Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants

Targeting p53 pathways: mechanisms, structures, and advances in therapy

The TP53 tumor suppressor is the most frequently altered gene in human cancers, and has been a major focus of oncology research. The p53 protein is a transcription factor that can activate the expression of multiple target genes and plays critical roles in regulating cell cycle, apoptosis, and genomic stability, and is widely regarded as the "guardian of the genome". Accumulating evidence has shown that p53 also regulates cell metabolism, ferroptosis, tumor microenvironment, autophagy and so on, all of which contribute to tumor suppression. Mutations in TP53 not only impair its tumor suppressor function, but also confer oncogenic properties to p53 mutants. Since p53 is mutated and inactivated in most malignant tumors, it has been a very attractive target for developing new anti-cancer drugs. However, until recently, p53 was considered an "undruggable" target and little progress has been made with p53-targeted therapies. Here, we provide a systematic review of the diverse molecular mechanisms of the p53 signaling pathway and how TP53 mutations impact tumor progression. We also discuss key structural features of the p53 protein and its inactivation by oncogenic mutations. In addition, we review the efforts that have been made in p53-targeted therapies, and discuss the challenges that have been encountered in clinical development.

Markov State Modeling Analysis Captures Changes in the Temperature-Sensitive N-Terminal and β-Turn Regions of the p53 DNA-Binding Domain

The transcription factor p53 is one of the most widely studied cancer proteins. Its temperature-sensitive nature suggests reduction in functionality at physiological temperatures. Temperature-induced conformational variations and their impact on its functional ability still remain unexplored. A total of 20.8 μs molecular dynamics simulations of wildtype p53 in the apo and the DNA-bound states have been performed at 300 K and 310 K. Further, Markov State Modeling (MSM) analyses were performed, considering C_α-C_α distances as reaction coordinates. Filtering of these distances based on correlation with the time-independent components (tICs) resulted in 16 and 32 distances for apo and DNA-bound systems, respectively. Individual MSM analyses using these filtered distances were performed for both p53 systems. These C_α-C_α residue pairs belonged to the N-terminal, S6/7 β-turn, loop L2, loop L3, and hydrophobic core residues. At physiological temperatures, apo-p53 exhibits exposure of its hydrophobic core, where the temperature-sensitive hotspot residues were also located. This exposure was the result of the S6/7 β-turn and N-terminal moving apart. In the DNA-bound p53 system, loop L1 attains an open conformation at physiological temperatures, which weakens the DNA binding. It is already known that p53 mutants that lack DNA binding also tend to show similar conformational variations. The S6/7 β-turn along with the already known functionally important loop L2 may pose as regions to be targeted to overcome the loss in binding of temperature-sensitive wildtype p53. Rescue strategies directed toward these temperature-sensitive regions may be useful to recuperate its strong binding at physiological temperatures.

GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3

Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.

Computational Prediction and Experimental Validation of the Unique Molecular Mode of Action of Scoulerine

Scoulerine is a natural compound that is known to bind to tubulin and has anti-mitotic properties demonstrated in various cancer cells. Its molecular mode of action has not been precisely known. In this work, we perform computational prediction and experimental validation of the mode of action of scoulerine. Based on the existing data in the Protein Data Bank (PDB) and using homology modeling, we create human tubulin structures corresponding to both free tubulin dimers and tubulin in a microtubule. We then perform docking of the optimized structure of scoulerine and find the highest affinity binding sites located in both the free tubulin and in a microtubule. We conclude that binding in the vicinity of the colchicine binding site and near the laulimalide binding site are the most likely locations for scoulerine interacting with tubulin. Thermophoresis assays using scoulerine and tubulin in both free and polymerized form confirm these computational predictions. We conclude that scoulerine exhibits a unique property of a dual mode of action with both microtubule stabilization and tubulin polymerization inhibition, both of which have similar affinity values.

Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures

Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.

