Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure.

The structure of a protein plays a pivotal role in determining its function. Often, the protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a protein's surface representation poses significant challenges for its curvature-based characterization. In the present study, we employ unsupervised machine learning to segment the protein surface into patches. To measure the surface curvature of a patch, we present an algebraic sphere fitting method that is fast, accurate, and robust. Moreover, we use local curvatures to show the existence of "shape complementarity" in protein-protein, antigen-antibody, and protein-ligand interfaces. We believe that the current approach could help understand the relationship between protein structure and its biological function and can be used to find binding partners of a given protein.

Accurate prediction of B-form/A-form DNA conformation propensity from primary sequence: A machine learning and free energy handshake.

DNA carries the genetic code of life, with different conformations associated with different biological functions. Predicting the conformation of DNA from its primary sequence, although desirable, is a challenging problem owing to the polymorphic nature of DNA. We have deployed a host of machine learning algorithms, including the popular state-of-the-art LightGBM (a gradient boosting model), for building prediction models. We used the nested cross-validation strategy to address the issues of "overfitting" and selection bias. This simultaneously provides an unbiased estimate of the generalization performance of a machine learning algorithm and allows us to tune the hyperparameters optimally. Furthermore, we built a secondary model based on SHAP (SHapley Additive exPlanations) that offers crucial insight into model interpretability. Our detailed model-building strategy and robust statistical validation protocols tackle the formidable challenge of working on small datasets, which is often the case in biological and medical data.

Reversible cerebral and brain stem dysfunction in n: Hexane neuropathy.

A 18-year-old male, screen printer by profession developed sensory motor polyneuropathy, change in his behavior, bilateral 6(th) and 7(th) cranial nerve palsies, down beat nystagmus and cerebellar dysarthria. He had bilaterally prolonged P100 latency; left: 137 ms; right: 144 ms. P 37 was not recordable on either side while N 20 was normal. The inter latency difference between Ipsilateral R2 and Contralateral R2 was 6.15 ms, on the left side and normal on the right side. In the follow-up, there was normalization of the blink reflex study, improvement in P100 latency [left: 114 ms; right: 120 ms.] but worsening of peripheral nerve conductions. The sequential clinical recovery was of the behavioral dysfunction, down beat nystagmus, 6(th) nerve, 7(th) nerve involvement and ataxia, in that order. Sural nerve biopsy showed loss of large diameter myelinated fibers.