Fine-tuning Protein Embeddings for Functional Similarity Evaluation.

MOTIVATION: Protein's with unknown function are frequently compared to better characterized relatives, either using sequence similarity, or recently through similarity in a learned embedding space. Through comparison, protein sequence embeddings allow for interpretable and accurate annotation of proteins, as well as for downstream tasks such as clustering for unsupervised discovery of protein families. However, it's unclear whether embeddings can be deliberately designed to improve their use in these downstream tasks. RESULTS: We find that for functional annotation of proteins, as represented by Gene Ontology (GO) terms, direct fine-tuning of language models on a simple classification loss has an immediate positive impact on protein embedding quality. Fine-tuned embeddings show stronger performance as representations for K-nearest neighbor classifiers, reaching stronger performance for GO annotation than even directly comparable fine-tuned classifiers, while maintaining interpretability through protein similarity comparisons. They also maintain their quality in related tasks, such as rediscovering protein families with clustering. AVAILABILITY: github.com/mofradlab/go_metric. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrin mechanosensing relies on a pivot-clip mechanism to reinforce cell adhesion.

Cells intricately sense mechanical forces from their surroundings, driving biophysical and biochemical activities. This mechanosensing phenomenon occurs at the cell-matrix interface, where mechanical forces resulting from cellular motion, such as migration or matrix stretching, are exchanged through surface receptors, primarily integrins, and their corresponding matrix ligands. A pivotal player in this interaction is the α5ß1 integrin and fibronectin (FN) bond, known for its role in establishing cell adhesion sites for migration. However, upregulation of the α5ß1-FN bond is associated with uncontrolled cell metastasis. This bond operates through catch bond dynamics, wherein the bond lifetime paradoxically increases with greater force. The mechanism sustaining the characteristic catch bond dynamics of α5ß1-FN remains unclear. Leveraging molecular dynamics simulations, our approach unveils a pivot-clip mechanism. Two key binding sites on FN, namely the synergy site and the RGD (Arg-Gly-Asp) motif, act as active points for structural changes in α5ß1 integrin. Conformational adaptations at these sites are induced by a series of hydrogen bond formations and breaks at the synergy site. We disrupt these adaptations through a double mutation on FN, known to reduce cell adhesion. A whole-cell finite-element model is employed to elucidate how the synergy site may promote dynamic α5ß1-FN binding, resisting cell contraction. In summary, our study integrates molecular- and cellular-level modeling to propose that FN's synergy site reinforces cell adhesion through enhanced binding dynamics and a mechanosensitive pivot-clip mechanism. This work sheds light on the interplay between mechanical forces and cell-matrix interactions, contributing to our understanding of cellular behaviors in physiological and pathological contexts.

HGTDR: Advancing drug repurposing with heterogeneous graph transformers.

MOTIVATION: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages. RESULTS: We propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.

Comparison of psychological reactions, fear of negative evaluation and mood-emotional alexithymia in successful and unsuccessful people in addiction recovery: a survey of Baluchestan in Iran.

The aim of this study was to examine psychological responses, fear of negative evaluation, and mood-related alexithymia in individuals in addiction recovery who have succeeded versus those who have not. This study employed a causal-comparative design. The research focused on all individuals who sought treatment for addiction at clinics within a specific district in Zahedan city, Iran. Out of the group, 100 individuals were chosen (50 who successfully stopped their addiction and 50 who were unsuccessful in their attempts to quit) through convenience sampling. Data were gathered using the depression, Anxiety, and Stress Scale (DASS-21), Lori's fear of negative evaluation scale, and Toronto's mood-emotional alexithymia scale. Data were examined through both descriptive statistics and multivariate analysis of variance. Based on the results, successful and unsuccessful individuals in addiction recovery showed varying levels of depression, stress, fear of negative evaluation, and having no fear of negative evaluation (p < .01). In simpler terms, unsuccessful individuals in addiction recovery scored higher in depression, stress, fear of negative evaluation, inability feeling emotions, inability to express feelings compared to successful individuals. But successful individuals in addiction recovery obtained higher mean scores than unsuccessful individuals in addiction recovery on having no fear of negative evaluation. According to the findings psychological reactions, fear of negative evaluation and mood-emotional alexithymia play a significant role in addiction treatment and can be helpful in addiction recovery in unsuccessful people in addiction recovery. It is feasible to enhance the effectiveness of addiction recovery by implementing interventions that target the reduction of depression, stress, fear of negative evaluation, and alexithymia.