Markov state models and NMR uncover an overlooked allosteric loop in p53

The tumor suppressor p53 is the most frequently mutated gene in human cancer, and thus reactivation of mutated p53 is a promising avenue for cancer therapy. Analysis of wildtype p53 and the Y220C cancer mutant long-timescale molecular dynamics simulations with Markov state models and validation by NMR relaxation studies has uncovered the involvement of loop L6 in the slowest motions of the protein. Due to its distant location from the DNA-binding surface, the conformational dynamics of this loop has so far remained largely unexplored. We observe mutation-induced stabilization of alternate L6 conformations, distinct from all experimentally-determined structures, in which the loop is both extended and located further away from the DNA-interacting surface. Additionally, the effect of the L6-adjacent Y220C mutation on the conformational landscape of the functionally-important loop L1 suggests an allosteric role to this dynamic loop and the inactivation mechanism of the mutation. Finally, the simulations reveal a novel Y220C cryptic pocket that can be targeted for p53 rescue efforts. Our approach exemplifies the power of the MSM methodology for uncovering intrinsic dynamic and kinetic differences among distinct protein ensembles, such as for the investigation of mutation effects on protein function.

Wildtype and Y220C L1 and L6 loops conformational landscape, with MSM-identified L6 states highlighted on the right.

Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

The wide application of new DNA sequencing technologies is generating vast quantities of genetic variation data at unprecedented speed. Developing methodologies to decode the pathogenicity of the variants is imperatively demanding. We hypothesized that as deleterious variants may function through disturbing structural stability of their affected proteins, information from structural change caused by genetic variants can be used to identify the variants with deleterious effects. In order to measure the structural change for proteins with large size, we designed a method named RP-MDS composed of Ramachandran plot (RP) and Molecular Dynamics Simulation (MDS). Ramachandran plot captures the variant-caused secondary structural change, whereas MDS provides a quantitative measure for the variant-caused globular structural change. We tested the method using variants in TP53 DNA binding domain of 219 residues as the model. In total, RP-MDS identified 23 of 38 (60.5%) TP53 known Pathogenic variants and 17 of 42 (41%) TP53 VUS that caused significant changes of P53 structure. Our study demonstrates that RP-MDS method provides a powerful protein structure-based tool to screen deleterious genetic variants affecting large-size proteins.

Tumorigenic p53 mutants undergo common structural disruptions including conversion to α-sheet structure

The p53 protein is a commonly studied cancer target because of its role in tumor suppression. Unfortunately, it is susceptible to mutation-associated loss of function; approximately 50% of cancers are associated with mutations to p53, the majority of which are located in the central DNA-binding domain. Here, we report molecular dynamics simulations of wild-type (WT) p53 and 20 different mutants, including a stabilized pseudo-WT mutant. Our findings indicate that p53 mutants tend to exacerbate latent structural-disruption tendencies, or vulnerabilities, already present in the WT protein, suggesting that it may be possible to develop cancer therapies by targeting a relatively small set of structural-disruption motifs rather than a multitude of effects specific to each mutant. In addition, α-sheet secondary structure formed in almost all of the proteins. α-Sheet has been hypothesized and recently demonstrated to play a role in amyloidogenesis, and its presence in the reported p53 simulations coincides with the recent re-consideration of cancer as an amyloid disease.

Roles of computational modelling in understanding p53 structure, biology, and its therapeutic targeting

The transcription factor p53 plays pivotal roles in numerous biological processes, including the suppression of tumours. The rich availability of biophysical data aimed at understanding its structure–function relationships since the 1990s has enabled the application of a variety of computational modelling techniques towards the establishment of mechanistic models. Together they have provided deep insights into the structure, mechanics, energetics, and dynamics of p53. In parallel, the observation that mutations in p53 or changes in its associated pathways characterize several human cancers has resulted in a race to develop therapeutic modulators of p53, some of which have entered clinical trials. This review describes how computational modelling has played key roles in understanding structural-dynamic aspects of p53, formulating hypotheses about domains that are beyond current experimental investigations, and the development of therapeutic molecules that target the p53 pathway.

