Predicting the shapes of bones at a joint: application to the shoulder

This paper presents a novel method to explore the intrinsic morphological correlation between the bones of a shoulder joint (humerus and scapula). To model this correlation, canonical correlation analysis (CCA) is used. We also propose a technique to predict a three-dimensional (3D) bone shape from its adjoining segment at a joint based on partial least squares regression (PLS). The high dimensional 3D surface information of a bone is represented by a few variables using principal component analysis, which also captures the pattern of variability of the shapes in our datasets. Our results show that the humerus set and scapula set have highly linear morphological relationship and that the correlation information can be used as a classifier. In this study, primate shoulder bone datasets were categorised into two clusters: great apes (including humans) and monkeys. A leave one out experiment was performed to test the robustness of this prediction method. The prediction behaviour using this method shows statistically significantly better results than using the mean shape from the training set.     

A cartilage growth mixture model for infinitesimal strains: solutions of boundary-value problems related to in vitro growth experiments

A cartilage growth mixture (CGM) model is linearized for infinitesimal elastic and growth strains. Parametric studies for equilibrium and nonequilibrium boundary-value problems representing the in vitro growth of cylindrical cartilage constructs are solved. The results show that the CGM model is capable of describing the main biomechanical features of cartilage growth. The solutions to the equilibrium problems reveal that tissue composition, constituent pre-stresses, and geometry depend on collagen remodeling activity, growth symmetry, and differential growth. Also, nonhomogeneous growth leads to nonhomogeneous tissue composition and constituent pre-stresses. The solution to the nonequilibrium problem reveals that the tissue is nearly in equilibrium at all time points. The results suggest that the CGM model may be used in the design of tissue engineered cartilage constructs for the repair of cartilage defects; for example, to predict how dynamic mechanical loading affects the development of nonuniform properties during in vitro growth. Furthermore, the results lay the foundation for future analyses with nonlinear models that are needed to develop realistic models of cartilage growth.     

The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling

Biomechanics and Modeling in Mechanobiology - The human heart is enclosed in the pericardial cavity. The pericardium consists of a layered thin sac and is separated from the myocardium by a thin...     

BFL: a node and edge betweenness based fast layout algorithm for large scale networks

Background  

Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements.     

Daphnias: from the individual based model to the large population equation

The class of deterministic ‘Daphnia’ models treated by Diekmann et al. (J Math Biol 61:277–318, 2010) has a long history going back to Nisbet and Gurney (Theor Pop Biol 23:114–135, 1983) and Diekmann et al. (Nieuw Archief voor Wiskunde 4:82–109, 1984). In this note, we formulate the individual based models (IBM) supposedly underlying those deterministic models. The models treat the interaction between a general size-structured consumer population (‘Daphnia’) and an unstructured resource (‘algae’). The discrete, size and age-structured Daphnia population changes through births and deaths of its individuals and through their aging and growth. The birth and death rates depend on the sizes of the individuals and on the concentration of the algae. The latter is supposed to be a continuous variable with a deterministic dynamics that depends on the Daphnia population. In this model setting we prove that when the Daphnia population is large, the stochastic differential equation describing the IBM can be approximated by the delay equation featured in (Diekmann et al., loc. cit.).     

Heteroduplex mobility assay for the identification of Listeria sp and Listeria monocytogenes strains: application to characterisation of strains from sludge and food samples

One hundred and ten Listeria sp. isolates from sewage sludge were identified according to phenotypic and genotypic methods. The Listeria sp. strains isolated from five types of sludge from three sewage treatment plants in Angers (France) and the surrounding area included L. monocytogenes (55.5%), L. innocua (29.1%), L. seeligeri (13.6%) and L. welshimeri (1.8%). The majority of L. monocytogenes strains belonged to serotypes 4b, 1/2b and 1/2a. Moreover, a heteroduplex mobility assay based on the 16S rRNA sequences was tested for its ability to identify the six species of the genus Listeria. This study, performed on 283 Listeria sp. strains from human, food and sewage sludge samples, showed that all the species were distinguishable from one another. L. innocua and L. seeligeri showed respectively three and two distinct banding patterns. Within L. monocytogenes, four groups (I-IV) were defined. The majority of food and environmental isolates were clustered in group I and it is noteworthy that group IV clustered epidemiologic isolates and strains belonging to serotypes 4b, 1/2a and 1/2b.     

