Anna Rychkova

On the energetics of translocon-assisted insertion of charged transmembrane helices into membranes.

The understanding of the mechanism of insertion of transmembrane (TM) helixes through the translocon presents a major open challenge. Although the experimental information about the partition of the inserted helices between the membrane and the solution contains crucial information about this process, it is not clear how to extract this information. In particular, it is not clear how to rationalize the small apparent insertion energy, ΔGapp, of an ionized residue in the center of a TM helix. Here we explore the nature of the insertion energies, asking what should be the value of these parameters if their measurements represent equilibrium conditions. This is done using a coarse-grained model with advanced electrostatic treatment. Estimating the energetics of ionized arginine of a TM helix in the presence of neighboring helixes or the translocon provides a rationale for the observed ΔGapp of ionized residues. It is concluded that the apparent insertion free energy of TM with charged residues reflects probably more than just the free energy of moving the isolate single helix from water into the membrane. The present approach should be effective not only in exploring the mechanism of the operation of the translocon but also for studies of other membrane proteins.

An effective coarse-grained model for biological simulations: recent refinements and validations.

Exploring the free energy landscape of proteins and modeling the corresponding functional aspects presents a major challenge for computer simulation approaches. This challenge is due to the complexity of the landscape and the enormous computer time needed for converging simulations. The use of various simplified coarse grained (CG) models offers an effective way of sampling the landscape, but most current models are not expected to give a reliable description of protein stability and functional aspects. The main problem is associated with insufficient focus on the electrostatic features of the model. In this respect, our recent CG model offers significant advantage as it has been refined while focusing on its electrostatic free energy. Here we review the current state of our model, describing recent refinements, extensions, and validation studies while focusing on demonstrating key applications. These include studies of protein stability, extending the model to include membranes, electrolytes and electrodes, as well as studies of voltage‐activated proteins, protein insertion through the translocon, the action of molecular motors, and even the coupling of the stalled ribosome and the translocon. The examples discussed here illustrate the general potential of our approach in overcoming major challenges in studies of structure function correlation in proteins and large macromolecular complexes. Proteins 2014; 82:1168–1185.

Exploring the nature of the translocon-assisted protein insertion

The elucidation of the molecular nature of the translocon-assisted protein insertion is a challenging problem due to the complexity of this process. Furthermore, the limited availability of crucial structural information makes it hard to interpret the hints about the insertion mechanism provided by biochemical studies. At present, it is not practical to explore the insertion process by brute force simulation approaches due to the extremely lengthy process and very complex landscape. Thus, this work uses our previously developed coarse-grained model and explores the energetics of the membrane insertion and translocation paths. The trend in the calculated free-energy profiles is verified by evaluating the correlation between the calculated and observed effect of mutations as well as the effect of inverting the signal peptide that reflects the “positive-inside” rule. Furthermore, the effect of the tentative opening induced by the ribosome is found to reduce the kinetic barrier. Significantly, the trend of the forward and backward energy barriers provides a powerful way to analyze key energetics information. Thus, it is concluded that the insertion process is most likely a nonequilibrium process. Moreover, we provided a general formulation for the analysis of the elusive apparent membrane insertion energy, ΔGapp, and conclude that this important parameter is unlikely to correspond to the free-energy difference between the translocon and membrane. Our formulation seems to resolve the controversy about ΔGapp for Arg.

Simulating the pulling of stalled elongated peptide from the ribosome by the translocon

The nature of the coupling between the stalling of the elongated nascent peptide chain in the ribosome and its insertion through the translocon is analyzed, focusing on the recently discovered biphasic force that overcomes the stalling barrier. The origin of this long-range coupling is explored by coarse-grained simulations that combine the translocon (TR) insertion profile and the effective chemical barrier for the extension of the nascent chain in the ribosome. Our simulation determined that the inserted H segment is unlikely to climb the TR barrier in parallel with the peptide synthesis chemical step and that the nascent chain should first overcome the chemical barriers and move into the ribosome–TR gap region before the insertion into the TR tunnel. Furthermore, the simulations indicate that the coupled TR-chemistry free energy profile accounts for the biphasic force. Apparently, although the overall elongation/insertion process can be depicted as a tug-of-war between the forces of the TR and the ribosome, it is actually a reflection of the combined free-energy landscape. Most importantly, the present study helps to relate the experimental observation of the biphasic force to crucial information about the elusive path and barriers of the TR insertion process.