A novel hotspot and rare somatic mutation p.A138V, at TP53 is associated with poor survival of pancreatic ductal and periampullary adenocarcinoma patients

BACKGROUND

Pancreatic Ductal Adenocarcinoma (PDAC) is a cancer of the exocrine pancreas and 5-year survival rates remain constant at 7%. Along with PDAC, Periampullary Adenocarcinoma (PAC) accounts for 0.5-2% of all gastrointestinal malignancies. Genomic observations were well concluded for PDAC and PACs in western countries but no reports are available from India till now.

METHODS

Targeted Next Generation Sequencing were performed in 8 (5 PDAC and 3 PAC) tumour normal pairs, using a panel of 412 cancer related genes. Primary findings were replicated in 85 tumour samples (31 PDAC and 54 PAC) using the Sanger sequencing. Mutations were also validated by ASPCR, RFLP, and Ion Torrent sequencing. IHC along with molecular dynamics and docking studies were performed for the p.A138V mutant of TP53. Key polymorphisms at TP53 and its associated genes were genotyped by PCR-RFLP method and association with somatic mutations were evaluated. All survival analysis was done using the Kaplan-Meier survival method which revealed that the survival rates varied significantly depending on the somatic mutations the patients harboured.

RESULTS

Among the total 114 detected somatic mutations, TP53 was the most frequently mutated (41%) gene, followed by KRAS, SMAD4, CTNNB1, and ERBB3. We identified a novel hotspot TP53 mutation (p.A138V, in 17% of all patients). Low frequency of KRAS mutation (33%) was detected in these samples compared to patients from Western counties. Molecular Dynamics (MD) simulation and DNA-protein docking analysis predicted p.A138V to have oncogenic characteristics. Patients with p.A138V mutation showed poorer overall survival (p = 0.01). So, our finding highlights elevated prevalence of the p53p.A138V somatic mutation in PDAC and pancreatobiliary PAC patients.

CONCLUSION

Detection of p.A138V somatic variant in TP53 might serve as a prognostic marker to classify patients. It might also have a role in determining treatment regimes. In addition, low frequency of KRAS hotspot mutation mostly in Indian PDAC patient cohort indicates presence of other early drivers in malignant transformation.

Experimental Protein Molecular Dynamics: Broadband Dielectric Spectroscopy coupled with nanoconfinement

Protein dynamics covers multiple spatiotemporal scale processes, among which slow motions, not much understood even though they are underlying protein folding and protein functions. Protein slow motions are associated with structural heterogeneity, short-lived and poorly populated conformations, hard to detect individually. In addition, they involve collective motions of many atoms, not easily tracked by simulation and experimental devices. Here we propose a biophysical approach, coupling geometrical nanoconfinement and broadband dielectric spectroscopy (BDS), which distinguishes protein conformations by their respective molecular dynamics. In particular, protein-unfolding intermediates, usually poorly populated in macroscopic solutions are detected. The protein dynamics is observed under unusual conditions (sample nanoconfinement and dehydration) highlighting the robustness of protein structure and protein dynamics to a variety of conditions consistent with protein sustainability. The protein dielectric signals evolve with the temperature of thermal treatments indicating sensitivity to atomic and molecular interaction changes triggered by the protein thermal unfolding. As dipole fluctuations depend on both collective large-scale motions and local motions, the approach offers a prospect to track in-depth unfolding events.

Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations

Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process.

