ClusPro ranks first in 7th CAPRI evaluation round

CAPRI (Critical Assessment of Predicted Interactions) experiment is a community-wide effort dedicated to evaluating the current state of methods for prediction of protein complex structure.

The evaluation of results for the last three years, was recently published in Proteins.

Automated protein docking server ClusPro developed by our group was ranked first in the server category for all targets . The summary of the results is shown below. For each predictor group, the table shows the number of acceptable or better predictions, and among those the number of high quality models, indicated by three stars, as well as the number of medium quality solutions, indicated by two stars.

Server rankings
ServerTop 5
Predictions
ClusPro10/6**
HDOCK8/1***/5**
HADDOCK8/2***/2**
LZERD8/1***/4**
MDOCKPP9/1***/3**
GalaxyPPDock6/4**
Swarmdock6/1***/1
PYDOCKWEB3/1**

In addition our human group was the top performer in protein-protein docking category . The results for the 10 best-performing groups are provided below for comparison.

Human predictor rankings
GroupPredictions
Kozakov/Vajda6/1***/6**
Venclovas5/2***/3**
Seok5/1***/4**
Pierce5/2***/2**
Andreani/Guerois5/1***/3**
Zou4/1***/3**
Zacharias5/1***/2**
Kihara5/1***/2**
Gray5/1***/2**
Shen4/1***/2**
D3R Grand Challenge 4 Blind pose prediction results

D3R Grand Challenge 4 Blind pose prediction results

D3R (Drug Design Data Resource) Grand Challenge is a blinded prediction challenge for the computational chemistry community, with components addressing pose-prediction, affinity ranking, and free energy calculations. Its fourth installment, D3R GC4, was held from October to December 2018.

Our group participated in the pose prediction challenge for the macrocyclic inhibitors for Beta secretase 1 (BACE1). This protein is involved in the generation of beta-amyloid peptides and presents an important target for developing drugs for Alzheimer’s disease. In the stage 1a of the challenge, the organizers presented participants with the apo-structure of the receptor and SMILES strings describing 20 ligands.

According to the official rankings, the template-based method developed in our group scored best, out of 74 total submitted entries, in terms of the Mean RMSD. Our group achieved sub-angstrom mean and median RMSD for this challenge.

Our method relies on finding structures of distant homologs of the target protein that bind similar ligands, and using them at all stages of the protocol: initial pose generation, structure refinement, and the final scoring. More details are available in our paper, published in the Journal of Computer-Aided Molecular Design.

We refined and automated the approach used in this challenge. It is now available for free academic and non-commercial use as a user-friendly web-server, ClusPro LigTBM. This version of the protocol is described in our Journal of Molecular Biology paper.

New fast method for modeling of Protein Interactions

We present an ultra-fast approach to modeling protein interactions.

Protein-protein interactions (PPIs) are the basis of cellular functions, and when these processes are compromised diseases such as cancer emerge. For years scientists have tried with mixed success to map out PPIs to understand cellular processes. Now our group has outlined a method that could pave the way to designing new drugs that prevent problematic protein interactions that lead to disease. The findings are published in the early online edition of PNAS.

Proteins are the major building blocks of the cell. Many proteins perform their function by interacting with other proteins. In a typical cell, hundreds of thousands of different protein interactions take place. Characterizing the structure of these interactions helps elucidate how organisms function normally and during disease development.

The problem considered is given three dimensional structures of two individual proteins to predict how these protein interact with each other. We can liken the method to characterizing all the possible structures that pairs of “lego blocks” form out of a huge set of different individual starting blocks.

In the paper titled “Protein-protein docking by fast generalized Fourier transforms on 5D rotational manifolds,” we explain a new algorithm used to create ultra-fast approach to modeling protein interactions. We discovered that the method runs an order of magnitude faster than previous state-of-the-art methods and has comparable accuracy.

proteincomplexstructure
Correct structure of a protein-protein complex together with an ensemble of alternative structures sampled by the new algorithm.

The algorithm features a fast Manifold Fourier transform (FMFT) that helps to speed the calculations, enabling us to sample a large number of putative protein-protein complex conformations.

The new algorithm will soon be available to the scientific community through our publicly available protein-protein docking server called ClusPro. This resource, with more than 15000 academic users worldwide, supported by the National Science Foundation and the Binational Science Foundation, is being developed by our group in collaboration with scientists at Boston University. ClusPro was judged to be the best automated docking server in the latest rounds of the international blind protein docking competition called CAPRI (Critical Assessment of Prediction Interaction).

ClusPro server is ranked first in the latest rounds of CAPRI experiment

CAPRI (Critical Assessment of Predicted Interactions) experiment is a community-wide effort dedicated to evaluating the current state of methods for prediction of protein complex structure.

The evaluation of results for the last 7 rounds comprising a total of 20 prediction targets, was recently presented in Tel Aviv at the sixth CAPRI Evaluation Meeting. More then 6000 models submitted by 68 predictor groups and 14 automated servers were processed.

Automated protein docking server ClusPro developed by our own Applied BioComputation group in collaboration with Structural BioInformatics lab at Boston University was ranked first in the server category. The summary of the results is shown below. For each predictor group, the table shows the number of acceptable or better predictions, and among those the number of high quality models, indicated by three stars, as well as the number of medium quality solutions, indicated by two stars.

Server rankings
Server Predictions
ClusPro 9/3**
PyDockWeb 6/2**
LZerD 4/1***/1**
HADDOCK 4/2**
SwarmDock 3/2**
GalaxyPPDock 1**
PatchDock-FiberDock, DOCK/PIERR, MegaDock, GRAMM-X 1
SurFit 0

Interestingly, the server’s performance was comparable to that of the best human predictor groups, although the latter had access to all information available in the literature. The results for the 10 best-performing groups are provided below for comparison.

Human predictor rankings
Group Predictions
Guerois 10/1***/8**
Zacharias 10/3***/2**
Vajda/Kozakov, Seok 8/3***/2**
Weng 6/1***/4**
Fernandez-Recio 7/1***/3**
Vakser 6/2***/2**
Eisenstein 4/2***/2**
Zou 7/1***/2**
Bates 6/3**
Huang 5/3***