The nearest-neighbor (NN) model is a general tool for the evaluation for oligonucleotide thermodynamic stability. It is primarily used for the prediction of melting temperatures but has also found use in RNA secondary structure prediction and theoretical models of hybridization kinetics. One of the key problems is to obtain the NN parameters from melting temperatures, and VarGibbs was designed to obtain those parameters directly from melting temperatures. Here we will describe the basic workflow from RNA melting temperatures to NN parameters with the use of VarGibbs. We start by a brief revision of the basic concepts of RNA hybridization and of the NN model and then show how to prepare the data files, run the parameter optimization, and interpret the results.

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

Understanding a drug candidate\'s mechanism of action is crucial for its further development. However, kinetic schemes are often complex and multi-parametric, especially for proteins in oligomerization equilibria. Here, we demonstrate the use of particle swarm optimization (PSO) as a method to select between different sets of parameters that are too far apart in the parameter space to be found by conventional approaches. PSO is based upon the swarming of birds: each bird in the flock assesses multiple landing spots while at the same time sharing that information with its neighbors. We applied this approach to the kinetics of HSD17β13 enzyme inhibitors, which displayed unusually large thermal shifts. Thermal shift data for HSD17β13 indicated that the inhibitor shifted the oligomerization equilibrium toward the dimeric state. Validation of the PSO approach was provided by experimental mass photometry data. These results encourage further exploration of multi-parameter optimization algorithms as tools in drug discovery.

The inhibition of the YAP-TEAD protein-protein interaction constitutes a promising therapeutic approach for the treatment of cancers linked to the dysregulation of the Hippo signaling pathway. The identification of a class of small molecules which potently inhibit the YAP-TEAD interaction by binding tightly to the Ω-loop pocket of TEAD has previously been communicated. This report details the further multi-parameter optimization of this class of compounds resulting in advanced analogs combining nanomolar cellular potency with a balanced ADME and off-target profile, and efficacy of these compounds in tumor bearing mice is demonstrated for the first time.

Nitroalkanes are important toxic pollutants for which there is no effective removal method at present. Although genetic engineering bacteria have been developed as a promising bioremediation strategy for years, their actual performance is far lower than expected. In this study, important factors affecting the application of engineered Geobacillus for nitroalkanes degradation were comprehensively optimized. The deep-reconstructed engineered strains significantly raised the expression and activity level of catalytic enzymes, but failed to fully enhance the degradation efficiency. However, further debugging of a variety of key parameters effectively improved the performance of the engineering strains. The increased cell membrane permeability, trace supplementation of vital nutritional factors, synergy of multifunctional enzyme engineered bacteria, switch of oxygen-supply mode, and moderate initial biomass all effectively boosted the degradation efficiency. Finally, a low-cost and highly effective bioreactor test for high-concentration nitroalkanes degradation proved the multi-parameter optimization mode helps to maximize the performance of genetically engineered bacteria.

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30-40 cm and 90-100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R2 of 0.642, RMSE of 0.283, and MAE of 0.213 at 30-40 cm depth, and with the R2 of 0.450, RMSE of 0.276, and MAE of 0.220 at 90-100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher; in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30-40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90-100 cm showed fluctuating increasing trend.

Microalgae have garnered much contemplation as candidates to fix CO2 into valuable compounds. Although microalgae have been studied to produce various metabolites, they have not yet proved successful for commercialization. Since, handling such problems practically requires satisfying multiple parameters simultaneously, we put forth a multi-parameter optimization strategy to manipulate the carbon metabolism of Scenedesmus sp. to improve biomass production and enhance CO2 fixation to increase the production of fuel-related metabolites. The Box-Behnken design method was applied with CO2 concentration, CO2 sparging time and glucose concentration as independent variables; biomass and total fatty acid methyl ester (total FAME) content were analyzed as response variables. The strain is supplemented with both CO2 and glucose with an aim to enhance carbon flux and rechannel it towards carbon fixation. As per the results obtained in this study, Scenedesmus sp. could effectively exploit high CO2 concentration (15%) for longer duration under high concentration of glucose supplementation (9 g/L) producing a biomass of 635.24 ± 39.9 μg/mL with a high total fatty acid methyl ester (FAME) content of 71.29 ± 4.2 μg/mg, significant acetyl-CoA carboxylase enzyme activity and a favorable fatty acid profile: 35.8% palmitic acid, 10.5% linoleic acid and 30.6% linolenic acid. The carbohydrate content was maximum at 10% CO2 sparged for the longest duration of 90 min under glucose concentration of 9 g/L. This study puts forth an optimal design that can provide evidence on comprehending the carbon assimilation mechanism to enhance production of biomass and biofuels and provide conditions to microalgal species to tolerate CO2 rich flue gas.

Identification of purposeful chemical matter on a broad range of drug targets is of high importance to the pharmaceutical industry. However, disease-relevant but more complex hit-finding plans require flexibility regarding the subset of the compounds that we screen. Herein we describe a strategy to design high-quality small molecule screening subsets of two different sizes to cope with a rapidly changing early discovery portfolio. The approach taken balances chemical tractability, chemical diversity and biological target coverage. Furthermore, using surveys, we actively involved chemists within our company in the selection process of the diversity decks to ensure current medicinal chemistry principles were incorporated. The chemist surveys revealed that not all published PAINS substructure alerts are considered productive by the medicinal chemistry community and in agreement with previously published results from other institutions, QED scores tracked quite well with chemists\' notions of chemical attractiveness.