Discovery and functional characterization of a neomorphic PTEN mutation.

Significance Identification of putative functional genetic mutations involved in cancer has been dramatically accelerated by developments in next generation sequencing technologies. However, analyzing an individual patient genome and interpreting mutation spectra to inform cancer origin and targeted treatment have been challenging. This study presents a framework interpreting a single patient’s genome and identifies a novel causal mutation in the phosphatase and tensin homolog (PTEN) tumor suppressor. Through computational and experimental approaches, we demonstrate that this mutation causes PTEN to retain known tumor suppressor function while gaining protumor activity. This finding suggests a new role for PTEN and other tumor suppressor involvement in cancer formation and reveals the potential wealth of biological information currently underexploited by the lack of systematic approaches for cancer genome interpretation services. Although a variety of genetic alterations have been found across cancer types, the identification and functional characterization of candidate driver genetic lesions in an individual patient and their translation into clinically actionable strategies remain major hurdles. Here, we use whole genome sequencing of a prostate cancer tumor, computational analyses, and experimental validation to identify and predict novel oncogenic activity arising from a point mutation in the phosphatase and tensin homolog (PTEN) tumor suppressor protein. We demonstrate that this mutation (p.A126G) produces an enzymatic gain-of-function in PTEN, shifting its function from a phosphoinositide (PI) 3-phosphatase to a phosphoinositide (PI) 5-phosphatase. Using cellular assays, we demonstrate that this gain-of-function activity shifts cellular phosphoinositide levels, hyperactivates the PI3K/Akt cell proliferation pathway, and exhibits increased cell migration beyond canonical PTEN loss-of-function mutants. These findings suggest that mutationally modified PTEN can actively contribute to well-defined hallmarks of cancer. Lastly, we demonstrate that these effects can be substantially mitigated through chemical PI3K inhibitors. These results demonstrate a new dysfunction paradigm for PTEN cancer biology and suggest a potential framework for the translation of genomic data into actionable clinical strategies for targeted patient therapy.

The strength of transcription-factor binding modulates co-variation in transcriptional networks

An appreciable fraction of the transcriptome differs in level of expression among individuals. Transcription factor (TF) expression and DNA binding causes cell-specific activation and repression of downstream targets, and TF expression levels vary across individuals. However, it is not clear how the strength of DNA binding for individual TFs translates into regulatory control, or whether a different set of binding motifs is used for strongly regulated modules. Here we integrate two publicly available data sets in Drosophila melanogaster, as well as conduct novel analyses, to address these questions.

On the nature of the apparent free energy of inserting amino acids into membrane through the translocon.

The nature of the biological free energy scale (ΔGapp), obtained from translocon mediated insertion studies, has been a major puzzle and the subject of major controversies. Part of the problem has been the complexity of the insertion process that discouraged workers from considering the feasible kinetics schemes and left the possible impression that ΔGapp presents some simple partition. Here we extend and clarify our recent analysis of the insertion problem using well-defined kinetics schemes and a free energy profile. We point out that although the rate constants of some steps are far from being obvious, it is essential to consider explicitly such schemes in order to advance in analyzing the meaning of ΔGapp. It is then shown that under some equilibrium conditions the kinetics scheme leads to a simple formula that allows one to relate ΔGapp to the actual free energy of partitioning between the water, the membrane, and the translocon. Other options are also considered (including limits with irreversible transitions that can be described by linear free energy relationships (LFERs)). It is concluded that it is unlikely that a kinetics plus thermodynamic based analysis can lead to a result that identifies ΔGapp with the partition between the membrane and the translocon. Thus, we argue that unless such analysis is presented, it is unjustified to assume that ΔGapp corresponds to the membrane translocon equilibrium or to some other arbitrary definition. Furthermore, we point out that the presumption that it is sufficient to just calculate the PMF for going from the translocon (TR) to the membrane and then to assume irreversible diffusive motion to water and for further entrance to the membrane is not a valid analysis. Overall, we point out that it is important to try to relate ΔGapp to a well-defined kinetics scheme (regardless of the complication of the system) in order to determine whether the energies of inserting positively charged residues to the membrane are related to the corresponding ΔGapp. It is also suggested that deviations from our simple formula for equilibrium conditions can help in identifying and analyzing kinetics barriers.