An in-silico study on the mechanical behavior of colorectal cancer cell lines in the micropipette aspiration process.

Cancer alters the structural integrity and morphology of cells. Consequently, the cell function is overshadowed. In this study, the micropipette aspiration process is computationally modeled to predict the mechanical behavior of the colorectal cancer cells. The intended cancer cells are modeled as an incompressible Neo-Hookean visco-hyperelastic material. Also, the micropipette is assumed to be rigid with no deformation. The proposed model is validated with an in-vitro study. To capture the equilibrium and time-dependent behaviors of cells, ramp, and creep tests are respectively performed using the finite element method. Through the simulations, the effects of the micropipette geometry and the aspiration pressure on the colorectal cancer cell lines are investigated. Our findings indicate that, as the inner radius of the micropipette increases, despite the increase in deformation rate and aspirated length, the time to reach the equilibrium state increases. Nevertheless, it is obvious that increasing the tip curvature radius has a small effect on the change of the aspirated length. But, due to the decrease in the stress concentration, it drastically reduces the equilibrium time and increases the deformation rate significantly. Interestingly, our results demonstrate that increasing the aspiration pressure somehow causes the cell stiffening, thereby reducing the upward trend of deformation rate, equilibrium time, and aspirated length. Our findings provide valuable insights for researchers in cell therapy and cancer treatment and can aid in developing more precise microfluidic.

Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment.

BACKGROUND: Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake environments, understanding the model's decision-making process is critical. This study assesses the performance of different pretrained Bidirectional Encoder Representations from Transformers (BERT) models and delves into understanding its decision-making within the context of medical image protocol assignment. METHODS: Four different pre-trained BERT models (BERT, BioBERT, ClinicalBERT, RoBERTa) were fine-tuned for the medical image protocol classification task. Word importance was measured by attributing the classification output to every word using a gradient-based method. Subsequently, a trained radiologist reviewed the resulting word importance scores to assess the model's decision-making process relative to human reasoning. RESULTS: The BERT model came close to human performance on our test set. The BERT model successfully identified relevant words indicative of the target protocol. Analysis of important words in misclassifications revealed potential systematic errors in the model. CONCLUSIONS: The BERT model shows promise in medical image protocol assignment by reaching near human level performance and identifying key words effectively. The detection of systematic errors paves the way for further refinements to enhance its safety and utility in clinical settings.

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics.

Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.

Antimicrobial peptide interactions with bacterial cell membranes.

Antimicrobial peptides (AMPs) are potential alternatives for common antibiotics because of their greater activity and efficiency against a broad range of viruses, bacteria, fungi, and parasites. In this project, two antimicrobial peptides including magainin 2 and protegrin 1 with α-helix and ß-sheet secondary structures were selected to investigate their interactions with different lipid bilayers such as 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoserine (POPS), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), POPC/POPG (7:3), POPC/POPS (7:3), POPG/POPE(1:3), and POPG/POPE(3:1). The obtained structures of the AMPs illustrated that protegrin 1 cannot maintain its secondary structure in the solution phase in contrast to magainin 2. The head groups of the lipid units play a key role in the stability of the lipid bilayers. The head parts of the lipid membranes by increasing the internal H-bond contribute to membrane compactness. The POPG and POPS units inside the POPC/POPG and POPC/POPS membranes increase the order of the POPC units. The cationic residues of the AMPs form remarkable electrostatic interactions with the negatively charged membrane surfaces, which play a key role in the stabilization process of the peptide secondary structures. The Arg residues of protegrin 1 and the Gly1, Lys4, Lys10, Lys11, Lys14, and Glu19 of the magainin 2 have the most important roles in the complexation process. The values of Gibbs binding energies (ΔG) indicate that the complexation process between AMPs and different bacterial membranes is favorable from the thermodynamic viewpoint and AMPs could form stable complexes with the lipid bilayers. As a result of ΔG values, protegrin 1 forms a more stable complex with POPG/POPE(3:1), while the α-helix has more affinity to the POPG/POPE(1:3) bacterial membranes. Therefore, it can be considered that ß-sheet and α-helix AMPs are more effective against gram-positive and gram-negative bacteria, respectively. The results of this study can provide useful details about the antimicrobial peptide interactions with the bacterial cell, which can be employed for designing new antimicrobial materials with greater efficiency.Communicated by Ramaswamy H. Sarma.

Force transmission and SUN-KASH higher-order assembly in the LINC complex models.

