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Cassava root-rot incited by soil-borne pathogens is one of the major diseases that reduces root yield. Although the use of resistant cultivars is the most effective method of management, the genetic basis for root-rot resistance remains poorly understood. Therefore, our work analyzed the transcriptome of two contrasting genotypes (BRS Kiriris/resistant and BGM-1345/susceptible) using RNA-Seq to understand the molecular response and identify candidate genes for resistance. Cassava seedlings (resistant and susceptible to root-rot) were both planted in infested and sterilized soil and samples from Initial-time and Final-time periods, pooled. Two controls were used: (i) seedlings collected before planting in infested soil (absolute control) and, (ii) plants grown in sterilized soil (mock treatments). For the differentially expressed genes (DEGs) analysis 23.912 were expressed in the resistant genotype, where 10.307 were differentially expressed in the control treatment, 15 DEGs in the Initial Time-period and 366 DEGs in the Final Time-period. Eighteen candidate genes from the resistant genotype were related to plant defense, such as the MLP-like protein 31 and the peroxidase A2-like gene. This is the first model of resistance at the transcriptional level proposed for the cassava × root-rot pathosystem. Gene validation will contribute to screening for resistance of germplasm, segregating populations and/or use in gene editing in the pursuit to develop most promising cassava clones with resistance to root-rot.

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Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

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Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ b =0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ b =0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

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Cassava root rot disease is caused by a complex of soil-borne pathogens and has high economic impacts because it directly affects the tuberous roots, which are the main commercial product. This study aimed to evaluate cassava genotypes for resistance to root rot disease in a field with a previous history of high disease incidence. It also aimed to identify possible genomic regions associated with field resistance based on genome-wide association studies. A total of 148 genotypes from Embrapa Mandioca and Fruticultura were evaluated over two years, including improved materials and curated germplasms. Analysis of phenotypic data was conducted, as well as a genomic association analysis, based on the general linear model, mixed linear model, and fixed and random model circulating probability unification. The observed high disease index (ω) was directly correlated with genotype survival, affecting plant height, shoot yield, and fresh root yield. The genotypes were grouped into five clusters, which were classified according to level of root rot resistance (i.e., extremely susceptible, susceptible, moderately susceptible, moderately resistant, and resistant). The 10 genotypes with the best performance in the field were selected as potential progenitors for the development of segregating progenies. Estimates of genomic kinship between these genotypes ranged from -0.183 to 0.671. The genotypes BGM-1171 and BGM-1190 showed the lowest degree of kinship with the other selected sources of resistance. The genotypes BGM-0209, BGM-0398, and BGM-0659 showed negative kinship values with most elite varieties, while BGM-0659 presented negative kinship with all landraces. A genome-wide association analysis detected five significant single nucleotide polymorphisms related to defense mechanisms against biotic and abiotic stresses, with putative association with fresh root yield in soil infested with root rot pathogens. These findings can be utilized to develop molecular selection for root rot resistance in cassava.

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While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent-exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decreases and relative hydrophobicity increases. Across a series of 13 inhibitor-bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from nuclear magnetic resonance studies suggesting that residual side-chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.

Proteins are the workhorses of our cells. They are large molecules that 'fold' into specific, often highly complex, three-dimensional configurations. These structures are not static, but rather dynamic and flexible. In other words, proteins can shift between different three-dimensional shapes to perform their tasks within the cell. To perform their roles, many proteins have to bind to small molecule ligands. Many ligands are drugs, which means that their effectiveness depends on their ability to bind to and impact the proteins involved in the disease they are treating. When a ligand binds to a protein, it can reshape the protein. For example, certain conformations of the protein, which were difficult for the protein to be in on its own, may become more stable when the ligand binds. Additionally, upon ligand binding, some parts of the protein may move relative to each other. Previous studies have shown that these movements can affect the interaction between ligand and protein. However, these studies only examined a small number of proteins. Therefore, Wankowicz et al. set out to determine, in greater detail, what happens to protein flexibility upon ligand binding. First, a pipeline was created to model alternative configurations of the protein both with and without ligands attached. These models measured flexibility within protein structures. The models revealed that when ligands bind to proteins, the flexibility of different regions of the protein changes ­ and does so in a consistent way. Proteins that become more rigid in the region interacting with their ligands become less rigid in other, distant regions, and vice versa. In other words, the rest of the protein is able to compensate for any changes in flexibility caused by ligand binding, which may contribute to how well a ligand binds to a protein. This study demonstrates the ability of ligands to affect the entire structure of the proteins they bind to, and therefore sheds new light on the role of proteins' innate conformational flexibility during this process. These results will contribute to our understanding of how the ligands and proteins involved in different cellular processes interact with each other ­ and, potentially, how these interactions can be manipulated.

