10.7 Protein folding prediction
9 min read•august 21, 2024
Protein folding prediction is a crucial aspect of bioinformatics, helping researchers understand protein structure and function. This field combines computational approaches with experimental techniques to determine protein structures faster and more cost-effectively than traditional methods alone.
The process of protein folding involves complex interactions at various levels of structure. From primary amino acid sequences to quaternary arrangements, understanding these hierarchies is essential for predicting how proteins fold and function in biological systems.
Fundamentals of protein folding
- Protein folding prediction plays a crucial role in bioinformatics by enabling researchers to understand protein structure and function
- Accurate prediction methods contribute to drug discovery, protein engineering, and understanding disease mechanisms
- Computational approaches in protein folding complement experimental techniques, allowing for faster and more cost-effective structure determination
Protein structure hierarchy
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- Primary structure consists of the linear amino acid sequence
- forms local patterns (alpha helices, beta sheets)
- Alpha helices involve hydrogen bonding between residues 3-4 positions apart
- Beta sheets involve hydrogen bonding between adjacent strands
- represents the overall 3D conformation of a single polypeptide chain
- Quaternary structure describes the arrangement of multiple folded protein subunits
Thermodynamics of folding
- Gibbs free energy () determines the spontaneity of protein folding
- Enthalpy () reflects the formation of non-covalent interactions
- Entropy () accounts for the hydrophobic effect and conformational changes
- Folding occurs when becomes negative
- Hydrophobic collapse drives the initial stages of folding
- Hydrogen bonding and van der Waals interactions stabilize the final structure
Levinthal's paradox
- Highlights the discrepancy between theoretical folding time and observed folding rates
- Theoretical time for random sampling of all possible conformations exceeds the age of the universe
- Actual protein folding occurs within milliseconds to seconds
- Resolved by understanding folding as a guided process on an energy landscape
- Folding funnels explain how proteins avoid sampling all possible conformations
- Intermediate states and folding nuclei further accelerate the folding process
Computational approaches
- Computational methods in protein folding prediction aim to overcome limitations of experimental techniques
- These approaches leverage various algorithms, databases, and physical principles to model protein structures
- Advancements in computational power and algorithms have significantly improved prediction accuracy
Ab initio methods
- Predict protein structure based solely on amino acid sequence
- Utilize physics-based force fields to simulate atomic interactions
- Employ conformational sampling techniques (, )
- algorithm uses fragment assembly and
- method combines fragment assembly with replica exchange Monte Carlo
- Computationally intensive but applicable to novel protein folds
Homology modeling
- Predicts structure based on similarity to known protein structures
- Requires a template with >30% sequence identity for accurate predictions
- Steps include template selection, alignment, backbone generation, loop modeling, and refinement
- and serve as popular tools
- Accuracy depends on the quality of the template and the alignment
- Widely used for predicting structures of proteins with close homologs
Threading techniques
- Align target sequence to known structural templates
- Evaluate the fitness of the sequence to the template's 3D structure
- Use scoring functions to assess sequence-structure compatibility
- and represent well-known threading algorithms
- Effective for detecting remote homologs and predicting structures of distantly related proteins
- Combine elements of both ab initio and homology-based approaches
Machine learning in folding prediction
- Machine learning techniques have revolutionized protein structure prediction in recent years
- These methods can capture complex patterns and relationships in protein sequence and structure data
- Integration of machine learning with traditional approaches has led to significant improvements in prediction accuracy
Neural networks for structure prediction
- Utilize artificial to learn patterns in protein sequences and structures
- Convolutional neural networks () extract local sequence features
- Recurrent neural networks () capture long-range dependencies in protein sequences
- employs deep bidirectional long short-term memory (LSTM) networks for secondary structure prediction
- combines CNNs and LSTMs to predict secondary structure and solvent accessibility
- Neural networks can predict contact maps and distance matrices for tertiary structure modeling
Deep learning architectures
- Transformer-based models have shown remarkable performance in protein structure prediction
