Introduction to protein folding for physicists

The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the conformational behaviour of flexible molecules is nothing more than a complex physical problem, increasingly more physicists are moving into the study of protein systems, bringing with them powerful mathematical and computational tools, as well as the sharp intuition and deep images inherent to the physics discipline. This work attempts to facilitate the first steps of such a transition. In order to achieve this goal, we provide an exhaustive account of the reasons underlying the protein folding problem enormous relevance and summarize the present-day status of the methods aimed to solving it. We also provide an introduction to the particular structure of these biological heteropolymers, and we physically define the problem stating the assumptions behind this (commonly implicit) definition. Finally, we review the 'special flavor' of statistical mechanics that is typically used to study the astronomically large phase spaces of macromolecules. Throughout the whole work, much material that is found scattered in the literature has been put together here to improve comprehension and to serve as a handy reference.Comment: 53 pages, 18 figures, the figures are at a low resolution due to arXiv restrictions, for high-res figures, go to http://www.pabloechenique.co

Structural interrogation of phosphoproteome identified by mass spectrometry reveals allowed and disallowed regions of phosphoconformation

High-throughput mass spectrometric (HT-MS) study is the method of choice for monitoring global changes in proteome. Data derived from these studies are meant for further validation and experimentation to discover novel biological insights. Here we evaluate use of relative solvent accessible surface area (rSASA) and DEPTH as indices to assess experimentally determined phosphorylation events deposited in PhosphoSitePlus. Based on accessibility, we map these identifications on allowed (accessible) or disallowed (inaccessible) regions of phosphoconformation. Surprisingly a striking number of HT- MS/MS derived events (1461/5947 sites or 24.6%) are present in the disallowed region of conformation. By considering protein dynamics, autophosphorylation events and/or the sequence specificity of kinases, 13.8% of these phosphosites can be moved to the allowed region of conformation. We also demonstrate that rSASA values can be used to increase the confidence of identification of phosphorylation sites within an ambiguous MS dataset. While MS is a stand-alone technique for the identification of vast majority of phosphorylation events, identifications within disallowed region of conformation will benefit from techniques that independently probe for phosphorylation and protein dynamics. Our studies also imply that trapping alternate protein conformations may be a viable alternative to the design of inhibitors against mutation prone drug resistance kinases

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Energy Landscape and Global Optimization for a Frustrated Model Protein

The three-color (BLN) 69-residue model protein was designed to exhibit frustrated folding. We investigate the energy landscape of this protein using disconnectivity graphs and compare it to a Go model, which is designed to reduce the frustration by removing all non-native attractive interactions. Finding the global minimum on a frustrated energy landscape is a good test of global optimization techniques, and we present calculations evaluating the performance of basin-hopping and genetic algorithms for this system.Comparisons are made with the widely studied 46-residue BLN protein.We show that the energy landscape of the 69-residue BLN protein contains several deep funnels, each of which corresponds to a different β-barrel structure

Molecular modelling studies of binding of DACD derivatives into G-Quadruplex DNA: comparison of force field and quantum polarized ligand docking methods

The DNA G-qadruplexes are one of the targets being actively explored for anti-cancer therapy by inhibiting them through small molecules. This computational study was conducted to predict the binding strengths and orientations of a set of novel dimethyl-amino-ethyl-acridine (DACA) analogues that are designed and synthesized in our laboratory, but did not diffract in Synchrotron light.Thecrystal structure of DNA G-Quadruplex(TGGGGT)4(PDB: 1O0K) was used as target for their binding properties in our studies.We used both the force field (FF) and QM/MM derived atomic charge schemes simultaneously for comparing the predictions of drug binding modes and their energetics. This study evaluates the comparative performance of fixed point charge based Glide XP docking and the quantum polarized ligand docking schemes. These results will provide insights on the effects of including or ignoring the drug-receptor interfacial polarization events in molecular docking simulations, which in turn, will aid the rational selection of computational methods at different levels of theory in future drug design programs. Plenty of molecular modelling tools and methods currently exist for modelling drug-receptor or protein-protein, or DNA-protein interactionssat different levels of complexities.Yet, the capasity of such tools to describevarious physico-chemical propertiesmore accuratelyis the next step ahead in currentresearch.Especially, the usage of most accurate methods in quantum mechanics(QM) is severely restricted by theirtedious nature. Though the usage of massively parallel super computing environments resulted in a tremendous improvement in molecular mechanics (MM) calculations like molecular dynamics,they are still capable of dealing with only a couple of tens to hundreds of atoms for QM methods. One such efficient strategy that utilizes thepowers of both MM and QM are the QM/MM hybrid methods. Lately, attempts have been directed towards the goal of deploying several different QM methods for betterment of force field based simulations, but with practical restrictions in place. One of such methods utilizes the inclusion of charge polarization events at the drug-receptor interface, that is not explicitly present in the MM FF

