Three-dimensional Structure Prediction Research Articles

In Silico Design and Evaluation of a Novel Therapeutic Agent Against the Spike Protein as a Novel Treatment Strategy for COVID-19 Treatment.

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that is associated with severe damage to other human organs. It causes by a novel coronavirus, and it is spreading all over the world. To date, there is some approved vaccine or therapeutic agent which could be effective against this disease. But their effectiveness against mutated strains is not studied completely. The spike glycoprotein on the surface of the coronaviruses gives the virus the ability to bind to host cell receptors and enter cells. Inhibition of attachment of these spikes can lead to virus neutralization by inhibiting viral entrance. In this study, we tried to use the virus entrance strategy against itself by utilizing virus receptor (ACE-2) in order to design an engineered protein consisting of a human Fc antibody fragment and a part of ACE-2, which reacts with virus RBD, and we also evaluated this interaction by computational methods and in silico methods. Subsequently, we have designed a new protein structure to bind with this site and inhibit the virus from attaching to its cell receptor, mechanically or chemically. Various in silico software, bioinformatics, and patent databases were used to retrieve the requested gene and protein sequences. The physicochemical properties and possibility of allergenicity were also examined. Three-dimensional structure prediction and molecular docking were also performed to develop the most suitable therapeutic protein. The designed protein consisted of a total of 256 amino acids with a molecular weight of 28984.62 and 5.92 as a theoretical isoelectric point. Instability and aliphatic index and grand average of hydropathicity are 49.99, 69.57 and -0.594, respectively. In silico studies can provide a good opportunity to study viral proteins and new drugs or compounds since they do not need direct exposure to infectious agents or equipped laboratories. The suggested therapeutic agent should be further characterized in vitro and in vivo.

Phylum-level studies of bacterial cutinases for unravelling enzymatic specificity toward PET degradation: an in silico approach.

The overwhelming use of PET plastic in various day-to-day activities led to the voluminous increase in PET waste and growing environmental hazards. A plethora of methods have been used that are associated with secondary pollutants. Therefore, microbial degradation of PET provides a sustainable approach due to its versatile metabolic diversity and capacity. The present work highlights the cutinase enzyme's role in PET degradation. This study focuses on the bacterial cutinases homologs screened from 43 reported phylum of bacteria. The reported bacterial cutinases for plastic degradation have been chosen as reference sequences, and 917 sequences have shown homology across the bacterial phyla. The dienelactone hydrolase (DLH) domain was identified for attaining specificity towards PET binding in 196 of 917 sequences. Various computational tools have been used for the physicochemical characterization of 196 sequences. The analysis revealed that most selected sequences are hydrophilic, extracellular, and thermally stable. Based on this analysis, 17 sequences have been further pursued for three-dimensional structure prediction and validation. The molecular docking studies of 17 selected sequences revealed efficient PET binding with the three sequences derived from the phylum Bacteroidota, the lowest binding energy of -5.9kcal/mol, Armatimonadota, and Nitrososphaerota with -5.8kcal/mol. The two enzyme sequences retrieved from the phylum Bacteroidota and Armatimonadota are metagenomically derived. Therefore, the present studies concluded that there is a high probability of finding cutinase homologs in various environmental resources that can be further explored for PET degradation.

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

Precision Vaccine Development: Unraveling the Immunoinformatics Code Against Epstein-Barr Virus's T41 Glycoprotein 350/220

Epstein-Barr Virus (EBV) poses a substantial global health threat, with a 90% morbidity rate in adults worldwide. Linked to infectious mononucleosis, autoimmune diseases, and cancers, there is an imperative for effective preventive measures, given the absence of current vaccines or medications. This study employs a bioinformatics strategy to design an epitope vaccine targeting EBV. The primary target for immunity is the EBV glycoprotein 350/220 (gp350), which mediates attachment to B cells through complement receptor 2 (CR2/CD21). The selected protein, EBV glycoprotein 350/220 was chosen based on its role in pathogenicity. In the post-therapeutic approach, three-dimensional structure prediction, validation, and verification processes were conducted, followed by molecular docking studies to identify potential drug-binding sites and suitable therapeutic inhibitors for the target protein. The protein's ability to stimulate humoral and cell-mediated immunity was assessed, leading to the identification of a peptide segment ATNLFLLEL from positions 342 to 356 as the predominant T cell epitope. This epitope demonstrated compatibility with various HLA profiles, enhanced by its interaction with eight HLA-A and HLA-B alleles. Population coverage analysis revealed broad responsiveness across geographies, suggesting its potential as a widely applicable vaccine component. Its non-allergic classification further supports its potential efficacy for diverse individuals. Docking studies with HLA-B*3501 confirmed its significant affinity for MHC-I molecules, establishing it as a promising vaccination candidate. Additionally, a region from amino acid positions 253 to 310 was identified as containing potential B-cell epitopes, with the sequence "LGAGELALTMRSKKLP" showing promise due to its antigenic potential. This computational approach to epitope-based peptide vaccine design and target site prediction against EBV represents a pioneering advancement in EBV virus research, emphasizing the need for validation through <i>in vitro</i> and <i>in vivo</i> trials.

