AlphaFold 2 and Protein Structure Prediction

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Questions and Answers

What is a significant limitation of AlphaFold 2 in predicting protein structures?

Nucleic acid and small molecule predictions are more accurate than protein predictions.

It can generate multiple states of the same protein structure.

You need to specify the number of chains for oligomeric structures. (correct)

It always predicts oligomeric complexes accurately.

Which method has AlphaFold 2 effectively replaced in predicting protein structures?

Molecular dynamics simulations

Quantum mechanical calculations

Thermodynamic modeling

Protein-protein docking (correct)

How does AlphaFold 3 improve upon AlphaFold 2?

It allows predictions without a sequence input.

It can generate complete oligomeric structures by default.

It has a reduced prediction time. (correct)

It increases the accuracy of nucleic acid predictions.

What can be derived from the ability to predict protein structures with high accuracy?

It supports the detailed mapping of evolutionary history for various proteins. Signup and view all the answers

What was highlighted as a drawback in the predictions made by AlphaFold?

They reflect the most stable state, but miss dynamic features. Signup and view all the answers

What is the ultimate aim of de novo protein design?

To create proteins that have no natural analogs with new functions. Signup and view all the answers

In comparison to older methods, how does AlphaFold contribute to structural insights?

It makes previous methods like homology modeling obsolete. Signup and view all the answers

What is the primary goal of the RFdiffusion algorithm?

To design proteins from scratch with structural constraints Signup and view all the answers

How does ProteinPMNN contribute to protein design?

By encoding structures as distances between backbone atoms Signup and view all the answers

What limitation is currently faced in computational structural biology related to protein modeling?

Insufficient computational resources for simulations Signup and view all the answers

What is a notable feature of the designed hemagglutinin binder?

It was the best result from many candidate designs Signup and view all the answers

What aspect do AlphaFold predictions typically lack?

Consistency in multiple structural states Signup and view all the answers

How does RFdiffusion utilize symmetric noise in protein design?

To evolve oligomeric proteins Signup and view all the answers

In what way does Rosettafold diffusion model differ from traditional all-atom modeling?

It simplifies the modeling to backbone structures only Signup and view all the answers

What type of algorithm does RFdiffusion use to start its design process?

A de-noising algorithm with randomly placed atoms Signup and view all the answers

What advantage does incorporating pre-chosen structural motifs provide in protein design?

It guides the design towards specific functional interactions Signup and view all the answers

Why is establishing a ground truth through experimental structure still important?

To evaluate the predictive power of computational models Signup and view all the answers

What aspect of protein structure prediction is addressed by the AlphaFold 3 algorithm?

It combines sequence information with homologous structures and pairwise alignments. Signup and view all the answers

How does AlphaFold 3 utilize homologous structures in its predictions?

To improve the initial distance probability distribution between residues. Signup and view all the answers

What limitation is noted regarding predictions made by ESMfold?

Its predictions are less accurate than those of AlphaFold. Signup and view all the answers

What is a characteristic of the pairformer module in the AlphaFold 3 algorithm?

It updates distance distributions based on residue interactions. Signup and view all the answers

Which of the following is a method used by the AlphaFold 3 algorithm to improve its structure predictions?

Iteratively cycling information between the pairformer and diffusion modules. Signup and view all the answers

What is the significance of plDDT scores in AlphaFold 3 predictions?

They measure the accuracy and reliability of atomic position predictions. Signup and view all the answers

What is a feature of the ESMfold structure prediction tool?

It rapidly produces de novo predictions from amino acid sequences. Signup and view all the answers

What role does the triangle rule play in AlphaFold 3's predictions?

It states a correlation between distance measures and reliable backbone structure predictions. Signup and view all the answers

What does AlphaFold 3 do with low energy conformers of small molecule ligands?

It generates them and includes them in the prediction process. Signup and view all the answers

How do LLMs trained on protein sequences function in terms of understanding amino acids?

They learn to predict features based on single sequences without needing homologs. Signup and view all the answers

What are the two main types of interfaces in protein docking, and how do they differ in terms of complexity?

