Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures model variations inter-residue network, evolutionary information, complex network metrics and energetic terms generate an...

Abstract Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin that accumulates in terrestrial and marine food webs, with potential impacts on human health. This process requires the gene pair hgcAB, which encodes for proteins actuate Hg methylation, has been well described anoxic environments. However, recent studies report MeHg formation suboxic seawater, although microorganisms involved remain poorly understood. In this study, we conducted large-scale...

Aberrant phase separation of globular proteins is associated with many diseases. Here, we use a model protein system to understand how the unfolded states drive and formation deposits (UPODs). We find that for UPODs form, concentrations molecules must be above threshold value. Additionally, possess appropriate sequence grammars separation. While recruit molecular chaperones, their compositional profiles are also influenced by synergistic physicochemical interactions governed cellular...

Abstract Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate drugs human body, are described from four perspectives: absorption, distribution, metabolism excretion—all closely related to fifth perspective, toxicity (ADMET). Since obtaining ADMET data vitro, vivo or pre-clinical stages time consuming expensive, many efforts have been made...

Abstract The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range computational tools has been developed over the past two decades assist in epitope prediction; however, they have presented limited performance generalization, identification conformational B-cell epitopes. Here, we present epitope3D, a novel scalable machine learning...

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development understanding diseases. However, current predictive tools often struggle to balance efficiency precision predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, deep learning model that efficiently accurately predicts changes PPI binding free energy upon single multiple point mutations. Building robust Siamese network...

Abstract Motivation A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used predict the effects mutations on Ab binding affinity. Results Here we present an updated and refined version this approach, mCSM-AB2, capable accurately modelling Ab–antigen affinity, through inclusion evolutionary energetic terms. Using new expanded database over...

Abstract Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because new drug candidates are usually analogous to or derived from NC. In order express physically realistically using a computer, it is essential descriptor set that can adequately represent characteristics belonging NC’s chemical space. Although several topological descriptors developed describe physical, chemical,...

Understanding antibody-antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of landscapes, guiding design. Despite this, little effort has been devoted accurately predicting the affinity between antibodies antigens develop tailored docking scoring functions this type interaction. Here, we developed CSM-AB, a machine learning method...

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. there have many efforts to develop computational tools guide rational antibody engineering, most approaches of limited accuracy when applied design, analysing a single point mutation at time. To overcome this gap, we curated dataset 242 experimentally determined changes binding affinity upon...

Abstract Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations gene rpoB. Previously we have highlighted that these reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, used insights to develop computational rifampicin predictor capable of identifying resistant even outside well-defined determining region (RRDR),...

EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users go from selecting protein target with known structure tailoring purchasable performing visualizing docking in few clicks. Our system also filter screening libraries based on properties, cluster molecules by similarity personalize parameters.EasyVS freely available as easy-to-use web interface at...

While antibodies have been ground-breaking therapeutic agents, the structural determinants for antibody binding specificity remain to be fully elucidated, which is compounded by virtually unlimited repertoire of antigens they can recognize. Here, we explored landscapes antibody-antigen interfaces identify driving target recognition assessing concavity and interatomic interactions.We found that complementarity-determining regions utilized deeper with their longer H3 loops, especially loops...

Glycosylation, a crucial and the most common post-translational modification, coordinates multitude of biological functions through attachment glycans to proteins lipids. This process, predominantly governed by glycosyltransferases (GTs) glycoside hydrolases (GHs), decides not only biomolecular functionality but also protein stability solubility. Mutations in these enzymes have been implicated spectrum diseases, prompting critical research into structural functional consequences such genetic...

Abstract While protein–nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these a challenge. Consequently, most understand effects missense mutations on protein-nucleic affinity have focused single-point and presented performance independent sets. To overcome this, we curated largest dataset experimentally measured nucleic binding date, encompassing 856 141 multiple-point...

Abstract Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form DNA variants. Ultimately, goal many these is to associate variants phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range approaches for encoding genotypic phenotypic information any organism disease, from individual population, three-dimensional (3D) space. It allows mutation mapping annotation,...

Abstract While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic combinations has been a challenging venture. Computational methods have emerged this context as promising tool for prioritizing further evaluation, though they presented limited performance, utility, and interpretability. Here, we propose predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures accurately...

Abstract Alzheimer's disease (AD) is one of the most common forms dementia and neurodegenerative diseases, characterized by formation neuritic plaques neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, missense mutations can alter folding functions these proteins, significantly increasing risk AD. However, many methods to identify AD‐causing variants did not consider effect from perspective a protein three‐dimensional environment. Here, we...

Bilayers prepared from sorbitan fatty acid esters (Span) have been frequently used for delivery of drugs including flavonoids. We applied molecular dynamics simulation to characterize the structure a monostearate (Span 60) bilayer in complex with three representative flavones, subclass At low concentration, unsubstituted flavone, most hydrophobic member, was able flip over and cross large diffusion coefficient. high it accumulated at center resulting phase separation. The leaflets were...

SUMMARY The emergence of the COVID-19 pandemic has spurred a global rush to uncover basic biological mechanisms, inform effective vaccine and drug development. Despite viral novelty, sequencing efforts have already identified genomic variation across isolates. To enable easy exploration spatial visualization potential implications SARS-CoV-2 mutations on infection, host immunity development we developed COVID-3D ( http://biosig.unimelb.edu.au/covid3d/ ).

Variants in non-homologous end joining (NHEJ) DNA repair genes are associated with various human syndromes, including microcephaly, growth delay, Fanconi anemia, and different hereditary cancers. However, very little has been done previously to systematically record the underlying molecular consequences of NHEJ variants their link phenotypic outcomes. In this study, a list over 2983 missense principal components system, Ligase IV, DNA-PKcs, Ku70/80 XRCC4, reported clinical literature, was...

Summary Aberrant phase separation of globular proteins is associated with many diseases. Here, we use a model protein system to understand how unfolded states drive and the formation deposits (UPODs). For UPODs form, concentrations molecules must be above threshold value. Additionally, possess appropriate sequence grammars separation. While recruit molecular chaperones, their compositional profiles are also influenced by synergistic physicochemical interactions governed features cellular...

Nitration of tyrosine and tryptophan residues is common in cells under nitrative stress. However, physiological consequences protein nitration are not well characterized on a molecular level due to limited availability the 3D structures nitrated proteins. Molecular dynamics (MD) simulation can be an alternative tool probe structural perturbations induced by nitration. In this study we developed mechanics parameters for 3-nitrotyrosine (NIY) 6-nitrotryptophan (NIW) that compatible with...