Interferon-induced transmembrane protein-1 competitively blocks Ephrin receptor A2-mediated Epstein-Barr virus entry into epithelial cells.

Epstein-Barr virus (EBV) can infect both B cells and epithelial cells (ECs), causing diseases such as mononucleosis and cancer. It enters ECs via Ephrin receptor A2 (EphA2). The function of interferon-induced transmembrane protein-1 (IFITM1) in EBV infection of ECs remains elusive. Here we report that IFITM1 inhibits EphA2-mediated EBV entry into ECs. RNA-sequencing and clinical sample analysis show reduced IFITM1 in EBV-positive ECs and a negative correlation between IFITM1 level and EBV copy number. IFITM1 depletion increases EBV infection and vice versa. Exogenous soluble IFITM1 effectively prevents EBV infection in vitro and in vivo. Furthermore, three-dimensional structure prediction and site-directed mutagenesis demonstrate that IFITM1 interacts with EphA2 via its two specific residues, competitively blocking EphA2 binding to EBV glycoproteins. Finally, YTHDF3, an m6A reader, suppresses IFITM1 via degradation-related DEAD-box protein 5 (DDX5). Thus, this study underscores IFITM1's crucial role in blocking EphA2-mediated EBV entry into ECs, indicating its potential in preventing EBV infection.

Characterization of a novel gammapartitivirus infecting the phytopathogenic fungus Pyricularia oryzae.

In this study, we identified a novel double-strand RNA (dsRNA) mycovirus in Pyricularia oryzae, designated "Magnaporthe oryzae partitivirus 4" (MoPV4). The genome of MoPV4 consists of a dsRNA-1 segment encoding an RNA-dependent RNA polymerase (RdRP) and a dsRNA-2 segment encoding a capsid protein (CP). Phylogenetic analysis indicated that MoPV4 belongs to the genus Gammapartitivirus within family Partitiviridae. The particles of MoPV4 are isometric with a diameter of about 32.4 nm. Three-dimensional structure predictions indicated that the RdRP of MoPV4 forms a classical right-handed conformation, while the CP has a reclining-V shape.

An interactive visualization tool for educational outreach in protein contact map overlap analysis.

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

Characterization of a novel 3-quinuclidinone reductase possessing remarkable thermostability

The 3-quinuclidinone reductase plays an irreplaceable role in the biopreparation of (R)-3-quinuclidinol, an intermediate vital for synthesis of various pharmaceuticals. Thermal robustness is a critical factor for enzymatic synthesis in industrial applications. This study characterized a new 3-quinuclidinone reductase, named SaQR, with significant thermal stability. The SaQR was overexpressed in a GST-fused state, and substrate and cofactor screening were conducted. Additionally, three-dimensional structure prediction using AlphaFold and analysis were performed, along with relevant thermostability tests, and the evaluation of factors influencing enzyme activity. The findings highlight the remarkable thermostability of SaQR, retaining over 90% of its activity after 72 h at 50°C, with an optimal operational temperature of 85°C. SaQR showed typical structural traits of the SDR superfamily, with its cofactor-determining residue being aspartic acid, conferring nicotinamide adenine dinucleotide (NAD(H)) preference. Moreover, K+ and Na+, at a concentration of 400 mM, could significantly enhance the activity, while Mg2+ and Mn2+ only display inhibitory effects within the tested concentration range. The findings of molecular dynamics simulations suggest that high temperatures may disrupt the binding of enzyme to substrate by increasing the flexibility of residues 205–215. In conclusion, this study reports a novel 3-quinuclidinone reductase with remarkable thermostability.