Rigid interface is straightforward, while plastic interface is highly challenging. Signup and view all the answers

What is the ultimate goal of ab initio structure prediction in protein modeling?

To model protein structures from first principles without using prior structural knowledge. Signup and view all the answers

What are 'constraints' in the context of protein modeling?

Features that should be true based on experiments or bioinformatics analysis. Signup and view all the answers

In protein docking, what is the role of side chain flexibility during interactions?

Modeling side chain flexibility helps improve docking accuracy. Signup and view all the answers

Which factor can reduce the reliability of protein docking results?

Working with homology models that are less accurate. Signup and view all the answers

Which aspect of protein modeling is particularly challenging with ab initio approaches?

Simulating protein folding through molecular dynamics. Signup and view all the answers

What is typically the first step in the protein docking process?

Sampling all possible interaction geometries. Signup and view all the answers

What are the primary challenges associated with protein-ligand interactions compared to protein-protein interactions?

The conformational changes during protein-ligand binding introduce additional complexity. Signup and view all the answers

What challenges arise due to the nature of molecular dynamics in protein modeling?

Molecular dynamics requires high computational resources for folding simulations. Signup and view all the answers

Which type of docking typically requires separate treatment for protein-protein and protein-ligand interactions?

Molecular docking. Signup and view all the answers

What is the role of force fields in molecular dynamics?

To compute the potential energy of molecular systems based on various interactions Signup and view all the answers

Which of the following best describes ab initio structure prediction in protein modeling?

Deriving protein structures from first principles without experimental data Signup and view all the answers

What limitation affects the practical use of simulating protein folding over real-time?

Excessive time scales required for protein folding simulations Signup and view all the answers

Which technique is considered a valuable approach to predict protein interactions by using known structures?

Homology modeling Signup and view all the answers

Which statement about the computational costs of simulating protein behavior is true?

The complexity of protein simulations often necessitates the use of heuristics to manage costs. Signup and view all the answers

Study Notes

Computational Structural Biology

Computational methods provide powerful insights into protein function.
Structural experiments are resource-intensive and limited by expertise and sample size.
Obtaining protein structures from first principles is challenging.
Simulating protein folding is impractical due to the vast time scales.
Homology modeling and docking are more tractable approaches.
Homology modeling uses known structures as templates.
The realization that homolog sequences offer important insights into interactions has enabled significant progress.
The forces governing protein structure, dynamics, and energetics are well understood.
Protein behavior can be simulated using computer programs.
Computational costs limit simulations due to the complexity of proteins.
Heuristics are used to overcome computational limitations.
Force fields facilitate the calculation of potential energy in molecular systems.
Force fields account for various interactions like bond lengths, angles, torsions, van der Waals, and electrostatics.
Newtonian molecular mechanics uses force fields to calculate the net force on each atom.
The acceleration of each atom is determined using Newton's second law.
Calculating the position of atoms after a small time step allows for system evolution.
The method iterates to produce a molecular simulation that approximates the protein's behavior.
Molecular dynamics provides a more realistic simulation of protein behavior at a given temperature.
Molecular dynamics uses random velocities, following Boltzmann distribution, for the protein atoms.
Protein simulations require simulating the surrounding environment including water, ions and potentially lipids.
The box model system is used during simulations due to limited atom handling capacity.
Molecular dynamics gives a window into protein motion and protein interactions.
Force fields use several classical approximations of the quantum reality.
Optimizing force fields for accurate representation is not trivial.
Accurately modeling water molecules is challenging for computational models.
Electrostatic interactions decrease with distance but are theoretically infinite.
Protein size and interactions significantly impact computational resources.
Covalent bond formation or breaking requires quantum simulations.

Force Fields

A force field is a set of equations and parameters, allowing the calculation of potential energy within a molecular system.
Force fields calculate the overall energy of a system by considering bond lengths, angles, torsions, van der Waals interactions, and electrostatics.
Force fields combine the influence of all interaction types on a given atom.

