Activation of RAS oncogenes in normal cells triggers a stable cell cycle arrest known as RAS-induced senescence (RIS), marked by persistent DNA damage and extensive epigenetic remodeling. Although bypassing RIS promotes tumorigenesis, the molecular mechanisms underlying this transition remain poorly defined. Here, we demonstrate that RIS cells accumulate high levels of R-loops-three-stranded DNA-RNA hybrids-that frequently co-localize with DNA G-quadruplexes formed on the non-template DNA strand, generating G-loop-like structures. RIS bypass is characterized by the resolution of these structures through the heterotrimeric RPA complex, which facilitates RNase H1-mediated R-loop processing. In pre-RIS and RIS cells, hyperphosphorylation of RPA32 disrupts the ability of RPA to enhance RNase H1 activity, thereby impairing its enzymatic processivity. Consequently, R-loops and G-loops remain unresolved, contributing to the accumulation of γH2AX. Remarkably, forced restoration of RPA-regulated RNase H1 activity in RAS-expressing cells reduces DNA damage and enables cell cycle re-entry, effectively bypassing senescence. These findings identify a regulatory axis involving RPA phosphorylation and RNase H1 activity that governs R-loop and G-loop resolution, acting as a critical genome maintenance mechanism during oncogene-induced stress.

Introduction:Microtubules are essential components of the neuronal cytoskeleton. The α- and β-tubulins, variably expressed in the central nervous system, play key roles in neurogenesis and brain development. Pathogenic variants in TUBB2A have recently been identified as an ultra-rare cause of pediatric neurodevelopmental disorders (NDDs). However, the neurological and behavioral manifestations, genotype-phenotype correlations, and underlying disease mechanisms remain poorly understood due to the limited number of reported families. Methods:We describe a cohort of families presenting with microcephaly, global developmental delay, speech impairment, seizures and/or EEG abnormalities, movement disorders and severe behavioral disorders. Clinical assessments and brain imaging studies were conducted over a 10-year follow-up period. Genetic analysis was performed via whole-exome sequencing (WES), and structural modeling was used to investigate the functional impact of the identified variants. Results:WES revealed a novel recurrent heterozygous pathogenic variant in TUBB2A (NM_001069.3:c.1172G > A; NP_001060.1:p.Arg391His), identified as the cause of disease in multiple affected individuals from unrelated families. Comparative analysis with previously reported TUBB2A de novo variants confirmed that this novel recurrent mutation affects a highly conserved Arg391 residue within the longitudinal E-site heterodimer interface. Computational modeling demonstrated that the variant disrupts α/β-tubulin heterodimer formation, impairing binding stability at this critical interaction site. Discussion:Our findings expand the phenotypic and genotypic spectrum of TUBB2A-related disorders and identify Arg391 as a mutational hotspot linked to severe brain developmental disorders due to aberrant tubulin dynamics, highlighting the disruption of the α/β-tubulin heterodimer formation as the disease mechanism associated to this novel hotspot variant. These results provide new insights into disease mechanisms and offer a foundation for potential future therapeutic approaches aimed at stabilizing α/β-tubulin interactions.

(1) Background: Electrostatics plays a capital role in protein–protein and protein–ligand interactions. Implicit solvent models are widely used to describe electrostatics and complementarity at interfaces. Electrostatic complementarity at the interface is not trivial, involving surface potentials rather than the charges of surfacial contacting atoms. (2) Results: The program bluues_cplx, here used in conjunction with the software NanoShaper to compute molecular surfaces, has been used to compute the electrostatic properties of 756 protein–protein and 189 protein–ligand complexes along with the corresponding isolated molecules. (3) Methods: The software we make available here uses Generalized Born (GB) radii, computed by a molecular surface integral, to output several descriptors of electrostatics at protein (and in general, molecular) interfaces. We illustrate the usage of the software by analyzing a dataset of protein–protein and protein–ligand complexes, thus extending and refining previous analyses of electrostatic complementarity at protein interfaces. (4) Conclusions: The complete analysis of a molecular complex is performed in tens of seconds on a PC, and the results include the list of surfacial contacting atoms, their charges and Pearson correlation coefficient, the list of contacting surface points with the electrostatic potential (computed for the isolated molecules) and Pearson correlation coefficient, the electrostatic and hydrophobic free energy with different contributions for the isolated molecules, their complex and the difference for all terms. The software is readily usable for any molecular complex in solution.