Expression, purification, and characterization of transmembrane protein homogentisate solanesyltransferase

Homogentisate solanesyltransferase (HST) is a crucial enzyme in the plastoquinone biosynthetic pathway and has recently emerged as a promising target for herbicides. In this study, we successfully expressed and purified a stable and highly pure form of seven times transmembrane protein Chlamydomonas reinhardtii HST (CrHST). The final yield of CrHST protein obtained was 12.2 mg per liter of M9 medium. We evaluated the inhibitory effect on CrHST using Des-Morpholinocarbony Cyclopyrimorate (DMC) and found its IC50 value to be 3.63 ± 0.53 μM, indicating significant inhibitory potential. Additionally, we investigated the substrate affinity of CrHST with two substrates, determining the Km values as 22.76 ± 1.70 μM for FPP and 48.54 ± 3.89 μM for HGA. Through sequence alignment analyses and three-dimensional structure predictions, we identified conserved amino acid residues forming the active cavity in the enzyme. The results from molecular docking and binding energy calculations indicate that DMC has a greater binding affinity with HST compared to HGA. These findings represent substantial progress in understanding CrHST’s properties and potential for herbicide development.Key points• First high-yield transmembrane CrHST protein via E. coli system• Preliminarily identified active cavity composition via activity testing• Determined substrate and inhibitor modes via molecular docking

Comprehensive Evolutionary Analysis of the SMXL Gene Family in Rosaceae: Further Insights into Its Origin, Expansion, Diversification, and Role in Regulating Pear Branching.

SMXL genes constitute a conserved gene family that is ubiquitous in angiosperms and involved in regulating various plant processes, including branching, leaf elongation, and anthocyanin biosynthesis, but little is known about their molecular functions in pear branching. Here, we performed genome-wide identification and investigation of the SMXL genes in 16 angiosperms and analyzed their phylogenetics, structural features, conserved motifs, and expression patterns. In total, 121 SMXLs genes were identified and were classified into four groups. The number of non-redundant SMXL genes in each species varied from 3 (Amborella trichopoda Baill.) to 18 (Glycine max Merr.) and revealed clear gene expansion events over evolutionary history. All the SMXL genes showed conserved structures, containing no more than two introns. Three-dimensional protein structure prediction revealed distinct structures between but similar structures within groups. A quantitative real-time PCR analysis revealed different expressions of 10 SMXL genes from pear branching induced by fruit-thinning treatment. Overall, our study provides a comprehensive investigation of SMXL genes in the Rosaceae family, especially pear. The results offer a reference for understanding the evolutionary history of SMXL genes and provide excellent candidates for studying fruit tree branching regulation, and in facilitating pear pruning and planting strategies.

Identification and Characterization of CC-AMP1-like and CC-AMP2-like Peptides in Capsicum spp.

Antimicrobial peptides (AMPs) are compounds with a variety of bioactive properties. Especially promising are their antibacterial activities, often toward drug-resistant pathogens. Across different AMP sources, AMPs expressed within plants are relatively underexplored with a limited number of plant AMP families identified. Recently, we identified the novel AMPs CC-AMP1 and CC-AMP2 in ghost pepper plants (Capsicum chinense x frutescens), exerting promising antibacterial activity and not classifying into any known plant AMP family. Herein, AMPs related to CC-AMP1 and CC-AMP2 were identified within both Capsicum annuum and Capsicum baccatum. In silico predictions throughout plants were utilized to illustrate that CC-AMP1-like and CC-AMP2-like peptides belong to two broader AMP families, with three-dimensional structural predictions indicating that CC-AMP1-like peptides comprise a novel subfamily of α-hairpinins. The antibacterial activities of several closely related CC-AMP1-like peptides were compared with a truncated version of CC-AMP1 possessing significantly more activity than the full peptide. This truncated peptide was further characterized to possess broad-spectrum antibacterial activity against clinically relevant ESKAPE pathogens. These findings illustrate the value in continued study of plant AMPs toward characterization of novel AMP families, with CC-AMP1-like peptides possessing promising bioactivity.

Three-dimensional structure prediction of [GADS]-proteins as tentative primitive proteins

Abstract [GADV]-protein is a random peptide constructed from glycine (G), alanine (A), aspartic acid (D), and valine (V), whereas [GADS]-protein is constructed from G, A, D, and serine (S). Both [GADV]- and [GADS]-proteins are candidates of primitive proteins. In this study, the three-dimensional structure formation ability of [GADS]-proteins was compared with that of [GADV]-proteins. Based on the results of molecular dynamics simulations, the secondary structure formation ability and rigid structure formation ability of [GADS]-proteins were inferior to those of [GADV]-proteins.

