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Wang X, Xu K, Zeng X, Linghu K, Zhao B, Yu S, Wang K, Yu S, Zhao X, Zeng W, Wang K, Zhou J. Machine learning-assisted substrate binding pocket engineering based on structural information. Brief Bioinform 2024; 25:bbae381. [PMID: 39101501 PMCID: PMC11299021 DOI: 10.1093/bib/bbae381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/25/2024] [Accepted: 07/23/2024] [Indexed: 08/06/2024] Open
Abstract
Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4 Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme-substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering.
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Affiliation(s)
- Xinglong Wang
- School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Kangjie Xu
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Xuan Zeng
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Kai Linghu
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Beichen Zhao
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Shangyang Yu
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Kun Wang
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Shuyao Yu
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Xinyi Zhao
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Weizhu Zeng
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Kai Wang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
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Zhou H, Skolnick J. Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening. J Phys Chem B 2024; 128:5363-5370. [PMID: 38783525 PMCID: PMC11163432 DOI: 10.1021/acs.jpcb.4c01875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems
Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jeffrey Skolnick
- Center for the Study of Systems
Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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3
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In II, Deng W. Construction and validation of a diagnostic model for rheumatoid arthritis based on mitochondrial autophagy-related genes. Heliyon 2024; 10:e24818. [PMID: 38327427 PMCID: PMC10847616 DOI: 10.1016/j.heliyon.2024.e24818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 02/09/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease associated with an increased risk of disability. Due to its slow progression, timely diagnosis and treatment during the early stages can effectively decelerate disease advancement. Consequently, there is a pressing need to investigate additional biomarkers and therapeutic targets relevant to RA diagnosis. Mitochondrial autophagy, a biological process that regulates the quantity of mitochondria, is intricately linked to the development of tumor diseases. However, the role of autophagy in RA remains unclear. To address this, transcriptome data from the GEO database were collected for RA and its controls and subjected to differential expression analysis. The differentially expressed genes obtained were then intersected with mitochondrial autophagy-related genes. Subsequently, the overlapping genes were further intersected with genes from critical modules obtained through the weighted co-expression network algorithm. Diagnostic genes were identified, and diagnostic models were constructed for the resulting genes using the random forest and LASSO algorithms. The model achieved an AUC of 0.916 in the GSE93272 dataset and 0.951 in the GSE17755 dataset. Additionally, qPCR experiments were conducted on the diagnostic genes. Finally, we explored the correlation between the abundance of immune cell infiltration and diagnostic genes, constructing a drug-gene interaction network. The diagnostic genes identified in this study can serve as a reference for early diagnosis and the discovery of therapeutic targets in RA.
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Affiliation(s)
- Iong Iok In
- Department of Rheumatology, Kiang Wu Hospital, Rua de Coelho do Amaral 60–70, Macao, SAR, China
| | - Weiming Deng
- Department of Rheumatology, Guangdong Second Provincial General Hospital, No. 466 Xingang Middle Road, Guangdong, China
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4
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Zhou H, Skolnick J. FRAGSITE2: A structure and fragment-based approach for virtual ligand screening. Protein Sci 2024; 33:e4869. [PMID: 38100293 PMCID: PMC10751727 DOI: 10.1002/pro.4869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023]
Abstract
Protein function annotation and drug discovery often involve finding small molecule binders. In the early stages of drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing. While our recent ligand homology modeling (LHM)-machine learning VLS method FRAGSITE outperformed approaches that combined traditional docking to generate protein-ligand poses and deep learning scoring functions to rank ligands, a more robust approach that could identify a more diverse set of binding ligands is needed. Here, we describe FRAGSITE2 that shows significant improvement on protein targets lacking known small molecule binders and no confident LHM identified template ligands when benchmarked on two commonly used VLS datasets: For both the DUD-E set and DEKOIS2.0 set and ligands having a Tanimoto coefficient (TC) < 0.7 to the template ligands, the 1% enrichment factor (EF1% ) of FRAGSITE2 is significantly better than those for FINDSITEcomb2.0 , an earlier LHM algorithm. For the DUD-E set, FRAGSITE2 also shows better ROC enrichment factor and AUPR (area under the precision-recall curve) than the deep learning DenseFS scoring function. Comparison with the RF-score-VS on the 76 target subset of DEKOIS2.0 and a TC < 0.99 to training DUD-E ligands, FRAGSITE2 has double the EF1% . Its boosted tree regression method provides for more robust performance than a deep learning multiple layer perceptron method. When compared with the pretrained language model for protein target features, FRAGSITE2 also shows much better performance. Thus, FRAGSITE2 is a promising approach that can discover novel hits for protein targets. FRAGSITE2's web service is freely available to academic users at http://sites.gatech.edu/cssb/FRAGSITE2.