Combinatorial Pattern Discovery Approach for the Folding Trajectory Analysis of a β-Hairpin

The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated) approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters)—each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity c∈RO((N + nm) log n), where N is the size of the output patterns and (n × m) is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a β-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1) The method recovers states previously obtained by visually analyzing free energy surfaces. (2) It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the β-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3) The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the choice of reaction coordinates. (An abstract version of this work was presented at the 2005 Asia Pacific Bioinformatics Conference [1].

Computational methodologies applied to Protein-Protein Interactions for molecular insights in Medicinal Chemistry

In living systems, proteins usually team up into \u201cmolecular machinery\u201d implementing several protein-to-protein physical contacts \u2013 or protein-protein interactions (PPIs) \u2013 to exert biological effects at both cellular and systems levels. Deregulations of protein-protein contacts have been associated with a huge number of diseases in a wide range of medical areas, such as oncology, cancer immunotherapy, infectious diseases, neurological disorders, heart failure, inflammation and oxidative stress. PPIs are very complex and usually characterised by specific shape, size and complementarity. The protein interfaces are generally large, broad and shallow, and frequently protein-protein contacts are established between non-continuous epitopes, that conversely are dislocated across the protein interfaces. For this reason, in the past two decades, PPIs were thought to be \u201cundruggable\u201d targets by the scientific research community with scarce or no chance of success. However, in recent years the Medicinal Chemistry frontiers have been changing and PPIs have gained popularity amongst the research groups due to their key roles in such a huge number of diseases. Until recently, PPIs were determined by experimental evidence through techniques specifically developed to target a small group of interactions. However, these methods present several limitations in terms of high costs and labour- and time-wasting. Nowadays, a large number of computational methods have been successfully applied to evaluate, validate, and deeply analyse the experimentally determined protein interactomes. In this context, a high number of computational tools and techniques have been developed, such as methods designed to construct interaction databases, quantum mechanics and molecular mechanics (QM/MM) to study the electronic properties, simulate chemical reactions, and calculate spectra, and all-atom molecular dynamics simulations to simulate temporal and spatial scales of inter- and intramolecular interactions. These techniques have allowed to explore PPI networks and predict the related functional features. In this PhD work, an extensive use of computational techniques has been reported as valuable tool to explore protein-protein interfaces, identify their hot spot residues, select small molecules and design peptides with the aim of inhibiting six different studied PPIs. Indeed, in this thesis, a success story of in silico approaches to PPI study has been described, where MD simulations, docking and pharmacophore screenings led to the identification of a set of PPI modulators. Among these, two molecules, RIM430 and RIM442, registered good inhibitory activity with IC50 values even within the nanomolar range against the interaction between MUC1 and CIN85 proteins in cancer disease. Furthermore, computational alanine scanning, all-atom molecular dynamics simulations, docking and pharmacophore screening were exploited to (1) rationally predict three potential interaction models of NLRP3PYD-ASCPYD complex involved in inflammatory and autoimmune diseases; (2) identify a potentially druggable region on the surface of SARS-CoV-2 Spike protein interface and select putative inhibitors of the interaction between Spike protein and the host ACE2 receptor against COVID-19 (CoronaVIrus Disease 2019); (3) investigate intramolecular modifications as a consequence of a point mutation on C3b protein (R102G) associated with the age-related macular degeneration (AMD) disease; (4) design non-standard peptides to inhibit transcriptional events associated with HOX-PBX complex involved in cancer diseases; and (5) to optimise a patented peptide sequence by designing helix-shaped peptides embedded with the hydrogen bond surrogate approach to tackle cocaine abuse relapses associated with Ras-RasGRF1 interaction. Although all the herein exploited techniques are based on predictive calculations and need experimental evidence to confirm the findings, the results and molecular insights retrieved and collected show the potential of the computer-aided drug design applied to the Medicinal Chemistry, guaranteeing labour- and time-saving to the research groups. On the other hand, computing ability, improved algorithms and fast-growing data sets are rapidly fostering advances in multiscale molecular modelling, providing a powerful emerging paradigm for drug discovery. It means that more and more research efforts will be done to invest in novel and more precise computational techniques and fine-tune the currently employed methodologies

