Journal of Chemical Information and Modeling 最新文献

{"title":"Unravelling the Complexity of Amyloid Peptide Core Interfaces","authors":"Máté Sulyok-Eiler, Veronika Harmat, András Perczel","doi":"10.1021/acs.jcim.4c01479","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01479","url":null,"abstract":"Amyloids, large intermolecular sandwiched β-sheet structures, underlie several protein misfolding diseases but have also been shown to have functional roles and can be a basis for designing smart and responsive nanomaterials. Short segments of proteins, called aggregation-prone regions (APRs), have been identified that nucleate amyloid formation. Here we present the database of 173 APR crystal structures currently available in the PDB, and a tool, ACW, for analyzing their topologies and the 267 inter-β-sheet interfaces of zipper regions assigned in these structures. We defined a new descriptor of zipper interfaces, the surface detail index (SDi), which quantifies the intertwining between the side chains of both β-sheets of the zipper, an important factor for the molecular recognition and self-assembly of these mesostructures. This allowed a comparative analysis of the zipper interfaces and identification of 6 clusters with different intertwining, steric fit, and size characteristics using three complementary descriptors, SDi, shape complementarity, and buried surface area. 60% of the APR structures are formed by parallel β-sheets, of which 52% belong to the topological class 1. This could be explained by the better fit and a deeper entanglement of the zipper regions of the parallel structures than of the antiparallel structures, as the analysis showed that both their shape complementarity (0.79 vs 0.70) and SDi (1.53 vs 1.32) were higher. The higher abundance of certain residues (Asn and Gln in parallel and Leu and Ala in antiparallel β-sheets) can be explained by their ability to form different ladder-like secondary interaction patterns within β-sheets. Analogous to the hierarchy of protein structure, we interpreted the primary, secondary, tertiary, and quaternary structure levels of APRs revealing different characteristics of the zipper regions for both parallel and antiparallel β-sheet structures, which may provide clues to the structural conditions of amyloid core formation and the rational design of amyloid polymorphs.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"130 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding","authors":"Daniel P. Devore,&nbsp; and ,&nbsp;Kevin L. Shuford*,&nbsp;","doi":"10.1021/acs.jcim.4c0142710.1021/acs.jcim.4c01427","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01427https://doi.org/10.1021/acs.jcim.4c01427","url":null,"abstract":"<p >The ability to predict the strength of halogen bonds and properties of halogen bond (XB) donors has significant utility for medicinal chemistry and materials science. XBs are typically calculated through expensive ab initio methods. Thus, the development of tools and techniques for fast, accurate, and efficient property predictions has become increasingly more important. Herein, we employ three machine learning models to classify the XB donors and complexes by their principal halogen atom as well as predict the values of the maximum point on the electrostatic potential surface (<i>V</i>_S,max) and interaction strength of the XB complexes through a molecular fingerprint and data-based analysis. The fingerprint analysis produces a root-mean-square error of ca. 7.5 and ca. 5.5 kcal mol^–1 while predicting the <i>V</i>_S,max for the halobenzene and haloethynylbenzene systems, respectively. However, the prediction of the binding energy between the XB donors and ammonia acceptor is shown to be within 1 kcal mol^–1 of the density functional theory (DFT)-calculated energy. More accurate predictions can be made from the precalculated DFT data when compared to the fingerprint analysis.