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Objective To evaluate, through a biomechanical assay, the maximum load, energy, and displacement necessary for the occurrence of fractures in synthetic models of femurs after the removal of cannulated screws and the performance of a reinforcement technique with polymethylmethacrylate (PMMA) in different combined positions. Methods In total, 25 synthetic bones were used, and they were divided into 4 groups: the control group (CG), with 10 models without perforation, and the test groups (A, B and C), with 5 models each. The test groups were fixed with cannulated screws using the Asnis technique, and they had the synthesis removed, and two of the holes formed by the reinforcement technique with PMMA were filled. The biomechanical analysis was performed simulating a fall on the large trochanter using a servo-hydraulic machine. Results All specimens of the CG and of groups A, B and C presented basal-cervical fracture of the femoral neck, except for a single model in group B, which presented a longitudinal fracture. An average of 5.4 mL of PMMA were used to reinforce the groups with filling. According to the analysis of variance (ANOVA) and the Tukey multiple comparison test, at the level of 5%, we observed that the CG presented significant differences in relation to groups A and C in the following parameters: maximum load, energy up to the fracture, and displacement. Conclusion We observed that groups A and C, when compared to the CG, showed significant differences in the observation of displacement, maximum load, and energy until the fracture.

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New X-ray crystallography and cryo-electron microscopy (cryo-EM) approaches yield vast amounts of structural data from dynamic proteins and their complexes. Modeling the full conformational ensemble can provide important biological insights, but identifying and modeling an internally consistent set of alternate conformations remains a formidable challenge. qFit efficiently automates this process by generating a parsimonious multiconformer model. We refactored qFit from a distributed application into software that runs efficiently on a small server, desktop, or laptop. We describe the new qFit 3 software and provide some examples. qFit 3 is open-source under the MIT license, and is available at https://github.com/ExcitedStates/qfit-3.0.

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Robotic-assisted surgery has been actively applied in several fields of minimally invasive surgery, and its use in cancer treatment is increasing. This technology has diffused rapidly, despite high costs and limited reimbursement. Studies have proven the positive results of robotic surgery for patients' quality of life. The objective of this article is to describe the impacts of robotic-assisted cancer surgery adoption at the Brazilian National Cancer Institute. A single qualitative case study was conducted with a descriptive approach, and to have triangulation, data were collected from 3 different sources - participant observation, technical reviews, and seven semi-structured interviews with the main actors involved in the case - and analyzed through content analysis. Despite the considerable costs associated with robotic surgery, this innovation dramatically decreases the length of hospital stay and reduces complications and rehabilitates the patient in a much faster way.

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A survey to investigate the occurrence of cassava anthracnose disease (CAD) and distribution of Colletotrichum spp. in cassava plantations in different eco-zones of the Reconcavo Region in Bahia, Brazil, investigated during the rainy season of 2014. A total of 50 cassava fields distributed among 18 municipalities were visited and intensity of anthracnose evaluated. The highest disease incidence (DI) (83.3%) was in samples collected in São Félix, and the lowest (34.4%), in Varzedo. Municipalities that presented the highest values for DI were located within the 'Af' Köppen-Geiger eco-zone, also presenting the highest values for the estimated McKinney disease index. Based on previous studies of multilocus phylogeny, seven different species of Colletotrichum were identified (Colletotrichum fructicola, Colletotrichum tropicale, Colletotrichum gloeosporioides s.s, Colletotrichum theobromicola, Colletotrichum siamense, Colletotrichum brevisporum and Colletotrichum plurivorum) and a new approach based on ERIC-PCR was used aiming to group the 82 isolates according to these findings. The highest percentage of genetic variance (> 78%) was among isolates within fields. Based on the survey and genetic analysis, C. fructicola is probably the main causal agent of cassava anthracnose in the Recôncavo Region, since this species was present with highest incidence in all eco-zones, 47.61, 42.86 and 57.14% for Af (tropical rainforest climate), As (tropical dry savanna climate) and Aw (tropical wet savanna climate), respectively. This study is the first report of C. fructicola lineages as the most likely pathogen causing anthracnose disease of cassava in Brazil, and these findings may be used to guide the selection of resistant varieties.