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyAtlantaGeorgiaUSA
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von Beck T, Mena Hernandez L, Zhou H, Floyd K, Suthar MS, Skolnick J, Jacob J. Atovaquone and Pibrentasvir Inhibit the SARS-CoV-2 Endoribonuclease and Restrict Infection In Vitro but Not In Vivo. Viruses 2023; 15:1841. [PMID: 37766247 PMCID: PMC10534768 DOI: 10.3390/v15091841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
The emergence of SARS-CoV-1 in 2003 followed by MERS-CoV and now SARS-CoV-2 has proven the latent threat these viruses pose to humanity. While the SARS-CoV-2 pandemic has shifted to a stage of endemicity, the threat of new coronaviruses emerging from animal reservoirs remains. To address this issue, the global community must develop small molecule drugs targeting highly conserved structures in the coronavirus proteome. Here, we characterized existing drugs for their ability to inhibit the endoribonuclease activity of the SARS-CoV-2 non-structural protein 15 (nsp15) via in silico, in vitro, and in vivo techniques. We have identified nsp15 inhibition by the drugs pibrentasvir and atovaquone which effectively inhibit SARS-CoV-2 and HCoV-OC43 at low micromolar concentrations in cell cultures. Furthermore, atovaquone, but not pibrentasvir, is observed to modulate HCoV-OC43 dsRNA and infection in a manner consistent with nsp15 inhibition. Although neither pibrentasvir nor atovaquone translate to clinical efficacy in a murine prophylaxis model of SARS-CoV-2 infection, atovaquone may serve as a basis for the design of future nsp15 inhibitors.
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Affiliation(s)
- Troy von Beck
- Emory Vaccine Center, Emory National Primate Research Center, Emory University, 954 Gatewood Road, Atlanta, GA 30329, USA; (T.v.B.); (L.M.H.); (K.F.); (M.S.S.)
| | - Luis Mena Hernandez
- Emory Vaccine Center, Emory National Primate Research Center, Emory University, 954 Gatewood Road, Atlanta, GA 30329, USA; (T.v.B.); (L.M.H.); (K.F.); (M.S.S.)
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA; (H.Z.); (J.S.)
| | - Katharine Floyd
- Emory Vaccine Center, Emory National Primate Research Center, Emory University, 954 Gatewood Road, Atlanta, GA 30329, USA; (T.v.B.); (L.M.H.); (K.F.); (M.S.S.)
| | - Mehul S. Suthar
- Emory Vaccine Center, Emory National Primate Research Center, Emory University, 954 Gatewood Road, Atlanta, GA 30329, USA; (T.v.B.); (L.M.H.); (K.F.); (M.S.S.)
- Department of Pediatrics, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA; (H.Z.); (J.S.)
| | - Joshy Jacob
- Emory Vaccine Center, Emory National Primate Research Center, Emory University, 954 Gatewood Road, Atlanta, GA 30329, USA; (T.v.B.); (L.M.H.); (K.F.); (M.S.S.)
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6
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Aftab F, Rodriguez-Fuguet A, Silva L, Kobayashi IS, Sun J, Politi K, Levantini E, Zhang W, Kobayashi SS, Zhang WC. An intrinsic purine metabolite AICAR blocks lung tumour growth by targeting oncoprotein mucin 1. Br J Cancer 2023; 128:1647-1664. [PMID: 36810913 PMCID: PMC10133251 DOI: 10.1038/s41416-023-02196-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Lung cancer cells overexpress mucin 1 (MUC1) and active subunit MUC1-CT. Although a peptide blocks MUC1 signalling, metabolites targeting MUC1 are not well studied. AICAR is a purine biosynthesis intermediate. METHODS Cell viability and apoptosis were measured in AICAR-treated EGFR-mutant and wild-type lung cells. AICAR-binding proteins were evaluated by in silico and thermal stability assays. Protein-protein interactions were visualised by dual-immunofluorescence staining and proximity ligation assay. AICAR-induced whole transcriptomic profile was determined by RNA sequencing. EGFR-TL transgenic mice-derived lung tissues were analysed for MUC1 expression. Organoids and tumours from patients and transgenic mice were treated with AICAR alone or in combination with JAK and EGFR inhibitors to evaluate treatment effects. RESULTS AICAR reduced EGFR-mutant tumour cell growth by inducing DNA damage and apoptosis. MUC1 was one of the leading AICAR-binding and degrading proteins. AICAR negatively regulated JAK signalling and JAK1-MUC1-CT interaction. Activated EGFR upregulated MUC1-CT expression in EGFR-TL-induced lung tumour tissues. AICAR reduced EGFR-mutant cell line-derived tumour formation in vivo. Co-treating patient and transgenic mouse lung-tissue-derived tumour organoids with AICAR and JAK1 and EGFR inhibitors reduced their growth. CONCLUSIONS AICAR represses the MUC1 activity in EGFR-mutant lung cancer, disrupting protein-protein interactions between MUC1-CT and JAK1 and EGFR.