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"64 21","pages":"8201–8214 8201–8214"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142609509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations","authors":"Shu Li,&nbsp;Lu Peng,&nbsp;Liuqing Chen,&nbsp;Linjie Que,&nbsp;Wenqingqing Kang,&nbsp;Xiaojun Hu,&nbsp;Jun Ma,&nbsp;Zengru Di and Yu Liu*,&nbsp;","doi":"10.1021/acs.jcim.4c0100610.1021/acs.jcim.4c01006","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01006https://doi.org/10.1021/acs.jcim.4c01006","url":null,"abstract":"<p >Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce <i>PepHiRe</i>, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied <i>PepHiRe</i> to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, <i>PepHiRe</i> effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC₅₀ values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"64 21","pages":"8164–8175 8164–8175"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142608691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding and Quantifying Molecular Flexibility: Torsion Angular Bin Strings.","authors":"Jessica Braun, Paul Katzberger, Gregory A Landrum, Sereina Riniker","doi":"10.1021/acs.jcim.4c01513","DOIUrl":"10.1021/acs.jcim.4c01513","url":null,"abstract":"<p><p>Molecular flexibility is a commonly used, but not easily quantified term. It is at the core of understanding composition and size of a conformational ensemble and contributes to many molecular properties. For many computational workflows, it is necessary to reduce a conformational ensemble to meaningful representatives, however defining them and guaranteeing the ensemble's completeness is difficult. We introduce the concepts of torsion angular bin strings (TABS) as a discrete vector representation of a conformer's dihedral angles and the number of possible TABS (nTABS) as an estimation for the ensemble size of a molecule, respectively. Here, we show that nTABS corresponds to an upper limit for the size of the conformational space of small molecules and compare the classification of conformer ensembles by TABS with classifications by RMSD. Overcoming known drawbacks like the molecular size dependency and threshold picking of the RMSD measure, TABS is shown to meaningfully discretize the conformational space and hence allows e.g. for fast checks of the coverage of the conformational space. The current proof-of-concept implementation is based on the ETKDGv3 conformer generator as implemented in the RDKit and known torsion preferences extracted from small-molecule crystallographic data.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7917-7924"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-Guided Molecular Modeling of Nisin and Lipid II Assembly and Membrane Pore Formation.","authors":"Hugo A Perez, Zhe Wang, Bernard S Gerstman, Jin He, Prem P Chapagain","doi":"10.1021/acs.jcim.4c01050","DOIUrl":"10.1021/acs.jcim.4c01050","url":null,"abstract":"<p><p>The lantibiotic pore-forming peptide nisin is a promising candidate in the fight against multidrug-resistant bacteria due to its unique structure, which allows it to disrupt bacteria in two distinct ways─Lipid II trafficking and transmembrane pore formation. However, exactly how nisin and Lipid II assemble into oligomeric pore structures in the bacterial membrane is not known. Spontaneous peptide assembly into pores is difficult to observe in even the very long-time scale molecular dynamics (MD) simulations. In this study, we adopted an MD-guided modeling approach to investigate the nisin-nisin and nisin-Lipid II associations in the membrane environment. Through extensive microsecond-time scale all-atom MD simulations, we established that nisin monomers dimerize by forming β-sheets in a POPE:POPG lipid bilayer and oligomerize further to form stable transmembrane channels. We determined that these nisin dimers use Lipid II as a dimer interface to incur enhanced stability. Our results provide a clearer understanding of the self-assembly of nisin monomers within the membrane and insights into the role of Lipid II in the structural integrity of oligomeric structures.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7977-7986"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142379406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior of Trapped Molecules in Lantern-Like Carcerand Superphanes.","authors":"Andrzej Eilmes, Mirosław Jabłoński","doi":"10.1021/acs.jcim.4c01040","DOIUrl":"10.1021/acs.jcim.4c01040","url":null,"abstract":"<p><p>Superphanes are a group of organic molecules from the cyclophane family. They are characterized by the presence of two parallel benzene rings joined together by six bridges. If these bridges are sufficiently long, the superphane cavity can be large enough to trap small molecules or ions. Using ab initio (time scale of 80 ps) and classical (up to 200 ns) molecular dynamics (MD) methods, we study the behavior of five fundamental molecules (M = H₂O, NH₃, HF, HCN, MeOH) encapsulated inside the experimentally reported lantern-like superphane and its two derivatives featuring slightly modified side bridges. The main focus is studying the dynamics of hydrogen bonds between the trapped M molecule and the imino nitrogen atoms of the side chains of the host