Controlled exploration of chemical space by machine learning of coarse-grained representations

The size of chemical compound space is too large to be probed exhaustively. This leads high-throughput protocols to drastically subsample and results in sparse and non-uniform datasets. Rather than arbitrarily selecting compounds, we systematically explore chemical space according to the target property of interest. We first perform importance sampling by introducing a Markov chain Monte Carlo scheme across compounds. We then train an ML model on the sampled data to expand the region of chemical space probed. Our boosting procedure enhances the number of compounds by a factor 2 to 10, enabled by the ML model's coarse-grained representation, which both simplifies the structure-property relationship and reduces the size of chemical space. The ML model correctly recovers linear relationships between transfer free energies. These linear relationships correspond to features that are global to the dataset, marking the region of chemical space up to which predictions are reliable---a more robust alternative to the predictive variance. Bridging coarse-grained simulations with ML gives rise to an unprecedented database of drug-membrane insertion free energies for 1.3 million compounds.Comment: 9 pages, 5 figure

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Molecular Flexibility in Crystal Structure Prediction

The packing of molecules in solids greatly affects the properties of the bulk materials. This is particularly important for the pharmaceutical industry, where the discovery of crystal forms at a late stage of process development can have disastrous consequences. As a result, the importance of polymorphism in crystal structures of organic molecules has been recognised for many years. This thesis presents computational developments that can complement experimental form screening of molecules for which conformational flexibility is significant. Current methods for crystal structure prediction are limited by the extent of molecular flexibility that can be practically handled due to the prohibitive computational cost associated with quantum mechanical calculations integrated in most of the successful approaches. In order to reduce the number of quantum mechanical evaluations, local approximate models can be defined for the estimation of the intramolecular energy, molecular geometry and the conformationally dependent intermolecular electrostatic model. A novel algorithm, CrystalOptimizer, for the accurate local minimisation of the lattice energy of crystals involving flexible organic molecules is presented. The main novelty of the algorithm is the use of dynamically constructed and updated local approximate models which essentially make available the full accuracy of quantum mechanical models at each and every iteration of the minimisation algorithm, requiring only a small number of explicit quantum mechanical calculations. This has made possible the accurate treatment of molecules involving a relatively large numbers of atoms with significant flexibility in torsional and bond angles and even bond lengths. The performance of the algorithm is critically assessed and demonstrated on a set of single and multi-component crystals. An extension of an existing algorithm for the identification of low energy crystal structures of flexible molecules, CrystalPredictor, is also described. In the proposed modification, the intramolecular energy and the molecular conformation are modelled using local approximate models. This provides a more realistic model for the effects of the flexible degrees of freedom on the molecular geometry and lattice energy. The use of deterministic low-discrepancy sequences ensures an extensive and uniform coverage of the multivariable search space. A parallelised implementation of the algorithm allows minimisations from several hundreds of thousands of initial guesses to be carried out in reasonable time. A further computational benefit is derived by the storage of the information used to construct the local approximate models in databases, which can be re-used in subsequent re-minimisation of structures with more accurate models for the lattice energy. The usefulness of these modifications is demonstrated on the ROY molecule, for which the structures of all experimentally known polymorphs are identified by the algorithm. By combining the above algorithms, a comprehensive multi-stage methodology for ab initio determination of the crystal structure of a given molecule based solely on its atomic connectivity is presented. The application of the methodology to two large and flexible molecules of pharmaceutical interest is also demonstrated

Kinetically Trapped Liquid-State Conformers of a Sodiated Model Peptide Observed in the Gas Phase

We investigate the peptide AcPheAla5LysH+, a model system for studying helix formation in the gas phase, in order to fully understand the forces that stabilize the helical structure. In particular, we address the question of whether the local fixation of the positive charge at the peptide's C-terminus is a prerequisite for forming helices by replacing the protonated C-terminal Lys residue by Ala and a sodium cation. The combination of gas-phase vibrational spectroscopy of cryogenically cooled ions with molecular simulations based on density-functional theory (DFT) allows for detailed structure elucidation. For sodiated AcPheAla6, we find globular rather than helical structures, as the mobile positive charge strongly interacts with the peptide backbone and disrupts secondary structure formation. Interestingly, the global minimum structure from simulation is not present in the experiment. We interpret that this is due to high barriers involved in re-arranging the peptide-cation interaction that ultimately result in kinetically trapped structures being observed in the experiment.Comment: 28 pages, 10 figure