Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related to mechanistic, kinetic and structural aspects of protein and peptide aggregation. The database has been updated to CPAD 2.0 by significantly expanding datasets and improving the user-interface. Key features of CPAD 2.0 are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures related to aggregation, which are classified into proteins, fibrils, and protein-ligand complexes, (iii) 2031 aggregating/non-aggregating peptides with pre-calculated aggregation properties, and (iv) 912 aggregation-prone regions in amyloidogenic proteins. This database will help the scientific community (a) by facilitating research leading to improved understanding of protein aggregation, (b) by helping develop, validate and benchmark mechanistic and kinetic models of protein aggregation, and (c) by assisting experimentalists with design of their investigations and dissemination of data generated by their studies. CPAD 2.0 can be accessed at https://web.iitm.ac.in/bioinfo2/cpad2/index.html.

Type 1 diabetes (T1D) is a T cell–mediated disease with a strong immunogenetic human leukocyte antigen (HLA) dependence. HLA allelic influence on the T cell receptor (TCR) repertoire shapes thymic selection and controls activation of diabetogenic clones yet remains largely unresolved in T1D. We sequenced the circulating TCRβ chain repertoire from 2250 HLA-typed participants across three cross-sectional cohorts, including individuals with T1D and healthy related and unrelated controls. We found that HLA risk alleles show higher restriction of TCR repertoires in individuals with T1D. We leveraged deep learning to identify T1D-associated TCR subsequence motifs that were also observed in independent TCR cohorts residing in pancreas-draining lymph nodes of individuals with T1D. Collectively, our data demonstrate T1D-related TCR motif enrichment based on genetic risk, offering a potential metric for autoreactivity and groundwork for TCR-based diagnostics and therapeutics.

Coronaviruses are responsible for several epidemics, including the 2002 SARS, 2012 MERS, and COVID‐19. The emergence of recent COVID‐19 pandemic due to SARS‐CoV‐2 virus in December 2019 has resulted in considerable research efforts to design antiviral drugs and other therapeutics against coronaviruses. In this context, it is crucial to understand the biophysical and structural features of the major proteins that are involved in virus‐host interactions. In the current study, we have compared spike proteins from three strains of coronaviruses NL63, SARS‐CoV, and SARS‐CoV, known to bind human angiotensin‐converting enzyme 2 (ACE2), in terms of sequence/structure conservation, hydrophobic cluster formation and importance of binding site residues. The study reveals that the severity of coronavirus strains correlates positively with the interaction area, surrounding hydrophobicity and interaction energy and inversely correlate with the flexibility of the binding interface. Also, we identify the conserved residues in the binding interface of spike proteins in all three strains. The systematic point mutations show that these conserved residues in the respective strains are evolutionarily favored at their respective positions. The similarities and differences in the spike proteins of the three viruses indicated in this study may help researchers to deeply understand the structural behavior, binding site properties and etiology of ACE2 binding, accelerating the screening of potential lead molecules and the development/repurposing of therapeutic drugs.