Protein Interactions: Computational Methods, Analysis and Applications

Contents
Preface
Acknowledgments
About the Editor
Part I Protein–Protein Interactions
Chapter 1 Structural and dynamical aspects of evolutionarily conserved protein–protein complexes
1.1. Introduction
1.1.1. Classification of protein–protein complexes
1.1.2. Characteristics of protein–protein interfaces
1.2. Evolutionary perspective on protein–protein complexes (PPCs)
1.2.1. Sequence and functional similarities dictate the conservation of PPIs
1.2.2. Variations in quaternary structure and quaternary state of homologous homomeric complexes during evolution
1.3. Conservation of structural features of interface residues of interologs
1.3.1. Similarity in the structural features of interface regions in interologs
1.3.2. Structural features of interfacial residues in the bound and unbound forms of proteins
1.4. Protein–protein complexes (PPCs) are fuzzy entities
1.4.1. Protein interfaces show variation in dynamics in different conditions
1.4.2. Dynamics may alter in regions other than interface in PPCs on complexation
1.4.3. Role of protein dynamics in assemblies
1.5. Conclusion
Acknowledgments
References
Chapter 2 A comprehensive overview of sequence-based protein-binding residue predictions for structured and disordered regions
2.1. Introduction
2.2. Computational prediction of protein-binding residues from sequence
2.2.1. Overview of sequence-based predictors of PBRs
2.2.2. Architectures of the predictors of PBRs
2.3. Summary and recommendations
Acknowledgments
References
Chapter 3 Prediction of protein–protein complex structures by docking
3.1. Introduction
3.2. Rigid body protein–protein docking approaches
3.3. Inclusion of protein flexibility during systematic docking searches
3.4. Inclusion of experimental and bioinformatics data during protein–protein docking
3.5. Flexible refinement and final scoring of docked complexes
3.6. Conclusion and future developments
References
Chapter 4 Binding affinity of protein–protein complexes: Experimental techniques, databases and computational methods
4.1. Introduction
4.2. Protein–protein binding affinity
4.3. Experimental methods to estimate protein–protein binding affinity
4.3.1. Surface plasmon resonance
4.3.2. Isothermal titration calorimetry
4.3.3. Fluorescence-based techniques
4.4. Important features influencing the binding affinity
4.4.1. Structure-based features
4.4.1.1. Buried surface area
4.4.1.2. Hot spots and anchor residues
4.4.1.3. Non-covalent interactions and binding affinity
4.4.2. Sequence-based features
4.5. Computational resources for binding affinity
4.5.1. PPA-Pred Web server
4.6. Analysis of PPI networks based on binding affinity
4.7. Effect of mutations on binding affinity
4.7.1. Databases for change in binding affinity upon mutation
4.7.2. Methods to predict the change in binding affinity
4.7.3. Additivity of ΔΔG for multiple mutations
4.8. Conclusion
Acknowledgments
References
Chapter 5 Mutational effects on protein–prote ininteractions
5.1. Introduction
5.2. Protein–protein interaction and mutation
5.2.1. Mutations of interface and non-interface residues
5.3. Mutation on PPIs and genetic diseases
5.4. Mutation in protein interfaces and cancer
5.5. Experimental measurement of mutational effects on PPIs strength
5.6. Experimental databases
5.7. Computational methods
5.7.1. The drawbacks to computational methods
5.8. Conclusion
References
Chapter 6 Predicting the consequences of mutations
6.1. Introduction
6.2. Not all mutations are made equal
6.3. Predicting changes in binding free energy due to mutation
6.3.1. Sequence-based prediction of ΔΔGbind
6.3.2. Structure-based prediction of ΔΔGbind
6.3.3. Predicting global interface features
6.4. Predicting the effects of post-translational modifications
6.5. Predictions of coevolving mutations in PPIs