- Attention mechanisms allow for capturing global context in protein sequences
- Residual networks enable training of very deep architectures for improved feature extraction
- uses masked language modeling to learn protein sequence representations
- employs a large-scale language model trained on millions of protein sequences
- can model protein structures as graphs of interacting residues
AlphaFold vs traditional methods
- , developed by DeepMind, represents a breakthrough in protein structure prediction
- Utilizes attention-based neural networks and evolutionary information
- Achieves near-experimental accuracy for many protein targets
- Outperforms traditional methods in CASP14 competition by a significant margin
- Incorporates multiple sequence alignments and residue-residue distance prediction
- Iterative refinement process allows for high-resolution structure prediction
- Traditional methods still valuable for specific cases and as complementary approaches
Energy landscape theory
- Energy landscape theory provides a framework for understanding protein folding mechanisms
- Describes the relationship between protein conformation and free energy
- Helps explain how proteins overcome Levinthal's paradox and fold efficiently
Funnel-shaped landscapes
- Represent the overall shape of the energy landscape for most proteins
- Broad top corresponds to unfolded states with high energy and entropy
- Narrow bottom represents the native state with lowest energy
- Folding progresses down the funnel, reducing both energy and conformational freedom
- Multiple pathways can lead to the native state, explaining folding heterogeneity
- Smooth funnels correspond to fast-folding proteins, while rough funnels indicate slower folding
Kinetic traps and intermediates
- Local energy minima on the landscape can trap partially folded proteins
- Kinetic traps slow down folding and may lead to misfolded states
- Intermediates represent partially folded states with some native-like structure
- Molten globule states often occur as early folding intermediates
- Chaperone proteins can help proteins escape kinetic traps
- Some proteins fold through obligate intermediates, while others follow two-state folding
Folding pathways
- Describe the sequence of events leading from the unfolded to the native state
- Nucleation-condensation model proposes formation of a folding nucleus
- Diffusion-collision model suggests assembly of pre-formed secondary structure elements
- Framework model involves hierarchical formation of secondary, then tertiary structure
- Folding pathways can be mapped using phi-value analysis and hydrogen exchange experiments
- Understanding folding pathways aids in protein engineering and designing folding inhibitors
Experimental validation techniques
- Experimental methods provide crucial data for validating and improving computational predictions
- Combine multiple techniques to obtain a comprehensive understanding of protein structure
- Advancements in these methods continue to push the boundaries of structural biology
X-ray crystallography
- Determines atomic-resolution structures of crystallized proteins
- Involves growing protein crystals and analyzing X-ray diffraction patterns
- Provides high-resolution data (often <2Å) for static protein structures
- Phasing methods include molecular replacement and anomalous dispersion
- Refinement process improves model fit to experimental data
- Challenges include obtaining high-quality crystals and capturing dynamic structures
NMR spectroscopy
- Analyzes protein structure and dynamics in solution
- Utilizes nuclear magnetic resonance phenomena to measure atomic interactions
- Provides information on protein flexibility and conformational changes
- 2D and 3D NMR experiments (COSY, NOESY, HSQC) yield distance and angle constraints
- Structure calculation involves satisfying experimental constraints
- Limited by protein size (typically <30 kDa) and requirement for isotope labeling
Cryo-electron microscopy
- Images frozen-hydrated protein samples using electron microscopy
- Single-particle analysis allows structure determination of large complexes
- Recent advances (direct electron detectors, improved algorithms) enable near-atomic resolution
- Captures proteins in native-like environments without crystallization
- Suitable for studying large assemblies and membrane proteins
- Challenges include sample preparation and image processing of heterogeneous samples
Protein misfolding and disease
- Protein underlies numerous neurodegenerative and systemic diseases
- Understanding misfolding mechanisms is crucial for developing therapeutic strategies
- Computational approaches aid in predicting aggregation propensity and designing stabilizing mutations
Amyloid formation
- Involves the aggregation of proteins into β-sheet-rich fibrillar structures
- Associated with diseases such as Alzheimer's, Parkinson's, and type II diabetes
- Nucleation-dependent polymerization model describes amyloid growth kinetics