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Affiliation(s)
- Fareesa Aftab
- Department of Cancer Division, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Boulevard, Orlando, FL, 32827, USA
| | - Alice Rodriguez-Fuguet
- Department of Cancer Division, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Boulevard, Orlando, FL, 32827, USA
| | - Luis Silva
- Department of Cancer Division, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Boulevard, Orlando, FL, 32827, USA
| | - Ikei S Kobayashi
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, E/CLS-409, Boston, MA, 02215, USA
| | - Jiao Sun
- Department of Computer Science, College of Engineering and Computer Science, University of Central Florida, 4000 Central Florida Boulevard, Orlando, FL, 32816, USA
| | - Katerina Politi
- Departments of Pathology and Internal Medicine (Section of Medical Oncology) and the Yale Cancer Center, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Elena Levantini
- Harvard Stem Cell Institute, 330 Brookline Avenue, Harvard Medical School, Boston, MA, 02215, USA
- Institute of Biomedical Technologies, National Research Council (CNR), Area della Ricerca di Pisa, 56124, Pisa, Italy
| | - Wei Zhang
- Department of Computer Science, College of Engineering and Computer Science, University of Central Florida, 4000 Central Florida Boulevard, Orlando, FL, 32816, USA
| | - Susumu S Kobayashi
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, E/CLS-409, Boston, MA, 02215, USA
- Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8575, Japan
| | - Wen Cai Zhang
- Department of Cancer Division, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Boulevard, Orlando, FL, 32827, USA.
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Rather MA, Agarwal D, Bhat TA, Khan IA, Zafar I, Kumar S, Amin A, Sundaray JK, Qadri T. Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture. Int J Biol Macromol 2023; 233:123549. [PMID: 36740117 DOI: 10.1016/j.ijbiomac.2023.123549] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.
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Affiliation(s)
- Mohd Ashraf Rather
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir, India.
| | - Deepak Agarwal
- Institute of Fisheries Post Graduation Studies OMR Campus, Vaniyanchavadi, Chennai, India
| | | | - Irfan Ahamd Khan
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir, India
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Punjab, Pakistan
| | - Sujit Kumar
- Department of Bioinformatics and Computational Biology, Virtual University Punjab, Pakistan
| | - Adnan Amin
- Postgraduate Institute of Fisheries Education and Research Kamdhenu University, Gandhinagar-India University of Kurasthra, India; Department of Aquatic Environmental Management, Faculty of Fisheries Rangil- Ganderbel -SKUAST-K, India
| | - Jitendra Kumar Sundaray
- ICAR-Central Institute of Freshwater Aquaculture, Kausalyaganga, Bhubaneswar, Odisha 751002, India
| | - Tahiya Qadri
- Division of Food Science and Technology, SKUAST-K, Shalimar, India
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Trudeau SJ, Hwang H, Mathur D, Begum K, Petrey D, Murray D, Honig B. PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions. Protein Sci 2023; 32:e4594. [PMID: 36776141 PMCID: PMC10019447 DOI: 10.1002/pro.4594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/14/2023]
Abstract
We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.