Biophysical simulations and structure-based modeling of residue interaction networks in the tumor suppressor proteins reveal functional role of cancer mutation hotspots in molecular communication

In the current study, we have combined molecular simulations and energetic analysis with dynamics-based network modeling and perturbation response scanning to determine molecular signatures of mutational hotspot residues in the p53, PTEN, and SMAD4 tumor suppressor proteins. By examining structure, energetics and dynamics of these proteins, we have shown that inactivating mutations preferentially target a group of structurally stable residues that play a fundamental role in global propagation of dynamic fluctuations and mediating allosteric interaction networks. Through integration of long-range perturbation dynamics and network-based approaches, we have quantified allosteric potential of residues in the studied proteins. The results have revealed that mutational hotspot sites often correspond to high centrality mediating centers of the residue interaction networks that are responsible for coordination of global dynamic changes and allosteric signaling. Our findings have also suggested that structurally stable mutational hotpots can act as major effectors of allosteric interactions and mutations in these positions are typically associated with severe phenotype. Modeling of shortest inter-residue pathways has shown that mutational hotspot sites can also serve as key mediating bridges of allosteric communication in the p53 and PTEN protein structures. Multiple regression models have indicated that functional significance of mutational hotspots can be strongly associated with the network signatures serving as robust predictors of critical regulatory positions responsible for loss-of-function phenotype. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of mutational hotspots, providing a plausible rationale for explaining and localizing disease-causing mutations in tumor suppressor genes.

Computational Approaches to Prioritize Cancer Driver Missense Mutations

Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.

In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few

A disease has distinct genetic and molecular hallmarks such as sequence variants that are likely to produce the alternative protein structures accountable for individual responses to drugs and disease development.

Insights into the Effect of the G245S Single Point Mutation on the Structure of p53 and the Binding of the Protein to DNA

The transcription factor p53 is a potent tumor suppressor dubbed as the "guardian of the genome" because of its ability to orchestrate protective biological outputs in response to a variety of oncogenic stresses. Mutation and thus inactivation of p53 can be found in 50% of human tumors. The majority are missense mutations located in the DNA binding region. Among them, G245S is known to be a structural hotspot mutation. To understand the behaviors and differences between the wild-type and mutant, both a dimer of the wild type p53 (wt-p53) and its G245S mutant (G245S-mp53), complexed with DNA, were simulated using molecular dynamics for more than 1 μs. wt-p53 and G245S-mp53 apo monomers were simulated for 1 μs as well. Conformational analyses and binding energy evaluations performed underline important differences and therefore provide insights to understand the G245S-mp53 loss of function. Our results indicate that the G245S mutation destabilizes several structural regions in the protein that are crucial for DNA binding when found in its apo form and highlight differences in the mutant-DNA complex structure compared to the wt protein. These findings not only provide means that can be applied to other p53 mutants but also serve as structural basis for further studies aimed at the development of cancer therapies based on restoring the function of p53.

Protein structural robustness to mutations: an in silico investigation

Proteins possess qualities of robustness and adaptability to perturbations such as mutations, but occasionally fail to withstand them, resulting in loss of function. Herein, the structural impact of mutations is investigated independently of the functional impact. Primarily, we aim at understanding the mechanisms of structural robustness pre-requisite for functional integrity. The structural changes due to mutations propagate from the site of mutation to residues much more distant than typical scales of chemical interactions, following a cascade mechanism. This can trigger dramatic changes or subtle ones, consistent with a loss of function and disease or the emergence of new functions. Robustness is enhanced by changes producing alternative structures, in good agreement with the view that proteins are dynamic objects fulfilling their functions from a set of conformations. This result, robust alternative structures, is also coherent with epistasis or rescue mutations, or more generally, with non-additive mutational effects and compensatory mutations. To achieve this study, we developed the first algorithm, referred to as Amino Acid Rank (AAR), which follows the structural changes associated with mutations from the site of the mutation to the entire protein structure and quantifies the changes so that mutations can be ranked accordingly. Assessing the paths of changes opens the possibility of assuming secondary mutations for compensatory mechanisms.

An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass

Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.

