different interactors. Experimental structures of p53 in complex with other proteins strengthen the importance of this interface for protein-protein interactions. We here characterized with structure-based approaches somatic and germline variants of p53 which could have a marginal effect in terms of stability and act locally or allosterically on the region 207-213 with consequences on the cytosolic functions of this protein. To this goal, we studied 1132 variants in the p53 DBD with structure-based approaches, accounting also for protein dynamics. We focused on variants predicted with marginal effects on structural stability. We then investigated each of these variants for their impact on DNA binding, dimerization of the p53 DBD, and intramolecular contacts with the 207-213 region. Furthermore, we identified variants that could modulate long-range the conformation of the region 207-213 using a coarse-grain model for allostery and all-atom molecular dynamics simulations. Our predictions have been further validated using enhanced sampling methods for 15 variants. The methodologies used in this study could be more broadly applied to other p53 variants or cases where conformational changes of loop regions are essential in the function of disease-related proteins.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

Structure-function relationships of the disease-linked A218T oxytocin receptor variant

Various single nucleotide polymorphisms (SNPs) in the oxytocin receptor (OXTR) gene have been associated with behavioral traits, autism spectrum disorder (ASD) and other diseases. The non-synonymous SNP rs4686302 results in the OXTR variant A218T and has been linked to core characteristics of ASD, trait empathy and preterm birth. However, the molecular and intracellular mechanisms underlying those associations are still elusive. Here, we uncovered the molecular and intracellular consequences of this mutation that may affect the psychological or behavioral outcome of oxytocin (OXT)-treatment regimens in clinical studies, and provide a mechanistic explanation for an altered receptor function. We created two monoclonal HEK293 cell lines, stably expressing either the wild-type or A218T OXTR. We detected an increased OXTR protein stability, accompanied by a shift in Ca²⁺ dynamics and reduced MAPK pathway activation in the A218T cells. Combined whole-genome and RNA sequencing analyses in OXT-treated cells revealed 7823 differentially regulated genes in A218T compared to wild-type cells, including 429 genes being associated with ASD. Furthermore, computational modeling provided a molecular basis for the observed change in OXTR stability suggesting that the OXTR mutation affects downstream events by altering receptor activation and signaling, in agreement with our in vitro results. In summary, our study provides the cellular mechanism that links the OXTR rs4686302 SNP with genetic dysregulations associated with aspects of ASD.

The conformational and mutational landscape of the ubiquitin-like marker for autophagosome formation in cancer

Macroautophagy/autophagy is a cellular process to recycle damaged cellular components, and its modulation can be exploited for disease treatments. A key autophagy player is the ubiquitin-like protein MAP1LC3B/LC3B. Mutations and changes in MAP1LC3B expression occur in cancer samples. However, the investigation of the effects of these mutations on MAP1LC3B protein structure is still missing. Despite many LC3B structures that have been solved, a comprehensive study, including dynamics, has not yet been undertaken. To address this knowledge gap, we assessed nine physical models for biomolecular simulations for their capabilities to describe the structural ensemble of MAP1LC3B. With the resulting MAP1LC3B structural ensembles, we characterized the impact of 26 missense mutations from pan-cancer studies with different approaches, and we experimentally validated our prediction for six variants using cellular assays. Our findings shed light on damaging or neutral mutations in MAP1LC3B, providing an atlas of its modifications in cancer. In particular, P32Q mutation was found detrimental for protein stability with a propensity to aggregation. In a broader context, our framework can be applied to assess the pathogenicity of protein mutations or to prioritize variants for experimental studies, allowing to comprehensively account for different aspects that mutational events alter in terms of protein structure and function.Abbreviations: ATG: autophagy-related; Cα: alpha carbon; CG: coarse-grained; CHARMM: Chemistry at Harvard macromolecular mechanics; CONAN: contact analysis; FUNDC1: FUN14 domain containing 1; FYCO1: FYVE and coiled-coil domain containing 1; GABARAP: GABA type A receptor-associated protein; GROMACS: Groningen machine for chemical simulations; HP: hydrophobic pocket; LIR: LC3 interacting region; MAP1LC3B/LC3B microtubule associated protein 1 light chain 3 B; MD: molecular dynamics; OPTN: optineurin; OSF: open software foundation; PE: phosphatidylethanolamine, PLEKHM1: pleckstrin homology domain-containing family M 1; PSN: protein structure network; PTM: post-translational modification; SA: structural alphabet; SLiM: short linear motif; SQSTM1/p62: sequestosome 1; WT: wild-type.