Bartletts principal regressive and arbitrary African buffalo optimizatized three-dimensional protein structure prediction

Bartletts principal regressive and arbitrary African buffalo optimizatized three-dimensional protein structure prediction

Machine learning for <i>in silico</i> protein research

<i>In silico</i> protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of the <i>in silico</i> protein research that combines with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend on the parameters of force field, which is necessary for obtaining accurate results. Machine learning can help researchers to obtain more accurate force field parameters. In molecular dynamics simulation, machine learning can also help to perform the free energy calculation in relatively low cost. Structure prediction is generally used to predict the structure given a protein sequence. Structure prediction is of high complexity and data volume, which is exactly what machine learning is good at. By the help of machine learning, scientists have gained great achievements in three-dimensional structure prediction of proteins. On the other hand, the predicting of protein properties based on its known information is also important to study protein. More challenging, however, is molecule design. Though marching learning has made breakthroughs in drug-like small molecule design and protein design in recent years, there is still plenty of room for exploration. This review focuses on summarizing the above four fields andlooks forward to the application of marching learning to the <i>in silico</i> protein research.

A Rare Variant in MDH2 (rs111879470) Is Associated with Predisposition to Recurrent Breast Cancer in an Extended High-Risk Pedigree.

A significant fraction of breast cancer recurs, with lethal outcome, but specific genetic variants responsible have yet to be identified. Five cousin pairs with recurrent breast cancer from pedigrees with a statistical excess of recurrent breast cancer were sequenced to identify rare, shared candidate predisposition variants. The candidates were tested for association with breast cancer risk with UKBiobank data. Additional breast cancer cases were assayed for a subset of candidate variants to test for co-segregation. Three-dimensional protein structure prediction methods were used to investigate how the mutation under consideration is predicted to change structural and electrostatic properties in the mutated protein. One hundred and eighty-one rare candidate predisposition variants were shared in at least one cousin pair from a high-risk pedigree. A rare variant in MDH2 was found to segregate with breast-cancer-affected relatives in one extended pedigree. MDH2 is an estrogen-stimulated gene encoding the protein malate dehydrogenase, which catalyzes the reversible oxidation of malate to oxaloacetate. The molecular simulation results strongly suggest that the mutation changes the NAD+ binding pocket electrostatics of MDH2. This small sequencing study, using a powerful approach based on recurrent breast cancer cases from high-risk pedigrees, identified a set of strong candidate variants for inherited predisposition for breast cancer recurrence, including MDH2, which should be pursued in other resources.

Rendering protein mutation movies with MutAmore

BackgroundThe success of AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations.ResultsWe introduce MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures.ConclusionMutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins.

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

D-allose Inhibits TLR4/PI3K/AKT Signaling to Attenuate Neuroinflammation and Neuronal Apoptosis by Inhibiting Gal-3 Following Ischemic Stroke

BackgroundIschemic stroke (IS) occurs when a blood vessel supplying the brain becomes obstructed, resulting in cerebral ischemia. This type of stroke accounts for approximately 87% of all strokes. Globally, IS leads to high mortality and poor prognosis and is associated with neuroinflammation and neuronal apoptosis. D-allose is a bio-substrate of glucose that is widely expressed in many plants. Our previous study showed that D-allose exerted neuroprotective effects against acute cerebral ischemic/reperfusion (I/R) injury by reducing neuroinflammation. Here, we aimed to clarify the beneficial effects D-allose in suppressing IS-induced neuroinflammation damage, cytotoxicity, neuronal apoptosis and neurological deficits and the underlying mechanism in vitro and in vivo.MethodsIn vivo, an I/R model was induced by middle cerebral artery occlusion and reperfusion (MCAO/R) in C57BL/6 N mice, and D-allose was given by intraperitoneal injection within 5 min after reperfusion. In vitro, mouse hippocampal neuronal cells (HT-22) with oxygen–glucose deprivation and reperfusion (OGD/R) were established as a cell model of IS. Neurological scores, some cytokines, cytotoxicity and apoptosis in the brain and cell lines were measured. Moreover, Gal-3 short hairpin RNAs, lentiviruses and adeno-associated viruses were used to modulate Gal-3 expression in neurons in vitro and in vivo to reveal the molecular mechanism.ResultsD-allose alleviated cytotoxicity, including cell viability, LDH release and apoptosis, in HT-22 cells after OGD/R, which also alleviated brain injury, as indicated by lesion volume, brain edema, neuronal apoptosis, and neurological functional deficits, in a mouse model of I/R. Moreover, D-allose decreased the release of inflammatory factors, such as IL-1β, IL-6 and TNF-α. Furthermore, the expression of Gal-3 was increased by I/R in wild-type mice and HT-22 cells, and this factor further bound to TLR4, as confirmed by three-dimensional structure prediction and Co-IP. Silencing the Gal-3 gene with shRNAs decreased the activation of TLR4 signaling and alleviated IS-induced neuroinflammation, apoptosis and brain injury. Importantly, the loss of Gal-3 enhanced the D-allose-mediated protection against I/R-induced HT-22 cell injury, inflammatory insults and apoptosis, whereas activation of TLR4 by the selective agonist LPS increased the degree of neuronal injury and abolished the protective effects of D-allose.ConclusionsIn summary, D-allose plays a crucial role in inhibiting inflammation after IS by suppressing Gal-3/TLR4/PI3K/AKT signaling pathway in vitro and in vivo.

