Computational Biology

(BIOSC 1540)

Oct 10, 2024

Lecture 12:
Protein structure prediction

Announcements

  • No class on Tuesday (10/15)
  • No office hours (mine or UTA) next week - will resume on 10/22
  • Will have Programming+ recitations
  • A05 will be posted tomorrow
  • David Baker, John Jumper, and Demis Hassabis won the Nobel Prize in Chemistry for "computational protein design" and "protein structure prediction"

After today, you should be able to

Why are we learning about protein structure prediction?

Why predict protein structure?

Protein structure dictates interactions, signaling, and biochemical roles

Experimental methods (X-ray, Cryo-EM) provide high-resolution structures but are resource-intensive and time-consuming

Structural insights can accelerate ... everything?

  • Drug Discovery: Designing small-molecule inhibitors or antibodies that target specific protein conformations.
  • Biotechnology: Engineering proteins for industrial or therapeutic applications.
  • Disease Research: Mutations causing structural defects linked to diseases like Alzheimer’s and cystic fibrosis.

Prediction is critical for the future of biology

Advances in predictive accuracy are opening new frontiers in biology

Structure prediction complements genomics and transcriptomics to create a holistic understanding of biological function

Integrating predictive models with experimental data is the way forward

After today, you should be able to

Identify what makes structure prediction challenging

What makes structure prediction hard: Conformational space

Proteins can adopt a large number of possible conformations

Levinthal’s Paradox: A protein can’t sample all conformations in a biologically reasonable time, yet it folds quickly

Example: A protein with 100 amino acids, each capable of adopting about 3 torsion angles, results in ~         possible conformations3100

3^{100}

What makes structure prediction hard: Complex energy landscape

​Energy calculations are computationally intensive and depend on accurate force fields

Proteins fold to the lowest free-energy state, but this landscape is highly rugged

A potential energy surface (PES) is a represents the energy of a system as a function of the positions of its atoms

Understand how the system's energy changes upon reactions or movements

What makes structure prediction hard: Flexibility and dynamics

Proteins are not static; they adopt multiple conformations (flexibility) based on their environment or interactions with other molecules

Some proteins or regions do not adopt a fixed 3D structure but remain disordered or flexible under physiological conditions

What makes structure prediction hard: Environmental effects

Proteins fold differently in different environments

Predictions need to capture interactions with solvent molecules, ions, and cofactors

7MHX

Example: Predicting transmembrane protein structures, where the lipid bilayer plays a key role in folding, is particularly complex.

AlphaFold 3

pH-gated K+ channel

What makes structure prediction hard: Post-translational modifications

PTMs such as phosphorylation, glycosylation, and methylation can alter protein folding and function

Example: eIF4E is a eukaryotic translation initiation factor involved in directing ribosomes to the cap structure of mRNAs

Ser209 is phosphorylated by MNK1

AlphaFold 3 accurately predicts these changes when they are already known

What makes structure prediction hard: Methods are data driven

Example: AlphaFold has made strides, but predicting de novo structures remains challenging, especially for proteins with no templates

Our predictions rely on similarity to known structures, but novel sequences or folds (for which no homologous structures exist) are difficult to predict accurately

After today, you should be able to

Explain homology modeling

Homology modeling predicts protein structures based on evolutionary relationships

Homology modeling is the most accurate when sequence identity to other proteins is high (>30%)

Common tools for homology modeling include MODELLER, SWISS-MODEL, and Phyre2

The main principle is that proteins with similar sequences tend to fold into similar structures

Hidden Markov Models (HMMs) Capture Evolutionary Patterns in Proteins

HMMs are statistical models representing sequences using probabilities for matches, insertions, and deletions

Essentially more robust alignments

A Markov model predicts outcomes based on transitional probabilities

Suppose I collect weather data in Pittsburgh for the past 30 days: Sunny, Cloudy, or Rain