The linkers of the nucleoskeleton and cytoskeleton (LINC) complex comprises Sad-1 and UNC-84 (SUN) and Klarsicht, ANC-1, SYNE homology (KASH) domain proteins, whose conserved interactions provide a physical coupling between the cytoskeleton and the nucleoskeleton, thereby mediating the transfer of physical forces across the nuclear envelope. The LINC complex can perform distinct cellular functions by pairing various KASH domain proteins with the same SUN domain protein. Recent studies have suggested a higher-order assembly of SUN and KASH instead of a more widely accepted linear trimer model for the LINC complex. In the present study, we use molecular dynamics simulations to investigate the mechanism of force transfer across the two proposed models of LINC complex assembly, namely the 3:3 linear trimer model and the 6:6 higher-order model. Employing steered molecular dynamics simulations with various structures using forces at different rates and directions, we examine the structural stability of the two models under various biologically relevant conditions. Our results suggest that both models can withstand and transfer significant levels of force while retaining their structural integrity. However, the force response of various SUN/KASH assemblies depend on the force direction and pulling rates. Slower pulling rates result in higher mean square fluctuations of the 3:3 assembly compared to the fast pulling. Interestingly, the 6:6 assembly tends to provide an additional range of motion flexibility and might be more advantageous to the structural rigidity and pliability of the nuclear envelope. These findings offer insights into how the SUN and KASH proteins maintain the structural integrity of the nuclear membrane.

On the interactions of peptides with gold nanoparticles: effects of sequence and size.

Peptide-based self-assembly and synthesis techniques have emerged as a viable approach to designing active and stable inorganic nanostructures in aqueous media. In the present study, we use all-atom molecular dynamic (MD) simulations to study the interactions of ten short peptides (namely A3, AgBP1, AgBP2, AuBP1, AuBP2, GBP1, Midas2, Pd4, Z1, and Z2) with different gold nanoparticles (of different diameters ranging from 2 to 8 nm). Our MD simulation results imply that the gold nanoparticles have a remarkable effect on the stability and conformational properties of peptides. Moreover, the size of the gold nanoparticles and the type of peptide amino acid sequences play important roles in the stability of the peptide-AuNP complexes. Our results reveal that some amino acids such as Tyr, Phe, Met, Lys, Arg, and Gln have direct contact with the metal surface in comparison with Gly, Ala, Pro, Thr, and Val residues. The peptide adsorption on the surface of the gold nanoparticles is favorable from the energetic viewpoint, in which the van der Waals (vdW) interactions between the peptides and metal surface can be considered as one of the driving forces for the complexation process. The calculated Gibbs binding energies indicate that AuNPs have more sensitivity against the GBP1 peptide in the presence of different peptides. Overall, the results of this study can provide new insight into the peptide interaction with the gold nanoparticles from the molecular viewpoint, which can be important for designing new biomaterials based on the peptides and gold nanoparticles.Communicated by Ramaswamy H. Sarma.

Mercapto-pyrimidines are reversible covalent inhibitors of the papain-like protease (PLpro) and inhibit SARS-CoV-2 (SCoV-2) replication.

The papain-like protease (PLpro) plays a critical role in SARS-CoV-2 (SCoV-2) pathogenesis and is essential for viral replication and for allowing the virus to evade the host immune response. Inhibitors of PLpro have great therapeutic potential, however, developing them has been challenging due to PLpro's restricted substrate binding pocket. In this report, we screened a 115 000-compound library for PLpro inhibitors and identified a new pharmacophore, based on a mercapto-pyrimidine fragment that is a reversible covalent inhibitor (RCI) of PLpro and inhibits viral replication in cells. Compound 5 had an IC50 of 5.1 µM for PLpro inhibition and hit optimization yielded a derivative with increased potency (IC50 0.85 µM, 6-fold higher). Activity based profiling of compound 5 demonstrated that it reacts with PLpro cysteines. We show here that compound 5 represents a new class of RCIs, which undergo an addition elimination reaction with cysteines in their target proteins. We further show that their reversibility is catalyzed by exogenous thiols and is dependent on the size of the incoming thiol. In contrast, traditional RCIs are all based upon the Michael addition reaction mechanism and their reversibility is base-catalyzed. We identify a new class of RCIs that introduces a more reactive warhead with a pronounced selectivity profile based on thiol ligand size. This could allow the expansion of RCI modality use towards a larger group of proteins important for human disease.

A covalent inhibitor targeting the papain-like protease from SARS-CoV-2 inhibits viral replication.

Covalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 have great potential as antivirals, but their non-specific reactivity with thiols has limited their development. In this report, we performed an 8000 molecule electrophile screen against PLpro and identified an α-chloro amide fragment, termed compound 1, which inhibited SARS-CoV-2 replication in cells, and also had low non-specific reactivity with thiols. Compound 1 covalently reacts with the active site cysteine of PLpro, and had an IC50 of 18 µM for PLpro inhibition. Compound 1 also had low non-specific reactivity with thiols and reacted with glutathione 1-2 orders of magnitude slower than other commonly used electrophilic warheads. Finally, compound 1 had low toxicity in cells and mice and has a molecular weight of only 247 daltons and consequently has great potential for further optimization. Collectively, these results demonstrate that compound 1 is a promising lead fragment for future PLpro drug discovery campaigns.

Gut-on-a-chip models for dissecting the gut microbiology and physiology.

Microfluidic technologies have been extensively investigated in recent years for developing organ-on-a-chip-devices as robust in vitro models aiming to recapitulate organ 3D topography and its physicochemical cues. Among these attempts, an important research front has focused on simulating the physiology of the gut, an organ with a distinct cellular composition featuring a plethora of microbial and human cells that mutually mediate critical body functions. This research has led to innovative approaches to model fluid flow, mechanical forces, and oxygen gradients, which are all important developmental cues of the gut physiological system. A myriad of studies has demonstrated that gut-on-a-chip models reinforce a prolonged coculture of microbiota and human cells with genotypic and phenotypic responses that closely mimic the in vivo data. Accordingly, the excellent organ mimicry offered by gut-on-a-chips has fueled numerous investigations on the clinical and industrial applications of these devices in recent years. In this review, we outline various gut-on-a-chip designs, particularly focusing on different configurations used to coculture the microbiome and various human intestinal cells. We then elaborate on different approaches that have been adopted to model key physiochemical stimuli and explore how these models have been beneficial to understanding gut pathophysiology and testing therapeutic interventions.

Smartphone-assisted lab-in-a-tube device using gold nanocluster-based aptasensor for detection of MUC1-overexpressed tumor cells.

Developing smartphone technology for point-of-care diagnosis is one of the current favorable trends in the field of biosensors. In fact, using smartphones can provide better accessibility and facility for rapid diagnosis of diseases. On the other hand, the detection of circulating tumor cells (CTCs) is one of the recent methods for the early diagnosis of cancer. Here, a new smartphone-assisted lab-in-a-tube device is introduced for the detection of Mucin 1 (MUC1) overexpressed tumor-derived cell lines using gold nanoclusters (GNCs)-based aptasensor. Accordingly, commercial polyurethane (PU) foam was first coated with graphene oxide (GO) to increase its surface area (8.45-fold), and improve its wettability. The surface of the resulting three-dimensional PU-GO (3DPU-GO) platform was then modified by MUC1 aptamer-GNCs to provide the required sensitivity and specificity through a turn "on/off" detection system. The proposed biosensor was first optimized with a spectrophotometer method. Afterward, findings were evaluated based on the red color intensity of the lab-in-a-tube system; and indicated the high ability of the biosensor for detection of MUC1-overexpressed tumor cell lines in the range of 250-20,000 cells mL-1 with a limit of detection of 221 cells mL-1. In addition, the developed biosensor showed a decent selectivity against positive-control cell lines (MCF-7, and HT-29) in comparison to negative-control cell lines (HEK293, and L929). Notably, the results represented good accordance with reference methods including spectroscopy devices. Ultimately, the results of this work bring a new perspective to the field of point-of-care detection and can be considered in future biosensors.

Adsorption Process of Various Antimicrobial Peptides on Different Surfaces of Cellulose.

Current antimicrobial challenges in hospitals, pharmaceutical production units, and food packaging have motivated the development of antimicrobial agents, among them the antimicrobial compounds based on cellulose and peptides. Herein, we develop molecular dynamics (MD) models to dissect and characterize the adsorption process of antimicrobial peptides (AMPs) such as protegrin 1, magainin 2, and cyclic indolicidin on various surfaces of cellulose including [-1-10], [1-10], [-100], [100], [-110], and [110]. Our results suggest that the magainin 2 antimicrobial peptide loses most of its initial helix form, spreads on the cellulose surface, and makes the most rigid structure with [110] surface. The cyclic indolicidin peptide has the lowest affinity to adsorb on the cellulose surfaces, and the protegrin 1 peptide successfully adsorbs on all the proposed cellulose surfaces. Our MD simulations confirmed that cellulose can improve the corresponding peptides' structural stability and change their secondary structures during adsorption. The [-1-10] and [100] surfaces of cellulose show considerable affinity against the AMPs, exhibiting greater interactions with and adsorption to the peptides. Our data imply that the stronger adsorptions are caused by a set of H-bonds, van der Waals, and electrostatic interactions, where van der Waals interactions play a prominent role in the stability of the AMP-cellulose structures. Our energy analysis results suggest that glutamic acid and arginine amino acids have key roles in the stability of AMPs on cellulose surfaces due largely to stronger interactions with the cellulose surfaces as compared with other residues. Our results can provide useful insight at the molecular level that can help design better antimicrobial biomaterials based on cellulose.