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Affiliation(s)
- Stephen J. Trudeau
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Integrated Graduate Program in Cellular, Molecular and Biomedical Studies (CMBS), Columbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Howook Hwang
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Schrodinger, Inc.New YorkNew YorkUSA
| | - Deepika Mathur
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Kamrun Begum
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Donald Petrey
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Diana Murray
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Barry Honig
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of Biochemistry and Molecular BiophysicsColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of MedicineColumbia UniversityNew YorkNew YorkUSA
- Zuckerman Mind Brain and Behavior InstituteColumbia UniversityNew YorkNew YorkUSA
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9
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Raheem KY, Ibukunoluwa FP, Olorundare SA, Nandwa JO, Abayomi MA, Uchechukwu EJ, Adewunmi M, Blessing KZ, Anthony MM, Gbadebo MI, Daniel FT. Therapeutic capability of selected medicinal plants' bioactive constituents against the mutant ovarian TP53 gene; a computational approach. ADVANCES IN BIOMARKER SCIENCES AND TECHNOLOGY 2023. [DOI: 10.1016/j.abst.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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10
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Skolnick J, Zhou H. Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions. J Phys Chem B 2022; 126:6853-6867. [PMID: 36044742 PMCID: PMC9484464 DOI: 10.1021/acs.jpcb.2c04525] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/18/2022] [Indexed: 11/28/2022]
Abstract
Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems
Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia 30332, United States
| | - Hongyi Zhou
- Center for the Study of Systems
Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia 30332, United States
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Astore C, Zhou H, Ilkowski B, Forness J, Skolnick J. LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity. Commun Biol 2022; 5:870. [PMID: 36008469 PMCID: PMC9411158 DOI: 10.1038/s42003-022-03816-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 08/08/2022] [Indexed: 11/09/2022] Open
Abstract
To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease comorbidities from shared mode of action proteins predicted by the artificial intelligence-based MEDICASCY algorithm. LeMeDISCO was applied to predict the occurrence of comorbid diseases for 3608 distinct diseases. Benchmarking shows that LeMeDISCO has much better comorbidity recall than the two molecular methods XD-score (44.5% vs. 6.4%) and the SAB score (68.6% vs. 8.0%). Its performance is somewhat comparable to the phenotype method-based Symptom Similarity Score, 63.7% vs. 100%, but LeMeDISCO works for far more cases and its large comorbidity recall is attributed to shared proteins that can help provide an understanding of the molecular mechanism(s) underlying disease comorbidity. The LeMeDISCO web server is available for academic users at: http://sites.gatech.edu/cssb/LeMeDISCO .
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Affiliation(s)
- Courtney Astore
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Bartosz Ilkowski
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Jessica Forness
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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Gene Interaction Network Analysis Reveals IFI44L as a Drug Target in Rheumatoid Arthritis and Periodontitis. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092749. [PMID: 35566100 PMCID: PMC9104995 DOI: 10.3390/molecules27092749] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/20/2022] [Accepted: 04/20/2022] [Indexed: 11/18/2022]
Abstract
Simple Summary In spite of substantial investigation, the biological link between periodontitis and rheumatoid arthritis remains unexplained. This study intended to correlate periodontitis and rheumatoid arthritis gene expression patterns to find shared targets for both the disease. We identified the differentially expressed genes (DEGs) in periodontitis and rheumatoid arthritis. The network was built by integrating DEGs and ranking the genes using GeneMANIA. FINDSITEcomb2.0 was used to find a possible inhibitor for the top-ranked gene. Further, the binding effectiveness and protein-ligand complex stability were then determined by molecular docking and molecular dynamics. The network analysis showed IFI44L as a highly ranking gene implicated in most immunological pathways. A virtual screening of 6507 compounds revealed vemurafenib as the best candidate for the IFI44L target. Molecular docking and molecular dynamics modelling revealed the stability of the IFI44L-vemurafenib complex, which suggest IFI44L is potential target and vemurafenib could be the better candidate to treat both diseases. Abstract Objective: Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions. Materials and Methods: Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITEcomb2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively. Results: From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of −7.7 Kcal/mol, and the simulation results show stability till 100 ns. Conclusions: The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.