I want to figure out how to predict tomorrow's weather based on today's

Today's weather

Tomorrow's weather

Transition probability

Example: If today is cloudy, there is a 57% chance it will be Sunny tomorrow

We can represent these states and probabilities as a (cursed?) graph

Each edge represents the probability of transitioning from one state to the next

Hidden Markov models also include additional information in "hidden states"

Suppose my friend lives in a remote location where it is either Rainy or Sunny

I cannot look up the weather but I have last year's weathers reports

  • Walking
  • Shopping
  • Cleaning

We know how weather patterns transitions, but we don't have this information from our friend

Obervables

Hidden states

Note: If we had previous observable data, we could fit/learn transition probabilities of hidden states

My friend can only tell me

HMMs Model Protein Sequences as a Series of Probabilistic States

Hidden states represent the underlying biological events that are not directly observable

Observables are the actual amino acids (residues) in the protein sequence that we can observe

  • Match states: conserved positions in the sequence
  • Insertion states: positions where extra residues are added
  • Deletion states: positions where residues are missing

HMMER Uses HMMs to Search Protein Databases for Homology

HMMER is a tool that uses HMMs to search databases for sequences that match a given profile HMM

It is used to find homologous sequences, identifying evolutionary relationships across protein families

SWISS-MODEL

swissmodel.expasy.org

MTLSILVAHDLQRVIGFENQLPWHLPNDLKHVKKLSTGHTLVMGRKTFESIGKPLPNRRNVVLTSDTSFNVEGVDVIHSIEDIYQLPGHVFIFGGQTLFEEMIDKVDDMYITVIEGKFRGDTFFPPYTFEDWEVASSVEGKLDEKNTIPHTFLHLIRKK

DHFR (UniProt)

SWISS-MODEL

SWISS-MODEL

SWISS-MODEL

What happens with a novel protein?

MGKKEVILLFLAVIFVALNTLVVAVYFRETADEQVVYGKNNINQKLIQLKDGTYGFEPALPHVGTFKVLDSNRVPQIAQEIIRNKVKRYLQEAVRIEGTYPIVDGLVNAKYTVANPNNLHGYEGFLFKDNVPLTYPQEFILSNLDGKVRSLQNYDYDLDVLFGEKEEVKSEILRGLYYNTYTRAFSPYKL

Novel protein (ChatGPT)

Novel proteins are too challenging

After today, you should be able to

Know when to use threading instead of homology modeling

Why Use Threading?

In cases where sequence similarity to known structures is low (< 30%), homology modeling becomes unreliable

Phyre2, RaptorX, MUSTER, and I-TASSER are commonly used for threading and takes much longer than homology modeling

Threading matches sequences to known structural folds based on structural rather than sequence similarity

Identifying the Right Fold

After today, you should be able to

Interpret a contact map for protein structures

Contact Maps Visualize Residue Interactions in Proteins

A contact map is a 2D representation of which residues are in close proximity

Each point on the map corresponds to two residues that are close in 3D space

mapiya.lcbio.pl

Contact Maps Represent Spatial Proximity, Not Sequence Order

Contacts are determined by spatial proximity, typically within a certain distance threshold

Residues far apart in the sequence can still be close in the 3D structure, reflected in the contact map

Residues on the diagonal are adjacent in sequence (and spatially)

After today, you should be able to

Comprehend how coevolution provides structural insights

The Rise of Machine Learning in Structural Biology

Traditional methods like homology modeling and threading rely on templates and known structures

AlphaFold (DeepMind) and RosettaFold (Baker Lab) lead the charge in this area

ML predicts 3D structures only from sequence data

What is AlphaFold?