PartTree: an algorithm to build an approximate tree from a large number of unaligned sequences

MOTIVATION: To construct a multiple sequence alignment (MSA) of a large number (> approximately 10,000) of sequences, the calculation of a guide tree with a complexity of O(N2) to O(N3), where N is the number of sequences, is the most time-consuming process. RESULTS: To overcome this limitation, we have developed an approximate algorithm, PartTree, to construct a guide tree with an average time complexity of O(N log N). The new MSA method with the PartTree algorithm can align approximately 60,000 sequences in several minutes on a standard desktop computer. The loss of accuracy in MSA caused by this approximation was estimated to be several percent in benchmark tests using Pfam. AVAILABILITY: The present algorithm has been implemented in the MAFFT sequence alignment package (http://align.bmr.kyushu-u.ac.jp/mafft/software/). SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.     

Evolving intentions for social interaction: from entrainment to joint action

This article discusses four different scenarios to specify increasingly complex mechanisms that enable increasingly flexible social interactions. The key dimension on which these mechanisms differ is the extent to which organisms are able to process other organisms' intentions and to keep them apart from their own. Drawing on findings from ecological psychology, scenario 1 focuses on entrainment and simultaneous affordance in 'intentionally blind' individuals. Scenario 2 discusses how an interface between perception and action allows observers to simulate intentional action in others. Scenario 3 is concerned with shared perceptions, arising through joint attention and the ability to distinguish between self and other. Scenario 4 illustrates how people could form intentions to act together while simultaneously distinguishing between their own and the other's part of a joint action. The final part focuses on how combining the functionality of the four mechanisms can explain different forms of social interactions. It is proposed that basic interpersonal processes are put to service by more advanced functions that support the type of intentionality required to engage in joint action, cultural learning, and communication.     

Progressive combinatorial algorithm for multiple structural alignments: application to distantly related proteins

MALECON is a progressive combinatorial procedure for multiple alignments of protein structures. It searches a library of pairwise alignments for all three-protein alignments in which a specified number of residues is consistently aligned. These alignments are progressively expanded to include additional proteins and more spatially equivalent residues, subject to certain criteria. This action involves superimposing the aligned proteins by their hitherto equivalent residues and searching for additional Calpha atoms that lie close in space. The performance of MALECON is illustrated and compared with several extant multiple structure alignment methods by using as test the globin homologous superfamily, the OB and the Jellyrolls folds. MALECON gives better definitions of the common structural features in the structurally more diverse proteins of the OB and Jellyrolls folds, but it yields comparable results for the more similar globins. When no consistent multiple alignments can be derived for all members of a protein group, our procedure is still capable of automatically generating consistent alignments and common core definitions for subgroups of the members. This finding is illustrated for proteins of the OB fold and SH3 domains, believed to share common structural features, and should be very instrumental in homology modeling and investigations of protein evolution.     

Chain-selective isotopic labeling for NMR studies of large multimeric proteins: application to hemoglobin

Multidimensional, multinuclear NMR has the potential to elucidate the mechanisms of allostery and cooperativity in multimeric proteins under near-physiological conditions. However, NMR studies of proteins made up of non-equivalent subunits face the problem of severe resonance overlap, which can prevent the unambiguous assignment of resonances, a necessary step in interpreting the spectra. We report the application of a chain-selective labeling technique, in which one type of subunit is labeled at a time, to carbonmonoxy-hemoglobin A (HbCO A). This labeling method can be used to extend previous resonance assignments of key amino acid residues, which are important to the physiological function of hemoglobin. Among these amino acid residues are the surface histidyls, which account for the majority of the Bohr effect. In the present work, we report the results of two-dimensional heteronuclear multiple quantum coherence (HMQC) experiments performed on recombinant (15)N-labeled HbCO A. In addition to the C2-proton (H epsilon(1)) chemical shifts, these spectra also reveal the corresponding C4-proton (H delta(2)) resonances, correlated with the N epsilon(2) and N delta(1) chemical shifts of all 13 surface histidines per alpha beta dimer. The HMQC spectrum also allows the assignment of the H delta(1), H epsilon(1), and N epsilon(1) resonances of all three tryptophan residues per alpha beta dimer in HbCO A. These results indicate that heteronuclear NMR, used with chain-selective isotopic labeling, can provide resonance assignments of key regions in large, multimeric proteins, suggesting an approach to elucidating the solution structure of hemoglobin, a protein with molecular weight 64.5 kDa.     