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Kumar Yadalam P, Krishnamurthi I, Srimathi R, Alzahrani KJ, Mugri MH, Sayed M, Almadi KH, Alkahtanyj MF, Almagbol M, Bhandi S, Ali Baeshen H, Thirumal Raj A, Patil S. Gene and Protein Interaction Network Analysis in Epithelial-Mesenchymal Transition of Hertwig's Epithelial Root Sheath reveals periodontal regenerative drug targets - An in silico study. Saudi J Biol Sci 2022; 29:3822-3829. [PMID: 35844389 PMCID: PMC9280257 DOI: 10.1016/j.sjbs.2022.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/12/2022] [Accepted: 03/06/2022] [Indexed: 12/14/2022] Open
Abstract
Background and aim Hertwig’s Εpithelial Root Sheath (HΕRS) has a major function in the developing tooth roots. Earlier research revealed that it undergoes epithelial–mesenchymal transition, a vital process for the morphogenesis and complete development of the tooth and its surrounding periodontium. Few studies have demonstrated the role of HERS in cementogenesis through ΕMΤ. The background of this in-silico system biology approach is to find a hub protein and gene involved in the EMT of HERS that may uncover novel insights in periodontal regenerative drug targets. Materials and methods The protein and gene list involved in epithelial–mesenchymal transition were obtained from literature sources. The protein interaction was constructed using STRING software and the protein interaction network was analyzed. Molecular docking simulation checks the binding energy and stability of protein-ligand complex. Results Results revealed the hub gene to be DYRK1A(Hepcidin), and the ligand was identified as isoetharine. SΤRIΝG results showed a confidence cutoff of 0.9 in sensitivity analysis with a condensed protein interaction network. Overall, 98 nodes from 163 nodes of expected edges were found with an average node degree of 11.9. Docking results show binding energy of −4.70, and simulation results show an RMSD value of 5.6 Å at 50 ns. Conclusion Isoetharine could be a potential drug for periodontal regeneration.
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14
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Zhang W, Huang J. EViS: An Enhanced Virtual Screening Approach Based on Pocket-Ligand Similarity. J Chem Inf Model 2022; 62:498-510. [PMID: 35084171 DOI: 10.1021/acs.jcim.1c00944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein-ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket-ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein-ligand structure database.
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Affiliation(s)
- Wenyi Zhang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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15
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Masibag AN, Bergin CJ, Haebe JR, Zouggar A, Shah MS, Sandouka T, Mendes da Silva A, Desrochers FM, Fournier-Morin A, Benoit YD. Pharmacological targeting of Sam68 functions in colorectal cancer stem cells. iScience 2021; 24:103442. [PMID: 34877499 PMCID: PMC8633986 DOI: 10.1016/j.isci.2021.103442] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/09/2021] [Accepted: 11/10/2021] [Indexed: 01/20/2023] Open
Abstract
Cancer stem cells (CSCs) are documented to play a key role in tumorigenesis and therapy resistance. Despite significant progress in clinical oncology, CSC reservoirs remain elusive and difficult to eliminate. Reverse-turn peptidomimetics were characterized as disruptors of CBP/beta-Catenin interactions and represent a promising avenue to curb hyperactive canonical Wnt/beta-Catenin signaling in CSCs. Recent studies suggested Sam68 as a critical mediator of reverse-turn peptidomimetics response in CSC populations. Using computational and biochemical approaches we confirmed Sam68 as a primary target of reverse-turn peptidomimetics. Furthermore, we executed an in silico drug discovery pipeline to identify yet uncharacterized reverse-turn peptidomimetic structures displaying superior anti-CSC activity in transformed pluripotent and colorectal cancer cell models. Thus, we identified YB-0158 as a reverse-turn peptidomimetic small molecule with enhanced translational potential, altering key hallmarks of human colorectal CSCs in patient-derived ex vivo organoids and in vivo serial tumor transplantation. Sam68 is a direct protein target of reverse-turn peptidomimetic small molecules YB-0158 is a peptidomimetic structure with high predicted affinity for Sam68 YB-0158 elicits a cancer-selective response impeding main cancer stem cell hallmarks YB-0158 blocks cancer stem cell activity in tumor organoids and in vivo systems
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Affiliation(s)
- Angelique N Masibag
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Christopher J Bergin
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Joshua R Haebe
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Aïcha Zouggar
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Muhammad S Shah
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Tamara Sandouka
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Amanda Mendes da Silva
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - François M Desrochers
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Aube Fournier-Morin
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Yannick D Benoit
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
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16
