Ercheng Wang

Zhejiang Lab

The use of quantum mechanical potentials in protein-ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation of such potentials on real-world challenges is necessary. To this end, we have collated an extensive set of over a thousand galectin inhibitors with known affinities and docke...

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-lig...

Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop,...

Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the...

The first study to evaluate the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding poses for RNA-ligand systems.

Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by the rapid emergence of drug resistance. However, the lack of the detailed interaction between AR and its antagonists poses a major obstacle to the design of novel AR antagonists. Here, funnel metady...

Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development of algorithms for this task, existing methods tend to rely on noisy predicted residues located in the local neighborhood when generating sequences. To address this limita...

Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the...

Compound–protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model fo...

Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactio...

Ligand docking is one of the core technologies in structure-based virtual screening for drug discovery. However, conventional docking tools and existing deep learning tools may suffer from limited performance in terms of speed, pose quality and binding affinity accuracy. Here we propose KarmaDock, a deep learning approach for ligand docking that in...

Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield predictio...

Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development of algorithms for this task, existing methods tend to leverage limited and noisy residue environment when generating sequences. In this paper, we develop an iterative seq...

DNA methyltransferase 3A (DNMT3A) has been regarded as a potential epigenetic target for the development of cancer therapeutics. A number of DNMT3A inhibitors have been reported, but most of them do not have good potency, high selectivity and/or low cytotoxicity. It has been suggested that a non-conserved region around the target recognition domain...

Binding of different ligands to glucocorticoid receptor (GR) may induce different conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within the GR ligand binding domain, the folding pathway of helix 12 (H12) induced by the binding of the agonist dexamethasone (DEX), antagonis...

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein–ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance...

Cotranslational folding is one of the most important features of protein folding in vivo. Although many studies have shown that the folding pathways of cotranslational folding are different from free folding in vitro, the detailed mechanism of folding dynamics is lacking. Here we combine all-atom molecular simulations with an ideal ribosome tunnel...

A large number of protein-protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein-peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, a...

In structure-based drug design (SBDD), the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) approach has been widely used in ranking the binding affinity of small molecule ligands. However, accurate estimation of protein-ligand binding affinity still remains a challenge due to the intrinsic limitation of the standard generalized Born (GB...

Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa), and therefore it has been regarded as the most important therapeutic target for both hormone-sensitive prostate cancer (HSPC) and advanced PCa. In this study, a novel hit (C18) with IC50 of 2.4 μM against AR transcriptional activity in LNCaP cell was identifie...

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the Molecular Mechanics/Poisson Bo...

The villin headpiece subdomain (HP35) is a fast-folding protein with 35 residues and its folding pathways have been extensively studied experimentally and theoretically but remain controversial. While experiments showed that HP35 might have multiple folding pathways, most theoretical studies only found one major pathway, although a few theoretical...

Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBS...

Protein-protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server,...

DNA methyltransferases (DNMTs), responsible for regulation of DNA methylation, have been regarded as promising drug targets for cancer therapy. However, high structural conservation of the catalytic domains of DNMTs poses a big challenge to design selective inhibitors towards a specific DNMT isoform. In this study, molecular dynamics (MD) simulatio...

A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth...

NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I1/2 inhibitors that can not only bind in the...

Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors have been approved by the Food and Drug Administration, along with a large number of kinase inhibitors being evaluated in clinical and preclinical trials. Among all, allosteric inhibitors, such as type II kinase inhibito...

Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due...

Translation speed can affect the cotranslational folding of nascent peptide. Experimental observations have indicated that slowing down translation rates of codons can increase the probability of protein cotranslational folding. Recently, a kinetic modeling indicates that fast translation can also increase the probability of cotranslational protein...

Fault diagnosis is very important to ensure the safe operation of hydraulic generator units (HGU). Shaft orbit identification has been highlighted as an effective method for HGU fault diagnosis in the past few years. The purpose of this paper is to propose a novel shaft orbit identification method based on comprehensive geometric characteristics an...

To investigate the role of the ribosomal exit tunnel on protein folding, we simulate the initial-stage folding behavior of the protein villin headpiece subdomain HP35 (PDB id: 1yrf) with and without prefolding in the exit tunnel by using an all-atom model and find that prefolding in the exit tunnel could effectively help the protein form native sec...