The yin-yang of kinase activation and unfolding explains the peculiarity of Val600 in the activation segment of BRAF

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.

Dabrafenib for Pilocytic Astrocytoma With BRAF V599ins

This report highlights the first pediatric case of pilocytic astrocytoma with BRAF V599ins mutation, successfully treated with dabrafenib.

DNA Sequencing of CD138 Cell Population Reveals TP53 and RAS-MAPK Mutations in Multiple Myeloma at Diagnosis

Multiple myeloma is a hematologic neoplasm caused by abnormal proliferation of plasma cells. Sequencing studies suggest that plasma cell disorders are caused by both cytogenetic abnormalities and oncogene mutations. Therefore, it is necessary to detect molecular abnormalities to improve the diagnosis and management of MM. The main purpose of this study is to determine whether NGS, in addition to cytogenetics, can influence risk stratification and management. Additionally, we aim to establish whether mutational analysis of the CD138 cell population is a suitable option for the characterization of MM compared to the bulk population. Following the separation of the plasma cells harvested from 35 patients newly diagnosed with MM, we performed a FISH analysis to detect the most common chromosomal abnormalities. Consecutively, we used NGS to evaluate NRAS, KRAS, BRAF, and TP53 mutations in plasma cell populations and in bone marrow samples. NGS data showed that sequencing CD138 cells provides a more sensitive approach. We identified several variants in BRAF, KRAS, and TP53 that were not previously associated with MM. Considering that the presence of somatic mutations could influence risk stratification and therapeutic approaches of patients with MM, sensitive detection of these mutations at diagnosis is essential for optimal management of MM.

Computational analysis of natural product B-Raf inhibitors

B-Raf protein is a serine-threonine kinase and an important signal transduction molecule of the MAPK signaling pathway that mediates signals from RAS to MEK, ultimately promoting various essential cellular functions. The B-Raf kinase domain is divided into two subdomains: a small N-terminal lobe and a large C-terminal lobe, with a deep catalytic cleft between them. The N-terminal lobe contains a phosphate-binding loop (P-loop) and nucleotide-binding pocket, while the C-terminal lobe binds the protein substrates and contains the catalytic loop. The ligand pharmacophore was generated by using 17 different natural products and the receptor pharmacophore was generated by using protein structures. The reported natural product B-Raf inhibitors were analyzed according to the pharmacophore analysis (HipHop fit), virtual screening tools by Lipinski's rule of five. Thirteen out of seventeen molecules share the best ligand based pharmacophoric model (HipHop_5). The best receptor based pharmacophoric model came as AADHR. The compounds were docked against the B-Raf receptors (PDB ID: 3OG7, 4XV2, 5C9C). The compound DHSilB with cDOCKER interaction energy of -62.7 kcal/mol, -83.3 kcal/mol, -73.6 kcal/mol as well as the compound DHSilA with cDOCKER interaction energy of -63.9 kcal/mol, -63.2 kcal/mol, -74.7 kcal/mol showed satisfactory interaction with the respective receptors. Finally, the MD simulation was run for 100 ns for the top docked compounds DHSilA and DHSilB with the B-Raf proteins (PDB ID: 3OG7, 4XV2 and 5C9C). After the MD simulation run for 100 ns, the ligand 2,3-dehydrosilybin A (DHSilA) was found to be more stable in terms of the trajectories of RMSD, RMSF, Rg and H-bonds.

Non-Equilibrium Protein Folding and Activation by ATP-Driven Chaperones

Recent experimental studies suggest that ATP-driven molecular chaperones can stabilize protein substrates in their native structures out of thermal equilibrium. The mechanism of such non-equilibrium protein folding is an open question. Based on available structural and biochemical evidence, I propose here a unifying principle that underlies the conversion of chemical energy from ATP hydrolysis to the conformational free energy associated with protein folding and activation. I demonstrate that non-equilibrium folding requires the chaperones to break at least one of four symmetry conditions. The Hsp70 and Hsp90 chaperones each break a different subset of these symmetries and thus they use different mechanisms for non-equilibrium protein folding. I derive an upper bound on the non-equilibrium elevation of the native concentration, which implies that non-equilibrium folding only occurs in slow-folding proteins that adopt an unstable intermediate conformation in binding to ATP-driven chaperones. Contrary to the long-held view of Anfinsen’s hypothesis that proteins fold to their conformational free energy minima, my results predict that some proteins may fold into thermodynamically unstable native structures with the assistance of ATP-driven chaperones, and that the native structures of some chaperone-dependent proteins may be shaped by their chaperone-mediated folding pathways.

PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification

High-throughput sequencing technologies have identified millions of genetic mutations in multiple human diseases. However, the interpretation of the pathogenesis of these mutations and the discovery of driver genes that dominate disease progression is still a major challenge. Combining functional features such as protein post-translational modification (PTM) with genetic mutations is an effective way to predict such alterations. Here, we present PTMsnp, a web server that implements a Bayesian hierarchical model to identify driver genetic mutations targeting PTM sites. PTMsnp accepts genetic mutations in a standard variant call format or tabular format as input and outputs several interactive charts of PTM-related mutations that potentially affect PTMs. Additional functional annotations are performed to evaluate the impact of PTM-related mutations on protein structure and function, as well as to classify variants relevant to Mendelian disease. A total of 4,11,574 modification sites from 33 different types of PTMs and 1,776,848 somatic mutations from TCGA across 33 different cancer types are integrated into the web server, enabling identification of candidate cancer driver genes based on PTM. Applications of PTMsnp to the cancer cohorts and a GWAS dataset of type 2 diabetes identified a set of potential drivers together with several known disease-related genes, indicating its reliability in distinguishing disease-related mutations and providing potential molecular targets for new therapeutic strategies. PTMsnp is freely available at: http://ptmsnp.renlab.org.

Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. SIGNIFICANCE: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

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.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.

NQO1: A target for the treatment of cancer and neurological diseases, and a model to understand loss of function disease mechanisms

NAD(P)H quinone oxidoreductase 1 (NQO1) is a multi-functional protein that catalyses the reduction of quinones (and other molecules), thus playing roles in xenobiotic detoxification and redox balance, and also has roles in stabilising apoptosis regulators such as p53. The structure and enzymology of NQO1 is well-characterised, showing a substituted enzyme mechanism in which NAD(P)H binds first and reduces an FAD cofactor in the active site, assisted by a charge relay system involving Tyr-155 and His-161. Protein dynamics play important role in physio-pathological aspects of this protein. NQO1 is a good target to treat cancer due to its overexpression in cancer cells. A polymorphic form of NQO1 (p.P187S) is associated with increased cancer risk and certain neurological disorders (such as multiple sclerosis and Alzheimer´s disease), possibly due to its roles in the antioxidant defence. p.P187S has greatly reduced FAD affinity and stability, due to destabilization of the flavin binding site and the C-terminal domain, which leading to reduced activity and enhanced degradation. Suppressor mutations partially restore the activity of p.P187S by local stabilization of these regions, and showing long-range allosteric communication within the protein. Consequently, the correction of NQO1 misfolding by pharmacological chaperones is a viable strategy, which may be useful to treat cancer and some neurological conditions, targeting structural spots linked to specific disease-mechanisms. Thus, NQO1 emerges as a good model to investigate loss of function mechanisms in genetic diseases as well as to improve strategies to discriminate between neutral and pathogenic variants in genome-wide sequencing studies.

Hierarchical Organization Endows the Kinase Domain with Regulatory Plasticity

The functional diversity of kinases enables specificity in cellular signal transduction. Yet how more than 500 members of the human kinome specifically receive regulatory inputs and convey information to appropriate substrates-all while using the common signaling output of phosphorylation-remains enigmatic. Here, we perform statistical co-evolution analysis, mutational scanning, and quantitative live-cell assays to reveal a hierarchical organization of the kinase domain that facilitates the orthogonal evolution of regulatory inputs and substrate outputs while maintaining catalytic function. We find that three quasi-independent "sectors"-groups of evolutionarily coupled residues-represent functional units in the kinase domain that encode for catalytic activity, substrate specificity, and regulation. Sector positions impact both disease and pharmacology: the catalytic sector is significantly enriched for somatic cancer mutations, and residues in the regulatory sector interact with allosteric kinase inhibitors. We propose that this functional architecture endows the kinase domain with inherent regulatory plasticity.

