Methods for Molecular Modelling of Protein Complexes.

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes

생물정보학과 물리화학적 접근법을 통한 단백질 상호작용 예측

학위논문 (박사)-- 서울대학교 대학원 : 화학부 물리화학 전공, 2016. 2. 석차옥.Proteins play key roles in many biological systems through protein interactions. Research of protein interactions can help to understand protein functions and develop new drugs. Protein interactions can be classified into homo-oligomer interactions, protein-peptide interactions, and protein-protein interactions. Protein interactions can be studied based on co-crystallized complex structure determined by X-ray crystallography or Nucleic Magnetic Resonance method, but experimentally determined structures cover only small part of the known protein- protein interactions. Therefore, there are many interests to develop computational methods for predicting protein interactions. Predicting protein interactions can be classified into methods based on bioinformatics and physical chemistry approaches. According to bioinformatics approaches, proteins with high sequence similarity convey similar interfaces and similar interactions. According to physical chemistry approaches, the funnel-like energy landscape is a general feature of protein interactions and protein interactions can be predicted by a global optimization method. In this thesis, I show bioinformatics and physical chemistry approaches for predicting homo-oligomer interactions, protein-peptide interactions, and protein- protein interactions. Both bioinformatics approaches and physical chemistry approaches played important roles to achieve improvement in predicting protein interactions.1. INTRODUCTION 1 2. GalaxyGemini: a program for protein homo-oligomer structure prediction based on similarity 5 2.1. Introduction 5 2.2. Methods 7 2.2.1. Overall procedure of GalaxyGemini 7 2.2.2. Oligomer database and test sets 9 2.2.3. Oligomer structure prediction 9 2.2.4. Scoring function for predicting oligomer state 10 2.2.5. Scoring function for predicting oligomer interactions 12 2.2.6. Energy minimization 15 2.2.7. Assessment measures 15 2.3. Results and Discussion 17 2.3.1. Performance of GalaxyGemini on training set and test set 17 2.3.2. Contribution of score components 24 2.3.3. Oligomer states for improvement cases on CASP9 targets 26 2.4. Conclusions 28 3. GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization 29 3.1. Introduction 29 3.2. Methods 32 3.2.1. Overall procedure of GalaxyPepDock 32 3.2.2. Template selection 34 3.2.3. Model-building 38 3.2.4. Evaluation measure 40 3.3. Results and Discussion 41 3.3.1. Performance compared to other protein-peptide docking programs 41 3.3.2. Template search of GalaxyPepDock 45 3.3.3. Energy-based optimization of GalaxyPepDock 48 3.3.4. Performance of GalaxyPepDock on CAPRI target 51 3.3.5. Limits of template-based docking 54 3.4. Conclusions 56 4. GalaxyPPDock: a protein-protein docking program based on cluster-guided conformational space annealing 57 4.1. Introduction 57 4.2. Methods 60 4.2.1. Overall procedure of GalaxyPPDock 60 4.2.2. Sets of protein complexes used for method development 62 4.2.3. Training of energy parameters 62 4.2.4. Overview of the conformational space annealing 66 4.2.5. Cluster-guided conformational space annealing 67 4.2.6. Assessment measure 69 4.3. Results and Discussion 70 4.3.1. Performance of cluster-guided conformational space annealing 70 4.3.2. Comparison to other protein-protein docking methods 78 4.3.3. Performance of GalaxyPPDock on recent CAPRI targets 82 4.3.4. Protein-protein docking with side-chain flexibility 85 4.3.5. Contribution of GalaxyPPDock energy components 89 4.4. Conclusions 91 5. Conclusions 92 BIBLIOGRAPHY 94 국문초록 105Docto