Structural Evolution and Dynamics of the p53 Proteins

The family of the p53 tumor suppressive transcription factors includes p73 and p63 in addition to p53 itself. Given the high degree of amino-acid-sequence homology and structural organization shared by the p53 family members, they display some common features (i.e., induction of cell death, cell-cycle arrest, senescence, and metabolic regulation in response to cellular stress) as well as several distinct properties. Here, we describe the structural evolution of the family members with recent advances on the molecular dynamic studies of p53 itself. A crucial role of the carboxy-terminal domain in regulating the properties of the DNA-binding domain (DBD) supports an induced-fit mechanism, in which the binding of p53 on individual promoters is preferentially regulated by the K_OFF over K_ON.

Model of the Ankyrin and SOCS Box Protein, ASB9, E3 Ligase Reveals a Mechanism for Dynamic Ubiquitin Transfer

Cullin-RING E3 ligases (CRLs) are elongated and bowed protein complexes that transfer ubiquitin over 60 Å to proteins targeted for proteasome degradation. One such CRL contains the ankyrin repeat and SOCS box protein 9 (ASB9), which binds to and partially inhibits creatine kinase (CK). While current models for the ASB9-CK complex contain some known interface residues, the overall structure and precise interface of the ASB9-CK complex remains unknown. Through an integrative modeling approach, we report a third-generation model that reveals precisely the interface interactions and also fits the shape of the ASB9-CK complex as determined by small-angle X-ray scattering. We constructed an atomic model for the entire CK-targeting CRL to uncover dominant modes of motion that could permit ubiquitin transfer. Remarkably, only the correctly docked CK-containing E3 ligase and not incorrectly docked structures permitted close approach of ubiquitin to the CK substrate.

Investigating DNA Binding and Conformational Variation in Temperature Sensitive p53 Cancer Mutants Using QM-MM Simulations

The tp53 gene is found to be mutated in 50% of all the cancers. The p53 protein, a product of tp53 gene, is a multi-domain protein. It consists of a core DNA binding domain (DBD) which is responsible for its binding and transcription of downstream target genes. The mutations in p53 protein are responsible for creating cancerous conditions and are found to be occurring at a high frequency in the DBD region of p53. Some of these mutations are also known to be temperature sensitive (ts) in nature. They are known to exhibit partial or strong binding with DNA in the temperature range (298–306 K). Whereas, at 310 K and above they show complete loss in binding. We have analyzed the changes in binding and conformational behavior at 300 K and 310 K for three of the ts-mutants viz., V143A, R249S and R175H. QM-MM simulations have been performed on the wild type and the above mentioned ts-mutants for 30 ns each. The optimal estimate of free energy of binding for a particular number of interface hydrogen bonds was calculated using the maximum likelihood method as described by Chodera et. al (2007). This parameter has been observed to be able to mimic the binding affinity of the p53 ts-mutants at 300 K and 310 K. Thus the correlation between MM-GBSA free energy of binding and hydrogen bonds formed by the interface residues between p53 and DNA has revealed the temperature dependent nature of these mutants. The role of main chain dihedrals was obtained by performing dihedral principal component analysis (PCA). This analysis, suggests that the conformational variations in the main chain dihedrals (ϕ and ψ) of the p53 ts-mutants may have caused reduction in the overall stability of the protein. The solvent exposure of the side chains of the interface residues were found to hamper the binding of the p53 to the DNA. Solvent Accessible Surface Area (SASA) also proved to be a crucial property in distinguishing the conformers obtained at 300 K and 310 K for the three ts-mutants from the wild type at 300 K.