6.6. Applications of computational studies
6.6.1. Designing mutations that alter binding affinity and specificity
6.6.2. Predicting disease-causing mutations
6.7. Future directions
References
Part II Protein–Nucleic Acid Interactions
Chapter 7 Computational approaches for understanding the recognition mechanism of protein–nucleic acid complexes
7.1. Introduction
7.2. Databases for protein–nucleic acid complexes/interactions
7.2.1. Databases for protein–nucleic acid complexes
7.2.2. Databases for interactions between amino acidresidues and nucleotides in protein–nucleic acid complexes
7.2.3. Databases for binding affinities of protein–nucleic acid interactions
7.2.4. Databases for transcription and posttranscriptional regulation factors and their nucleic acid-binding sites
7.3. Structural analysis of protein–nucleic acid complexes
7.3.1. Noncovalent interactions
7.3.1.1. Hydrogen bonds
7.3.1.2. π-Interactions
7.3.1.3. Contributions of different noncovalent interactions
7.3.2. Propensity of amino acid residues at the interfaces of protein–DNA and protein–RNA complexes
7.3.3. Conformational changes of DNA
7.3.4. Protein–DNA/RNA interfacial properties and functional implications
7.3.5. Residues play dual roles on binding and stabilizing in protein–nucleic acid complexes
7.3.6. Frequency of occurrence of stabilizing, binding and KRs in protein–DNA/RNA complexes
7.3.7. Preference of atomic contacts and DOT in KRs
7.3.8. Functional importance and diseases associated with KRs
7.4. Disorder-to-order transition
7.4.1. Examples
7.4.2. Statistical analysis on DOT residues in protein–nucleic acid complexes
7.5. Mechanisms for protein–DNA recognition
7.5.1. Direct and indirect readout mechanisms
7.5.1.1. Direct readout
7.5.1.2. Computation of intermolecular interaction energy
7.5.1.3. Indirect readout
7.5.1.4. Computation of intramolecular interaction energy
7.5.1.5. Examples
7.5.1.6. Combination of inter- and intramolecular interactions
7.5.2. Role of water molecules in protein–DNA recognition
7.6. Organism-specific recognition of protein–nucleic acid complexes
7.7. Molecular dynamics simulations of protein–nucleic acid complexes
7.7.1. Force fields
7.7.2. Conformational changes
7.7.3. MD simulation studies on protein–DNA complexes
7.7.4. Free energy calculations
7.8. Binding affinity
7.9. Conclusion
Acknowledgment
References
Chapter 8 Prediction of nucleic acid binding proteins and their binding sites
8.1. Introduction
8.2. Identification of nucleic acid binding proteins
8.3. Specific methods for identifying DNA-binding proteins
8.4. Methods for identifying RNA-binding proteins
8.5. Identification of binding site residues from protein–nucleic acid complex structures
8.6. Prediction of binding site residues in DNA-binding proteins
8.8. Best predictor for identifying the binding sites in protein–nucleic acid complexes
8.9. Binding site prediction in disordered regions
8.10. Conclusion
Acknowledgment
References
Chapter 9 Predicting protein-binding sites in nucleic acids
9.1. Introduction
9.2. Definition of a binding site between proteins and nucleic acids
9.3. Data of binding sites between proteins and nucleic acids
9.4. Predicting protein-binding sites in DNA
9.5. Predicting protein-binding sites in RNA
9.6. Conclusion and future perspective
Acknowledgments
References
Chapter 10 Docking algorithms and scoring functions
10.1. Introduction
10.2. Specificity of protein–RNA/DNA interaction
10.3. Docking algorithms and approaches
10.3.1. FFT algorithm in GRAMM7, and FTDock
10.3.2. Distance geometry algorithm in DOCK
10.3.4. Distance-dependent atomic interaction potential ITScore-PR
10.3.5. Quasi-chemical potential (QUASI-RNP) and the decoys as the reference state potential (DARS-RNP)
10.3.6. Simultaneous docking and folding approach for ribonucleoprotein (RNP) -denovo method (Rosetta)
10.3.7. Combinational scoring functions
