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Taking a Numeric Path. Idan Szpektor. The Input. A partial description of a molecule: The atoms The bonds The bonds lengths and angles Spatial constraints on some of the atoms of a molecule. The Questions. Is there a conformation of the molecule that answers the spatial constraints?

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the input
The Input
  • A partial description of a molecule:
    • The atoms
    • The bonds
    • The bonds lengths and angles
  • Spatial constraints on some of the atoms of a molecule.
the questions
The Questions
  • Is there a conformation of the molecule that answers the spatial constraints?
  • Provide explicit description of such conformations.
  • How many “really” different conformations are there? Which one is the best?
hard life
Hard life…

Torsion angles are (usually) unconstrained

a large number of degrees of freedom

The answers to the questions are not easy to find analytically. They are also resource consuming (running time).

a numeric path
A Numeric Path
  • Two algorithms take different approaches to limit the space of possible conformations to search in:
    • Numeric randomized algorithm
    • Semi numeric algorithm on a simpler problem

A Randomized Kinematics-Based Approach to Pharmacophore-Constrained Conformational Search and Database Screening

S. M. Lavalle, P. W. Finn, L. E. Kavraki, J. C. Latombe (2000)

the pharmacophore problem
The Pharmacophore Problem
  • The problem: to find a molecule conformation that satisfies a set of constraints out of a database of flexible molecules (e.g. for ligand docking).
  • Constraints:
    • Different atoms and their features in the molecule (types, charges etc.)
    • The bond length and angles between bonds
    • Static bond torsion angles
    • Relative locations of some atoms from an anchor atom
the molecule model cont
The Molecule Model (Cont…)
  • An atom ai carries standard information
  • A bond bi carries the following information:
    • li – the bond length
    • αi – the angle from the previous bond
    • The set of possible torsion angles θi [0, 2π)
  • All info besides the torsion angles is fixed.
  • Θis the m dimensional vector of variables that defines the conformation
the pharmacophore model
The Pharmacophore Model
  • A finite set of features corresponding to a subset of atoms
  • Constraints on the relative positions between features (the atoms), when one of the features is designated as aanch, the origin of a global xyz coordinate
the kinematic model
The Kinematic Model
  • The bond length, angles and the torsion angles Θ can be used to give the positions of all of the atoms, relative to aanch
  • We look at each atom center as a local coordinate frame. We would like to use the transformation from one coordinate frame to the another.
the kinematic model cont13
The Kinematic Model (Cont…)
  • The homogeneous transformation is:
the kinematic model cont14
The Kinematic Model (Cont…)
  • The xyz position of atom ai is given by:
the kinematic model cont15
The Kinematic Model (Cont…)

The coordinate frame of the molecule could be rotated with respect to the global coordinate frame of the pharmacophore feature positions

Another global coordinate frame transformation is needed

the kinematic model cont16
The Kinematic Model (Cont…)
  • Global rotation transformation based on Euler angles γ,φ,ψ:
the kinematic model cont17
The Kinematic Model (Cont…)
  • The complete xyz position of atom ai is given by:
the kinematic error function
The Kinematic Error Function
  • Given Θ, γ,φ and ψ, the total amount of error between the requested feature positions G and the actual feature positions g can be measured as:
the energy function
The Energy Function
  • In a sense, the energy function measures the likelihood that the molecule will achieve a conformation in nature.
  • An example for an energy function:
two questions
Two Questions
  • For a given molecule from a database and a pharmacophore:
  • Can the molecule achieve a low-energy conformation that satisfies the given pharmacophore?
  • What are the “distinct” low-energy conformations that satisfy the pharmacophore?
randomized conformation search with constraints the motivation
Randomized Conformation Search with Constraints – The Motivation
  • For the problem, the system of equations is generally under constrained, which leads to a complicated multidimensional solution set.
  • This consideration, and the need for efficiency, led to the choice of a numerical randomized technique.
the search gradient descent
The Search – Gradient Descent
  • Randomly sample the neighborhood of Pi
  • Search for a point Pi+1 such that f(Pi+1) < f(Pi)
  • If such Pi+1 is found, move to Pi+1 and repeat the search
integrating into a database search
Integrating into a Database Search
  • A small set (~several hundreds) of candidate molecules (configurations) are chosen from a database (using 2D information)
  • Kinematics-based conformational search is performed to further reduce the set of candidates
search in a set of candidates
Search in a Set of Candidates
  • Each time a sample conformation fails to match, the likelihood that the molecule will ever succeed decrease
  • On the other hand, after any number of ‘fail’ iterations, it is impossible to conclude that the molecule will never succeed
search in a set of candidates cont
Search in a Set of Candidates (cont…)
  • Perform one attempt (random sample + match search) per molecule
  • Go through all the molecules in the set and repeat
  • Stop when a requested number of matches was found or a maximum number of iterations was reached
conformation clustering
Conformation Clustering
  • In general, having alternative low-energy conformations is useful because in many cases it is not the lowest-energy conformation that results in docking.
conformation clustering cont
Conformation Clustering (cont...)
  • Use a metric m(θ1, θ2), such as RMS of the displacements of the atoms between two conformations
  • A threshold mmax is the maximum distance that still regards two conformations as identical (in the same cluster)
clustering algorithm idea
Clustering Algorithm – Idea
  • Always keep one representative for each cluster
  • The representative is the conformation with the lowest energy
clustering algorithm detailed
Clustering Algorithm – Detailed
  • for a new conformation θi:
    • if exists another conformation θk such that m(θk, θi) ≤ mmax and e(θk) ≤ e(θi), discard θi
    • otherwise:
      • add θi as a new cluster
      • remove all θk such that m(θk, θi) ≤ mmax
  • 2 different pharmacophores, for ACE and Thermolysin inhibitors
  • 6 different molecules for each pharmacophore, kept in the database in randomly picked conformations
  • The docked conformations are known
experiments cont
Experiments (Cont…)
  • Cluster distance mmax=1.5 Ǻ
  • 20 iterations for the different candidates
experiments cont35
Experiments (Cont…)
  • In general, conformations’ energies are within 2–7 kcal/mol of the energy of the known docked conformation.
  • The RMS distance of the conformations from the known docked conformations:
    • Thermolysin inhibitors – 0.50, 2.96, 0.59, 0.81, 2.40, and 2.56 Å.
    • ACE inhibitors – 1.26, 1.79, 0.94, 2.03, 1.87, and 1.98 Å.
experiments cont36
Experiments (Cont…)
  • A sufficient clustering record for a single molecule required about 5–20 min.
  • A claim: “Our previous work with randomized techniques has shown that if we continue iterating our algorithm we increase our chances of covering the conformational space of the molecule, and hence, our chances of providing exhaustive information about the constrained conformations of the molecule.”
cyclic coordinate descent a robotics algorithm for protein loop closure