Multiplexing mutation rate assessment: determining pathogenicity of Msh2 variants in Saccharomyces cerevisiae

Despite the fundamental importance of mutation rate as a driving force in evolution and disease risk, common methods to assay mutation rate are time-consuming and tedious. Established methods such as fluctuation tests and mutation accumulation experiments are low-throughput and often require significant optimization to ensure accuracy. We established a new method to determine the mutation rate of many strains simultaneously by tracking mutation events in a chemostat continuous culture device and applying deep sequencing to link mutations to alleles of a DNA-repair gene. We applied this method to assay the mutation rate of hundreds of Saccharomyces cerevisiae strains carrying mutations in the gene encoding Msh2, a DNA repair enzyme in the mismatch repair pathway. Loss-of-function mutations in MSH2 are associated with hereditary nonpolyposis colorectal cancer, an inherited disorder that increases risk for many different cancers. However, the vast majority of MSH2 variants found in human populations have insufficient evidence to be classified as either pathogenic or benign. We first benchmarked our method against Luria-Delbrück fluctuation tests using a collection of published MSH2 missense variants. Our pooled screen successfully identified previously characterized nonfunctional alleles as high mutators. We then created an additional 185 human missense variants in the yeast ortholog, including both characterized and uncharacterized alleles curated from ClinVar and other clinical testing data. In a set of alleles of known pathogenicity, our assay recapitulated ClinVar's classification; we then estimated pathogenicity for 157 variants classified as uncertain or conflicting reports of significance. This method is capable of studying the mutation rate of many microbial species and can be applied to problems ranging from the generation of high-fidelity polymerases to measuring the frequency of antibiotic resistance emergence.

Analysis and Interpretation of the Impact of Missense Variants in Cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.

Bcl-xL Dynamics under the Lens of Protein Structure Networks

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-x_L) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-x_L prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-x_L upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-x_L in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-x_L found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.

Three Simple Properties Explain Protein Stability Change upon Mutation

Accurate prediction of protein stability upon mutation enables rational engineering of new proteins and insights into protein evolution and monogenetic diseases caused by single-point amino acid substitutions. Many tools have been developed to this aim, ranging from energy-based models to machine-learning methods that use large amounts of experimental data. However, as the methods become more complex, the interpretation of the chemistry underlying the protein stability effects becomes obscure. It is thus of interest to identify the simplest prediction model that retains complete amino acid specific interpretation; for a given number of input descriptors, we expect such a model to be almost universal. In this study, we identify such a limiting model, SimBa, a simple multilinear regression model trained on a substitution-type-balanced experimental data set. The model accounts only for the solvent accessibility of the site, volume difference, and polarity difference caused by mutation. Our results show that this very simple and directly applicable model performs comparably to other much more complex, widely used protein stability prediction methods. This suggests that a hard limit of ∼1 kcal/mol numerical accuracy and an R ∼ 0.5 trend accuracy exists and that new features, such as account of unfolded states, water colocalization, and amino acid correlations, are required to improve accuracy to, e.g., 1/2 kcal/mol.