Insight into the specificity and severity of pathogenic mechanisms associated with missense mutations through experimental and structural perturbation analyses

Most pathogenic missense mutations cause specific molecular phenotypes through protein destabilization. However, how protein destabilization is manifested as a given molecular phenotype is not well understood. We develop here a structural and energetic approach to describe mutational effects on specific traits such as function, regulation, stability, subcellular targeting or aggregation propensity. This approach is tested using large-scale experimental and structural perturbation analyses in over thirty mutations in three different proteins (cancer-associated NQO1, transthyretin related with amyloidosis and AGT linked to primary hyperoxaluria type I) and comprising five very common pathogenic mechanisms (loss-of-function and gain-of-toxic function aggregation, enzyme inactivation, protein mistargeting and accelerated degradation). Our results revealed that the magnitude of destabilizing effects and, particularly, their propagation through the structure to promote disease-associated conformational states largely determine the severity and molecular mechanisms of disease-associated missense mutations. Modulation of the structural perturbation at a mutated site is also shown to cause switches between different molecular phenotypes. When very common disease-associated missense mutations were investigated, we also found that they were not among the most deleterious possible missense mutations at those sites, and required additional contributions from codon bias and effects of CpG sites to explain their high frequency in patients. Our work sheds light on the molecular basis of pathogenic mechanisms and genotype–phenotype relationships, with implications for discriminating between pathogenic and neutral changes within human genome variability from whole genome sequencing studies.

Recent advances of RAF (rapidly accelerated fibrosarcoma) inhibitors as anti-cancer agents

Frequent oncogenic mutations have been identified in MAPK (mitogen-activated protein kinase) signaling pathway components. As a result, MAPK pathway is associated with human cancer initiation, in particular RAF (rapidly accelerated fibrosarcoma) component. The mutation in RAF component leads to auto-activation of MAPK signaling pathway, stimulating the uncontrolled cell growth and proliferation. In last few years, diverse chemical scaffolds have been identified as RAF inhibitors. Most of these scaffolds show potent anti-cancer activity. The present review highlights the recent investigations of RAF inhibitors during the last five years.

Biophysical and functional perturbation analyses at cancer-associated P187 and K240 sites of the multifunctional NADP(H):quinone oxidoreductase 1

Once whole-genome sequencing has reached the clinical practice, a main challenge ahead is the high-throughput and accurate prediction of the pathogenicity of genetic variants. However, current prediction tools do not consider explicitly a well-known property of disease-causing mutations: their ability to affect multiple functional sites distant in the protein structure. Here we carried out an extensive biophysical characterization of fourteen mutant variants at two cancer-associated sites of the enzyme NQO1, a paradigm of multi-functional protein. We showed that the magnitude of destabilizing effects, their molecular origins (structural vs. dynamic) and their efficient propagation through the protein structure gradually led to functional perturbations at different sites. Modulation of these structural perturbations also led to switches between molecular phenotypes. Our work supports that experimental and computational perturbation analyses would improve our understanding of the molecular basis of many loss-of-function genetic diseases as well as our ability to accurately predict the pathogenicity of genetic variants in a high-throughput fashion.

Evolutionary Divergent Suppressor Mutations in Conformational Diseases

Neutral and adaptive mutations are key players in the evolutionary dynamics of proteins at molecular, cellular and organismal levels. Conversely, largely destabilizing mutations are rarely tolerated by evolution, although their occurrence in diverse human populations has important roles in the pathogenesis of conformational diseases. We have recently proposed that divergence at certain sites from the consensus (amino acid) state during mammalian evolution may have rendered some human proteins more vulnerable towards disease-associated mutations, primarily by decreasing their conformational stability. We herein extend and refine this hypothesis discussing results from phylogenetic and structural analyses, structure-based energy calculations and structure-function studies at molecular and cellular levels. As proof-of-principle, we focus on different mammalian orthologues of the NQO1 (NAD(P)H:quinone oxidoreductase 1) and AGT (alanine:glyoxylate aminotransferase) proteins. We discuss the different loss-of-function pathogenic mechanisms associated with diseases involving the two enzymes, including enzyme inactivation, accelerated degradation, intracellular mistargeting, and aggregation. Last, we take into account the potentially higher robustness of mammalian orthologues containing certain consensus amino acids as suppressors of human disease, and their relation with different intracellular post-translational modifications and protein quality control capacities, to be discussed as sources of phenotypic variability between human and mammalian models of disease and as tools for improving current therapeutic approaches.

