Devlina Chakravarty

AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model protein folds with atomic-level accuracy. Nevertheless, previous work has shown that these predictions tend to be inaccurate for structurally heterogeneous proteins. To systematically assess factors that contribute to this inaccuracy, we tested AlphaFold2's performance on 98-fold-switching proteins, which assume at least two distinct-yet-stable secondary and tertiary structures. Topological similarities were quantified between five predicted and two experimentally determined structures of each fold-switching protein. Overall, 94% of AlphaFold2 predictions captured one experimentally determined conformation but not the other. Despite these biased results, AlphaFold2's estimated confidences were moderate-to-high for 74% of fold-switching residues, a result that contrasts with overall low confidences for intrinsically disordered proteins, which are also structurally heterogeneous. To investigate factors contributing to this disparity, we quantified sequence variation within the multiple sequence alignments used to generate AlphaFold2's predictions of fold-switching and intrinsically disordered proteins. Unlike intrinsically disordered regions, whose sequence alignments show low conservation, fold-switching regions had conservation rates statistically similar to canonical single-fold proteins. Furthermore, intrinsically disordered regions had systematically lower prediction confidences than either fold-switching or single-fold proteins, regardless of sequence conservation. AlphaFold2's high prediction confidences for fold switchers indicate that it uses sophisticated pattern recognition to search for one most probable conformer rather than protein biophysics to model a protein's structural ensemble. Thus, it is not surprising that its predictions often fail for proteins whose properties are not fully apparent from solved protein structures. Our results emphasize the need to look at protein structure as an ensemble and suggest that systematic examination of fold-switching sequences may reveal propensities for multiple stable secondary and tertiary structures.

In biological fluids, nanoparticles are always surrounded by proteins. As the protein is adsorbed on the surface, the extent of adsorption and the effect on the protein conformation and stability are dependent on the chemical nature, shape, and size of the nanoparticle (NP). We have carried out a detailed investigation on the interaction of bovine serum albumin (BSA) with polyethyleneimine-functionalized ZnO nanoparticles (ZnO-PEI). ZnO-PEI was synthesized using a wet chemical method with a core size of ~3-7 nm (from transmission electron microscopy). The interaction of BSA with ZnO-PEI was examined using a combination of calorimetric, spectroscopic, and computational techniques. The binding was studied by ITC (isothermal titration calorimetry), and the result revealed that the complexation is enthalpy-driven, indicating the possible involvement of electrostatic interaction. To investigate the nature of the interaction and the location of the binding site, a detailed domain-wise surface electrostatic potential calculation was performed using adaptive Poisson-Boltzmann software (APBS). The result shows that the protein surface can bind the nanoparticle. On binding ZnO-PEI, the protein gets destabilized to some extent, as displayed by CD (circular dichroism) and FTIR (Fourier transform infrared) spectroscopy. Chemical and thermal denaturation of BSA, when carried out in the presence of ZnO-PEI, also indicated a small perturbation in the protein structure. A comparison of the enthalpy and entropy components of binding with those derived for the interaction of BSA with ZnO nanoparticles explains the effect of hydrophilic cationic species attached on the NP surface. The effect of the NP surface modification on the structure and stability of BSA would find useful applications in nanobiotechnology.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Recent work suggests that AlphaFold (AF)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF's training sets. AF2's confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations.

BACKGROUND: Osteoporosis is an important public health problem in older adults. It is more common in postmenopausal women and not only gives rise to morbidity but also markedly diminishes the quality of life in this population. There is lack of information about the risk factor of osteoporosis in developing countries. In this study we aimed to assess the risk factors for osteoporosis in postmenopausal women from selected BMD centers of two developing Asian countries (Iran and India). METHODS: This study is a multicenter interview-based study conducted in selected hospitals and health centers from urban areas in Iran and India. The case group included postmenopausal osteoporotic women who were identified as patients with bone density higher than 2.5 SD below average of young normal bone density (in L1-L4) spine region interest and/or total femoral region) by using DEXA method. The controls were chosen from postmenopausal women with normal bone density (in L1-L4 spine and total femoral regions using DEXA method) matching in age groups was strategy of choice.The sample sizes included from Iran a total of 363 subjects (178 osteoporotic and 185 normal) and from India a total of 354 subjects (203 osteoporotic and 151 normal). RESULTS: The significant (p < 0.05) risk factors in present study population with their Odds Ratios (in parenthesis, respectively in Iran and India) were as follow:Lower education defined as less than class 12 or nil college (2.1) (2.7), duration of menopause greater than 5 years: (2.2) (1.4), Menarche age (after 14 years): (1.9) (1.6), Menopause age (before 45 years): (1.1) (2), Parity more than 3: (1.1) (1), Bone and joint problem (2.3) (2.2). Calcium supplementation (0.6) and HRT (0.4) were shown as protective factors and steroid therapy (3.3) was found as a risk factor in Iran. Calcium supplementation more than 1 year (0.3) was shown as a protective factor in India.Pure vegetarianism: (2.2) and Red meat consumption more than 4 times per week (1.4) was shown as a risk factor in Indian and Iranian subjects respectively. Regular consumption of Soya (0.3), almond (0.5), fish (0.5), fruits (0.4) and milk tea 4 cups per day and more (0.4) appeared to be significant protective factors in India. Regular consumption of cheese (0.5), milk (0.5), chicken (0.4), egg (0.6), fruit (0.4), tea 7 cups per day and more (0.3) were found to be significant protective factors in Iran. Exercises were shown as protective factor in Iran (0.4) and India (0.4). There were no significant differences in association of risk factors and osteoporosis between Iranian and Indian subjects. CONCLUSION: Osteoporosis in Iranian and Indian subjects also appears to be associated with several known risk factors that well described in the literature. There were no significant differences in association of risk factors and osteoporosis between Iranian and Indian subjects. It was shown a protective role of certain nutritional dietary components and also exercises in both populations and can be exploited in preventive educational strategies on osteoporosis in these populations.