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.

Co-evolution of drug resistance and broadened substrate recognition in HIV protease variants isolated from an Escherichia coli genetic selection system.

A genetic selection system for activity of HIV protease is described that is based on a synthetic substrate constructed as a modified AraC regulatory protein that when cleaved stimulate l-arabinose metabolism in an Escherichia coli araC strain. Growth stimulation on selective plates was shown to depend on active HIV protease and the scissile bond in the substrate. In addition, the growth of cells correlated well with the established cleavage efficiency of the sites in the viral polyprotein, Gag, when these sites were individually introduced into the synthetic substrate of the selection system. Plasmids encoding protease variants selected based on stimulation of cell growth in the presence of saquinavir or cleavage of a site not cleaved by wild-type protease, were indistinguishable with respect to both phenotypes. Also, both groups of selected plasmids encoded side chain substitutions known from clinical isolates or displayed different side chain substitutions but at identical positions. One highly frequent side chain substitution, E34V, not regarded as a major drug resistance substitution was found in variants obtained under both selective conditions and is suggested to improve protease processing of the synthetic substrate. This substitution is away from the substrate-binding cavity and together with other substitutions in the selected reading frames supports the previous suggestion of a substrate-binding site extended from the active site binding pocket itself.

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.

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.

Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome.

Defective mismatch repair leads to increased mutation rates, and germline loss-of-function variants in the repair component MLH1 cause the hereditary cancer predisposition disorder known as Lynch syndrome. Early diagnosis is important, but complicated by many variants being of unknown significance. Here we show that a majority of the disease-linked MLH1 variants we studied are present at reduced cellular levels. We show that destabilized MLH1 variants are targeted for chaperone-assisted proteasomal degradation, resulting also in degradation of co-factors PMS1 and PMS2. In silico saturation mutagenesis and computational predictions of thermodynamic stability of MLH1 missense variants revealed a correlation between structural destabilization, reduced steady-state levels and loss-of-function. Thus, we suggest that loss of stability and cellular degradation is an important mechanism underlying many MLH1 variants in Lynch syndrome. Combined with analyses of conservation, the thermodynamic stability predictions separate disease-linked from benign MLH1 variants, and therefore hold potential for Lynch syndrome diagnostics.

Protein stability and degradation in health and disease.

The cellular proteome performs highly varied functions to sustain life. Since most of these functions require proteins to fold properly, they can be impaired by mutations that affect protein structure, leading to diseases such as Alzheimer's disease, cystic fibrosis, and Lynch syndrome. The cell has evolved an intricate protein quality control (PQC) system that includes degradation pathways and a multitude of molecular chaperones and co-chaperones, all working together to catalyze the refolding or removal of aberrant proteins. Thus, the PQC system limits the harmful consequences of dysfunctional proteins, including those arising from disease-causing mutations. This complex system is still not fully understood. In particular the structural and sequence motifs that, when exposed, trigger degradation of misfolded proteins are currently under investigation. Moreover, several attempts are being made to activate or inhibit parts of the PQC system as a treatment for diseases. Here, we briefly review the present knowledge on the PQC system and list current strategies that are employed to exploit the system in disease treatment.

Blocking protein quality control to counter hereditary cancers.

Inhibitors of molecular chaperones and the ubiquitin-proteasome system have already been clinically implemented to counter certain cancers, including multiple myeloma and mantle cell lymphoma. The efficacy of this treatment relies on genomic alterations in cancer cells causing a proteostatic imbalance, which makes them more dependent on protein quality control (PQC) mechanisms than normal cells. Accordingly, blocking PQC, e.g. by proteasome inhibitors, may cause a lethal proteotoxic crisis in cancer cells, while leaving normal cells unaffected. Evidence, however, suggests that the PQC system operates by following a better-safe-than-sorry principle and is thus prone to target proteins that are only slightly structurally perturbed, but still functional. Accordingly, implementing PQC inhibitors may also, through an entirely different mechanism, hold potential for other cancers. Several inherited cancer susceptibility syndromes, such as Lynch syndrome and von Hippel-Lindau disease, are caused by missense mutations in tumor suppressor genes, and in some cases, the resulting amino acid substitutions in the encoded proteins cause the cellular PQC system to target them for degradation, although they may still retain function. As a consequence of this over-meticulous PQC mechanism, the cell may end up with an insufficient amount of the abnormal, but functional, protein, which in turn leads to a loss-of-function phenotype and manifestation of the disease. Increasing the amounts of such proteins by stabilizing with chemical chaperones, or by targeting molecular chaperones or the ubiquitin-proteasome system, may thus avert or delay the disease onset. Here, we review the potential of targeting the PQC system in hereditary cancer susceptibility syndromes.

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

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases.

3did: identification and classification of domain-based interactions of known three-dimensional structure.

