Accurate ligand–protein docking in <scp>CASP15</scp> using the <scp>ClusPro LigTBM</scp> server

Kotelnikov, Sergei; Ashizawa, Ryota; Popov, Konstantin I.; Khan, Omeir; Ignatov, Mikhail; Li, Stan Xiaogang; Hassan, Mosavverul; Coutsias, Evangelos A.; Poda, Gennady; Padhorny, Dzmitry; Tropsha, Alexander; Vajda, Sandor; Kozakov, Dima

doi:10.1002/prot.26587

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…In the Alchemy_LIG team [ 145 ] protein structures were constructed using AlphaFold2, and ligands were docked utilizing the AutoDock Vina docking method and a machine learning model trained to detect native binding modes. The ClusPro group [ 146 ] employed AlphaFold2 for constructing monomer protein structures and created multimeric assemblies via a template-based docking algorithm, ClusPro LigTBM [ 146 ], for general ligand placement, alongside the Glide program [ 147 ], for direct docking in cases when no templates were found.…”

Section: An Overview Of Protein Structure Predictionmentioning

confidence: 99%

Recent Progress of Protein Tertiary Structure Prediction

Wuyun,

Chen,

Shen

et al. 2024

Molecules

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The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

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Section: An Overview Of Protein Structure Predictionmentioning

confidence: 99%

Recent Progress of Protein Tertiary Structure Prediction

Wuyun,

Chen,

Shen

et al. 2024

Molecules

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show abstract

“…Currently, when predicting the structures of protein–ligand complexes, researchers are employing similar strategies, template-based methods, with success in the ligand prediction category of the Critical Assessment of Structure Prediction 15 (CASP15) competition. − This method consists of two key steps. The first step is to find the template, which has a homologous sequence and a crystal structure with the co-crystal substrate.…”

Section: Introductionmentioning

confidence: 99%

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling

Wang,

Zhou,

Wang

et al. 2024

J. Chem. Inf. Model.

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Accurate protein−ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method�Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein−ligand docking problems, facilitating the development of small molecule drugs. Zalign is freely available as a web server at https://cloud.zelixir.com/zalign/home.

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“…Currently, when predicting the structures of protein-ligand complexes, researchers are employing similar strategies, template-based methods, with success in the ligand prediction category of the Critical Assessment of Structure Prediction 15 (CASP15) competition [30,31,32]. This method consists of two key steps.…”

Section: Introductionmentioning

confidence: 99%

Fully flexible molecular alignment enables accurate ligand structure modelling

Wang,

Zhou,

Wang

et al. 2023

Preprint

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Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction, and also provide the structural basis for in depth lead optimization in small molecule drug design. Ligand-based modeling approaches primarily extract valuable information from the structural features of small molecules to assess their potential as drug candidates against specific targets. However, it is challenging to provide reasonable predictions of binding poses for different molecules, due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlating to higher success rates. However, molecular similarity isn’t consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method—Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved significantly higher success rates than other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs.

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Accurate ligand–protein docking in CASP15 using the ClusPro LigTBM server

Cited by 3 publications

References 29 publications

Recent Progress of Protein Tertiary Structure Prediction

Recent Progress of Protein Tertiary Structure Prediction

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling

Fully flexible molecular alignment enables accurate ligand structure modelling

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