10.4. Protein and DNA/RNA flexibility problem
10.4.1. Fragment-based docking approach for a single-stranded RNA-binding problem
10.4.2. Information-driven docking approach in HADDOCK (High Ambiguity Driven DOCKing)
10.5. Conclusion
References
Chapter 11 Recent progress of methodology development for protein–RNA docking
11.1. Introduction
11.2. Algorithms of protein–RNA docking
11.2.1. Protein–RNA docking has its own characteristics
11.2.2. General pipeline for implementing protein–RNA docking algorithms
11.2.2.1. Conformation sampling
11.2.2.2. Scoring function design
11.2.2.3. Clustering and refinement
11.2.2.4. Benchmark data sets for evaluating protein–RNA docking
11.3. On existing protein–RNA docking software
11.4. Conclusion and outlook
Acknowledgments
References
Part III Protein–Ligand Interactions
Chapter 12 Protein–carbohydrate complexes: Binding site analysis, prediction, binding affinity and molecular dynamics simulations
12.1. Introduction
12.2. Databases for carbohydrates and protein–carbohydrate complexes
12.2.1. Structural databases of protein–carbohydrate complexes and carbohydrate-binding proteins
12.2.2. Sequence databases
12.2.3. Binding affinity databases
12.2.4. Carbohydrates databases
12.2.5. Web portals and integrated resources
12.3. Identification of binding site residues in protein–carbohydrate complexes
12.3.1. Distance-based criterion
12.3.2. ASA-based method
12.3.3. Energy-based approach
12.4. Analysis of binding sites
12.4.1. Residues involved in binding and stabilizing in protein–carbohydrate complexes
12.4.2. Frequency of occurrence of stabilizing, binding and KRs
12.5. Binding affinity
12.6. Prediction of binding site residues
12.7. Molecular dynamics simulations and recognition mechanism
12.7.1. Conformational analysis of monosaccharides
12.7.2. Conformational structures for disaccharides and oligosaccharides
12.7.3. Carbohydrate binding with toxins and viruses
12.8. Applications
12.9. Conclusion
Acknowledgments
References
Chapter 13 Quantitative structure–activity relationship in ligand-based drug design: Concepts and applications
13.1. Introduction
13.2. Goals of QSAR
13.3. Methods based on QSAR
13.4. QSAR based on dimensionality of properties
13.5. QSAR based on correlation and classification
13.6. QSAR model development
13.6.1. Molecular descriptors
13.6.2. 2D descriptors
13.6.3. 3D descriptors
13.6.4. Feature selection
13.6.5. Validation of QSAR models
13.7. Pitfalls of QSAR modeling
13.8. Pathway for a successful QSAR model
13.9. Applications of QSAR modeling
13.9.1. QSAR modeling of Bcl-2 protein–protein interaction inhibitors
13.9.2. QSAR modeling of mutation-specific inhibitors for epidermal growth factor receptor in cancer
13.10. Conclusion
Acknowledgments
References
Chapter 14 Protein–ligand interactions in molecular modeling and structure-based drug design
14.1. Introduction
14.2. Drug discovery in the past and present
14.3. Flexibility of macromolecular targets
14.4. Sampling of ligands
14.4.1. Scoring functions
14.4.2. Lipinski’s rule of 5
14.4.3. Fragment-based drug design
14.4.4. Active site prediction and homology modeling
14.5. Visualization of protein–ligand interactions
14.6. Conclusion and outlook
Acknowledgments
References
Chapter 15 An overview of protein–ligand docking and scoring algorithms
15.1. Introduction
15.2. Docking algorithms
15.2.1. Rigid docking
15.2.2. Flexible ligand docking
15.2.3. Flexible protein docking
15.3. Scoring functions for binding free energy estimations
15.3.1. Physics or force-field-based methods
15.3.2. Knowledge-based or potential of mean force methods
15.3.3. Empirical or regression-based methods
15.3.4. Descriptor or machine learning-based methods
15.4. CASF, D3R, CASR and other challenges
15.5. Advantages and pitfalls of machine learning methods
15.6. Summary and perspectives
Acknowledgments
References
Index