Biology subjects

Basu Mallik, B.

Publications and source records attributed to Basu Mallik, B..

3 recordsLinked to original sources

bioRxiv2026

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

De novo protein design has advanced rapidly, yet designing binders to polar, solvent-exposed epitopes and small, flexible ligands remains challenging. Such hydrated surfaces and flexible molecules, including carbohydrates, provide few of the hydrophobic contacts favoured by current methods and have largely resisted de novo binders. To address this challenge, here we introduce latent generative search for binder design, a novel framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model. The model codesigns sequence and structure - generating them together in a continuous latent space - and thereby removes the inverse-folding step on which current methods rely. In a screen of more than one million designs by multiplexed phage display, latent generative search produced more validated binders than every other method tested, its codesigned sequences surpassing post hoc redesign. It delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets. Our approach also accessed previously untapped biology, generating the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens - a polar, flexible target class beyond the reach of current design methods.

bioengineering↗

bioRxiv2024

RAF2Net: Automated grading of Renal cell Carcinoma utilizing Attention-enhanced deep learning models through Feature Fusion

It is anticipated that the number of instances of kidney cancer will continue to rise globally, which motivates changes to the current diagnostic framework in order to address emerging issues. Renal cell carcinoma (RCC) accounts for 80-85% of all renal tumors and is the most common kind of kidney cancer. Based on kidney histopathology images, this study presented a completely automated, robust, and computationally efficient Renal Cell Carcinoma Grading Network (RAF 2Net). Our suggested model incorporates 3 different Mobilenet backbones with intelligent feature fusion. Moreover, the attention blocks help us give more importance to the important pixels, which are majorly responsible for classification. For comparison purposes, Similar tests were conducted using transfer learning methods with pre-trained ImageNet weights and deep learning models created from scratch. To show the efficacy of the suggested method, we have computed evaluation parameters like Accuracy, Precision, F score, Recall, Confusion Matrix, and TSNE. Based on the provided KMC dataset, the experimental result demonstrates that the proposed RAF 2Net outperforms the nine most recent classification methods regarding prediction Accuracy, Recall, Precision, and F score with a value greater than 92%.

bioinformatics↗

bioRxiv2023

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. For example, clathrin coats adopt a wide variety of architectures to adapt to vesicular cargos of various sizes. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, most existing methods focus on designing static structures with high accuracy. Here we characterize the structures of three distinct computationally designed protein assemblies that each form multiple unanticipated architectures, and identify flexibility in specific regions of the subunits of each assembly as the source of structural diversity. Cryo-EM single-particle reconstructions and native mass spectrometry showed that only two distinct architectures were observed in two of the three cases, while we obtained six cryo-EM reconstructions that likely represent a subset of the architectures present in solution in the third case. Structural modeling and molecular dynamics simulations indicated that the surprising observation of a defined range of architectures, instead of non-specific aggregation, can be explained by constrained flexibility within the building blocks. Our results suggest that deliberate use of structural flexibility as a design principle will allow exploration of previously inaccessible structural and functional space in designed protein assemblies.

bioengineering↗