Mapping the degradation pathway of a disease-linked aspartoacylase variant

Canavan disease is a severe progressive neurodegenerative disorder that is characterized by swelling and spongy degeneration of brain white matter. The disease is genetically linked to polymorphisms in the aspartoacylase (ASPA) gene, including the substitution C152W. ASPA C152W is associated with greatly reduced protein levels in cells, yet biophysical experiments suggest a wild-type like thermal stability. Here, we use ASPA C152W as a model to investigate the degradation pathway of a disease-causing protein variant. When we expressed ASPA C152W in Saccharomyces cerevisiae, we found a decreased steady state compared to wild-type ASPA as a result of increased proteasomal degradation. However, molecular dynamics simulations of ASPA C152W did not substantially deviate from wild-type ASPA, indicating that the native state is structurally preserved. Instead, we suggest that the C152W substitution interferes with the de novo folding pathway resulting in increased proteasomal degradation before reaching its stable conformation. Systematic mapping of the protein quality control components acting on misfolded and aggregation-prone species of C152W, revealed that the degradation is highly dependent on the molecular chaperone Hsp70, its co-chaperone Hsp110 as well as several quality control E3 ubiquitin-protein ligases, including Ubr1. In addition, the disaggregase Hsp104 facilitated refolding of aggregated ASPA C152W, while Cdc48 mediated degradation of insoluble ASPA protein. In human cells, ASPA C152W displayed increased proteasomal turnover that was similarly dependent on Hsp70 and Hsp110. Our findings underscore the use of yeast to determine the protein quality control components involved in the degradation of human pathogenic variants in order to identify potential therapeutic targets.

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.

Galactosemia: Towards Pharmacological Chaperones

Galactosemia is a rare inherited metabolic disease resulting from mutations in the four genes which encode enzymes involved in the metabolism of galactose. The current therapy, the removal of galactose from the diet, is inadequate. Consequently, many patients suffer lifelong physical and cognitive disability. The phenotype varies from almost asymptomatic to life-threatening disability. The fundamental biochemical cause of the disease is a decrease in enzymatic activity due to failure of the affected protein to fold and/or function correctly. Many novel therapies have been proposed for the treatment of galactosemia. Often, these are designed to treat the symptoms and not the fundamental cause. Pharmacological chaperones (PC) (small molecules which correct the folding of misfolded proteins) represent an exciting potential therapy for galactosemia. In theory, they would restore enzyme function, thus preventing downstream pathological consequences. In practice, no PCs have been identified for potential application in galactosemia. Here, we review the biochemical basis of the disease, identify opportunities for the application of PCs and describe how these might be discovered. We will conclude by considering some of the clinical issues which will affect the future use of PCs in the treatment of galactosemia.

Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk

The lack of functional evidence for the majority of missense variants limits their clinical interpretability and poses a key barrier to the broad utility of carrier screening. In Lynch syndrome (LS), one of the most highly prevalent cancer syndromes, nearly 90% of clinically observed missense variants are deemed “variants of uncertain significance” (VUS). To systematically resolve their functional status, we performed a massively parallel screen in human cells to identify loss-of-function missense variants in the key DNA mismatch repair factor MSH2. The resulting functional effect map is substantially complete, covering 94% of the 17,746 possible variants, and is highly concordant (96%) with existing functional data and expert clinicians’ interpretations. The large majority (89%) of missense variants were functionally neutral, perhaps unexpectedly in light of its evolutionary conservation. These data provide ready-to-use functional evidence to resolve the ∼1,300 extant missense VUSs in MSH2 and may facilitate the prospective classification of newly discovered variants in the clinic.

Understanding protein structural changes for oncogenic missense variants

Understanding and predicting the changes of protein structure and function upon mutation and their relationship to human health is a critical element to translate the genomic revolution into actionable interventions. Therefore, it is pertinent to explore how mutations result in structural changes leading to pathogenic proteins, but due to the protein structural knowledge gap, experimental approaches are lacking. Protein structure prediction methods, such as I-TASSER, have made it possible to predict the structure of a given amino acid sequence, thus opening a new way to explore protein structure changes upon mutations when experimental information is not available. Using known mutations from the Catalogue of Somatic Mutation in Cancer (COSMIC) and ClinVar databases, we compare predicted structure-derived properties from wild type (WT) and mutated proteins and find differences between the local and global 3D protein structures of the WT and the mutants. The studies in this relatively small sample reveal that the structural changes are quite diverse.