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Guterres H, Park SJ, Cao Y, Im W. CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site. J Chem Inf Model 2021; 61:5336-5342. [PMID: 34757752 DOI: 10.1021/acs.jcim.1c01156] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Rational drug design involves a task of finding ligands that would bind to a specific target protein. This work presents CHARMM-GUI Ligand Designer that is an intuitive and interactive web-based tool to design virtual ligands that match the shape and chemical features of a given protein binding site. Ligand Designer provides ligand modification capabilities with 3D visualization that allow researchers to modify and redesign virtual ligands while viewing how the protein-ligand interactions are affected. Virtual ligands can also be parameterized for further molecular dynamics (MD) simulations and free energy calculations. Using 8 targets from 8 different protein classes in the directory of useful decoys, enhanced (DUD-E) data set, we show that Ligand Designer can produce similar ligands to the known active ligands in the crystal structures. Ligand Designer also produces stable protein-ligand complex structures when tested using short MD simulations. We expect that Ligand Designer can be a useful and user-friendly tool to design small molecules in any given potential ligand binding site on a protein of interest.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Sang-Jun Park
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Yiwei Cao
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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17
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Skolnick J, Gao M, Zhou H, Singh S. AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function. J Chem Inf Model 2021; 61:4827-4831. [PMID: 34586808 DOI: 10.1021/acs.jcim.1c01114] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Mu Gao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Suresh Singh
- Twilight Design, 4 Adams Road, Kendall Park, New Jersey 08824, United States
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18
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Astore C, Zhou H, Jacob J, Skolnick J. Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications. Sci Rep 2021; 11:20864. [PMID: 34675303 PMCID: PMC8531388 DOI: 10.1038/s41598-021-00368-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 10/06/2021] [Indexed: 01/08/2023] Open
Abstract
Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.
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Affiliation(s)
- Courtney Astore
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA
| | - Joshy Jacob
- Emory Vaccine Center, Emory University, Atlanta, GA, 30329, USA
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30329, USA
- Department of Microbiology and Immunology, Emory Vaccine Center, School of Medicine, Emory University, Atlanta, GA, 30329, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA.
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19
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Murray D, Petrey D, Honig B. Integrating 3D structural information into systems biology. J Biol Chem 2021; 296:100562. [PMID: 33744294 PMCID: PMC8095114 DOI: 10.1016/j.jbc.2021.100562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/18/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.
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Affiliation(s)
- Diana Murray
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Donald Petrey
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Barry Honig
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Department of Medicine, Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, USA.
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20
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Zhou H, Cao H, Skolnick J. FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening. J Chem Inf Model 2021; 61:2074-2089. [PMID: 33724022 DOI: 10.1021/acs.jcim.0c01160] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia 30332-2000, United States
| | - Hongnan Cao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia 30332-2000, United States
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia 30332-2000, United States
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21
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Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions. J Mol Biol 2020; 432:4872-4890. [PMID: 32652079 DOI: 10.1016/j.jmb.2020.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 07/05/2020] [Accepted: 07/07/2020] [Indexed: 11/23/2022]
Abstract
G protein-coupled receptors (GPCRs) are a large family of integral membrane proteins responsible for cellular signal transductions. Identification of therapeutic compounds to regulate physiological processes is an important first step of drug discovery. We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein structure prediction and structure-based binding-site identification, followed by homologous GPCR detections through structure and orthosteric binding-site comparisons. Ligand profiles constructed from the homologous ligand-GPCR complexes are then used to thread through compound databases for VS. The pipeline was first tested in a large-scale retrospective screening experiment against 224 human Class A GPCRs, where MAGELLAN achieved a median enrichment factor (EF) of 14.38, significantly higher than that using individual ligand profiles. Next, MAGELLAN was examined on 5 and 20 GPCRs from two public VS databases (DUD-E and GPCR-Bench) and resulted in an average EF of 9.75 and 13.70, respectively, which compare favorably with other state-of-the-art docking- and ligand-based methods, including AutoDock Vina (with EF = 1.48/3.16 in DUD-E and GPCR-Bench), DOCK 6 (2.12/3.47 in DUD-E and GPCR-Bench), PoLi (2.2 in DUD-E), and FINDSITECcomb2.0 (2.90 in DUD-E). Detailed data analyses show that the major advantage of MAGELLAN is attributed to the power of ligand profiling, which integrates complementary methods for ligand-GPCR interaction recognition and thus significantly improves the coverage and sensitivity of VS models. Finally, cases studies on opioid and motilin receptors show that new connections between functionally related GPCRs can be visualized in the minimum spanning tree built on the similarities of predicted ligand-binding ensembles, suggesting a novel use of MAGELLAN for GPCR deorphanization.