What AlphaFold tells us about cohesin's retention on and release from chromosomes.

Cohesin is a trimeric complex containing a pair of SMC proteins (Smc1 and Smc3) whose ATPase domains at the end of long coiled coils (CC) are interconnected by Scc1. During interphase, it organizes chromosomal DNA topology by extruding loops in a manner dependent on Scc1's association with two large hook-shaped proteins called SA (yeast: Scc3) and Nipbl (Scc2). The latter's replacement by Pds5 recruits Wapl, which induces release from chromatin via a process requiring dissociation of Scc1's N-terminal domain (NTD) from Smc3. If blocked by Esco (Eco)-mediated Smc3 acetylation, cohesin containing Pds5 merely maintains pre-existing loops, but a third fate occurs during DNA replication, when Pds5-containing cohesin associates with Sororin and forms structures that hold sister DNAs together. How Wapl induces and Sororin blocks release has hitherto remained mysterious. In the 20 years since their discovery, not a single testable hypothesis has been proposed as to their role. Here, AlphaFold 2 (AF) three-dimensional protein structure predictions lead us to propose formation of a quarternary complex between Wapl, SA, Pds5, and Scc1's NTD, in which the latter is juxtaposed with (and subsequently sequestered by) a highly conserved cleft within Wapl's C-terminal domain. AF also reveals how Scc1's dissociation from Smc3 arises from a distortion of Smc3's CC induced by engagement of SMC ATPase domains, how Esco acetyl transferases are recruited to Smc3 by Pds5, and how Sororin prevents release by binding to the Smc3/Scc1 interface. Our hypotheses explain the phenotypes of numerous existing mutations and are highly testable.

What AlphaFold tells us about cohesin’s retention on and release from chromosomes

Cohesin is a trimeric complex containing a pair of SMC proteins (Smc1 and Smc3) whose ATPase domains at the end of long coiled coils (CC) are interconnected by Scc1. During interphase, it organizes chromosomal DNA topology by extruding loops in a manner dependent on Scc1’s association with two large hook-shaped proteins called SA (yeast: Scc3) and Nipbl (Scc2). The latter’s replacement by Pds5 recruits Wapl, which induces release from chromatin via a process requiring dissociation of Scc1’s N-terminal domain (NTD) from Smc3. If blocked by Esco (Eco)-mediated Smc3 acetylation, cohesin containing Pds5 merely maintains pre-existing loops, but a third fate occurs during DNA replication, when Pds5-containing cohesin associates with Sororin and forms structures that hold sister DNAs together. How Wapl induces and Sororin blocks release has hitherto remained mysterious. In the 20 years since their discovery, not a single testable hypothesis has been proposed as to their role. Here, AlphaFold 2 (AF) three-dimensional protein structure predictions lead us to propose formation of a quarternary complex between Wapl, SA, Pds5, and Scc1’s NTD, in which the latter is juxtaposed with (and subsequently sequestered by) a highly conserved cleft within Wapl’s C-terminal domain. AF also reveals how Scc1’s dissociation from Smc3 arises from a distortion of Smc3’s CC induced by engagement of SMC ATPase domains, how Esco acetyl transferases are recruited to Smc3 by Pds5, and how Sororin prevents release by binding to the Smc3/Scc1 interface. Our hypotheses explain the phenotypes of numerous existing mutations and are highly testable.