Naturally-Occurring Rare Mutations Cause Mild to Catastrophic Effects in the Multifunctional and Cancer-Associated NQO1 Protein

The functional and pathological implications of the enormous genetic diversity of the human genome are mostly unknown, primarily due to our unability to predict pathogenicity in a high-throughput manner. In this work, we characterized the phenotypic consequences of eight naturally-occurring missense variants on the multifunctional and disease-associated NQO1 protein using biophysical and structural analyses on several protein traits. Mutations found in both exome-sequencing initiatives and in cancer cell lines cause mild to catastrophic effects on NQO1 stability and function. Importantly, some mutations perturb functional features located structurally far from the mutated site. These effects are well rationalized by considering the nature of the mutation, its location in protein structure and the local stability of its environment. Using a set of 22 experimentally characterized mutations in NQO1, we generated experimental scores for pathogenicity that correlate reasonably well with bioinformatic scores derived from a set of commonly used algorithms, although the latter fail to semiquantitatively predict the phenotypic alterations caused by a significant fraction of mutations individually. These results provide insight into the propagation of mutational effects on multifunctional proteins, the implementation of in silico approaches for establishing genotype-phenotype correlations and the molecular determinants underlying loss-of-function in genetic diseases.

Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation

Germline mutations in the folliculin (FLCN) tumor suppressor gene are linked to Birt-Hogg-Dubé (BHD) syndrome, a dominantly inherited genetic disease characterized by predisposition to fibrofolliculomas, lung cysts, and renal cancer. Most BHD-linked FLCN variants include large deletions and splice site aberrations predicted to cause loss of function. The mechanisms by which missense variants and short in-frame deletions in FLCN trigger disease are unknown. Here, we present an integrated computational and experimental study that reveals that the majority of such disease-causing FLCN variants cause loss of function due to proteasomal degradation of the encoded FLCN protein, rather than directly ablating FLCN function. Accordingly, several different single-site FLCN variants are present at strongly reduced levels in cells. In line with our finding that FLCN variants are protein quality control targets, several are also highly insoluble and fail to associate with the FLCN-binding partners FNIP1 and FNIP2. The lack of FLCN binding leads to rapid proteasomal degradation of FNIP1 and FNIP2. Half of the tested FLCN variants are mislocalized in cells, and one variant (ΔE510) forms perinuclear protein aggregates. A yeast-based stability screen revealed that the deubiquitylating enzyme Ubp15/USP7 and molecular chaperones regulate the turnover of the FLCN variants. Lowering the temperature led to a stabilization of two FLCN missense proteins, and for one (R362C), function was re-established at low temperature. In conclusion, we propose that most BHD-linked FLCN missense variants and small in-frame deletions operate by causing misfolding and degradation of the FLCN protein, and that stabilization and resulting restoration of function may hold therapeutic potential of certain disease-linked variants. Our computational saturation scan encompassing both missense variants and single site deletions in FLCN may allow classification of rare FLCN variants of uncertain clinical significance.

Towards Accurate Genotype-Phenotype Correlations in the CYP2D6 Gene

Establishing accurate and large-scale genotype-phenotype correlations and predictions of individual response to pharmacological treatments are two of the holy grails of Personalized Medicine. These tasks are challenging and require an integrated knowledge of the complex processes that regulate gene expression and, ultimately, protein functionality in vivo, the effects of mutations/polymorphisms and the different sources of interindividual phenotypic variability. A remarkable example of our advances in these challenging tasks is the highly polymorphic CYP2D6 gene, which encodes a cytochrome P450 enzyme involved in the metabolization of many of the most marketed drugs (including SARS-Cov-2 therapies such as hydroxychloroquine). Since the introduction of simple activity scores (AS) over 10 years ago, its ability to establish genotype-phenotype correlations on the drug metabolizing capacity of this enzyme in human population has provided lessons that will help to improve this type of score for this, and likely many other human genes and proteins. Multidisciplinary research emerges as the best approach to incorporate additional concepts to refine and improve such functional/activity scores for the CYP2D6 gene, as well as for many other human genes associated with simple and complex genetic diseases.