On the Sensitivity and Affinity of Gold, Silver, and Platinum Surfaces against the SARS-CoV-2 Virus: A Comparative Computational Study.

The novel coronavirus disease and its complications have motivated the design of new sensors with the highest sensitivity, and affinity for the detection of the SARS-CoV-2 virus is considered in many research studies. In this research article, we employ full atomistic molecular dynamics (MD) models to study the interactions between the receptor binding domain (RBD) and spike protein of the coronavirus and different metals such as gold (Au), platinum (Pt), and silver (Ag) to analyze their sensitivity against this virus. The comparison between the RBD interactions with ACE2 (angiotensin-converting enzyme 2) and different metals indicates that metals have remarkable effects on the structural features and dynamical properties of the RBD. The binding site of the RBD has more affinity to the surfaces of gold, platinum, and silver than to the other parts of the protein. Moreover, the initial configuration of the RBD relative to the metal surface plays an important role in the stability of metal complexes with the RBD. The binding face of the protein to the metal surface has been changed in the presence of different metals. In other words, the residues of the RBD that participate in RBD interactions with the metals are different irrespective of the initial configurations in which the [Asn, Thr, Tyr], [Ser, Thr, Tyr], and [Asn, Asp, Tyr] residues of the protein have a greater affinity to Ag, Au, and Pt, respectively. The corresponding metals have a considerable affinity to the RBD, which due to strong interactions with the protein can change the secondary structure and structural features. Based on the obtained results during the complexation process between the protein and metals, the helical structure of the protein changes to the bend and antiparallel ß-sheets. The calculated binding energies for the RBD complexes with silver, gold, and platinum are -95.03, -138.03, and -133.96 kcal·mol-1, respectively. The adsorption process of the spike protein on the surfaces of different metals represents similar results and indicates that the entire spike protein of the coronavirus forms a more stable complex with the gold surface compared with other metals. Moreover, the RBD of the spike protein has more interactions with the surfaces than with the other parts of the protein. Therefore, it is possible to predict the properties of the coronavirus on the metal surface based on the dynamical behavior of the RBD. Overall, our computational results confirm that the gold surface can be considered as an outstanding substrate for developing new sensors with the highest sensitivity against SARS-CoV-2.

GO Bench: shared hub for universal benchmarking of machine learning-based protein functional annotations.

MOTIVATION: Gene annotation is the problem of mapping proteins to their functions represented as Gene Ontology (GO) terms, typically inferred based on the primary sequences. Gene annotation is a multi-label multi-class classification problem, which has generated growing interest for its uses in the characterization of millions of proteins with unknown functions. However, there is no standard GO dataset used for benchmarking the newly developed new machine learning models within the bioinformatics community. Thus, the significance of improvements for these models remains unclear. RESULTS: The Gene Benchmarking database is the first effort to provide an easy-to-use and configurable hub for the learning and evaluation of gene annotation models. It provides easy access to pre-specified datasets and takes the non-trivial steps of preprocessing and filtering all data according to custom presets using a web interface. The GO bench web application can also be used to evaluate and display any trained model on leaderboards for annotation tasks. AVAILABILITY AND IMPLEMENTATION: The GO Benchmarking dataset is freely available at www.gobench.org. Code is hosted at github.com/mofradlab, with repositories for website code, core utilities and examples of usage (Supplementary Section S.7). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

On the potentials of sialic acid derivatives as inhibitors for the mumps virus: A molecular dynamics and quantum chemistry investigation.