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22
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Zhou H, Cao H, Matyunina L, Shelby M, Cassels L, McDonald JF, Skolnick J. MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action. Mol Pharm 2020; 17:1558-1574. [PMID: 32237745 PMCID: PMC7319183 DOI: 10.1021/acs.molpharmaceut.9b01248] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332
| | - Hongnan Cao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332
| | - Lilya Matyunina
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - Madelyn Shelby
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - Lauren Cassels
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - John F. McDonald
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332
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Cao H, Jin M, Gao M, Zhou H, Tao YJ, Skolnick J. Differential kinase activity of ACVR1 G328V and R206H mutations with implications to possible TβRI cross-talk in diffuse intrinsic pontine glioma. Sci Rep 2020; 10:6140. [PMID: 32273545 PMCID: PMC7145857 DOI: 10.1038/s41598-020-63061-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 03/19/2020] [Indexed: 01/17/2023] Open
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a lethal pediatric brain cancer whose median survival time is under one year. The possible roles of the two most common DIPG associated cytoplasmic ACVR1 receptor kinase domain mutants, G328V and R206H, are reexamined in the context of new biochemical results regarding their intrinsic relative ATPase activities. At 37 °C, the G328V mutant displays a 1.8-fold increase in intrinsic kinase activity over wild-type, whereas the R206H mutant shows similar activity. The higher G328V mutant intrinsic kinase activity is consistent with the statistically significant longer overall survival times of DIPG patients harboring ACVR1 G328V tumors. Based on the potential cross-talk between ACVR1 and TβRI pathways and known and predicted off-targets of ACVR1 inhibitors, we further validated the inhibition effects of several TβRI inhibitors on ACVR1 wild-type and G328V mutant patient tumor derived DIPG cell lines at 20–50 µM doses. SU-DIPG-IV cells harboring the histone H3.1K27M and activating ACVR1 G328V mutations appeared to be less susceptible to TβRI inhibition than SF8628 cells harboring the H3.3K27M mutation and wild-type ACVR1. Thus, inhibition of hidden oncogenic signaling pathways in DIPG such as TβRI that are not limited to ACVR1 itself may provide alternative entry points for DIPG therapeutics.
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Affiliation(s)
- Hongnan Cao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia, 30332, United States
| | - Miao Jin
- Department of BioSciences, Rice University, Houston, Texas, 77005, United States
| | - Mu Gao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia, 30332, United States
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia, 30332, United States
| | - Yizhi Jane Tao
- Department of BioSciences, Rice University, Houston, Texas, 77005, United States
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, Georgia, 30332, United States.
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Pedraza-González L, Marín MDC, Jorge AN, Ruck TD, Yang X, Valentini A, Olivucci M, De Vico L. Web-ARM: A Web-Based Interface for the Automatic Construction of QM/MM Models of Rhodopsins. J Chem Inf Model 2020; 60:1481-1493. [PMID: 31909998 PMCID: PMC7101466 DOI: 10.1021/acs.jcim.9b00615] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
This article introduces Web-ARM, a specialized tool, online available, designed to build quantum mechanical/molecular mechanical models of rhodopsins, a widely spread family of light-responsive proteins. Web-ARM allows the rapidly building of models of rhodopsins with a documented quality and the prediction of trends in UV-vis absorption maximum wavelengths, based on their excitation energies computed at the CASPT2//CASSCF/Amber level of theory. Web-ARM builds upon the recently reported, python-based a-ARM protocol [J. Chem. Theory Comput., 2019, 15, 3134-3152] and, as such, necessitates only a crystallographic structure or a comparative model in PDB format and a very basic knowledge of the studied rhodopsin system. The user-friendly web interface uses such input to generate congruous, gas-phase models of rhodopsins and, if requested, their mutants. We present two possible applications of Web-ARM, which showcase how the interface can be employed to assist both research and educational activities in fields at the interface between chemistry and biology. The first application shows how, through Web-ARM, research projects (e.g., rhodopsin and rhodopsin mutant screening) can be carried out in significantly less time with respect to using the required computational photochemistry tools via a command line. The second application documents the use of Web-ARM in a real-life educational/training activity, through a hands-on experience illustrating the concepts of rhodopsin color tuning.
