Arf1 belongs to the Arf family of small GTPases that localise at the Golgi and plasma membrane. Active Arf1 plays a crucial role in regulating Golgi organisation and function. In mouse fibroblasts, loss of adhesion triggers a consistent drop (∼50%) in Arf1 activation that causes the Golgi to disorganise but not fragment. In suspended cells, the trans-Golgi (GalTase) disperses more prominently than cis-Golgi (Man II), accompanied by increased active Arf1 (detected using GFP-ABD: ARHGAP10 Arf1 binding domain) associated with the cis-Golgi compartment. Re-adhesion restores Arf1 activation at the trans-Golgi as it reorganises. Arf1 activation at the Golgi is regulated by Arf1 Guanine nucleotide Exchange Factors (GEFs), GBF1, and BIG1/2. In non-adherent fibroblasts, the cis-medial Golgi provides a unique setting to test and understand the role GEF-mediated Arf1 activation has in regulating Golgi organisation. Labelled with Man II-GFP non-adherent fibroblasts treated with increasing concentrations of Brefeldin-A (BFA) (inhibits BIG1/2 and GBF1) or Golgicide A (GCA) (inhibits GBF1 only) comparably decrease active Arf1 levels. They, however, cause a concentration-dependent increase in cis-medial Golgi fragmentation and fusion with the endoplasmic reticulum (ER). Using selected BFA and GCA concentrations, we find a change in the kinetics of Arf1 inactivation could mediate this by regulating cis-medial Golgi localisation of GBF1. On loss of adhesion, a ∼50% drop in Arf1 activation over 120 minutes causes the Golgi to disorganise. The kinetics of this drop, when altered by BFA or GCA treatment causes a similar decline in Arf1 activation but over 10 minutes. This causes the Golgi to now fragment which affects cell surface glycosylation and re-adherent cell spreading. Using non-adherent fibroblasts this study reveals the kinetics of Arf1 inactivation, with active Arf1 levels, to be vital for Golgi organisation and function.

SUMOylation is a post translational modification that involves covalent attachment of SUMO C-terminus to side chain amino group of lysine residues in target proteins. Disruption of the modification has been linked to neurodegenerative diseases and cancer. Recent improvements in mass spectrometry-coupled proteomics experiments have enabled high-throughput identification of SUMOylated lysines in mammalian cells. One such study was Hendriks et al, 2018, wherein the authors identified SUMOylated lysines in human and mouse cells. Information from this study was used as an input to a sequence homology based method to annotate putative SUMOylatable lysines from the proteome of fruit fly Drosophila melanogaster. 5283 human and 468 mouse SUMOylated proteins led to the identification of 8539 and 1700 fly homologs and putative SUMOylation sites therein respectively. Clustering analysis was carried out on these annotated sites to obtain three typs of information. First type of information revealed amino acid preferences in the local sequence vicinity of the annotated sites. This exercise confirmed that ψKx(E/D), where ψ = I/V/L, is the most frequently occurring sequence motif involving SUMOylated lysines. Second type of information revealed protein families that contain the annotated sites. Results from this exercise reveal that members of thousands of protein families contain annotated SUMOylation sites. Third type of information revealed preferred biological and cellular functions of proteins containing the annotated lysines. This exercise revealed that nucleus and transcription are preferred cellular localization and biological function respectively.

SUMOylation is a posttranslational modification that involves lysine residues from eukaryotic proteins. Misregulation of the modification has been linked to neuro-degenrative diseases and cancer. All the presently available tools to predict SUMOylation sites are sequence based. Here, we propose a novel structure based prediction tool to discriminate between SUMOylated and non-SUMOylated lysines. We demonstrate the method by carrying out a proof-of-concept study. The method achieved an accuracy of 81% and a Matthews’ Correlation Coefficient of 0.4.

Molecular interactions play a central role in all biological processes. The strength of these interactions is often characterized by their dissociation constants (KD). The high affinity interactions (KD less than or equal to 10-6) are crucial for the proper execution of cellular processes and are thus extensively investigated. Detailed molecular and biochemical analyses of such high affinity interactions have lent considerable support to the concept of binary on/off switches in different biological contexts. However, such studies have typically discounted the presence of low-affinity binders (KD > 10-3) in the cellular environment. In this study, we have assessed the potential influence of these low affinity binders on high affinity interactions. By employing Gillespie stochastic simulations and ordinary differential equations we demonstrate that the presence of low-affinity binders can indeed alter the kinetics and equilibrium of high-affinity interactions. We evaluated the possible impact of such binders in two different contexts including sex determination in Drosophila melanogaster and in signaling systems that employ molecular thresholds. Based on these analyses, we speculate that low-affinity binders may be more prevalent in different biological contexts where the outcomes depend critically on threshold value determinants and could impact their homeostatic regulation.

In this study, we mined the PDB and created a structural library of 178,465 interfaces that mediate protein–protein/domain–domain interactions. Interfaces involving the same CATH fold(s) were clustered together. Our analysis of the library reveals similarities between chain–chain and domain–domain interactions. The library also illustrates how a single protein fold can interact with multiple folds using similar interfaces. The library is hence a useful resource to study the types of interactions between protein folds. Analyzing the data in the library reveals various interesting aspects of protein–protein and domain–domain interactions such as how proteins belonging to folds that interact with many other folds also have high number of Enzyme Commission terms. These data could be utilized to seek potential binding partners. It can also be utilized to investigate the different ways in which two or more folds interact with one another structurally. We constructed a statistical potential of pair preferences of amino acids across the interface for chain–chain and domain–domain interactions separately. They are quite similar further lending credence to the notion that domain–domain interfaces could be used to study chain–chain interactions. We analyzed protein complexes modeled by AlphaFold2 and RoseTTAFold and noticed that some of the modes of interaction involve folds and interfaces that have not been observed to bind in the PDB. Lastly and importantly, the library includes predicted small molecule‐binding sites at protein–protein interfaces. This has applications as interfaces containing small molecule‐binding sites can be easily targeted to prevent the interaction and perhaps form a part of a therapeutic strategy.