DEVELOPING GENE-SPECIFIC META-PREDICTOR OF VARIANT PATHOGENICITY

Rapid, accurate, and inexpensive genome sequencing promises to transform medical care. However, a critical hurdle to enabling personalized genomic medicine is predicting the functional impact of novel genomic variation. Various methods of missense variants pathogenicity prediction have been developed by now. Here we present a new strategy for developing a pathogenicity predictor of improved accuracy by applying and training a supervised machine learning model in a gene-specific manner. Our meta-predictor combines outputs of various existing predictors, supplements them with an extended set of stability and structural features of the protein, as well as its physicochemical properties, and adds information about allele frequency from various datasets. We used such a supervised gene-specific meta-predictor approach to train the model on the CFTR gene, and to predict pathogenicity of about 1,000 variants of unknown significance that we collected from various publicly available and internal resources. Our CFTR-specific meta-predictor based on the Random Forest model performs better than other machine learning algorithms that we tested, and also outperforms other available tools, such as CADD, MutPred, SIFT, and PolyPhen-2. Our predicted pathogenicity probability correlates well with clinical measures of Cystic Fibrosis patients and experimental functional measures of mutated CFTR proteins. Training the model on one gene, in contrast to taking a genome wide approach, allows taking into account structural features specific for a particular protein, thus increasing the overall accuracy of the predictor. Collecting data from several separate resources, on the other hand, allows to accumulate allele frequency information, estimated as the most important feature by our approach, for a larger set of variants. Finally, our predictor will be hosted on the ClinGen Consortium database to make it available to CF researchers and to serve as a feasibility pilot study for other Mendelian diseases.

Computational Approach to Study Membrane Protein Topology

Membrane proteins represent an important class of proteins with a variety of biological functions and a major fundamental and pharmaceutical interest. The protein-conducting channel translocon is responsible for protein-membrane integration. Despite significant progress it is unknown how membrane proteins achieve different topologies. Experimental studies suggest that the location of the positive charges on the signal peptide may influence its orientation (so-called “positive-inside” rule). In addition the topology of the membrane proteins may be affected by the prl (protein localization) mutations on the translocon.In this study we tried to estimate the barrier for the polypeptide insertion into the translocon using our renormalization approach (1). In this approach the insertion dynamics is first simulated with coarse grain model (2) where the whole insertion profile is divided on intermediate states and the time dependence response of each step is obtained. The second step involves the simulation with an implicit Langevin Dynamics (LD) model of reduced dimensions. By adjusting the friction and the barrier of the implicit LD model we can get the agreement between the time dependence responses of both models. By combining the barriers from different intermediate steps we can get the barrier for the whole insertion process.The renormalization approach allowed us to compare the barriers for different signal peptides, as well as to study the effect of mutations of the translocon on the orientation of the signal peptide.(1) Kamerlin et al., Annual Reviews, 2010.(2) Rychkova et al., PNAS, 2010.

Computational Studies of Translocon-Assisted Processes of Membrane Protein Insertion and Translocation

Membrane proteins make up about 30% of all the proteins in the body and represent more than 50% of drug targets. The protein-conducting channel, called translocon, is responsible for protein-membrane integration and the understanding of the mechanism of translocon-associated membrane protein folding has a significant biological and pharmaceutical interest.The translocon is a very large multidomain protein complex and the structural information about the protein-translocon complex is very tentative. Thus brute force all atom MD computer simulations are not expected to be very useful at this stage. In order to advance this challenging direction we introduce a unique coarse grain (CG) method with extended electrostatic treatment. This model allowed us to explore the energetics of the insertion of transmembrane helixes into lipid bilayer trough the translocon and to study the controversial question of the charged residue location in the membrane. More recently we stated to use the CG model in exploring the effect of key mutations that allow the secretion of proteins with defective or absent signal sequences. The preliminary insight provided by this study will also be described.