How Molecular Topology Can Help in Amyotrophic Lateral Sclerosis (ALS) Drug Development: A Revolutionary Paradigm for a Merciless Disease

Even if amyotrophic lateral sclerosis is still considered an orphan disease to date, its prevalence among the population is growing fast. Despite the efforts made by researchers and pharmaceutical companies, the cryptic information related to the biological and physiological onset mechanisms, as well as the complexity in identifying specific pharmacological targets, make it almost impossible to find effective treatments. Furthermore, because of complex ethical and economic aspects, it is usually hard to find all the necessary resources when searching for drugs for new orphan diseases. In this context, computational methods, based either on receptors or ligands, share the capability to improve the success rate when searching and selecting potential candidates for further experimentation and, consequently, reduce the number of resources and time taken when delivering a new drug to the market. In the present work, a computational strategy based on Molecular Topology, a mathematical paradigm capable of relating the chemical structure of a molecule to a specific biological or pharmacological property by means of numbers, is presented. The result was the creation of a reliable and accessible tool to help during the early in silico stages in the identification and repositioning of potential hits for ALS treatment, which can also apply to other orphan diseases. Considering that further computational and experimental results will be required for the final identification of viable hits, three linear discriminant equations combined with molecular docking simulations on specific proteins involved in ALS are reported, along with virtual screening of the Drugbank database as a practical example. In this particular case, as reported, a clinical trial has been already started for one of the drugs proposed in the present study.

Minimal-waste design of timber layouts from non-standard reclaimed elements: A combinatorial approach based on structural reciprocity

The use of timber allows reducing the environmental impact in the construction sector. However, as the demand for construction timber rises, the pressure on the world’s forest is increasing too. To maintain an adequate supply of timber from sustainable forests in the coming decades, the building industry must adopt practices that reduce the impact on forestry. Reuse is one of the principles of Circular Economy (CE). Among the technical challenges of reuse are the variability and the short size of the stock of elements coming either from demolition or from new construction, such as cut-offs and temporary scaffolding. This work presents a study for the design of structural configurations with short and non-regular sized elements that would normally be considered waste. The configurations are based on the principle of structural reciprocity and are generated by an optimization algorithm that allows minimizing the material waste and maximizing the stock elements use. A computational strategy based on the SPEA-II multi-objective method is employed for the investigation of optimal trade-offs between competing objective functions, such as structural lightness and optimal use of stock inventory. The goal of this work is demonstrating the feasibility of an industrial process, borrowing key elements from the Industry 4.0 paradigm, for a streamlined and economical production of standardized building components using non-standard reclaimed elements.

Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task

The neocortex can be viewed as a tapestry consisting of variations of rather stereotypical local cortical microcircuits. Hence understanding how these microcircuits compute holds the key to understanding brain function. Intense research efforts over several decades have culminated in a detailed model of a generic cortical microcircuit in the primary visual cortex from the Allen Institute. We are presenting here methods and first results for understanding computational properties of this large-scale data-based model. We show that it can solve a standard image-change-detection task almost as well as the living brain. Furthermore, we unravel the computational strategy of the model and elucidate the computational role of diverse subtypes of neurons. Altogether this work demonstrates the feasibility and scientific potential of a methodology based on close interaction of detailed data and large-scale computer modelling for understanding brain function.

A Computational Strategy of Variable Step, Variable Order for Solving Stiff Systems of Ordinary Differential Equations

This research study focuses on a computational strategy of variable step, variable order (CSVSVO) for solving stiff systems of ordinary differential equations. The idea of Newton’s interpolation formula combine with divided difference as the basis function approximation will be very useful to design the method. Analysis of the performance strategy of variable step, variable order of the method will be justified. Some examples of stiff systems of ordinary differential equations will be solved to demonstrate the efficiency and accuracy.

Unravelling the Graded Millisecond Allosteric Activation Mechanism of Imidazole Glycerol Phosphate Synthase

Deciphering the molecular mechanisms of enzymatic allosteric regulation requires the structural characterization of key functional states and also their time evolution toward the formation of the allosterically activated ternary complex. The transient nature and usually slow millisecond timescale interconversion between these functional states hamper their detailed experimental and computational characterization. Here, we design a computational strategy tailored to reconstruct millisecond timescale events to describe the graded allosteric activation of imidazole glycerol phosphate synthase (IGPS) in the ternary complex. IGPS is a heterodimeric bienzyme complex responsible for the hydrolysis of glutamine to glutamate in the HisH subunit and delivering ammonia for the cyclase activity in HisF. Despite significant advances in understanding the underlying allosteric mechanism, essential molecular details of the long-range millisecond allosteric activation pathway of wild-type IGPS remain hidden. Without using a priori information of the active state, our simulations uncover how IGPS, with the allosteric effector bound in HisF, spontaneously captures glutamine in a catalytically inactive HisH conformation, subsequently attains a closed HisF:HisH interface, and finally forms the oxyanion hole in HisH for efficient glutamine hydrolysis. We show that effector binding in HisF dramatically decreases the conformational barrier associated with the oxyanion hole formation in HisH, in line with the experimentally observed 4500-fold activity increase in glutamine production. The formation of the allosterically active state is controlled by time-evolving dynamic communication networks connecting the effector and substrate binding sites. This computational strategy can be generalized to study other unrelated enzymes undergoing millisecond timescale allosteric transitions.

scTriangulate: Decision-level integration of multimodal single-cell data

Hundreds of bioinformatics approaches now exist to define cellular heterogeneity from single-cell genomics data. Reconciling conflicts between diverse methods, algorithm settings, annotations or modalities have the potential to clarify which populations are real and establish reusable reference atlases. Here, we present a customizable computational strategy called scTrianguate, which leverages cooperative game theory to intelligently mix-and-match clustering solutions from different resolutions, algorithms, reference atlases, or multi-modal measurements. This algorithm relies on a series of robust statistical metrics for cluster stability that work across molecular modalities to identify high-confidence integrated annotations. When applied to annotations from diverse competing cell atlas projects, this approach is able to resolve conflicts and determine the validity of controversial cell population predictions. Tested with scRNA-Seq, CITE-Seq (RNA + surface ADT), multiome (RNA + ATAC), and TEA-Seq (RNA + surface ADT + ATAC), this approach identifies highly stable and reproducible, known and novel cell populations, while excluding clusters defined by technical artifacts (i.e., doublets). Importantly, we find that distinct cell populations are frequently attributed with features from different modalities (RNA, ATAC, ADT) in the same assay, highlighting the importance of multimodal analysis in cluster determination. As it is flexible, this approach can be updated with new user-defined statistical metrics to alter the decision engine and customized to new measures of stability for different measures of cellular activity.

Structure of the stress-related LHCSR1 complex determined by an integrated computational strategy

Light-harvesting complexes (LHCs) are pigment-protein complexes whose main function is to capture sunlight and transfer the energy to reaction centers of photosystems. In response to varying light conditions, LH complexes also play photoregulation and photoprotection roles. In algae and mosses, a sub-family of LHCs, Light-Harvesting complex stress related (LHCSR), is responsible for photoprotective quenching. Despite their functional and evolutionary importance, no direct structural information on LHCSRs is available that can explain their unique properties. In this work we propose a structural model of LHCSR1 from the moss P. Patens, obtained through an integrated computational strategy that combines homology modeling, molecular dynamics, and multiscale quantum chemical calculations. The model is validated by reproducing the spectral properties of LHCSR1. Our model reveals the structural specificity of LHCSR1, as compared with the CP29 LH complex, and poses the basis for understanding photoprotective quenching in mosses.