- Amyloid precursor proteins often contain intrinsically disordered regions
- Computational methods (TANGO, Zyggregator) predict aggregation-prone sequences
- Therapeutic strategies target various stages of amyloid formation (oligomers, fibrils)
Prion diseases
- Caused by misfolded prion proteins that can induce misfolding in normal proteins
- Include Creutzfeldt-Jakob disease, bovine spongiform encephalopathy, and scrapie
- Prion proteins undergo conformational change from α-helical to β-sheet-rich structure
- Propagation occurs through templated misfolding and fragmentation
- Computational models simulate prion propagation and strain behavior
- Challenges in prediction due to the complexity of prion conformational changes
Chaperone proteins
- Assist in proper protein folding and prevent aggregation
- Heat shock proteins (HSPs) play a crucial role in cellular stress response
- Chaperonins (GroEL/GroES) provide isolated folding environments
- Hsp70 and Hsp90 families aid in folding and stabilization of client proteins
- Computational prediction of chaperone binding sites and interaction networks
- Therapeutic potential in enhancing chaperone activity to combat misfolding diseases
Structure prediction tools
- Various computational tools and resources are available for protein structure prediction
- Continuous development and improvement of these tools drive progress in the field
- Integration of multiple approaches often yields the most accurate predictions
CASP competition overview
- Critical Assessment of protein Structure Prediction evaluates prediction methods
- Held biannually since 1994, providing benchmark datasets for the community
- Targets include experimentally determined structures not yet publicly available
- Categories include template-based modeling, free modeling, and refinement
- Metrics such as and RMSD assess prediction accuracy
- Recent CASP competitions have seen significant improvements due to deep learning approaches
Popular prediction software
- I-TASSER combines threading, ab initio modeling, and iterative refinement
- SWISS-MODEL offers automated homology modeling through a web server
- Rosetta suite provides tools for and protein design
- MODELLER automates comparative protein structure modeling
- AlphaFold2 represents the state-of-the-art in deep learning-based prediction
- RaptorX employs deep learning for contact prediction and structure modeling
Limitations of current methods
- Accuracy decreases for larger proteins and multi-domain structures
- Prediction of protein-protein interactions and complexes remains challenging
- Membrane proteins pose difficulties due to their unique folding environment
- Intrinsically disordered regions are hard to predict accurately
- Time and computational resources can be limiting factors for some methods
- Integration of experimental data with predictions needs further development
Applications in biotechnology
- Protein structure prediction has numerous applications in biotechnology and medicine
- Accurate structural information enables rational design and engineering of proteins
- Computational approaches accelerate the discovery and development process
Drug design
- Structure-based drug design utilizes protein target structures for ligand discovery
- Virtual screening methods dock small molecules into predicted binding sites
- Fragment-based approaches build up drug candidates from small chemical fragments
- De novo drug design generates novel compounds tailored to specific targets
- Protein-protein interaction inhibitors can be designed based on interface predictions
- Machine learning models integrate structural information for ADMET prediction
Protein engineering
- Rational design modifies protein sequences based on structural insights
- Directed evolution combines random mutagenesis with selection or screening
- Computational protein design tools (Rosetta, FoldX) predict effects of mutations
- Enzyme engineering improves catalytic efficiency and substrate specificity
- Antibody engineering enhances affinity, stability, and pharmacokinetics
- Designing novel protein folds and functions pushes the boundaries of synthetic biology
Synthetic biology
- De novo protein design creates proteins with desired structures and functions
- Protein origami techniques design self-assembling nanostructures
- Computational design of orthogonal protein-protein interfaces
- Engineering protein-based logic gates and circuits for cellular computation
- Designing protein cages and nanocontainers for drug delivery
- Predicting and optimizing the folding of designed proteins in vivo
Future directions
- The field of protein folding prediction continues to evolve rapidly
- Integration of diverse data sources and methods will drive further improvements
- Applications of protein structure prediction are expanding into new areas of research
Quantum computing approaches
- Quantum algorithms may accelerate sampling of protein conformations
- Quantum annealing could optimize energy functions in structure prediction
- Hybrid quantum-classical algorithms for folding simulations