The database of three-dimensional interacting domains (3did) is a collection of protein interactions for which high-resolution three-dimensional structures are known. 3did exploits the availability of structural data to provide molecular details on interactions between two globular domains as well as novel domain-peptide interactions, derived using a recently published method from our lab. The interface residues are presented for each interaction type individually, plus global domain interfaces at which one or more partners (domains or peptides) bind. The 3did web server at http://3did.irbbarcelona.org visualizes these interfaces along with atomic details of individual interactions using Jmol. The complete contents are also available for download.

Novel peptide-mediated interactions derived from high-resolution 3-dimensional structures.

Many biological responses to intra- and extracellular stimuli are regulated through complex networks of transient protein interactions where a globular domain in one protein recognizes a linear peptide from another, creating a relatively small contact interface. These peptide stretches are often found in unstructured regions of proteins, and contain a consensus motif complementary to the interaction surface displayed by their binding partners. While most current methods for the de novo discovery of such motifs exploit their tendency to occur in disordered regions, our work here focuses on another observation: upon binding to their partner domain, motifs adopt a well-defined structure. Indeed, through the analysis of all peptide-mediated interactions of known high-resolution three-dimensional (3D) structure, we found that the structure of the peptide may be as characteristic as the consensus motif, and help identify target peptides even though they do not match the established patterns. Our analyses of the structural features of known motifs reveal that they tend to have a particular stretched and elongated structure, unlike most other peptides of the same length. Accordingly, we have implemented a strategy based on a Support Vector Machine that uses this features, along with other structure-encoded information about binding interfaces, to search the set of protein interactions of known 3D structure and to identify unnoticed peptide-mediated interactions among them. We have also derived consensus patterns for these interactions, whenever enough information was available, and compared our results with established linear motif patterns and their binding domains. Finally, to cross-validate our identification strategy, we scanned interactome networks from four model organisms with our newly derived patterns to see if any of them occurred more often than expected. Indeed, we found significant over-representations for 64 domain-motif interactions, 46 of which had not been described before, involving over 6,000 interactions in total for which we could suggest the molecular details determining the binding.

3did Update: domain-domain and peptide-mediated interactions of known 3D structure.

The database of 3D interacting domains (3did) is a collection of protein interactions for which high-resolution 3D structures are known. 3did exploits structural information to provide the crucial molecular details necessary for understanding how protein interactions occur. Besides interactions between globular domains, the new release of 3did also contains a hand-curated set of transient peptide-mediated interactions. The interactions are grouped in Interaction Types, based on the mode of binding, and the different binding interfaces used in each type are also identified and catalogued. A web-based tool to query 3did is available at http://3did.irbbarcelona.org.

Approved drug mimics of short peptide ligands from protein interaction motifs.

Most biological functions are regulated through complex networks of transient protein interactions, and, thus, finding effective ways to modulate them would represent an important step towards defining the next generation of drugs. In this study, we set out to determine if existing approved drugs may represent a good source of compounds from which initial lead inhibitors of protein-protein interactions mediated by short peptide regions may be drawn. Peptide structures were defined in terms of pharmacophores and searched against U.S. Food and Drug Administration (FDA)-approved drugs to identify similar compounds. The top ranking matches (using a score that corrects root-mean-square deviation (rmsd) for the number of matched pharmacophores and for the number of drug rotatable bonds) included a number of nuclear receptor ligands that matched allosterically to the corepressor binding site of peroxisome proliferator-activated receptor alpha (PPARalpha). The top ranking drug matches were docked to the peptide-binding site using AUTODOCK. The majority of the top-ranking matches showed a negative estimated free energy change upon binding that is comparable to, or greater than, that of the original peptide. We conclude that the usage of certain approved drugs may represent a useful strategy in inhibiting specific protein-protein interactions. Such a strategy may benefit from the increased likelihood that developed compounds might have favorable bioactivity and safety profiles in clinical use.

Contextual specificity in peptide-mediated protein interactions.

Most biological processes are regulated through complex networks of transient protein interactions where a globular domain in one protein recognizes a linear peptide from another, creating a relatively small contact interface. Although sufficient to ensure binding, these linear motifs alone are usually too short to achieve the high specificity observed, and additional contacts are often encoded in the residues surrounding the motif (i.e. the context). Here, we systematically identified all instances of peptide-mediated protein interactions of known three-dimensional structure and used them to investigate the individual contribution of motif and context to the global binding energy. We found that, on average, the context is responsible for roughly 20% of the binding and plays a crucial role in determining interaction specificity, by either improving the affinity with the native partner or impeding non-native interactions. We also studied and quantified the topological and energetic variability of interaction interfaces, finding a much higher heterogeneity in the context residues than in the consensus binding motifs. Our analysis partially reveals the molecular mechanisms responsible for the dynamic nature of peptide-mediated interactions, and suggests a global evolutionary mechanism to maximise the binding specificity. Finally, we investigated the viability of non-native interactions and highlight cases of potential cross-reaction that might compensate for individual protein failure and establish backup circuits to increase the robustness of cell networks.