MMR Deficiency is Homogeneous in Pancreatic Carcinoma and Associated with High Density of Cd8-Positive Lymphocytes

BACKGROUND

Microsatellite instability (MSI) has emerged as a predictive biomarker for immune checkpoint inhibitor therapy. Cancer heterogeneity represents a potential obstacle for the analysis of predicitive biomarkers. MSI has been reported in pancreatic cancer, but data on the possible extent of intratumoral heterogeneity are lacking.

METHODS

To study MSI heterogeneity in pancreatic cancer, a tissue microarray (TMA) comprising 597 tumors was screened by immunohistochemistry with antibodies for the mismatch repair (MMR) proteins MLH1, PMS2, MSH2, and MSH6.

RESULTS

In six suspicious cases, large section immunohistochemistry and microsatellite analysis (Bethesda panel) resulted in the identification of 4 (0.8%) validated MSI cases out of 480 interpretable pancreatic ductal adenocarcinomas. MSI was absent in 55 adenocarcinomas of the ampulla of Vater and 7 acinar cell carcinomas. MMR deficiency always involved MSH6 loss, in three cases with additional loss of MSH2 expression. Three cancers were MSI-high and one case with isolated MSH6 loss was MSS in PCR analysis. The analysis of 44 cancer-containing tumor blocks revealed that the loss of MMR protein expression was always homogeneous in affected tumors. Automated digital image analysis of CD8 immunostaining demonstrated markedly higher CD8 + tumor infiltrating lymphocytes in tumors with (mean = 685, median = 626) than without (mean = 227; median = 124) MMR deficiency (p < 0.0001), suggesting a role of MSI for immune response.

CONCLUSIONS

Our data suggest that MSI occurs early in a small subset of ductal adenocarcinomas of the pancreas and that immunohistochemical MMR analysis on limited biopsy or cytology material may be sufficient to estimate MMR status of the entire cancer mass.

A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy

Autophagy is a key clearance process to recycle damaged cellular components. One important upstream regulator of autophagy is ULK1 kinase. Several three-dimensional structures of the ULK1 catalytic domain are available, but a comprehensive study, including molecular dynamics, is missing. Also, an exhaustive description of ULK1 alterations found in cancer samples is presently lacking. We here applied a framework which links -omics data to structural protein ensembles to study ULK1 alterations from genomics data available for more than 30 cancer types. We predicted the effects of mutations on ULK1 function and structural stability, accounting for protein dynamics, and the different layers of changes that a mutation can induce in a protein at the functional and structural level. ULK1 is down-regulated in gynecological tumors. In other cancer types, ULK2 could compensate for ULK1 downregulation and, in the majority of the cases, no marked changes in expression have been found. 36 missense mutations of ULK1, not limited to the catalytic domain, are co-occurring with mutations in a large number of ULK1 interactors or substrates, suggesting a pronounced effect of the upstream steps of autophagy in many cancer types. Moreover, our results pinpoint that more than 50% of the mutations in the kinase domain of ULK1, here investigated, are predicted to affect protein stability. Three mutations (S184F, D102N, and A28V) are predicted with only impact on kinase activity, either modifying the functional dynamics or the capability to exert effects from distal sites to the functional and catalytic regions. The framework here applied could be extended to other protein targets to aid the classification of missense mutations from cancer genomics studies, as well as to prioritize variants for experimental validation, or to select the appropriate biological readouts for experiments.