DynaBiS: A hierarchical sampling algorithm to identify flexible binding sites for large ligands and peptides

Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein–ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein–ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.     

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.     

Multi-conformational peptide dynamics derived from NMR data: A new search algorithm and its application to antamanide

Summary A search algorithm, called MEDUSA, is presented which allows the determination of multiple conformations of biomolecules in solution with exchange rate constants typically between 10³ and 10⁷ s^–1 on the basis of experimental high-resolution NMR data. Multiples of structures are generated which are consistent as ensembles with NMR cross-relaxation rates (NOESY, ROESY), scalar J-coupling constants, and T_1p measurements. The algorithm is applied to the cyclic decapeptide antamanide dissolved in chloroform. The characteristic radio-frequency field dependence of the T_1p relaxation rates found for the NH protons of Val¹ and Phe⁶ can be explained by a dynamical exchange between two structures.     

PoPMuSiC, an algorithm for predicting protein mutant stability changes: application to prion proteins

A novel tool for computer-aided design of single-site mutations in proteins and peptides is presented. It proceeds by performing in silico all possible point mutations in a given protein or protein region and estimating the stability changes with linear combinations of database-derived potentials, whose coefficients depend on the solvent accessibility of the mutated residues. Upon completion, it yields a list of the most stabilizing, destabilizing or neutral mutations. This tool is applied to mouse, hamster and human prion proteins to identify the point mutations that are the most likely to stabilize their cellular form. The selected mutations are essentially located in the second helix, which presents an intrinsic preference to form beta-structures, with the best mutations being T183-->F, T192-->A and Q186-->A. The T183 mutation is predicted to be by far the most stabilizing one, but should be considered with care as it blocks the glycosylation of N181 and this blockade is known to favor the cellular to scrapie conversion. Furthermore, following the hypothesis that the first helix might induce the formation of hydrophilic beta-aggregates, several mutations that are neutral with respect to the structure's stability but improve the helix hydrophobicity are selected, among which is E146-->L. These mutations are intended as good candidates to undergo experimental tests.     

Estimating a multivariate familial correlation using joint models for canonical correlations: application to memory score analysis from familial Hispanic Alzheimer's disease study

Summary .  Analysis of multiple traits can provide additional information beyond analysis of a single trait, allowing better understanding of the underlying genetic mechanism of a common disease. To accommodate multiple traits in familial correlation analysis adjusting for confounders, we develop a regression model for canonical correlation parameters and propose joint modeling along with mean and scale parameters. The proposed method is more powerful than the regression method modeling pairwise correlations because it captures familial aggregation manifested in multiple traits through maximum canonical correlation.     

Adjuvant immunotherapy for solid tumors: from promise to clinical application

Although surgery remains the mainstay for the treatment of most solid tumors, investigators are seeking complementary therapies to eradicate microscopic disease, which causes tumor relapse even after an apparently complete surgical excision. Although adjuvant chemotherapy has achieved some significant results, the control of minimal residual disease is still a challenge for clinicians. Among novel therapeutic approaches, immunotherapy holds promise. This anticancer strategy aims at triggering a highly specific endogenous killing machine against tumor cells. Recent progress in tumor immunology has improved our understanding of host-immune system interactions. In particular, new technologies have fostered the identification of potentially immunogenic tumor antigens that can be used as suitable targets for immune effector cells. After observing immunotherapy-mediated clinical responses in patients with metastatic disease, investigators have started evaluating this anticancer modality in the adjuvant setting. Here, we review the immunological strategies so far explored in humans and report worldwide results following the clinical application of adjuvant immunotherapy for solid tumors.