The study of solid–liquid equilibria offers critical insights into the molecular interactions between constituents in binary mixtures. Predicting these equilibria often requires comprehensive thermodynamic models, yet simplified approaches can provide valuable perspectives. In this work, we explore the application of the Bragg–Williams model to solid–liquid equilibria in binary mixtures leading to the formation of eutectic solvents. This model relies on a single parameter—the molar energy change upon mixing compounds—and demonstrates noteworthy features: the parameter can be estimated from a few (in principle, from a single) experimental melting points, and it correlates strongly with interaction energy parameters from more complex models, such as the PC-SAFT molecular-based equation of state. By using the Bragg–Williams model, we provide a straightforward and informative framework for characterizing solid–liquid equilibria, enabling insights into molecular interactions while requiring few data points as input. Despite its simplicity, the model effectively captures the essence of binary mixture energetics, positioning it as a practical tool for advancing the understanding of phase behavior in eutectic solvent systems.

The characterization of the statistical ensemble of conformations of Intrinsically Disordered Regions (IDRs) is a great challenge both from the experimental and com- putational points of view. In this respect, a number of protocols have been developed using molecular dynamics (MD) simulations to sample the huge conformational space of the molecule. In this work, we consider one of the best methods available, Replica exchange solute tempering (REST), as a reference to compare with the results obtained using other methods, in terms of experimentally measurable quantities. Along with the methods assessed, we propose here a novel protocol called probabilistic MD chain growth (PMD-CG), which combines the flexible-mecano and hierarchical chain growth methods with the statistical data obtained from tripeptide MD trajectories as start- ing point. The system chosen for testing is a 20-residue region from the C-terminal domain of p53 tumor suppressor protein (p53-CTD). Our results show that PMD-CG provides an ensemble of conformations extremely fast, after suitable computation of the conformational pool for all peptide triplets of the IDR sequence. The measurable quantities computed on the ensemble of conformations agrees well with those based on the REST conformational ensemble.

Lauroylcholine chloride is the product of lipase catalysed esterification of a choline chloride/glucose/water (2 : 1 : 1) reactive natural deep eutectic solvent (R-NADES).

MOTIVATION:Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. RESULTS:In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on High Performance Computing (HPC) systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. AVAILABILITY AND IMPLEMENTATION:The source code is available at https://github.com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/. The data underlying this article are available at https://github.com/pgbarletta/suppl_info_locuaz.

A novel approach for the enzymatic esterification of carbohydrate polyols with fatty acids was investigated, with reactive natural deep eutectic solvents (R-NADES) as reaction medium and source of reagents. Three binary hydrophilic R-NADES consisting of choline chloride (ChCl) as hydrogen bond acceptor (HBA) and sugar alcohols (D-sorbitol, xylitol, D-arabitol) as hydrogen bond donors (HBD), were prepared and characterized. The carbohydrate polyol-based R-NADES were durable viscous fluids between 40 and 800C, the common temperature range for an enzymatic reaction. The commercially accessible lipase B from Candida antarctica immobilized on acrylic resins (LAR) showed significant esterification activity and exceptional thermal stability in all three tested R-NADES and successfully catalyzed the synthesis of polyol esters. Box Behnken factorial design with three levels-three variables was used for optimizing the reaction conditions. The preparative esterification of D-arabitol with lauric acid (LA) at predicted optimal values for enzyme load, (i.e., 800 U/gram D-arabitol), temperature (70 degrees C) and LA/D-arabitol molar ratio of 1), attained 80 mol% LA conversion after 24 h reaction time. Structural analysis based on mass, infrared and nuclear magnetic resonance spectroscopy demonstrated that the reaction product is solely the diester 1,5-dilauryl-D-arabitol (1,5-DLDA). The 1,5-DLDA product, with an NMR-purity above 99.9%, was isolated in 95% yield. Under similar conditions, 1,5-dilauryl-xylitol (1,5-DLX) and 1,6dilauryl-D-sorbitol (1,6-DLDS) were obtained, at a LA conversion of 56 mol% and 62 mol%, respectively. A combination of docking and molecular dynamics simulations allowed to rationalize the structural stability of the lipase B from Candida antarctica (CalB) active site in R-NADES, as well as the selectivity of the catalyzed esterification.