Co-Chaperones in Targeting and Delivery of Misfolded Proteins to the 26S Proteasome

Protein homeostasis (proteostasis) is essential for the cell and is maintained by a highly conserved protein quality control (PQC) system, which triages newly synthesized, mislocalized and misfolded proteins. The ubiquitin-proteasome system (UPS), molecular chaperones, and co-chaperones are vital PQC elements that work together to facilitate degradation of misfolded and toxic protein species through the 26S proteasome. However, the underlying mechanisms are complex and remain partly unclear. Here, we provide an overview of the current knowledge on the co-chaperones that directly take part in targeting and delivery of PQC substrates for degradation. While J-domain proteins (JDPs) target substrates for the heat shock protein 70 (HSP70) chaperones, nucleotide-exchange factors (NEFs) deliver HSP70-bound substrates to the proteasome. So far, three NEFs have been established in proteasomal delivery: HSP110 and the ubiquitin-like (UBL) domain proteins BAG-1 and BAG-6, the latter acting as a chaperone itself and carrying its substrates directly to the proteasome. A better understanding of the individual delivery pathways will improve our ability to regulate the triage, and thus regulate the fate of aberrant proteins involved in cell stress and disease, examples of which are given throughout the review.

Computational analysis of androgen receptor (AR) variants to decipher the relationship between protein stability and related-diseases

Although more than 1,000 androgen receptor (AR) mutations have been identified and these mutants are pathologically important, few theoretical studies have investigated the role of AR protein folding stability in disease and its relationship with the phenotype of the patients. Here, we extracted AR variant data from four databases: ARDB, HGMD, Cosmic, and 1,000 genome. 905 androgen insensitivity syndrome (AIS)-associated loss-of-function mutants and 168 prostate cancer-associated gain-of-function mutants in AR were found. We analyzed the effect of single-residue variation on the folding stability of AR by FoldX and guanidine hydrochloride denaturation experiment, and found that genetic disease-associated mutations tend to have a significantly greater effect on protein stability than gene polymorphisms. Moreover, AR mutants in complete androgen insensitivity syndrome (CAIS) tend to have a greater effect on protein stability than in partial androgen insensitive syndrome (PAIS). This study, by linking disease phenotypes to changes in AR stability, demonstrates the importance of protein stability in the pathogenesis of hereditary disease.

Yeast mismatch repair components are required for stable inheritance of gene silencing

Alterations in epigenetic silencing have been associated with ageing and tumour formation. Although substantial efforts have been made towards understanding the mechanisms of gene silencing, novel regulators in this process remain to be identified. To systematically search for components governing epigenetic silencing, we developed a genome-wide silencing screen for yeast (Saccharomyces cerevisiae) silent mating type locus HMR. Unexpectedly, the screen identified the mismatch repair (MMR) components Pms1, Mlh1, and Msh2 as being required for silencing at this locus. We further found that the identified genes were also required for proper silencing in telomeres. More intriguingly, the MMR mutants caused a redistribution of Sir2 deacetylase, from silent mating type loci and telomeres to rDNA regions. As a consequence, acetylation levels at histone positions H3K14, H3K56, and H4K16 were increased at silent mating type loci and telomeres but were decreased in rDNA regions. Moreover, knockdown of MMR components in human HEK293T cells increased subtelomeric DUX4 gene expression. Our work reveals that MMR components are required for stable inheritance of gene silencing patterns and establishes a link between the MMR machinery and the control of epigenetic silencing.

Structure-function investigation of 3-methylaspartate ammonia lyase reveals substrate molecular determinants for the deamination reaction

The enzymatic reactions leading to the deamination of β-lysine, lysine, or 2-aminoadipic acid are of great interest for the metabolic conversion of lysine to adipic acid. Enzymes able to carry out these reactions are not known, however ammonia lyases (EC 4.3.1.-) perform deamination on a wide range of substrates. We have studied 3-methylaspartate ammonia lyase (MAL, EC 4.3.1.2) as a potential candidate for protein engineering to enable deamination towards β-lysine, that we have shown to be a competitive inhibitor of MAL. We have characterized MAL activity, binding and inhibition properties on six different compounds that would allow to define the molecular determinants necessary for MAL to deaminate our substrate of interest. Docking calculations showed that β-lysine as well as the other compounds investigated could fit spatially into MAL catalytic pocket, although they probably are weak or very transient binders and we identified molecular determinants involved in the binding of the substrate. The hydrophobic interactions formed by the methyl group of 3-methylaspartic acid, together with the presence of the amino group on carbon 2, play an essential role in the appropriate binding of the substrate. The results showed that β-lysine is able to fit and bind in MAL catalytic pocket and can be potentially converted from inhibitor to substrate of MAL upon enzyme engineering. The characterization of the binding and inhibition properties of the substrates tested here provide the foundation for future and more extensive studies on engineering MAL that could lead to a MAL variant able to catalyse this challenging deamination reaction.