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Affiliation(s)
- Laura Pedraza-González
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Via A. Moro 2, I-53100 Siena, Italy
| | - María Del Carmen Marín
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Via A. Moro 2, I-53100 Siena, Italy
| | - Alejandro N Jorge
- Department of Chemistry, Bowling Green State University, Bowling Green, Ohio 43403, United States of America
| | - Tyler D Ruck
- Department of Chemistry, Bowling Green State University, Bowling Green, Ohio 43403, United States of America
| | - Xuchun Yang
- Department of Chemistry, Bowling Green State University, Bowling Green, Ohio 43403, United States of America
| | - Alessio Valentini
- Theoretical Physical Chemistry, Research Unit MolSys, Université de Liège, Allée du 6 Août, 11, 4000 Liège, Belgium
| | - Massimo Olivucci
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Via A. Moro 2, I-53100 Siena, Italy
- Department of Chemistry, Bowling Green State University, Bowling Green, Ohio 43403, United States of America
| | - Luca De Vico
- Department of Biotechnology, Chemistry and Pharmacy, Università degli Studi di Siena, Via A. Moro 2, I-53100 Siena, Italy
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Sweeney-Jones AM, Gagaring K, Antonova-Koch J, Zhou H, Mojib N, Soapi K, Skolnick J, McNamara CW, Kubanek J. Antimalarial Peptide and Polyketide Natural Products from the Fijian Marine Cyanobacterium Moorea producens. Mar Drugs 2020; 18:E167. [PMID: 32197482 PMCID: PMC7142784 DOI: 10.3390/md18030167] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 03/12/2020] [Accepted: 03/16/2020] [Indexed: 12/21/2022] Open
Abstract
A new cyclic peptide, kakeromamide B (1), and previously described cytotoxic cyanobacterial natural products ulongamide A (2), lyngbyabellin A (3), 18E-lyngbyaloside C (4), and lyngbyaloside (5) were identified from an antimalarial extract of the Fijian marine cyanobacterium Moorea producens. Compounds 1 and 1 exhibited moderate activity against Plasmodium falciparum blood-stages with EC50 values of 0.89 and 0.99 µM, respectively, whereas 3 was more potent with an EC50 value of 0.15 nM, respectively. Compounds 1, 4, and 5 displayed moderate liver-stage antimalarial activity against P. berghei liver schizonts with EC50 values of 1.1, 0.71, and 0.45 µM, respectively. The threading-based computational method FINDSITEcomb2.0 predicted the binding of 1 and 2 to potentially druggable proteins of Plasmodiumfalciparum, prompting formulation of hypotheses about possible mechanisms of action. Kakeromamide B (1) was predicted to bind to several Plasmodium actin-like proteins and a sortilin protein suggesting possible interference with parasite invasion of host cells. When 1 was tested in a mammalian actin polymerization assay, it stimulated actin polymerization in a dose-dependent manner, suggesting that 1 does, in fact, interact with actin.
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Affiliation(s)
| | - Kerstin Gagaring
- Calibr, a division of The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Jenya Antonova-Koch
- Calibr, a division of The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hongyi Zhou
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nazia Mojib
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Katy Soapi
- Institute of Applied Sciences, University of the South Pacific, Suva, Fiji
| | - Jeffrey Skolnick
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Case W. McNamara
- Calibr, a division of The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Julia Kubanek
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA;
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9214538] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.
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Pulley JM, Rhoads JP, Jerome RN, Challa AP, Erreger KB, Joly MM, Lavieri RR, Perry KE, Zaleski NM, Shirey-Rice JK, Aronoff DM. Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. Annu Rev Pharmacol Toxicol 2019; 60:333-352. [PMID: 31337270 DOI: 10.1146/annurev-pharmtox-010919-023537] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.
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Affiliation(s)
- Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jillian P Rhoads
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Rebecca N Jerome
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Anup P Challa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kevin B Erreger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Meghan M Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Robert R Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kelly E Perry
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Nicole M Zaleski
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jana K Shirey-Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - David M Aronoff
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.,Departments of Obstetrics and Gynecology, and Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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Lee HS, Im W. Stalis: A Computational Method for Template-Based Ab Initio Ligand Design. J Comput Chem 2019; 40:1622-1632. [PMID: 30829435 DOI: 10.1002/jcc.25813] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/23/2019] [Accepted: 02/17/2019] [Indexed: 12/20/2022]
Abstract
Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Hui Sun Lee
- Departments of Biological Sciences and Bioengineering, Lehigh University, 111 Research Drive, Bethlehem, Pennsylvania 18015
| | - Wonpil Im
- Departments of Biological Sciences and Bioengineering, Lehigh University, 111 Research Drive, Bethlehem, Pennsylvania 18015
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