- Potential for solving larger protein systems more efficiently
- Challenges in developing quantum-compatible force fields and algorithms
- Early-stage research, with practical applications still years away
Integration with systems biology
- Incorporating protein structure information into metabolic and signaling networks
- Predicting the structural effects of genetic variations on cellular pathways
- Modeling protein-protein interaction networks based on structural information
- Integrating structure prediction with gene expression and proteomics data
- Simulating the behavior of entire proteomes under different conditions
- Challenges in scaling up predictions to proteome-wide levels
Personalized medicine implications
- Predicting the structural effects of disease-associated mutations
- Designing personalized drugs based on patient-specific protein structures
- Assessing the impact of genetic variations on protein folding and stability
- Predicting individual responses to drugs based on target protein structures
- Challenges in handling the vast amount of genomic and structural data
- Ethical considerations in using structural predictions for medical decisions
Key Terms to Review (32)
Ab initio prediction: Ab initio prediction refers to a computational approach that predicts the structure and function of biological molecules based solely on their primary sequence, without relying on prior experimental data. This method uses physical and chemical principles to model interactions at an atomic level, making it particularly relevant for understanding genome annotation and protein folding. By leveraging algorithms and simulations, ab initio prediction provides insights into the potential characteristics and behaviors of biomolecules.
AlphaFold: AlphaFold is an advanced artificial intelligence system developed by DeepMind that predicts protein structures with remarkable accuracy based on their amino acid sequences. This breakthrough has transformed the field of structural biology, providing insights into protein folding and allowing researchers to better understand the functions of proteins within biological systems.
Chaperones: Chaperones are specialized proteins that assist in the proper folding of other proteins, ensuring they achieve their functional three-dimensional structures. They play a crucial role in protein folding prediction by preventing misfolding and aggregation, which can lead to cellular dysfunction and diseases. Understanding how chaperones interact with nascent polypeptides helps scientists predict protein structures and functions more accurately.
Chimera: In biological terms, a chimera refers to an organism or cell that contains genetically distinct tissues, originating from two or more different zygotes. This phenomenon can occur naturally, such as in the case of individuals who develop from the fusion of multiple embryos, or it can be artificially created in laboratories for various research purposes. Chimeras are significant in understanding genetic variation, cell lineage tracing, and developmental biology, especially within the realms of structural and protein databases, as well as protein folding prediction.
CNNs: Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed to process structured grid data, such as images. They utilize convolutional layers to automatically detect patterns and features in the input data, making them especially powerful for tasks like image recognition and classification. Their ability to learn spatial hierarchies allows them to excel in fields such as bioinformatics, particularly in predicting protein folding.
Energy minimization: Energy minimization is a computational method used to find the lowest energy conformation of a molecular structure, which often correlates with the most stable state of that molecule. By optimizing the arrangement of atoms, energy minimization helps predict structural configurations that are crucial for understanding molecular interactions and behaviors. This technique is essential in fields like protein structure prediction, molecular docking, and protein folding analysis.
Esm-1b: esm-1b is a deep learning model developed for protein structure prediction that utilizes the principles of evolutionary scale modeling. This model leverages large-scale sequence and structural data to predict protein folding more accurately, contributing to the understanding of protein functions and interactions in biological systems. By encoding amino acid sequences and their evolutionary relationships, esm-1b enables more precise predictions of 3D protein structures from primary sequences.
Gdt-ts: gdt-ts, or Global Distance Test for Template Structures, is a scoring function used to assess the accuracy of protein structure predictions by comparing the predicted model to a reference structure. It provides a quantitative measure of how well the predicted structure aligns with known structural data, which is crucial for evaluating models generated through computational techniques in protein folding prediction.