Newtonian Molecular Mechanics

Using a force field, the net force acting on each atom in a simulated molecular system can be calculated.
Newton's Second Law is used to calculate the acceleration of each atom.
Calculating atom positions with velocity and Newton's Laws yields the next system state.
This iteration process produces a simulation of the system's dynamics over time.
A "movie" of the molecular motion can be visualized by iteratively calculating and visualizing the states.

Molecular Dynamics

Molecular dynamics aims for realistic simulations of protein behavior at a specific temperature.
Protein atoms are assigned initial random velocities based on the Boltzmann distribution.
The simulation evolves by calculating the forces on atoms then updating the system's velocity and positions repeatedly.
The method provides an important view of protein motions.

Molecular Dynamics - Setting Up The System

Protein simulations always require the environment to be simulated as well.
Simulations include water molecules, ions and possibly lipids (membrane proteins).
Water within a small volume will boil off.
The system's setup uses a simulation box filled with repeating copies of the medium (water).
Atoms interact with periodic copies, such that atoms exiting at one end reappear on the opposite end.
This model acts as a large universe with repetitive unit cells.

Molecular Dynamics - Gives a Window Into Protein Motion

Molecular dynamics provides insights into phenomena like channel gating.
MD simulations use long time scales to observe, for example, gating in a voltage-gated channel.
Side chains, solvent, and membranes are all simulated, but may not be always explicitly displayed.
Domains undergo unfolding and reorganization. This is typical of processes over various time-scales.

Force Fields Require Trade-offs

Force fields are approximate classical representations of quantum reality.
Optimizing force fields to behave well is challenging and non-trivial.
Accurately modelling water molecules is a major challenge

Computational Resources Limit Simulations

Proteins have many atoms, all interacting with each other.
Covalent bonds' formation/breaking processes require quantum simulations.
Interactions with water are significant and computationally expensive.
Accurate simulations need extremely short time scales and enormous computational resources.
Computations are often simplified to attain practicality; such approximations can reduce accuracy.

Uses of Molecular Dynamics

MD simulations allow observation of protein behavior over time.
They provide insight into natural ranges of motion (e.g. domain closure, protein flexibility).
Models like substrate complexes can be assessed for stability.
Approximate binding energies can be derived from simulations.
Studying enzyme reactions is more complex with current computational tools.

Molecular Dynamics Can Be Extended to Chemistry

Typical MD simulations don't allow for bond-breaking, limiting their use in enzyme modeling.
To understand enzyme reactions, a combination of classical and quantum simulations (hybrid models) is often necessary.
Only key atoms are treated quantum mechanically while others use standard MD force fields.
Hybrid models can have higher computational costs.

Protein Folding by Molecular Dynamics Simulation

MD can successfully simulate the folding of some smaller proteins, which fold rapidly.
The simulations provide valuable insight into the folding process.
These simulations are computationally expensive.

Predicting Protein Structures and Complexes

Docking, homology modeling and other methods can predict protein structures.

Homology Modeling

Related proteins have similar overall structures but differ in details.
Homology modeling uses known structures as templates.
The process involves essentially replacing the sequence of the known structure with the target's sequence.

Homology Modeling Workflow

Sequences of interest are aligned to a known structure.
Structural alignments are used to determine similar parts within sequences.
Amino acids are replaced by the aligned sequence, then side chains are adjusted.
Loops are built or modified, and energy from the known structure is minimized for the whole structure.
The predicted structure is scored. Validation to known structural information (like Ramachandran analysis) is done.

Homology Modeling Limitations

Homology modeling relies on the accuracy of template alignment and coverage.
With sequence identities less than 50% and/or poor sequence coverage, accuracy suffers.
Predicted structures generally have similar accuracy as the starting template.
Accurately modeling loops and structurally divergent segments is challenging

Docking and Ligand Binding

Given two structures, predicting the complex of the two structures is in principle ‘relatively simple’.
Protein interactions dependent on 6 variables: Orientation and position of domains.
Conformational changes in proteins/ligands during binding increase complexity in modeling.
Protein-ligand and protein-protein interactions pose differing modeling challenges.

