Computational Approaches To Molecular Recognition

Molecular recognition is a very interesting problem, and foremost, a current challenge for biophysical chemistry. Having reliable predictions on the specific recognition between molecules is highly priority as it will provide an insight of fundamental problems and will raise relevant technological applications. The dissertation presented here is centered on a quantitative analysis of molecular recognition in solution for host-guest, protein-ligand binding and catalysis. The statistical mechanics framework used to describe the state-of-the-art for receptor-ligand binding is an inflection point for the developing of new improved and methods. In fact, a highly performanced and accurate model was obtained for the analysis of host-guest binding. Finally, the presented models were used as a reliable predictive tools for discovering new chemical entities for enhance catalysis in solution.

Supramolecular chemistry has been defined by J.-M. Lehn as "a highly interdisciplinary field of science covering the chemical, physical, and biological features of chemical species of higher complexity, that are held together and organized by means of intermolecular (noncovalent) binding interactions" (Science, 1993). Recognition, reactivity, and transport represent three basic functional features, in essence dynami~s, which may be translated into structural features. The purpose of the NATO workshop which took place september 1-5, 1993 at the Bischenberg (near Strasbourg) was to present computations which may contribute to the atomic level understanding of the structural and thermodynamical features involved in the processes of molecular recognition and supramolecular organization. of "supra-molecular modeling". Other The main focus was therefore, on the many facets applications of computers in chemistry, such as automation, simulation of processes, procedures for fitting kinetic or thermodynamic data, computer assisted synthetic strategies, use of data bases for structure elucidation or for bibliographic searches, have an obvious impact in supramolecular chemistry as well, but were not presented at the workshop.

The aim of this thesis was to improve computational methods for structure based drug design, particularly for RNA targets. For the first portion, we present a critical evaluation of various computational drug design algorithms for their ability to predict experimental binding poses and rank libraries of small molecules against protein targets. In particular, we characterize the strengths and weaknesses of the ligand sampling method for the DOCK suite of programs. In the second portion of the thesis, we apply the lessons learned from protein targets to the disruption of protein-RNA interactions critical to the life cycle of the HIV virus. As a class, RNA historically has presented a difficult computational challenge both due to its highly localized charge and flexibility. Therefore, we have extended protocols and added new protocols in existing software packages such as DOCK and AMBER to predict experimental binding poses, once again validating our results using experimental data. Finally, we apply these protocols both to develop libraries of small molecules against druggable RNA targets and to establish a fragment-based library designed to find new scaffolds for RNA.

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

This title covers a wide range of topics relevant to the development of drugs. It provides a comprehensive description of the major methodological strategies available for rational drug discovery.

This thesis focuses on molecular recognition in chymosin complexes using various computational approaches used for studying protein-ligand systems. Three computational investigations are presented in this thesis. The first research project, titled, 'Allosteric-Activation Mechanism Of BovineChymosin', is presented in chapter 5. The study investigates the aspartic protease, bovine chymosin, which catalyses the proteolysis of k-casein proteins in milk. The research presented in this chapter employed two computational techniques, molecular dynamics and bias exchange metadynamics simulations, to study the mechanism of allosteric-activation and to compute the free energy surface for the process. The simulations reveal that allosteric activation is initiated by interactions between the HPHPH sequence of k-casein and a small a-helical region of chymosin (residues 112-116). A small conformational change in the a-helix causes the side chain of Phe114 to vacate a pocket that may then be occupied by the sidechain of Tyr77. The free energy surface for the self-inhibited to open transition is significantly altered by the presence of the HPHPH sequence of k-casein. The second research project, named, 'Effect of Mutations in Bovine or Camel Chymosin on the Thermodynamics of Binding k-Caseins', is presented in chapter 6. Both bovine and camel chymosin catalyse the proteolysis of a milk protein, k-casein, which helps to initiate milk coagulation. The research in this chapter reports computational alanine scanning calculations in four chymosin k-casein complexes, helping to elucidate the influence that individual residues have on the protein-ligand binding thermodynamics. Of the 12 sequence differences in the binding sites of bovine and camel chymosin, eight are shown to be particularly important for understanding differences in the binding thermodynamics (Asp112Glu, Lys221Val, Gln242Arg, Gln278Lys. Glu290Asp, His292Asn,Gln294Glu, and Lys295Leu. Residue in bovine chymosin written first). The final research project of this thesis titled, 'Comparative Molecular Field Analysis using Molecular Integral Equation Theory', is delivered in chapter 7. The study reports, and thoroughly benchmarks, a new method for 3D-QSAR that uses a classical statistical mechanics based solvent model combined with machine learning. Recently, Gussregen et al. used solute-solvent distribution functions calculated by the 3D Reference Interaction Site Model (3D-RISM) in a 3D-QSAR model to predict the binding affinities of serine protease inhibitors. The work carried out for this thesis extends this idea by introducing probe atoms into the 3D-RISM solvent model in order to capture other molecular interactions in addition to those related to hydration/dehydration. The CARMa models have been thoroughly benchmarked against other 3D-QSAR methods across six different datasets, demonstrating that CARMa is an extremely robust method, outperforming other field-based QSAR methods.