Graph Neural Networks: Graph neural networks (GNNs) are a type of deep learning architecture designed to operate on data represented as graphs, where entities are represented as nodes and relationships as edges. GNNs leverage the structure of graphs to learn complex patterns and relationships, making them particularly useful for tasks such as protein function prediction and protein folding prediction. By propagating information across connected nodes, GNNs capture both local and global dependencies in graph-structured data.
Hhpred: hhpred is a powerful bioinformatics tool used for protein structure prediction based on hidden Markov models (HMMs). It enables users to predict the three-dimensional structures of proteins by comparing their sequences to known structures in a database, helping researchers understand protein function and interactions.
Homology Modeling: Homology modeling is a computational technique used to predict the three-dimensional structure of a protein based on its similarity to one or more known protein structures. This method is particularly useful when the target protein's structure has not yet been experimentally determined, allowing researchers to infer its structure from related proteins, thereby connecting sequence information to functional predictions and drug design.
Markov Models: Markov models are mathematical systems that undergo transitions from one state to another on a state space. These models rely on the principle that the future state depends only on the current state, not on the sequence of events that preceded it. This property, known as the Markov property, makes these models especially useful in predicting sequences, such as those involved in protein folding prediction, where the conformation of a protein can be represented as a series of states.
Misfolding: Misfolding refers to the incorrect folding of proteins into their functional three-dimensional structures, which can lead to loss of function and the development of various diseases. This phenomenon occurs when the amino acid sequence fails to adopt its proper configuration, often due to environmental factors or genetic mutations. Misfolded proteins can accumulate in cells, forming aggregates that disrupt normal cellular processes.
Modeller: A modeller is a computational tool or software used to predict the three-dimensional structures of biological macromolecules, primarily proteins, based on known structures of related homologous proteins. It plays a vital role in various fields, such as drug discovery and structural biology, by providing insights into protein function and interactions through modeling techniques. Modellers utilize algorithms and statistical methods to refine these predicted structures, making them essential for understanding biological processes at a molecular level.
Molecular dynamics: Molecular dynamics is a computer simulation method used to analyze the physical movements of atoms and molecules over time. By employing classical mechanics, it allows researchers to study the time-dependent behavior of molecular systems, providing insights into their structure, dynamics, and interactions. This technique is especially relevant for predicting how proteins fold and how they can be modeled from first principles.
Monte Carlo: Monte Carlo refers to a computational algorithm that relies on random sampling to obtain numerical results, often used to model complex systems and processes. This technique is particularly useful in predicting outcomes where deterministic solutions are difficult to achieve, making it a powerful tool in various scientific fields, including protein folding prediction.
Netsurfp-2.0: netsurfp-2.0 is a computational tool used for predicting the solvent accessibility and secondary structure of proteins based on their amino acid sequences. This software is significant in bioinformatics for helping researchers understand protein folding and structure by providing insights into which parts of a protein are likely to be exposed to solvent and which are buried inside, aiding in the overall prediction of protein folding.
Neural Networks: Neural networks are a set of algorithms modeled after the human brain, designed to recognize patterns and solve complex problems through a system of interconnected nodes or neurons. They excel in tasks like classification and regression by learning from data, making them particularly valuable in predicting protein structures and functions, as well as modeling biological processes like protein folding.
PDB: PDB stands for the Protein Data Bank, which is a comprehensive repository for three-dimensional structural data of biological macromolecules, primarily proteins and nucleic acids. It serves as a critical resource for researchers in various fields, providing access to a wealth of structural information that helps in understanding protein functions, interactions, and mechanisms. The PDB facilitates the integration of structural data with sequence databases and supports tools for data retrieval and submission, making it an essential hub in bioinformatics and structural biology.
Protbert: Protbert is a computational tool designed for predicting protein structures from amino acid sequences, using machine learning algorithms and deep learning techniques. This innovative approach helps researchers understand how proteins fold and how their structures relate to their functions, which is essential for various applications in biology and medicine.