The p53 tetramer shows an induced-fit interaction of the C-terminal domain with the DNA-binding domain

The Trp53 gene is the most frequently mutated gene in all human cancers. Its protein product p53 is a very powerful transcription factor that can activate different biochemical pathways and affect the regulation of metabolism, senescence, DNA damage response, cell cycle and cell death. The understanding of its function at the molecular level could be of pivotal relevance for therapy. Investigation of long-range intra- and interdomain communications in the p53 tetramer–DNA complex was performed by means of an atomistic model that included the tetramerization helices in the C-terminal domain, the DNA-binding domains and a consensus DNA-binding site of 18 base pairs. Nonsymmetric dynamics are illustrated in the four DNA-binding domains, with loop L1 switching from inward to outward conformations with respect to the DNA major groove. Direct intra- and intermonomeric long-range communications between the tetramerization and DNA-binding domains are noted. These long-distance conformational changes link the C terminus with the DNA-binding domain and provide a biophysical rationale for the reported functional regulation of the p53 C-terminal region. A fine characterization of the DNA deformation caused by p53 binding is obtained, with ‘static' deformations always present and measured by the slide parameter in the central thymine–adenine base pairs; we also detect ‘dynamic' deformations switched on and off by particular p53 tetrameric conformations and measured by the roll and twist parameters in the same base pairs. These different conformations can indeed modulate the electrostatic potential isosurfaces of the whole p53–DNA complex. These results provide a molecular/biophysical understanding of the evident role of the C terminus in post-translational modification that regulates the transcriptional function of p53. Furthermore, the unstructured C terminus is able to facilitate contacts between the core DNA-binding domains of the tetramer.

Molecular docking to flexible targets

It is widely accepted that protein receptors exist as an ensemble of conformations in solution. How best to incorporate receptor flexibility into virtual screening protocols used for drug discovery remains a significant challenge. Here, stepwise methodologies are described to generate and select relevant protein conformations for virtual screening in the context of the relaxed complex scheme (RCS), to design small molecule libraries for docking, and to perform statistical analyses on the virtual screening results. Methods include equidistant spacing, RMSD-based clustering, and QR factorization protocols for ensemble generation and ROC analysis for ensemble selection.

Discovery of an inhibitor of Z-alpha1 antitrypsin polymerization

Polymerization of the Z variant alpha-1-antitrypsin (Z-α1AT) results in the most common and severe form of α1AT deficiency (α1ATD), a debilitating genetic disorder whose clinical manifestations range from asymptomatic to fatal liver and/or lung disease. As the altered conformation of Z-α1AT and its attendant aggregation are responsible for pathogenesis, the polymerization process per se has become a major target for the development of therapeutics. Based on the ability of Z-α1AT to aggregate by recruiting the reactive center loop (RCL) of another Z-α1AT into its s4A cavity, we developed a high-throughput screening assay that uses a modified 6-mer peptide mimicking the RCL to screen for inhibitors of Z-α1AT polymer growth. A subset of compounds from the Library of Pharmacologically Active Compounds (LOPAC) with molecular weights ranging from 300 to 700 Da, was used to evaluate the assay's capabilities. The inhibitor S-(4-nitrobenzyl)-6-thioguanosine was identified as a lead compound and its ability to prevent Z-α1AT polymerization confirmed by secondary assays. To further investigate the binding location of S-(4-nitrobenzyl)-6-thioguanosine, an in silico strategy was pursued and the intermediate α1AT M* state modeled to allow molecular docking simulations and explore various potential binding sites. Docking results predict that S-(4-nitrobenzyl)-6-thioguanosine can bind at the s4A cavity and at the edge of β-sheet A. The former binding site would directly block RCL insertion whereas the latter site would prevent β-sheet A from expanding between s3A/s5A, and thus indirectly impede RCL insertion. Altogether, our investigations have revealed a novel compound that inhibits the formation of Z-α1AT polymers, as well as in vitro and in silico strategies for identifying and characterizing additional blocking molecules of Z-α1AT polymerization.

