Recovering Mantle Memories from River Profiles

Researchers use a closed-loop modeling strategy to validate regional uplift patterns recorded in river profiles across the African continent.

Analyzing complexities in the Brazilian soybean supply chain: a systems thinking and modeling approach

Purpose This study aims to analyze the complexities of the Brazilian soybean supply chain (SSC) and develop strategic interventions to improve the origin system’s performance. Design/methodology/approach This study used stakeholder interviews to identify the SSC bottlenecks and determine and assess drivers of competitiveness. A methodological framework based on the systems thinking approach for developing long-term structural changes was used. The problem was structured using behavior over time graph and causal loop modeling to propose three investment strategies to solve the logistics problem in SSC. Findings This study highlights the gaps in coordination between stakeholders and the public sector regarding the public policy for infrastructure investment. Three strategic interventions were developed to address the agro-industrial logistical problem, namely, investment in storage, multimodal transport systems and improvements in existing transport infrastructure. To overcome transport and storage logistics limitations, the authors suggest different forms of partnerships, including public-private partnerships. Research limitations/implications This research is limited to evaluating an agricultural commodity (soybean) and does not include its by-products. The sample of stakeholders was limited and the boundary of analysis was Brazil. Nevertheless, the study showed how strategic interventions could be developed following a holistic analysis. Practical implications The proposed integrated approach illustrates the development of three strategic initiatives. It can be implemented by stakeholders, including the public sector, which is the basis for providing assertive long-term investments in Brazilian logistics. Social implications The SSC analysis could promote the implementation of systemically determined interventions and strategies. It could significantly improve the performance of agricultural systems and help the formulation of public policies aimed at rural development. Originality/value The use of system dynamics to identify intervention points is an essential contribution to mitigating the SSC’s hindrances. Moreover, the combining methodologies resulted in comprehensive intervention strategies.

Enhanced Conformational Sampling of Nanobody CDR H3 Loop by Generalized Replica-Exchange with Solute Tempering

The variable domains of heavy-chain antibodies, known as nanobodies, are potential substitutes for IgG antibodies. They have similar affinities to antigens as antibodies, but are more heat resistant. Their small size allows us to exploit computational approaches for structural modeling or design. Here, we investigate the applicability of an enhanced sampling method, a generalized replica-exchange with solute tempering (gREST) for sampling CDR-H3 loop structures of nanobodies. In the conventional replica-exchange methods, temperatures of only a whole system or scaling parameters of a solute molecule are selected for temperature or parameter exchange. In gREST, we can flexibly select a part of a solute molecule and a part of the potential energy terms as a parameter exchange region. We selected the CDR-H3 loop and investigated which potential energy term should be selected for the efficient sampling of the loop structures. We found that the gREST with dihedral terms can explore a global conformational space, but the relaxation to the global equilibrium is slow. On the other hand, gREST with all the potential energy terms can sample the equilibrium distribution, but the structural exploration is slower than with dihedral terms. The lessons learned from this study can be applied to future studies of loop modeling.

Protein loop modeling and refinement using deep learning models

Loops in proteins play essential roles in protein functions and interactions. The structural characterization of loops is challenging because of their conformational flexibility and relatively poor conservation in multiple sequence alignments. Many experimental and computational approaches have been carried out during the last few decades for loop modeling. Although the latest AlphaFold2 achieved remarkable performance in protein structure predictions, the accuracy of loop regions for many proteins still needs to be improved for downstream applications such as protein function prediction and structure based drug design. In this paper, we proposed two novel deep learning architectures for loop modeling: one uses a combined convolutional neural network (CNN)-recursive neural network (RNN) structure (DeepMUSICS) and the other is based on refinement of histograms using a 2D CNN architecture (DeepHisto). In each of the methods, two types of models, conformation sampling model and energy scoring model, were trained and applied in the loop folding process. Both methods achieved promising results and worth further investigations. Since multiple sequence alignments (MSA) were not used in our architecture, the energy scoring models have less bias from MSA. We believe the methods may serve as good complements for refining AlphaFold2 predicted structures.

Closed-Loop Modeling to Evaluate the Performance of a Scaled-Up Lithium–Sulfur Battery in Electric Vehicle Applications

A closed-loop modeling method was established here to evaluate the performance of new battery technology from lab research to scaled-up developed electric vehicle (EV) applications. As an emerging energy-storage device, the lithium–sulfur battery (LSB) is a very promising candidate for the next generation of rechargeable batteries. However, it has been difficult to commercialize the LSB up to now. In this work, we designed and built a battery, EV, and driver system loop model to study the key performance parameters of LSB operation in EVs, in which the tested data from the lab were introduced into the model followed by simulating driving cycles and fast charging. A comparison with the lithium-ion batteries used in real vehicles verified the high reliability of the model. Meanwhile, the simulation results showed that the LSB needs more improvements for EV application; in particular, developments are still highly needed to overcome the high power and energy loss and sharp voltage vibration for practical applications. The novelty of this work relies on the created closed-loop modeling method to simulate lab research results for evaluating new battery technology in scaled-up EV applications in order to not only vividly predict EV operation performance and commercialization feasibility, but also thoughtfully guide researchers and developers for further optimization and problem solutions. Therefore, this method holds great promise as a powerful tool for both lab research and the industrial development of new batteries for EV applications.

Robustification of RosettaAntibody and Rosetta SnugDock

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.

Molecular Dynamics Analysis of a Flexible Loop at the Binding Interface of the SARS-CoV-2 Spike Protein Receptor-Binding Domain

Since the identification of the SARS-CoV-2 virus as the causative agent of the current COVID-19 pandemic, considerable effort has been spent characterizing the interaction between the Spike protein receptor-binding domain (RBD) and the human angiotensin converting enzyme 2 (ACE2) receptor. This has provided a detailed picture of the end point structure of the RBD-ACE2 binding event, but what remains to be elucidated is the conformation and dynamics of the RBD prior to its interaction with ACE2. In this work we utilize molecular dynamics simulations to probe the flexibility and conformational ensemble of the unbound state of the receptor-binding domain from SARS-CoV-2 and SARS-CoV. We have found that the unbound RBD has a localized region of dynamic flexibility in Loop 3 and that mutations identified during the COVID-19 pandemic in Loop 3 do not affect this flexibility. We use a loop-modeling protocol to generate and simulate novel conformations of the CoV2-RBD Loop 3 region that sample conformational space beyond the ACE2 bound crystal structure. This has allowed for the identification of interesting substates of the unbound RBD that are lower energy than the ACE2-bound conformation, and that block key residues along the ACE2 binding interface. These novel unbound substates may represent new targets for therapeutic design.