Cyclic coordinate descent: A robotics algorithm for protein loop closure

A. A. Canutescu and R. L. Dunbrack Jr. (2003)

the loop closure problem
The Loop Closure Problem
  • The problem: matching a given loop to a given backbone (e.g. for Homology modeling).
  • Constraint: connecting the two protein segments on either end of the loop, termed N and C-terminal anchors.
previous solutions
Previous Solutions
  • Number of available conformation is enormous.
  • Analytical: for 6 degrees of freedom.
  • Numerical: changing all torsion angles at once to the next “best” position.
  • Numerical methods are computationally expensive and sometimes unstable.
cyclic coordinate descent ccd
Cyclic Coordinate Descent (CCD)
  • Originally developed for robotics.
  • An iterative relaxation algorithm.
  • Adjust only one degree of freedom at a time.
ccd algorithm
CCD Algorithm
  • Proceed in an iterative fashion along the chain of degrees of freedom.
  • Modify each torsion angle so that the end of the loop gets as close as possible to the desired position.
ccd simplicity
CCD Simplicity
  • One equation in one unknown for each degree of freedom.
  • The equation provides:
    • Optimum setting for the variable
    • First and second derivatives
the main equation cont47
The Main Equation (Cont...)
  • Multiplying the last two terms by:
  • Defining:
  • We get:
ccd benefits
CCD Benefits
  • Computationally Fast.
  • Analytically simple - no singularities.
  • Constraints can be placed on any degree of freedom.
  • Using derivatives, small increments in change can be done in preference to of large changes.
test 1 success percentage
Test 1 – Success Percentage
  • 2752 different loops.
  • 100 randomly different starting conformations for each loop.
  • A match (closed loop) is when distance from the terminals is less than 0.08 Ǻ.
  • Maximum 5000 iterative cycles (through all torsion angles) per search.
test 1 cont
Test 1 (cont…)
  • Two test frameworks:
    • No constraints
    • Using a Ramachandran Map
  • When using the map:
    • for a new proposed angle θnew, find (ψnew,θnew) in the map and compare to (ψold,θold)
    • Change to θnew with probability:
analysis of test 1
Analysis of Test 1
  • Loops that fail to converge arrive to a local minimum (usage of Monte Carlo method to escape local minimum).
  • CCD favors large changes in the first residues.
  • Loops that failed to converge are usually extended loops.
test 2 minimum rms to original
Test 2 – Minimum RMS to Original
  • CCD is meant to be a component of a matching algorithm that should also include loop generation and energy function.
  • How well CCD alone converge to the original loop conformation.
  • 30 original loops. For each loop, 5000 random conformations were generated and closed.
test 3 structure convergence
Test 3 – Structure Convergence
  • Using Ramachandran map and starting with the same loop conformation.
  • Each time using different random generated numbers.
  • 500 tests. do all final conformations are the same?
  • Compared to 500 closures of the same loop with different starting conformations.
comparison to random tweak
Comparison to Random TWEAK
  • 5000 trails (10 loops, 500 initial configurations each). Closure when RMS < 0.08 Ǻ.
  • CCD – closed 5000. ~7 min running time
  • TWEAK – closed 4841. ~40 min running time