Protein Docking

Docking is a method that takes two protein structures and tries to predict their complex.
Methods use structures from various sources, like experiments or homology models.
Accurate docking is usually improved when proteins reorganize the least compared to the starting structures

Protein-protein Docking

Docking tries to predict the complex of two proteins or similar molecules.
Docking requires sampling various interaction geometries.
Optimizing interaction interfaces to find likely interaction conformations is necessary.
Modeling side chain flexibility during simulations is crucial.
Backbones may shift during protein interaction; modeling these significant changes pose challenges.

Ab Initio Structure Prediction

The ultimate goal of protein modeling is predicting structures from first principles, without prior structure knowledge.
Ab initio modelling was deemed a hard problem in science.
The difficulty arises because the physics of proteins don't immediately lead to a correct structure.
MD calculations are computationally prohibitive for simulating folding.
The key is extracting useful information from sequence data for better structure prediction.

Constraints

"Constraints" represent key features predicted to be true of the model.
Experimental constraints (like disulfide bonds, residue proximity or surface exposure) can be included in modeling.
Bioinformatic analysis (secondary structure predictions) can provide constraints.
Constraints are included in modeling to guide the search for the correct structure.
For accurate ab initio structure prediction, a reliable system to generate constraints from raw sequences data alone is required.

Amino Acid Covariance as a Source of Structural Constraints

Two amino acids close in the core of a protein structure are often correlated.
Changes in one part lead to changes in correlated residues.
Finding correlated residue pairs provides insight into important interactions and structural constraints.
Analyzing sequence data from many protein structures can reveal these correlations.
Intermolecular interactions (between molecules) can introduce additional correlations.

Covariance Analysis

Statistical methods applied to structural sequence data can allow accurate prediction of interactions between residues.
Large numbers of sequences are needed for more robust prediction of interactions.
Analysis reveals co-varying residues—residues which strongly correlate in different protein sequences.
Using this information as constraints guides structure prediction.

Co-varying Residues Constrain Structure Predictions

Co-varying residues are usually close in space.
Covariance can be used predictably as proximity constraints for modeling interactions and structural prediction.
Models incorporating such inferences are often more accurately oriented.
For homo-oligomers, contacts between protomers can result in unsatisfied covariance constraints (e.g. the program cannot find the correct distances in the structure).
Such constraints can be used to guide docking of oligomers.
Modeling hetero complexes requires considering both sequences.

Examples of Predicted Structures

Combining covariance analysis and Rosetta, highly accurate structures for membrane proteins and other complex systems can be predicted.
Successful predictions do not guarantee perfect accuracy.

Ab Initio Predictions by Deep Learning

Subtle patterns in protein sequences are predictive for structure.
Machine learning methods are effective at capturing these patterns.
AlphaFold 2 was a early pioneering method to solve the general case of structure prediction.
AlphaFold 3 extends capabilities, including improvements in its speed and robustness.
Subsequent tools using similar approaches continued to emerge and improve in speed and accuracy.

Neural Networks

Neural networks mimic aspects of neuron organization.
Neural networks excel at identifying patterns in data without additional human guidance.
When trained with sample data, they can discover important patterns.
After training, neural networks can extrapolate learned patterns to previously unseen data.
Deep learning networks (neural networks with > 3 hidden layers) underpin most artificial intelligence applications.

Neural Networks Process (Details)

Neural networks process inputs into outputs by passing inputs through layers.
In each layer, each node calculates the weighted sum of its inputs.
When the weighted sum is beyond some threshold, the node passes output 1 to the next layer; otherwise it passes 0.
The network is tuned via iteratively adjusting weights. Weight adjustments are based on a ‘back-propagation’ method.

Large Language Models

Large language models are neural networks trained on vast text datasets.
They are trained to predict masked words in new text.
Such models can generate new, coherent text like that produced by ChatGPT.
LLMs trained on amino acid sequences as ‘text’ can predict amino acid identity.
This prediction relies on links in the primary sequence.
The method relies on predictive relationships between residues distant in the primary protein sequence.
LLMs are useful for structural prediction in situations where homologous sequences or structures are unavailable.

LLM - ESMfold

ESMfold is a pure LLM tool for structure prediction.
It is based on Meta's models.
It's capable of accurate speed for de novo structure prediction from sequence, without the need for homologous sequences.

