A multiwell plasmonic reader was designed and validated for higher throughput analysis of biological interactions with a platform of the same size as standard 96-well plates. While the plasmonic sensor can be read with standard 96-well plate readers, a custom 96-well plate reader was designed to analyze nanohole arrays at high incident angles required for higher sensitivity. Gold nanohole arrays were manufactured on a 4 in. glass wafer using a photolithographic process. In comparison to single channel measurements with nanohole arrays fabricated with nanosphere lithography, the nanohole array sensors greatly enhanced the signal-to-noise ratio of the plasmonic signal and precision of the measurements with the multiwell plate system. As proof of concept, the detection of IgG in the low nanomolar range was achieved with the multiwell plate reader. The multiwell plasmonic plate reader was also applied to the screening of several prostate specific (PSA) antibodies for secondary detection of PSA and for the analysis of an anticancer drug through a competitive assay between methotrexate (MTX) and folic acid Au nanoparticle (FaNP) for human dihydrofolate reductase (hDHFR). The multiwell plasmonic reader based on nanohole array technology offers the rapid, versatile, sensitive, and simple high throughput detection of biomolecules.

Comparison of amino acid sequence similarity is the fundamental concept behind the protein phylogenetic tree formation. By virtue of this method, we can explain the evolutionary relationships, but further explanations are not possible unless sequences are studied through the chemical nature of individual amino acids. Here we develop a new methodology to characterize the protein sequences on the basis of the chemical nature of the amino acids. We design various algorithms for studying the variation of chemical group transitions and various chemical group combinations as patterns in the protein sequences. The amino acid sequence of conventional myosin II head domain of 14 family members are taken to illustrate this new approach. We find two blocks of maximum length 6 aa as ‘FPKATD’ and ‘Y/FTNEKL’ without repeating the same chemical nature and one block of maximum length 20 aa with the repetition of chemical nature which are common among all 14 members. We also check commonality with another motor protein sub-family kinesin, KIF1A. Based on our analysis we find a common block of length 8 aa both in myosin II and KIF1A. This motif is located in the neck linker region which could be responsible for the generation of mechanical force, enabling us to find the unique blocks which remain chemically conserved across the family. We also validate our methodology with different protein families such as MYOI, Myosin light chain kinase (MLCK) and Rho-associated protein kinase (ROCK), Na+/K+-ATPase and Ca2+-ATPase. Altogether, our studies provide a new methodology for investigating the conserved amino acids’ pattern in different proteins.

Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment. Expected final online publication date for the Annual Review of Biophysics, Volume 50 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Photo-luminescence (P-L) intermittency (or blinking) in semiconductor nanocrystals (NCs), a phenomenon ubiquitous to single-emitters, is generally considered to be temporally random intensity fluctuations between "bright" ("On") and "dark" ("Off") states. However, individual quantum-dots (QDs) rarely exhibit such telegraphic signal, and yet, the vast majority of single-NC blinking data are analyzed using a single fixed threshold (FT) which generates binary trajectories. Further, blinking dynamics can vary dramatically over NCs in the ensemble, and it is unclear whether the exponents ( ) of single-particle On-/Off-time distributions ( ), which are used to validate mechanistic models of blinking, are narrowly distributed. Here, we sub-classify an ensemble based on the emissivity of QDs, and subsequently compare the (sub)ensembles" behaviors. To achieve this, we analyzed a large number (>1000) of blinking trajectories for a model system, Mn +2 doped ZnCdS QDs, which exhibits diverse blinking dynamics. An intensity histogram dependent thresholding method allowed us to construct distributions of relevant blinking parameters (such as, ). Interestingly, we find that single QD s follow either truncated power law or power law, and their relative proportion vary over sub-populations. Our results reveal a remarkable variation in amongst as well as within sub-ensembles, which implies multiple blinking mechanisms being operational amongst various QDs. We further show that the obtained via cumulative single-particle is distinct from the weighted mean value of all single-particle , an evidence for the lack of ergodicity. Thus, investigation and analyses of a large number of QDs, albeit for a limited time-span of few decades, is crucial to characterize possible blinking mechanisms or heterogeneity therein.

Biophysics experiments performed at single-molecule resolution contain exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. Here, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous manner to incorporate information from multiple experiments into a single analysis and to find the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.