doi: bioRxiv preprint their components rapidly with the surrounding medium (Hyman et al., 2014;. Most of the liquid condensates possess common characteristics, which include their formation mechanism as well as their physical properties. For instance, multivalent proteins or nucleic acids associate through weak intermolecular interactions and reach a solubility limit to form liquid condensates (Banani et al., 2017;. These condensates are highly mobile, spherical, but get deformed on physical contact, fuse and eventually relax back to their spherical shape (Brangwynne et al., 2009;Brangwynne et al., 2011;Molliex et al., 2015;Nott et al., 2015). Several proteins undergoing LLPS, however, contain intrinsically disordered regions (IDRs) that are closely associated with prion-like domains (PLDs) and low complexity domains (LCDs) (

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Parkinson's disease is mainly a sporadic disorder in which both environmental and cellular factors play a major role in the initiation of this disease. Glycosaminoglycans (GAG) are integral components of the extracellular matrix and are known to influence amyloid aggregation of several proteins, including α-synuclein (α-Syn). However, the mechanism by which different GAGs and related biological polymers influence protein aggregation and the structure and intercellular spread of these aggregates remains elusive. In this study, we used three different GAGs and related charged polymers to establish their role in α-Syn aggregation and associated biological activities of these aggregates. Heparin, a representative GAG, affected α-Syn aggregation in a concentration-dependent manner, whereas biphasic α-Syn aggregation kinetics was observed in the presence of chondroitin sulfate B. Of note, as indicated by 2D NMR analysis, different GAGs uniquely modulated α-Syn aggregation because of the diversity of their interactions with soluble α-Syn. Moreover, subtle differences in the GAG backbone structure and charge density significantly altered the properties of the resulting amyloid fibrils. Each GAG/polymer facilitated the formation of morphologically and structurally distinct α-Syn amyloids, which not only displayed variable levels of cytotoxicity but also exhibited an altered ability to internalize into cells. Our study supports the role of GAGs as key modulators in α-Syn amyloid formation, and their distinct activities may regulate amyloidogenesis depending on the type of GAG being up- or down-regulated .

IntroductionThere is lack of information on the proportion of new smear—positive pulmonary tuberculosis (PTB) patients treated with a 6-month thrice-weekly regimen under Revised National Tuberculosis Control Programme (RNTCP) who develop recurrent TB after successful treatment outcome.ObjectiveTo estimate TB recurrence among newly diagnosed PTB patients who have successfully completed treatment and to document endogenous reactivation or re-infection. Risk factors for unfavourable outcomes to treatment and TB recurrence were determined.MethodologyAdult (aged ≥ 18 yrs) new smear positive PTB patients initiated on treatment under RNTCP were enrolled from sites in Tamil Nadu, Karnataka, Delhi, Maharashtra, Madhya Pradesh and Kerala. Those declared “treatment success” at the end of treatment were followed up with 2 sputum examinations each at 3, 6 and 12 months after treatment completion. MIRU-VNTR genotyping was done to identify endogenous re-activation or exogenous re-infection at TB recurrence. TB recurrence was expressed as rate per 100 person-years (with 95% confidence interval [95%CI]). Regression models were used to identify the risk factors for unfavourable response to treatment and TB recurrence.ResultsOf the1577 new smear positive PTB patients enrolled, 1565 were analysed. The overall cure rate was 77% (1207/1565) and treatment success was 77% (1210 /1565). The cure rate varied from 65% to 86%. There were 158 of 1210 patients who had TB recurrence after treatment success. The pooled TB recurrence estimate was 10.9% [95%CI: 0.2–21.6] and TB recurrence rate per 100 person–years was 12.7 [95% CI: 0.4–25]. TB recurrence per 100 person–years varied from 5.4 to 30.5. Endogenous reactivation was observed in 56 (93%) of 60 patients for whom genotyping was done. Male gender was associated with TB recurrence.ConclusionA substantial proportion of new smear positive PTB patients successfully treated with 6 –month thrice-weekly regimen have TB recurrence under program settings.