Novel HARS2 missense variants identified in individuals with sensorineural hearing impairment and Perrault syndrome

Biallelic variants in HARS2 have been associated with Perrault syndrome, characterized by sensorineural hearing impairment and premature ovarian insufficiency. Here we report three novel families, compound heterozygous for missense variants in HARS2 identified by next-generation sequencing, namely c.172A > G (p.Lys58Glu) and c.448C > T (p.Arg150Cys) identified in two sisters aged 13 and 16 years and their older brother, c.448C > T (p.Arg150Cys) and c.980G > A (p.Arg327Gln) identified in a seven year old girl, and finally c.137T > A (p.Leu46Gln) and c.259C > T (p.Arg87Cys) identified in a 32 year old woman. Clinically, all five individuals presented with early onset, rapidly progressive hearing impairment. Whereas the oldest female fulfilled the criteria of Perrault syndrome, the three younger females, aged 7, 13 and 16, all had apparently normal ovarian function, apart from irregular menstrual periods in the oldest female at age 16. The present report expands the list of HARS2 variants and helps gain further knowledge to the phenotype.

S-nitrosylation affects TRAP1 structure and ATPase activity and modulates cell response to apoptotic stimuli

The mitochondrial chaperone TRAP1 has been involved in several mitochondrial functions, and modulation of its expression/activity has been suggested to play a role in the metabolic reprogramming distinctive of cancer cells. TRAP1 posttranslational modifications, i.e. phosphorylation, can modify its capability to bind to different client proteins and modulate its oncogenic activity. Recently, it has been also demonstrated that TRAP1 is S-nitrosylated at Cys501, a redox modification associated with its degradation via the proteasome. Here we report molecular dynamics simulations of TRAP1, together with analysis of long-range structural communication, providing a model according to which Cys501 S-nitrosylation induces conformational changes to distal sites in the structure of the protein. The modification is also predicted to alter open and closing motions for the chaperone function. By means of colorimetric assays and site directed mutagenesis aimed at generating C501S variant, we also experimentally confirmed that selective S-nitrosylation of Cys501 decreases ATPase activity of recombinant TRAP1. Coherently, C501S mutant was more active and conferred protection to cell death induced by staurosporine. Overall, our results provide the first in silico, in vitro and cellular evidence of the relevance of Cys501 S-nitrosylation in TRAP1 biology.

Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1

Despite the rapid expansion in recent years of databases reporting either benign or pathogenic genetic variations, the interpretation of novel missense variants remains challenging, particularly for clinical or genetic testing laboratories where functional analysis is often unfeasible. Previous studies have shown that thermodynamic analysis of protein structure in silico can discriminate between groups of benign and pathogenic missense variants. However, although structures exist for many human disease‒associated proteins, such analysis remains largely unexploited in clinical laboratories. Here, we analyzed the predicted effect of 338 known missense variants on the structure of menin, the MEN1 gene product. Results provided strong discrimination between pathogenic and benign variants, with a threshold of >4 kcal/mol for the predicted change in stability, providing a strong indicator of pathogenicity. Subsequent analysis of seven novel missense variants identified during clinical testing of patients with MEN1 showed that all seven were predicted to destabilize menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the effect of missense variants in MEN1 and that integration of proteomic with genomic data could potentially contribute to the classification of novel variants in this disease.