Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

Enabling structure-based drug discovery utilizing predicted models.

High-quality predicted structures enable structure-based approaches to an expanding number of drug discovery programs. We propose that by utilizing free energy perturbation (FEP), predicted structures can be confidently employed to achieve drug design goals. We use structure-based modeling of hERG inhibition to illustrate this value of FEP.

Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation.

Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. However, before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a bound ligand, we employ induced fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced fit docking technology, IFD-MD, on a retrospective set of public experimental GPCR structures with 95% of cross-docks producing a pose with a ligand RMSD ≤ 2.5 Å in the top two predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experimental structure from this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol, and explore the challenges of model validation under scenarios of limited ligand data, ample ligand data, and categorical data.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Theoretical analysis of the effects of counterions on the supramolecular arrangement of sulfamethoxazole.

Active pharmaceutical ingredients are formulated as the salt form, aiming to modulate their physicochemical properties. In this regard, the optimization and choice of the salt former have a strong influence on toxicity, therapeutic efficiency, and bioavailability. Sulfamethoxazole (SMZ) salts with Na+, Cl-, and Br- counterions influence in the supramolecular arrangement as well as in their thermodynamic and kinetic parameters. Herein, we analyzed the interactions of the Na+, Cl-, and Br- counterions on the supramolecular arrangement of the sulfamethoxazole salts by Hirshfeld surfaces, fingerprint plots, and theoretical methods-quantum theory of atoms in molecules and natural bond orbitals. Moreover, we evaluated their electronic structure by density functional theory using calculation of the frontier molecular orbitals. Molecular electrostatic potential maps were also obtained to predict the interactions of the counterions along crystalline arrangements. We observed that the structures of [SMZ]+ and [SMZ]- ions differ slightly from the SMZ. The chemical reactivity indices show that the SMZ is kinetically more stable than its respective ions, while its anion is more polarizable, and its cation has a higher global electrophilicity index. The molecular electrostatic potential maps show high charge density in the sulfonyl group (nucleophilic region) and the heterocyclic amino group (electrophilic region). Although the molecular skeleton is identical among the three SMZ species and the presence of different counterions in the formation of the crystalline structure of the salts results in supramolecular arrangements with different patterns of intermolecular interactions, despite being very similar in terms of intensities.

An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation.

After two years since the outbreak, the COVID-19 pandemic remains a global public health emergency. SARS-CoV-2 variants with substitutions on the spike (S) protein emerge increasing the risk of immune evasion and cross-species transmission. Here, we analyzed the evolution of the S protein as recorded in 276,712 samples collected before the start of vaccination efforts. Our analysis shows that most variants destabilize the S protein trimer, increase its conformational heterogeneity and improve the odds of the recognition by the host cell receptor. Most frequent substitutions promote overall hydrophobicity by replacing charged amino acids, reducing stabilizing local interactions in the unbound S protein trimer. Moreover, our results identify "forbidden" regions that rarely show any sequence variation, and which are related to conformational changes occurring upon fusion. These results are significant for understanding the structure and function of SARS-CoV-2 related proteins which is a critical step in vaccine development and epidemiological surveillance.

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d-1) and yield [g kg-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.

Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl.

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods' ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.

Back and forth transmission of SARS-CoV-2 between humans and animals may lead to wild reservoirs of virus that can endanger efforts toward long-term control of COVID-19 in people, and protecting vulnerable animal populations that are particularly susceptible to lethal disease. Predicting high risk host species is key to targeting field surveillance and lab experiments that validate host zoonotic potential. A major bottleneck to predicting animal hosts is the small number of species with available molecular information about the structure of ACE2, a key cellular receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with 3D modeling of virus and host cell protein interactions using machine learning methods. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals - an order of magnitude more species than previously possible. The high accuracy predictions achieved by this approach are strongly corroborated by in vivo empirical studies. We identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may further enhance the risk of spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. With molecular sequence data available for only a small fraction of potential host species, predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.

PDB-tools web: A user-friendly interface for the manipulation of PDB files.

The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose. Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.

Insights on cross-species transmission of SARS-CoV-2 from structural modeling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity-enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results.

Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design.

Emergence of coronaviruses poses a threat to global health and economy. The current outbreak of SARS-CoV-2 has infected more than 28,000,000 people and killed more than 915,000. To date, there is no treatment for coronavirus infections, making the development of therapies to prevent future epidemics of paramount importance. To this end, we collected information regarding naturally-occurring variants of the Angiotensin-converting enzyme 2 (ACE2), an epithelial receptor that both SARS-CoV and SARS-CoV-2 use to enter the host cells. We built 242 structural models of variants of human ACE2 bound to the receptor binding domain (RBD) of the SARS-CoV-2 surface spike glycoprotein (S protein) and refined their interfaces with HADDOCK. Our dataset includes 140 variants of human ACE2 representing missense mutations found in genome-wide studies, 39 mutants with reported effects on the recognition of the RBD, and 63 predictions after computational alanine scanning mutagenesis of ACE2-RBD interface residues. This dataset will help accelerate the design of therapeutics against SARS-CoV-2, as well as contribute to prevention of possible future coronaviruses outbreaks.

Bacterial flagellar motor PL-ring disassembly subcomplexes are widespread and ancient.