AlphaFold 3

AlphaFold uses known experimental structures to train neural networks.
The structural prediction problem is handled via multiple modules to improve efficiency, and is iterated.
Protein structure prediction can be broken down into various subproblems that are handled separately to increase performance.

AlphaFold 3 Algorithm

AlphaFold takes the input sequence and finds homologous sequences.
It searches for template structures to build the model (but these templates are often not strictly required).
It performs sequence alignments.
It then reasons at the residue-residue interaction matrix level.
The diffusion module generates 3D models and improves predictions through iterative calculations. The pairformer and diffusion module work iteratively.

AlphaFold 3 Algorithm - First Steps

The input information includes protein/nucleic acid sequences, ligands, (provided in SMILES representation)
Homolog sequences are used, and pairwise sequence alignments are constructed.
Structural templates from the PDB database are searched and considered, as well as the number of copies.
Multiple conformations are generated for the proteins, or for small ligands.

AlphaFold 3 – Pairwise Distances

The pairformer predicts inter-residue distances.
For residue pairs, a distance probability distribution is updated.
Information from existing homologous structures is used if available.
Sequence data and inferred relationships (covariance-like) can influence distance distributions.
The method includes patterns inherent in the sequence itself (e.g., LLM-like approaches).

Reasoning About Distances - Triangle Rule

The triangle inequality is used to predict residue distances reliably.
The algorithm ensures internal consistency in the distance distributions from residue-residue interactions.

AlphaFold: Pairformer

A pairformer module of AlphaFold predicts and updates a probability distribution for distance between pairs of residues.
Sequences provide the main data source for these probabilistic predictions (co-variance).
Template structures are employed when available.
Relationships, such as triangle inequalities, influence distance predictions.

Diffusion Models

Diffusion models in AlphaFold are neural networks designed to identify specific categories.
The method works through training by repeatedly adding noise to true structures. The algorithm corrects the structure through iterative processing.
Algorithms learn methods to generate physically plausible structures from non-structure input information.
This method does not explicitly model intermolecular forces. Instead, patterns in known structures are recognized and used for correct predictions.

AlphaFold's Diffusion Module

The diffusion module acts as a check to refine predictions from the pairformer.
Information on residue distances is used from sequence alignments (pairformer).
The diffusion module is used to improve predicted structures.
Results are used to further refine predictions further through iterative calculations by the pairformer and the diffusion module.

AlphaFold 3 Predicts Structures

AlphaFold 3 predicts various protein structures, including single proteins, complexes, DNA, RNA and small molecules.
It predicts protein structures with high reliability, often achieving accurate predictions for most residues (95% within 1 Å).
Sequences and structures of similar proteins aid predictions, and when available, such 'templates' improve structure prediction accuracy.
AlphaFold can predict the binding sites of proteins and other relevant molecular data/structure to known and unknown complexes and ligands.

AlphaFold 3 Predicts Arbitrary Complexes

AlphaFold 3 accurately predicts a large set of arbitrary protein, DNA/RNA, and small molecule complexes.
This includes structures predicted for several types of complexes, such as protein-drug complexes, protein-nucleic acid complexes, and complexes including post-translational modifications and antibodies.

Per Residue Accuracy (pLDDT Scores)

pLDDT scores are reported for each residue, and reliably predict Ca local distance difference test accuracy for known structures.
High pLDDT values (>90) suggest high accuracy. The predicted structure includes an accurate backbone. A similar score accuracy is for X₁ rotatemer prediction.
pLDDT values> 70 suggest a generally accurate backbone structure prediction.
Lower values indicate that the prediction is less reliable in that regions.

Extended Low pLDDT Scores Predict Disorder

AlphaFold predicts atomic locations for every residue.
Extended regions (pLDDT<50) generally correspond to disorder.
Prediction of disordered regions is accurate compared with previous approaches.
These regions typically do not pack with secondary structure, though they can have preferred secondary structure.