PyMOL: PyMOL is an open-source molecular visualization system that is widely used in bioinformatics and structural biology for visualizing and analyzing molecular structures, particularly proteins and nucleic acids. Its powerful graphical capabilities allow users to manipulate 3D representations of biomolecules, making it an essential tool for studying interactions, structural databases, and protein folding predictions.
Qmean score: The qmean score is a quantitative measure used to evaluate the accuracy of predicted protein structures based on their alignment with known experimental data. This score provides a way to assess how well a computational model predicts the folding and spatial arrangement of proteins, which is critical in understanding their function and interactions. A higher qmean score indicates better agreement between the predicted model and actual structure, making it a valuable tool in protein folding prediction.
Quark: A quark is an elementary particle and a fundamental constituent of matter that combines to form protons and neutrons, which make up atomic nuclei. Quarks come in six different types, known as flavors, and they are never found in isolation due to a property called confinement. They play a critical role in the strong force, which holds atomic nuclei together, making them essential to understanding the structure of matter at a subatomic level.
RNNs: Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series or natural language. Unlike traditional neural networks, RNNs have connections that loop back on themselves, allowing them to maintain a memory of previous inputs, which is crucial for tasks that depend on context and sequential information, like protein folding prediction.
Root-mean-square deviation (rmsd): Root-mean-square deviation (rmsd) is a statistical measure used to quantify the differences between predicted and observed values, particularly in the context of comparing molecular structures. It calculates the square root of the average squared differences between corresponding atoms in two structures, providing a single numerical value that indicates their similarity or dissimilarity. In bioinformatics, rmsd is crucial for assessing the accuracy of protein folding predictions and for comparing different conformations in protein structure databases.
Rosetta: Rosetta is a powerful software suite used for predicting and modeling protein structures, protein-protein interactions, and docking simulations. It employs various computational methods including ab initio modeling, allowing researchers to understand and visualize complex biological processes at the molecular level. Rosetta's versatility makes it a key tool in areas such as drug design, structural biology, and bioinformatics.
Secondary structure: Secondary structure refers to the local folding patterns of a protein that are stabilized by hydrogen bonds between the backbone atoms. Common types of secondary structures include alpha helices and beta sheets, which play crucial roles in determining the overall shape and function of proteins, impacting their interactions and biological activities.
Spot-1d: Spot-1d is a computational tool used in the prediction of protein folding by analyzing one-dimensional sequences of amino acids. This method focuses on identifying patterns and features in the linear arrangement of amino acids to infer how they may fold into three-dimensional structures. Spot-1d contributes to understanding protein function and stability, making it essential in bioinformatics and structural biology.
Swiss-model: The swiss-model is a widely used computational tool for homology modeling of protein structures, allowing researchers to predict the three-dimensional conformation of proteins based on their sequence similarity to known structures. This method is crucial for understanding protein function and interaction, providing a structural framework that can aid in drug design and functional analysis.
Tertiary structure: Tertiary structure refers to the overall three-dimensional shape of a protein that is formed by the folding of its secondary structures, such as alpha helices and beta sheets, into a compact, functional form. This structure is crucial because it determines how the protein interacts with other molecules and performs its biological functions, linking it to aspects like protein function prediction and structure databases.
Threader: In the context of protein folding prediction, a threader is a computational tool used to predict the three-dimensional structure of a protein based on its amino acid sequence. It achieves this by threading the sequence through known protein structures in a database, assessing how well the sequence fits into those structures. This method is crucial for understanding protein function and dynamics, as it helps predict how proteins fold and interact.
UniProt: UniProt is a comprehensive protein sequence and functional information database that provides a rich source of data for the scientific community. It aims to support the understanding of protein function, structure, and interactions by providing well-annotated protein sequences along with associated biological information. UniProt serves as a critical resource for studying protein sequences, predicting their functions, and understanding their folding mechanisms.