HtrA2, a trimeric proapoptotic serine protease is involved in several diseases including cancer and neurodegenerative disorders. Its unique ability to mediate apoptosis via multiple pathways makes it an important therapeutic target. In HtrA2, C-terminal PDZ domain upon substrate binding regulates its functions through coordinated conformational changes the mechanism of which is yet to be elucidated. Although allostery has been found in some of its homologs, it has not been characterized in HtrA2 so far. Here, with an in silico and biochemical approach we have shown that allostery does regulate HtrA2 activity. Our studies identified a novel non-canonical selective binding pocket in HtrA2 which initiates signal propagation to the distal active site through a complex allosteric mechanism. This non-classical binding pocket is unique among HtrA family proteins and thus unfolds a novel mechanism of regulation of HtrA2 activity and hence apoptosis.

The involvement of α-synuclein (α-Syn) amyloid formation in Parkinson’s disease (PD) pathogenesis is supported by the discovery of α-Syn gene (SNCA) mutations linked with familial PD, which are known to modulate the oligomerization and aggregation of α-Syn. Recently, the A53V mutation has been discovered, which leads to late-onset PD. In this study, we characterized for the first time the biophysical properties of A53V, including the aggregation propensities, toxicity of aggregated species, and membrane binding capability, along with those of all familial mutations at the A53 position. Our data suggest that the A53V mutation accelerates fibrillation of α-Syn without affecting the overall morphology or cytotoxicity of fibrils compared to those of the wild-type (WT) protein. The aggregation propensity for A53 mutants is found to decrease in the following order: A53T > A53V > WT > A53E. In addition, a time course aggregation study reveals that the A53V mutant promotes early oligomerization similar to the case for the A53T mutation. It promotes the largest amount of oligomer formation immediately after dissolution, which is cytotoxic. Although in the presence of membrane-mimicking environments, the A53V mutation showed an extent of helix induction capacity similar to that of the WT protein, it exhibited less binding to lipid vesicles. The nuclear magnetic resonance study revealed unique chemical shift perturbations caused by the A53V mutation compared to those caused by other mutations at the A53 site. This study might help to establish the disease-causing mechanism of A53V in PD pathology.

High-temperature requirement protease A2 (HtrA2), a multitasking serine protease that is involved in critical biological functions and pathogenicity, such as apoptosis and cancer, is a potent therapeutic target. It is established that the C-terminal post-synaptic density protein, Drosophila disc large tumor suppressor, zonula occludens-1 protein (PDZ) domain of HtrA2 plays pivotal role in allosteric modulation, substrate binding and activation, as commonly reported in other members of this family. Interestingly, HtrA2 exhibits an additional level of functional modulation through its unique N-terminus, as is evident from 'inhibitor of apoptosis proteins' binding and cleavage. This phenomenon emphasizes multiple activation mechanisms, which so far remain elusive. Using conformational dynamics, binding kinetics and enzymology studies, we addressed this complex behavior with respect to defining its global mode of regulation and activity. Our findings distinctly demonstrate a novel N-terminal ligand-mediated triggering of an allosteric switch essential for transforming HtrA2 to a proteolytically competent state in a PDZ-independent yet synergistic activation process. Dynamic analyses suggested that it occurs through a series of coordinated structural reorganizations at distal regulatory loops (L3, LD, L1), leading to a population shift towards the relaxed conformer. This precise synergistic coordination among different domains might be physiologically relevant to enable tighter control upon HtrA2 activation for fostering its diverse cellular functions. Understanding this complex rheostatic dual switch mechanism offers an opportunity for targeting various disease conditions with tailored site-specific effector molecules. Abbreviations BIR, baculovirus IAP repeat; GST, glutathione S-transferase; HtrA2, high-temperature requirement protease A2; IAP, inhibitor of apoptosis proteins; IBM, IAP-binding motif; ITC, isothermal titration calorimetry; MBP, maltose-binding protein; MD, molecular dynamics; OMP, outer membrane porins; PDZ, post-synaptic density protein, Drosophila disc large tumor suppressor, zonula occludens-1 protein; rmsf, root mean square fluctuation; SPR, surface plasmon resonance; XIAP, X-linked inhibitor of apoptosis protein. Structured digital abstract

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.