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Pseudocercospora musae, causal agent of Sigatoka leaf spot, or yellow Sigatoka disease, is considered a major pathogen of banana (Musa spp.). Widely disseminated in Brazil, this study explored the genetic diversity in field populations of the pathogen from production areas in the Distrito Federal and the States of Bahia, Minas Gerais, and Rio Grande do Norte. Resistance to demethylation inhibitor (DMI) fungicides was also examined. For 162 isolates from 10 banana growing regions, analysis of mating type idiomorph frequency was conducted, together with estimation of genetic diversity at 15 microsatellite loci. A total of 149 haplotypes were identified across the examined populations, with an average genetic diversity of 4.06. In general, populations displayed 1:1 proportions of idiomorphs MAT1-1 and MAT1-2, providing evidence for sexual recombination. Multilocus linkage disequilibrium also indicated asexual reproduction contributing to the genetic structure of certain populations. AMOVA revealed that 86.3% of the genetic differentiation of the pathogen occurred among isolates within populations. Discriminant Analysis of Principal Components (DAPC) identified six most probable genetic groups, with no population structure associated with geographic origin or collection site. Although genetic similarity was observed among certain populations from different states, data revealed increasing genetic differentiation with increasing geographic distance, as validated by Mantel's test (r = 0.19, P < 0.001). On the basis of DMI fungicide sensitivity testing and CYP51 gene sequence polymorphism, isolates from the Distrito Federal separated into two main groups, one with generally higher EC50 values against eight DMI fungicides. A clear phenotype-to-genotype relationship was observed for isolates carrying the CYP51 alteration Y461N. Conventionally adopted fungicides for control of Sigatoka leaf spot are likely to be overcome by combined sexual and asexual reproduction mechanisms in P. musae driving genetic variability. Continued analysis of pathogen genetic diversity and monitoring of DMI sensitivity profiles of Brazilian field populations is essential for the development of integrated control strategies based on host resistance breeding and rational design of fungicide regimes.

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The use of cephalomedullary devices has gained popularity in the treatment of proximal femoral fractures. Despite their biomechanical advantages, several complications are well described in the literature. One of these complications, which is rarely reported, is the medial migration of the cephalic screw. The authors present this unusual complication in a case of a long-nail implant, which was treated with removal of the implants as a first step, and posterior osteosynthesis with a locked proximal femur plate as a second step, as well the outcome until fracture consolidation and resolution of the case.

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Cassava (Manihot esculenta Crantz) is an important food security crop in many parts of the developing world. The crop's high yield potential and multitude of uses-both for nutrition and processing-render cassava a promising driver for the development of rural value chains. It is traditionally propagated from stem cuttings of up to 30 cm in length, giving a multiplication rate as low as 1:10. Propagating cassava traditionally is very inefficient, which leads to challenges in the production and distribution of quality planting material and improved cultivars, greatly limiting the impact of investments in crop breeding. The work described in the present study aimed to develop a seed treatment approach to facilitate the use of shorter seed pieces, increasing the multiplication rate of cassava and thus making the crop's seed systems more efficient. After several tests, formulation was identified, consisting of thiamethoxam 21 g ha-1, mefenoxam 1.0 g ha-1, fludioxonil 1.3 g ha-1, thiabendazole 7.5 g ha-1 and Latex 2% as a binder. Plant growing from seed pieces treated with this formulation displayed increased crop establishment and early crop vigor, leading to an improved productivity throughout a full growing cycle. This allowed to reduce the cassava seed piece size to 8 cm with no negative effects on germination and crop establishment, leading to yields comparable to those from untreated 16 cm pieces. This, in turn, will allow to increase the multiplication ratio of cassava by a factor of up to 3. Notably, this was possible under regular field conditions and independently of any specialised treatment facilities. Compared with existing seed production protocols, the increased multiplication rates allowed for efficiency gains of between 1 to 1.9 years compared to conventional five-year cycles. We believe that the technology described here holds considerable promise for developing more reliable and remunerative delivery channels for quality cassava planting material and improved genetics.