The bacterial flagellum is an amazing nanomachine. Understanding how such complex structures arose is crucial to our understanding of cellular evolution. We and others recently reported that in several Gammaproteobacterial species, a relic subcomplex comprising the decorated P and L rings persists in the outer membrane after flagellum disassembly. Imaging nine additional species with cryo-electron tomography, here, we show that this subcomplex persists after flagellum disassembly in other phyla as well. Bioinformatic analyses fail to show evidence of any recent horizontal transfers of the P- and L-ring genes, suggesting that this subcomplex and its persistence is an ancient and conserved feature of the flagellar motor. We hypothesize that one function of the P and L rings is to seal the outer membrane after motor disassembly.

Less Is More: Coarse-Grained Integrative Modeling of Large Biomolecular Assemblies with HADDOCK.

Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing for modeling larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modeling platform. Docking and refinement are performed at the coarse-grained level, and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ∼7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

CO2 adsorption in nitrogen-doped single-layered graphene quantum dots: a spectroscopic investigation.

In this work, we investigate the adsorption process of CO2 in graphene quantum dots from the electronic structure and spectroscopic properties point of view. We discuss how a specific doping scheme could be employed to further enhance the adsorbing properties of the quantum dots. This is evaluated by considering the depth of the potential well, the spectroscopic constants, and the lifetime of the compound. Electronic structure calculations are carried out in the scope of the density functional theory (DFT), whereas discrete variable representation (DVR) and Dunham methodologies are employed to obtain spectroscopic constants and hence the lifetimes of the systems. Our results suggest that nitrogen-doped graphene quantum dots are promising structures as far as sensing applications of CO2 are concerned. Graphical Abstract Adsorption mechanism of the CO2 molecule in (a) a pristine and (b) a nitrogendoped Graphene Quantum Dot.

Publisher Correction: Structural insights into binding specificity, efficacy and bias of a ß2AR partial agonist.

In the version of this paper originally published, the structure for epinephrine shown in Figure 1a was redrawn with an extra carbon. The structure has been replaced in the HTML and PDF versions of the article. The original and corrected versions of the structure are shown below.

Evaluation of buffer solutions and urea addition for estimating the in vitro digestibility of feeds.

The aim of this study was to compare the in vitro digestibility of dry matter (IVDMD) and neutral detergent fiber (IVNDFD) using 2 buffer solutions with or without urea addition. The study was comprised of 2 separate experiments. In both experiments, the treatments were composed of Kansas or McDougall's buffer solutions with or without urea addition, according to a 2 × 2 factorial arrangement. In Exp. I, the IVDMD and IVNDFD of 25 forages and 25 concentrates were evaluated. Samples were incubated for 48 h using an artificial fermenter and nonwoven textile filter bags (100 g/m2). In this experiment, the repeatability and discriminatory power among samples were calculated within forage or concentrate samples, for each treatment. In Exp. II, Tifton hay and ground corn samples were incubated for 48 h. The pH and ammonia nitrogen (NH3-N) concentration were measured after 0, 3, 6, 12, 18, 24, and 48 h of incubation. In Exp. I, the interaction between buffer solution and urea addition impacted the IVDMD and IVNDFD of forages (P < 0.05), with greater values being exhibited when using McDougall's buffer with urea (P < 0.05). For concentrates, the effect of buffer and urea interaction did not affect IVDMD and IVNDFD (P > 0.05). However, greater IVDMD and IVNDFD were observed for McDougall's buffer (P < 0.05), while urea addition increased IVDMD and IVFDFD estimates (P < 0.05) regardless of buffer solution used. In general, repeatability of the digestibility was better using McDougall's buffer and improved when urea was added. Urea addition also decreased the discriminatory power among samples for both buffers. In Exp. II, a buffer solution × urea addition × incubation time interaction was detected (P < 0.05) for pH and NH3-N in both Tifton hay and ground corn. Kansas buffer exhibited lower pH values with a greater decrease observed throughout incubation time when compared to McDougall's buffer. The use of Kansas buffer with urea addition was the only treatment exhibiting NH3-N accumulation throughout incubation. In conclusion, McDougall's buffer provides both better conditions for in vitro fiber digestion and greater precision in digestibility estimates, and is recommended over Kansas buffer. In spite of urea addition increases the precision of in vitro digestibility estimates, it decreases discriminatory power among samples.

Structural insights into binding specificity, efficacy and bias of a ß2AR partial agonist.

Salmeterol is a partial agonist for the ß2 adrenergic receptor (ß2AR) and the first long-acting ß2AR agonist to be widely used clinically for the treatment of asthma and chronic obstructive pulmonary disease. Salmeterol's safety and mechanism of action have both been controversial. To understand its unusual pharmacological action and partial agonism, we obtained the crystal structure of salmeterol-bound ß2AR in complex with an active-state-stabilizing nanobody. The structure reveals the location of the salmeterol exosite, where sequence differences between ß1AR and ß2AR explain the high receptor-subtype selectivity. A structural comparison with the ß2AR bound to the full agonist epinephrine reveals differences in the hydrogen-bond network involving residues Ser2045.43 and Asn2936.55. Mutagenesis and biophysical studies suggested that these interactions lead to a distinct active-state conformation that is responsible for the partial efficacy of G-protein activation and the limited ß-arrestin recruitment for salmeterol.