Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38‐46

Christoffer, Charles; Terashi, Genki; Shin, Woong‐Hee; Aderinwale, Tunde; Subramaniya, Sai Raghavendra Maddhuri Venkata; Peterson, Lenna X.; Verburgt, Jacob

doi:10.1002/prot.25850

Cited by 14 publications

(27 citation statements)

References 70 publications

(71 reference statements)

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“…In the recent CAPRI rounds 38–45 (2016–2018) there were seven targets which were modelled by using LZerD ( 17 ). There were 2 targets (T123 and T124) where individual input subunits were not well-modeled, with backbone RMSDs exceeding 10 Å.…”

Section: Discussionmentioning

confidence: 99%

“…Ranksum uses the sum of model ranks by the knowledge-based scoring functions DFIRE ( 14 ), GOAP ( 15 ), and ITScorePro ( 16 ) to select models by a consensus of these scoring functions. Ranksum has been demonstrated effective in several rounds of the CAPRI blind community-wide experiment, where it was used to achieve top performance in scoring models ( 11 , 17 ). In the webserver output we provide the model ranking by ranksum as well as ranks by the individual scores as reference.…”

Section: Methodsmentioning

confidence: 99%

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LZerD webserver for pairwise and multiple protein–protein docking

Christoffer

Chen

Bharadwaj

et al. 2021

Nucleic Acids Research

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Protein complexes are involved in many important processes in living cells. To understand the mechanisms of these processes, it is necessary to solve the 3D structures of the protein complexes. When protein complex structures have not yet been determined by experiment, protein-protein docking tools can be used to computationally model the structures of these complexes. Here, we present a webserver which provides access to LZerD and Multi-LZerD protein docking tools. The protocol provided by the server have performed consistently among the top in the CAPRI blind evaluation. LZerD docks pairs of structures, while Multi-LZerD can dock three or more structures simultaneously. LZerD uses a soft protein surface representation with 3D Zernike descriptors and explores the binding pose space using geometric hashing. Multi-LZerD performs multi-chain docking by combining pairwise solutions by LZerD. Both methods output full-atom docked models of the input proteins. Users can also input distance constraints between interacting or non-interacting residues as well as residues that locate at the interface or far from the interface. The webserver is equipped with a user-friendly panel that visualizes the distribution and structures of binding poses of top scoring models. The LZerD webserver is available at https://lzerd.kiharalab.org.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

LZerD webserver for pairwise and multiple protein–protein docking

Christoffer

Chen

Bharadwaj

et al. 2021

Nucleic Acids Research

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“…Rigid-body search for the binding site most often rely on the Fast Fourier Transform [6][7][8]. Other successful approaches include 3D Zernike descriptor-based docking [9,10] or geometric hashing [11].…”

Section: Introductionmentioning

confidence: 99%

Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations

Kurciński

Kmiecik

Zalewski

et al. 2021

Preprint

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Structure prediction of protein-protein complexes is one of the most critical challenges in computational structural biology. It is often difficult to predict the complex structure, even for relatively rigid proteins. Modeling significant structural flexibility in protein docking remains an unsolved problem. This work demonstrates a protein-protein docking protocol with enhanced sampling that accounts for large-scale backbone flexibility. The docking protocol starts from unbound x-ray structures and is not using any binding site information. In docking, one protein partner undergoes multiple fold rearrangements, rotations, and translations during docking simulations, while the other protein exhibits small backbone fluctuations. Including significant backbone flexibility during the search for the binding site has been made possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics. In our simulations, we obtained acceptable quality models for the set of 12 protein-protein complexes, while for selected cases, models were close to high accuracy.

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“…additions of the blocks of potentials) and weight parameters that balance the energy terms. All decoy models were ranked by ranksum 41 , a sum of the ranks of three scoring functions, GOAP 42 , DFire 43 , and ITScore 44 . The best scoring model was selected as the predicted structure.…”

Section: Methodsmentioning

confidence: 99%

Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction

Jain

Terashi

Kagaya

et al. 2021

Sci Rep

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Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.

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Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38‐46

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 14 publications

References 70 publications

LZerD webserver for pairwise and multiple protein–protein docking

LZerD webserver for pairwise and multiple protein–protein docking

Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations

Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction

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