Pre-computed AF2 Structures in UniProt

AlphaFold 2 has generated a vast database of predicted structures for almost all UniProt sequences.
These pre-calculated structures cover a wide range of protein sizes (16 to 2700 amino acids).
Oligomeric structures are also efficiently predicted, in terms of the protomer structures only.
Predictions are linked to UniProt database entries, available online for quick retrieval.

Free to Use Alphafold 3 Website (AlphaFold Server)

AlphaFold Server is a website with an easy to use interface that allows users to upload protein sequences and predict structures.
The server handles up to 5000 amino acid residues or a few ligands.
Results are returned in a reasonable time.

Example: Ribf Transferase

A detailed example provides an illustration of a structure's limitations and insights from using AlphaFold 3.

Example: Using AF2 to Find a Key Fertilization Protein

Researchers used AlphaFold 2 to analyze various protein interactions.
Specifically, for sperm fertilization research, AlphaFold 2 improved the research process significantly, by helping to identify an essential new partner protein (TMEM81),
These approaches showcase the rapid progress in identifying key proteins necessary for important biological processes.

Predicted Structure Limitations

The predicted structures are typically very accurate, yet they differ in details.
Automatic oligomer predictions are not usually included and need to be requested explicitly from the tools.
Structures are generally predicted in a state consistent with most probable interactions.
Nucleic acid and small molecule predictions can have lower accuracy than purely protein predictions.
Tools' current versions may not handle all possible ligand types.

AlphaFold Makes Many Older Approaches to Gaining Structural Insights Near Obsolete

AlphaFold's effectiveness outperforms other structural prediction and modeling methods (homology modeling, docking, ab initio prediction).
AlphaFold accuracy is often better for secondary structure prediction, membrane topology, and disordered regions.
These methods are valuable in sequence-based prediction, especially for regions like membrane helices, protein disorder, or domain boundaries.

Deep Learning Looks Poised to Revolutionize (Structural) Biology

Predicting protein structure is a significant advance toward understanding function.
Comparing protein folds reveals evolutionary history, especially in diverse and quickly evolving organisms like viruses.
Identifying protein interactions lets us build models of multi-protein complexes and map out cellular pathways.
Predictions of Protein-ligand complex structures can help find important substrates or allosteric modifiers for useful protein and enzyme functions.
The latest neural networks (e.g. AlphaFold 3) used in this field are capable of drug discovery.

De Novo Protein Design

Design of proteins that do not exist in nature is possible due to increased understanding of protein structural rules.
Protein design through engineering new genes and characterising their properties allows investigation of unique protein behavior and/or function.
Deep learning is increasingly playing an essential role in this field, alongside other methods like empirical approaches.

Rosettafold Diffusion

RFdiffusion is an algorithm for de novo protein structure design.
It creates structures by de-noising randomly positioned atoms.
Side chains have to be optimized to complete the molecule.
AlphaFold then checks if the generated model is biologically plausible.

Additional Criteria Can Be Built into the Design

Design-specific constraints, like binding to a target protein, can be incorporated to improve predictions in focused applications.
Designing targeted proteins, including oligomers and proteins for specific binding events are easier with AlphaFold or similar models.

Proteins Can Be Designed to Bind a Specific Target

Proteins are modeled using RFdiffusion to design proteins that bind existing proteins, either interacting directly/indirectly.
The protein design is guided by the structure and properties of the pre-existing target protein.

###ProteinPMNN

ProteinPMNN is a program for training models of protein structure, that handles side chain placement in a separately trained model.
It is a neural network trained on the PDB structures to add in side chain information.
It works by using distances between the backbone atoms to construct a model of the side chains.
It also recovers information from distances between residues, improving the overall structure and sequence prediction accuracy.

Additional Information

Accuracy of AlphaFold varies depending on the characteristics of the protein sequence (e.g., disorder versus highly structured sequences; proteins with high versus low sequence identity with known proteins).

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Description

This quiz explores the advancements and limitations of AlphaFold 2 in predicting protein structures. It covers comparison with previous methods, unexpected outcomes in protein interaction studies, and improvements brought by AlphaFold 3. Test your knowledge on how these tools impact de novo protein design and structural insights.