Natural (and Unnatural) Small Molecules as Pharmacological Chaperones and Inhibitors in Cancer

Mutations causing single amino acid exchanges can dramatically affect protein stability and function, leading to disease. In this chapter, we will focus on several representative cases in which such mutations affect protein stability and function leading to cancer. Mutations in BRAF and p53 have been extensively characterized as paradigms of loss-of-function/gain-of-function mechanisms found in a remarkably large fraction of tumours. Loss of RB1 is strongly associated with cancer progression, although the molecular mechanisms by which missense mutations affect protein function and stability are not well known. Polymorphisms in NQO1 represent a remarkable example of the relationships between intracellular destabilization and inactivation due to dynamic alterations in protein ensembles leading to loss of function. We will review the function of these proteins and their dysfunction in cancer and then describe in some detail the effects of the most relevant cancer-associated single amino exchanges using a translational perspective, from the viewpoints of molecular genetics and pathology, protein biochemistry and biophysics, structural, and cell biology. This will allow us to introduce several representative examples of natural and synthetic small molecules applied and developed to overcome functional, stability, and regulatory alterations due to cancer-associated amino acid exchanges, which hold the promise for using them as potential pharmacological cancer therapies.

Atomistic simulations and network-based modeling of the Hsp90-Cdc37 chaperone binding with Cdk4 client protein: A mechanism of chaperoning kinase clients by exploiting weak spots of intrinsically dynamic kinase domains

A fundamental role of the Hsp90 and Cdc37 chaperones in mediating conformational development and activation of diverse protein kinase clients is essential in signal transduction. There has been increasing evidence that the Hsp90-Cdc37 system executes its chaperoning duties by recognizing conformational instability of kinase clients and modulating their folding landscapes. The recent cryo-electron microscopy structure of the Hsp90-Cdc37-Cdk4 kinase complex has provided a framework for dissecting regulatory principles underlying differentiation and recruitment of protein kinase clients to the chaperone machinery. In this work, we have combined atomistic simulations with protein stability and network-based rigidity decomposition analyses to characterize dynamic factors underlying allosteric mechanism of the chaperone-kinase cycle and identify regulatory hotspots that control client recognition. Through comprehensive characterization of conformational dynamics and systematic identification of stabilization centers in the unbound and client- bound Hsp90 forms, we have simulated key stages of the allosteric mechanism, in which Hsp90 binding can induce instability and partial unfolding of Cdk4 client. Conformational landscapes of the Hsp90 and Cdk4 structures suggested that client binding can trigger coordinated dynamic changes and induce global rigidification of the Hsp90 inter-domain regions that is coupled with a concomitant increase in conformational flexibility of the kinase client. This process is allosteric in nature and can involve reciprocal dynamic exchanges that exert global effect on stability of the Hsp90 dimer, while promoting client instability. The network-based rigidity analysis and emulation of thermal unfolding of the Cdk4-cyclin D complex and Hsp90-Cdc37-Cdk4 complex revealed weak spots of kinase instability that are present in the native Cdk4 structure and are targeted by the chaperone during client recruitment. Our findings suggested that this mechanism may be exploited by the Hsp90-Cdc37 chaperone to recruit and protect intrinsically dynamic kinase clients from degradation. The results of this investigation are discussed and interpreted in the context of diverse experimental data, offering new insights into mechanisms of chaperone regulation and binding.

Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.

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.

The protein quality control system targets misfolded proteins for degradation. So far it has not been possible from sequence or structural data to predict the biological stability of a misfolded protein, or the effect of mutations on intracellular protein levels. Here we demonstrate that in silico saturation mutagenesis and biophysical calculations of the structural stability of the human mismatch repair protein MSH2 correlate with cellular protein levels, turnover and function. Of 24 different MSH2 variants, some of which are linked to Lynch syndrome, a destabilization of as little as 3 kcal/mol is sufficient to cause rapid degradation via the ubiquitin-proteasome pathway. Thus, biophysical modeling can, to a large extent, predict the metabolic stability of proteins. We also show that the same biophysical calculations can be used to distinguish with high accuracy neutral sequence variation from pathogenic variants, and that the calculations outperform several traditionally used disease predictors. We therefore suggest the method to be of potential value for patient stratification in Lynch syndrome, and perhaps other hereditary diseases.

Network-based modelling and percolation analysis of conformational dynamics and activation in the CDK2 and CDK4 proteins: dynamic and energetic polarization of the kinase lobes may determine divergence of the regulatory mechanisms

Network modeling and percolation analysis of conformational dynamics and energetics of regulatory mechanisms in cyclin-dependent kinases.