Mumps virus is an infectious pathogen causing major health problems for humans such as encephalitis, orchitis, and parotitis. Therefore, designing an inhibitor for this virus is of great medical and public health importance. With this goal in mind, we investigate the affinity of different sialic acid-based compounds (ligands) against the hemagglutinin-neuraminidase (HN) protein of the mumps virus, using a combination of molecular dynamics (MD) simulations and quantum chemistry calculations. Our MD simulation results indicate that the ligands form stable complexes with the HN protein through a combination of electrostatic, van der Waals (vdW), and hydrogen bond (H-bond) interactions, which the electrostatic interactions play a more important role in the complexation process. Based on the obtained results from the structural analysis Arg381, Arg291, and Arg49 play a key role in the binding site interactions with the different ligands, in comparison with other residues. There are some candidates such as Neu5Acα2-6Galß1-4GlcNAcß, Neu5Acα2-3Galß1-3GlcNacß1-3Galß1-4Glc, and Neu5Acα2-6Galß1-4GlcNAcß1-3Galß1-4Glc that form more stable complexes with the HN than the α2-3-Sialyllactose confirmed by the calculated Gibbs binding energies (-39.65, -46.93, and -36.49 kcal.mol-1, respectively). To investigate the relationship between the molecular properties of the selected compounds and their affinity to the HN receptor, density functional theory dispersion corrected (DFT-D3) calculations were employed. According to our DFT-D3 results, neutral sialic acid-based compounds have lower reactivity to the mumps virus than the negativity charge structures. Moreover, by increasing the electronic chemical potential (µ) the vdW and H-bond interactions between drugs and the HN protein increase. In other words, by elevating the electron tendency of the selected ligands their affinity to the mumps virus increases. Our quantum chemistry calculations reveal that in addition to the structural features the molecular properties of the drugs can play important roles in their affinity and reactivity against the virus. The results of this study can provide useful details to design new compounds or improve their properties against the mumps virus.

The effectiveness of cognitive-behavioural group therapy on infertile men, a randomised clinical trial.

BACKGROUND: Infertility can result in the emergence of depression and anger, the loss of one's identity, and the feeling of guilt in the infertile person. Present study aimed to investigate the effects of cognitive behavioural group therapy (CBGT) on infertile men's anger and positive and negative affect. MATERIALS AND METHODS: Forty-three men with infertility for at least five years were selected for the present randomised clinical study. Spielberger's State-Trait Anger Expression Inventory-II (STAXI-II) and the PANAS scale (positive and negative affect) were filled out by the participants, and they were randomly divided into the experimental (22) and control (21) groups. The experimental group received 18 ninety-minute sessions of hybrid group therapy held twice a week, while the control group received no intervention. When the intervention program was over, all participants performed the post-test. Moreover, the post-hoc stage was held two months later. RESULTS: The effects of the intervention were significant in terms of state anger, trait anger, anger expression in, anger expression out, positive effects, and negative effects (p < 0.004), but they were not significant concerning the anger control out index (p = 0.241). The significant differences between the two groups were also observed in the post-hoc stage. CONCLUSIONS: The CBGT therapy seems to be helpful for infertile men to reduce their anger and negative affect and increase their positive affect by implementing a set of techniques like cognitive regeneration, assertiveness, daily joyful activities, exercises to relax one's muscles, diaphragm respiration, the identification of various fillings, and emotional expression.

Design of a flexing organ-chip to model in situ loading of the intervertebral disc.

The leading cause of disability of all ages worldwide is severe lower back pain. To address this untreated epidemic, further investigation is needed into the leading cause of back pain, intervertebral disc degeneration. In particular, microphysiological systems modeling critical tissues in a degenerative disc, like the annulus fibrosus (AF), are needed to investigate the effects of complex multiaxial strains on AF cells. By replicating these mechanobiological effects unique to the AF that are not yet understood, we can advance therapies for early-stage degeneration at the cellular level. To this end, we designed, fabricated, and collected proof-of-concept data for a novel microphysiological device called the flexing annulus-on-a-chip (AoC). We used computational models and experimental measurements to characterize the device's ability to mimic complex physiologically relevant strains. As a result, these strains proved to be controllable, multi-directional, and uniformly distributed with magnitudes ranging from - 10 % to 12% in the axial, radial, and circumferential directions, which differ greatly from applied strains possible in uniaxial devices. Furthermore, after withstanding accelerated life testing (66 K cycles of 10% strain) and maintaining 2000 bovine AF cells without loading for more than three weeks the AoC proved capable of long-term cell culture. Additionally, after strain (3.5% strain for 75 cycles at 0.5 Hz) was applied to a monolayer of AF cells in the AoC, a population remained adhered to the channel with spread morphology. The AoC can also be tailored for other annular structures in the body such as cardiovascular vessels, lymphatic vessels, and the cervix.