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

Diversification of a single ancestral gene into a successful toxin superfamily in highly venomous Australian funnel-web spiders

Background: Spiders have evolved pharmacologically complex venoms that serve to rapidly subdue prey and deter predators. The major toxic factors in most spider venoms are small, disulfide-rich peptides. While there is abundant evidence that snake venoms evolved by recruitment of genes encoding normal body proteins followed by extensive gene duplication accompanied by explosive structural and functional diversification, the evolutionary trajectory of spider-venom peptides is less clear. Results: Here we present evidence of a spider-toxin superfamily encoding a high degree of sequence and functional diversity that has evolved via accelerated duplication and diversification of a single ancestral gene. The peptides within this toxin superfamily are translated as prepropeptides that are posttranslationally processed to yield the mature toxin. The N-terminal signal sequence, as well as the protease recognition site at the junction of the propeptide and mature toxin are conserved, whereas the remainder of the propeptide and mature toxin sequences are variable. All toxin transcripts within this superfamily exhibit a striking cysteine codon bias. We show that different pharmacological classes of toxins within this peptide superfamily evolved under different evolutionary selection pressures. Conclusions: Overall, this study reinforces the hypothesis that spiders use a combinatorial peptide library strategy to evolve a complex cocktail of peptide toxins that target neuronal receptors and ion channels in prey and predators. We show that the ω-hexatoxins that target insect voltage-gated calcium channels evolved under the influence of positive Darwinian selection in an episodic fashion, whereas the κ-hexatoxins that target insect calcium-activated potassium channels appear to be under negative selection. A majority of the diversifying sites in the ω-hexatoxins are concentrated on the molecular surface of the toxins, thereby facilitating neofunctionalisation leading to new toxin pharmacology. © 2014 Pineda et al.; licensee BioMed Central Ltd

Folding by Numbers: Primary Sequence Statistics and Their Use in Studying Protein Folding

The exponential growth over the past several decades in the quantity of both primary sequence data available and the number of protein structures determined has provided a wealth of information describing the relationship between protein primary sequence and tertiary structure. This growing repository of data has served as a prime source for statistical analysis, where underlying relationships between patterns of amino acids and protein structure can be uncovered. Here, we survey the main statistical approaches that have been used for identifying patterns within protein sequences, and discuss sequence pattern research as it relates to both secondary and tertiary protein structure. Limitations to statistical analyses are discussed, and a context for their role within the field of protein folding is given. We conclude by describing a novel statistical study of residue patterning in β-strands, which finds that hydrophobic (i,i+2) pairing in β-strands occurs more often than expected at locations near strand termini. Interpretations involving β-sheet nucleation and growth are discussed

FREE ENERGIES IN BIOMOLECULAR SIMULATIONS: FROM PROTEIN-PROTEIN INTERACTIONS TO UNFOLDING INHIBITION

Part I - Microtubules are polymeric structures formed by the self association of tubulin dimers. They are extremely dynamical structures, that can undergo phases of growing and shrinking, playing a key role during cells proliferation process. Due to its importance for mitosis, tubulin is the target of many anticancer drugs currently in use or under clinical trial. The success of these molecules, however, is limited by the onset of resistant tumor cells during the treatment, so new resistance-proof compounds need to be developed. We analyze the protein-protein interactions allowing microtubules formation using molecular dynamics and free energy calculations. We were able to identify the most important amino acids for tubulin-tubulin binding and thus to design peptides, corresponding to tubulin subsequences. These peptides, able to interfere with microtubules formations, were proved to exhibit antitumoral activity. Part II - Understanding the molecular mechanisms that allow some organisms to survive in extremely harsh conditions is an important achievement that might disclose a wide range of applications and that is constantly drawing the attention of many research fields. The simple small organic molecules, called osmolytes, responsible for the high adaptability of these living creatures are well known and of common use; nevertheless a full disclosure of the machinery behind their activity is still to be obtained. We developed a computational approach that, taking advantage of advanced simulation techniques, allowed to fully describe the effects of osmo-protectants on a small hairpin peptide and on a full mini-protein. The computational study allowed to highlight interesting new features and to develop a theory on the \u201cosmoprotection driving force\u201d