Motivation: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. Results: In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics (MD) simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on HPC systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. Availability and implementation: the source code is available at https://github. com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.

The study of proteins with intrinsically disordered regions (IDRs) has emerged as an active field of research due to their intriguing nature. Although IDRs lack a well-defined folded structure, they play important functional roles in cells, following biological mechanisms different from those of the traditional structured proteins. Consequently, it has been necessary to re-design experimental and theoretical methods in order to face the challenges introduced by the dynamic nature of IDRs. In this work, we present an accurate and cost-effective method to study the conformational dynamics of IDRs based on the use of residue-local probabilistic expressions that characterize the conformational ensembles obtained from finite-temperature molecular dynamics (MD) simulations. It is shown that the good performance and the high convergence rates achieved with our method are independent of the IDR lengths, since the method takes advantage of the major influence of the identity and conformation of the nearest residue neighbors on the amino-acid conformational preferences to evaluate the IDR conformational ensembles. This allows us to characterize the conformational space of IDRs using a reduced number of probabilities which can be obtained from comparatively short MD simulations or experimental databases. To exemplify the usefulness of our approach, we present an application to directly detect Molecular Recognition Features (MoRFs) in an IDR domain of the protein p53, and to follow the time evolution of the thermodynamic magnitudes of this system during its exploration of the conformational space.

Biomolecular electrostatics is of key importance for biological function and recognition. The continuum electrostatic model based on the Poisson-Boltzmann (PB) equation has been widely used to study biomolecular electrostatics. The solution of the PB equation gives the electrostatic potential over the space enclosing the molecule(s) of interest, typically obtained at points of a suitable grid. The Generalized Born (GB) model has been used to provide a useful approximation to the solution obtained by solving numerically the PB equation. The main advantage of the GB approach is to express the electrostatic energy of the system as the sum of pairwise interactions. The latter depend on geometric parameters (the GB radii) which depend on all atoms, and their radii, of the system. In this work we present a tutorial through examples for a more efficient and general version of the program Bluues which is able to compute:1) the generalized Born radius of each atom;2) the electrostatic potential at the surface of the molecule mapped to solvent accessible atoms;3) the solvent accessible surface in a PDB formatted file;4) the electrostatic potential in the volume surrounding the molecule;5) the electrostatic free energy and different contributions to it;6) the pH-dependent properties of proteins (total charge and pH-dependent free energy of folding) in the pH range -4 to 18;7) the pKa shifts due to molecular structure of all ionizable groups. Program summary Program Title: bluues2CPC Library link to program files: https://doi .org /10 .17632 /bx98gcrzbg .1Developer's repository link: https://github .com /federico -fogolari /bluues2Code Ocean capsule: https://codeocean .com /capsule /9338710 Licensing provisions: GPLv3Programming language: CNature of problem: Biomolecular electrostatics is of key importance for biological function and recognition. Computation of electrostatic effects and their dissection in atomic contributions is a general problem in molecular modeling.Solution method: The GBR6 version of the Generalized Born (GB) model is implemented through surface integrals and all common molecular electrostatic analyses are performed by a single command line program.Additional comments including restrictions and unusual features: The program requires, as input, files in pqr format, which may be generated by independent software (e.g. babel, pdb2pqr). For the optional generation of the solvent excluded surface, the software msms must be installed and present in the path.(c) 2023 Elsevier B.V. All rights reserved.

In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.