superphane. The length of the N···H hydrogen bond increases in the following order: HF < HCN < H₂O < MeOH < NH₃. The mobility of the trapped molecule and its preferred position inside the superphane cage depend not only on the type of this molecule but also largely on the in/out conformational arrangement of the imino nitrogens in the side chains of the superphane. Their inward-pointing positions allow the formation of strong N···H hydrogen bonds. For this reason, these nitrogens are the preferred sites of interaction. The mobility of the molecules and their residence times on each side of the superphane have been explained by referring to the symmetry and conformation of the given superphane cage. All force field MD simulations have shown that the encapsulated molecule remained inside the superphane cage for 200 ns without any escape event to the outside. Moreover, our simulations based on some endohedral complexes in the water box also showed no exchange event. Thus, the superphanes we study are true carcerand molecules. We attribute this property to the hydrophobic side chains and their pinwheel arrangement, which makes the side walls of the studied superphanes fairly impenetrable to small molecules.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7925-7937"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NetSci: A Library for High Performance Biomolecular Simulation Network Analysis Computation.","authors":"Andrew M Stokely, Lane W Votapka, Marcus T Hock, Abigail E Teitgen, J Andrew McCammon, Andrew D McCulloch, Rommie E Amaro","doi":"10.1021/acs.jcim.4c00899","DOIUrl":"10.1021/acs.jcim.4c00899","url":null,"abstract":"<p><p>We present the NetSci program-an open-source scientific software package designed for estimating mutual information (MI) between data sets using GPU acceleration and a k-nearest-neighbor algorithm. This approach significantly enhances calculation speed, achieving improvements of several orders of magnitude over traditional CPU-based methods, with data set size limits dictated only by available hardware. To validate NetSci, we accurately compute MI for an analytically verifiable two-dimensional Gaussian distribution and replicate the generalized correlation (GC) analysis previously conducted on the B1 domain of protein G. We also apply NetSci to molecular dynamics simulations of the Sarcoendoplasmic Reticulum Calcium-ATPase (SERCA) pump, exploring the allosteric mechanisms and pathways influenced by ATP and 2'-deoxy-ATP (dATP) binding. Our analysis reveals distinct allosteric effects induced by ATP compared to dATP, with predicted information pathways from the bound nucleotide to the calcium-binding domain differing based on the nucleotide involved. NetSci proves to be a valuable tool for estimating MI and GC in various data sets and is particularly effective for analyzing intraprotein communication and information transfer.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7966-7976"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Mechanism-Driven Discovery of Novel Small Molecule Inhibitors against Drug-Resistant SARS-CoV-2 M^pro Variants.","authors":"Jingyi Yang, Beibei Fu, Rongpei Gou, Xiaoyuan Lin, Haibo Wu, Weiwei Xue","doi":"10.1021/acs.jcim.4c01206","DOIUrl":"10.1021/acs.jcim.4c01206","url":null,"abstract":"<p><p>Under the selective pressure of nirmatrelvir, a peptidomimetic covalent drug targeting SARS-CoV-2 M^pro, various drug-resistant mutations on M^pro have been acquired <i>in vitro</i>. Among the mutations, L50F and E166V, along with the combination of L50F and E166V, are particularly representative and pose considerable obstacles to the effective treatment of COVID-19. Our previous study identified NMI-001 and NMI-002 as novel nonpeptide inhibitors that target SARS-CoV-2 M^pro, possessing unique scaffolds and binding modes different from those of nirmatrelvir. In view of these findings, we proposed a drug design strategy aimed at rapidly identifying inhibitors that can combat mutation-induced drug resistance. Initially, molecular dynamics (MD) simulation was employed to investigate the binding mechanisms of NMI-001 and NMI-002 against the three drug-resistant mutants (M^pro_L50F, M^pro_E166V, and M^pro_L50F+E166V). Then, we conducted two phases of high-throughput virtual screening. In the first phase, NMI-001 served as a template to perform scaffold hopping-based similarity search in a library of 15,742,661 compounds. In the second phase, 968 compounds exhibiting similarity to NMI-001 were evaluated via molecular docking and MD simulations. Six compounds that may be effective against at least one mutant were identified, and five compounds were procured for conducting <i>in vitro</i> assays. Finally, the compound Z1557501297 (NMI-003) exhibiting inhibitory effects against the E166V (IC₅₀ = 27.81 ± 2.65 μM) and L50F+E166V (IC₅₀ = 8.78 ± 0.74 μM) mutants was discovered. The binding modes referring to NMI-003-M^pro_E166V and NMI-003-M^pro_L50F+E166V were further elucidated at the atomic level. In summary, NMI-003 reported herein is the first compound with activity against E166V and L50F+E166V, which provides a good starting point to design novel antiviral drugs for the treatment of drug-resistant SARS-CoV-2.