Structural and sequential context of p53: A review of experimental and theoretical evidence

Approximately 27 million people are suffering from cancer that contains either an inactivating missense mutation of TP53 gene or partially abrogated p53 signaling pathway. Concerted action of folded and intrinsically disordered domains accounts for multi-faceted role of p53. The intricacy of dynamic p53 structure is believed to shed light on its cellular activity for developing new cancer therapies. In this review, insights into structural details of p53, diverse single point mutations affecting its core domain, thermodynamic understanding and therapeutic strategies for pharmacological rescue of p53 function has been illustrated. An effort has been made here to bridge the structural and sequential evidence of p53 from experimental to computational studies. First, we focused on the individual domains and the crucial protein-protein or DNA-protein contacts that determine conformation and dynamic behavior of p53. Next, the oncogenic mutations associated with cancer and its contribution to thermodynamic fluctuation has been discussed. Thus the emerging anti-cancer strategies include targeting of destabilized cancer mutants with selective inhibition of its negative regulators. Recent advances in development of small molecule inhibitors and peptides exploiting p53-MDM2 interaction has been included. In a nutshell, this review attempts to describe structural biology of p53 which provide new openings for structure-guided rescue.

Molecular dynamics of the full-length p53 monomer

The p53 protein is frequently mutated in a very large proportion of human tumors, where it seems to acquire gain-of-function activity that facilitates tumor onset and progression. A possible mechanism is the ability of mutant p53 proteins to physically interact with other proteins, including members of the same family, namely p63 and p73, inactivating their function. Assuming that this interaction might occurs at the level of the monomer, to investigate the molecular basis for this interaction, here, we sample the structural flexibility of the wild-type p53 monomeric protein. The results show a strong stability up to 850 ns in the DNA binding domain, with major flexibility in the N-terminal transactivations domains (TAD1 and TAD2) as well as in the C-terminal region (tetramerization domain). Several stable hydrogen bonds have been detected between N-terminal or C-terminal and DNA binding domain, and also between N-terminal and C-terminal. Essential dynamics analysis highlights strongly correlated movements involving TAD1 and the proline-rich region in the N-terminal domain, the tetramerization region in the C-terminal domain; Lys120 in the DNA binding region. The herein presented model is a starting point for further investigation of the whole protein tetramer as well as of its mutants.

Computational identification of a transiently open L1/S3 pocket for reactivation of mutant p53

The tumour suppressor p53 is the most frequently mutated gene in human cancer. Reactivation of mutant p53 by small molecules is an exciting potential cancer therapy. Although several compounds restore wild-type function to mutant p53, their binding sites and mechanisms of action are elusive. Here computational methods identify a transiently open binding pocket between loop L1 and sheet S3 of the p53 core domain. Mutation of residue Cys124, located at the centre of the pocket, abolishes p53 reactivation of mutant R175H by PRIMA-1, a known reactivation compound. Ensemble-based virtual screening against this newly revealed pocket selects stictic acid as a potential p53 reactivation compound. In human osteosarcoma cells, stictic acid exhibits dose-dependent reactivation of p21 expression for mutant R175H more strongly than does PRIMA-1. These results indicate the L1/S3 pocket as a target for pharmaceutical reactivation of p53 mutants.

About 40% of human cancers carry missense mutations in the tumour suppressor protein p53. Here the authors identify a transiently open pocket in the protein, and by targeting a small molecule to it, partially restore mutant p53 tumour suppressor activity.

Molecular modelling and simulations in cancer research

The complexity of cancer and the vast amount of experimental data available have made computer-aided approaches necessary. Biomolecular modelling techniques are becoming increasingly easier to use, whereas hardware and software are becoming better and cheaper. Cross-talk between theoretical and experimental scientists dealing with cancer-research from a molecular approach, however, is still uncommon. This is in contrast to other fields, such as amyloid-related diseases, where molecular modelling studies are widely acknowledged. The aim of this review paper is therefore to expose some of the more common approaches in molecular modelling to cancer scientists in simple terms, illustrating success stories while also revealing the limitations of computational studies at the molecular level.