Advancing Loop Prediction to Ultra-High Resolution Sampling

Homology modeling is integral to structure-based drug discovery. Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. For loops identified to possess α-helical segments, an alternative dihedral library is employed composed of (phi,psi) angles commonly found in helices. Even with imperfect knowledge coming from sequence-based secondary structure, helix or hairpin embedded loops, up to 17 residues in length, are successfully predicted to median sub-angstrom RMSD. Having demonstrated success with these cases, performance costs for these and other similar long loop predictions will be discussed. Dramatic improvements in both speed and accuracy are possible through the development of a Cβ-based scoring function, applicable to hydrophobic residues, that can be applied as early as half-loop buildup. With this scoring function, up to a 30-fold reduction in the cost to produce competitive sub-2 A loops are observed. Through the use of this scoring function, an efficient method will be presented to achieve ultra-high resolution buildup that restrains combinatorial explosion and offers an alternative to the current approach to full-loop buildup. This novel method is designed to be inherently suitable for homology model refinement

Self-Assembling Peptide Nanomaterials: Molecular Dynamics Studies, Computational Designs And Crystal Structure Characterizations

Peptides present complicated three-dimensional folds encoded in primary amino acid sequences of no more than 50 residues, providing cost-effective routes to the development of self-assembling nanomaterials.� The complexity and subtlety of the molecular interactions in such systems make it interesting to study and to understand the fundamental principles that determine the self-assembly of nanostructures and morphologies in solution. Such principles can then be applied to design novel self-assembling nanomaterials of precisely defined local structures and to controllably engineer new advanced functions into the materials. We first report the rational engineering of complementary hydrophobic interactions to control β-fibril type peptide self-assemblies that form hydrogel networks. Complementary to the experimental observations of the two distinct branching morphologies present in the two β-fibril systems that share a similar sequence pattern, we investigated on network branching, hydrogel properties by molecular dynamics simulations to provide a molecular picture of the assemblies. Next, we present the theory-guided computational design of novel peptides that adopt predetermined local nanostructures and symmetries upon solution assembly. Using such an approach, we discovered a non-natural, single peptide tetra-helical motif that can be used as a common building block for distinct predefined material nanostructures. The crystal structure of one designed peptide assembly demonstrates the atomistic match of the motif structure to the prediction, as well as provides fundamental feedback to the methods used to design and evaluate the computationally designed peptide candidates. This study could potentially improve the success rate of future designs of peptide-based self-assembling nanomaterials

Study of Proteoforms, DNA and Complexes using Trapped Ion Mobility Spectrometry-Mass Spectrometry

The characterization of biomolecules and biomolecular complexes represents an area of significant research activity because of the link between structure and function. Drug development relies on structural information in order to target certain domains. Many traditional biochemical techniques, however, are limited by their ability to characterize only certain stable forms of a molecule. As a result, multidimensional approaches, such as ion mobility mass spectrometry coupled to mass spectrometry (IMS-MS), are becoming very attractive tools as they provide fast separation, detection and identification of molecules, in addition to providing three-dimensional shape for structural elucidation. The present work expands the use and application of trapped ion mobility spectrometry-coupled to mass spectrometry (TIMS-MS) by analyzing a range of biomolecules (including proteoforms, intrinsically disordered peptides, DNA and molecular complexes). The aim is to i) evaluate the TIMS platform measuring sensitivity, selectivity, and separation of targeted compounds, ii) pioneer new applications of TIMS for a more efficient and higher throughput methodologies for identification and characterization of biomolecular ions, and iii) characterize the dynamics of selected biomolecules for insight into the folding pathways and the intra-or intermolecular interactions that define their conformational space