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Ramanathan Sowdhamini

Publications and source records attributed to Ramanathan Sowdhamini.

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Using protein blocks to build custom fragment libraries from protein structures

The remarkable structural diversity of modern proteins reflects millions of years of evolution, during which sequence space has expanded while many structural features remain conserved. This conservation is evident not only among homologous proteins but also in the recurrence of supersecondary motifs across unrelated proteins, underscoring the abundance and robustness of these structural units. Here, we present a novel pipeline for generating customized protein fragment libraries using protein blocks (PBs) - a structural alphabet that encodes local backbone conformations. Our method efficiently extracts structurally similar fragments from a curated, non-redundant protein structure database by converting three-dimensional structures into one-dimensional PB sequences. By integrating predicted PB sequences with the PB-ALIGN and PB-kPRED tools, our approach identifies relevant fragments independently of sequence homology. Fragment quality is further assessed using a new scoring function that combines secondary structure similarity and PB alignment metrics. The resulting libraries contain fragments of at least seven PBs (11 amino acid residues), covering over 70% of the local backbone structure. Our results demonstrate that PBs enable efficient mining of high-quality structural fragments from diverse protein spaces, including proteins with disordered regions. The pipeline is accessible as an online tool (PB-Frag, https://pbpred-us2b.univ-nantes.fr/pbfrag ).

q-bio.QM

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.

q-bio.QM

A glance into the evolution of template-free protein structure prediction methodologies

Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (> 150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.

q-bio.QM