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7998-8009"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142386398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Predictive Efficacy for Drug Resistance in Novel HIV-1 Protease Inhibitors through Transfer Learning Mechanisms.","authors":"Huseyin Tunc, Sumeyye Yilmaz, Busra Nur Darendeli Kiraz, Murat Sari, Seyfullah Enes Kotil, Ozge Sensoy, Serdar Durdagi","doi":"10.1021/acs.jcim.4c01037","DOIUrl":"10.1021/acs.jcim.4c01037","url":null,"abstract":"<p><p>The human immunodeficiency virus presents a significant global health challenge due to its rapid mutation and the development of resistance mechanisms against antiretroviral drugs. Recent studies demonstrate the impressive performance of machine learning (ML) and deep learning (DL) models in predicting the drug resistance profile of specific FDA-approved inhibitors. However, generalizing ML and DL models to learn not only from isolates but also from inhibitor representations remains challenging for HIV-1 infection. We propose a novel drug-isolate-fold change (DIF) model framework that aims to predict drug resistance score directly from the protein sequence and inhibitor representation. Various ML and DL models, inhibitor representations, and protein representations were analyzed through realistic validation mechanisms. To enhance the molecular learning capacity of DIF models, we employ a transfer learning approach by pretraining a graph neural network (GNN) model for activity prediction on a data set of 4855 HIV-1 protease inhibitors (PIs). By performing various realistic validation strategies on internal and external genotype-phenotype data sets, we statistically show that the learned representations of inhibitors improve the predictive ability of DIF-based ML and DL models. We achieved an accuracy of 0.802, AUROC of 0.874, and <i>r</i> of 0.727 for the unseen external PIs. By comparing the DIF-based models with a null model consisting of isolate-fold change (IF) architecture, it is observed that the DIF models significantly benefit from molecular representations. Combined results from various testing strategies and statistical tests confirm the effectiveness of DIF models in testing novel PIs for drug resistance in the presence of an isolate.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7844-7863"},"PeriodicalIF":5.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution","authors":"Emile de Bruyn,&nbsp;Anton Emil Dorn,&nbsp;Giulia Rossetti*,&nbsp;Claudio Fernandez,&nbsp;Tiago F. Outeiro,&nbsp;Jörg B. Schulz and Paolo Carloni,&nbsp;","doi":"10.1021/acs.jcim.4c0117210.1021/acs.jcim.4c01172","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01172https://doi.org/10.1021/acs.jcim.4c01172","url":null,"abstract":"<p >Serine 129 can be phosphorylated in pathological inclusions formed by the intrinsically disordered protein human α-synuclein (AS), a key player in Parkinson’s disease and other synucleinopathies. Here, molecular simulations provide insight into the structural ensemble of phosphorylated AS. The simulations allow us to suggest that phosphorylation significantly impacts the structural content of the physiological AS conformational ensemble in aqueous solution, as the phosphate group is mostly solvated. The hydrophobic region of AS contains β-hairpin structures, which may increase the propensity of the protein to undergo amyloid formation, as seen in the nonphysiological (nonacetylated) form of the protein in a recent molecular simulation study. Our findings are consistent with existing experimental data with the caveat of the observed limitations of the force field for the phosphorylated moiety.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"64 21","pages":"8215–8226 8215–8226"},"PeriodicalIF":5.6,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.4c01172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142608676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}