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Abstract The use of cephalomedullary devices has gained popularity in the treatment of proximal femoral fractures. Despite their biomechanical advantages, several complications are well described in the literature. One of these complications, which is rarely reported, is the medial migration of the cephalic screw. The authors present this unusual complication in a case of a long-nail implant, which was treated with removal of the implants as a first step, and posterior osteosynthesis with a locked proximal femur plate as a second step, as well the outcome until fracture consolidation and resolution of the case.

Resumo O uso dos dispositivos cefalomedulares tem ganhado popularidade no tratamento das fraturas do fêmur proximal. Apesar das vantagens biomecânicas, várias complicações são descritas, entre as quais a migração medial do parafuso cefálico é pouco conhecida. Os autores apresentam um caso dessa complicação incomum em um implante de haste longa tratada em dois tempos cirúrgicos para a retirada dos implantes e posterior osteossíntese com placa bloqueada para fêmur proximal, assim como o desfecho até a consolidação da fratura e resolução do caso.

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MOTIVATION: Over the last few years, the field of protein structure prediction has been transformed by increasingly accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments (MSAs). However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV and DNCON2, as examples of direct coupling analysis, meta-prediction and deep learning. RESULTS: We considered correctly predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy, whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from MSAs. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. AVAILABILITY AND IMPLEMENTATION: We use publicly available databases. Our code is available for download at https://opig.stats.ox.ac.uk/. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

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How changes in enzyme structure and dynamics facilitate passage along the reaction coordinate is a fundamental unanswered question. Here, we use time-resolved mix-and-inject serial crystallography (MISC) at an X-ray free electron laser (XFEL), ambient-temperature X-ray crystallography, computer simulations, and enzyme kinetics to characterize how covalent catalysis modulates isocyanide hydratase (ICH) conformational dynamics throughout its catalytic cycle. We visualize this previously hypothetical reaction mechanism, directly observing formation of a thioimidate covalent intermediate in ICH microcrystals during catalysis. ICH exhibits a concerted helical displacement upon active-site cysteine modification that is gated by changes in hydrogen bond strength between the cysteine thiolate and the backbone amide of the highly strained Ile152 residue. These catalysis-activated motions permit water entry into the ICH active site for intermediate hydrolysis. Mutations at a Gly residue (Gly150) that modulate helical mobility reduce ICH catalytic turnover and alter its pre-steady-state kinetic behavior, establishing that helical mobility is important for ICH catalytic efficiency. These results demonstrate that MISC can capture otherwise elusive aspects of enzyme mechanism and dynamics in microcrystalline samples, resolving long-standing questions about the connection between nonequilibrium protein motions and enzyme catalysis.

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While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources.

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Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.

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Nowadays Brazil has a complex cancer care scenario. There are nearly 600.000 new cancer cases each year in Brazil, and the huge majority of patients have some contact with hospital services. However, long waiting queues for diagnostics and treatments have become common. One of the critical success factors in a cancer treatment is early diagnosis. The reduction of waiting time to start therapeutic procedures is one of the main issues for improvement of patient's quality of life and possibilities of cure. The objective of this work is to describe the development of a decision support system that improves the identification of access alternatives, appointment scheduling and employment of available resources. The Theory of Constraints was used to identify bottlenecks in patient treatment flow and a Discrete Events Simulation model was used to reduce patients' waiting time to start cancer treatment.

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In last decades Information Technology (IT) has been established as the main tool to support processes. Medical area has noticed the opportunity for improvement and interacting with IT has enabled solid interdisciplinary instruments for improvement. The aim in this project is composed by an analysis of the relationship between the application of information technologies, oncological assistance and innovation. Next, the project will focus to present the case of a proposal to reformulate the national cancer care system, with IT and Innovation being the base instrument for this process.

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Nowadays Brazil has a complex cancer care scenario. There are nearly 600.000 new cancer cases each year in Brazil, and the huge majority of patients have some contact with hospital services. However, long waiting queues for diagnostics and treatments have become common. One of the critical success factors in a cancer treatment is early diagnosis. The reduction of waiting time to start therapeutic procedures is one of the main issues for improvement of patient's quality of life and possibilities of cure. The objective of this work is to describe the development of a decision support system that improves the identification of access alternatives, appointment scheduling and employment of available resources. The Theory of Constraints was used to identify bottlenecks in patient treatment flow and a Discrete Events Simulation model was used to reduce patients' waiting time to start cancer treatment.

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