BACKGROUND:P-21-activated kinases (PAKs) are protein serine/threonine kinases, part of the RAS/mitogen-activated protein kinase pathway. PAK1 is highly expressed in the central nervous system and crucially involved in neuronal migration and brain developmental processes. Recently, de novo heterozygous missense variants in PAK1 have been identified as an ultrarare cause of pediatric neurodevelopmental disorders. METHODS:We report a series of children affected with postnatal macrocephaly, neurodevelopmental impairment, and drug-resistant epilepsy. Repeated electroencephalographic (EEG) and video-EEG evaluations were performed over a two- to 10-year period during follow-up to delineate electroclinical histories. Genetic sequencing studies and computational evaluation of the identified variants were performed in our patient cohort. RESULTS:We identified by whole-exome sequencing three novel de novo variants in PAK1 (NM_001128620: c.427A>G, p.Met143Val; c.428T>C, p.Met143Thr; c.428T>A, p.Met143Lys) as the underlying cause of the disease in our families. The three variants affected the same highly conserved Met143 residue within the cysteine-rich inhibitor of PAK1 (CRIPaK) domain, which was identified before as a PAK1 inhibitor target. Computational studies suggested a defective autoinhibition presumably due to impaired PAK1 autoregulation as a result of the recurrent substitution. CONCLUSIONS:We delineated the electroclinical phenotypes of PAK1-related neurological disorders and highlight a novel mutational hotspot that may involve defective autoinhibition of the PAK1 protein. The three novel variants affecting the same hotspot residue within the CRIPaK domain highlight potentially impaired PAK1-CRIPaK interaction as a novel disease mechanism. These findings shed light on possible future treatments targeted at the CRIPaK domain, to modulate PAK1 activity and function.

BACKGROUND:Pathogenic variants in PEX-genes can affect peroxisome assembly and function and cause Zellweger spectrum disorders (ZSDs), characterized by variable phenotypes in terms of disease severity, age of onset and clinical presentations. So far, defects in at least 15 PEX-genes have been implicated in Mendelian diseases, but in some of the ultra-rare ZSD subtypes genotype-phenotype correlations and disease mechanisms remain elusive.METHODS:We report five families carrying biallelic variants in PEX13. The identified variants were initially evaluated by using a combination of computational approaches. Immunofluorescence and complementation studies on patient-derived fibroblasts were performed in two patients to investigate the cellular impact of the identified mutations.RESULTS:Three out of five families carried a recurrent p.Arg294Trp non-synonymous variant. Individuals affected with PEX13-related ZSD presented heterogeneous clinical features, including hypotonia, developmental regression, hearing/vision impairment, progressive spasticity and brain leukodystrophy. Computational predictions highlighted the involvement of the Arg294 residue in PEX13 homodimerization, and the analysis of blind docking predicted that the p.Arg294Trp variant alters the formation of dimers, impairing the stability of the PEX13/PEX14 translocation module. Studies on muscle tissues and patient-derived fibroblasts revealed biochemical alterations of mitochondrial function and identified mislocalized mitochondria and a reduced number of peroxisomes with abnormal PEX13 concentration.CONCLUSIONS:This study expands the phenotypic and mutational spectrum of PEX13-related ZSDs and also highlight a variety of disease mechanisms contributing to PEX13-related clinical phenotypes, including the emerging contribution of secondary mitochondrial dysfunction to the pathophysiology of ZSDs.

Antibodies have become the Swiss Army tool for molecular biology and nanotechnology. Their outstanding ability to specifically recognise molecular antigens allows their use in many different applications from medicine to the industry. Moreover, the improvement of conventional structural biology techniques (e.g., X-ray, NMR) as well as the emergence of new ones (e.g., Cryo-EM), have permitted in the last years a notable increase of resolved antibody-antigen structures. This offers a unique opportunity to perform an exhaustive structural analysis of antibody-antigen interfaces by employing the large amount of data available nowadays. To leverage this factor, different geometric as well as chemical descriptors were evaluated to perform a comprehensive characterization.