Developed by DeepMind, AlphaFold predicts protein structures with atomic accuracy by using deep learning models trained on large structural datasets

Breakthroughs

  • AlphaFold 2 achieved near-experimental level accuracy in the 2020 CASP14 competition (Critical Assessment of protein Structure Prediction)
  • AlphaFold 3 (2024) predicts proteins, DNA, RNA, ligands, and post-translational modifications

Coevolving residues mutate in a correlated manner

Mutations in one residue often result in compensatory mutations in its interacting partner

This is observed across species through analysis of homologous protein sequences

Correlated mutations indicate functionally significant residue pairs

Arg (positive)

Asp (negative)

Lys (positive)

Glu (negative)

Trp (hydrophobic)

Val (hydrophobic)

Evolution

Evolutionary Analysis Reveals Structural Insights

Coevolution analysis helps predict which residues are close in the 3D structure

Residues showing correlated mutations are likely to be spatially close in the folded protein

This is particularly useful when no experimental structure is available

Multiple Sequence Alignments Enable Coevolution Detection

Coevolution is detected using large MSAs from homologous proteins

The more diverse the sequences in the MSA, the better the resolution of coevolving residues

Evolutionary information from MSAs guides predictions for residue-residue contacts

evcouplings.org

Coevolution example: DHFR

Residues with a high Score (i.e., coevolve) are near each other in the protein's structure (i.e., small distance)

Val14 and Gly120 coevolved

Models predict these residues are spatially close

evcouplings.org

Coevolutionary signals can be noisy

Not all correlated mutations are due to direct physical interactions; some may be indirect

Noise in the data can come from random mutations or insufficient evolutionary diversity.

Large and diverse sequence data sets are needed for reliable coevolution predictions.

Machine learning leverages coevolution for high-accuracy predictions

AlphaFold and RosettaFold utilize coevolutionary data from MSAs to predict residue interactions

These models incorporate evolutionary information along with structural features, leading to highly accurate predictions

After today, you should be able to

Explain why ML models are dominate protein structure prediction

AlphaFold pipeline, simplified

Given the following data

MTLSILVAHDLQRVIGFENQLPWHLPNDLKHVKKLSTGHTLVMGRKTFESIGKPLPNRRNVVLTSDTSFNVEGVDVIHSIEDIYQLPGHVFIFGGQTLFEEMIDKVDDMYITVIEGKFRGDTFFPPYTFEDWEVASSVEGKLDEKNTIPHTFLHLIRKK

Input sequence

Multiple Sequence Alignment

Predict

Atomistic structure

ML models

AlphaFold 2 pipeline: Evoformer

Using MSAs and contact maps, DeepMind trained a model to predict protein structures

Contact maps are converted into dihedral angles

What is new in AlphaFold 3?

Biggest change is the use of a diffusion model

Diffusion models essentially learn to unscramble atoms into a structure

AlphaFold 3 is supercharged for any biomolecule

Proteins, DNA, RNA, ligands, PTMs, protein-proteins, etc.

AlphaFold 3

MTLSILVAHDLQRVIGFENQLPWHLPNDLKHVKKLSTGHTLVMGRKTFESIGKPLPNRRNVVLTSDTSFNVEGVDVIHSIEDIYQLPGHVFIFGGQTLFEEMIDKVDDMYITVIEGKFRGDTFFPPYTFEDWEVASSVEGKLDEKNTIPHTFLHLIRKK

DHFR (UniProt)

alphafoldserver.com

MGKKEVILLFLAVIFVALNTLVVAVYFRETADEQVVYGKNNINQKLIQLKDGTYGFEPALPHVGTFKVLDSNRVPQIAQEIIRNKVKRYLQEAVRIEGTYPIVDGLVNAKYTVANPNNLHGYEGFLFKDNVPLTYPQEFILSNLDGKVRSLQNYDYDLDVLFGEKEEVKSEILRGLYYNTYTRAFSPYKL

Novel protein (ChatGPT)

AlphaFold 3 is a breakthrough, not the final solution

Caveat: Proteins are dynamic

What about intrinsically disordered proteins?

At least 40% of proteins have disordered regions

LARP1

AlphaFold (and all other methods) struggle with disordered regions

Before the next class, you should

  • Work on A05
  • Review material

Lecture 12:
Protein structure prediction

Today

Thursday

Lecture 13:
Molecular simulation princples